This study addresses a critical challenge in medical imaging diagnostics by proposing a unified, lightweight deep learning model capable of diagnosing multiple diseases across diverse imaging modalities, including chest X-rays, MRIs, skin images, and endoscopic images, within a single efficient framework. Each modality presents unique feature characteristics, introducing complexities in the diagnostic process. To enhance image quality, we apply Contrast Limited Adaptive Histogram Equalization and utilize the Vision Transformer for improved feature extraction and diagnostic performance. To tackle remaining challenges, we introduce the ChirpMBPru-Net model, designed to analyze multiple image modalities in medical imaging while minimizing computational demands. This model employs the efficient MobileNet architecture as its backbone and systematically applies pruning to remove redundant layers. Moreover, a dense module for multi-scale feature extraction and the Chirplet transformation are employed in the pruned model, capturing both frequency and spatial patterns at varying scales. Additionally, the ChirpMBPru-Net model demonstrates its versatility by adapting to domain shifts in engineering fields, such as defect detection in industrial applications (e.g., scholar defect detection), where it can classify multiple categories of the same object or defect type. The model achieves an impressive accuracy of 97% across 16 disease categories and proves effective in handling real-world domain shifts, demonstrating its potential for both medical and engineering applications.

Conventional standalone approaches for diagnosing individual diseases often fail to achieve robust generalization because they are severely impacted by overfitting. This results in poor adaptability to diverse image representations and an inability to balance performance with computational efficiency. In this study, we propose KDLight, a lightweight, novel CNN model designed for efficient medical image classification across diverse modalities, including MRI, X-ray, radiography, skin images, and histopathology. We employ Knowledge Distillation (KD), where insights from an efficient teacher model (MobileNet) guide the learning process of the KDLight student model. The KDLight model minimizes the number of parameters while enhancing feature learning across diverse medical image representations. Experimental results show that KDLight achieves 95.55% classification accuracy with only 2.96 seconds and a compact 7.5 MB disk size, significantly reducing parameter size, accelerating inference, and lowering computational costs compared to traditional pre-trained models. Additionally, KDLight ability to efficiently learn diverse image representations can be extended to other domains, such as crack classification (e.g., road, window, and building cracks), enabling high-performance detection across different surface defect categories.

Mpox is now a pandemic disease, and all its public health concerns have inspired very heated debates on Instagram. This paper is trying to capture possible public perceptions, concerns, and narratives through their Instagram posts on Mpox by applying two cutting-edge unsupervised topic models called BERTopic and Latent Dirichlet Allocation (LDA). A dataset of 60127 multilingual posts from July 2022 to October 2024 was processed and raised some clear thematic issues and vaccination hesitance, including disease awareness, awareness of misinformation, and geographic differences. BERTopic investigated nuanced location-specific issues such as humor on social media and technical risks; LDA gave a more extensive framework centered on broader topics like health emergencies and worldwide impact. The base of the analysis is a dataset of Mpox-related Instagram posts collected over a specified time. Pre-processing consists of text extraction and removal of URLs, tokenization, and removal of stop words, and also uses countVectorization. These findings serve to shed light on the interaction between public sentiment and health communication and the critical importance of customized outreach programs to counter misinformation and enhance awareness and public health response in outbreaks of infectious diseases. Findings yield insights into public health communication in setting proactive plans to curb misinformation and boost awareness, with future studies along the lines of cross-platform analysis as well as with multi-media data touching on the greater public perception.

Alzheimer’s Disease (AD) remains one of the most challenging neurodegenerative disorders to diagnose due to the complexity of its underlying causes and progression patterns. The study involved Gradient Boosting, XGBoost, and Random Forests to achieve improvements in diagnostic accuracy. The study was performed on a dataset of 2,149 patient records featuring 35 attributes, showing that ensemble methods outperform conventional approaches in working with the complexity of medical data. The best model is Gradient Boosting showing the highest accuracy of 95%, which represents the possibility for the real-world application in medical diagnosis. The results enlighten about the role of preprocessing, hyperparameter tuning, and feature analysis to achieve reliability in prediction. Thereby, these findings contribute to the ever-accumulating compendium of machine learning underpinnings in medical research as they provide leads for future AD diagnostic frameworks.

