Aug 2026· International Journal of Electrical and Computer Engineering (IJECE)· Vol 16, pp. 1669· 0 citations
TL;DR
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.
Abstract
The rapid evolution of intelligent systems has shifted the focus of electrical and computer engineering from isolated data processing toward integrated models of machine cognition. This editorial introduces a foundational perspective on machine intelligence systems, emphasizing the transformation from raw data to meaningful intelligence through learning systems, representation mechanisms, and computational perception. In contemporary AI-driven environments, intelligence is no longer defined solely by algorithmic performance, but by the ability to construct structured representations of the world and interpret complex multimodal signals. Learning systems, particularly those grounded in machine learning and deep learning paradigms, serve as the core mechanism enabling this transformation. Representation learning provides the bridge between unstructured data and abstract knowledge, while computational perception enables machines to interpret visual, auditory, and sensor-based information in real time. Together, these components form the foundational architecture of intelligent systems that underpin emerging applications in engineering, automation, and cyber-physical environments. 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.
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.
Smitha Rajagopal, S. Nirkhi, Bharati S. Pochal et al.· Future Technology· 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
Artificial Intelligence (AI) is a multidisciplinary field focused on designing and developing intelligent systems capable of performing tasks that typically require human intelligence. It encompasses the creation of smart computer programs that can perceive, reason, learn, and make decisions. Although AI draws inspiration from the study of human intelligence, it is not restricted to biologically inspired methods or processes. Despite the absence of a universally accepted definition, AI is generally recognized as the study of computational techniques that enable machines to perceive their environment, reason logically, and take appropriate actions. In the modern digital era, the volume of data generated by humans and machines has increased dramatically, surpassing the capacity of individuals to efficiently process, analyze, and make informed decisions based on such information. AI addresses this challenge by enabling computers to learn from data, identify patterns, and support intelligent decision-making. As a result, artificial intelligence has become a fundamental technology driving automation, data analysis, and advanced computing applications across numerous industries. This paper presents an overview of artificial intelligence 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
This study proposes CognitiveNet, a cognitive computing model designed for processing multi-modal long-sequence data. The framework integrates BiLSTM for sequence context extraction, low-rank self-attention (LRSA) for key segment focus, cognitive memory units (CMU) for cross-segment historical information retrieval, and sparse gating for efficient feature selection. Experimental evaluation on the MIMIC-III dataset for classification and NASA CMAPSS dataset for regression demonstrates that CognitiveNet outperforms baseline models, achieving higher accuracy, lower error rates, and improved inference efficiency while maintaining interpretability. Ablation and disturbance experiments confirm the critical contributions of CMU and sparse gating in capturing long-term dependencies and prioritizing core features. While the study focuses on healthcare and aero-engine prognostics, the proposed model provides a generalizable framework for multi-sensor signal processing, with potential applications in electromagnetic signal monitoring, antenna array diagnostics, and high-frequency system predictive maintenance, where efficient handling of long, complex sequences is crucial. This work highlights the capacity of cognitive computing to support real-time, interpretable, and high-precision analysis of complex temporal signals in industrial and engineering contexts.
The integration of artificial intelligence into instrumentation and measurement systems has emerged as a transformative force across industrial, environmental, and scientific domains. This article provides a systematic overview of AI applications in instrumentation, encompassing intelligent sensor data processing, predictive maintenance, automated meter reading, and fault diagnosis. Drawing upon recent advances documented in the literature, we examine the methodological landscape ranging from conventional machine learning to deep learning and large language models. Key benefits include enhanced measurement accuracy through intelligent compensation, reduced downtime through predictive maintenance, and improved operational efficiency through automation. However, significant challenges persist, including the blackbox nature of AI models, data scarcity, uncertainty quantification, and the gap between laboratory performance and realworld deployment. We argue that the future of intelligent instrumentation lies in hybrid approaches that integrate datadriven AI with conventional modeldriven methods, thereby combining the pattern recognition capabilities of AI with the interpretability and rigor of physicsbased models.
Fujie Lu· Academic Journal of Manageme...· 0 citations
Artificial intelligence (AI) systems are often discussed as if they think and learn, and are capable of generating, but these claims are typically presented as metaphors rather than analyses. The development of machine learning and deep learning, particularly neural and generative models, has sparked debate over whether artificial systems truly resemble human cognition in any meaningful way or merely reproduce its features through statistical computation. In this paper, the working principles of modern AI systems are examined by situating machine learning and deep learning within the intellectual traditions of neuroscience and cognitive science. Based on recent surveys, theoretical studies, and critical views, the paper discusses how learning processes in artificial systems are motivated by and differ radically from biological thinking. It compares supervised, unsupervised, and reinforcement learning paradigms, the formation of representations in deep neural networks, and how generative models can produce outputs that seem creative but lack comprehension or intention. The paper critiques neural metaphors, cognitive analogies, and assertions of machine intelligence, arguing that interpreting what AI systems can and cannot do requires careful, specific analysis. Finally, the paper offers an interdisciplinary synthesis that helps clarify the conceptual underpinnings of contemporary AI, where anthropomorphic interpretations are flawed. It underscores the need to synthesize insights from cognitive science, neuroscience, and philosophy.
Unknown authors· American Journal of Science,...· 0 citations