Utilization of Artificial Intelligence in Cyber Security System Prototype to Face Quantum Computing

The integration of artificial intelligence (AI) in education has the potential to revolutionize teaching and learning, particularly in the development of students’ critical thinking skills. This study explores science instructors' familiarity, perceptions, and experiences with using AI to enhance students' critical thinking skills, as well as the level of institutional support for AI integration in teaching. A quantitative survey was conducted among 20 science instructors from higher education institutions in Isabela, Philippines. The findings reveal that while instructors acknowledge AI's potential to improve educational outcomes, there is a significant gap in formal AI training and literacy among educators. Positive correlations were found between AI literacy, AI integration, and critical thinking development, suggesting that as AI literacy increases, AI integration and enhancement of critical thinking skills also increase. Regression analysis identified AI integration as a significant predictor of critical thinking development. Challenges remain in the effective implementation of AI, including concerns about overreliance on AI-generated responses and the need for clear assessment guidelines. Interestingly, years of teaching experience did not significantly influence participants’ AI literacy, perceptions, or integration. This study highlights the importance of developing comprehensive AI literacy programs for educators and integrating AI into curriculum structures to balance AI-enhanced learning with human-centered pedagogy. These findings emphasize the need for thoughtful implementation and ongoing research to effectively leverage AI in promoting critical thinking skills in science education.

Integrating blockchain technology with artificial intelligence (AI) i.e., blockchain Intelligence makes an extremely powerful tool that solves many multidimensional problems in several domains. Blockchain technology has the potential to provide links to shared data, transactions, and records in a decentralized, safe, and reliable manner, including the information and decision-making capability of AI which makes machines similar as capable as humans. This study is intended to present an updated systematic review of the integration of Blockchain and AI in various application areas. We have studied and summarized more than 100 research papers to present an updated version of the review. We also discuss the future of Blockchain technologies with AI. By integrating these two technologies results increases the security, efficiency, and productivity of the applications. Past works feature a few possible advantages of integration of Blockchain and AI, yet just give a restricted hypothetical system to depict forthcoming certifiable combination instances of the two advances. We survey and orchestrate surviving exploration on the integration of AI and Blockchain are other ways around to thoroughly build up a future research plan on the fusion of the two innovations. We also proposed an agenda to develop a secure system of cyber threat intelligence information exchange by using features of blockchain and artificial intelligence. This paper mainly focusses on explaining how collaboration of blockchain and AI gives immense boost in latest domains like Cybersecurity, Healthcare, Supply Chain Management, Finance and Banking and Social Media Analytics.

This study investigates the influence of artificial intelligence (AI) integration on teachers’ professional identity and job satisfaction using a quantitative research design involving 251 respondents. Descriptive statistics showed relatively high levels of AI integration (M = 3.98, SD = 0.62) and professional identity (M = 4.12, SD = 0.58), indicating strong engagement with AI tools and a well-defined sense of professional role among teachers. Pearson correlation analysis revealed a moderately strong, statistically significant positive relationship between AI integration and professional identity (r = 0.612, p = 0.000), demonstrating that increased use of AI is associated with a strengthened professional identity. Mediation analysis further indicated that institutional factors significantly influence the relationship between AI integration and job satisfaction, with AI integration positively predicting institutional support (β = 0.54, p = 0.000) and institutional factors strongly predicting job satisfaction (β = 0.47, p = 0.000). Both a significant direct effect (β = 0.29, p = 0.001) and a strong indirect effect (β = 0.25, p = 0.000) were found, confirming partial mediation. These findings highlight that AI not only enhances teachers’ identity and satisfaction but that successful implementation relies heavily on institutional readiness and support. Overall, the results underscore the importance of adopting teacher-centered AI strategies that reinforce professional identity, reduce workload, and enhance well-being.