This study introduces an innovative Low-Rank Autoencoder (LRAE) for anomaly detection in automotive Controller Area Network (CAN) systems, demonstrating significant advancements over traditional Standard Autoencoders (SAEs). By factorizing weight matrices into compact subspaces (rank$\leq 16$), the LRAE achieves a 91.3 % reduction in parameters (2,010 vs. 23,198) and a 9 times lower memory footprint (0.01 MB vs. 0.09 MB), enabling efficient deployment on resourceconstrained Electronic Control Units. Training dynamics reveal LRAE converges 50 % faster (5 vs. 10 epochs) with a validation loss stabilizing at 0.35, compared to SAE's at 0.05. Evaluated on the SynCAN dataset, LRAE outperforms SAE with a$1,233 \%$higher recall (0.04 vs. 0.003) and$1,440 \%$higher F1-score (0.077 vs. 0.005) in continuous attacks, a 148.4 % recall gain (0.226 vs. 0.091) in suppress attacks, and a 48.1 % higher PrecisionRecall Area Under the Curve (PR-AUC) (0.228 vs. 0.154) in plateau scenarios. Despite minor trade-offs in flooding (2.7 % lower PR-AUC, 0.741 vs. 0.761) and playback (22.2 % lower F1score, 0.112 vs. 0.144), LRAE's accelerated convergence and enhanced specificity position it as a pioneering advancement. These results highlight LRAE's potential to revolutionize automotive cybersecurity, blending efficiency with state-of-the-art detection performance.

Multi-modal neuroimaging analysis is crucial for a comprehensive understanding of brain function and pathology, as it allows for the integration of different imaging techniques, thus overcoming the limitations of individual modalities. However, the high costs and limited availability of certain modalities pose significant challenges. To address these issues, this paper proposes the Condition-Aligned Temporal Diffusion (CATD) framework for end-to-end cross-modal synthesis of neuroimaging, enabling the generation of functional magnetic resonance imaging (fMRI)-detected Blood Oxygen Level Dependent (BOLD) signals from more accessible Electroencephalography (EEG) signals. By constructing Conditionally Aligned Block (CAB), heterogeneous neuroimages are aligned into a latent space, achieving a unified representation that provides the foundation for cross-modal transformation in neuroimaging. The combination with the constructed Dynamic Time-Frequency Segmentation (DTFS) module also enables the use of EEG signals to improve the temporal resolution of BOLD signals, thus augmenting the capture of the dynamic details of the brain. Experimental validation demonstrates that the framework improves the accuracy of brain activity state prediction by 9.13% (reaching 69.8%), enhances the diagnostic accuracy of brain disorders by 4.10% (reaching 99.55%), effectively identifies abnormal brain regions, enhancing the temporal resolution of BOLD signals. The proposed framework establishes a new paradigm for cross-modal synthesis of neuroimaging by unifying heterogeneous neuroimaging data into a latent representation space, showing promise in medical applications such as improving Parkinson's disease prediction and identifying abnormal brain regions.

Machine learning (ML) models in healthcare are increasing but the lack of interpretability of these models results in them not being suitable for use in clinical practice. In the medical field, it is vital to clarify to clinicians and patients the rationale behind a model’s high probability prediction for a specific disease in an individual patient. This transparency fosters trust, facilitates informed decision-making, and empowers both clinicians and patients to understand the underlying factors driving the model’s output. This paper aims to incorporate explainability to ML models such as Random Forest (RF), eXtreme Gradient Boosting (XGBoost) and Multilyer Perceptron (MLP) for using with Clinical Practice Research Datalink (CPRD) data and interpret them in terms of feature importance to identify the top most features when distinguishing between lung cancer and non-lung cancer cases. The SHapley Additive exPlanations (SHAP) method has been used in this work to interpret the models. We use SHAP to gain insights into explaining individual predictions as well as interpreting them globally. The feature importance from SHAP is compared with the default feature importance of the models to identify any discrepancies between the results. Based on experimental findings, it has been found that the default feature importance from the tree-based models and SHAP is consistent with features ‘age’ and ‘smoking status’ which serve as the top features for predicting lung cancer among patients. Additionally, this work pinpoints that feature importance for a single patient may vary leading to a varied prediction depending on the employed model. Finally, the work concludes that individual-level explanation of feature importance is crucial in mission-critical applications like healthcare to better understand personal health and lifestyle factors in the early prediction of diseases that may lead to terminal illness.

