Open Access
ISSN:1079-8587 (print)
ISSN:2326-005X (online)
Publication Frequency:Continuously
Intelligent Automation & Soft Computing: An International Journal seeks to provide a common forum for the dissemination of accurate results in artificial intelligence, intelligent automation, control, computer science, modeling and systems engineering. The journal aims to publish articles covering both the short- and long-term developments in soft computing and other related fields, including robotics, control, cybersecurity, vision, speech recognition, pattern recognition, data mining, big data, data analytics, machine intelligence and deep learning. It also aims to explore existing and emerging relationships among automation, systems engineering, system of computer engineering and soft computing.
Scopus Citescore (Impact per Publication 2025): 7.7, EBSCO, OpenAIRE, OpenALEX, CNKI Scholar, PubScholar, Portico, etc.
Open Access
ARTICLE
Intelligent Automation & Soft Computing, Vol.41, pp. 139-166, 2026, DOI:10.32604/iasc.2026.088003 - 21 September 2026
（This article belongs to the Special Issue: Intelligent Control, Modeling, and Optimization for Autonomous and Renewable Energy Systems)
Abstract Connected Vehicle-to-Everything (C-V2X) networks rely on Basic Safety Messages (BSMs) for cooperative awareness, but authenticated vehicles can still transmit falsified kinematic data—an insider attack that cryptographic authentication cannot prevent. Existing misbehavior detection systems (MDSs) achieve high detection rates on static benchmarks yet provide no formal guarantee that honest behaviour is a rational vehicle’s dominant strategy. We present a four-layer hierarchical trust architecture for 5G New Radio (NR) C-V2X that integrates edge AI detection, cloud large language model (LLM)-driven weight calibration, game-theoretic conviction, and ledger-anchored payoff tracking. A Nash equilibrium gate, calibrated with a wide margin… More >
Open Access
ARTICLE
Intelligent Automation & Soft Computing, Vol.41, pp. 105-137, 2026, DOI:10.32604/iasc.2026.088941 - 21 September 2026
（This article belongs to the Special Issue: Soft Computing-Driven Intelligent Automation for Adaptive Cyber-Physical Systems)
Abstract Autonomous unmanned aerial vehicles (UAVs) operating in dense three-dimensional environments require predictive models that can represent multiple plausible futures while estimating the safety consequences of these futures. This paper presents a hybrid diffusion world model for short-horizon UAV trajectory forecasting and probabilistic collision-risk estimation. The model conditions on historical UAV states and executed actions, depth observations, and safety-context variables to generate multiple future relative-motion trajectories over a 1.0-s prediction horizon, while jointly estimating collision probability, near-miss probability, and obstacle-clearance information. A task-specific synthetic UAV dataset based on locations in Vietnam containing 1000 in-distribution episodes and… More >
Open Access
ARTICLE
Intelligent Automation & Soft Computing, Vol.41, pp. 73-103, 2026, DOI:10.32604/iasc.2026.087440 - 21 September 2026
Abstract Effective feature extraction is problematic due to subtle differences in skin texture, color, and shape. Moreover, imbalanced datasets are common in medical image analysis, which complicates classification by biasing models toward dominant classes, causing overfitting. The proposed approach is a hybrid one comprising transfer learning and self-attention. We have employed ResNet-50 pre-trained model as a feature extractor for dermatoscopic images. Max Pooling and Global Average Pooling were used to focus on the important patterns and reduce irrelevant background information. He normal kernel and L2-regularization initialization are applied to emphasize salient patterns and suppress irrelevant background… More >
Open Access
ARTICLE
Intelligent Automation & Soft Computing, Vol.41, pp. 49-72, 2026, DOI:10.32604/iasc.2026.082765 - 28 August 2026
Abstract Civilian Unmanned Aerial Systems (UAS) are increasingly deployed in smart-city monitoring, infrastructure inspection, logistics, and emergency response applications. However, their integration with wireless networks, cloud services, and AI-driven analytics significantly expands cybersecurity and privacy risks. Existing studies mainly focus on isolated technical vulnerabilities such as GNSS spoofing, jamming, and communication attacks, while lacking a unified framework that systematically connects cyber threats with quantitative privacy risk assessment. To address this research gap, this study proposes a layered threat-modeling framework for collaborative civilian UAS based on multidimensional attack-surface analysis and STRIDE-oriented threat mapping. In addition, a quantitative More >
Open Access
RETRACTION
Intelligent Automation & Soft Computing, Vol.41, pp. 47-47, 2026, DOI:10.32604/iasc.2026.089959 - 19 August 2026
Abstract This article has no abstract. More >
Open Access
ARTICLE
Intelligent Automation & Soft Computing, Vol.41, pp. 27-46, 2026, DOI:10.32604/iasc.2026.088039 - 11 August 2026
Abstract Decoding cognitive workload from electroencephalography (EEG) underpins passive brain–computer interfaces and adaptive learning technology, yet practical decoders share two weaknesses: they rely on a single family of features, and their accuracy collapses on recordings from an unfamiliar device, montage, or task. We address both. We first build a multi-specialist stacking decoder that fuses complementary spectral, Riemannian, and spatial views through a meta-learner. Evaluated across two public corpora under the leave-one-subject-out MOABB benchmarking protocol, it outperforms the best single specialist on the binary workload contrasts, and the strongest of these effects survives family-wide false-discovery-rate correction. The… More >
Open Access
CORRECTION
Intelligent Automation & Soft Computing, Vol.41, pp. 25-25, 2026, DOI:10.32604/iasc.2026.085938 - 04 June 2026
Abstract This article has no abstract. More >
Open Access
ARTICLE
Intelligent Automation & Soft Computing, Vol.41, pp. 1-24, 2026, DOI:10.32604/iasc.2026.078344 - 12 May 2026
Abstract Accurate crude oil price forecasting is critical for global economic stability but remains an exceptionally challenging task due to the data’s complex, non-linear, and non-stationary nature. Deep learning models like LSTMs are widely favored. However, the dominant research trend currently focuses on increasingly complex hybrid and ensemble architectures. These models often suffer from high computational overhead, intricate tuning processes, and potential overfitting, raising critical questions about their necessity. In this paper, we challenged the assumption that complexity is required for high performance by proposing and evaluating a streamlined 1D-CNN model. We conducted a comprehensive evaluation… More >
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