Distance learning has spread nowadays on a large scale across the world, which has led to many challenges in education such as invigilation and learning coordination. These challenges have attracted the attention of many researchers aiming at providing high quality and credibility monitoring of students. Distance learning has offered an effective education alternative to traditional learning in higher education. The lecturers in universities face difficulties in understanding students’ emotions and abnormal behaviors during educational sessions and e-exams. The purpose of this study is to use computer vision algorithms and deep learning algorithms to develop a new system that supports lecturers in monitoring and managing students during online learning sessions and e-exams. To achieve the proposed objective, the system employs software methods, computer vision algorithms, and deep learning algorithms. Semi-structural interviews were also used as feedback to enhance the system. The findings showed that the system achieved high accuracy for student identification in real time, student follow-up during the online session, and cheating detection. Future work can focus on developing additional tools to assist students with special needs and speech recognition to improve the follow-up facial recognition system’s ability to detect cheating during e-exams in distance learning.

The big rise in the existence of digital data contributed to creating many good chances, especially related to corporations, institutions and firms. Also, it gives the capability to scrimp data regarding its major or area, where the countries have benefited from the analysis of big data (BD) greatly in the face of epidemics and diseases, especially COVID-19 since BD is now available everywhere around us, from official reports and scientific studies related to virology and epidemiology. The general aim of this study is to clarify how the conjunction among both BD and machine learning (ML) created huge differences in data science and a big influence on the applications related to a lot of fields chiefly in COVID-19. The method which is used in this study ‘relevance tree’ by identifying papers related to ML and BD, especially in COVID-19. The results have been shown that the use of reinforcement learning in analyzing BD provides effective and tremendous results, although it faces many challenges and restrictions that have been explained in detail in this study. In addition, the results showed that most of the countries in the time of Corona turned into smart cities, totally dependent on smart applications based on the analysis of BD using ML, and one of the most important applications that were circulated around the world global positioning system. In addition to the results that have been found, data privacy is one of the most important challenges facing data analysis. Consequently, it recommended future researchers to focus on studying the challenges faced by ML in analyzing medical data in the COVID-19 era.

Machine learning is considered the most significant technique that processes and analyses educational big data. In this research paper, many previous papers related to analysing the educational big data that uses a lot of artificial intelligence techniques were studied. The purpose of the study is to identify weaknesses and gaps in previous researches. The results showed that many researches highlighted early expectations for academic performance. Unfortunately, no one thought of finding an effective way to guide high schooled students to reach their appropriate majors that can be suitable to their abilities. Those students need to be guided to pass this sensitive phase with high efficiency and good results. Thus, this school level is considered as the starting point for students’ academic lives, professional, and future success.

Performance scale application (PSA) usage in the classroom is underutilized, despite the rapid progress of mobile phone and e-learning technology. Lack of self-learning, evaluation, satisfaction, and inability to choose appropriate specialties influence students’ academic achievement in secondary school. The objective of this study is to investigate the development and testing of PSA on students’ learning achievement in secondary school. The PSA was developed on the Android mobile operating system using the extra trees regression algorithm to predict student achievement in secondary school. Students in the 11th grade basic specialty were considered. Three specialties were used, namely scientific, literary, and industry. The variables examined include improving evaluation (IME), improving communication (IMC), improving scientific (IMSC), and satisfaction of learning (SOL). The findings demonstrated that the PSA accurately predicted the students’ choice of specialty, IMC, IMSC, SOL, personalized learning (L), distance L, mobile L, self L, and specialty L. The findings also indicated a positive and significant effect of the PSA on students’ learning achievement. This validates that the extra trees regression is an effective tool for the development of PSA. In conclusion, the PSA has efficiently predicted the choice of specialties and academic achievements of students in secondary schools.

It is a wireless network with mobile nodes that function independently and communicate with one another with radio waves. When a node gets a packet, they evaluate all of the possible routes before selecting the optimal one. In this way, the capabilities of routing are incorporated into each node of the network. Researchers used ant colonies to find the best path between two sites. The Simple Ant Routing Protocol has improved with the assistant of Internet of Things (IoT). Energy Aware Simple Ant Routing Algorithm (EASARA), a new protocol, considers each node's energy usage. The change improved routing overhead and packet delivery ratio. It also improved communication. EASARA performed better with more hosts. Traffic congestion statistics followed. One parameter estimated host power reserve and connection congestion. Energy-congestion conscious protocol is basic routing algorithm is ECSARA (Energy-congestion based Simple Ant Routing Algorithm). The protocol improves with more hosts. It sends packets faster. Hence, transmission data transfer increased. Energy savings extended the route's lifespan. Signal strength predicted connection failures. This metric chooses a new route. Signal-to-Anchor-Receiver-Attendance based Simple Ant Routing Algorithm is SS-SARA. All host monitors detected strong signals. Communication uses substitute pathways when signal strength goes below a threshold. The protocol improved packet delivery and throughput on congested networks, according to experiments.

Local explanation rankings can depend on the classifier, masking operator and computational budget. This study compares LIME, KernelSHAP and Ablation or Occlusion in two controlled cases: Breast Cancer Wisconsin Diagnostic with a random forest and multilayer perceptron and Fashion-MNIST with a multilayer perceptron and convolutional neural network. Evaluation uses 104 shared correctly classified tabular cases and 100 class-balanced shared correctly classified images. Sensitivity analyses use 20 cases per modality, ten explanation seeds, three Gaussian noise scales, three prediction-preservation thresholds and three replacement baselines. Paired tests apply Holm adjustment across 60 method contrasts. KernelSHAP has the lowest mean tabular deletion AUC. Occlusion leads for the image multilayer perceptron and LIME for the convolutional network. Deterministic removal methods repeat exactly for nondegenerate vectors, while LIME generally has greater signed rank stability under retained input changes. Alternative image backgrounds change attribution vectors and can reverse deletion rankings. Evaluated-input costs range from 31 for tabular Ablation to approximately 15,001 for tabular KernelSHAP. These results support separate reporting of faithfulness, stability and cost under documented protocols. They establish neither a universal method ranking nor clinical utility or generalization across datasets and training seeds.