In recent times, the Visual Transformer (VT) has emerged as a powerful alternative to the conventional Convolutional Neural Networks (CNNs) for their superior attention mechanism and pattern recognition abilities. Within a short time, the VT paradigm has given rise to many variants, each showcasing enhanced accuracy and optimized performance for various computer vision applications. Our study introduces a multitransformer pipeline for optimal VT architecture exploration in AD detection and classification. Through a comparative evaluation among the VT variants, this study also aims to contribute valuable insights into the applicability of VTs in Alzheimer’s Disease (AD) classification using OASIS and ADNI datasets. Furthermore, VT performances are systematically compared with CNNs to determine the basic capabilities of the models and their limitations in capturing intricate patterns indicative of early AD stages under both data-rich and data-scarce situations. The results resonate with the fact that the attention mechanism of VTs is of pivotal importance for achieving superior performance in AD diagnosis. The codes used in the study are made publicly available.

To meet the demand of the world’s largest population, smart manufacturing has accelerated the adoption of smart factories—where autonomous and cooperative instruments across all levels of production and logistics networks are integrated through a Cyber-Physical Production System (CPPS). However, these networks are comprised of various heterogeneous devices with varying computational power and memory capabilities. As a result, many secure communication protocols—that demand considerably high computational power and memory—can not be verbatim employed on these networks, and thereby, leaving them more vulnerable to security threats and attacks over conventional networks. These threats can largely be tackled by employing a Trust Management Model (TMM) by exploiting the behavioural patterns of nodes to identify their trust class. In this context, ML-based models are best suited due to their ability to capture hidden patterns in data, learning and improving the pattern detection accuracy over time to counteract and tackle threats of a dynamic nature, which is absent in most of the conventional models. However, among the existing ML-based solutions in detecting attack patterns, many of them are computationally expensive, require a long training time, and a considerably large amount of training data—which are seldom available. An aid to this is the association rule learning (ARL) paradigm, whose models are computationally inexpensive and do not require a long training time. Therefore, this paper proposes an ARL-based intelligent Behavioural Trust Model (iBUST) for securing the CPPS. For this intelligent TMM, a variant of Frequency Pattern Growth (FP-Growth), called enhanced FP-Growth (EFP-Growth) algorithm is developed by altering the internal data structures for faster execution and by developing a modified exponential decay function (MEDF) to automatically calculate minimum supports for adapting trust evolution characteristics. In addition, a new optimisation model for finding optimum parameter values in the MEDF and an algorithm for transmuting a 1D quantitative feature into a respective categorical feature are developed to facilitate the model. Afterwards, the trust class of an object is identified employing the Naïve Bayes classifier. This proposed model is evaluated on a trust evolution-supported experimental environment along with other compared models taking a benchmark dataset into consideration, where it outperforms its counterparts.

Artificial intelligence (AI)-based approaches are crucial in computer-aided diagnosis (CAD) for various medical applications. Their ability to quickly and accurately learn from complex data is remarkable. Deep learning (DL) models have shown promising results in accurately classifying Alzheimer's disease (AD) and its related cognitive states, Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI), along with the healthy conditions known as Cognitively Normal (CN). This offers valuable insights into disease progression and diagnosis. However, certain traditional machine learning (ML) classifiers perform equally well or even better than DL models, requiring less training data. This is particularly valuable in CAD in situations with limited labeled datasets. In this paper, we propose an ensemble classifier based on ML models for magnetic resonance imaging (MRI) data, which achieved an impressive accuracy of 96.52%. This represents a 3-5% improvement over the best individual classifier. We evaluated popular ML classifiers for AD classification under both data-scarce and data-rich conditions using the Alzheimer's Disease Neuroimaging Initiative and Open Access Series of Imaging Studies datasets. By comparing the results to state-of-the-art CNN-centric DL algorithms, we gain insights into the strengths and weaknesses of each approach. This work will help users to select the most suitable algorithm for AD classification based on data availability.

