Open Access

ARTICLE

Ahmed Murtaza¹, Abdullah Memon², Sana Hafeez³, Muzammil Ali², Ghulam E Mustafa Abro^4,*

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 >

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50

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RETRACTION

Wei-Lung Mao^1,*, Yu-Ying Chiu¹, Chao-Ting Chu², Bing-Hong Lin¹, Jian-Jie Hung³

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 >

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248

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108

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ARTICLE

Sugeng Rifqi Mubaroq^1,*, Rolly Maulana Awangga², Tegar Ditya Pragama¹, Sidiq Fathummubin³, Ali Yusuf Abdulhaq¹

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 >

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139

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CORRECTION

S. Saranya^*, G. Usha

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 >

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1058

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2234

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ARTICLE

Thanh Tuan Nguyen¹, Cuong Nguyen Dinh Hoa^2,3,*

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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1042

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829

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ARTICLE

Erblin Halabaku, Eliot Bytyçi^*

Intelligent Automation & Soft Computing, Vol.39, No.6, pp. 987-1006, 2024, DOI:10.32604/iasc.2024.059429
Abstract Machine learning has emerged as a pivotal tool in deciphering and managing this excess of information in an era of abundant data. This paper presents a comprehensive analysis of machine learning algorithms, focusing on the structure and efficacy of random forests in mitigating overfitting—a prevalent issue in decision tree models. It also introduces a novel approach to enhancing decision tree performance through an optimized pruning method called Adaptive Cross-Validated Alpha CCP (ACV-CCP). This method refines traditional cost complexity pruning by streamlining the selection of the alpha parameter, leveraging cross-validation within the pruning process to achieve More >

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5914

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1936

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ARTICLE

Yutong Tian¹, Minghan Gao², Qiang Gao^1,*, Xiao-Hong Peng³

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 175-188, 2024, DOI:10.32604/iasc.2024.042762
Abstract How to find an effective trading policy is still an open question mainly due to the nonlinear and non-stationary dynamics in a financial market. Deep reinforcement learning, which has recently been used to develop trading strategies by automatically extracting complex features from a large amount of data, is struggling to deal with fast-changing markets due to sample inefficiency. This paper applies the meta-reinforcement learning method to tackle the trading challenges faced by conventional reinforcement learning (RL) approaches in non-stationary markets for the first time. In our work, the history trading data is divided into multiple… More >

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2980

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REVIEW

Ghulam E Mustafa Abro^1,*, Ayman M Abdallah^1,2, Faizan Zahid³, Saleem Ahmed⁴

Intelligent Automation & Soft Computing, Vol.40, pp. 99-123, 2025, DOI:10.32604/iasc.2025.060364
Abstract This review synthesises and assesses the most recent developments in Unmanned Aerial Vehicles (UAVs) and swarm robotics, with a specific emphasis on optimisation strategies, path planning, and formation control. The study identifies key methodologies that are driving progress in the field by conducting a comprehensive analysis of seven critical publications. The following are included: sensor-based platforms that facilitate effective obstacle avoidance, cluster-based hierarchical path planning for efficient navigation, and adaptive hybrid controllers for dynamic environments. The review emphasises the substantial contribution of optimisation techniques, including Max-Min Ant Colony Optimisation (MMACO), to the improvement of convergence… More >

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4820

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3018

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ARTICLE

Mustafa Youldash¹, Atta Rahman^2,*, Manar Alsayed¹, Abrar Sebiany¹, Joury Alzayat¹, Noor Aljishi¹, Ghaida Alshammari¹, Mona Alqahtani¹

Intelligent Automation & Soft Computing, Vol.40, pp. 125-143, 2025, DOI:10.32604/iasc.2025.058509
Abstract Diabetic retinopathy (DR) is a complication of diabetes that can lead to reduced vision or even blindness if left untreated. Therefore, early and accurate detection of this disease is crucial for diabetic patients to prevent vision loss. This study aims to develop a deep-learning approach for the early and precise diagnosis of DR, as manual detection can be time-consuming, costly, and prone to human error. The classification task is divided into two groups for binary classification: patients with DR (diagnoses 1–4) and those without DR (diagnosis 0). For multi-class classification, the categories are no DR,… More >

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4251

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2345

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RETRACTION

Nan Pan^1,*, Dilin Pan², Yi Liu²

Intelligent Automation & Soft Computing, Vol.40, pp. 147-147, 2025, DOI:10.32604/iasc.2025.062708
Abstract This article has no abstract. More >

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4089

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2273

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ARTICLE

Nada Mohammed Murad¹, Adnan Yousif Dawod², Saadaldeen Rashid Ahmed^3,4,*, Ravi Sekhar⁵, Pritesh Shah⁵

