Alzheimer’s disease represents a growing global health concern, emphasizing the need for early diagnosis to mitigate neurocognitive decline. Speech analysis has emerged as a promising, non-invasive approach, yet limited data availability hinders the development of robust Machine Learning (ML) models. To address this challenge, this study exploits the potentialities of Large Language Models (LLMs)—both their ability to generate synthetic data and their capacity to extract complex linguistic features from speech. We employ GPT-4 to generate synthetic transcripts, thus expanding the ADReSS2020 dataset and enhancing its diversity while preserving semantic and structural coherence. Moreover, we propose a novel multilevel feature extraction framework that integrates Bidirectional Encoder Representations from Transformers (BERT) embeddings fine-tuned with linguistic features obtained through Computerized Language Analysis (CLAN). The study involved two experiments: first, to identify the optimal feature extraction strategy and second, to evaluate the impact of synthetic data generated by GPT-4. In both experiments, the performance of five classifiers was evaluated to determine the most effective configuration. Our results demonstrated that fine-tuned BERT embeddings slightly improve classification performance compared to pre-trained models, highlighting the value of domain-specific fine-tuning. Although adding CLAN-like linguistic features yielded limited benefits, GPT-4-generated synthetic data demonstrated promising potential, particularly when combined with sentence embeddings. Classifiers such as Random Forest showed an improvement in accuracy, increasing from 0.79 to 0.88 when using the augmented dataset. This study paves the way for the use of LLMs to expand the diversity of datasets and improve the robustness of ML models in clinical applications.

Alzheimer’s disease (AD) represents a major public health challenge due to its irreversible progression and increasing prevalence among the aging population. Early diagnosis is crucial, and recent advances in Artificial Intelligence and data analytics have shown promising results in detection methods. This study proposes an approach based on deep neural networks for automatic AD detection from the speech data of the ADReSS2020 dataset, using log-Mel spectrogram representation. To address data limitations and enhance model performance, we applied five data augmentation techniques, which significantly improved accuracy by introducing greater variability in audio characteristics. We evaluated the performance of three models: a CNN-LSTM network and two transfer learning approaches based on ResNet50 and VGG16. Experimental results showed that the CNN-LSTM model performs best, achieving an accuracy of 68%, with a significant improvement of 9.67% over the baseline. ResNet50-LSTM and VGG16-LSTM followed with $\mathbf{6 7} \%$ and $\mathbf{6 6} \%$ accuracy, respectively. This work demonstrates the potential of deep learning-based speech-driven approaches as scalable and noninvasive tools for Alzheimer’s diagnosis and highlights the importance of data enhancement to improve model performance.

Stress is considered one of the most prevalent concerns among individuals. Studies have shown that experiencing long-term stress can cause severe health issues such as cardiovascular diseases, hypertension, depression, etc. Preventative measures, such as early stress detection, can help individuals mitigate these health issues. When a person gets stressed, physiological values like blood volume pulse, temperature, and electrodermal activity signals get affected. Machine Learning techniques can be utilized to identify stress by analyzing these physiological signals. This paper presents a machine learning method for detecting stress levels of an individual using the publicly available dataset called "Wearable Stress and Affect Detection"(WESAD), which has physiological data collected from the wrist-worn and chest-worn sensors attached to 15 different subjects. We used physiological signals, including Blood Volume Pulse(BVP), Body Temperature(TEMP), and Electrodermal Activity(EDA) signals, collected from wrist-worn sensors to detect the state of the mind. For the implementation, we used different Machine Learning models, like Logistic Regression, Decision Tree, Random Forest, and Stacking Ensemble Learning technique. During the investigation, personalized models, utilizing individual subject data, and generalized models, amalgamating all subject data, were developed. Evaluation reveals accuracy values reaching up to 99% and 91% for individual subject data and combined data, respectively.

