The present study focuses on improving Cognitive Radio Networks (CRNs) based on applying machine learning to spectrum sensing in remote learning scenarios. Remote education requires connection dependability and continuity that can be affected by the scarcity of the amount of usable spectrum and suboptimal spectrum usage. The solution for the proposed problem utilizes deep learning approaches, namely CNN and LSTM networks, to enhance the spectrum detection probability (92% detection accuracy) and consequently reduce the number of false alarms (5% false alarm rate) to maximize spectrum utilization efficiency. By developing the cooperative spectrum sensing where many users share their data, the system makes detection more reliable and energy-saving (achieving 92% energy efficiency) which is crucial for sustaining stable connections in educational scenarios. This approach addresses critical challenges in remote education by ensuring scalability across diverse network conditions and maintaining performance on resource-constrained devices like tablets and IoT sensors. Combining CRNs with new technologies like IoT and 5G improves their capabilities and allows these networks to meet the constantly changing loads of distant educational systems. This approach presents another prospect to spectrum management dilemmas in that education delivery needs are met optimally from any STI irrespective of the availability of resources in the locale. The results show that together with machine learning, CRNs can be considered a viable path to improving the networks’ performance in the context of remote learning and advancing the future of education in the digital environment. This work also focuses on how machine learning has enabled the enhancement of CRNs for education and provides robust solutions that can meet the increasing needs of online learning.

This longitudinal study is dedicated to the evaluation of the comprehensive impact of educational reforms through a mixed research methodology which is a combination of the quantitative- and qualitative-oriented research methods to check the students’ outcomes. Data was collected in the span of [mention the time frame] from various data sources for instance standardized test scores, school performance statistics, and through open-ended qualitative evaluation from both students and teachers. Data analysis carried on after the reforms had been put in place revealed that there was a considerable rise in mean test scores and success graduation rates. Therefore, formative evaluation demonstrates the need for implementing reforms that will eventually help the students in boosting academic performance. Besides, there is no difference among investor opinions on teachers, administrators, and students who are involved with the implementation of the reforms. Stakeholders manifest this new assistance as an outcome of lasting improvements in curriculum quality, methods of teaching, and student participation. The study approaches two main challenges that are confronted with education reform that is resourcelessness and to society the change of the educational system can be more suitable for the students to excel academically and it can have an impact on the whole community. Even though this study makes important advancements toward the realization of the complex education implementation process and its effect on student academics, there are elements in which it can be criticized. Both quantitative and qualitative performance improvement is important as well as all the important stakeholder participation. This way the transformation process becomes layered. In other words, these results point to the necessity of planning interventions for longer periods that target the challenges and the forces that maintain the low levels of education performance by the counties.

Brain tumors are a primary factor causing cancer-related deaths globally, and their classification remains a significant research challenge due to the variability in tumor intensity, size, and shape, as well as the similar appearances of different tumor types. Accurate differentiation is further complicated by these factors, making diagnosis difficult even with advanced imaging techniques such as magnetic resonance imaging (MRI). Recent techniques in artificial intelligence (AI), in particular deep learning (DL), have improved the speed and accuracy of medical image analysis, but they still face challenges like overfitting and the need for large annotated datasets. This study addresses these challenges by presenting two approaches for brain tumor classification using MRI images. The first approach involves fine-tuning transfer learning cutting-edge models, including SEResNet, ConvNeXtBase, and ResNet101V2, with global average pooling 2D and dropout layers to minimize overfitting and reduce the need for extensive preprocessing. The second approach leverages the Vision Transformer (ViT), optimized with the AdamW optimizer and extensive data augmentation. Experiments on the BT-Large-4C dataset demonstrate that SEResNet achieves the highest accuracy of 97.96%, surpassing ViT’s 95.4%. These results suggest that fine-tuning and transfer learning models are more effective at addressing the challenges of overfitting and dataset limitations, ultimately outperforming the Vision Transformer and existing state-of-the-art techniques in brain tumor classification.

In order to assess the effects of e-learning integration on university performance and competitiveness, this study uses Oman as a model for the Gulf. Analyzing how e-learning impacts technology integration, diversity, community engagement, infrastructure, financial strength, institutional reputation, student outcomes, research and innovation, and academic quality can reveal whether universities are effectively incorporating digital tools to enhance teaching and learning. By offering a framework for comparable institutions in the Gulf area, this study provides insights into optimizing e-learning techniques to improve university performance and competitiveness. This study uses the Structural Equation Modeling (SEM) with a dataset comprising 424 participants and 55 indicators, analyzed using both measurement and structural models. The results of the hypothesis testing, which indicate that e-learning has a positive effect on factors like student outcomes (B = 0.080, t = 2.859, P = 0.004) and institutional reputation (B = 0.058, t = 2.770, P = 0.005), lend credence to these beliefs. Omani universities need culturally sensitive e-learning, stronger institutional support, and training to enhance diversity (B = 0.002, t = 0.456, P = 0.647) and technology integration (B = −0.009, t = 0.864, P = 0.387). These improvements increase the visibility of Gulf institutions abroad, attracting the best students from all around the world and fostering an inclusive learning atmosphere. Financially speaking, e-learning offers reasonably priced solutions such as digital libraries and virtual laboratories, which are especially beneficial in a region where education plays a major role in socioeconomic development.

Lighting conditions in learning spaces can affect students’ emotions and influence their performance. This research seeks to verify the influence of classroom lighting on students’ academic performance under different conditions and measurement forms. The research method is based on the systematic review of research articles establishing case analyses characterizing lighting intensity and color temperature to determine ranges favorable to a higher level of attention and long-term memory. Also, this study shows relevant aspects of the cases representative of a sustainable solution and proposes a research model. The study found light intensity values between 350 and 1000 lux and color temperatures between 4000 and 5250 Kelvin that favor attention. Long-term memory reached the highest levels of measurement by analyzing different parameters sensitive to lighting conditions and questionnaires. In conclusion, it was demonstrated that an adequate light intensity and color temperature based on the greatest possible amount of natural light complemented with Light Emitting Diode (LED) light generates optimal lighting for the classroom, achieving energy efficiency in a sustainable solution and promoting student well-being and performance.

Shared education has the potential to foster pluralistic values and improve relations between individuals from diverse ethno-linguistic backgrounds. This study aims to contribute to the understanding of how shared learning experiences can promote pluralism and social equality by examining the pedagogical factors that influence their success. This study focuses on a shared English learning model implemented with 8th-grade Arab and Jewish students in homogenous Israeli cities. This qualitative study, involving observations, interviews, focus groups, and transcript analysis, engaged 42 students, two teachers, and two administrators. The findings suggest that shared education has positive social implications. It facilitated interaction between Arab and Jewish students and challenged negative stereotypes. Notably, the Jewish students’ limited Arabic language proficiency led to complex interactions, stimulating critical thinking about linguistic inequality and increasing motivation to learn Arabic. While shared education improved intergroup relations, it also encountered logistical challenges that necessitated institutional support to optimize its effectiveness.