An Algorithm-to-Intelligence Integration Framework is proposed that connects computer science foundations, artificial intelligence paradigms, system requirements, application domains, and future technologies to achieve responsible, secure, sustainable, and deployable technological advancement.
Abstract
Artificial intelligence and computer science will converge to produce the next set of emerging technologies. Artificial intelligence enables machines to learn, make intelligent decisions, and adapt, but it is built on the foundations of computer science, including algorithms, data structures, optimization, complexity theory, and computing platforms. This theoretical review examines the role of these foundations in modern artificial intelligence systems and their contributions to the development of scalable, explainable, efficient, and deployable technologies. The paper discusses the technical foundations of machine learning, deep learning, graph-based intelligence, neuro-symbolic systems, explainable artificial intelligence, and edge intelligence in terms of algorithmic reasoning, data structures, learning optimization, computational efficiency, and computational infrastructure. It also discusses the role of integrating artificial intelligence and computer science across major application areas, including smart health, cybersecurity, robotics, natural language processing, Internet of Things systems, edge computing, and sustainable digital infrastructure. To improve the paper's conceptual framework, it proposes an Algorithm-to-Intelligence Integration Framework that connects computer science foundations, artificial intelligence paradigms, system requirements, application domains, and future technologies. The survey finds that intelligent systems should, in the future, combine adaptive learning with robust computational design to achieve responsible, secure, sustainable, and deployable technological advancement.
An overview of artificial intelligence is presented by discussing its fundamental concepts, definitions, historical evolution, key characteristics, major applications, recent advancements, and notable achievements.
Mahendra Manilal Patel, Brijesh J. Gameti, Darshanaben D. Pandya· International Journal of Sci...· 0 citations
Artificial Intelligence (AI) has become a transformative technology for enhancing Intelligent Decision Support Systems (IDSS) by enabling accurate, adaptive, and data-driven decision-making across diverse computer science applications. This review examines the fundamental concepts of AI, its major techniques, including machine learning, deep learning, expert systems, fuzzy logic, reinforcement learning, explainable AI, and generative AI, and their roles in modern decision support systems. It further discusses emerging trends such as human-centered AI, edge AI, federated learning, digital twins, hybrid AI models, and responsible AI that are reshaping intelligent decision-making. The review also highlights the applications of AI-enabled IDSS in cybersecurity, software engineering, cloud computing, the Internet of Things, healthcare informatics, robotics, big data analytics, smart manufacturing, and education technologies. In addition, key challenges related to data quality, explainability, scalability, privacy, ethics, computational complexity, and user trust are critically discussed, followed by future research directions emphasizing foundation models, neuro-symbolic AI, Green AI, Quantum AI, AI governance, and autonomous decision intelligence. Overall, the review provides a comprehensive overview of recent advancements and identifies promising opportunities for developing trustworthy and efficient AI-driven intelligent decision support systems.
P. S· Journal of Intelligent Decis...· 0 citations
This editorial sets the stage for understanding intelligence as an emergent computational construct, highlighting its role as the first phase in the broader cognitive intelligence systems continuum that progresses toward adaptive, autonomous, and socio-cognitive systems in future research directions.
Tole Sutikno· International Journal of Ele...· 0 citations
The sections that follow trace the historical development of AI within computer science, review the principal technique families and their applications, and close with an original discussion of cross-cutting patterns, ethical obligations, and likely future directions.
Elayaraja Subbaiah, Manykandaprebou Vaitinadin, E. Kesavan· International Journal of Sci...· 0 citations
This review highlights the role of machine learning techniques, deep learning models, and their applications in various areas such as smart grid, renewable energy prediction, fault detection, energy prediction, and predictive maintenance, and covers benchmark datasets, evaluation platforms, and the performance of AI algorithms.
Fawad Khan· Global Trends in Science and...· 0 citations
Frontiers of Intelligent Computing and Cyber Technologies – Volume V: Data Science, Analytics, and Smart Digital Transformation presents a comprehensive exploration of modern data-driven technologies that are transforming industries and society. This volume introduces the core principles of data science, data analytics, machine learning, big data, cloud computing, and intelligent decision-making systems. It bridges theoretical concepts with practical applications through real-world case studies, analytical techniques, and emerging digital transformation strategies. Readers will gain valuable insights into data visualization, predictive analytics, AI-powered business intelligence, and smart enterprise solutions that support innovation across healthcare, finance, manufacturing, education, and smart cities. Written in a clear and structured manner, this book is ideal for students, researchers, educators, and industry professionals seeking to build expertise in intelligent computing and data-centric technologies. It serves as a valuable reference for understanding how advanced analytics and smart digital transformation are shaping the future of technology and sustainable innovation.
M. M, K. Y· 0 citations
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