With the Industrial Internet of Things (IIoT) continuing to expand, lots of data collection, exchange, and authentication generated from an increasing number of access devices is required with heterogeneity, multidimension, and multiobjective networks as its characteristics. However, traditional IIoT systems are vulnerable to security challenges, such as data leakage, theft, and tampering. As one of the most promising solutions, blockchain has played an essential role in ensuring security and transparency in the IIoT. But there are still some challenges that prevent the secure and effective implementation of blockchain-based IIoT systems in consensus security, consensus efficiency, and consensus application. To address these problems, we propose an effective security blockchain consensus algorithm for heterogeneous IIoT nodes aiming to defend against the consensus attack and improve consensus efficiency. First, we design a blockchain-based IIoT system architecture. Then, we present an identity authentication and transformation protocol to defend against consensus attacks. Furthermore, we introduce a method for constructing communication directed acyclic graphs (DAGs) and transaction set DAGs to enhance transaction throughput. Based on these two DAGs, we propose an efficient and security consensus algorithm (DAG-D). DAG-D employs transaction sets instead of single transactions or blocks, leveraging communication DAG propagation to swiftly confirm transaction set DAGs based on parent transactions for associated confirmation. Experimental results show that our proposed DAG-D outperforms DAG-M, DAG-Avalanche, and DAG-CoDAG, regarding transaction throughput, transaction latency, and communication overhead.

Heart disease is a leading cause of morbidity and mortality worldwide, necessitating the development of innovative diagnostic methodologies for early detection. This study presents a novel deep convolutional neural network model that leverages Mel-spectrograms to accurately classify heart sounds. Our approach demonstrates significant advancements in heart disease detection, achieving high accuracy, specificity, and unweighted average recall scores (UAR), which are critical factors for practical clinical applications. The comparison of our proposed model's performance with a PANN-based model from a previous study highlights the strengths of our approach, particularly in terms of specificity and UAR. The successful application of Mel-spectrograms in conjunction with deep learning techniques illustrates the potential for widespread clinical adoption of our model, ultimately contributing to early detection and improved patient outcomes. Furthermore, we discuss potential avenues for future research to enhance the model's effectiveness, such as incorporating additional features and exploring alternative deep learning architectures. In conclusion, our deep convolutional neural network model, combined with Mel-spectrograms, offers a significant step forward in the field of heart sound classification and the early detection of heart diseases, demonstrating its potential for real-world clinical applications and improved patient outcomes.

Breast cancer is a global health concern for women. The detection of breast cancer in its early stages is crucial, and screening mammography serves as a vital leading-edge tool for achieving this goal. In this study, we explored the effectiveness of Resnet 50v2 and Resnet 152v2 deep learning models for classifying mammograms using EMBED datasets for the first time. We preprocessed the datasets and utilized various techniques to enhance the performance of the models. Our results suggest that the choice of model architecture depends on the dataset used, with ResNet152 outperforming ResNet50 in terms of recall score. These findings have implications for cancer screening, where recall is an important metric. Our research highlights the potential of deep learning to improve breast cancer classification and underscores the importance of selecting the appropriate model architecture.

Intelligent sensing solutions bridge the gap between the physical world and the cyber-physical systems by digitizing the sensor data collected from sensor devices. Sensor cloud networks provide physical and virtual sensing device resources and enable uninterrupted intelligent solutions to end-users. Thanks to advancements in machine learning algorithms and big data, the automation of mundane tasks with artificial intelligence is becoming a reliable smart option. However, existing approaches based on centralized Machine Learning (ML) on sensor cloud networks fail to ensure data privacy. Moreover, centralized ML works with the pre-requisite to transfer the entire training dataset from end devices to a central server. To address this, we propose a Quantized Federated Learning (FL) based approach, called CloudFL, to ensure data privacy on end devices in a sensor cloud network. Our framework enables a personalized version of FL implementation and enhances privacy and security with cryptosystem tools to obfuscate the information of the FL process from unauthorized access. Furthermore, microservices of our approach provide software as a service implementation of FL with instances of cloud servers that require zero-touch on local data for training.

