Journal Information

Discover Computing

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Impact Factor:
1.9
Publisher:
Springer
ISSN:
2948-2992
Viewed:
31101
Tracked:
15
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Call For Papers

Discover Computing is an academic journal published by Springer. (ISSN 2948-2992, impact factor 1.9, CCF C).

Aims and scope Discover Computing (formerly Information Retrieval Journal) is a fully open access, peer-reviewed journal that supports multidisciplinary research and policy developments across all fields relevant to computer science. The journal aims to be a resource for researchers, policy makers and the general public for recent advances in computer science, and its uses in research development and society. As a fully open access journal, we ensure that our research is highly discoverable and instantly available globally to everyone. The journal particularly welcomes work that aims to address the United Nations Sustainable Development Goals, especially, Industry, Innovation and Infrastructure. Topics Topics welcomed at Discover Computing include but are not limited to the following: Foundational computing theories: Algorithms, data structures, computational complexity Automata theory, graph theory, formal languages Turing machines, P vs NP problem, lambda calculus Modern computing architectures and systems: Quantum computing, distributed systems, parallel computing Microarchitecture, multicore processors, memory hierarchies Cloud computing, edge computing, serverless architectures Human-computer interaction: User experience (UX), user interface (UI), accessibility Cognitive ergonomics, adaptive systems, virtual reality User-centered design, haptic feedback, gesture recognition Artificial intelligence and machine learning: Neural networks, deep learning, reinforcement learning Natural language processing, computer vision, robotics Generative adversarial networks (GANs), transfer learning, explainable AI Cybersecurity and privacy: Cryptography, firewall, intrusion detection systems Digital forensics, malware analysis, blockchain security Data anonymization, end-to-end encryption, zero trust architectures Emergent technologies: Augmented reality (AR), virtual reality (VR), mixed reality (MR) Internet of Things (IoT), 5G, smart cities Drones, autonomous vehicles, wearable tech Societal impacts of computing: Digital ethics, algorithmic bias, technological unemployment Digital divide, accessibility, information equity Digital literacy, e-governance, surveillance capitalism Content types Discover Computing welcomes a variety of article types – please see our submission guidelines for details. The journal also publishes guest-edited Topical Collections of relevance to all aspects of computer science and its applications. For more information, please follow up with our journal publishing contact.
Last updated by Admin Agent on

Special Issues

Special Issue on Theoretical and Methodological Integration of High-Performance Computing and Machine Learning Submission Date: 2026-10-31 This collection focuses on the theoretical foundations, methodological innovations, and computational strategies that underpin the integration of High-Performance Computing (HPC) and Machine Learning (ML). It aims to provide insights into how HPC accelerates ML model training, scaling, and optimization, while ML techniques enhance HPC computational efficiency and performance. The collection explores how novel algorithms, frameworks, and system architectures enable scalable, efficient, and robust integration of ML and HPC. Potential topics of interest include, but are not limited to: - Theoretical models and frameworks for HPC-ML integration - Architectures and design principles for scalable ML on HPC systems - Energy-optimized GPU scheduling - Trends in HPC-enabled ML frameworks and libraries (e.g., PyTorch, Horovod) - Distributed training and parallelization of deep learning models using HPC infrastructure - Deep Learning and Reinforcement Learning on HPC systems - Techniques for parallelizing ML algorithms across heterogeneous computing environments - Optimization of resource allocation, memory management, and scheduling for large-scale ML tasks - Co-design of HPC hardware and ML software, including AI accelerators and exascale computing platforms - Methodologies for integrating quantum, neuromorphic, and edge computing into HPC–ML workflows - Generative AI and large language models at HPC scale - Leveraging HPC to enhance cybersecurity with AI-driven threat detection, anomaly detection, and real-time attack analysis - Demonstrating the practical integration of HPC with ML through mini-labs or live demos (e.g., setting up distributed training environments, optimizing model performance on HPC clusters) This Collection supports and amplifies research related to SDG 9. Keywords: High-Performance Computing (HPC); Machine Learning (ML); HPC-Accelerated Machine Learning; Distributed Deep Learning; Scalable Machine Learning; Parallel Computing for AI; Edge-to-Cloud AI Systems; AI Model Optimization on HPC; Resource Optimization in HPC-ML Systems; Exascale Computing for AI
Last updated by Dou Sun on
Special Issue on Trustworthy Visual Intelligence and Adaptive Learning for Safety-Critical Systems Submission Date: 2026-12-31 This Collection focuses on advancing trustworthy and adaptive visual intelligence for safety-critical systems. With deep learning increasingly deployed in industrial inspection, environmental monitoring, and predictive maintenance, ensuring reliability, interpretability, and adaptability is essential. We welcome original research and reviews on vision-based defect inspection, anomaly detection, predictive maintenance, and intelligent surveillance, emphasizing explainable, uncertainty-aware, and domain-adaptive learning. Topics of interest include diffusion and self-training models, uncertainty-aware perception, edge-AI for real-time monitoring, and human-in-the-loop decision systems. Works validated in manufacturing, energy, or public-safety environments are especially encouraged. By integrating trustworthy AI principles with visual intelligence and adaptive learning, this Collection aims to advance reliable, transparent, and resilient intelligent systems that enhance safety, operational efficiency, and confidence in next-generation AI applications. This Collection supports and amplifies research related to SDG 9. Keywords: Trustworthy AI; Visual Intelligence; Adaptive Learning; Safety-Critical Systems; Deep Learning; Vision-Based Defect Inspection; Predictive Maintenance; Anomaly Detection; Intelligent Surveillance; Uncertainty-Aware Learning
Last updated by Dou Sun on

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