Chronic kidney disease (CKD) is among the top 20 causes of death worldwide and affects approximately 10 of the world adult population. CKD is a disorder that disrupts normal kidney function. Due to the increasing number of people with CKD, effective prediction measures for the early diagnosis of CKD are required. The novelty of this study lies in developing the diagnosis system to detect chronic kidney diseases. This study assists experts in exploring preventive measures for CKD through early diagnosis using machine learning techniques. This study focused on evaluating a dataset collected from 400 patients containing 24 features. The mean and mode statistical analysis methods were used to replace the missing numerical and the nominal values. To choose the most important features, Recursive Feature Elimination (RFE) was applied. Four classification algorithms applied in this study were support vector machine (SVM), k-nearest neighbors (KNN), decision tree, and random forest. All the classification algorithms achieved promising performance. The random forest algorithm outperformed all other applied algorithms, reaching an accuracy, precision, recall, and F1-score of 100 for all measures. CKD is a serious life-threatening disease, with high rates of morbidity and mortality. Therefore, artificial intelligence techniques are of great importance in the early detection of CKD. These techniques are supportive of experts and doctors in early diagnosis to avoid developing kidney failure.

Corona Virus Disease 2019 (COVID-19) pandemic has increased the importance of Virtual Learning Environments (VLEs) instigating students to study from their homes. Every day a tremendous amount of data is generated when students interact with VLEs to perform different activities and access learning material. To make the generated data useful, it must be processed and managed by the proper machine learning (ML) algorithm. ML algorithms’ applications are many folds with Education Data Mining (EDM) and Learning Analytics (LA) as their major fields. ML algorithms are commonly used to process raw data to discover hidden patterns and construct a model to make future predictions, such as predicting students’ performance, dropouts, engagement, etc. However, in VLE, it is important to select the right and most applicable ML algorithm to give the best performance results. In this study, we aim to improve those ML and DL algorithms’ performance that give an inferior performance in terms of performance, accuracy, precision, recall, and F1 score. Several ML algorithms were applied on Open University Learning Analytics (OULA) dataset to reveal which one offers the best results in terms of performance, accuracy, precision, recall, and F1 score. Two popular ML algorithms called Decision Tree (DT) and Feed-Forward Neural Network (FFNN) provided unsatisfactory results. They were selected and experimented with various techniques such as grid search cross-validation, adaptive boosting, extreme gradient boosting, early stopping, feature engineering, and dropping inactive neurons to improve their performance scores. Moreover, we also determined the feature weights/importance in predicting the students’ study performance, leading to the design and development of the adaptive learning system. The ML techniques and the methods used in this research study can be used by instructors/administrators to optimize learning content and provide informed guidance to students, thus improving their learning experience and making it exciting and adaptive.

Learning activities are considerably supported and improved by the rapid advancement of e-learning systems. This gives students a tremendous chance to participate in learning activities worldwide. The Massive Open Online Courses (MOOCs) platform has emerged as one of the most significant platforms for e-learning as a result of the rapid growth of network information technologies. Due to the increased number of online courses available, it is harder for individual learners to choose the appropriate courses, activities, and learning paths for the actual necessities they want, which reduces their learning performance. Moreover, a sequential Recommender System (RS) can identify the learner’s future interest and suggest the subsequent item or learning content given a sequence of past interactions. This is in contrast to interactive recommendation methods that can create recommendations based on the learner’s feedback via constant interactions. The goal of this paper is to propose a Reinforcement Learning (RL) based smart e-learning framework with Markov Decision Process (MDP) that have the potential to enhance the learning experience for each student by providing them with a personalized and effective learning path. Applying the MDP and RL-based techniques such as Q-learning for Sequential Path Recommendation (SPR) and learning development is more achievable. This is because the MDP allows for adjusting the recommendations method to find new activities and learning paths based on the learners’ feedback on recommendations results. Experimental findings reveal that the suggested model obtains significant improvements and provides viable performance under different parameters optimization. Furthermore, we also show that the proposed method outperforms a long session (long-term rewards such that they maximize learning progress while minimizing frustration and disengagement). This demonstrates the model’s improvements in simulating the learner’s sequential behavior, learning activities, various learning materials, and learning paths simultaneously. These promising initial results provide a possible solution to assess this challenge further in future work.

Realizing secure and private communications on the Internet of Things (IoT) is challenging, primarily due to IoT's projected vast scale and extensive deployment. Recent efforts have explored the use of blockchain in decentralized protection and privacy supported. Such solutions, however, are highly demanding in terms of computation and time requirements, barring these solutions from the majority of IoT applications. Specifically, in this paper, we introduce a resource-efficient, blockchain-based solution for secure and private IoT. The solution is made possible through novel exploitation of computational resources in a typical IoT environment (e.g., smart homes), along with the use of an instance of Deep Extreme Learning Machine (DELM). In this proposed approach, the Smart Home Architecture based in Blockchain is protected by carefully evaluating its reliability in regard to the essential security aims of privacy, integrity, and accessibility. In addition, we present simulation results to emphasize that the overheads created by our method (in terms of distribution, processing time, and energy consumption) are marginal related to their protection and privacy benefits.