Intelligent Automation & Soft Computing, Vol.40, pp. 1-27, 2025, DOI:10.32604/iasc.2024.058624
Abstract This study introduces an innovative hybrid approach that integrates deep learning with blockchain technology to improve cybersecurity, focusing on network intrusion detection systems (NIDS). The main goal is to overcome the shortcomings of conventional intrusion detection techniques by developing a more flexible and robust security architecture. We use seven unique machine learning models to improve detection skills, emphasizing data quality, traceability, and transparency, facilitated by a blockchain layer that safeguards against data modification and ensures auditability. Our technique employs the Synthetic Minority Oversampling Technique (SMOTE) to equilibrate the dataset, therefore mitigating prevalent class imbalance difficulties… More >

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3894

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2505

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ARTICLE

Abdulghani M. Abdulghani, Mokhles M. Abdulghani, Wilbur L. Walters, Khalid H. Abed^*

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 337-352, 2024, DOI:10.32604/iasc.2024.047017
Abstract Multi-Agent Reinforcement Learning (MARL) has proven to be successful in cooperative assignments. MARL is used to investigate how autonomous agents with the same interests can connect and act in one team. MARL cooperation scenarios are explored in recreational cooperative augmented reality environments, as well as real-world scenarios in robotics. In this paper, we explore the realm of MARL and its potential applications in cooperative assignments. Our focus is on developing a multi-agent system that can collaborate to attack or defend against enemies and achieve victory with minimal damage. To accomplish this, we utilize the StarCraft… More >

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2579

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ARTICLE

Mohammed Salih Ahmed¹, Atta Rahman^2,*, Yahya Alhabboub¹, Khalid Alzahrani¹, Hassan Baragbah¹, Basel Altaha¹, Hussein Alkatout¹, Sardar Asad Ali Biabani^3,4, Rashad Ahmed⁵, Aghiad Bakry²

Intelligent Automation & Soft Computing, Vol.40, pp. 149-175, 2025, DOI:10.32604/iasc.2025.059080
（This article belongs to the Special Issue: Medical Imaging Decision Support Systems Using Deep Learning and Machine Learning Algorithms)
Abstract This study rigorously evaluates the potential of transfer learning in diagnosing retinal eye diseases using advanced models such as YOLOv8, Xception, ConvNeXtTiny, and VGG16. All models were trained on the esteemed RFMiD dataset, which includes images classified into six critical categories: Diabetic Retinopathy (DR), Macular Hole (MH), Diabetic Neuropathy (DN), Optic Disc Changes (ODC), Tesselated Fundus (TSLN), and normal cases. The research emphasizes enhancing model performance by prioritizing recall metrics, a crucial strategy aimed at minimizing false negatives in medical diagnostics. To address the challenge of imbalanced data, we implemented effective preprocessing techniques, including cropping,… More >

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3541

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2379

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ARTICLE

Ali Hasan Dakheel^1,*, Mohammed Raheem Mohammed¹, Zainab Ali Abd Alhuseen¹, Wassan Adnan Hashim^2,3

Intelligent Automation & Soft Computing, Vol.40, pp. 195-220, 2025, DOI:10.32604/iasc.2025.061622
（This article belongs to the Special Issue: Medical Imaging Decision Support Systems Using Deep Learning and Machine Learning Algorithms)
Abstract The prediction of pregnancy-related hazards must be accurate and timely to safeguard mother and fetal health. This study aims to enhance risk prediction in pregnancy with a novel deep learning model based on a Long Short-Term Memory (LSTM) generator, designed to capture temporal relationships in cardiotocography (CTG) data. This methodology integrates CTG signals with demographic characteristics and utilizes preprocessing techniques such as noise reduction, normalization, and segmentation to create high-quality input for the model. It uses convolutional layers to extract spatial information, followed by LSTM layers to model sequences for superior predictive performance. The overall More >

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3493

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2518

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ARTICLE

Phan-Anh-Huy Nguyen^*, Luu-Luyen Than

Intelligent Automation & Soft Computing, Vol.40, pp. 221-234, 2025, DOI:10.32604/iasc.2025.058976
Abstract The burgeoning e-commerce industry has made online customer reviews a crucial source of feedback for businesses. Sentiment analysis, a technique used to extract subjective information from text, has become essential for understanding consumer sentiment and preferences. However, traditional sentiment analysis methods often struggle with the nuances and context of natural language. To address these issues, this study proposes a comparison of deep learning models that figure out the optimal method to accurately analyze consumer reviews on women's clothing. CNNs excel at capturing local features and semantic information, while LSTMs are adept at handling long-range dependencies… More >

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3235