Artificial Intelligence in Computer Science

Introduction: The integration of artificial intelligence (AI) into higher education is swiftly revolutionizing pedagogical methodologies, in conjunction with learning processes and research paradigms. The interdisciplinary potential of AI within academic settings was examined in this study, employing a case study conducted at the University of Tirana. Through the utilization of bibliometric analysis and survey-based research, this study comprehensively investigates the swiftly emerging trends in AI applications, students' significant familiarity with AI technologies, as well as the substantial challenges impeding broader adoption. The bibliometric analysis highlights significant exponential growth in AI research, particularly within pivotal domains such as finance and accounting, thereby emphasizing the swiftly increasing relevance of blockchain and automation. The survey indicates a robust enthusiasm among students for AI in educational settings. More than 90 percent of students actively incorporate AI tools in their project work. Nonetheless, resource limitations and ethical considerations, including privacy, data security, and algorithmic bias, pose considerable challenges to the widespread adoption of AI, despite the prevailing enthusiasm. Objective: The aim of this research is to examine the incorporation of Artificial Intelligence (AI) within the realm of higher education. This analysis concentrates on the applications, advantages, and challenges associated with AI, with the intent of advancing interdisciplinary research and educational methodologies at the University of Tirana. Theoretical Framework: This research expands upon the principles of AI adoption within the educational sector, alongside an examination of ethical considerations and multidisciplinary collaboration. Theories pertaining to technological integration and adaptive learning systems serve as the foundational framework for comprehending the implications of AI in the realm of education. Method: The methodology employs bibliometric analysis to examine AI-related research trends utilizing data from SCOPUS and conducts a survey to assess students' familiarity with and perceptions of AI. Data collection was facilitated through bibliometric instruments and an online survey incorporating Likert-scale and open-ended questions. Results and Discussion: The results underscore an increased focus on artificial intelligence (AI) and blockchain within scholarly research, wherein students exhibit considerable engagement and interest in AI applications. Nevertheless, limitations in resources and ethical issues, including privacy and bias, persist as primary challenges. The discourse underscores the imperative for investment in infrastructure and the incorporation of ethical education. Research Implications: This research highlights the imperative for higher education institutions to integrate artificial intelligence tools, cultivate adaptive curricula, and address ethical considerations in order to adequately prepare students for a future shaped by AI. The implications of these findings also pertain to educational policy and the formulation of interdisciplinary research strategies. Originality/Value: The study contributes by delivering a comprehensive bibliometric analysis and gives insights into student engagement with artificial intelligence. Its significance resides in presenting actionable recommendations to enhance the integration of artificial intelligence in higher education.

The advent of Artificial Intelligence (AI) has revolutionized various domains, and Content Management Systems (CMS) are no exception. Traditional CMS workflows, often reliant on manual processes, have struggled to keep up with the growing demands for scalability, speed, and personalization in managing digital content. AI - powered CMS address these challenges by introducing automation, enhancing efficiency, and delivering personalized user experiences. Through the integration of AI technologies such as Natural Language Processing (NLP), Machine Learning (ML), and Computer Vision, modern CMS have evolved into intelligent platforms capable of automating workflows, optimizing content delivery, and driving data - driven decision - making. This paper explores the transformative potential of AI in CMS, focusing on its applications in workflow automation and user experience enhancement. AI enables the automation of repetitive tasks like content tagging, categorization, and scheduling, significantly reducing manual effort and human error. At the same time, it enhances user engagement by offering personalized content recommendations, optimizing search functionalities, and dynamically adapting content to user contexts. These advancements allow businesses to manage content more effectively and cater to diverse audience needs in real time. However, the integration of AI into CMS is not without challenges. Issues such as data privacy concerns, bias in AI models, and the complexity of integrating AI into legacy systems remain significant barriers. This paper addresses these challenges and proposes actionable recommendations, including the development of explainable AI systems, robust data security frameworks, and scalable architectures that accommodate diverse business needs. As businesses increasingly adopt AI - powered CMS, their potential to transform content management is undeniable. These systems not only streamline workflows and improve operational efficiency but also empower organizations to deliver meaningful and personalized experiences to users. By addressing current challenges and advancing AI technologies, the future of content management will be defined by intelligent, adaptive, and user - centric platforms that bridge the gap between technology and human creativity. This comprehensive review highlights the opportunities and challenges of leveraging AI in CMS and underscores the importance of continued research and innovation to fully realize its potential. AI - powered CMS are not merely tools for managing content but catalysts for digital transformation, enabling organizations to thrive in an increasingly dynamic digital landscape.

