With the rise in applications of artificial intelligence (AI) across various sectors, security concerns have become paramount. Traditional AI systems often lack robust security measures, making them vulnerable to adversarial attacks, data breaches, and privacy violations. Cryptography has emerged as a crucial component in enhancing AI security by ensuring data confidentiality, authentication, and integrity. This paper presents a comprehensive bibliometric review to understand the intersection between cryptography, AI, and security. A total of 495 journal articles and reviews were identified using Scopus as the primary database. The results indicate a sharp increase in research interest between 2020 and January 2025, with a significant rise in publications in 2023 and 2024. The key application areas include computer science, engineering, and materials science. Key cryptographic techniques such as homomorphic encryption, secure multiparty computation, and quantum cryptography have gained prominence in AI security. Blockchain has also emerged as an essential technology for securing AI-driven applications, particularly in data integrity and secure transactions. This paper highlights the crucial role of cryptography in safeguarding AI systems and provides future research directions to strengthen AI security through advanced cryptographic solutions.

In this paper, we will describe a statistical study of data analyzed by SPSS software. The aim of our study is to show the importance of sharing emotional states between learners during online communication, which helps to create an adequate environment and a positive cognitive exchange between the teammates. Our study is based on three stages. The first, discussed the satisfaction of learners with the distance textual communication. The second stage, demonstrates the importance of sharing emotional states of learners with their teammates in a flexible manner without disrupting their concentration, and in the final stage, we show the role of psychology science to improve the distance collaborative work. The results confirm that the presence of emotional states can enrich the online communication, and ensure the quality work.

This article investigates the significant influence of smart radios on the energy efficiency of Wireless Sensor Networks (WSNs). It explores the incorporation of intelligent radios as a crucial element to enhance WSNs’ sustainability, providing a novel approach to tackle energy-related challenges. Smart radios are essential for prolonging the lifespan of sensor devices and reducing the environmental effect of WSNs by autonomously adjusting communication protocols and optimizing energy consumption. This paper also highlights the differences between 5G and 6G technologies within the WSNs framework. While 5G brought improvements in data speed and connectivity, 6G represents a significant progress by prioritizing energy efficiency as a fundamental goal. This shift represents a fundamental change, with a primary focus on achieving extremely low energy consumption and ensuring the sustainable operation of WSNs. The paper examines crucial technological factors enabling 6G to outperform its predecessor, establishing it as a revolutionary force in the field of wireless communication for sensor networks. This research illuminates the crucial function performed by smart radios in connecting the desire for energy efficiency with the changing environment of wireless sensor networks.

With the broad application of machine learning (ML) and deep learning (DL) models in financial markets, it has become increasingly important to evaluate their performance compared with traditional methods, especially for credit risk scoring in financial services. This mechanism determines the probability of default (PD) for borrowers, in other words, how likely a client is to fail to repay their debt. With the rapid development and growing availability of ML/DL technologies, it is essential for banks, financial institutions, auditors and regulatory bodies to assess whether these approaches truly outperform traditional models in terms of accuracy, stability and interpretability or whether their complexity comes at a cost. A systematic review following PRISMA examined credit risk scoring models. From 520 initial articles, 117 were analyzed to compare ML/DL approaches with traditional methods. This review evaluates accuracy, stability and interpretability, offering guidance for model selection in real-world credit scoring. Logistic regression remains essential in regulated contexts requiring transparency, supporting informed decisions on balancing performance and explainability.