Open Access

ARTICLE

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 >

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41

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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 - 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 >

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93

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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 - 28 February 2025
（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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258

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ARTICLE

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 >

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303

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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 - 12 February 2025
（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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245

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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 - 29 January 2025
Abstract This article has no abstract. More >

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194

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RETRACTION

Nan Pan^1,*, Yi Liu², Dilin Pan², Junbing Qian¹, Gang Li³
Intelligent Automation & Soft Computing, Vol.40, pp. 145-145, 2025, DOI:10.32604/iasc.2025.062707 - 29 January 2025
Abstract This article has no abstract. More >

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181

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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 - 23 January 2025
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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266

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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 - 23 January 2025
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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242

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ARTICLE

Ayoub Ghani^1,*, Ahmed Zinedine¹, Mohammed El Mohajir²
Intelligent Automation & Soft Computing, Vol.40, pp. 75-98, 2025, DOI:10.32604/iasc.2025.060143 - 23 January 2025
Abstract The Internet of Things (IoT) advances allow healthcare providers to distantly gather and immediately analyze patient health data for diagnostic purposes via connected health devices. In a COVID-19-like pandemic, connected devices can mitigate virus spread and make essential information, such as respiratory patterns, available to healthcare professionals. However, these devices generate vast amounts of data, rendering them susceptible to privacy breaches, and data leaks. Blockchain technology is a robust solution to address these issues in telemedicine systems. This paper proposes a blockchain-based access management solution to enhance patient privacy and secure telehealth and telemedicine data.… More >

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215

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ARTICLE

Haider H. Al-Mahmood^1,*, Saad N. Alsaad²
Intelligent Automation & Soft Computing, Vol.40, pp. 53-74, 2025, DOI:10.32604/iasc.2024.059691 - 10 January 2025
Abstract In secure communications, lightweight encryption has become crucial, particularly for resource-constrained applications such as embedded devices, wireless sensor networks, and the Internet of Things (IoT). As these systems proliferate, cryptographic approaches that provide robust security while minimizing computing overhead, energy consumption, and memory usage are becoming increasingly essential. This study examines lightweight encryption techniques utilizing chaotic maps to ensure secure data transmission. Two algorithms are proposed, both employing the Logistic map; the first approach utilizes two logistic chaotic maps, while the second algorithm employs a single logistic chaotic map. Algorithm 1, including a two-stage mechanism… More >

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262

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ARTICLE

Mahmood Alsaadi, Mohammed E. Seno^*, Mohammed I. Khalaf
Intelligent Automation & Soft Computing, Vol.40, pp. 29-52, 2025, DOI:10.32604/iasc.2024.060390 - 10 January 2025
（This article belongs to the Special Issue: Machine Learning for Privacy and Security in Internet of Things (IoT))
Abstract The agility of Internet of Things (IoT) software engineering is benchmarked based on its systematic insights for wide application support infrastructure developments. Such developments are focused on reducing the interfacing complexity with heterogeneous devices through applications. To handle the interfacing complexity problem, this article introduces a Semantic Interfacing Obscuration Model (SIOM) for IoT software-engineered platforms. The interfacing obscuration between heterogeneous devices and application interfaces from the testing to real-time validations is accounted for in this model. Based on the level of obscuration between the infrastructure hardware to the end-user software, the modifications through device replacement, More >

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224

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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 - 10 January 2025
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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