Mental health has become a major concern in recent years. Social media have been increasingly used as platforms to gain insight into a person’s mental health condition by analysing the posts and comments, which are textual in nature. By analysing these texts, depressive posts can be detected. To facilitate this process, this work presents an attention-based bidirectional Long Short-Term Memory (LSTM)- Convolutional Neural Network (CNN) based model to detect depressive Bangla social media texts, which is lighter and more robust than the conventional models and provides better performance. A dataset containing such Bangla texts was also developed in this work to mitigate the scarcity. Different preprocessing stages were followed, and three embeddings were used in this task. Thanks to the attention mechanism, the proposed model achieved an accuracy of 94.3% with 92.63% of sensitivity and 95.12% of specificity. When tested on other languages, such as English, the proposed model performed remarkably. The robustness and explainability of the proposed model were also discussed in this paper. Additionally, when compared with classical machine learning models, ensemble approaches, transformers, other similar models, and existing architectures, the proposed model outperformed them.

Novel Coronavirus 2019 disease or COVID-19 is a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The use of chest X-rays (CXRs) has become an important practice to assist in the diagnosis of COVID-19 as they can be used to detect the abnormalities developed in the infected patients' lungs. With the fast spread of the disease, many researchers across the world are striving to use several deep learning-based systems to identify the COVID-19 from such CXR images. To this end, we propose an inverted bell-curve-based ensemble of deep learning models for the detection of COVID-19 from CXR images. We first use a selection of models pretrained on ImageNet dataset and use the concept of transfer learning to retrain them with CXR datasets. Then the trained models are combined with the proposed inverted bell curve weighted ensemble method, where the output of each classifier is assigned a weight, and the final prediction is done by performing a weighted average of those outputs. We evaluate the proposed method on two publicly available datasets: the COVID-19 Radiography Database and the IEEE COVID Chest X-ray Dataset. The accuracy, F1 score and the AUC ROC achieved by the proposed method are 99.66%, 99.75% and 99.99%, respectively, in the first dataset, and, 99.84%, 99.81% and 99.99%, respectively, in the other dataset. Experimental results ensure that the use of transfer learning-based models and their combination using the proposed ensemble method result in improved predictions of COVID-19 in CXRs.

Recent complex network analysis reflected the brain network as a modular network with small-world architecture in Autism Spectrum Disorder (ASD). Network hierarchy, which can provide important information to comment on brain networks, especially in ASD, has not yet been fully explored. The present work proposes a Weighted Hierarchical Complexity (WHC) metric to study network topology using the node degree concept. To do so, brain networks have been constructed using a visibility algorithm. To ensure proper mapping of network characteristics by the proposed metric, it is statistically compared to other network measures of brain connectivity related to integration, segregation and centrality. Further, for automated ASD classification, these network metrics were fed to explainable machine learning algorithms and the results revealed that brain regions tend to hierarchically coordinate in ASD, but the hierarchical architecture is attenuated after a few steps compared to networks in Typically Developing individuals (TDs). The value of WHC (0.55) reveals architecture up to three levels (four-degree nodes) with an abundance of 2-degree hubs in ASD indicating high intra-connectivity compared to TDs (WHC = 0.78; four-level spread). The explainable Support Vector Machine (SVM)-classifier model highlighted the role of WHC in classifying ASD with 98.76% of accuracy. The graph-theory metrics ensured that weaker long-range connections and stronger intra-connections are markers of ASD. Thus, it becomes evident that whole-brain architecture can be characterised by a chain-like hierarchical modular structure representing atypical brain topology as in ASD.

