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 >

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 >

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 >

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 >

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 >

Pikkili Gopala Krishna^1,*, Jalari Somasekar²

Intelligent Automation & Soft Computing, Vol.40, pp. 235-259, 2025, DOI:10.32604/iasc.2025.063657 - 28 April 2025

Abstract Stress is mental tension caused by difficult situations, often experienced by hospital workers and IT professionals who work long hours. It is essential to detect the stress in shift workers to improve their health. However, existing models measure stress with physiological signals such as PPG, EDA, and blink data, which could not identify the stress level accurately. Additionally, the works face challenges with limited data, inefficient spatial relationships, security issues with health data, and long-range temporal dependencies. In this paper, we have developed a federated learning-based stress detection system for IT and hospital workers, integrating… More >

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

Intelligent Automation & Soft Computing, Vol.40, pp. 221-234, 2025, DOI:10.32604/iasc.2025.058976 - 14 April 2025

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 >

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 - 28 February 2025

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 >

Mukhsar^1,*, Andi Tenriawaru², Gusti Ngurah Adhi Wibawa¹, Bahriddin Abapihi¹, Sitti Wirdhana Ahmad³, I Putu Sudayasa⁴

Intelligent Automation & Soft Computing, Vol.40, pp. 177-193, 2025, DOI:10.32604/iasc.2025.058884 - 24 February 2025

Abstract Despite extensive prevention efforts and research, dengue hemorrhagic fever (DHF) remains a major public health challenge, particularly in tropical regions, with significant social, economic, and health consequences. Statistical models are crucial in studying infectious DHF by providing a structured framework to analyze transmission dynamics between humans (hosts) and mosquitoes (vectors). Depending on the disease characteristics, different stochastic compartmental models can be employed. This research applies Bayesian Integrated Nested Laplace Approximation (INLA) to the SIR-SI model for DHF data. The method delivers accurate parameter estimates, improved computational efficiency, and effective integration with early warning systems. The… More >

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 - 12 February 2025

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 >