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 about the world of artificial intelligence, intelligent automation, control, computer science, modeling and systems engineering. It is intended that the articles published in the journal will encompass both the short and the long term effects of soft computing and other related fields such as robotics, control, computer, cyber security, vision, speech recognition, pattern recognition, data mining, big data, data analytics, machine intelligence and deep learning. It further hopes it will address the existing and emerging relationships between automation, systems engineering, system of computer engineering and soft computing.
EBSCO, OpenAIRE, OpenALEX, CNKI Scholar, PubScholar, Portico, etc.
Starting from January 2025, Intelligent Automation & Soft Computing will transition to a continuous publication model, and accepted articles will be promptly published online upon completion of the peer review and production processes.
Refers to the articles published on the journal within the last three years that have gained the most viewing times to date (Statistics provided by TSP database)
Refers to articles published on the journal since 2020 that have received the most frequent citation to date (Statistics provided by TSP database)
Open Access
ARTICLE
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 >
Open Access
ARTICLE
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 >
Open Access
ARTICLE
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 >
Open Access
RETRACTION
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 >
Open Access
RETRACTION
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 >
Open Access
ARTICLE
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 >
Open Access
REVIEW
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 >
Open Access
ARTICLE
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 >
Open Access
ARTICLE
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 >
Open Access
ARTICLE
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 >
Open Access
ARTICLE
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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