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Discover Computing

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影响因子:
1.9
出版商:
Springer
ISSN:
2948-2992
浏览:
29609
关注:
15

征稿

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.
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Special Issues

Special Issue on Hardware Accelerated AI/ML Applications 截稿日期: 2026-09-16 Modern Artificial Intelligence (AI) and Machine Learning (ML) models require advanced hardware platforms that allow task parallelism both for the training and the deployment of these models in real time. Generative AI, for example, that is used to create new audio and video content or Large Language Models (LLM) such as ChatGPT have been trained and run on expensive supercomputers that can provide the hardware resources for the fast execution of these large deep learning models. Common AI/ML models for object detection, classification, tracking, tilt/orientation classification, etc, do not require the hardware resources of Generative AI or LLMs but still, cannot be executed on ordinary computing machines (desktops, laptops, smart phones). Lightweight versions of these models (e.g., pruning can be applied to reduce the stored parameters) are often offered for faster operation on platforms with lower computational power such as microcontrollers or other devices operating at the edge of an Internet of Things (IoT) infrastructure. These lightweight models offer lower inference latency but with a slight precision degradation. Graphic Processing Unit (GPU) powered computers are often employed for training, testing and using in real time AI/ML models. In embedded applications reconfigurable hardware is also an option to execute fast these models e.g., at the IoT edge. In this Topical Collection we are interested in AI/ML models and applications that are based on hardware acceleration for efficient training, testing and real time operation. Hardware acceleration techniques can exploit data pipelining, loop unrolling, use of redundant resources and wide ports/buses to satisfy different Neural Network (NN) architectures that require a high degree of parallelism. The operations that need to be executed fast include NN weight transfer/update, multiply/accumulate, vector operations, etc. The efficient use of the hardware resources in order to achieve the highest operating speed with low cost platforms and a reasonable power consumption is very important for many AI/ML applications. This Collection of Discover Computing, provides a forum for presenting novel approaches for hardware accelerated AI/ML applications. Therefore, papers that do not focus on hardware acceleration cannot be accepted. This Collection supports and amplifies research related to SDG 9, SDG 11 and SDG 12. Keywords: Artificial Intelligence; Machine Learning; Hardware Acceleration; Neural Networks; Hardware resources; Data pipelining; Loop Unrolling; High Speed Inference; Low Power
由 Dou Sun 最后更新于
Special Issue on Bridging Computational Intelligence with Control Theory 截稿日期: 2026-09-20 The modeling, analysis, and control of complex dynamical systems are increasingly critical for solving real-world problems as computing, automation, and intelligent technologies advance. The high dimensionality, nonlinearity, and uncertainty inherent in these systems often make traditional control methods insufficient. This collection explores how integrating computational intelligence techniques (e.g., reinforcement learning, neural networks) with control theory methodologies (e.g., model predictive control, stability analysis, event-triggered control) can address challenges such as real-time control, multi-agent collaboration, and decision-making in dynamic, high-dimensional, and uncertain environments. Topics of Interest include, but are not limited to: - Hybrid Control Strategies: Integrating reinforcement learning, neural networks, fuzzy logic, and classical control methods for improved performance in large-scale, nonlinear systems like autonomous vehicles, drones, and robots. Emphasis on event-triggered control for adaptive, real-time decision-making with reduced communication and computation. - Deep Reinforcement Learning (DRL) and Model Predictive Control (MPC): Combining DRL with MPC to enable real-time optimization and adaptive decision-making in dynamic, uncertain environments, particularly for autonomous and multi-agent systems. Enhances MPC with computational intelligence to handle uncertainty and improve stability. - Network Control and Event-Triggered Mechanisms: Optimizing distributed systems with event-triggered strategies to reduce communication and computation while ensuring robust performance under uncertainty. Focus on delayed feedback and fault tolerance to maintain stability in large-scale systems. - Multi-Agent Collaboration and Distributed Control: Improving decision-making in multi-agent