The demand for secure handling and confidentiality of information has heightened immensely in the contemporary era, particularly due to high volumes of data exchanged through the Internet. Steganography and cryptography are two widely used techniques for safeguarding data, each with its inherent strengths and limitations. Cryptography protects information by converting it into an unreadable form, guaranteeing confidentiality. However, it does not hide the fact that a communication is present, which can draw unwanted attention. Steganography conceals information within digital media to render its presence less noticeable. Nevertheless, this approach can diminish the quality of the cover media and faces challenges in achieving lossless recovery of the cover. Reversible Data Hiding in Encrypted Images (RDHEI) has been proposed to transcend these limitations, enhancing security and recoverability. RDHEI provides a robust method for extracting and embedding data while maintaining the integrity of the original image. Separable Reversible Data Hiding in Encrypted Images (SRDHEI) takes it a step further by enabling data to be decrypted and extracted separately, without requiring access to or modification of the original image. This paper presents a novel SRDHEI solution with a high embedding capacity of 0.691 bits per pixel (bpp), which is achieved by combining Wang’s “multiple-bit RDH scheme without histogram shifting” with additive homomorphic encryption concepts. Experimental comparisons demonstrate that the new solution outperforms other methods in terms of embedding capacity, visual quality, and overall security.

The digitization of healthcare has led to an unprecedented growth in health-related data, offering new opportunities to transform clinical decision-making, disease prediction, and patient engagement. However, extracting actionable insights from diverse data sources such as electronic health records, wearable devices, and mobile health apps requires a fusion of domain knowledge in healthcare and technical expertise in data science. This paper presents a structured, interdisciplinary framework for digital health that emphasizes practical strategies for data acquisition, preprocessing, feature engineering, machine learning, and ethical data use. The model promotes a holistic understanding of digital health challenges and opportunities, preparing future professionals to apply computational tools in real-world healthcare environments responsibly.

Researchers are looking into Human Activity Recognition to find out different activities using affordable sensors. They are also using machine learning for real-time monitoring with sensor data. In healthcare, it's important to know how much energy is used during activities, especially for people who cannot move much, like older folks with Chronic Fatigue Syndrome. Figuring out their daily energy use is crucial to help them manage fatigue better. So, there's a need for a detailed study on finding different activities and understanding how much energy is used in every activity, especially in healthcare. In this survey paper, we explored articles about recognizing physical and social activities. We looked into the detailed methods used to calculate energy expenditure, carefully comparing the sensors used in various research projects. We investigated pre-processing techniques for sensor data. We uncovered how to extract features and studied the machine-learning methods used with raw sensor data. Bringing all this together not only broadened our understanding but also gave us a nuanced view of recognizing activities and calculating energy expenditure. This paper provides a detailed survey of recent sensor-based research focused on detecting and measuring physical activities. There is a scarcity of systems utilizing sensors for this purpose. As the number of individuals facing challenges in performing day-to-day activities continues to rise, there is an imperative need for enhanced methods to develop such systems.

Chronic Fatigue Syndrome (CFS) is a disorder with complex symptoms among patients. In most cases, CFS sufferers describe severe body weakness, poor sleep and inability to perform their usual work as their primary complaints. Symptoms worsen when the patient attempts to do similar work as tolerated. To prevent the worsening of symptoms, the patients need to be aware of what intensity of work they can manage. In this paper, we propose CFSCare, a hardware and software-based system that uses ML models to measure CFS patients' daily activity and energy expenditure objectively. Through our developed App, CFSCare submits to the user a summary of the comprehensive reports of the user's activity and sends a recommendation to the user on how they can prevent acquiring symptoms of CFS brought about by over-exertion. We use an Android smartphone and wrist sensor (MetamotionC) to monitor their leg and hand activity. We develop ML models based on SVM and DT algorithms to predict particular leg and hand activities. Among the applied ML models, SVM exhibited a brilliant performance with 98% accuracy in predicting leg activities and an average to-fold cross-validation score of 94%. For the hand activities prediction, DT recorded the best accuracy of 96%, and the average score of to cross-validations is also 96%. Since CFS patients can tire with exertion after a small amount of daily activity, CFSCare can playa vital role in preventing the patient from over-exertion through the monitoring features.

In this paper, we introduce a machine learning (ML)-based approach for Chronic Kidney Disease (CKD) and diabetes prediction and perform feature effect analysis through SHAP (SHapley Additive exPlanations). We utilize two publicly available clinical datasets and build five ML classifier models for the analysis. Among all models, CatBoost provides the best performance for both CKD and diabetes prediction. Our CatBoost model has an accuracy of 0.95, mean 10-fold cross-validation of 0.96, ROC-AUC score of 0.99, precision of 0.96, recall of 0.96, and F1-score of 0.96 for CKD. The CatBoost model shows an accuracy of 0.99, mean 10-fold cross-validation of 0.97, ROC-AUC score of 1.0, precision of 1.0, recall of 0.98, and F1-score of 0.99 for diabetes. We perform comprehensive feature effect analysis by computing SHAP values. This gives insights into the contribution of every feature to the model's predictions. The achieved results highlight that CatBoost is a robust choice for accurate and reliable predictions of CKD and diabetes. The findings of this study contribute to the corresponding field of ML approaches for medical diagnosis and illustrate the significance of feature effect analysis in understanding model predictions. With excellent results, our research has the potential to enhance the clinical decision-making process and improve patient outcomes.