Alzheimer's disease (AD) is the most prevalent form of dementia, and its early diagnosis is essential for slowing disease progression. Recent studies on multimodal neuroimaging fusion using MRI and PET have achieved promising results by integrating multi-scale complementary features. However, most existing approaches primarily emphasize cross-modal complementarity while overlooking the diagnostic importance of modality-specific features. In addition, the inherent distributional differences between modalities often lead to biased and noisy representations, degrading classification performance. To address these challenges, we propose a Collaborative Attention and Consistent-Guided Fusion framework for MRI and PET based AD diagnosis. The proposed model introduces a learnable parameter representation (LPR) block to compensate for missing modality information, followed by a shared encoder and modality-independent encoders to preserve both shared and specific representations. Furthermore, a consistency-guided mechanism is employed to explicitly align the latent distributions across modalities. Experimental results on the ADNI dataset demonstrate that our method achieves superior diagnostic performance compared with existing fusion strategies.

Wearable intelligence technologies have advanced rapidly in recent years, achieving considerable results in Parkinson's disease (PD) severity classification. Recently, end-to-end deep time-series models have become more popular than traditional machine-learning classifiers, as they do not rely on handcrafted feature extraction. However, for PD severity recognition, most existing studies overlook the time-frequency complementarity and axis correlations present in multivariate signals. To address these gaps, we propose a multivariate time-series fusion framework that converts raw accelerometer and gyroscope signals into continuous wavelet transform (CWT) and recurrence plot (RP) images to capture complementary frequency and recurrence domain information, and jointly integrates these with the original time-domain sequences in end-to-end deep classifiers, as well as handcrafted features extracted at the patient level. We conduct preliminary experiments on wrist IMU data from 95 PD patients using five-fold, patient-level cross-validation focusing on walking activity. Our proposed fusion framework achieves a precision of 72.6 %, outperforming the latest end-toend multivariate time-series baseline model. Moreover, the fusion results are substantially higher than those obtained using CWT images, RP images, or handcrafted features alone, highlighting the effectiveness of jointly leveraging heterogeneous representations. Overall, this study highlights the lack of cross-axis and time-frequency complementary modeling in current end-to-end time-series methods and provides new insights into interpretable deep learning for PD severity classification, thereby offering additional priors and support for advancing disease recognition technologies.

Alzheimer's Disease (AD) is the most prevalent neurodegenerative disorder in aging populations, posing a significant and escalating burden on global healthcare systems. While Multi-Tusk Learning (MTL) has emerged as a powerful computational paradigm for modeling longitudinal AD data, existing frameworks do not account for the time-varying nature of feature correlations. To address this limitation, we propose a novel MTL framework, named Feature Similarity Laplacian graph Multi-Task Learning (MTL-FSL). Our framework introduces a novel Feature Similarity Laplacian (FSL) penalty that explicitly models the time-varying relationships between features. By simultaneously considering temporal smoothness among tasks and the dynamic correlations among features, our model enhances both predictive accuracy and biological interpretability. To solve the non-smooth optimization problem arising from our proposed penalty terms, we adopt the Alternating Direction Method of Multipliers (ADMM) algorithm. Experiments conducted on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate that our proposed MTLFSL framework achieves state-of-the-art performance, outperforming various baseline methods. The implementation source can be found at https://github.com/huatxxx/MTL-FSL.

Severity assessment of Parkinson's disease (PD) using wearable sensors offers an effective, objective basis for clinical management. However, general-purpose time series models often lack pathological specificity in feature extraction, making it difficult to capture subtle signals highly correlated with PD. Furthermore, the temporal sparsity of PD symptoms causes key diagnostic features to be easily “diluted” by traditional aggregation methods, further complicating assessment. To address these issues, we propose the Multi-scale Frequency-Aware Adversarial Multi-Instance Network (MFAM). This model enhances feature specificity through a frequency decomposition module guided by medical prior knowledge. Furthermore, by introducing an attention-based multi-instance learning (MIL) framework, the model can adaptively focus on the most diagnostically valuable sparse segments. We comprehensively validated MFAM on both the public PADS dataset for PD versus differential diagnosis (DD) binary classification and a private dataset for four-class severity assessment. Experimental results demonstrate that MFAM outperforms general-purpose time series models in handling complex clinical time series with specificity, providing a promising solution for automated assessment of PD severity.

With the rapid proliferation of edge devices, such as those in the Internet of Things (IoT), which generate critical data for machine learning applications, it is essential to enable their participation in privacy-preserving Federated Learning (FL) systems. Given their limited computational resources, an effective approach is to adapt and reduce their training workload to align with their capabilities. Previous FL research has focused primarily on workload reduction through lightweight models at the edge, with limited attention given to optimizing on-device training efficiency by reducing the amount of data required for training. In this work, we propose FedFT-EDS, a novel approach that combines Fine-Tuning of partial client models with Entropy-based Data Selection to reduce training workloads on edge devices. By actively selecting the most informative local instances for learning, FedFT-EDS significantly reduces the training data in FL and demonstrates that not all user data are equally beneficial across training rounds. We show that FedFT-EDS uses only 50% of the available training data while improving the global model performance compared to the baseline methods, FedAvg and FedProx. Importantly, FedFT-EDS improves the learning efficiency of client models by up to 3×, to achieve a similar performance to the baselines in only one third of their training time. This work underscores the critical role of data selection in Federated Learning and offers a promising direction for achieving scalable and efficient FL systems.

Nearly half of global anthropogenic N2O emissions are accounted for by nitrogen fertiliser application. Therefore, accurate prediction of fertiliser-induced N2O fluxes is crucial for optimising fertiliser strategies and mitigating climate change. In this work, we introduce DA-Mamba: a data augmentation-enhanced state space model that can capture long-range N2O flux dynamics and their interactions with agri-environmental variables, even when data is limited. Using a publicly available dataset of fertiliser-induced N2O emissions, DA-Mamba achieves state-of-the-art performance, outperforming six baseline models. Additionally, we have integrated DA-Mamba as a containerised microservice within ParallelFarm, our cloud-based precision fertilisation and farm management system. The service uses real-time weather, soil and management data to generate optimised fertiliser plans and field-level N2O–yield predictions, thereby supporting sustainable agricultural decision-making.

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that significantly diminishes patients’ quality of life. Early and accurate diagnosis is critical for reducing both individual and societal burdens. Although current diagnostic methods can effectively differentiate between PD patients and healthy individuals, they tend to ignore the diversity of PD symptoms and the differences with other similar diseases, such as essential tremor or multiple system atrophy, leading to a higher risk of misdiagnosis. Additionally, existing supervised learning methods rely on subjective labeling by physicians, which is both time-consuming and subjective. To overcome these limitations, we collect multi-sensor activity data from 102 participants in free-living environments, and propose a novel self-supervised learning framework that redefines PD diagnosis as an anomaly detection problem. Additionally, we utilize two large public PD datasets as external cohorts to verify their validity. Extensive experiments demonstrate that our framework not only learns more discriminative features but also significantly enhances the model’s generalization ability, providing a promising solution to reduce misdiagnosis in PD.

The prediction of fertilization rates is a critical area of research in the agricultural field and is essential for ensuring global food security. With the ongoing expansion of the global population and the escalating repercussions of climate change, precise crop fertilization rate predictions have become paramount. This is because accurate predictions can optimize resource allocation and improve agricultural productivity. Moreover, they can provide scientific support for policy-making and agricultural input management, thereby promoting sustainable agricultural development. Despite its importance, the complexity of agricultural systems, which is influenced by multiple factors including climate, geography, soil conditions, and management practices, poses significant challenges to prediction accuracy. In this paper, we propose a deep learning framework based on Graph Neural Networks (GNNs) that effectively incorporates geographical knowledge and multi-dimensional feature information. By modeling spatial relationships through graph structures (nodes and edges), our framework enhances fertilization rate prediction accuracy. We validate the model using two datasets of different scales. The results demonstrate excellent predictive performance across all datasets and strong scalability, highlighting its potential for agricultural fertilization rate prediction.

With the widespread deployment of sensors in portable devices such as smartphones and smartwatches, sensor-based human activity recognition (HAR) has emerged as a key research focus in the field of ubiquitous computing. However, the sequential nature of sensor data makes manual annotation extremely labor-intensive and time-consuming, severely limiting the scalability of traditional supervised learning approaches. To address this challenge, self-supervised learning (SSL) has become a highly promising alternative by enabling the extraction of effective feature representations from large amounts of unlabeled data.In this work, we propose a novel attention-guided time-channel masking strategy for self-supervised learning in HAR tasks. Unlike traditional random masking methods, our approach utilizes multi-head attention mechanisms to dynamically select and mask the most discriminative portions of the sequence, thereby guiding the model to learn deeper structural patterns and salient features within the data. Extensive experiments conducted on three public datasets — MotionSense, USC-HAD, and UCI-HAR — demonstrate that our method significantly outperforms both traditional random masking and fully supervised baselines in terms of macro-averaged F1-score and classification accuracy. Furthermore, ablation studies validate the robustness and effectiveness of the proposed attention-guided masking strategy under different masking ratios, with particularly notable advantages observed at lower masking rates. These results collectively confirm the effectiveness and potential applicability of our method for enhancing self-supervised representation learning in HAR tasks.

Agriculture is crucial for food production and rural economies, yet pest infestations significantly reduce crop yields. Traditional pest management heavily relies on manual monitoring and expert experience, limiting its automation potential. While recent advances in automated pest detection have improved early identification, integrating expert knowledge to support automated decision-making remains challenging. To address these challenges, this study proposes an intelligent diagnostic framework integrating object detection, retrieval-augmented technology, and Large Language Models (LLMs). In the object detection stage, a customized YOLOv8 model is optimized through data augmentation and an Adaptive Feature Pyramid Network, achieving pest identification and lightweight optimization. Subsequently, automated online retrieval technology extracts relevant pest control information, while a locally deployed DeepSeek LLM analyzes, filters, and summarizes the retrieved content to generate professional recommendations. Experimental results validate the effectiveness of the framework. In the information processing stage, retrieval-augmented LLMs effectively mitigate the "hallucination" phenomenon, significantly enhancing the professionalism and credibility of generated recommendations. Concurrently, the optimized object detection stage achieves remarkable results. On the Pest24 dataset, the improved YOLOv8 model demonstrates a significant performance boost, with [email protected] increasing by 13%, model size reducing by 30%, and an inference speed of 275 FPS. Compared to standalone LLM-based systems, our proposed intelligent diagnostic framework demonstrates enhanced decision-making reliability through multi-stage collaborative optimization, addressing both model hallucination and detection efficiency. This work provides a scalable solution for intelligent pest management.

Privacy concerns have escalated due to companies’ misuse of user data and the occurrence of data breaches and leaks worldwide. Uploading personal data from personal devices to a central server over the network poses a danger in obtaining an inference. Hence, a different approach is needed for this scenario. Federated learning enables collaborative training on devices while maintaining the privacy of user data. Federated learning originally aimed to address privacy concerns but is vulnerable to certain privacy attacks. Although certain privacy-enhancing strategies are available, researchers are actively seeking a more effective option. This research suggests two privacy improvement methods using proxies as a better option for personal devices in a federated learning environment, achieving good performance and cost effective without accuracy loss. We studied and assessed how the methodology compared to other methodologies. Finally, we discussed how this proposed technique can address the limitations of other techniques and possible collaborations with them.

Pests significantly threaten global agricultural production, which causes severe yield losses through feeding and virus transmission. To mitigate yield losses caused by pests, timely and precise pest management practices are critical. Although previous efforts have advanced automated solutions for real-time environmental monitoring in agriculture, implementing precise pest management decision-making and suggestion generation remains a challenge due to complex reasoning processes in practice. In response, an enhanced pest management system, PEZEGO, is proposed to provide precise management suggestions through multimodal environmental data, a fine-tuned open vocabulary detector (OVD), and large language models (LLMs). Specifically, a mobile application and low-cost Internet of Things (IoT) devices are developed to capture images and environmental information. A hybrid convolutional low-rank adaptation method (HCLoRA) is proposed to fine-tune pretrained OVDs, enabling zero-shot pest detection for converting images to pest species and quantity information. In addition, a structured data-based retrieval augmented generation (SRAG) workflow for LLMs is proposed to provide precise pest management suggestions through automatically extracted agriculture management knowledge and Chain-of-Thought. The effectiveness of PEZEGO is validated in a case study of pest management in the U.K., including pest detection in field scenarios and management suggestion generation. Compared to advanced model fine-tuning methods, HCLoRA for YOLOWorld achieves the highest detection performance with$0.1759~AP^{h}$on pest detection. Additionally, the proposed SRAG workflow demonstrates the ability to support pest management with a 68.7% average F1 score for knowledge extraction and 77.33% accuracy for suggestion generation. Eventually, a mobile application demonstrates the practical effectiveness of the proposed system.