Diabetes mellitus is a chronic metabolic disorder with significant global prevalence and associated healthcare burdens, necessitating early detection and effective management strategies. The integration of Machine Learning (ML) and Artificial Intelligence (AI) has revolutionized diabetes care, offering innovative approaches to prediction, monitoring, and personalized management. This study conducted a systematic review of 82 high-quality peer-reviewed articles, following the PRISMA guidelines, to provide a comprehensive evaluation of ML and AI applications in diabetes prediction and management. The review highlights the widespread adoption of supervised learning models, such as Random Forest and Support Vector Machines (SVM), which consistently demonstrate high accuracy and reliability in predicting diabetes risk. Ensemble learning methods, particularly Gradient Boosting, emerged as superior techniques for predictive performance, while deep learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), proved effective in analyzing unstructured data such as medical images and time-series glucose data. The integration of AI into wearable devices and mobile health applications has further enhanced real-time monitoring and glycemic control, bridging the gap between technological advancements and practical healthcare solutions. Despite these advancements, challenges such as data imbalance, limited external validation, and the need for explainable AI frameworks persist, underscoring the necessity for methodological rigor and standardization. This review provides critical insights into the current state, limitations, and opportunities of ML and AI in diabetes care, emphasizing their transformative potential in addressing this global health challenge.

The rapid advancement of Artificial Intelligence (AI) has fundamentally reshaped contemporary security systems, leading to significant improvements in surveillance, threat detection, and incident response across both physical and digital domains. By leveraging machine learning algorithms, neural networks, and predictive analytics, AI enhances the speed and precision of security operations while reducing human error and reliance on purely manual decision-making. In physical security contexts, AI-driven systems analyze real-time video feeds to detect anomalous behavior, identify individuals, and track suspicious movements with high accuracy. In digital security, AI supports malware detection, network intrusion prevention, and phishing mitigation through continuous learning from evolving threat patterns. Moreover, the integration of AI with big data analytics enables comprehensive risk assessment by processing large volumes of heterogeneous data, thereby facilitating early threat identification and proactive mitigation. Natural language processing techniques further contribute by detecting potential insider threats through the analysis of communication patterns and textual data. This paper adopts a qualitative, literature-based methodology grounded in thematic analysis. This paper draws on secondary data obtained from peer-reviewed journal articles, policy reports, and authoritative industry publications related to AI applications in security systems. The analysis synthesizes existing scholarship to identify dominant trends, operational benefits, and critical challenges associated with AI-driven security frameworks.The central objective of this paper is to examine how AI is transforming modern security systems while critically assessing the ethical, legal, and governance challenges that accompany its adoption. The findings indicate that although AI significantly enhances efficiency, predictive capability, and system resilience, persistent concerns related to data privacy, algorithmic bias, transparency, and over-reliance on automated decision-making continue to pose serious risks. This paper underscores the necessity of robust regulatory frameworks, transparent AI decision processes, and sustained human oversight to ensure responsible and trustworthy AI deployment. Ultimately, the paper argues that AI should be viewed not as a replacement for human judgment, but as an augmentative tool that strengthens security systems when embedded within ethical, legal, and organizational safeguards.

It's virtually not possible to measures the reasons why cybersecurity is crucial in the expanding battlefield of cybersecurity. Permitting mischievous threats to operate amok anywhere, whenever, and in any circumstance is not acceptable, particularly when it comes to the complex network of client and organization data that cyber security teams are tasked with safeguarding. Individuals and families, as well as corporations, agencies, and educational institutions, must consider cyber security. Machine Learning Can Help Advance the Cyber Security Landscape. Today's businesses collect massive quantities of information/data. Data is considered as the heart of almost every business-focused system imaginable. Infrastructure systems are included in this. In today’s era high-tech structure, network and cybersecurity systems, collects massive quantities of data and analytics on most mission-critical systems' key elements. While humans also provide critical serviceable oversight and brilliant insights in today's infrastructure, machine learning and artificial intelligence are rapidly gaining traction in almost all domain of today's systems, be it on premises or on the cloud.

