This study explores the role of Large Language Models (LLMs) in information retrieval within the digital environment through a theoretical analysis of their concepts, operational mechanisms, and a comparison with traditional methods, alongside identifying key challenges and contemporary applications. The findings reveal that LLMs represent a qualitative shift in processing natural language due to their ability to understand context and generate precise responses. The study highlights their superiority in enhancing retrieval systems through integration with cognitive technologies such as Retrieval-Augmented Generation (RAG) and Knowledge Graphs (KGs), thereby improving the reliability and effectiveness of results, especially in specialized domains. Despite these capabilities, the study identifies technical and methodological challenges, including hallucination and limited interpretability. It emphasizes that LLMs do not replace traditional retrieval methods but complement them, depending on task nature and user behavior. The study recommends developing hybrid models, enhancing multimodal capabilities, and expanding real-world evaluations—particularly in low-resource languages and specialized fields. It concludes that integrating LLMs with structured knowledge representations offers a promising path toward building more accurate, equitable, and intelligent information retrieval systems.

The current study examines the role of digital public relations in reputation management via Instagram, applying it to charitable organizations in the Kingdom of Bahrain, and the effectiveness of Instagram in improving their public image. It aims to identify the nature of digital media and its impact on promoting cultural exchange and dialogue in the Kingdom of Bahrain. A survey approach was used, and the available sample, consisting of (80) individuals from Bahraini society, was selected. (26) Males and (45) females were selected through questionnaire analysis and interviews. The study reached several conclusions, the most important of which is that digital public relations has a positive impact on managing the reputation of charitable organizations via Instagram. It also recommended the need to pay attention to the importance of digital public relations, develop clear digital public relations strategies, and attempt to implement them on the ground to achieve the desired goals.

This research paper is titled: Applications of Information and Communication Technology in Education. The researchers employed the analytical descriptive approach, which is the most suitable for the research topic based on gathering information, analyzing it, discussing it, and reaching conclusions and recommendations to address the issues surrounding the subject. The paper was divided into five main sections: the first section addresses the methodological framework of the research, the second focuses on the use of the internet and modern web applications, the third discusses modern technologies in the field of education and learning, the fourth covers practical applications of modern technologies in education and learning, and the fifth includes the findings, recommendations, and a list of sources and references. Among the outcomes of this paper are: enhancing the efficiency of educational staff, improving communication between educational administrations, teachers, and parents, and the emergence of distance learning systems, which have become an effective means in the educational process, contributing to the continuity of education and learning, and meeting the diverse needs of society.

The proliferation of Android malware poses an ever-growing menace to billions of mobile users worldwide. Detection systems are updated constantly to address these threats. Nevertheless, a counteraction arises in the form of evasion attacks, where an opponent modifies malicious samples in a way that causes them to be incorrectly classified as benign. In this paper, the proposed method aimed to investigate the signs of malware on Android devices, and to develop a malware detection model for Android systems based on the Drebin and the MH-100K datasets. We used each of the LSTM, MLP, and RNNs for reducing and detecting the threats and malware to enhance security over the Android systems. Each algorithm works separately and calls the sub-algorithms in the feature selection (PCA, and CFS). We used several scenarios for testing the performance of each algorithm according to the number of attributes in both datasets and the number of epochs for each algorithm. The experiment results showed the preference of results for the MH-100K dataset compared to the Drebin dataset. On the other hand, the results showed that the accuracy for the LSTM algorithm reached (98.31%) and outperformed both the MLP and the RNN algorithms for malware detection for both datasets.

This study aimed to analyze the role of cloud computing in the education sector, as this technology brings positive transformations by making education more accessible, effective, and efficient. However, educational institutions face challenges related to security, privacy, cost, and technical infrastructure, highlighting the need for an analytical study to examine the benefits and challenges of implementing cloud computing in this sector. The study adopted a descriptive analytical approach by reviewing and analyzing intellectual production related to cloud computing in education without collecting field data. The research procedures involved gathering, classifying, and analyzing previous studies, scientific reports, and relevant statistics to derive key trends and insights. The findings revealed that cloud computing offers numerous benefits, including reducing operational costs, increasing flexibility and scalability, and enhancing data security. Additionally, cloud computing supports digital transformation in education, improves academic data management, and provides an integrated learning environment powered by artificial intelligence. However, institutions face challenges such as dependence on internet connectivity, privacy concerns, and the need to select appropriate cloud models. The study recommended a well-planned transition to cloud computing by carefully selecting suitable models, strengthening technical infrastructure, and ensuring data security. It also emphasized the importance of providing comprehensive training for teachers and students and conducting periodic assessments to measure the effectiveness of cloud computing in improving the educational process.