With the global ageing population, the diagnosis of Alzheimer's disease (AD) has become an urgent public health priority. Gene expression techniques offer the advantages of being less invasive and cost-effective, but their high dimensionality and small sample sizes make them prone to the curse of dimensionality in AD diagnosis. This study proposes a novel interpretable deep learning approach to address these challenges. We introduce a shallow sparse autoencoder for dimensionality reduction and combine it with XGBoost for classification, achieving an Area Under the Receiver Operating Characteristic curve (AUROC) of up to 95.13% . Additionally, we develop a fast, low-cost feature selection algorithm that dynamically adjusts feature elimination to enhance model efficiency. Comprehensive cross-dataset evaluation demonstrates the model's strong generalisation performance on the public datasets: Alzheimer's Disease Neuroimaging Initiative (ADNI), AddNeuroMed1 (ANM1), and ANM2. Our method also provides biological interpretability through enrichment analysis, offering insights into the mechanisms underlying AD and potential therapeutic targets. This makes our approach a promising tool for early, accurate diagnosis and clinical application.

As the population rapidly ages, Alzheimer’s disease (AD), the most common form of dementia, urgently requires the identification of reliable structural brain biomarkers and the development of effective therapeutic strategies. Multiple multi-task learning (MTL) paradigms have been developed to enhance model generalization by sharing information between tasks to predict AD progression and accurately identify MRI-associated biomarkers. Unlike previous MTL approaches that consider only a single kind of cognitive score to predict the complicated AD progression over time, we have developed an innovative MTL method to deal with various cognitive scores simultaneously, with each focusing on different aspects of patient cognition. To effectively capture the intricate associations among different cognitive scores at multiple time points, we first propose an Adaptive Multiple Cognitive Objective Temporal (AMCOT) task-relationship binding penalty mechanism. This mechanism adaptively reveals temporal correlations between various cognitive scores at different time points and uses these relationships to predict cumulative disease progression accurately. To select the most informative MRI features in AD progression, we consider integrating the sparse group Lasso into our model. Our algorithms are designed to handle large datasets efficiently. Empirical evaluation on the Alzheimer’s disease dataset shows that our approach significantly outperforms existing state-of-the-art algorithms in both overall and individual task performance. Additionally, we applied stability selection techniques to identify stable MRI biomarkers and analyzed their temporal patterns to gain insights into AD progression. The implementation source can be found at https://github.com/XuanhanFan/MTL-AMCOT-BB.

Automated Parkinson’s diagnosis(PD) through wearable intelligence technologies has achieved considerable results, but its application in the wild environment presents challenges due to small on-state samples and sparse distribution across time, activity patterns, and subjects. To tackle these challenges, this study proposes a novel data augmentation model to improve PD recognition results in the wild. Our model utilizes a three-level augmentation strategy across different times, patterns, and subjects. Specifically, we apply temporal-level augmentation and aggregation to learn distinct representations, while using pattern/subject-level combinations and augmentations to generate additional samples. As a result, this augmentation approach not only facilitates the acquisition of diverse representations for symptoms but also addresses challenges such as missing data and small sample sizes, which are common for medical data in a free-living environments. This proposed model has applied to a real Parkinson’s Disease (PD) dataset collected in low-resource settings, where it achieves impressive accuracy in the fine-grained classification of PD severity (mild, moderate, severe). In conclusion, this study contributes to more accurate PD self-diagnosis in real-world environments, thereby enabling remote drug intervention guidance from doctors.

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by tremors as its most typical symptom. Wearable accelerometer sensors, along with corresponding machine learning algorithms, can effectively assist in the diagnosis of PD tremors. However, due to the variations in disease progression and symptoms caused by individual differences among PD patients, it is challenging for existing algorithms to eliminate label noise and accurately identify and extract disease-related features across diverse patient data. In this study, we propose a Learnable Discriminative Instance Pool (LDIP) algorithm based on multi-instance learning, which integrates the concept of learnable shapelets. This method transforms the traditional DIP algorithm into a learnable instance pool that can be adaptively adjusted according to discriminative criteria, thereby enhancing the separability between different classes after bag mapping. We evaluated the proposed method on two clinical datasets using three different machine learning classifiers, achieving a maximum 73% accuracy for 5-class classification. The experimental results demonstrate that our proposed method consistently outperforms current baselines across various settings.

Parkinson’s disease (PD) is a progressive neurode-generative disorder characterized by motor symptoms such as tremors, rigidity, and bradykinesia. Accurate and early diagnosis is crucial for effective management and treatment. Some quantitative studies have combined wearable technology with machine learning methods, demonstrating a high potential for practical application. However, these studies mostly use single-location, single-sensor data collected from PD patients in clinical settings, neglecting the diversity of PD symptoms and the real-world application scenarios in free-living environments. This paper proposes an auxiliary diagnosis framework for PD based on multi-location, multi-sensor fusion, and unsupervised domain adaptation. The multi-location, multi-sensor fusion can mitigate the asymmetry of Parkinson’s symptoms, while unsupervised domain adaptation helps transfer in-hospital data to free-living environments without the need for manual labeling of the free-living data. Additionally, this paper designs a multi-head attention mechanism that focuses the disease classifier on sensors with strong feature discrimination and good distribution alignment. This experiment relies on wearable sensor data from 60 PD patients and 12 healthy controls, achieving an impressive accuracy of 90.46%, a precision of 88.28%, a recall of 88.09%, and an F1-score of 88.14%.

Wearable sensors combined with machine learning provide an effective solution for assessing Parkinson’s Disease (PD) severity. However, time-series data from wearable sensors often lack window-level labels for PD severity, resulting in weak supervision, which introduces the challenge of label noise. Additionally, patient variability causes distributional discrepancies, further complicating the learning process. To address these issues, we propose Adaptive Domain-Adversarial Multi-Instance Learning (ADAMIL), which combines and refines Multiple-Instance Learning (MIL) with domain-adversarial techniques. We improve traditional MIL by incorporating self-attention mechanisms and learnable positional encoding, enabling ADAMIL to capture temporal dependencies more effectively, thus making it better suited for mitigating label noise in weakly supervised time-series data. Furthermore, ADAMIL refines domain-adversarial learning to autonomously align latent distributions, ensuring robust domain-invariant feature learning without relying on predefined labels. Experimental results show that ADAMIL achieves 85.29% accuracy and 80.57% F1-score in fine-grained PD severity classification, outperforming existing methods. Notably, this performance is achieved using only a single wrist-worn sensor, underscoring its potential for practical use in clinical and home settings. The code is available at https://github.com/xzxzy12345XZY/ADAMIL.

Alzheimer’s disease (AD) is the leading cause of dementia worldwide, characterized by its gradual progression and the subtle variations across disease stages, which pose significant challenges for accurate diagnosis. While deep representation learning algorithms have shown promise in the early detection of AD using MRI data, existing approaches often overlook the meaningful relationships between continuous labels in AD progression, and the learned representations frequently lack interpretability due to the black-box nature of deep learning models. To address these limitations, we propose ICReL (Interpretable Continuous Representation Learning), a novel concept based on coding rate principles that captures continuous representations across AD stages while maintaining a high degree of interpretability. Extensive experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset demonstrate that ICReL not only outperforms multiple baseline methods using 2D slice or 3D MRI in terms of learning continuous representations, but also exhibits enhanced robustness to label corruption and superior predictive performance. This work offers a new, interpretable approach to representation learning for computer-aided diagnosis of neurodegenerative diseases.

Alzheimer’s disease (AD) is the most common dementia in today’s aging society. Accurately predicting its progress remains a major challenge. Multi-task learning methods are widely used in AD research to help understand the progression of AD by predicting cognitive performance and identifying key imaging biomarkers. Previous work has selected representative feature subsets from magnetic resonance imaging (MRI) features. The design of these models is based on the assumption that correlations are consistent across different tasks. Specifically, the model only focuses on a single cognitive score in each prediction and ignores the correlation between different cognitive scores. However, clinicians often use a combination of assessment scores and other tests to more comprehensively assess cognitive status and make a diagnosis. Combining scores from multiple cognitive assessments helps improve accurate predictions of disease progression. Previous research models have primarily focused on predicting a single cognitive score longitudinally. In this paper, we propose a multi-target, multi-task learning method that comprehensively considers the correlation between different cognitive scores and the relationship between longitudinal tasks to simultaneously predict multiple cognitive scores to more comprehensively capture the disease characteristics of development, thereby effectively predicting disease progression. We also adopt a structure matrix to explicitly represent the correlation between tasks, further improving the accuracy and interpretability of the model. Results from extensive experiments using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset show that our method exhibits balanced multi-target performance when dealing with three cognitive scores. Compared to models focusing on a single cognitive target score, our method performs better in the early prediction of cognitive scores.

Promoting sustainable agriculture plays a crucial role in reducing greenhouse gas (GHG) emissions, lowering the carbon footprint, and improving farm resilience. Three challenges must be overcome to achieve sustainable agriculture management. Firstly, there is a lack of reliable and sustainable fertiliser solutions to improve fertiliser use efficiency, reduce GHG emissions while maintaining crop production. In addition, how to cost-effectively quantify the response of soil carbon and GHG fluxes to different fertilisation practices. Thirdly, there is a requirement to integrate multi-source farming data and AI models into a farm management information system (FMIS) to support intelligent decisions for farmers. To address these challenges, we developed the ParellelFarm, an AI-enabled sustainable farming management system that integrates multi-source farming data and AI-driven fertiliser and soil carbon models into a multi-tenant cloud platform, to support sustainable farming. It also provides remote field visualisation and management as well as instant messaging via web and mobile clients, supporting fast and accurate labour allocation with fewer resources. It is a potential solution for a cost-effective, highly productive and sustainable modern net-zero farm.

Agent-based modeling (ABM) is a powerful simulation methodology employed to analyze complex systems by representing individual entities, known as agents, which interact autonomously within a defined set of rules. This approach is particularly effective for epidemic simulation, where capturing the nuanced interactions and behaviors of individuals is crucial for understanding disease dynamics and spread. However ABM often come with complected and slow implementation. To address the performance issue, in this paper, we introduce a Framework for Large-scale Agent-based Rapid Epidemic Simulation (FLARES), a novel GPU-accelerated framework designed to enhance the performance of agent-based infectious disease transmission model. FLARES provides robust support for parallel execution, significantly reducing the overall time consumption for high computational cost tasks, enables researchers to conduct detailed and accurate epidemic analysis of disease spread.

In the rapidly evolving field of artificial intelligence, large language models (LLMs) have attracted much attention from researchers in various fields due to their unexpected text generation and comprehension capabilities. However, the applications of LLMs for sustainable pest management are under-explored due to the heavy reliance on specialized expert knowledge. In addition, evaluating the quality of LLMs' content is another technological challenge for applying LLMs in sustainable pest management. Therefore, we propose an instruction-based prompting method that integrates pest expert knowledge into the prompt, equipping LLMs with the necessary context to generate more accurate and relevant pest management advice. Furthermore, we propose an LLM-based evaluation framework to score the generated content on Coherence, Logical Consistency, Fluency, Relevance, Comprehension, and Exhaustion. Additionally, we integrate an Expert System based on crop threshold data as a baseline to obtain scores for Accuracy on whether pests found in crop fields should take management action. Each model's score is weighted by percentage to get a final score. The results show that GPT-3.5 and GPT-4 outperform the FLAN models in most evaluation dimensions. Furthermore, while using instruction-based prompting containing domain-specific knowledge outperforms other prompting methods with an accuracy of 72%, ongoing refinements and assessments of end-user satisfaction are essential to enhance the LLMs' effectiveness and practical helpfulness in providing pest management advice.

Multimodal learning is widely used in automated early diagnosis of Alzheimer's disease. However, the current studies are based on an assumption that different modalities can provide more complementary information to help classify the samples from the public dataset Alzheimer's Disease Neuroimaging Initiative (ADNI). In addition, the combination of modalities and different tasks are external factors that affect the performance of multimodal learning. Above all, we summrise three main problems in the early diagnosis of Alzheimer's disease: (i) unimodal vs multimodal; (ii) different combinations of modalities; (iii) classification of different tasks. In this paper, to experimentally verify these three problems, a novel and reproducible multi-classification framework for Alzheimer's disease early automatic diagnosis is proposed to evaluate and verify the above issues. The multi-classification framework contains four layers, two types of feature representation methods, and two types of models to verify these three issues. At the same time, our framework is extensible, that is, it is compatible with new modalities generated by new technologies. Following that, a series of experiments based on the ADNI-1 dataset are conducted and some possible explanations for the early diagnosis of Alzheimer's disease are obtained through multimodal learning. Experimental results show that SNP has the highest accuracy rate of 57.09% in the early diagnosis of Alzheimer's disease. In the modality combination, the addition of Single Nucleotide Polymorphism modality improves the multi-modal machine learning performance by 3% to 7%. Furthermore, we analyse and discuss the most related Region of Interest and Single Nucleotide Polymorphism features of different modalities.