systems by incorporating neural network-based or fuzzy logic models for real-time collaboration, synchronization, and robust handling of uncertainties and delays. - High-Dimensional Nonlinear Control and Optimization: Using computational intelligence techniques like neural networks and reinforcement learning to enhance optimization and decision-making in complex, high-dimensional systems such as robotics, smart grids, and industrial automation. - Modeling and Simulation with Differential Equations: Leveraging differential and difference equations to model complex dynamical systems, with a focus on delays and uncertainties, key aspects of robust control and event-triggered mechanisms. - Hybrid Models in Control: Combining classical control theory with neural networks and data-driven models for adaptive, intelligent control, ensuring both stability and performance guarantees. This Collection supports and amplifies research related to SDG 9. Keywords: Hybrid Control; Computational Intelligence; Event-triggered Control; Reinforcement Learning; Model Predictive Control; Distributed Control Systems; Multi-agent Systems; Neural Networks; Nonlinear Control; High-Dimensional Systems; Complex Dynamical Systems; Differential Equations and Simulation
由 Dou Sun 最后更新于
Special Issue on Sensor-Driven Intelligence and Embodied Interactive Systems 截稿日期: 2026-09-30 Modern intelligent systems increasingly rely on rich, heterogeneous sensor data from cameras, depth sensors, wearable devices, robots, drones, and IoT platforms. These environments present challenges such as noisy or incomplete data, dynamic conditions, and real-time constraints, requiring integrated approaches in machine learning, computer vision, robotics, and computational intelligence. This collection focuses on AI methods that transform sensor data into meaningful perception, enable intelligent decision-making, and support embodied interaction or real-world deployment. By framing research along this sensor-to-action pipeline, the collection provides a cohesive venue for contributions that bridge machine learning, sensing technologies, robotics, and AIoT systems, emphasizing practical, robust, and human-centered AI across healthcare monitoring, underwater sensing, assistive technologies, smart environments, educational robotics, and AIoT applications. Topics of interest include, but are not limited to: Sensor Acquisition and Perception - Depth, stereo, fisheye, ultrasonic, and multimodal sensor fusion - Visual perception in constrained environments (underwater, occlusion, low light) - Medical and biological imaging with deep learning (CT, MRI, biomarkers) Computational Intelligence & Decision-Making - Swarm intelligence, PSO, evolutionary methods, and reinforcement learning - Learning-based rule extraction for medical or behavioral prediction - Scheduling, prediction, and optimization in sensor-rich environments Embodied Interaction & Real-World Deployment - Human–robot interaction and socially assistive robotics - Sensor-based interactive systems (Kinect, gesture recognition, VR/AR) - Edge and AIoT-based monitoring and assistive devices for health, safety, and environmental applications This Collection supports and amplifies research related to SDG 9. Keywords: Sensor Fusion; Artificial Intelligence (AI); Machine Learning; Computer Vision; Multimodal Sensor Data; Robotics; Embodied Systems; Human-Robot Interaction; AIoT (Artificial Intelligence of Things); Assistive Technologies; Healthcare Monitoring; Smart Environments
由 Dou Sun 最后更新于
Special Issue on Intelligent Medicine: Machine Learning and Explainable AI for Next-Generation Healthcare 截稿日期: 2026-10-05 The healthcare sector is undergoing a profound digital transformation driven by Machine Learning (ML) and Artificial Intelligence (AI). As these technologies increasingly support diagnosis, prognosis, and clinical decision-making, the challenge is to balance predictive performance with interpretability, fairness, and trust. This Collection invites high-quality research that advances ML theory, methods, and applications specifically designed for clinical, epidemiological, and public-health contexts. A central emphasis of the Collection is explainability as both a transparency requirement and an educational aid: model explanations that support clinicians in understanding complex patient dynamics, uncovering novel relationships, and enhancing causal reasoning. Contributions that integrate structured electronic health records with imaging, signals, or clinical text, as well as studies addressing fairness, uncertainty quantification, and human-centered design, are particularly encouraged. Likewise, approaches that enable federated, privacy-preserving, and regulation-compliant collaboration across healthcare institutions are welcome. Topics of Interest - Predictive Modeling for Diagnosis and Prognosis: Advanced ML architectures for risk