Aug 2026· Journal of Intelligent Decision Making and Information Science· 0 citations· 18 references
TL;DR
This review offers a brief overview of current research progress and prospects on the development of intelligent AI systems and advanced machine learning, which will facilitate the future generation of trustworthy and ethical AI-based solutions.
Abstract
AI and Machine Learning (ML) are powerful and rapidly evolving technologies, reshaping intelligent decision-making, automation, and data-driven problem-solving in various industries. In recent years, the potential of intelligent systems to manage complex data, to learn adaptive patterns, and to assist in autonomous decision-making has been greatly improved by the emergence of new technologies, such as Deep Learning, Transformer-based Models, Generative AI, Large Language Models (LLMs), Explainable AI (XAI), Federated Learning, Edge AI, and Digital Twin. These advancements have empowered the healthcare, manufacturing, agriculture, finance, transportation, education, cybersecurity, and smart city sectors with enhanced efficiency, productivity, and service quality, driving faster AI adoption across these industries. The innovations have contributed to improved efficiency, productivity, and service quality, leading to increased adoption of AI across the healthcare, manufacturing, agriculture, finance, transportation, education, cybersecurity, and smart city sectors. The fundamentals of intelligent AI systems, key learning paradigms, notable technological advances, and applications are discussed in this chapter, providing a comprehensive review of intelligent AI systems and advanced ML. It also explores the potential of AI to solve real-world problems and discusses some of the critical challenges associated with data privacy, model interpretability, computational complexity, algorithmic bias, and ethical considerations. Finally, the chapter proposes new research directions towards the development of trustworthy, explainable, sustainable, and human-centric AI systems. This review offers a brief overview of current research progress and prospects on the development of intelligent AI systems and advanced machine learning, which will facilitate the future generation of trustworthy and ethical AI-based solutions.
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
Artificial Intelligence (AI) has emerged as an important technology for transforming processes, decision-making, and services across diverse industries. This paper provides a detailed examination of major AI techniques, including Machine Learning, Deep Learning, Computer Vision, Cognitive Computing, Natural Language Processing, and Neural Networks, with emphasis on their applications, advantages, and obstacles. The study examines the evolution of AI and its growing importance in modern technological and industrial environments. It further explores the application of AI across healthcare, logistics and transportation, e-commerce, the food industry, smart farming, real estate, banking and finance, entertainment, manufacturing, education, cybersecurity, energy, telecommunications, environmental management, travel and hospitality, marketing, construction, biotechnology, professional services, and aerospace and defence. The paper highlights major benefits of AI, including automation, improved efficiency, prediction, personalization, enhanced decision-making, and effective analysis of large volumes of data. At the same time, challenges related to data quality, privacy, cybersecurity, bias, explainability, implementation costs, computational requirements, ethics, and responsible use are examined. The study also identifies potential areas for further development, particularly through the integration of AI with IoT, robotics, digital twins, real-time data, and advanced analytical systems. Overall, the paper presents AI as a rapidly developing technology with significant potential to support innovation and transformation across diverse industries while emphasizing the need for secure, transparent, responsible, and human-centred implementation.
Suhas B. Shirol, Rajeev Kumar Singh, Sunil Gagare et al.· International journal of com...· 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
A methodology is advanced to embed explainability in the AI decision-making process, starting from data preprocessing to generating explanations and human evaluation, and the results highlight the potential of explainability to enhance human comprehension and foster responsible use of AI systems in high-stakes decision-making scenarios.
Mahabala H. N.· International Journal of Mod...· 0 citations
Artificial Intelligence (AI) has become a game-changer technology with potential applications in many sectors including healthcare, finance, education, manufacturing, transportation, agriculture, cyber security, retail, energy, and smart cities. The study examines key AI techniques and how they are applied in various fields, focusing on their advantages, drawbacks, and feasibility. The study covers supervised and unsupervised learning, semi-supervised learning, neural networks, deep learning, natural language processing, ensemble methods, fuzzy logic, expert systems and robotics. Literature review was done to gain insight into the development and cross-domain applications of AI, and comparative analysis was performed on the capabilities and challenges of various AI techniques. The results show that AI can enhance efficiency, automation, prediction, pattern recognition, and decision-making processes in various industries, but there are concerns and challenges associated with its use, such as data quality, privacy and security, ethics, transparency, cost, computational demand, and human involvement. The study also indicates that the appropriateness of the AI technique is dependent on the problem type, data properties and special application requirements. The study suggests that fostering a cross-domain perspective of AI can help in selecting the most suitable technique and promote responsible, effective, and human-centred AI adoption in various fields.
R. Baskar, Rajkumar Sivamani, Subrat Sahu et al.· International journal of com...· 0 citations
This review aimed to synthesize current advances in artificial intelligence (AI) and intelligent digital platforms applied to animal production, highlighting their main techniques, applications, challenges, and future perspectives. Recent developments in digital agriculture have accelerated the adoption of AI-based tools capable of supporting data-driven decision-making in livestock systems. Among the available technologies, Machine Learning, Deep Learning, and Computer Vision have become the most established approaches for predicting productive performance, monitoring animal health and welfare, optimizing nutrition and reproduction, and improving management efficiency. More recently, intelligent platforms integrating AI, the Internet of Things, cloud computing, and sensor networks have enabled continuous monitoring and real-time decision support across multiple production systems. Emerging technologies, including large language models and explainable AI, are expected to further enhance knowledge management and decision support, although challenges related to interoperability, data quality, model validation, and producer adoption remain. Overall, AI is no longer an emerging technology but an essential component of Precision Livestock Farming, with increasing potential to promote more efficient, sustainable, and evidence-based animal production.
Maíse Dos Santos Macário, Isis Regina Santos de Oliveira· Revista Ibero-Americana de H...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