The integration of artificial intelligence (AI) within the financial market has ushered in an era of unprecedented innovation and disruption, redefining traditional paradigms and unveiling transformative opportunities. This study explores the multifaceted applications of AI in the financial sector, including algorithmic trading, risk management, fraud detection, and portfolio optimization. By analyzing cutting-edge advancements such as machine learning, natural language processing, and predictive analytics, the research highlights how AI enhances market efficiency, decision-making accuracy, and operational agility. Moreover, the paper delves into the challenges and ethical considerations surrounding AI adoption, including data privacy, regulatory compliance, and the potential for market destabilization. Drawing on empirical evidence and case studies, this work offers a comprehensive examination of the symbiotic relationship between AI technologies and financial systems, while proposing innovative frameworks to harness their full potential responsibly. By unraveling the transformative capabilities of AI, this article aims to provide valuable insights for academics, practitioners, and policymakers striving to navigate the rapidly evolving landscape of the financial market.

The integration of artificial intelligence (AI) in healthcare presents significant promise to enhance clinical procedures and patient outcomes. This research examines the setting, methodology, conclusions, and issues associated with AI in healthcare. The swift proliferation of digital health data, encompassing medical imaging and clinical records, has generated substantial prospects for AI applications. Artificial intelligence methodologies, including machine learning, natural language processing, and computer vision, facilitate the derivation of significant insights from intricate datasets, hence improving clinical decision-making. A thorough literature review examines the practical applications of AI, encompassing its roles in medical diagnostics, treatment planning, and patient outcome prediction. The report also examines ethical issues, data protection, and legal frameworks, which are crucial for the responsible application of AI in healthcare. The results illustrate AI's capacity to enhance diagnostic precision, facilitate administrative efficiency, and optimise resource distribution, resulting in tailored therapies and improved healthcare administration. Nonetheless, obstacles persist, such as data integrity, algorithm transparency, and ethical considerations, which must be resolved to guarantee the secure and efficient deployment of AI. Continuous research, cooperation between healthcare and AI experts, and the establishment of comprehensive regulatory frameworks are essential for optimising the advantages of AI while minimising hazards. This research highlights AI's capacity to transform healthcare, stressing the necessity for a multidisciplinary strategy to effectively harness its benefits and tackle the associated ethical and regulatory dilemmas.

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.

This chapter explores the transformative role of Artificial Intelligence (AI) in enhancing Geospatial Intelligence (GEOINT), focusing on how key AI technologies such as machine learning, computer vision, and natural language processing are revolutionizing the analysis of geospatial data. The chapter examines the evolution of GEOINT through AI advancements and delves into practical applications across various domains, including defense, disaster management, environmental monitoring, and urban planning. Future trends, such as the integration of AI with quantum computing, autonomous systems, and augmented reality, are discussed to highlight emerging possibilities. Additionally, the chapter addresses the challenges and ethical considerations associated with AI in GEOINT, including data privacy, algorithmic bias, and data quality issues. This comprehensive analysis aims to provide insights into the future trajectory of AI in geospatial intelligence, unlocking new opportunities for innovation and strategic decision-making.

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.