As an emerging learning paradigm, lifelong learning solves multiple consecutive tasks based upon previously accumulated knowledge. When facing with a new task, existing lifelong learning approaches need both input and desired output data to construct task models before knowledge transfer can succeed. However, labeling each task requires extensive labors and time, which can be prohibitive for real-world lifelong regression problems. To reduce this burden, we propose to incorporate unsupervised feature into lifelong regression via coupled dictionary learning, enabling to learn new tasks without target output data. Specifically, the input data for each task is encoded as unsupervised feature while both input and output data are used to construct task predictor. The unsupervised feature is linked with task predictor through two dictionaries that are coupled by a joint sparse representation. Because of the learned coupling between the two spaces, the task predictor for the new coming task can be recovered given only the input data. We further incorporate active task selection into this framework, enabling actively choosing tasks to learn in a task-efficient manner. Three case studies are used to evaluate the effectiveness of our method, in comparison with existing lifelong learning approaches. Results show that our method is able to accurately predict new tasks through unsupervised transfer, eliminating the need to label tasks before constructing the predictor.

Alzheimer's disease (AD), the most prevalent dementia, gradually reduces the cognitive abilities of patients while also posing a significant financial burden on the healthcare system. A variety of multi-task learning methods have recently been proposed in order to identify potential MRI-related biomarkers and accurately predict the progression of AD. These methods, however, all use a predefined task relation structure that is rigid and insufficient to adequately capture the intricate temporal relations among tasks. Instead, we propose a novel mechanism for directly and automatically learning the temporal relation and constructing it as an Automatic Temporal relation Graph (AutoTG). We use the sparse group Lasso to select a universal MRI feature set for all tasks and particular sets for various tasks in order to find biomarkers that are useful for predicting the progression of AD. To solve the biconvex and non-smooth objective function, we adopt the alternating optimization and show that the two related sub-optimization problems are amenable to closed-form solution of the proximal operator. To solve the two problems efficiently, the accelerated proximal gradient method is used, which has the fastest convergence rate of any first-order method. We have preprocessed three latest AD datasets, and the experimental results verify our proposed novel multi-task approach outperforms several baseline methods. To demonstrate the high interpretability of our approach, we visualise the automatically learned temporal relation graph and investigate the temporal patterns of the important MRI features. The implementation source can be found athttps://github.com/menghui-zhou/MAGPP.

User counterparts, such as user attributes in social networks or user interests, are the keys to more natural Human–Computer Interaction (HCI) . In addition, users’ attributes and social structures help us understand the complex interactions in HCI. Most previous studies have been based on supervised learning to improve the performance of HCI. However, in the real world, owing to signal malfunctions in user devices, large amounts of abnormal information, unlabeled data, and unsupervised approaches (e.g., the clustering method) based on mining user attributes are particularly crucial. This paper focuses on improving the clustering performance of users’ attributes in HCI and proposes a deep graph embedding network with feature and structure similarity (called DGENFS ) to cluster users’ attributes in HCI applications based on feature and structure similarity. The DGENFS model consists of a Feature Graph Autoencoder (FGA) module, a Structure Graph Attention Network (SGAT) module, and a Dual Self-supervision (DSS) module. First, we design an attributed graph clustering method to divide users into clusters by making full use of their attributes. To take full advantage of the information of human feature space, a k-neighbor graph is generated as a feature graph based on the similarity between human features. Then, the FGA and SGAT modules are utilized to extract the representations of human features and topological space, respectively. Next, an attention mechanism is further developed to learn the importance weights of different representations to effectively integrate human features and social structures. Finally, to learn cluster-friendly features, the DSS module unifies and integrates the features learned from the FGA and SGAT modules. DSS explores the high-confidence cluster assignment as a soft label to guide the optimization of the entire network. Extensive experiments are conducted on five real-world data sets on user attribute clustering. The experimental results demonstrate that the proposed DGENFS model achieves the most advanced performance compared with nine competitive baselines.

Multi-task learning models based on temporal smoothness assumption, in which each time point of a sequence of time points concerns a task of prediction, assume the adjacent tasks are similar to each other. However, the effect of outliers is not taken into account. In this paper, we show that even only one outlier task will destroy the performance of the entire model. To solve this problem, we propose two Robust Temporal Smoothness (RoTS) frameworks. Compared with the existing models based on temporal relation, our methods not only chase the temporal smoothness information but identify outlier tasks, however, without increasing the computational complexity. Detailed theoretical analyses are presented to evaluate the performance of our methods. Experimental results on synthetic and real-life datasets demonstrate the effectiveness of our frameworks. We also discuss several potential specific applications and extensions of our RoTS frameworks.

Identifying and utilising various biomarkers for tracking Alzheimer’s disease (AD) progression have received many recent attentions and enable helping clinicians make the prompt decisions. Traditional progression models focus on extracting morphological biomarkers in regions of interest (ROIs) from MRI/PET images, such as regional average cortical thickness and regional volume. They are effective but ignore the relationships between brain ROIs over time, which would lead to synergistic deterioration. For exploring the synergistic deteriorating relationship between these biomarkers, in this paper, we propose a novel spatio-temporal similarity measure based multi-task learning approach for effectively predicting AD progression and sensitively capturing the critical relationships between biomarkers. Specifically, we firstly define a temporal measure for estimating the magnitude and velocity of biomarker change over time, which indicate a changing trend(temporal). Converting this trend into the vector, we then compare this variability between biomarkers in a unified vector space(spatial). The experimental results show that compared with directly ROI based learning, our proposed method is more effective in predicting disease progression. Our method also enables performing longitudinal stability selection to identify the changing relationships between biomarkers, which play a key role in disease progression. We prove that the synergistic deteriorating biomarkers between cortical volumes or surface areas have a significant effect on the cognitive prediction.

Recently, there have been a wide spectrum of multitask learning (MTL) methods developed to model Alzheimer’s disease (AD) progression. Typical MTL studies related cognitive ability prediction focus on modeling AD progression using high-quality clinical data such as MRI and cognitive scores. These studies follow a unified regularised MTL framework to process each follow-up data from patients over time. Beginning at baseline, the framework regards cognitive ability at each followup as a task and organise task relationship through temporal smoothness in cognitive ability. There is little attention on how to design feasible experimental protocols and normalisation for reliably evaluating those regularised MTL models. In this paper, we present an empirical analysis for investigate above issues. Four typical structural regularization approaches are revisited. Four issues affecting evaluation process of regularised MTL models are evaluated by experiments: 1) evaluation indicators, 2) repeated experimental times, 3) training data size and 4) number of tasks in MTL. The results demonstrate that regularised MTL models are capable of predicting AD progression with effectiveness, in many challenging cases of curse of dimensionality, data insufficiency or single MRI data input. One important finding is that MTL can effectively reduce the over-fitting risk of model, even with limited sample size. We also discover that the temporal smoothness assumption instead limits the performance of later tasks. It encourages us to revisit the relationship between patients’ cognitive ability changes between 2 and 3 years when using MTL to model AD progression.

Alzheimer’s disease (AD), the most prevalent dementia, gradually reduces the cognitive abilities of patients while also posing a significant financial burden on the healthcare system. A variety of multi-task learning methods have recently been proposed to identify potential MRI-related biomarkers and accurately predict the progression of AD. These methods, however, all use a predefined task relation structure that is rigid and insufficient to adequately capture the intricate temporal relations among tasks. Instead, we propose a novel mechanism for directly and automatically learning the temporal relation and constructing it as an Automatic Temporal relation Graph (AutoTG). We use the sparse group Lasso to select a universal MRI feature set for all tasks and particular sets for various tasks in order to find biomarkers that are useful for predicting the progression of AD. To solve the biconvex and nonsmooth objective function, we adopt the alternating optimization and show that the two related suboptimization problems are amenable to closed-form solution of the proximal operator. To solve the two problems efficiently, the accelerated proximal gradient method is used, which has the fastest convergence rate of first-order method. We have preprocessed two latest AD datasets, and the experimental results verify our proposed novel multi-task approach outperforms several baseline methods. To demonstrate the high interpretability of our approach, we visualize the automatically learned temporal relation graph and investigate the temporal patterns of the important MRI features. The implementation source is at https://github.com/menghui-zhou/MAGPP.

Internet of Things (IoT) enabled technology has rapidly and efficiently facilitate healthcare diagnose and treatment with low-cost and lightweight devices. Big data generated from IoT offers valuable and crucial information to guide decision-making, improve patient outcomes, and decrease healthcare costs, etc. The workshop is aiming to provide an opportunity for researchers and practitioners from both academia and industry to present the state-of-the-art research and applications in utilizing IoT and big data technology for healthcare by presenting efficient scientific and engineering solutions, addressing the needs and challenges for integration with new technologies, and providing visions for future research and development.

Internet of Things (IoT) Wearable technology plays a crucial role in assisting the diagnosis of Parkinson’s disease (PD), and an efficient model for auxiliary diagnosis of the severity of PD can help reduce the workload for doctors. However, due to the influence of data collection environments and annotators, noisy label data is inevitable, which may have a negative impact on modeling the severity of PD. To address the above challenges, on the one hand, we collected a large number of activity signal data of Parkinson’s patients in free-living environments, and on the other hand, we proposed an efficient PD stage assessment framework, which includes a noisy label processing method to alleviate the noisy label negative impact. Specifically, we collected signal data from 15 healthy controls and 68 PD patients through 12 activities, and then we proposed a framework for noisy label detection and correction. The experimental results on real PD data sets demonstrated that the proposed framework achieve 75.9% accuracy in PD stage assessment and significantly improve the classification performance of different types of basic classifiers, which is better than other noisy label detection algorithms and other PD stage assessment frameworks. Overall, in this work, we focus on modeling PD severity in free-living environments using a single wearable sensor and reducing the negative impact of noisy label data to better help PD patients manage the disease.

An effective auxiliary diagnostic model for the severity of Parkinson’s disease (PD) could help hospitals reduce their workload, particularly in nations or regions where medical resources are limited. However, a critical challenge persists that hampers the progress of such endeavors. Previous studies have employed label propagation techniques that assign uniform labels to all activity signal segments of a patient, neglecting the complex expression of PD symptoms, thereby introducing label noise. To confront this challenge, we have collected an extensive set of PD activity signals from a clinical setting and have proposed an efficient and robust framework for assessing PD severity. Specifically, we gathered wearable device data on 14 daily activities from 70 PD patients, based on the Unified Parkinson’s Disease Rating Scale Part III. Our data analysis indicates that many segments within the activities were incorrectly labeled, significantly impairing the classification performance of the model. We introduced a novel framework based on Multi-Instance Learning with a Re-weighted Discriminative Instance Mapping (RDIM) to model PD auxiliary diagnosis, aiming to eliminate the impact of label noise present in the data. The results demonstrate that our framework achieves an accuracy of 80.88% in classifying the severity of PD, effectively addressing the label noise caused by coarse-grained label propagation.

Pest management is an important factor affecting agricultural and food industry products. A large number of insect species and the subtle differences bring a challenge to the accurate recognition of pests. Many studies tackle the challenge of pest recognition through deep neural networks (DNNs) and achieve significant success in terms of accuracy. However, the complex structure and a large number of parameters make DNNs difficult for end users to understand the reasons for the decision of models, which causes distrust in the classification of harmful insects and overuse of insecticides. To address the lack of explainability of DNNs, we propose an explanation generation workflow to generate concept-based explanations for pest recognition. Specifically, the concept extraction method uses a clustering algorithm to extract image segments with meaningful concepts from a portion of the training dataset. Then, concept models are trained to detect the presence of concepts in the image. Finally, the explanation generation method provides concept-based global and local explanations in the form of weighted directed graphs and concept importances, respectively. Through qualitative and quantitative analysis, the proposed workflow extracts meaningful concepts for pest recognition effectively and detects the presence of concepts in images.