Abstract: This chapter delves into the transformative synergies between quantum computing, artificial intelligence (AI), and blockchain technology, focusing on their revolutionary impact on security, decentralized systems, and biomedical science. Quantum computing’s unparalleled computational power, combined with AI’s predictive analytics, enhances data security through quantum-resistant cryptography and advanced threat detection systems. In blockchain, the integration of quantum and AI optimizes scalability, improves transaction efficiency, and fortifies decentralized networks against quantum attacks. In biomedical science, these technologies accelerate drug discovery, enable precise genomic analysis, and enhance personalized medicine through AI-driven insights and quantum simulations. The chapter also addresses key challenges such as data privacy, ethical considerations, and scalability issues, while showcasing real-world applications and success stories in industries like healthcare, finance, and IoT. It concludes with a forward-looking perspective on fostering interdisciplinary collaboration and innovation to harness these quantum synergies for global societal benefit. Keywords Quantum computing, artificial intelligence, blockchain, quantum cryptography, decentralized systems, data security, AI-driven analytics, biomedical science, drug discovery, personalized medicine, quantum-resistant blockchain, genomic analysis, quantum simulations, interdisciplinary collaboration, innovation.

The digital landscape continues to evolve; ensuring robust internet safety has become a critical challenge in the face of growing cyber threats. This chapter delves into the transformative potential of artificial intelligence (AI) and blockchain technology in reinforcing cybersecurity measures. AI's ability to analyze large-scale data, identify patterns, and predict potential vulnerabilities makes it an invaluable tool in enhancing security protocols. When combined with blockchain's decentralized and immutable nature, which secures data by providing an incorruptible ledger, these technologies collectively offer an advanced framework for real-time threat detection and mitigation. The chapter explores several key applications, including decentralized identity management, cyber fraud detection, and privacy protection, illustrating how the fusion of AI and blockchain can help address significant security concerns such as data breaches, identity theft, and unauthorized access. However, the integration of AI and blockchain into cybersecurity systems is not without challenges. Issues such as scalability, energy efficiency, and the technical complexity of merging these technologies in practical settings remain obstacles. Despite these hurdles, significant advancements in blockchain protocols, such as sharding and AI-based optimization algorithms, show promise in overcoming these limitations through case studies and recent developments; this chapter demonstrates how AI and blockchain can work together to create a more secure, resilient, and transparent digital environment. This chapter is merely focused on the various technology aspects to secure date through various AI and blockchain techniques.

Background: Artificial intelligence (AI) is increasingly embedded in routine primary care, yet how the levels of integration might affect its acceptability is unknown, especially in relationship-based service models where patients expect visible human stewardship. Prior experimental studies often treat AI adoption as a binary condition, leaving the "intensity gradient" of automation and the role of model specialization under-explored. We examine whether increasing AI integration in the clinical encounter erodes patients' intention to seek care from family doctors in China, and whether labeling the AI as a medical-specific model buffers such erosion. Methods: We conducted a nationwide online survey experiment in China (N = 2790). Participants were randomly assigned to vignettes that varied by (i) the level of AI integration (low, medium, high) and (ii) the AI type (general-purpose vs. medical-specific large language model), with a human-only care scenario as a reference. Care-seeking intention from family doctors was assessed immediately after exposure. We estimated treatment effects using OLS regression with heteroskedasticity-robust standard errors, and examined the buffering hypothesis through an interaction term between AI integration intensity and AI type. Results: Care-seeking intention declined steadily as AI integration increased (p < 0.001), with the sharpest drop under high-intensity AI integration where clinical decisions were delegated to the AI system. Across all intensity levels, framing the system as a medical-specific AI consistently resulted in higher care-seeking intention than a general-purpose model. However, the interaction between AI intensity and the AI type was not statistically significant (p = 0.508). Conclusions: Patient acceptance of AI in primary care depends not only on whether AI is involved, but on how deeply AI is positioned in the encounter. Medical-specific AI labeling may enhance acceptance across all AI integration levels. The findings underscore the need to preserve human clinical agency in AI-embedded primary care. The results contribute to research on healthcare systems, digital health, and AI-patient interaction.