stratification, early detection, treatment-response prediction, postoperative outcome modeling, and survival analysis. - Comorbidity Analysis and Longitudinal Patient Trajectories: Representation learning and temporal modeling for disease interactions, multimorbidity networks, state-transition modeling, and dynamic patient phenotyping based on multivariate or multimodal time-series data. - Multimodal Data Integration: Techniques merging structured EHRs with imaging (MRI, CT, X-ray), physiological signals (ECG, EEG, wearable data), genomics, and clinical narratives through attention mechanisms, graph-based learning, transformers, and foundation-model adaptation. - Federated, Distributed, and Privacy-Preserving Learning: Federated optimization, secure aggregation, differential privacy, and decentralized architectures enabling cross-institutional collaboration while safeguarding patient confidentiality and ensuring regulatory compliance. - Fairness, Causality, Robustness, and Trustworthy ML: Approaches addressing algorithmic bias, causal inference and counterfactual reasoning, calibration and uncertainty quantification, out-of-distribution robustness, and explainability techniques designed for clinical auditability. - Ethical, Educational, and Human-Centered AI: Interpretable ML systems that enhance clinical training, support explainable decision pathways, improve AI literacy, and facilitate responsible deployment of AI-enabled healthcare tools. - Human–Robot Interaction and Intelligent Interfaces in Healthcare: Adaptive clinical interfaces, affective computing for patient engagement, assistive robotics, and cognitive-support systems for medical staff and learners. We warmly welcome submissions that advance explainable and trustworthy AI in healthcare, with a focus on methodological innovation and clinically relevant applications. To keep the Collection aligned with this focus, studies primarily centered on sentiment analysis or opinion mining of AI adoption fall outside the intended scope. This Collection supports and amplifies research related to SDG 9. Keywords: Machine Learning; Explainable AI; Healthcare; Comorbidity; Multimodal Learning; Time Series; Federated Learning; Causal Inference; Trustworthy AI; Medical Education; HCI for Personal Healthcare Assistant
由 Dou Sun 最后更新于
Special Issue on AI-Enhanced Cyber-Physical and Societal Systems 截稿日期: 2026-10-05 This Topical Collection focuses on cutting-edge research that integrates artificial intelligence with cyber-physical systems (CPS), intelligent infrastructure, and societal-scale technologies. As digital and physical worlds converge, AI is increasingly essential for enabling autonomy, resilience, and sustainability in complex engineered systems such as smart grids, intelligent transportation networks, healthcare technologies, and urban infrastructures. The collection invites contributions on AI-driven modeling, control, optimization, sensing, decision-making, and system intelligence within CPS and societal applications. Topics may include machine learning for smart cities, AI-based energy management, autonomous and connected mobility, intelligent environmental monitoring, resilient infrastructures, and socio-technical systems that leverage AI for large-scale impact. Emphasis will be placed on work supporting the United Nations Sustainable Development Goals through innovations that promote clean energy, industrial modernization, sustainable communities, and improved societal well-being. Both theoretical advances and real-world deployments are welcome. This Collection supports and amplifies research related to SDG 7, SDG 9 and SDG 11. Keywords: Cyber-Physical Systems (CPS); Smart Cities; Resilient Infrastructures; Artificial Intelligence; Computational Biology; Machine Learning; Human-machine Interactions; Big Data
由 Dou Sun 最后更新于
Special Issue on AI-Driven Remote Sensing and Sustainable Development 截稿日期: 2026-10-05 The amount of imagery collected from satellites, aircraft, UAVs, ground sensors, and mobile mapping systems is growing quickly. As these datasets become larger and more detailed, photogrammetry and remote sensing are increasingly working together with modern artificial intelligence and machine learning methods. This combination is opening new possibilities in Earth observation, 3D modelling, environmental monitoring, and the planning of sustainable infrastructure. With this Topical Collection, we hope to bring together studies that show how advanced imaging technologies can be combined with intelligent computational approaches in geospatial applications. We are interested in work that uses machine learning on optical, multispectral, hyperspectral, or SAR images, as well as research focused on processing point clouds from LiDAR, UAVs, or photogrammetric surveys. Topics such as interpretable AI for geographic decision making, optimization techniques for improving