Multi-task learning with temporal relation is a common prediction method for modelling the evolution of a wide range of systems. Considering the inherent relations between multiple time points, many works apply multi-task learning to jointly analyse all time points, with each time point corresponding to a prediction task. The most difficult challenge is determining how to fully explore and thus exploit the shared valuable temporal information between tasks to improve the generalization performance and robustness of the model. Existing works are classified as temporal smoothness and mean temporal relations. Both approaches, however, utilize a predefined and symmetric task relation structure that is too rigid and insufficient to adequately capture the intricate temporal relations between tasks. Instead, we propose a novel mechanism named Automatic Temporal Relation (AutoTR) for directly and automatically learning the temporal relation from any given dataset. To solve the biconvex objective function, we adopt the alternating optimization and show that the two related sub-optimization problems are amenable to closed-form computation of the proximal operator. To solve the two problems efficiently, the accelerated proximal gradient method is used, which has the fastest convergence rate of any first-order method. We have preprocessed six public real-life datasets and conducted extensive experiments to fully demonstrate the superiority of AutoTR. The results show that AutoTR outperforms several baseline methods on almost all datasets with different training ratios, in terms of overall model performance and every individual task performance. Furthermore, our findings verify that the temporal relation between tasks is asymmetrical, which has not been considered in previous works. The implementation source can be found at https://github.com/menghui-zhou/AutoTR.

Wearable technology has played a crucial role in computer-aided diagnosis and long-term monitoring of Parkinson’s disease (PD). How to efficiently and accurately assess the severity of Parkinson’s disease using wearable devices remains the essential problem. However, in the real free-living environment, we have encountered two issues: weak annotation and class imbalance, which could potentially impede the automatic assessment of Parkinson’s disease. To overcome these challenges, we propose a novel Parkinson’s disease assessment framework in free-living environment. Specifically, clustering methods are used to learn latent categories from the same activities, and use Latent Dirichlet allocation (LDA) topic models to capture latent features of multiple activities. Then, to mitigate the impact of data imbalance, we augment bag-level data while retaining key instance prototypes. The new framework is applied to a PD dataset collected by wearable sensors in the wild. It achieves an impressive 73.49% accuracy in the fine-grained (normal, mild, moderate, severe) classification of PD severity based on hand movements. Overall, this study contributes to more accurate PD self-diagnosis in the wild, enabling remote guidance for drug intervention from doctors.

Cardiovascular diseases (CVDs) are a significant cause of global mortality; thus, early intervention of CVDs can prevent their progression and effectively lower their death rate. However, one of the main obstacles to this is the lack of high-quality datasets for CVDs, which are essential for efficiently training machine learning prediction models. The available datasets are limited in size and have missing values and a high-class imbalance. Generative adversarial networks (GANs) have been shown to have high potential in producing medical synthetic tabular data, but the heterogeneity and complex dependencies of such data present challenges. This study proposes an enhanced sampling approach that aims to improve the quality of synthetic data and their resemblance to the original data distribution. The proposed method is evaluated on a medical dataset containing demographic, clinical and laboratory data from patients with CVDs. As part of the examination process, the performance of the proposed method was evaluated by comparing it with the data generated from the CTGAN. Our results show a higher degree of similarity between the distributions of the generated data and the real datasets, demonstrating the efficacy of the proposed sampling approach. The synthetic data generated by our model closely resembles the statistical properties of real data and shows strong potential for improving data quality. By accurately capturing the patterns and features of cardiovascular disease data, our model can contribute to enhancing the CVD risk prediction model.

Epidemiological studies have played an important role in explaining the risk factors associated with cardiovascular diseases (CVD) and identifying opportunities for prevention. Early CVD intervention can prevent the progression of these diseases and effectively lower the mortality rate. In particular, using coronary artery calcium (CAC) scores has been proven to enhance the prediction of coronary heart disease events. Thus, the early prediction of high-risk CAC allows individuals to prevent coronary heart disease (CHD) from progressing to extreme symptoms and illnesses. The process of identifying and using various predictors to predict CAC has received increasing attention, which has helped clinicians and patients make informed decisions. In this discipline, traditional methods rely on statistical approaches to calculate the effects of clinical and demographic factors on CAC score prediction, enabling the identification of relevant features. However, with the advancement of machine learning (ML) techniques, it is now possible to train CAC models that offer highly accurate and stable predictions. This research investigates the application of non-parametric methods to identify and present non-linear relationships within cardiovascular disease datasets derived from the King Faisal Specialist Hospital and Research Centre in Saudi Arabia, without assuming normal distributions. Furthermore, we will focus on efficient feature extraction to handle the dataset effectively. Additionally, we will explore the utility of regularised regression for predicting CAC scores.

On-device learning, such as federated learning, is gaining more popularity. It benefits users with faster inference and privacy preservation. However, the heterogeneity of personal devices makes its deployment not easy. With the increasing need for an on-demand learning platform, the web browser has become one of the leading solutions due to its availability and interoperability. Nevertheless, there is still a lack of research on evaluating the behaviour of federated learning on web browser platforms. This includes evaluating their compatibility, convergence in inference results, and performance. Our paper tries to address these concerns. Throughout our experiments, we found that there are still inconsistencies in inference results, compatibility issues, and varied performance among these platforms. Besides experiment analysis on this subject, we also recommend a model-platform-device compatibility report as our contribution.

The prediction of mobility change is a crucial aspect of infectious disease prevention and control, as individual movement can lead to the spread of infectious diseases. After an epidemic outbreak, countries usually implement a series of epidemic prevention and control policies to restrict personnel travel. This paper aims to measure the impact of national epidemic prevention and control policies on mobility change. To achieve this goal, we propose a mobility change prediction model based on LSTM and "local" fully connected layers. The results show that the model’s mobility change predictions in the six mobility categories are consistent with reality, and the model is optimal overall.

Every year, plant parasitic nematodes, one of the major groups of plant pathogens, cause a significant loss of crops worldwide. To mitigate crop yield losses caused by nematodes, an efficient nematode monitoring method is essential for plant and crop disease management. In other respects, efficient nematode detection contributes to medical research and drug discovery, as nematodes are model organisms. With the rapid development of computer technology, computer vision techniques provide a feasible solution for quantifying nematodes or nematode infections. In this paper, we survey and categorise the studies and available datasets on nematode detection through deep-learning models. To stimulate progress in related research, this survey presents the potential state-of-the-art object detection models, training techniques, optimisation techniques, and evaluation metrics for deep learning beginners. Moreover, seven state-of-the-art object detection models are validated on three public datasets and the AgriNema dataset for plant parasitic nematodes to construct a baseline for nematode detection.

In order to control the first wave of COVID-19 pandemic in 2020, many models have shown effectiveness in predicting the spread of new coronary pneumonia and the different interventions. However, few models can collect large amounts of high-quality real-time data faster under the premise of protecting privacy, considering the impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant and the mass vaccination program as a new intervention. Therefore, we developed a mobile intelligent application that can collect a large amount of real-time data while protecting privacy and conducted a feasibility study by defining a new COVID-19 mathematical model SEMCVRD. By simulating different intervention measures, the prediction model of the mobile intelligent application used in this article simulates the epidemic situation in the U.K. as an example. The findings are as below: the optimal intervention strategy is to suppress the intervention at$P=3$(intervention intensity: the average number of contacts per person per day) before the end of March 2021, then gradually release the intervention intensity at a rate of$P+2$, and finally release the intensity to$P=9$in June 2021. The COVID-19 pandemic will end at the end of June 2021, when the total number of deaths will reach 128772. This strategy will be able to balance the tradeoff between loss of life and economic loss. Compared with the official statistics released by the U.K. government on May 31, 2021, our model can accurately predict the relative error rate of the total number of cases is less than 6.9%, and the relative error rate of the total number of deaths is less than 1%. Furthermore, the model is also suitable for collecting data from countries/regions around the world.

In this paper, we propose a novel efficient multi-task learning formulation for the class of progression problems in which its state will continuously change over time. To use the shared knowledge information between multiple tasks to improve performance, existing multi-task learning methods mainly focus on feature selection or optimizing the task relation structure. The feature selection methods usually fail to explore the complex relationship between tasks and thus have limited performance. The methods centring on optimizing the relation structure of tasks are not capable of selecting meaningful features and have a bi-convex objective function which results in high computation complexity of the associated optimization algorithm. Unlike these multi-task learning methods, motivated by a simple and direct idea that the state of a system at the current time point should be related to all previous time points, we first propose a novel relation structure, termed adaptive global temporal relation structure (AGTS). Then we integrate the widely used sparse group Lasso, fused Lasso with AGTS to propose a novel convex multi-task learning formulation that not only performs feature selection but also adaptively captures the global temporal task relatedness. Since the existence of three non-smooth penalties, the objective function is challenging to solve. We first design an optimization algorithm based on the alternating direction method of multipliers (ADMM). Considering that the worst-case convergence rate of ADMM is only sub-linear, we then devise an efficient algorithm based on the accelerated gradient method which has the optimal convergence rate among first-order methods. We show the proximal operator of several non-smooth penalties can be solved efficiently due to the special structure of our formulation. Experimental results on four real-world datasets demonstrate that our approach not only outperforms multiple baseline MTL methods in terms of effectiveness but also has high efficiency.

In the Cooperative Intelligent Transportation System (C-ITS) paradigm, vehicles could communicate with roadside units to augment their traffic knowledge. Smart roadside units could provide second-order information (e.g., vehicle count) from raw first-order data (e.g., visual feed, point clouds), and this “smart” feature is usually provided using deep neural network models. However, implementing these useful models implies a cost for computational complexity that could hinder the future deployment of smart roadside units needed for sustainability in transportation systems. In this paper, we propose to use model compression on deep image processing models to promote its feasibility for usage in smart sensors. We formulated a controllable convolutional model compression (CCMC) algorithm that can perform filter-wise evolutionary pruning on image processing networks, along with a predefined compression ratio. CCMC is applicable for image processing networks, which have multiple possible traffic data sources (e.g., road camera surveillance). Furthermore, CCMC has a definable target compression ratio that is useful for controlling the trade-off between resource consumption and output performance. We tested our proposed method on depth estimation, which is useful for scene understanding and mapping the locations of objects in the 3D space. Our experiments show that the pruned model has minimal performance discrepancy from the original one, supporting the sustainability features needed for intelligent transportation systems.

Machine learning (ML) techniques for predicting the progression of Alzheimer’s disease (AD) can greatly assist researchers and clinicians in establishing effective AD prevention and treatment strategies. The problems of monotonicity of data forms and scarcity of medical data are the main reasons that currently limit the performance of ML approaches. In this research, we propose a novel similarity-based quantification approach that simultaneously considers the magnitude and direction relationships of structural variations among brain biomarkers, and encodes quantified data as third-order tensors to solve problem of data form monotonicity, then combining tensor multi-tasking learning model to predict AD progression. In this model, the prediction of each patient is considered as a task, and each task shares a set of latent factors obtained by tensor decomposition, knowledge sharing between tasks can improve the generalization of the model and solve the problem of scarcity of medical data. The model can be utilised to efficiently predict the progression of AD integrating magnetic resonance imaging (MRI) data and cognitive scores of AD patients at different stages. To evaluate the effectiveness of the proposed approach, we conducted extensive experiments utilising MRI data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The results reveal that the proposed model predicts AD progression more accurately and consistently than single-task and state-of-the-art multi-task regression approaches on various cognitive scores. The proposed approach can recognize brain structural variation in patients and apply it to reliably predict and diagnose AD progression.

In this paper, we propose a multi-task learning approach for predicting the progression of Alzheimer's disease (AD), known as the most common form of dementia. The vital challenge is to identify how the tasks are related and build learning models to capture such task relatedness. Unlike previous methods that assume low-rank structure, chase the predefined local temporal relatedness or utilize local approximation, we propose a novel penalty termed L ongitudinal S tability A djustment (LSA) to adaptively capture the intrinsic global temporal correlation among multiple time points and thus utilize the accumulated disease progression information. We combine LSA with sparse group Lasso to present a novel multi-task learning formulation to identify biomarkers closely related to cognitive measurement and predict AD progression. Two efficient algorithms are designed for large-scale dataset. Experimental results conducted on two AD data sets demonstrate our framework outperforms competing methods in terms of overall and each task performances. We also perform stability selection to identify stable biomarkers from the MRI feature set and analyze their temporal patterns in disease progression.