Quality sleep is very important for a healthy life. Nowadays, many people around the world are not getting enough sleep, which has negative impacts on their lifestyles. Studies are being conducted for sleep monitoring and better understanding sleep behaviors. The gold standard method for sleep analysis is polysomnography conducted in a clinical environment, but this method is both expensive and complex for long-term use. With the advancements in the field of sensors and the introduction of off-the-shelf technologies, unobtrusive solutions are becoming common as alternatives for in-home sleep monitoring. Various solutions have been proposed using both wearable and non-wearable methods, which are cheap and easy to use for in-home sleep monitoring. In this article, we present a comprehensive survey of the latest research works (2015 and after) conducted in various categories of sleep monitoring, including sleep stage classification, sleep posture recognition, sleep disorders detection, and vital signs monitoring. We review the latest research efforts using the non-invasive approach and cover both wearable and non-wearable methods. We discuss the design approaches and key attributes of the work presented and provide an extensive analysis based on ten key factors, with the goal to give a comprehensive overview of the recent developments and trends in all four categories of sleep monitoring. We also collect publicly available datasets for different categories of sleep monitoring. We finally discuss several open issues and future research directions in the area of sleep monitoring.

The Internet of Things (IoT) is made up of billions of physical devices connected to the Internet via networks that perform tasks independently with less human intervention. Such brilliant automation of mundane tasks requires a considerable amount of user data in digital format, which, in turn, makes IoT networks an open source of personally identifiable information data for malicious attackers to steal, manipulate, and perform nefarious activities. A huge interest has been developed over the past years in applying machine learning (ML)-assisted approaches in the IoT security space. However, the assumption in many current works is that big training data are widely available and transferable to the main server because data are born at the edge and are generated continuously by IoT devices. This is to say that classic ML works on the legacy set of entire data located on a central server, which makes it the least preferred option for domains with privacy concerns on user data. To address this issue, we propose the federated-learning (FL)-based anomaly detection approach to proactively recognize intrusion in IoT networks using decentralized on-device data. Our approach uses federated training rounds on gated recurrent units (GRUs) models and keeps the data intact on local IoT devices by sharing only the learned weights with the central server of FL. Also, the approach’s ensembler part aggregates the updates from multiple sources to optimize the global ML model’s accuracy. Our experimental results demonstrate that our approach outperforms the classic/centralized machine learning (non-FL) versions in securing the privacy of user data and provides an optimal accuracy rate in attack detection.

The convergence of the Internet of Things (IoT) and data analytics has great potential to accelerate knowledge discovery, while the traditional approach of centralized data collection then processing is becoming infeasible in many applications due to efficiency and privacy concerns. Federated learning (FL) has emerged as a new paradigm that enables model learning across distributed IoT devices without sharing raw data. However, previous works on FL are either relying on a single central server or fully decentralized. In this article, we propose a semihierarchical federated analytics framework combining the advantages of the above architectures. The proposed framework leverages multiple edge servers for aggregating updates from IoT devices and fusing learned model weights without the need of cloud or a central server. Besides, we develop a new local client update rule to further improve the communication efficiency by reducing the communication rounds between IoT devices and edge servers. We analyze the convergence properties of the presenting approach and investigate its characteristics considering the effects of varying parameters, unreliable links, and packet loss. The experimental results demonstrate the effectiveness of our proposed methodology in providing communication-efficient, robust, and fault-tolerant data analytics to IoT networks.

Billions of IoT devices are connected to networks all around us, enabling cyber-physical systems. These devices can carry and generate user-sensitive data, examples of such devices are smartwatches, medical equipment, and smart home gadgets. Individual IoT devices have some form of intrusion detection system integrated, but once they are all connected, a network threat to one device could mean a threat to many. IoT devices must have a robust intrusion detection system that would keep devices secure over a network. To aid with this, we provide a machine learning solution that adheres to Global Data Protection Regulation by keeping the user data secure locally on the IoT device itself. We propose a Federated Learning (FL) approach that capitalizes on a decentralized and collaborative way of training machine learning models. In this study, we practice federated learning technique to train and create a robust intrusion detection model for the security of IoT devices. We evaluate our proposed approach using three different use-cases to show the security enhancements that improve using the FL technique, resulting in a more reliable performance in this domain.