image quality or calibrating sensors, and the creation of digital twins of both natural and urban environments are also welcome. We also encourage studies in which AI and GIS are used together to support areas like renewable energy, disaster management, climate adaptation, or cultural heritage preservation. Our aim is to highlight methods that make geospatial imaging more accurate, more efficient, and easier to understand. By encouraging dialogue between researchers in photogrammetry, remote sensing, computer vision, GIS, environmental sciences, and engineering, we hope to support work that responds to real-world needs and global sustainability goals. Through this effort, we aim to contribute to the development of the next generation of responsible and effective geoimaging technologies. Discover Imaging welcomes submissions that focus on on advanced imaging technologies, data acquisition (optical, multispectral, hyperspectral, SAR, LiDAR), image processing, and geospatial applications such as Earth observation, 3D modeling, and GIS integration for sustainability. Discover Computing welcomes submissions that emphasize algorithmic innovation: machine learning and AI for image analysis, optimization techniques, interpretable AI frameworks, data fusion, and scalable computational models for large geospatial datasets. This Collection supports and amplifies research related to SDG 7, SDG 11 and SDG 13. Keywords: GeoAI; Explainable AI in Remote Sensin; Geospatial Machine Learning; Metaheuristic Optimization in Imaging; Sustainable Site Selection; Geospatial imaging; Remote Sensing; Environmental monitoring
由 Dou Sun 最后更新于
Special Issue on Advances in Geovisualization Evaluation: Methods and Applications 截稿日期: 2026-10-09 Maps are abstract representations that aim to effectively and efficiently communicate spatiotemporal information to the users. Different geovisualization methods, including simple or more sophisticated ones, are utilized to represent such information. Nowadays, maps can be static, animated, mobile, and/or interactive, while they are primarily distributed through the internet. At the same time, modern geovisualization also incorporates extended reality (XR) technologies. Undoubtedly, geovisualization may be characterized by a high level of visual and perceived complexity. Hence, it is crucial to establish robust methods towards the evaluation of their usability. Examining how well, how and why different types of geovisualization work mainly involves the implementation of experimental studies that employ behavioral and neuroimaging methods and techniques to measure visual perception and cognition. This collection aims to collect high-quality original research articles and systematic literature review studies in the field of geovisualization evaluation. We welcome new theories, methods, research frameworks, softwares, scientific datasets, and innovative applications. Potential topics include, but are not limited to, the following: -Cartographic design variables evaluation -Experimental data analysis for geovisualization evaluation -Mixed methods in geovisualization evaluation -New methods in geovisualization evaluation -Geovisualization and Human-Computer Interaction (HCI) -Geovisualization software tools -Geovisualization of Big Geospatial Data -Geovisualization and Artificial Intelligence (AI) applications -Map usability studies -Extended Reality (XR) applications For submissions to Discover Imaging, we welcome papers that focus on topics such as cartographic design variable evaluation, map usability studies, experimental and mixed-method approaches (including behavioral, eye-tracking, and neuroimaging), extended reality (XR) visualization for geospatial data, and human-computer interaction aspects centered on visual cognition and design. For Discover Computing, we welcome submissions that emphasize geovisualization software tools and frameworks, handling big geospatial data through scalable architectures and GPU/edge acceleration, computational methods for real-time rendering and uncertainty encoding, XR integration from a systems perspective, and AI-driven applications for adaptive visualization and usability prediction. This Collection supports and amplifies research related to SDG 4, SDG 9 and SDG 11. Keywords: Eye-Tracking; Geovisualization; Geovisualization Applications; Map Evaluation; Map Usability; Map Interaction; Methods; Tools & Datasets; Neuroimaging.
由 Dou Sun 最后更新于
Special Issue on Theoretical and Methodological Integration of High-Performance Computing and Machine Learning 截稿日期: 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
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Special Issue on Trustworthy Visual Intelligence and Adaptive Learning for Safety-Critical Systems 截稿日期: 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
由 Dou Sun 最后更新于

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