Precision fertilisation is the application of target variable fertilisation techniques based on soil fertility variations in specific regions. Precise fertilisation can help to balance soil nutrients, conserve fertiliser, prevent pollution, and boost crop yields. The lack of agricultural data is a key reason limiting the application of machine learning methods in agriculture. Due to the low-level network technology in farms, it is difficult to obtain diverse and complete agricultural data. The existing agricultural data is typically unstructured and difficult to mine. In this article, we extracted real-world agricultural dataset from four real farms with winter wheat and it includes different types of factors describing agriculture, such as climate, soil nutrients, crop yield information. Moreover, we present a novel multi-task learning (MTL) approach based on a tensor built of farm data to efficiently prediction both the amount and time of base fertiliser and topdressing. Specifically, real-world agricultural measurements (such as climate data, soil nutrients, etc.) are encoded into a three-dimensional tensor, and a set of interpretable temporal and spatial latent factors is extracted from the raw data through tensor decomposition. The latent factors are then utilised to train the spatio-temporal tensor prediction model. We have conducted extensive experiments utilising the real-world agricultural dataset. The experimental results show that our proposed methods have superior accuracy and stability in fertilisation prediction compared to state-of-the-art regression methods.

With the increasing demand for cost-effective crop pest management solutions, how to achieve effective and efficient automatic pest detection has become the primary research problem. Traditional object detection methods that rely on the quality of handcrafted feature selection are hardly used in pest detection due to the difficulty of designing the features of multiple types of pests. The application of deep learning which presents outstanding performances in object detection tasks faces the following challenges in the field of pest detection. First, the detection difficulties caused by tiny-size pests and protective colouration limit the accuracy of detection. Second, pest detection requires the employment of experts to obtain the annotation of pests for training models, which is costly. Finally, the ability to run on lightweight devices is required due to the limitations of the field environment on networks and equipment. To solve these problems, this paper focuses on a lightweight tiny object detection model, training on limited supervised samples through different data augmentation methods. Different components of object detection models and data augmentation methods are analysed in different sizes of training datasets. Finally, a method based on the Yolo detection model is proposed for pest detection. This pest detection model is evaluated on a real-world aphids data set containing 6k objects. Five sets of data augmentation methods are used on seven sizes of training data sets for analysis. Then the structure of the detection neck of the Yolo model is analysed. Our experimental results show that 54.35% mAP can be achieved by the PAN module and removing the Mosaic data augmentation method for tiny object detection with one hundred samples.

Alzheimer's Disease (AD) is the most common reason of dementia that causes serious problems in patients' congnitive functions. Multi-task learning (MTL) has performed well in studies of longitudinal processes in Alzheimer's disease for revealing the progression of AD. Combined with prior knowl-edges in disease progression or medical science, regularization MTL framework could introduce empirical constraints more flexibly. Meanwhile, it brings higher cost during optimization. While it shown that most of formulations could not define the disease progression precisely. Existing regression methods with temporal smoothness method eliminated abnormal fluctuation of cognitive scores, and neglected the sophisticated progression in disease. In this article, we proposed an analytic method to define the progression of AD, and a flexible bandwidth method to encourage the points of disease time sequence temporal smoothness in an appropriate way. To solve three non-smooth penalties in our method, we proposed an optimization method combined accelerated gradient descent (AGD) and alternating direction method of multipliers (ADMM).

The clinical progression of Alzheimer's disease( AD ) can't be accurately evaluated by single modality data alone. Multi-modal data have a good effect on the diagnosis of AD. Clarifying the complementarity between modalities is crucial for the assessment of each stage of AD. Few studies have specifically explored the complementarity between different modalities due to the lack of completely aligned and paired multi-modal data and the limitation of sample size. However, collecting the full set of aligned and paired data is expensive or even impractical. In addition, the limited number of samples poses a great challenge to the robustness of the model. In this paper, different machine learning( ML ) methods were used to explore data complementarity between T1-weighted magnetic resonance imaging ( MRI ), cerebrospinal fluid ( CSF ), and fluorodeoxyglucose-positron emission tomography ( FDG-PET ) modalities. The different modal data of Alzheimer's Neuroimaging Initiative ( ADNI ) and the self-extracted neuroimaging data were experimentally explored. Experiments show that there is obvious complementarity between MRI and CSF. By fusing MRI and CSF data, three binary classification tasks using multi-modal fusion data have achieved varying degrees of improvement. At the same time, we also explored the important features of multi-modal fusion data through SHapley Additive exPlanations ( SHAP ), and found that most important features are supported by relevant literature.

Abstract Angle closure glaucoma leads to fluid deposition in eye, and intraocular pressure occurs that damage the optic nerve, causes blindness and vision loss. Anterior chamber (AC) evaluation is imperative for determining the risk of angle‐closure. Previously, techniques were dependent on either Pentacam–Scheimpflug that interprets poor visual information, anterior segment optical coherence tomography is injurious to intercede opaque optical structures. Therefore, in this paper, an experimental protocol is designed for detailed disease analysis based on IBM SPSS statistics via ultrasound biomicroscopy which is superior in evaluating deep structures; first, the affected parameter for AC is analysed, and afterwards the direction that needs laser surgery is explored. Experiments are conducted on large‐scale clinical studies from an affiliated hospital in Shanghai, China. The dataset comprised 600 AC images in five directions of 60 subjects. The mean with standard deviation for anterior open distance is 0.158790.096779 mm, 0.158630.081435 mm, and anterior chamber angle is 18.74908.0315, 18.74108.3889 for left and right eye respectively. It is found that anterior chamber angle in the downside of the AC is wider than the upside. However, this decision is partly based on the narrowest part of the angle to widen the depth of the direction and eliminate pupil block.

Human activity recognition (HAR) using smartphone sensors have been recently studied in various applications including healthcare, fitness, and smart home. Their recognition accuracy often depends on high-quality feature design and effectiveness of classification algorithms, where existing work mostly replies on laborious hand-crafted design and shallow feature learning architecture. Recent deep learning techniques demonstrate outstanding effectiveness in performing automatic feature learning and outperform traditional models in terms of accuracy. But their performance is limited by the quality and volumes of available labelled data. It is challenging to achieve accurate multisubject HAR with only smartphone sensing data. This article proposes a novel optimal activity graph generation model incorporating a deep learning framework for automatic and accurate HAR with multiple subjects using only acceleration and gyroscope data. The activity graph generation model presents a multisensory integration mechanism with three-steps sorting algorithms for producing optimal activity graphs containing alignments of neighbored signals in their width and height. Then, we propose a deep convolutional neural network to automatically learn distinguishable features from the graphs for HAR. By leveraging superior presentation of correlations between human activities and neighbored signals alignments via optimal activity graphs, the learned features are endowed with more discriminative power. The experimental evaluation was carried out on several benchmark datasets (i.e., UCI, USCHAD, and UTD-MHAD). The results showed that our approach improved the average recognition accuracy by about 5% when compared with other state-of-the-art HAR methods. Particularly towards multisubject HAR cases (UTD-MHAD dataset with 21 subjects), it achieved up to 10% accuracy gain over other methods. These improvements show the advantage and potential of our method dealing with complex HAR problems with multiple subjects using limited sensing data.

Domestic waste classification was incorporated into legal provisions recently in China. However, relying on manpower to detect and classify domestic waste is highly inefficient. To that end, in this article, we propose a multimodel cascaded convolutional neural network (MCCNN) for domestic waste image detection and classification. MCCNN combined three subnetworks (DSSD, YOLOv4, and Faster-RCNN) to obtain the detections. Moreover, to suppress the false-positive predicts, we utilized a classification model cascaded with the detection part to judge whether the detection results are correct. To train and evaluate MCCNN, we designed a large-scale waste image dataset (LSWID), containing 30 000 domestic waste multilabeled images with 52 categories. To the best of our knowledge, the LSWID is the largest dataset on domestic waste images. Furthermore, a smart trash can is designed and applied to a Shanghai community, which helped to make waste recycling more efficient. Experimental results showed a state-of-the-art performance, with an average improvement of 10% in detection precision.

Alzheimer's disease (AD) is a typical progressive neurodegenerative disease with insidious onset. Utilising various biomarkers to track and predict AD progression for supporting clinic decisions has recently received wide attentions. Accurate prediction of disease progression will help clinicians and patients make the best decisions on disease prevention and treatment. Typical prediction models focus on extracting biomarker morphological information of different regions of interest (ROIs) from magnetic resonance imaging (MRI) or positron emission tomography (PET), such as the average regional cortical thickness and regional volume. They are effective in modeling AD progression and understanding AD biomarkers, but cannot make full utilise of the internal temporal and spatial relationships between these biomarkers to improve the accuracy and stability of AD prediction. In this paper, we propose a new multi-task learning (MTL) method based on the tensor composed of the spatio-temporal similarity measure between brain biomarkers, using MRI data and cognitive scores of AD patients in different stages can effectively predict the progression of AD. Specifically, we define a temporal and spatial feature similarity measure to calculate the rate of change and velocity of each biomarker in MRI to form a vector, which represents the morphological changing trend of the biomarker, then we calculate the similarity of the changing trend between two biomarkers and encode the data to the third-order tensor, and extract interpretable biomarker latent factors from the original data. The prediction of each patient sample in the tensor is a task and all prediction tasks share a set of latent factors obtained from tensor decomposition to train the AD progression prediction model, which learns task correlation from the spatiotemporal tensor itself. We conducted extensive experiments utilising the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Experimental results show that compared with ROI-based traditional single feature regression methods, our proposed method has better accuracy and stability in disease progression prediction in terms of root mean square error exhibiting an average of 4.10 decrease compared to Ridge regression, 0.19 decrease compared to Lasso regression and 0.18 decrease compared to Temporal Group Lasso (TGL) in the Mini Mental State Examination (MMSE) questionnaire.

Alzheimer’s disease (AD) is a neurodegenerative disease. Its condition is irreversible and ultimately fatal. Researchers have been studying approaches to support early diagnosis of Alzheimer disease and further delay the patient’s condition and improve AD patient’s quality of life. Gene expression data is a mature technology. It has many advantages such as high throughput, less-invasiveness, and affordability. It has great potential to help people diagnose Alzheimer’s disease in early stage. However, because the amount of information is too large compared to the number of samples in the Alzheimer’s database, researchers are facing “curse of dimensionality” when using gene expression data. In this work we are interested in the task of dimensionality reduction of gene expression data in Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. We investigated six dimensionality reduction algorithms: Principal component analysis, Kernel principal component analysis, Isometric feature mapping, Local linear embedding, Stacked denoising autoencoder, and Stacked sparse autoencoder. An SVM classifier is used to classify the samples in the ADNI dataset using the features obtained by dimensionality reduction. We first optimized the appropriate number of hidden layers for the two stacked autoencoders. Then we performed different degrees of dimensionality reduction with the other four algorithms and compared the classification performance of the features obtained by different algorithms with the SVM classifier. Our experimental results show that the features obtained by kernel principal component analysis and local linear embedding, Stacked denoising autoencoder, and Stacked sparse autoencoder dimensionality reduction have good AD classification performance.

With the aggravation of the population aging problem, Parkinson’s disease (PD) and other neurodegenerative diseases of the elderly are not only a medical problem but also an important social problem. Therefore, early detection of PD is particularly important for reducing complications. Currently, the diagnosis of PD is assessed by specialized physicians through the Uniform PD Rating Scale (UPDRS). This limits the detection rate of PD and the timely assessment of disease progression to a certain extent. Moreover, with the development of artificial intelligence, machine learning has been widely and effectively applied to the assessment and monitoring of PD. Therefore, we use machine learning to distinguish between healthy people and PD patients based on UPDRS. In this paper, we collaborated with the First People’s Hospital of Yunnan Province to collect exercise data from 15 healthy individuals and 15 PD patients using wearable motion sensors. The analysis found that not all activities collected according to the UPDRS were useful. According to our proposed Indicators for distinguishing activities (I-DA) method as defined in this article, the most differentiated activities are found. Retain the activities that contain the most discriminative information, and use these activities to distinguish between healthy people and PD patients. We verify the effectiveness of this method through experiments. We use k-Nearest Neighbor (KNN), eXtreme Gradient Boosting (XGB), and Support Vector Machine (SVM) to execute the classification method. When the selected activities were taken as the whole data set rather than all activities according to our proposed Indicators for distinguishing activities (I-DA) method, the classification accuracy of KNN and XGB were improved by 5.10% and 2.4% respectively. The classification accuracy of SVM was improved by 12.07%. The experimental results show that the accuracy is significantly improved.

