Chronic Kidney Disease (CKD) is considered a leading cause of high morbidity and mortality. Therefore, it needs early detection to allow timely intervention aimed at the enhancement of the patient outcome. The current study presents a Transparent CKD ML which combines the predictive power of efficient ML methods with the eXplainable AI techniques for transparent interpretibility of the prediction. This study has conducted an in-depth performance evaluation of the predictive power of the following eight machine learning algorithms: Logistic Regression, K-Nearest Neighbours (KNN), Support Vector Machine (SVM), Decision Tree, Random Forest, CatBoost, XGBoost, and AdaBoost on the 'Chronic Kidney Disease' dataset provided by UCI Machine Learning Repository. As a further study on algorithm performance, performance measures of accuracy, precision, recall, and F1 score were calculated; it was determined that Logistic Regression, Random Forest, and AdaBoost were performing very well and achieved 100% score in all metrics. This study further combined the ML models with eXplainable AI ( XAI) techniques to increase the transparency of the models.  SHapley Additive exPlanations (SHAP) an XAI technique was used to provide critical insights into the causality that dictates the predictions of CKD. Thus, this combination ensures the best performance of the model, increasing the trust in AI within clinical practice. The present study, therefore, unleashes the transformational potential of AI technologies in radically renovating the management of CKD and improving patient outcomes across the world.

This paper shows an analysis of features of email system using feature model in a Software Product Line (SPL). The core features that can be used by different SPLs are identified using feature model. The analysis is based on two primary measures – reusability and consistency. Reusability measures the level of frequency of usage of the feature in developing a new software product line and consistency ensures that the core features are consistent in a software product line. On the basis of reusability measure, the core features are classified into four different categories. These measures help in understanding the Return on Investment in a software product line.

The LED (Light emitting diode) based lighting systems are gaining popularity for its dual use i.e. for energy efficient lighting systems as well as for indoor optical wireless communication systems. Although, Visible light spectrum has the capability to provide very large system bandwidth (in THz), yet these systems have the limitation on account of limited modulation bandwidth. Besides, Visible light communication (VLC) systems also suffer due to multi-path propagation resulting in further depletion of system bandwidth due to pulse broadening. Therefore, one of the deployment objective of a visible light communication (VLC) system is to reduce the root mean square (RMS) delay parameter besides minimizing the number of LEDs. Hence, performance analysis of two geometrical shape structures mainly rectangular and circular models are explored for ubiquitous indoor coverage using hyper- heuristics evolutionary algorithm(HypEA) under spatial receiver mobility. Therefore, it is possible to achieve lower RMS delay spread and hence multi- fold increase in the overall system bandwidth without the use of complex system techniques like OFDM- MIMO etc.