Background: The field of sports science has been fundamentally transformed by the integration of artificial intelligence (AI) technologies. AI has enabled advances in areas such as performance optimization, training personalization, injury risk assessment and prevention, talent recognition, rehabilitation, athlete monitoring, and wellness optimization. Objective: This review aims to explore the diverse impact of AI in sports science, highlighting advances in AI techniques, the challenges and limitations of integrating AI tools, and the emerging role of AI chatbots in shaping the future of sports research and applications. Methods: The review examines the existing literature on the application of machine learning, deep learning and other AI techniques in various aspects of sports science research and practice. It provides an overview of how these technologies have been used to improve personalized training programs, video analysis, injury risk prediction, talent identification, and rehabilitation. Results: The article describes how AI-powered tools and techniques have revolutionized sports science, enabling personalized, data-driven and efficient approaches to performance optimization. Sophisticated machine learning algorithms such as artificial neural networks, decision trees and support vector machines have been used to develop predictive models for injury risk assessment and prevention, leading to improvements in athletes' wellbeing and long-term performance. AI-driven talent identification and selection processes have also shown promise in recognizing exceptional athletes with greater accuracy. In addition, the integration of AI into athlete monitoring and rehabilitation has led to greater personalization, better decision making and faster return to play. Conclusion: The integration of AI and machine learning techniques into sports science has the potential to transform the field and lead to improved athlete performance, reduced risk of injury, improved well-being, and more personalized and effective interventions. The emergence of AI chatbots expands the applications of these technologies in sports science and offers new opportunities to streamline research processes, provide personalized advice to athletes and support sports medicine and rehabilitation

Artificial intelligence (AI) is transforming the education sector of different nations of the world. In spite of its commonly attested huge prospects, AI is yet to be integrated into Nigerian educational systems at a significant extent. This study examines the prospects and challenges of AI integration into Nigerian educational systems. The study adopts Venkatesh’s Unified Theory of Acceptance and Use of Technology, which explains how technologies get accepted and used in society. Questionnaire, and library and internet materials are used to source data. Survey design and mixed method with their allied analytical tools are employed. The results show pedagogic, technological, informative, administrative and socio-economic benefits of AI integration into Nigerian educational systems. Besides, it identifies the challenges constraining AI integration into the sector and proffers some solutions. The study concludes that the current low level of AI awareness and integration in Nigeria limits the ideal extent of AI impact on Nigerian educational systems. Stakeholders should collaborate significantly in making efforts to increase AI integration and find lasting solutions to the problems of AI integration into the educational systems.

Mobility as a Service (MaaS) is anticipated to revolutionize transport by integrating conventional public transport with on-demand and shared services. This innovation promises enhanced convenience, flexibility, and sustainability in urban mobility, drawing interest from both researchers and industry. However, those systems heavily rely on the collection and sharing of personal data among various stakeholders, introducing security and privacy risks. To understand the scale and scope of cyber security and privacy concerns and risks associated with MaaS, we conducted a systematic literature review (SLR) covering 87 relevant research papers published between 2017 and April 2025. Our review represents the most comprehensive examination focusing on cyber security and privacy issues of MaaS systems. Our findings reveal three themes discussed within the MaaS literature: (i) cyber security and privacy risks inherent to MaaS systems, alongside proposed solutions to mitigate such risks; (ii) users’ concerns about these risks and how they affect MaaS adoption; and (iii) laws and policies that govern cyber security and privacy aspects of MaaS systems and solutions. As such, our research serves to not only inform MaaS service providers and users but also advise policymakers and legislators on the potential risks involved and the regulatory measures required to address them.

