Open Access
ISSN:1079-8587(print)
ISSN:2326-005X(online)
Publication Frequency:Bi-monthly
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 Volume 39, Number 1, 2024, Intelligent Automation & Soft Computing will transition to a bi-monthly publication schedule.
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.39, No.5, pp. 783-803, 2024, DOI:10.32604/iasc.2024.050819 - 31 October 2024
Abstract The confidentiality of pseudonymous authentication and secure data transmission is essential for the protection of information and mitigating risks posed by compromised vehicles. The Internet of Vehicles has meaningful applications, enabling connected and autonomous vehicles to interact with infrastructure, sensors, computing nodes, humans, and fellow vehicles. Vehicular hoc networks play an essential role in enhancing driving efficiency and safety by reducing traffic congestion while adhering to cryptographic security standards. This paper introduces a privacy-preserving Vehicle-to-Infrastructure authentication, utilizing encryption and the Moore curve. The proposed approach enables a vehicle to deduce the planned itinerary of Roadside More >
Open Access
ARTICLE
Intelligent Automation & Soft Computing, Vol.39, No.5, pp. 805-828, 2024, DOI:10.32604/iasc.2024.054611 - 31 October 2024
（This article belongs to the Special Issue: Combining Soft Computing with Machine Learning for Real-World Applications)
Abstract Signature verification involves vague situations in which a signature could resemble many reference samples or might differ because of handwriting variances. By presenting the features and similarity score of signatures from the matching algorithm as fuzzy sets and capturing the degrees of membership, non-membership, and indeterminacy, a neutrosophic engine can significantly contribute to signature verification by addressing the inherent uncertainties and ambiguities present in signatures. But type-1 neutrosophic logic gives these membership functions fixed values, which could not adequately capture the various degrees of uncertainty in the characteristics of signatures. Type-1 neutrosophic representation is also… More >
Open Access
ARTICLE
Intelligent Automation & Soft Computing, Vol.39, No.5, pp. 829-854, 2024, DOI:10.32604/iasc.2024.054645 - 31 October 2024
Abstract Education is the base of the survival and growth of any state, but due to resource scarcity, students, particularly at the university level, are forced into a difficult situation. Scholarships are the most significant financial aid mechanisms developed to overcome such obstacles and assist the students in continuing with their higher studies. In this study, the convoluted situation of scholarship eligibility criteria, including parental income, responsibilities, and academic achievements, is addressed. In an attempt to maximize the scholarship selection process, numerous machine learning algorithms, including Support Vector Machines, Neural Networks, K-Nearest Neighbors, and the C4.5… More >
Open Access
ARTICLE
Intelligent Automation & Soft Computing, Vol.39, No.5, pp. 855-887, 2024, DOI:10.32604/iasc.2024.055074 - 31 October 2024
Abstract Domain shift is when the data used in training does not match the ones it will be applied to later on under similar conditions. Domain shift will reduce accuracy in results. To prevent this, domain adaptation is done, which adapts the pre-trained model to the target domain. In real scenarios, the availability of labels for target data is rare thus resulting in unsupervised domain adaptation. Herein, we propose an innovative approach where source-free domain adaptation models and Generative Adversarial Networks (GANs) are integrated to improve the performance of computer vision or robotic vision-based systems in… More >
Open Access
ARTICLE
Intelligent Automation & Soft Computing, Vol.39, No.5, pp. 889-905, 2024, DOI:10.32604/iasc.2024.055133 - 31 October 2024
Abstract Birds play a crucial role in maintaining ecological balance, making bird recognition technology a hot research topic. Traditional recognition methods have not achieved high accuracy in bird identification. This paper proposes an improved ResNet18 model to enhance the recognition rate of local bird species in Yunnan. First, a dataset containing five species of local birds in Yunnan was established: C. amherstiae, T. caboti, Syrmaticus humiae, Polyplectron bicalcaratum, and Pucrasia macrolopha. The improved ResNet18 model was then used to identify these species. This method replaces traditional convolution with depth wise separable convolution and introduces an SE (Squeeze and Excitation) module to More >
Open Access
ARTICLE
Intelligent Automation & Soft Computing, Vol.39, No.5, pp. 907-928, 2024, DOI:10.32604/iasc.2024.055470 - 31 October 2024
Abstract Arabic dialect identification is essential in Natural Language Processing (NLP) and forms a critical component of applications such as machine translation, sentiment analysis, and cross-language text generation. The difficulties in differentiating between Arabic dialects have garnered more attention in the last 10 years, particularly in social media. These difficulties result from the overlapping vocabulary of the dialects, the fluidity of online language use, and the difficulties in telling apart dialects that are closely related. Managing dialects with limited resources and adjusting to the ever-changing linguistic trends on social media platforms present additional challenges. A strong… More >
Open Access
ARTICLE
Intelligent Automation & Soft Computing, Vol.39, No.5, pp. 929-947, 2024, DOI:10.32604/iasc.2024.056341 - 31 October 2024
Abstract This paper presents a deep learning Convolutional Neural Network (CNN) for predicting grain orientations from electron backscatter diffraction (EBSD) patterns. The proposed model consists of multiple neural network layers and has been trained on a dataset of EBSD patterns obtained from stainless steel 316 (SS316). Grain orientation changes when considering the effects of temperature and strain rate on material deformation. The deep learning CNN predicts material orientation using the EBSD method to address this challenge. The accuracy of this approach is evaluated by comparing the predicted crystal orientation with the actual orientation under different conditions, More >
Open Access
ARTICLE
Intelligent Automation & Soft Computing, Vol.39, No.5, pp. 949-983, 2024, DOI:10.32604/iasc.2024.056792 - 31 October 2024
Abstract The Internet of Medical Things (IoMT) is one of the critical emerging applications of the Internet of Things (IoT). The huge increases in data generation and transmission across distributed networks make security one of the most important challenges facing IoMT networks. Distributed Denial of Service (DDoS) attacks impact the availability of services of legitimate users. Intrusion Detection Systems (IDSs) that are based on Centralized Learning (CL) suffer from high training time and communication overhead. IDS that are based on distributed learning, such as Federated Learning (FL) or Split Learning (SL), are recently used for intrusion… More >
Open Access
CORRECTION
Intelligent Automation & Soft Computing, Vol.39, No.5, pp. 985-986, 2024, DOI:10.32604/iasc.2024.059591 - 31 October 2024
Abstract This article has no abstract. More >
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