Generative AI (GAI) technologies like ChatGPT are transforming the educational landscape. Their integration presents significant opportunities for personalized learning and enhanced student engagement but also raises challenges related to academic integrity and the role of human educators. This study aims to provide a comprehensive review on using multiple GAI tools and their influence on academic outcomes, bridging the gap in the current literature. A systematic literature review has been conducted, adhering to PRISMA guidelines, to discuss findings on the opportunities and challenges of GAI in education.This review includes theoretical and empirical studies employing qualitative, quantitative, and mixed-methods approaches. We have also examined conceptual frameworks and innovative AI applications, focusing on originality and ease of implementation. To gather insights into students’ experiences and perceptions regarding the use of Generative AI (GAI) in education, a survey has been conducted with 200 undergraduate university students. GAI offers opportunities for personalized learning, task automation, educator support, efficiency enhancement, innovative approaches, and increased student engagement. However, challenges include assessment integrity concerns, potential overshadowing of educational values, accuracy of AI-generated content, disruption of critical thinking skills, and ethical and privacy considerations. The student perception survey reveals that most students find AI systems helpful in assisting with academic work. Besides, they are aware of the challenges and limitations associated with the technology.

Mango leaf diseases represent a serious threat to world agriculture, necessitating prompt and accurate detection to avert catastrophic effects. In response, this study suggests a light-weight, deep learning-based method for automatically classifying mango leaf diseases. The model is based on the original DenseNet architecture, which is well known for its effectiveness in image classification tasks. Custom layers have been added over the existing layer of the original DenseNet model. The proposed model has been compared with other existing pre-trained models. Based on comparisons, the proposed model, DenseNet78, proved to be efficient even on a relatively small dataset, where the conventional model failed. The proposed model ensured generalization across regions, disease variants, and diverse datasets of mango leaves. The results demonstrate that the fine-tuned DenseNet architecture (DenseNet78), along with an ideal growth rate, modifying block size, and a number of layers, provides optimum accuracy, with 99.47% accuracy in identifying healthy mango leaves and 99.44% accuracy in detecting various mango leaf diseases. The results also demonstrate that the model is effective in accelerating the training process because of careful comparative analysis of all the available alternatives, including the most effective combination of optimizers, learning rate schedulers, and loss functions. The study's conclusion is an automated approach for diagnosing mango leaf disease using an improved and optimized DenseNet architecture (DenseNet78). Doi: 10.28991/ESJ-2024-08-01-03 Full Text: PDF

The agricultural sector in Bangladesh is a cornerstone of the nation's economy, with key crops such as rice, corn, wheat, potato, and tomato playing vital roles. However, these crops are highly vulnerable to various leaf diseases, which pose significant threats to crop yields and food security if not promptly addressed. Consequently, there is an urgent need for an automated system that can accurately identify and categorize leaf diseases, enabling early intervention and management. This study explores the efficacy of the latest state-of-the-art object detection model, YOLOv8 (You Only Look Once), in surpassing previous models for the automated detection and categorization of leaf diseases in these five major crops. By leveraging modern computer vision techniques, the goal is to enhance the efficiency of disease detection and management. A dataset comprising 19 classes, each with 150 images, totaling 2850 images, was meticulously curated and annotated for training and evaluation. The YOLOv8 framework, known for its capability to detect multiple objects simultaneously, was employed to train a deep neural network. The system's performance was evaluated using standard metrics such as mean Average Precision (mAP) and F1 score. The findings demonstrate that the YOLOv8 framework successfully identifies leaf diseases, achieving a high mAP of 98% and an F1 score of 97%. These results underscore the significant potential of this approach to enhance crop disease management, thereby improving food security and promoting agricultural sustainability in Bangladesh.

Vehicle-to-everything (V2X) communication has many benefits. It improves fuel efficiency, road safety, and traffic management. But it raises privacy and security concerns. These include the risk of cyberattacks and the loss of drivers’ personal data. Eavesdropping, data manipulation, and unauthorized vehicle monitoring are major problems that need immediate attention. This paper proposes a new approach to intrusion detection in V2X communications. It uses machine learning-based cryptographic protocols for intrusion detection (ML-CPIDSs). The goal is to improve privacy and security in vehicular ad hoc networks (VANETs). The ML-CPIDS combines advanced cryptographic protocols with machine learning. It provides strong authentication, encryption, and real-time threat detection. Robust authentication and encryption techniques in modern cryptographic systems protect sensitive information. Using machine learning algorithms, it is feasible to identify and address security risks in real-time. The proposed technology solves key privacy and security issues. It has applications in many areas, including autonomous vehicle networks, urban traffic management, and vehicle communication systems. Extensive simulations show the ML-CPIDS works in different VANET environments. Privacy, security, and the ability to identify threats in real time are some of the areas that are evaluated in these simulations. The proposed ML-CPIDS approach outperforms current methods on several metrics. It has better privacy and authentication, lower latency, and stronger threat detection. It also improves the integrity and efficiency of V2X communications in VANET networks.

A Comprehensive Review of Efficient Ray-Tracing Techniques for Wireless Communication

This study develops a grid-connected solar PV–biogas microgrid for a five-story residential building in Rajshahi, Bangladesh, optimized using HOMER Pro. Among six configurations, the optimal configuration, integrating solar PV and a biogas generator, achieves a 59.4% renewable fraction, reducing CO₂ emissions by 46% and lowering the cost of energy to $0.0306/kWh. The system’s net present cost is $46,813, with an annual operating cost of $1,187. It generates 31,168 kWh/year from solar PV and 3,040 kWh/year from biogas, with grid purchases and sales of 21,951 kWh/year and 13,411 kWh/year, respectively. Predictive modeling (R² = 1.0) enhances power estimation accuracy. MATLAB-based stability analysis confirms seamless voltage and frequency integration. A demand response strategy achieves 2,730.44 kWh/year savings, reducing non-renewable reliance. Sensitivity analysis identifies solar irradiance as the dominant cost factor. The results establish a scalable, cost-effective model for residential renewable microgrid adoption globally.