Diabetes, a significant global health crisis, is primarily driven in India by unhealthy diets and sedentary lifestyles, with rapid urbanization amplifying these effects through convenience-oriented living and limited physical activity opportunities, underscoring the need for advanced preventative strategies and technology for effective management. This study integrates Shapley Additive explanations (SHAPs) into ensemble machine learning models to improve the accuracy and efficiency of diabetes predictions. By identifying the most influential features using SHAP, this study examined their role in maintaining high predictive performance while minimizing computational demands. The impact of feature selection on model accuracy was assessed across ten models using three feature sets: all features, the top three influential features, and all except these top three. Models focusing on the top three features achieved superior performance, with the ensemble model attaining a better performance in most of the metrics, outperforming comparable approaches. Notably, excluding these features led to a significant decline in performance, reinforcing their critical influence. These findings validate the effectiveness of targeted feature selection for efficient and robust clinical applications.

Cloud computing delivers services through the Internet and enables the deployment of a diversity of apps to provide services to many businesses. At present, the low scalability of these cloud frameworks is their primary obstacle. As a result, they are unable to satisfy the demands of centralized computer systems, which are based on the Internet of Things (IoT). Applications such as disease surveillance and tracking and monitoring systems, which are highly latency sensitive, demand the computation of the Big Data communicated to centralized databases and from databases to cloud data centers, resulting in system performance loss. Recent concepts, such as fog and edge computing, offer novel approaches to data processing by relocating the processing power and other resources closer to the end user, thereby reducing latency and maximizing energy efficiency. Existing fog models, on the other hand, have a number of limitations and tend to prioritize either the precision of their findings or a faster response time, but not both. For the purpose of applying a healthcare solution in the real world, we developed and implemented a one-of-a-kind architecture that integrates quartet deep learning with edge computing devices. The paradigm that has been developed delivers health management as a fog service through the Internet of Things (IoT) devices and efficiently organizes the data from patients based on the requirements of the user. FogBus, a fog-enabled cloud framework, is used to measure the effectiveness of the proposed structure in regards to resource usage, network throughput, congestion, precision, and runtime. To maximize the QoS or forecast the accuracy in different fog computing settings and for different user requirements, the suggested technique can be set up to run in a number of different modes.

Robust Intrusion Detection Systems (IDS) are increasingly necessary in the age of big data due to the growing volume, velocity, and variety of data generated by modern networks. Metaheuristic algorithms offer a promising approach to enhance IDS performance in terms of optimal feature selection. Combining these algorithms along with Machine learning (ML) for the creation of an IDS makes it possible to improve detection accuracy, reduce false positives and negatives, and enhance the efficiency of network monitoring. Our study proposes using metaheuristic algorithms along with machine learning classifiers for feature selection to optimize the number of features from the data set of computer network traffic. We have tested several combinations of algorithms viz., Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) along with ML algorithms viz., Decision Tree (DT), Random Forest (RF), Gaussian Naïve Bayes (GNB) and Logistic Regression (LR). The combinations of algorithms have been tested over the NSS-KDD and kddcupdata_10% data sets. We have drawn several insights on feature selection scores with respect to test scores, FI scores, recall and precision for various algorithm combinations. The feature selection time has also been highlighted to showcase the fastest-performing algorithm combinations. Ultimately, we have presented three combinations of algorithms depending on organizational IDS requirements and provided separate solutions for each.