The Underwater sensor network (UWSN), also known as Marine Sensor Network (MSN), is gaining increasing attention due to its applications in the monitoring of the marine environment and assisting Marine Intelligent Transportation Systems (MITS). Such systems provide in-vehicle assistance services (i.e., traffic monitoring and driver alerts) by gathering transportation and environmental information. Though very promising, there are several barriers to developing energy-efficient communication protocols for heterogeneous MSN, including selecting optimal routing paths twinned with the lifetime of these sensor nodes along the path, which are restricted due to the limited energy storage capacity. Hereby, the selection of an optimal route path also necessitates harvesting and management of the sensor nodes’ energy. To facilitate this, the current work presents REER-H, a Reliable Energy Efficient Routing protocol with Harvesting for cluster-based MSN capable of multi-source energy harvesting and an incorporated energy management technique. Incorporating three separate layers of the protocol stack, namely, network, MAC, and physical layers, REER-H uses its proposed adaptive scheduling technique to support collision-free data transmission by assigning adaptive time slots based on demand and data load. Also, the proposed integrated energy harvesting and management solves the energy hole problem and enhances the overall network lifetime. In comparison to the existing cooperative and cluster-based energy-efficient routing protocols for underwater maritime communication, the simulated results using Network Simulator-3 (NS3) reveal that the proposed scheme remarkably enhances the overall network performance in terms of packet delivery ratio, throughput, lifetime energy consumption, and end-to-end delay for MSN.

The role of machine learning in neuroscience has been increasing through the years, in aiding diagnosis, biomarker discovery, signal analysis, and other applications. However, the lack of information of the decision-making of the models restricts their use and adoption by the community. In the process of neuronal signal acquisition, other electrical signals can distort the recording, for which a review process is necessary. Machine learning can aid by automatically detecting affected segments, speeding up the review process. However, as the ground-truth labelling is done manually or via a threshold, researchers must be able to identify the causes of false negatives and positives. This paper looks into explainable machine learning for artefact detection in invasively recorded neural signals through the use of different classifiers, trained with a feature subset produced by the combination of feature selection algorithms to reduce the dimensionality by two orders of magnitude. Our results show that the bagging decision tree model is best suited for creating a generalised model that is capable of classifying artefactual patterns in a multi-state dataset, which achieves an accuracy of 96.1%. Lastly, the predictor importance, Shapely values, and reduced feature space visualisation are used to gain insight into the model.

Brain structures and their varying connectivity patterns form complex networks that provide rich information to help in understanding high-order cognitive functions and their relationship with low-order sensory-motor processing. The brains with pathological conditions such as Autism Spectrum Disorder (ASD) exhibit diverse modular networks organised in hierarchies with small-world properties. However, much of the network hierarchy has not been carefully examined in ASD. Different machine learning architectures including Convolutional Neural Networks (CNN) have failed to extract related complex neuronal features and to exploit the hierarchical neural connectivity present at different electrode sites of the electroencephalogram (EEG) data. The presented work has addressed the mentioned limitations by developing a two-layered Visible-Graph Convolutional Network (VGCN) which projects each channel's EEG sample onto nodes of a graph with weighted edges formulated as per the hierarchical visibility among nodes. The proposed model has been applied to EEG signals obtained from ASD and Typical Individuals (TD) and has achieved a classification accuracy of 93.78% in comparison to state-of-the-art methods, including support vector machines (89.52%), deep neural network (78.21%), convolutional network (83.88%) and graph network (86.45%). Other performance metrics such as precision, recall, F1-score and Mathews correlation coefficient showed similar results, hence, supporting the proposed model's strengths. This evidence suggests that graph networks can confidently reveal hierarchical imbalances in the brain functioning of ASD.

A new distributed environment at less financial expenditure on communication over the Internet is presented by cloud computing. In recent times, the increased number of users has made network traffic monitoring a difficult task. Although traffic monitoring and security problems are rising in parallel, there is a need to develop a new system for providing security and reducing network traffic. A new method, iReTADS, is proposed to reduce the network traffic using a data summarization technique and also provide network security through an effective real-time neural network. Although data summarization plays a significant role in data mining, still no real methods are present to assist the summary evaluation. Thus, it is a serious endeavor to present four metrics for data summarization with temporal features such as conciseness, information loss, interestingness, and intelligibility. In addition, a new metric time is also introduced for effective data summarization. Finally, a new neural network known as Modified Synergetic Neural Network (MSNN) on summarized datasets for detecting the real-time anomaly-behaved nodes in network and cloud is introduced. Experimental results reveal that the iReTADS can effectively monitor traffic and detect anomalies. It may further drive studies on controlling the outbreaks and controlling pandemics while studying medical datasets, which results in smart healthy cities.