The rapid digitalisation of the Indian banking sector has revolutionised financial services, improving efficiency, accessibility, and customer experience. However, this transformation has also heightened the vulnerability of banks to sophisticated fraud, cybercrime, and financial misconduct. Conventional fraud detection techniques, often rule-based, struggle to keep pace with the evolving threat landscape. Consequently, artificial intelligence (AI)-powered fraud detection systems have emerged as a strategic solution to protect financial institutions and their customers. This paper explores the application of AI in fraud detection and risk management within the Indian banking system, analysing its impact on financial security, operational efficiency, and customer trust. It highlights how machine learning, natural language processing, predictive analytics, and behavioural biometrics are being deployed to detect anomalies, identify suspicious patterns, and prevent fraud in real time. Additionally, the study examines how AI enhances compliance with regulatory frameworks, particularly in areas such as anti-money laundering (AML) and Know Your Customer (KYC) obligations. Using secondary data sources, including research articles, industry reports, and case studies, this paper evaluates both the benefits and challenges of integrating AI into fraud detection frameworks. Findings suggest that while AI significantly reduces false positives, enhances scalability, and strengthens proactive fraud management, issues such as data privacy, model transparency, and infrastructural readiness remain pressing concerns. The research concludes that AI-powered fraud detection is not merely an optional innovation but a necessity for Indian banks to safeguard financial systems in an increasingly digital economy. Practical recommendations include fostering collaboration between banks and fintech firms, investing in AI research and training, adopting explainable AI models for regulatory trust, and strengthening cybersecurity awareness among customers. By leveraging AI responsibly and effectively, Indian banking institutions can build resilient systems that balance security with innovation, thereby enhancing long-term sustainability and public trust.

The global insurance industry is navigating a structural transition from a reactive "repair and replace" model toward a proactive "predict and prevent" paradigm. This evolution is driven by the integration of Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) into the core domains of underwriting and fraud detection. For executive stakeholders (CEOs and CFOs), the primary challenge lies in quantifying the Return on Investment (ROI) of these complex, data-intensive technologies. This report provides a comprehensive analysis of the economic and operational impacts of AI adoption, synthesizing empirical data from industry leaders. We examine the mitigation of operational friction where AI reduces policy processing times by up to 90%—and the improvement of the combined ratio through the identification of organized fraud rings and the elimination of "explainability debt." By mapping current implementations against the NIST AI Risk Management Framework (RMF) and the NAIC Model Bulletin, this white paper offers a rigorous methodology for evaluating AI's role in driving total shareholder return (TSR), which for AI leaders has historically outperformed laggards by a factor of 6.1. The findings underscore that while AI facilitates hyper-personalization and efficiency, long-term profitability is contingent upon robust governance, the management of technical debt, and the transition toward continuous, usage-based underwriting models.

The C* Conference on Computer Science and Software Engineering (C3S2E) was established in 2008. C3S2E 2010 is the third annual meeting in the C3S2E conference series and is held in Montréal, Québec, Canada, during May 19-20, 2010. The conference aims to address the need of the academic community in computer science and software engineering. It brings together academics, government and industry professionals to discuss recent progress and challenges in a broad range of areas in the field of computer science and software engineering such as algorithms and theory, artificial intelligence, bioinformatics, computer architecture, computer graphics, computer security, computer vision, data mining, databases, image processing, information retrieval, machine learning, as well as software engineering. C3S2E 2010 also serves as a platform for theoreticians and practitioners to exchange their original research ideas on academic or application aspects of computing and engineering of software systems, present their new findings on theoretical or practical aspects of computer science and software engineering, share their experiences on integrating new technologies into products and applications, discuss their work on applying computing and software engineering to real-life situations, describe their development and operations of challenging computing related systems, and identify unsolved challenges. Although it is the third time C3S2E is held, this comprehensive conference has grown from a Canadian conference into an international conference. For C3S2E 2010, we continue to keep our tradition for inviting international experts in various areas of computer science and software engineering to join our team of international program committee. As a result, our Program Committee consists of professionals from different parts of the world including Australia, Canada, Germany, Hong Kong, Italy, Japan, Portugal, South Korea, Turkey, UAE, and UK. This committee has done an excellent job in finishing the single-blind review and on-line double-blind debate processes in record time. On average, about 90% of reviews were submitted by the due date and about 3.67 reviews were received for each paper. In terms of submissions, we have attracted many high-quality papers submitted by authors globally. The paper selection process was thorough and competitive. On average, each paper was refereed by at least three reviewers, and about 18% of submissions were accepted as full research papers. This year, we have an intensive program that spans over two days. In addition to research papers and student posters, we also include position papers, which allow authors to give their opinions and vision on exploring new concepts and research directions.

The integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques into computational science has ushered in a new era of innovation and efficiency in various fields, with particular significance in computational fluid dynamics (CFD). Several methods based on AI and Machine Learning (ML) have been standardized in many fields of computational science, including computational fluid dynamics (CFD). This study aims to couple CFD with artificial neural networks (ANNs) to predict the fluid forces that arise when a flowing fluid interacts with obstacles installed in the flow domain. The momentum equation elucidating the flow has been simulated by adopting the finite element method (FEM) for a range of rheological and kinematic conditions. Hydrodynamic forces, including pressure drop between the back and front of the obstacle, surface drag, and lift variations, are measured on the outer surface of the cylinder via CFD simulations. This data has subsequently been fed into a Feed-Forward Back (FFB) propagation neural network for the prediction of such forces with completely unknown data. For all cases, higher predictivity is achieved for the drag coefficient (CD) and lift coefficient (CL) since the mean square error (MSE) is within ± 2% and the coefficient of determination (R) is approximately 99% for all the cases. The influence of pertinent parameters like the power law index (n) and Reynolds number (Re) on velocity, pressure, and drag and lift coefficients is also presented for limited cases. Moreover, a significant reduction in computing time has been noticed while applying hybrid CFD-ANN approach as compared with CFD simulations only.

Artificial Intelligence (AI) is revolutionizing healthcare by enhancing patient outcomes, reducing costs, and increasing the efficiency of medical professionals. This mini-review explores the diverse applications of AI in healthcare, including disease diagnosis, personalized treatment plans, and patient survival rate predictions. AI technologies such as Machine Learning (ML), deep learning, Natural Language Processing (NLP), and Robotic Process Automation (RPA) are becoming integral to modern healthcare practices. These technologies enable early disease detection, particularly in cases like cancer, by analyzing medical images and patient data, leading to more effective and personalized treatment strategies. Additionally, AI can predict patient outcomes by analyzing large datasets from electronic health records, providing valuable insights that can inform clinical decisions. However, the integration of AI in healthcare also presents significant ethical challenges. Issues such as data privacy, algorithmic bias, lack of transparency, and the potential for increased health inequalities need to be addressed. The World Health Organization (WHO) has provided guidelines emphasizing the ethical use of AI, highlighting the importance of designing AI systems that respect human rights and promote equity. As AI continues to advance, it is crucial to ensure its responsible and transparent use to maximize benefits and minimize risks. This review underscores the transformative potential of AI in healthcare while calling for vigilant ethical considerations to ensure that AI technologies are implemented in a manner that enhances patient care and upholds ethical standards.

