Wireless sensor networks (WSNs) have been commonly utilized in event detection and environmental observation applications. The main aim of event detection is to define the presence or absence of an event. Various existing studies in the field of event detection depend on static or threshold values to reveal the occurrence of an event, which can result in imprecise sensor readings. Recently, many studies have utilized fuzzy logic to treat fluctuating sensor readings; as a result, they have decreased the number of false alarms created. However, there is some attention required when utilizing fuzzy logic. One aspect is that the efficiency and accuracy of the fuzzy membership function can be impacted by the utilization of heterogeneous sensors, which may increase the complexity of the fuzzy logic operation as the number of inputs rises. To address these issues, this paper proposes an approach named Probabilistic Collaborative Event Detection (PCED), which is a hybrid event detection technique that is based on a cluster WSN topology. The PCED approach utilizes a validated probabilistic technique for heterogeneous sensor nodes to transform sensing values into probability formulas and introduces a Cluster Head Decision Mechanism to make decisions based on the aggregated data from the sensors. The proposed approach employs fuzzy logic at the fusion center level to enhance the precision of event detection. The effectiveness of this method is thoroughly evaluated using MATLAB software, demonstrating an improvement in the probability of detection and a decrease in the probability of false alarms. PCED is compared to well-established event detection mechanisms such as the REFD mechanism. The results show that PCED reduces the occurrence of false alarms from 37 to 3 in certain scenarios, while improving detection accuracy by up to 19.4% over REDF and decreasing detection latency by up to 17.5%.

Considering the weakness of traditional e-learning, a multiple of e-learning systems attempt to get individualization into the process of learning by offering learner-centered instruction, the adaptive E-learning system(AES)is seen as one of the more famous models. An AES can tailor its response to different circumstances. Various types and platforms in E-Learning systems have been discussed and explained such as blended, adaptive, and educational e-learning. Architectures, functions, and challenges also illustrated in details in this paper. Mostly focused on the Adaptive e-learning concept and importance for the learners. AES concentrates on adaptively delivering learning materials. This paper discusses some studies that approved the importance of adaption in the e-learning system in the years (2018-2023), and introduced some challenges faced by this system.

Chatbots are becoming increasingly significant entry points to digital services and are used in areas including job support, education, healthcare, and customer services. On the other hand, less is known about how chatbots affect people individually, in groups, and in society. Moreover, several obstacles must be overcome before chatbots can realize their full potential. As a result, chatbots have become a significant research topic in recent years. We propose a research agenda outlining future directions and issues to advance knowledge in chatbot research and education. This research involves the quantitative analysis of the impact of these chatbot tools on academic staff, with a count of 40, and students, with a count of 300, at Al Zahra College for Women (ZCW). The links are uploaded electronically in bilingual form for both staff and students, and the responses are retrieved from them. This analysis is conducted in IBM SPSS Statistics version 29, and a comparative report is also prepared based on the questionnaire responses from students and staff members of ZCW. The study investigates the effects of artificial intelligence (AI) chatbots on the faculty and students at ZCW. The use of AI-powered tools in educational settings is examined, along with their effects on administrative, instructional, and learning procedures. This will enable us to identify the advantages of using AI tools within E-Learning systems. The results demonstrate how well AI chatbots can streamline administrative duties, enhance student involvement, and provide academic help. However, there are drawbacks as well, such as user adjustment, privacy issues, and technological constraints. The study offers helpful suggestions for enhancing chatbot integration at Al Zahra College to enhance learning results and operational effectiveness.