Dementia directly influences the quality of life of a person suffering from this chronic illness. The caregivers or carers of dementia people provide critical support to them but are subject to negative health outcomes because of burden and stress. The intervention of mobile health (mHealth) has become a fast-growing assistive technology (AT) in therapeutic treatment of individuals with chronic illness. The purpose of this comprehensive study is to identify, appraise, and synthesize the existing evidence on the use of mHealth applications (apps) as a healthcare resource for people with dementia and their caregivers. A review of both peer-reviewed and full-text literature was undertaken across five (05) electronic databases for checking the articles published during the last five years (between 2014 and 2018). Out of 6195 searches yielded articles, 17 were quantified according to inclusion and exclusion criteria. The included studies distinguish between five categories, viz., (1) cognitive training and daily living, (2) screening, (3) health and safety monitoring, (4) leisure and socialization, and (5) navigation. Furthermore, two most popular commercial app stores, i.e., Google Play Store and Apple App Store, were searched for finding mHealth based dementia apps for PwD and their caregivers. Initial search generated 356 apps with thirty-five (35) meeting the defined inclusion and exclusion criteria. After shortlisting of mobile applications, it is observed that these existing apps generally addressed different dementia specific aspects overlying with the identified categories in research articles. The comprehensive study concluded that mobile health apps appear as feasible AT intervention for PwD and their carers irrespective of limited available research, but these apps have potential to provide different resources and strategies to help this community.

Due to the increasing of traffic accidents, there is an urgent need to control and reduce driving mistakes. Driver fatigue or drowsiness is one of these major mistakes. Many algorithms have been developed to address this issue by detecting fatigue and alerting the driver to this dangerous condition. The major problem of the developed algorithms is their detection accuracy, as well as the time required to detect fatigue status and alert the driver. The accuracy and the time represent a critical condition that affects the reduction of traffic accidents. Several datasets have been used in the development of fatigue or drowsy detection techniques. These data are gathered from the deriver’s brain Electroencephalogram (EEG) signals or from video streaming recordings of the driver behavior. This paper develops two distinct approaches, the first based on the use of machine learning classifiers and the second depends on the use of deep learning models to produce a high-performance fatigue detection system. The machine learning approach is used to process EEG signals, whereas the deep learning approach is used to process video streams. In machine learning classifiers, Support Vector Machine (SVM) provides up to 98% of detection accuracy, which is the highest accuracy among the other five deployed classifiers. In deep learning models, Convolutional Neural Network (CNN) provides up to 99% detection accuracy, which is the highest accuracy among the other two deployed models. The experimental results demonstrate that the two proposed algorithms provide the highest detection accuracy with the shortest Testing Time ( TT ) when compared to all other recent and efficient fatigue detection algorithms.

The rise of smart health technologies has offered benefits such as remote patient monitoring, personalized therapy, and early disease detection. However this advancement has also raised concerns about security and privacy of patient data. Authentication is essential for protecting patient data and smart health devices. The existing scheme is identified with security vulnerabilities in terms of stolen verifier, impersonation, and man in middle attacks. Motivated by this, authors have presented a secure, lightweight authentication technique for smart health environments for a remote monitoring scenario employing a Multiple Factor approach and a lightweight algorithm. The scheme is verified through formal and informal analysis. The scheme demonstrated enhanced authentication performance and security with processing overhead of (0.04 milliseconds) and transmission overhead of (704 bits), in comparison to the baseline scheme. This system has the potential to improve data security and privacy in a variety of smart health scenarios.

Background: Chronic obstructive pulmonary disease (COPD) comprises a group of progressive diseases that deteriorate lung functions. When patients cannot breathe, nothing else in their lives matter. Breathlessness has negative implications on patients’ lives, which leads to physical and psychological limitations. Moreover, the lack of relevant and updated information about the causes and consequences of the disease can exacerbate the problems of health literacy, information accessibility, and medical adherence. Objective: The objective of this study is to design an innovative mobile health (mHealth) app system called “MyLung” that provides complete solutions in order to increase self-awareness and promote better self-care management. This system, an information technology artifact, includes three novel integrative modules: education, risk reduction, and monitoring. Methods: The utility and effectiveness of the assistive mobile-based technology were evaluated using a mixed-methods approach. The study combined quantitative and qualitative research methods to thoroughly understand how the assistive mobile-based technology can influence patients’ behavioral intention to change their lifestyle. Thirty patients were categorized into two groups (intervention group and control group). Results: The results from the quantitative analysis led to four follow-up interviews in the qualitative study. The results of the quantitative study provided significant evidence to show that the design of MyLung leads to a change in the awareness level, self-efficacy, and behavioral intention for patients with COPD. The t tests revealed a significant difference before and after using the mobile-based app with regard to the awareness level (mean 3.28 vs 4.56; t10=6.062; P<.001), self-efficacy (mean 3.11 vs 5.56; t10=2.96; P=.01), and behavioral intention (mean 2.91 vs 4.55; t10=3.212; P=.009). Independent sample t tests revealed significant differences between the intervention group and the control group in terms of the awareness level (mean 4.56 vs 3.31; t19=4.80; P<.001) and self-efficacy (mean 5.56 vs 3.66; t19=2.8; P<.01). Integration of findings from quantitative and qualitative studies reveled the impact of the design in a comprehensive manner. These inferences are referred to as “meta-inferences” in this study. Conclusions: We designed an innovative assistive mobile-based technology to empower patients with COPD, which helped increase awareness and engage patients in self-care management activities. The assistive technology aims to inform patients about the risk factors of COPD and to improve access to relevant information. Meta-inferences that emerge from the research outputs contribute to research into chronic management information systems by helping us gain a more complete understanding of the potential impacts of this proposed mobile-based design on patients with chronic disease.

Industry-wide IoT networks have altered operations and increased vulnerabilities, notably DDoS attacks. IoT systems are decentralised. Therefore, these attacks flood networks with malicious traffic, creating interruptions, financial losses, and availability issues. We need scalable, privacy-preserving, and resource-efficient IoT intrusion detection algorithms to solve this essential problem. This paper presents a Federated-Learning (FL) framework using ResVGG-SwinNet, a hybrid deep-learning architecture, for multi-label DDoS attack detection. ResNet improves feature extraction, VGGNet optimises feature refining, and Swin-Transformer captures contextual dependencies, making the model sensitive to complicated attack patterns across varied network circumstances. Using the FL framework, decentralised training protects data privacy and scales and adapts across diverse IoT contexts. New preprocessing methods like Dynamic Proportional Class Adjustment (DPCA) and Dual Adaptive Selector (DAS) for feature optimisation improve system efficiency and accuracy. The model performed well on CIC-DDoS2019, UNSW-NB15, and IoT23 datasets, with 99.0% accuracy, 2.5% false alert rate, and 99.3% AUC. With a 93.0% optimisation efficiency score, the system balances computational needs with robust detection. With advanced deep-learning models, FL provides a scalable, safe, and effective DDoS detection solution that overcomes significant shortcomings in current systems. The framework protects IoT networks from growing cyber threats and provides a complete approach for current IoT-driven ecosystems.