Alzheimer’s Disease (AD) is the most common dementia characterized by loss of brain function. Multi-tasking learning methods have been widely used to predict cognitive performance and select important imaging biomarkers in AD research. The temporal smoothness assumption, prevalent for modeling AD progression, means the difference between cognitive scores at two consecutive time points is relatively small. However, it’s not appropriate due to the presence of sample disturbance and the effectiveness of drug therapy. In addition, many multi-task learning methods select discriminative feature subset from MRI features, assuming that correlations between tasks are consistent, which ignores the complex intrinsic correlation structure of tasks. In this paper, we present a multi-task learning framework which utilizes generalized fused Lasso and generalized group Lasso (GFGGL for abbreviation) to model the disease progression with the complex intrinsic nonlinear structures of disease. The proposed framework is more flexible to utilize the inherent nonlinear relation of AD than existing methods for the reason of we represent the intrinsic structure as three correlation matrices which are functions of super parameters. The framework involves (1) two nonlinear structures of disease progression and (2) one nonlinear structure among tasks. An efficient optimization method is designed for the difficult optimization problem due to the presence of three nonsmooth penalties. Extensive experimental results using dataset from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) demonstrate the effectiveness of the proposed method.

Glaucoma is an ocular disease that causes permanent blindness if not cured at an early stage. Cup-to-disk ratio (CDR), obtained by dividing the height of optic cup (OC) with the height of optic disk (OD), is a widely adopted metric used for glaucoma screening. Therefore, accurately segmenting OD and OC is crucial for calculating a CDR. Most methods have employed deep learning methods for the segmentation of OD and OC. However, these methods are very time consuming. In this article, we present a new fuzzy broad learning system-based technique for OD and OC segmentation with glaucoma screening. We comprehensively integrated extracting a region of interest from RGB images, data augmentation, extracting red and green channel images, and inputting them to the two separate fuzzy broad learning system-based neural networks for segmenting the OD and OC, respectively, and then calculated CDR. Experiments show that our fuzzy broad learning system-based technique outperforms many state-of-the-art methods.

This article substantially advances upon state-of-the-art to enhance liver vessels segmentation accuracy by leveraging advantages of synthetic PET-CT (SPET-CT) images in addition to computed tomography angiography (CTA) volumes. Our setup makes a hybrid solution of modified generative adversarial network-convolutional autoencoder (GAN-cAED) combining synthetic ability of GAN to deliver SPET-CT images with generative ability of cAED network in terms of latent learning to more refined segmentation of major liver vessels. We improve time complexity through a novel concept of controlled segmentation by introducing a threshold metric to stop segmentation up to a desired level. The innovative concept of controlled vessel segmentation with a stopping criterion via variant threshold levels will help surgeons to avoid unintentional major blood vessels cutting, reducing the risk of excessive blood loss. Clinically, such solutions offer computer-aided liver surgeries and drug treatment evaluation in a CTA-only environment, shorten the requirement of radioactive and expensive fused PET-CT images.

Specialized control of pests and diseases have been a high-priority issue for the agriculture industry in many countries. On account of automation and cost effectiveness, image analytic pest recognition systems are widely utilized in practical crops prevention applications. But due to powerless hand-crafted features, current image analytic approaches achieve low accuracy and poor robustness in practical large-scale multiclass pest detection and recognition. To tackle this problem, this article proposes a novel deep learning based automatic approach using hybrid and local activated features for pest monitoring. In the presented method, we exploit the global information from feature maps to build our global activated feature pyramid network to extract pests' highly discriminative features across various scales over both depth and position levels. It makes changes of depth or spatial sensitive features in pest images more visible during downsampling. Next, an improved pest localization module named local activated region proposal network is proposed to find the precise pest objects positions by augmenting contextualized and attentional information for feature completion and enhancement in local level. The approach is evaluated on our seven-year large-scale pest data-set containing 88.6 K images (16 types of pests) with 582.1 K manually labeled pest objects. The experimental results show that our solution performs over 75.03% mean average precision (mAP) in industrial circumstances, which outweighs two other state-of-the-art methods: Faster R-CNN with mAP up to 70% and feature pyramid network mAP up to 72%.

Proper scoring of sleep stages may offer more intuitive clinical information for assessing the sleep health and improving the diagnosis of sleep disorders in the smart home healthcare. It usually depends on an accurate analysis of the collected physiological signals, especially for the raw sleep Electroencephalogram (EEG). Most of the methods currently available just rely on the pre-processing or handcrafted features that need prior knowledge and preliminary analysis from the sleep experts and only a few of them take full advantage of the temporal information such as the inter-epoch dependency or transition rules among stages, which are more effective for identifying the differences among the sleep stages. In such cases, we proposed a novel hybrid neural network named HNSleepNet. It utilizes a two-branch CNN with multi-scale convolution kernels to capture the time-invariant features from the adjacent sleep EEG epochs both in time and frequency domains automatically, and attention-based residual encoder-decoder LSTM layers to learn the inter-epoch dependency and transition rules at the Sequence-wise level. After the two-step training, HNSleepNet can perform sequence-to-sequence automatic sleep staging with a raw single channel EEG in an end-to-end way. As the experimental results demonstrated, its performance achieved a better overall accuracy and macro F1-score (MASS: 88%, 0.85, Sleep-EDF: 87%-80%, 0.79-0.74) compared with the state-of-the-art approaches on various single-channels (F4-EOG (Left), Fpz-Cz and Pz-Oz) in two public datasets with different scoring standards (AASM and R&K), We hope this progress can make clinically practical value in promoting home sleep studies on various home health-care devices.

Facial expressions contain rich emotional information, which is an important method in human communication. At present, most of the researches on facial expression recognition is conducted on the frontal faces. However, in real life, the captu re device may capture facial expression data from various poses. Different from the existing techniques, in this paper, We propose a multitask deep learning method that uses the links between var ious poses and expressions to improve the accuracy of expression recognition. We use the adversarial network to supplement the e xpression information of the face for the head poses with a sever e lack of expression information. There are some advantages: Firstly, we use vgg16 to train the expression images of each deflecti on angle separately and find that the expression recognition acc uracy rate for small offset angle (-30 °, -15 °, 15°, 30 °) is larger t han 0 ° angle when the face angle is greater than 45 °, the accur acy of recognition decreases sharply. So we use multitask learnin g to jointly train these small offsets angle images and frontal ima ges, the multitask learning can learn the emotion-preserving repr esentations at various poses to predict the expression class label f rom the input face, and bring again to its recognition accuracy r ate. Secondly, for the poses of a severe lack of expression inform ation, we use TP-GAN to convert a large deflection pose image i nto a frontal face and supplement its expression information. Th e experimental results show that our proposed algorithm has a g ood recognition effect on facial expressions for all poses. Compa red with the most advanced expression recognition methods, this paper has also achieved the state-of-the-art recognition results.

Human activity recognition (HAR) has been widely used in some areas such as smart home, health care and so on. However, there are still some low recognition accuracy cases in actual scenarios. In order to improve the accuracy of recognition, we propose a multi-feature weighted ensemble classification method on triaxial accelerometer sensor data. We perform weighted integration on five base classifiers to obtain the final prediction classification label. Among these five base classifiers, three are K-nearest neighbor (KNN) classifiers with different features respectively using three traditional feature extraction methods from original data. Another two are currently popular deep learning models-Attention Mechanisms on Long Short-Term Memory Network (Attention-LSTM) and Convolutional Neural Network (CNN), which can automatically extract features and classify. We demonstrated the feasibility of this ensemble method on a dataset containing eight human daily activities. Comparing experimental results, our method achieved the best recognition effect, with an accuracy of 95.58%.

This paper presents a method on improving water pressure monitoring using wireless passive surface acoustic wave (WP-SAW) sensor. An experimental framework acting as water pipe simulator is built. Signal processing methods such as down-conversion, quadrature sampling, average, and multi-iterative two-point simple moving average (MI-2P-SMA) are utilized for improving sensing accuracy. The results show the relative errors of temperature at all testing points are less than 1% and the relative errors of pressure at all testing points are less than 2%.

In the context of the continuous development of Internet of Things (IoT) technology and Machine learning (ML) technology, its application in the medical field is becoming more and more extensive. However, with a dramatic increase in medical data obtained from the IoT-based medical auxiliary diagnosis system, the impact of label noise problems is also increasing. When training a machine learning algorithm for a supervised-learning task in some clinical applications, uncertainty in the labels of some patients may adversely affect the performance of the algorithm. For example, due to ambiguous patient conditions or poor reliability of diagnostic criteria, even clinical experts may lack confidence in making medical diagnoses for some patients. As a result, some samples used in algorithm training may be mislabeled, which adversely affects the performance of the algorithm. In this paper, we study a classification problem of sample labels with random damage. We propose a new hybrid label noise correction model that generalizes many learning problems, including supervised, unsupervised and semi-supervised learning. This hybrid model can withstand the negative effects of random noise and various non-random label noise. Extensive experimental results using real-world datasets from UCI machine learning repository are provided, the empirical study shows that our approach successfully improves data quality in many cases, in terms of classification accuracy, over existing label noise correction methods.

Traffic sign classification is the core of intelligent transportation and fundamental for constructing an automatic driving system. While supervised classification tasks demonstrate promising classification performance, a particular challenge is how to ensure the confidence for collecting labelled data. This paper presents a semi-supervised approach via label confidence for traffic sign classification to avoid the interference of uncertain labelled data. The idea of the proposed approach is to compare unsupervised information of the data, the supervised information carried by the learning data, and the supervised information which is given by the classification model in order to detect inconsistencies. The approach is able to build a robust classification model. Experimental results on benchmark and real-world dataset demonstrate that our approach significantly outperforms the existing approaches when uncertain labelled data exists.

As one of the next-generation network technologies for data centers, wireless data center networks have important research significance. Smart architecture optimization and management are vital for wireless data center networks. With the ever-increasing demand for data center resources, the deployment of the data servers are on the rise. However, traditional wired links among servers are expensive and inflexible. Benefitting from the development of intelligent optimization and other techniques, this article studies a high-speed wireless topology for wireless data center networks. A radio propagation model based on a heat map is constructed. The line-of-sight issue and the interference problem are also discussed. By simultaneously considering the objectives of coverage, propagation intensity, and interference intensity, as well as the constraint of connectivity, the topology optimization problem is formulated as a multiobjective optimization problem. To seek the solutions, several state-of-the-art serial multiobjective evolutionary algorithms (MOEAs), as well as parallel MOEAs, are employed. Prior knowledge is preferred for the grouping, and parameter adaptation is conducted in the distributed parallel algorithms. Experimental results demonstrate that the parallel MOEAs perform effectively in the optimization results and efficiently in time consumption.

Detection of lung cancer at early stages is critical, in most of the cases radiologists read computed tomography (CT) images to prescribe follow-up treatment. The conventional method for detecting nodule presence in CT images is tedious. In this article, we propose an enhanced multidimensional region-based fully convolutional network (mRFCN) based automated decision support system for lung nodule detection and classification. The mRFCN is used as an image classifier backbone for feature extraction along with the novel multilayer fusion region proposal network (mLRPN) with position-sensitive score maps being explored. We applied a median intensity projection to leverage three-dimensional information from CT scans and introduced deconvolutional layer to adopt proposed mLRPN in our architecture to automatically select the potential region of interest. Our system has been trained and evaluated using LIDC dataset, and the experimental results showed promising detection performance in comparison to the state-of-the-art nodule detection/classification methods, achieving a sensitivity of 98.1% and classification accuracy of 97.91%.

Recent emergence of “microblogging” services has been driving cyber-physical social system (CPSS) as a hot topic in real-world applications. How to efficiently detect and recognise spam and fake accounts becomes an important task where it requires analysis of microblog user behavior and prediction of their activity. This article attempts to investigate this challenge by proposing a new strategy to effectively model microblogging user activity and dynamically predicting their activities for the CPSS applications. We first analysis and define a set of benchmarks for measuring microblogging user activeness in considering serval key dynamic attributes including change rate of microblogging numbers, user attentions, etc. Then, we build up a new dynamic microblogging user activity prediction model (DUAPM) based on three important characteristics: personal information, social relationship, and user interaction. Finally, an improved logical regression algorithm is proposed for training the model and predicting user activity. Under the evaluation of a sample dataset containing Sina Weibo 3621 users over 20 weeks, it shows that our model deliver average up to 3% higher prediction accuracy than other social media user activity prediction models using traditional logical regression and random forest algorithms. We also take out a CPSS case study of evaluating DUAPM models for analysis and prediction of Twitter users' activity over 16 countries. The results show that our model effectively reflects the distribution and trends of Twitter users' activity with different background and cultures.

