Current Issue · Vol. 2, No. 1 (2026)
Journal of Smart Computing
Welcome to the Journal of Smart Computing (JSC), an international peer-reviewed journal dedicated to advancing intelligent and data-driven computational research. The journal provides a multidisciplinary platform for publishing high-quality theoretical, methodological, and applied studies in smart computing, artificial intelligence, operations research, and emerging computational paradigms.
Inspired by the growing importance of intelligent computing and AI-driven technologies in modern research and industry, the journal welcomes innovative contributions that promote efficient, adaptive, and intelligent decision-making systems.
JSC welcomes submissions addressing, but not limited to, the following areas:
• Artificial Intelligence and Machine Learning
• Data-Driven Optimization and Analytics
• Intelligent Operations Research
• Smart and Intelligent Systems
• Computational Intelligence
• Metaheuristic and Optimization Algorithms
• Deep Learning and Predictive Modeling
• Big Data and Decision Support Systems
• Digital Transformation and Industry 4.0
• Explainable and Human-Centered AI
• Edge, Cloud, and Smart Computing
• Intelligent Applications in Engineering and Business
JSC welcomes original research articles, review papers, case studies, and emerging applications that contribute to the future of smart and intelligent computing.
Recent articles
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01
Smart Supply Chain Management: A Comprehensive Framework Integrating Artificial Intelligence, Blockchain, and Big Data Analytics
Industry 4.0 technologies have changed how firms plan, execute, and monitor supply chain operations, increasing the need for frameworks that are simultaneously predictive, auditable, and analytically grounded. This paper…
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02
A Hybrid Metaheuristic Algorithm for Energy-Aware Task Scheduling
Energy use is now a primary constraint in heterogeneous computing systems: schedules that minimize the makespan of dependent tasks must also limit the power drawn by the processors that execute them. Energy-aware schedul…
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03
Lightweight Deep Learning for Real-Time Anomaly Detection on Intelligent Edge Devices
The proliferation of Internet-of-Things sensors has created a need to detect anomalies where data are generated, yet deep anomaly detectors are typically too large and power-hungry for microcontroller-class devices at th…
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04
A Hybrid Metaheuristic Algorithm for Energy-Aware Task Scheduling in Cloud-Edge Computing Environments
The growth of latency-sensitive applications at the network edge has made task scheduling a central problem in cloud-edge computing, where energy consumption, response time, and resource utilization must be balanced at t…
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05
Explainable AI for Predictive Maintenance in Industry 4.0
Predictive maintenance has become a cornerstone of Industry 4.0, where machine learning models analyze sensor data to anticipate equipment failures and reduce unplanned downtime. The most accurate models, however, are of…
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06
Federated Learning with Differential Privacy for Healthcare IoT: A Privacy-Preserving Framework for Collaborative Clinical Modeling
Wearable sensors and connected medical devices have made healthcare Internet of Things (IoT) systems a rich and highly sensitive source of clinical data. Centralizing these data for model training raises privacy, regulat…
Latest announcements
Special Issue Call — Privacy-Preserving Federated Learning
JSC invites submissions to a forthcoming special issue on privacy-preserving federated learning. We seek contributions on differential privacy in federated settings, secure aggregation protocols, and the systems aspects of FL at scale.
Deadline for submissions: rolling. Manuscripts will be reviewed as they arrive and the issue closes when fully populated.