This article explores the transformative potential of integrating artificial intelligence (AI) into blockchain technology and the cryptocurrency market. Highlighting the growing attention towards blockchain and cryptocurrencies, emphasizing their decentralized, secure, and transparent nature. However, challenges, including the need to address scalability issues and ensure responsible usage, prompt a focus on AI integration as a viable solution. The study delves into the benefits of AI in the cryptocurrency market, showcasing its ability to predict trends, identify risks, and optimize trading strategies. The research emphasizes the relevance of investigating the integration of AI into blockchain and its specific applications, particularly in detecting and preventing fraud. The article recognizes the potential for increased efficiency, reduced costs, and improved security in transactions through this integration. Acknowledging potential challenges such as AI decision-making implications and technical hurdles, the article advocates for ongoing research and development. It highlights the need for responsible AI adoption to maximize benefits while addressing concerns like algorithmic biases and potential market manipulation. Also explores AI's role in risk management, fraud detection, and investment management within the financial sector. It underscores the importance of ethical considerations, transparency, and accountability to ensure AI's integration aligns with the best interests of all stakeholders. As the article concludes, it emphasizes the dynamic and transformative potential of the integration of AI into blockchain and the cryptocurrency market. It advocates for a collaborative approach among stakeholders, policymakers, and developers to ensure responsible usage, compliance with regulations, and ongoing innovation. The synthesis of AI and blockchain technologies has the potential to revolutionize industries, enhance security, and contribute to a more efficient, transparent, and innovative future.

Artificial Intelligence (AI) is increasingly transforming healthcare by enhancing diagnostic accuracy, personalizing treatment, and improving operational efficiencies. This review explores AI's impact across several key areas: cancer medicine, fraud detection, and lessons from the petroleum industry. In cancer medicine, AI-driven advancements are leading to more accurate diagnostics, personalized treatment plans, and predictive models for patient outcomes. In fraud detection, AI techniques such as anomaly detection and natural language processing are effectively identifying and mitigating fraudulent activities, safeguarding financial and operational integrity. Insights from the petroleum industry reveal how AI applications, such as predictive maintenance and operational optimization, can be adapted to healthcare settings to enhance equipment reliability and resource management. Emerging trends include the integration of AI with genomics, telemedicine, and cross-disciplinary innovations, which promise further advancements in personalized care and operational efficiency. However, ethical considerations such as data privacy, bias, and transparency must be addressed to ensure responsible AI deployment. The review concludes by highlighting the need for continued innovation, collaboration, and patient-centric approaches to fully realize AI's potential in transforming healthcare and improving patient outcomes.

This research explores the integration and impact of artificial intelligence (AI) within Emirati media organizations, focusing on adoption determinants, obstacles, and interventions. Utilizing a mixed-methods approach, the study incorporates qualitative data from 21 in-depth interviews with media professionals and a thematic analysis of 30 scholarly articles published between 2019 and 2024. The research aims to provide a nuanced understanding of AI's transformative effects on the UAE media landscape. Key findings reveal that AI is significantly enhancing content creation, distribution, and audience engagement processes within Emirati media organizations. AI technologies such as machine learning, natural language processing, and predictive analytics are being leveraged to improve operational efficiency, streamline workflows, and deliver personalized content to audiences. These advancements are driving competitive advantages and elevating the overall quality of media output. Despite these benefits, several challenges hinder widespread AI adoption. Technological barriers, including limited access to advanced AI tools and infrastructure, pose significant obstacles. Additionally, there is a pronounced shortage of skilled personnel capable of implementing and managing AI systems, which exacerbates concerns over job displacement and workforce anxiety. Ethical considerations, such as ensuring unbiased AI algorithms and protecting user privacy, are also critical issues that require attention. The study underscores the importance of continuous training and upskilling for media professionals to adapt to the evolving technological landscape. It highlights the role of robust ethical guidelines and governance frameworks in promoting responsible AI use. Government support, through targeted policies, funding initiatives, and incentives, is identified as a crucial factor in facilitating AI integration and addressing adoption barriers. The research concludes by providing strategic recommendations for media organizations and policymakers. It advocates for the development of comprehensive AI adoption roadmaps that include capacity-building initiatives, ethical oversight, and collaborative efforts between industry stakeholders. By addressing these areas, Emirati media organizations can harness the full potential of AI to drive innovation, improve audience engagement, and maintain competitiveness in a rapidly digitizing global media environment. This study contributes to the growing body of knowledge on AI in media, offering valuable insights into the specific context of the UAE. It provides a foundation for future research on AI's role in media and serves as a guide for organizations seeking to navigate the complexities of AI adoption.