Intracranial blood vessels segmentation from computed tomography angiography (CTA) volumes is a promising biomarker for diagnosis and therapeutic treatment in cerebrovascular diseases. These segmentation outputs are a fundamental requirement in the development of automated decision support systems for preoperative assessment or intraoperative guidance in neuropathology. The state-of-the-art in medical image segmentation methods are reliant on deep learning architectures based on convolutional neural networks. However, despite their popularity, there is a research gap in the current deep learning architectures optimized to address the technical challenges in blood vessel segmentation. These challenges include: (i) the extraction of concrete brain vessels close to the skull; and (ii) the precise marking of the vessel locations. We propose an Optimally Fused Fully end-to-end Network (OFF-eNET) for automatic segmentation of the volumetric 3D intracranial vascular structures. OFF-eNET comprises of three modules. In the first module, we exploit the up-skip connections to enhance information flow, and dilated convolution for detailed preservation of spatial feature map that are designed for thin blood vessels. In the second module, we employ residual mapping along with inception module for speedy network convergence and richer visual representation. For the third module, we make use of the transferred knowledge in the form of cascaded training strategy to gradually optimize the three segmentation stages (basic, complete, and enhanced) to segment thin vessels located close to the skull. All these modules are designed to be computationally efficient. Our OFF-eNET, evaluated using 70 CTA image volumes, resulted in 90.75% performance in the segmentation of intracranial blood vessels and outperformed the state-of-the-art counterparts.

Epilepsy seizure prediction paves the way of timely warning for patients to take more active and effective intervention measures. Compared to seizure detection that only identifies the inter-ictal state and the ictal state, far fewer researches have been conducted on seizure prediction because the high similarity makes it challenging to distinguish between the pre-ictal state and the inter-ictal state. In this paper, a novel solution on seizure prediction is proposed using common spatial pattern (CSP) and convolutional neural network (CNN). Firstly, artificial pre-ictal EEG signals based on the original ones are generated by combining the segmented pre-ictal signals to solve the trial imbalance problem between the two states. Secondly, a feature extractor employing wavelet packet decomposition and CSP is designed to extract the distinguishing features in both the time domain and the frequency domain. It can improve overall accuracy while reducing the training time. Finally, a shallow CNN is applied to discriminate between the pre-ictal state and the inter-ictal state. Our proposed solution is evaluated on 23 patients' data from Boston Children's Hospital-MIT scalp EEG dataset by employing a leave-one-out cross-validation, and it achieves a sensitivity of 92.2% and false prediction rate of 0.12/h. Experimental result demonstrates that the proposed approach outperforms most state-of-the-art methods.

In medical text auxiliary diagnosis systems, there exists some problems including few labeled samples, imbalanced classes and domains are related but different. Taking advantages of transfer learning, we propose the multi-source transfer learning approach based on ensemble learning to address the above problems. Source data sampling method is designed to ensure the transfer ability of source samples. Then, three classifiers are ensembled to guarantee the robustness. Finally, classifiers from multiple domains are reasonably combined using mutual information to further improve performance. Our approach has been evaluated on the benchmark medical text datasets, and the results show that our approach is superior to the existing algorithms and can meet the requirement of an auxiliary diagnosis in certain extent.

Monitoring pest in agriculture has been a high-priority issue all over the world. Computer vision techniques are widely utilized in practical crop pest prevention applications due to the rapid development of artificial intelligence technology. However, current deep learning image analytic approaches achieve low accuracy and poor robustness in agriculture pest monitoring task. This paper targets at this challenge by proposing a novel two-stage deep learning based automatic pest monitoring system with hybrid global and local activated feature. In this approach, a Global activated Feature Pyramid Network (GaFPN) is firstly proposed for extracting highly representative features of pests over both depth and spatial position activation levels. Then, an improved Local activated Region Proposal Network (LaRPN) augmenting contextual and attentional information is represented for precisely locating pest objects. Finally, we design a fully connected neural network to estimate the severity of input image under the detected pests. The experimental results on our 88.6K images dataset (with 16 types of common pests) show that our approach outweighs the state-of-the-art methods in industrial circumstances.

Pulmonary cancer is one of the major causes of deaths caused by cancer around the globe. Early stage lung cancer detection can prove to be essential for the patients, for which the computed tomography (CT) images are analyzed by the radiologists to determine the presence of nodules and diagnose the disease. Conventional techniques used by the radiologists for nodule detection in CT images is time-consuming and inefficient; to assist in the diagnosis process and further enhance its efficiency and accuracy, decision support systems have been developed in the past few years. In our paper, we proposed a Multi-Dimension Region-based Fully Convolutional Network based decision support system for detection and classification of lung nodule. The Multi-Dimension RFCN serves as an image classifier backbone for our feature extraction step in addition to the proposed Tri-Level Region Proposal Network (3L-RPN) along with the position-sensitive score maps (PSSM) being explored. A novel median intensity projection method is used to leverage the multi-dimensional information from CT images and introduced an additional deconvolutional layer to adopt the proposed Tri-Level Region Proposal Network in our architecture to automatically identify the potential Region of Interest. We trained and evaluated our proposed decision support system using LIDC-IDRI dataset. The evaluation results demonstrated the high level performance of our proposed model in comparison to the state-of-the-art nodule detection and classification methods by attaining classification accuracy of 97.61% and sensitivity of 97.4%.

Translation is a data augmentation method widely used in the image classification tasks. We analyze the mechanism of translation and discover that the central area of the images is more likely to be selected as convolutional neural network's input. Inspired by the structure of human retina, we propose the hypothesis that the central area of the image contains more significant information than the marginal one. Comprehensive experiments are presented to prove that hypothesis and reach the conclusion that the receptive field is nonuniform. The central part of the image that is always selected by translation is called the focused area. Motivated by the demand to take use of different focused area and thus take use of different receptive fields, we propose a novel training mechanism that integrate different focused areas in one training process. Our method consists of several stages, each has its own focused area and learning rate, and achieves considerable result in the experiments. We call the integration of focused areas the ensemble of receptive fields.

Modeling and predicting progression of chronic diseases like Alzheimer's disease (AD) has recently received much attention. Traditional approaches in this field mostly rely on harnessing statistical methods into processing medical data like genes, MRI images, demographics, etc. Latest advances of machine learning techniques grant another chance of training disease progression models for AD. This trend leads on exploring and designing new machine learning techniques towards multi-modality medical and health dataset for predicting occurrences and modeling progression of AD. This paper aims at giving a systemic survey on summarizing and comparing several mainstream techniques for AD progression modeling, and discuss the potential and limitations of these techniques in practical applications. We summarize three key techniques for modeling AD progression: multi-task model, time series model and deep learning. In particular, we discuss the basic structural elements of most representative multi-task learning algorithms, and analyze a multi-task disease prediction model based on longitudinal time. Lastly, some potential future research direction is given.

In the harsh environmental conditions of cyber-physical systems (CPSs), the consensus problem seems to be one of the central topics that affect the performance of consensus-based applications, such as events detection, estimation, tracking, blockchain, etc. In this paper, we investigate the events detection based on consensus problem of CPS by means of compressed sensing (CS) for applications such as attack detection, industrial process monitoring, automatic alert system, and prediction for potentially dangerous events in CPS. The edge devices in a CPS are able to calculate a log-likelihood ratio (LLR) from local observation for one or more events via a consensus approach to iteratively optimize the consensus LLRs for the whole CPS system. The information-exchange topologies are considered as a collection of jointly connected networks and an iterative distributed consensus algorithm is proposed to optimize the LLRs to form a global optimal decision. Each active device in the CPS first detects the local region and obtains a local LLR, which then exchanges with its active neighbors. Compressed data collection is enforced by a reliable cluster partitioning scheme, which conserves sensing energy and prolongs network lifetime. Then the LLR estimations are improved iteratively until a global optimum is reached. The proposed distributed consensus algorithm can converge fast and hence improve the reliability with lower transmission burden and computation costs in CPS. Simulation results demonstrated the effectiveness of the proposed approach.

Due to the many beneficial effects on physical and mental health and strong association with many fitness and rehabilitation programs, physical activity (PA) recognition has been considered as a key paradigm for Internet of Things healthcare. Traditional PA recognition techniques focus on repeated aerobic exercises or stationary PA. As a crucial indicator in human health, it covers a range of bodily movement from aerobics to anaerobic that may all bring health benefits. However, existing PA recognition approaches are mostly designed for specific scenarios and often lack extensibility for application in other areas, thereby limiting their usefulness. In this paper, we attempt to detect more gym PAs (GPAs) in addition to traditional PA using acceleration, A two layer recognition framework is proposed that can classify aerobic, sedentary, and free weight activities, count repetitions and sets for the free weight exercises, and in the meantime, measure quantities of repetitions and sets for free weight activities. In the first layer, a one-class support vector machine is applied to coarsely classify free weight and nonfree weight activities. In the second layer, a neural network is utilized for aerobic and sedentary activities recognition; a hidden Markov model is to provide a further classification in free weight activities. The performance of the framework was tested on ten healthy subjects (age: 30 ± 5; BMI: 25 ± 5.5 kg/m2; and body fat: 20.5 ± 5.4), and compared with some typical classifiers. The results indicate the proposed framework has better performance in recognizing and measuring GPAs than other approaches. The potential of this framework can be extended in supporting more types of PA recognition in complex applications.

The expansion of automation techniques and increased risk of identity theft have led emphasis on the tremendous need of automated identification system. Due to the high recognition accuracy and robustness to changes in human physiology, retinal biometric identification system has drawn much attention in this research field. In this paper, we aim to propose an automatic fast and accurate retinal identification system for the multisample dataset. The proposed approach uses a hybrid segmentation technique to segment out both thick/thin vessels for effectively balancing the difference of wavelet response between thick/thin blood vessels. As a result, recognition accuracy is improved. A Principle Component Analysis-based feature processing approach is proposed for efficiently reducing the dimensionality of a large number of vessels features. It significantly reduces computation time and accelerates the matching process in the retinal identification system. The proposed technique is validated on DRIVE, STARE, VARIA, RIDB, HRF, Messidor, DIARETDB0, and a large multisample per subject database created by authors using the images provided by Dr. Chen (Shanghai Jiao Tong University Affiliated Sixth People Hospital). Experimental results demonstrated that the proposed approach outperforms other existing techniques. Segmentation achieves an overall accuracy of 99.65% with the recognition rate of 99.40% on all these databases.

The development of smart cities and the emergence of three-dimensional (3-D) urban terrain data have introduced new requirements and issues to the research on the 3-D deployment of wireless sensor networks. We study the deployment issue of heterogeneous wireless directional sensor networks in 3-D smart cities. Traditionally, studies on the deployment problem of WSNs focus on omnidirectional sensors on a 2-D plane or in full 3-D space. Based on 3-D urban terrain data, we transform the deployment problem into a multiobjective optimization problem, in which objectives of Coverage, Connectivity Quality, and Lifetime, as well as the Connectivity and Reliability constraints, are simultaneously considered. A graph-based 3-D signal propagation model employing the line-of-sight concept is used to calculate the signal path loss. Novel distributed parallel multiobjective evolutionary algorithms (MOEAs) are also proposed. For verification, real-world and artificial urban terrains are utilized. In comparison with other state-of-the-art MOEAs, the novel algorithms could more effectively and more efficiently address the deployment problem in terms of optimization performance and operation time.

As the rapid growth of social media technologies continues, Cyber-Physical-Social System (CPSS) has been a hot topic in many industrial applications. The use of “microblogging” services, such as Twitter, has rapidly become an influential way to share information. While recent studies have revealed that understanding and modelling microblog user behaviour with massive users’ data in social media are keen to success of many practical applications in CPSS, a key challenge in literatures is that diversity of geography and cultures in social media technologies strongly affect user behaviour and activity. The motivation of this article is to understand differences and similarities between microblogging users from different countries using social media technologies, and to attempt to design a Country-Level Micro-Blog User (CLMB) behaviour and activity model for supporting CPSS applications. We proposed a CLMB model for analysing microblogging user behaviour and their activity across different countries in the CPSS applications. The model has considered three important characteristics of user behaviour in microblogging data, including content of microblogging messages, user emotion index, and user relationship network. We evaluated CLBM model under the collected microblog dataset from 16 countries with the largest number of representative and active users in the world. Experimental results show that (1) for some countries with small population and strong cohesiveness, users pay more attention to social functionalities of microblogging service; (2) for some countries containing mostly large loose social groups, users use microblogging services as a news dissemination platform; (3) users in countries whose social network structure exhibits reciprocity rather than hierarchy will use more linguistic elements to express happiness in microblogging services.