COVID-19 shook the entire world with its highly infectious transmission and death rate. As per CDC guidelines, wearing a mask can effectively reduce the spread of COVID-19 and create a protective barrier against the virus until the efficacy of currently available vaccines reaches 100% and the majority of people get vaccinated. Wearing masks is highly recommended almost everywhere, in schools, stores, movies, etc. to prevent the spread of this virus, however, monitoring people to see whether they wear masks is not an easy task. As a result, different face mask detection models were proposed. In this paper, we introduce a face mask detection model using deep learning. We have taken into consideration three different categories to train our model, Mask, No Mask, and Incorrect Mask. The proposed model has achieved 96% accuracy.

This paper aims to explore the role of Machine Learning (ML) techniques in combating COVID-19. In this study, different ML techniques and data portioning methods will be used to predict RT-PCR results from ER-admitted patients. The data set contains 199 instances with 81 attributes. 5-Fold Cross Validation, 10-Fold Cross Validation, and 80% Training are the different data portioning methods utilized for this research. Decision tree (J48), Random Forest (RaF), and Rotation Forest (RoF), Multi-Layer Perceptron (MLP), Naïve Bayes (NB), K-Nearest Neighbors (kNN), Logistic Regression (LR), LogitBoost (LB), and Sequential Minimal Optimization (SMO) are the main classifiers we explore in this study. The results of our experiments indicate that Rotation Forest gives a highest accuracy of 90% on the data set.

Probabilistic topic models, which frequently represent topics as multinomial distributions over words, have been extensively used for discovering latent topics in text corpora. However, because topic models are entirely unsupervised, they may lead to topics that are not understandable in applications. Recently, several knowledge-based topic models have been proposed which primarily use word-level domain knowledge in the model to enhance the topic coherence and ignore the rich information carried by entities (e.g, persons, locations, organizations, etc.) associated with the documents. Additionally, there exists a vast amount of prior knowledge (background knowledge) represented as Linked Open Data (LOD) datasets and other ontologies, which can be incorporated into the topic models to produce coherent topics. In this paper, we introduce a novel regularization entity-based topic model ( RETM), which integrates an ontology with an entity-based topic model ( EntLDA) to increase the coherence of the identified topics through the topic modeling process. Our experimental results demonstrate the effectiveness of the proposed model in improving the coherence of topics.

With the advent of the Internet, the amount of Semantic Web documents that describe real-world entities and their inter-links as a set of statements have grown considerably. These descriptions are usually lengthy, which makes the utilization of the underlying entities a difficult task. Entity summarization, which aims to create summaries for real-world entities, has gained increasing attention in recent years. In this paper, we propose a probabilistic topic model, ES-LDA, that combines prior knowledge with statistical learning techniques within a single framework to create more reliable and representative summaries for entities. We demonstrate the effectiveness of our approach by conducting extensive experiments and show that our model outperforms the state-of-the-art techniques and enhances the quality of the entity summaries.

This paper aims to investigate and compare the accuracy of different data mining classification schemes, employing Ensemble Machine Learning Techniques, for the prediction of heart disease. The Cleveland data set for heart diseases, containing 303 instances, has been used as the main database for the training and testing of the developed system. 10-Fold Cross-Validation has been applied in order to increase the amount of data, which would otherwise have been limited. Different classifiers, namely Decision Tree (DT), Naïve Bayes (NB), Multilayer Perceptron (MLP), K-Nearest Neighbor (K-NN), Single Conjunctive Rule Learner (SCRL), Radial Basis Function (RBF) and Support Vector Machine (SVM), have been employed. Moreover, the ensemble prediction of classifiers, bagging, boosting and stacking, has been applied to the dataset. The results of the experiments indicate that the SVM method using the boosting technique outperforms the other aforementioned methods.