System safety and cyber security have a great effect on system reliability which is the foundation of keeping system running-normally. Although safety and security controls can reinforce each other mutually, contradictions between them also exist, such as resource competition that may cause serious damage. This paper puts forward a risk-based task scheduling approach for the integrated control of system safety and cyber security in industrial control systems (ICSs). In this approach, an integrated task model covering both safety task and security task is proposed, which describes the relationship between task and risk in detail. And the change of real-time risk during the enforcement of these tasks is analysed. These tasks are scheduled through an optimal scheduling scheme which means the risk decrease as soon as possible and is always acceptable based on genetic algorithm. Further, simulation results show that the proposed approach is effective for task scheduling and real-time risk control.

Introduction. Digitization of socio-economic processes has led to an increase in the share of electronic transactions in monetary transactions. Electronic transactions using electronic and cryptographic currencies are the object of increased attention of criminals. The goal of cyberattacks is not only to seize money, but also to steal confidential information, block information flows, create information chaos, damage software and publicly discredit the enterprise. Overcoming various cyber threats requires the organization of an effective system of cyber protection of electronic transactions. Purpose. The purpose of the article is to identify cyber threats to the functioning of the system of electronic transactions with proposals for their elimination through the selection of variable organizational forms of ensuring the cyber security of the enterprise. Results. It is proposed to consider accounting and control of electronic transactions as the information basis of the enterprise's cyber security system. The areas of effective cyber protection of electronic transactions are defined, which consist in ensuring: confidentiality, integrity, availability, comparability and addressability of accounting information about monetary transactions. To overcome cyber threats to the functioning of the electronic transaction system, it is proposed to use technologies: blockchain, chatbots with artificial intelligence, biometrics, cloud services, Internet traffic monitoring, etc. Organizational options for cyber protection of electronic transactions have been systematized in the direction of: uniting disparate employees into security teams or creating a separate security unit of the enterprise; remote involvement of the outsourcer's specialists or their periodic arrival at the enterprise – the customer of outsourcing services. An information scheme of a dual combination of internal and external security control has been developed, which is integrated into the cluster format of the organization of accounting for electronic transactions. It has been proven that the dualization of cyber security control ensures the balancing of cyber risks and the costs of organizing cyber protection of electronic transactions. This format of cyber security organization is adaptively relevant for various business entities. Taking into account the directions of cyber protection, the capabilities of modern technologies in overcoming cyber risks, the functional powers of security employees, as well as the organizational formats of cyber security of cash flows ensures the reliable functioning of the electronic transaction system at the enterprise. The provision of effective cyber protection in combination with the accounting and control of electronic transactions is fundamental to the organization of a comprehensive cyber security system of the financial and economic activities of the enterprise.

The rise of artificial intelligence (AI) heralds a significant revolution in healthcare, particularly in mental health.AI's potential spans diagnostic algorithms, data analysis from diverse sources, and real-time patient monitoring. It is essential for clinicians to remain informed about AI's progress and limitations. The inherent complexity of mental disorders, limited objective data, and retrospective studies pose challenges to the application of AI. Privacy concerns, bias, and the risk of AI replacing human care also loom. Regulatory oversight and physician involvement are needed for equitable AI implementation. AI integration and use in psychotherapy and other services are on the horizon. Patient trust, feasibility, clinical efficacy, and clinician acceptance are prerequisites. In the future, governing bodies must decide on AI ownership, governance, and integration approaches. While AI can enhance clinical decision-making and efficiency, it might also exacerbate moral dilemmas, autonomy loss, and issues regarding the scope of practice. Striking a balance between AI's strengths and limitations involves utilizing AI as a validated clinical supplement under medical supervision, necessitating active clinician involvement in AI research, ethics, and regulation. AI's trajectory must align with optimizing mental health treatment and upholding compassionate care.