In this paper, we present a method (Action-Fusion) for human action recognition from depth maps and posture data using convolutional neural networks (CNNs). Two input descriptors are used for action representation. The first input is a depth motion image that accumulates consecutive depth maps of a human action, whilst the second input is a proposed moving joints descriptor which represents the motion of body joints over time. In order to maximize feature extraction for accurate action classification, three CNN channels are trained with different inputs. The first channel is trained with depth motion images (DMIs), the second channel is trained with both DMIs and moving joint descriptors together, and the third channel is trained with moving joint descriptors only. The action predictions generated from the three CNN channels are fused together for the final action classification. We propose several fusion score operations to maximize the score of the right action. The experiments show that the results of fusing the output of three channels are better than using one channel or fusing two channels only. Our proposed method was evaluated on three public datasets: 1) Microsoft action 3-D dataset (MSRAction3D); 2) University of Texas at Dallas-multimodal human action dataset; and 3) multimodal action dataset (MAD) dataset. The testing results indicate that the proposed approach outperforms most of existing state-of-the-art methods, such as histogram of oriented 4-D normals and Actionlet on MSRAction3D. Although MAD dataset contains a high number of actions (35 actions) compared to existing action RGB-D datasets, this paper surpasses a state-of-the-art method on the dataset by 6.84%.

Convolutional neural networks (CNNs) have yielded state-of-the-art performance in image classification and other computer vision tasks. Their application in fire detection systems will substantially improve detection accuracy, which will eventually minimize fire disasters and reduce the ecological and social ramifications. However, the major concern with CNN-based fire detection systems is their implementation in real-world surveillance networks, due to their high memory and computational requirements for inference. In this paper, we propose an original, energy-friendly, and computationally efficient CNN architecture, inspired by the SqueezeNet architecture for fire detection, localization, and semantic understanding of the scene of the fire. It uses smaller convolutional kernels and contains no dense, fully connected layers, which helps keep the computational requirements to a minimum. Despite its low computational needs, the experimental results demonstrate that our proposed solution achieves accuracies that are comparable to other, more complex models, mainly due to its increased depth. Moreover, this paper shows how a tradeoff can be reached between fire detection accuracy and efficiency, by considering the specific characteristics of the problem of interest and the variety of fire data.

Wireless sensor networks (WSNs) are applied more and more widely in real life. In actual scenarios, 3-D directional wireless sensor nodes are constantly employed, thus, research on the real-time deployment optimization issue of 3-D directional WSNs based on terrain big data has more practical significance. Based on this, we study the deployment optimization issue of directional WSNs in the 3-D terrain through comprehensive consideration of coverage, lifetime, connectivity of sensor nodes, connectivity of cluster headers, and reliability of directional WSNs. We present a modified differential evolution algorithm by adopting crossover rate sort and polynomial-based mutation on the basis of the cooperative coevolutionary framework, and apply it to address the deployment problem of 3-D directional WSNs. In addition, to reduce computation time, we realize implementation of message passing interface parallelism. As is revealed by the experimentation results, the modified algorithm proposed in this paper achieves better performance with respect to either optimization results or operation time.

Social Internet-of-Things (SIoT) environment comprises not only smart devices but also the humans who interact with these IoT devices. The benefits of such system are overshadowed due to the cyber security issues. A novel approach is required to understand the security implication under such a dynamic environment while taking both the social and technical aspects into consideration. This paper addressed such challenges and proposed a 3-D security modeling platform that can capture and model the security requirements in the SIoT environment. The modeling process is graphical notation based and works as a security extension to the Business Process Model and Notation. Still, it utilizes the latest 3-D game technology; thus, the security extensions are generated through the third dimension. Consequently, the introduction of security extensions will not increase the complexity of the original SIoT scenario, while keeping all the key information on the same platform. Together with the proposed security ontology, these comprehensive security notations created a unique platform that aims at addressing the ever complicated security issues in the SIoT environment.

Small to medium enterprises (SMEs), particularly those whose business is focused on developing innovative produces, are limited by a major bottleneck in the speed of computation in many applications. The recent developments in GPUs have been the marked increase in their versatility in many computational areas. But due to the lack of specialist GPUprogramming skills, the explosion of GPU power has not been fully utilized in general SME applications by inexperienced users. Also, the existing automatic CPU-to-GPU code translators are mainly designed for research purposes with poor user interface design and are hard to use. Little attentions have been paid to the applicability, usability, and learnability of these tools for normal users. In this paper, we present an online automated CPU-to-GPU source translation system (GPSME) for inexperienced users to utilize the GPU capability in accelerating general SME applications. This system designs and implements a directive programming model with a new kernel generation scheme and memory management hierarchy to optimize its performance. A web service interface is designed for inexperienced users to easily and flexibly invoke the automatic resource translator. Our experiments with nonexpert GPU users in four SMEs reflect that a GPSME system can efficiently accelerate real-world applications with at least 4× and have a better applicability, usability, and learnability than the existing automatic CPU-to-GPU source translators.

Effectively monitoring maritime environments has become a vital problem in maritime applications. Traditional methods are not only expensive and time consuming but also restricted in both time and space. More recently, the concept of an industrial wireless sensor network (IWSN) has become a promising alternative for monitoring next-generation intelligent maritime grids, because IWSNs are cost-effective and easy to deploy. This paper focuses on solving the issue of 3-D IWSN deployment in a 3-D engine room space of a very large crude-oil carrier and also considers numerous power facilities. To address this 3-D IWSN deployment problem for maritime applications, a 3-D uncertain coverage model is proposed that uses a modified 3-D sensing model and an uncertain fusion operator. The deployment problem is converted into a multiobjective optimization problem that simultaneously addresses three objectives: coverage, lifetime, and reliability. Our goal is to achieve extensive coverage, long network lifetime, and high reliability. We also propose a distributed parallel cooperative coevolutionary multiobjective large-scale evolutionary algorithm for maritime applications. We verify the effectiveness of this algorithm through experiments by comparing it with five state-of-the-art algorithms. Numerical results demonstrate that the proposed method performs most effectively both in optimization performance and in minimizing the computation time.

Driven by the automation technologies and health informatics of Industry 4.0, hospitals in China have deployed a complete automation system/platform for healthcare services accessing. Without much more Internet knowledge, elderlies usually seek the third-party to assist them to get healthcare services from Web or APPs, it consequently results in an unexpected situation that scalpers could grab all healthcare services booking by unrighteous means in order to resell to elderlies for a much higher price. Moreover, it is hard for physicians to identify the scalpers due to the complexity, ad-hoc, and multiscenario nature of healthcare processes. In this paper, a novel method is proposed for the identification and creation of user groups of scalpers in mobile healthcare services. The approach utilizes and extends state of the art data analysis approaches in the event-logs of the mobile system to identify user groups. Based on the user groups, user profiles are extracted by identifying representative eventcases from hierarchical user-event clusters. A comprehensive evaluation is conducted in a selected test-set from the event-logs of a mobile healthcare APP. The result shows its accuracy and effectiveness in scalper detection in mobile healthcare APP. Further, a complete case study is deployed in a real word hospital to ensure its utility, efficacy, and reliability.

Internet of Things (IoT) technology offers opportunities to monitor lifelogging data by a variety of assets, like wearable sensors, mobile apps, etc. But due to heterogeneity of connected devices and diverse human life patterns in an IoT environment, lifelogging personal data contains huge uncertainty and are hardly used for healthcare studies. Effective validation of lifelogging personal data for longitudinal health assessment is demanded. In this paper, lifelogging physical activity (LPA) is taken as a target to explore how to improve the validity of lifelogging data in an IoT enabled healthcare system. A rule-based adaptive LPA validation (LPAV) model, LPAV-IoT, is proposed for eliminating irregular uncertainties (IUs) and estimating data reliability in IoT healthcare environments. A methodology specifying four layers and three modules in LPAV-IoT is presented for analyzing key factors impacting validity of LPA. A series of validation rules are designed with uncertainty threshold parameters and reliability indicators and evaluated through experimental investigations. Following LPAV-IoT, a case study on a personalized healthcare platform myhealthavatar connecting three state-of-the-art wearable devices and mobile apps are carried out. The results reflect that the rules provided by LPAV-IoT enable efficiently filtering at least 75% of IU and adaptively indicating the reliability of LPA data on certain condition of IoT environments.

Due to storage conditions and material's non‐planar shape, geometric distortion of the two‐dimensional content is widely present in scanned document images. Effective geometric restoration of these distorted document images considerably increases character recognition rate in large‐scale digitisation. For large‐scale digitisation of historical books, geometric restoration solutions expect to be accurate, generic, robust, unsupervised and reversible. However, most methods in the literature concentrate on improving restoration accuracy for specific distortion effect, but not their applicability in large‐scale digitisation. This study proposes an effective mesh based geometric restoration system (GRLSD) for large‐scale distorted historical document digitisation. In this system, an automatic mesh generation based dewarping tool is proposed to geometrically model and correct arbitrary warping historical documents. An XML‐based mesh recorder is proposed to record the mesh of distortion information for reversible use. A graphic user interface (GUI) toolkit is designed to visually display and manually manipulate the mesh for improving geometric restoration accuracy. Experimental results show that the proposed automatic dewarping approach efficiently corrects arbitrarily warped historical documents, with an improved performance over several state‐of‐the‐art geometric restoration methods. By using XML mesh recorder and GUI toolkit, the GRLSD system greatly aids users to flexibly monitor and correct ambiguous points of mesh for the prevention of damaging historical document images without distortions in large‐scale digitalisation.

Currently the false data injection (FDI) attack bring direct challenges in synchronized phase measurement unit (PMU) based network state estimation in wide-area measurement system, resulting in degraded system reliability and power supply security. This paper assesses the performance of state estimation in electric cyber-physical system paradigm considering the presence of FDI attacks. The adverse impact on network state estimation is evaluated through simulations for a range of FDI attack scenarios using IEEE 14-bus network model. In addition, an algorithmic solution is proposed to address the issue of additional PMU installation and placement with cyber security consideration and evaluated for a set of standard electric transmission networks (IEEE 14-bus, 30-bus, and 57-bus network). The numerical result confirms that the FDI attack can significantly degrade the state estimation and the cyber security can be improved by an appropriate placement of a limited number of additional PMUs.

Nowadays, sustainability and resilience have become a major consideration that cannot be neglected in urban development. People are starting to consider utilizing the urban infrastructure environment to maintain and improve the functionality and availability of the urban system when unexpected events take place. Traffic congestion is always a major issue in urban planning, especially when the vehicles in the roadway keep growing and the local authorities are lack of solutions to manage or distribute the traffics in the city. It has huge impact on urban sustainability and resilience such as overload of the city's infrastructure, and air pollution, etc. This paper presents a survey on the challenges of developing sustainable and resilient transportation networks and the current urban traffic optimisation methods, as a possible solution to address such challenges. It aims to describe and define the state of the art on the research on sustainable and resilient transportation networks in urban development and a taxonomy of different traffic optimisation methods used for avoiding traffic congestion and improve urban traffic management.

Summary The support vector machine (SVM) is a supervised learning algorithm used for recognizing patterns in data. It is a very popular technique in machine learning and has been successfully used in applications such as image classification, protein classification, and handwriting recognition. However, the computational complexity of the kernelized version of the algorithm grows quadratically with the number of training examples. To tackle this high computational complexity, we have developed a directive‐based approach that converts a gradient‐ascent based training algorithm for the CPU to an efficient graphics processing unit (GPU) implementation. We compare our GPU‐based SVM training algorithm to the standard LibSVM CPU implementation, a highly optimized GPU‐LibSVM implementation, as well as to a directive‐based OpenACC implementation. The results on different handwritten digit classification datasets demonstrate an important speed‐up for the current approach when compared to the CPU and OpenACC versions. Furthermore, our solution is almost as fast and sometimes even faster than the highly optimized CUBLAS‐based GPU‐LibSVM implementation, without sacrificing the algorithm's accuracy. Copyright © 2014 John Wiley & Sons, Ltd.

Due to cost-effectiveness and easy-deployment, radio-frequency identification (RFID) location systems are widely utilized into many industrial fields, particularly in the emerging environment of the Internet of Things (IoT). High accuracy and precision are key demands for these location systems. Numerous studies have attempted to improve localization accuracy and precision using either dedicated RFID infrastructures or advanced localization algorithms. But these effects mostly consider utilization of novel RFID localization solutions rather than optimization of this utilization. Practical use of these solutions in industrial applications leads to increased cost and deployment difficulty of RFID system. This paper attempts to investigate how accuracy and precision in passive RFID location systems (PRLS) are impacted by infrastructures and localization algorithms. A general experimental-based investigation strategy, PRLS-INVES, is designed for analyzing and evaluating the factors that impact the performance of a passive RFID location system. Through a case study on passive high frequency (HF) RFID location systems with this strategy, it is discovered that: 1) the RFID infrastructure is the primary factor determining the localization capability of an RFID location system and 2) localization algorithm can improve accuracy and precision, but is limited by the primary factor. A discussion on how to efficiently improve localization accuracy and precision in passive HF RFID location systems is given.