The improvement of hardware for the acquisition and processing of electroencephalography (EEG) has made its portability become a reality. This allows for studies to be carried outside lab settings, as well as many commercial applications. As recordings are done over extended periods, these devices generate large volumes of data, mainly if the neuronal activity is recorded through multiple channels. Machine learning (ML) techniques allow to effectively analyse and use this data for a wide range of applications. However the portability of these techniques can be challenging. In this article, we set out to review over 40 relevant articles where ML techniques in a diverse set of EEG applications that have successfully been incorporated into portable systems.

Recent advancements in the Internet of Things and the miniaturisation of low-cost sensing devices allow for the unobtrusive collection of data for human activity recognition and behaviour modelling. A useful application of this in the context of ambient assisted living is in the monitoring of older adults daily for improved wellbeing and quality of life. The existing solutions are based on per-individual monitoring, therefore the systems are managed independently for each ambient intelligent environment. In this paper, we proposed a centralised system for the collective monitoring of individuals in a community. The proposed approach is based on a cloud-based solution where data collection and processing are centralised. Since the data are aggregated for all the residents, the system has the potential of promoting social interaction among the community residents. Additionally, the cost of the in-home monitoring system can be reduced since only the sensing devices are required for data collection, while the processing is carried out on the cloud infrastructure. This also reduces the tedious tasks required in setting up individual home monitoring systems. The role of assistive robots, the possibility of remote monitoring and potential challenges of the proposed approach are explored.

The neural recordings known as Local Field Potentials (LFPs) provide important information on how neural circuits operate and relate. Due to the involvement of complex electronic apparatuses in the recording setups, these signals are often significantly contaminated by artifacts generated by a number of internal and external sources. To make the best use of these signals, it is imperative to detect and remove the artifacts from these signals. Hence, this work proposes a pattern recognition neural network based single-channel automatic artifact detection tool. The tool is capable of detecting the artifacts with an 93.2% of overall accuracy and requires an average computing time of 2.57 seconds to analyse LFPs of one minute duration, making it a strong candidate for online deployment without the need for employing high performance computing equipment.

Model optimisation is a key step in model development and traditionally this was limited to parameter tuning. However, recent developments and enhanced understanding of internal dynamics of model architectures have led to various exploration to optimise and enhance performance through model extension and development. In this paper, we extend the architecture of the Multi-recurrent Neural Network (MRN) to incorporate self-learning recurrent link ratios and periodically attentive hidden units. We contrast and show the superiority of these extensions to the standard MRN for a complex financial prediction task. The superiority is attributed to i) the ability of the self-learning recurrent link ratios to dynamically utilise data to identify optimal parameters of its memory mechanism and ii) the periodically attentive units enabling the hidden layer capture temporal features that are sensitive to different periods of time. Finally, we evaluate our extended MRNs (Self-Learning MRN (SL-MRN) and Periodically Attentive MRN (PA-MRN)), against two current state-of the-art models (Long-Short Term Memory and Support Vector Machines) for an eye state detection task. Our preliminary results demonstrate that the PA-MRN and SL-MRN outperform both state-of-the-art models. These results demonstrate that the MRN extensions are suitable models for machine learning applications and these findings would be further explored.

Academic feedback is essential in secondary schools to keep a rapport between students, teachers, and parents and guardians. There are three main factors that contribute towards a student's progress: attitude, attendance and aptitude. Monitoring their progress is key to a student's development in school and allows both teachers and parents or guardians to support them to a greater extent. Annual reports are sent to a student's home to summarise their performance over the academic year, following set criterion from the government. One aspect of a student's report is the teacher's written comment, providing more details on a student's attitude towards their learning. However, families whose primary language is not English may struggle to interpret this information. Working in schools has demonstrated the diversity of students and their wide range of backgrounds, including - but not limited to - language barriers. This work proposes a system called SENSE (Student pErformance quaNtifier using SEntiment analysis) for improving the information conveyed in secondary school reports through means of natural language processing. By combining the three key features which contribute towards a student's progress, a numerical representation is produced for an easier interpretation. This reduces the likelihood of a tarnished relationship between home and schools through better means of conveying information and maintains communication between students, teachers and parents or guardians.