The research deals with a group of problems and breakthroughs in the previous period and the largest attacks in history that caused failures in technical systems around the world. The research discussed cyber-attacks and disruptions and how to deal with them, better understand the difference, and enhance cyber security. Through the research, many examples of malfunctions, hacks, and cyber-attacks on modern global systems are discussed. The research aims to identify the basic causes, weaknesses, and methods that contribute to finding solutions security and the challenges of espionage and electronic penetration of countries through cyberspace ، it aims to clarify the various cyber challenges and risks that threaten the security of countries.

The rapid advancement of digital services and the increasing reliance on internet-based transactions have highlighted the crucial need for robust cybersecurity measures. Encryption is a fundamental technique used to secure sensitive data, preventing unauthorized access and data manipulation. However, traditional encryption methods face limitations in adapting to evolving cyber threats. Artificial Intelligence (AI) has emerged as a transformative force in enhancing encryption techniques, leveraging deep learning, neural networks, and predictive analytics to provide dynamic and adaptive encryption solutions. This paper explores the role of AI in website encryption, focusing on its contributions to smart encryption, real-time threat detection, and the enhancement of encryption protocols such as TLS and SSL. Additionally, the study examines the challenges associated with AI-driven encryption, including high computational resource requirements, the risks of AI-powered attacks, and implementation complexities. As AI continues to evolve, its integration into encryption methodologies is expected to play a pivotal role in strengthening cybersecurity frameworks and mitigating emerging cyber threats.

Resource-restrained gadgets pose sizable challenges for deploying synthetic intelligence (AI) packages, which include restricted computational power, reminiscence, and electricity resources. This research pursuits to broaden value-effective AI algorithms that cope with these limitations whilst retaining high overall performance and accuracy. The examine leverages superior optimization strategies, such as version pruning, quantization, and dynamic strength control, to layout light-weight models appropriate for low-strength environments. Experiments conducted on gadgets like the Raspberry Pi 4 and NVIDIA Jetson Nano screen giant improvements in inference time, electricity efficiency, and accuracy compared to conventional processes. The proposed algorithms acquire up to 50% reduction in energy consumption and 20% improvement in accuracy at the same time as lowering typical computational charges. These findings reveal the feasibility of deploying green AI solutions on constrained hardware without compromising on functionality or nice. The practical implications of this paintings make bigger to various applications, along with real-time healthcare monitoring, clever agriculture, and commercial IoT systems. The have a look at concludes by means of highlighting areas for destiny studies, which includes improving algorithmic adaptability and expanding trying out to embody diverse eventualities. This work gives a sturdy basis for advancing the deployment of AI in resource-restricted settings, bridging the gap between technological innovation and practical implementation.

This study aimed to measure the effect of designing an electronic training environment based on the theory of mind on improving the level of executive functions for students with autism spectrum disorder by identifying the appropriate executive functions for them, identifying the appropriate educational design for that environment, and measuring its effect on improving the level of executive functions in those students. The study adopted the quasi-experimental approach with a single-group experimental design in the pre- and post-applications. The study population consisted of students with autism spectrum disorder enrolled in a development center in Riyadh. The study sample consisted of (5) students who were selected intentionally based on the convergence of their scores on the Binet Intelligence Scale and the Gilliam Autism Severity Scale so that they would be within the category of those with autism spectrum disorder to a mild degree and with normal intelligence scores between 85-115 on the Stanford-Binet scale, and their ages ranged between 8-14 years. The researcher used a scale for executive functions for children with autism spectrum disorder prepared by Mustafa Aref (2020) and standardized it in the Saudi environment. The study reached a number of results, the most prominent of which are: There is a very significant effect of using the electronic training environment based on the theory of mind on improving the level of all executive functions among these students, as there are statistically significant differences at the significance level (0.05) between the average ranks of the sample members in the pre- and post-application of the executive functions scale in favor of the post-application. Therefore, the study recommended expanding the scope of using electronic training environments based on the theory of mind in teaching students with autism spectrum disorder; given the proven positive effect on developing the level of their executive functions.

This research paper was titled: (Artificial Intelligence in Education), and through it, the researchers sought to explore the main role that artificial intelligence represents as a modern and advanced technical achievement in the fields of education and learning at its various stages. The paper was divided into six sections, the first of which represents the methodological framework of the research, the second on the concept of artificial intelligence in education and its importance, the third included applications of artificial intelligence in education, the fourth shed light on the use of artificial intelligence in education and the challenges, controls, effects and ethical concerns that accompany it, the fifth was devoted to future trends in artificial intelligence in education in the Kingdom, and the sixth contained the results, recommendations and references. The researchers concluded with a number of results, the most prominent of which are: the importance of combining artificial intelligence and teachers to achieve the best educational results, the positive impact that artificial intelligence-supported tools achieve in improving the quality of education, and the role of artificial intelligence in reducing routine burdens on teachers and enabling them to focus on aspects of creative teaching.