Rapid developments in the fields of information and communication technology and microelectronics allowed seamless interconnection among various devices letting them to communicate with each other. This technological integration opened up new possibilities in many disciplines including healthcare and well-being. With the aim of reducing healthcare costs and providing improved and reliable services, several healthcare frameworks based on Internet of Healthcare Things (IoHT) have been developed. However, due to the critical and heterogeneous nature of healthcare data, maintaining high quality of service (QoS)-in terms of faster responsiveness and data-specific complex analytics-has always been the main challenge in designing such systems. Addressing these issues, this paper proposes a five-layered heterogeneous mist, fog, and cloud-based IoHT framework capable of efficiently handling and routing (near-)real-time as well as offline/batch mode data. Also, by employing software defined networking and link adaptation-based load balancing, the framework ensures optimal resource allocation and efficient resource utilization. The results, obtained by simulating the framework, indicate that the designed network via its various components can achieve high QoS, with reduced end-to-end latency and packet drop rate, which is essential for developing next generatione-healthcare systems.

One of the most ambitious goals of neuroscience and its neuroprosthetic applications is to interface intelligent electronic devices with the biological brain to cure neurological diseases. This emerging research field builds on our growing understanding of brain circuits and on recent technological advances in miniaturization of implantable multi-electrode-arrays (MEAs) to record brain signals at high spatiotemporal resolution. Data processing is needed to extract useful information from the recorded neural activity to better understand the function of underlying neural circuits and, in perspective, to operate neuroprosthetic devices. In this context, machine learning approaches are increasingly used in many application scenarios. This paper focuses on processing data of evoked local field potentials (LFPs) recorded from the rat barrel cortex using a miniaturized 16×16 MEA. We evaluated machine learning algorithms and trained an optimized classifier to detect at which cortical depth the neural activity is measured. We demonstrate with experimental results that machine learning can be applied successfully to noisy single-trial LFPs offering up to 99.11% of test accuracy in classifying signals acquired from different cortical layers. As such, the method is a very promising starting point toward real-time decoding of cerebral activities with low power consumption digital processors for brain-machine interfacing and neuroprosthetic applications.

The recent expansion of pervasive computing technology has contributed with novel means to pursue human activities in urban space. The urban dynamics unveiled by these means generate an enormous amount of data. These data are mainly endowed by portable and radio-frequency devices, transportation systems, video surveillance, satellites, unmanned aerial vehicles, and social networking services. This has opened a new avenue of opportunities, to understand and predict urban dynamics in detail, and plan various real-time services and applications in response to that. Over the last decade, certain aspects of the crowd, e.g., mobility, sentimental, size estimation and behavioral, have been analyzed in detail and the outcomes have been reported. This paper mainly conducted an extensive survey on various data sources used for different urban applications, the state-of-the-art on urban data generation techniques and associated processing methods in order to demonstrate their merits and capabilities. Then, available open-access crowd data sets for urban event detection are provided along with relevant application programming interfaces. In addition, an outlook on a support system for urban application is provided which fuses data from all the available pervasive technology sources and finally, some open challenges and promising research directions are outlined.

Rapid advances in hardware-based technologies during the past decades have opened up new possibilities for life scientists to gather multimodal data in various application domains, such as omics, bioimaging, medical imaging, and (brain/body)-machine interfaces. These have generated novel opportunities for development of dedicated data-intensive machine learning techniques. In particular, recent research in deep learning (DL), reinforcement learning (RL), and their combination (deep RL) promise to revolutionize the future of artificial intelligence. The growth in computational power accompanied by faster and increased data storage, and declining computing costs have already allowed scientists in various fields to apply these techniques on data sets that were previously intractable owing to their size and complexity. This paper provides a comprehensive survey on the application of DL, RL, and deep RL techniques in mining biological data. In addition, we compare the performances of DL techniques when applied to different data sets across various application domains. Finally, we outline open issues in this challenging research area and discuss future development perspectives.

An overview and introduction is given concerning CMOS chips used for neural tissue interfacing. Some basics in the biological domain are discussed as well as extracellular neural tissue interfacing approaches, design philosophies applied to high spatiotemporal resolution devices, in-vitro and in-vivo applications, and related challenges in the engineering domain.