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Abdulwahab Ali Almazroi^1,*, Mohammed A. Alqarni², Nida Aslam³, Rizwan Ali Shah⁴
Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2153-2174, 2023, DOI:10.32604/iasc.2023.039732
（This article belongs to the Special Issue: Computer Vision and Machine Learning for Real-Time Applications)
Abstract Nowadays days, the chief grounds of automobile accidents are driver fatigue and distractions. With the development of computer vision technology, a cutting-edge system has the potential to spot driver distractions or sleepiness and alert them, reducing accidents. This paper presents a novel approach to detecting driver tiredness based on eye and mouth movements and object identification that causes a distraction while operating a motor vehicle. Employing the facial landmarks that the camera picks up and sends to classify using a Convolutional Neural Network (CNN) any changes by focusing on the eyes and mouth zone, precision is achieved. One of the… More >

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Anuj Kumar, Shakti Kumar^*
Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2175-2198, 2023, DOI:10.32604/iasc.2023.039093
Abstract Melanoma is a perfidious form of skin cancer. The study offers a hybrid framework for the automatic classification of melanoma. An Automatic Melanoma Detection System (AMDS) is used for identifying melanoma from the infected area of the skin image using image processing techniques. A larger number of pre-existing automatic melanoma detection systems are either commercial or their accuracy can be further improved. The research problem is to identify the best preprocessing technique, feature extractor, and classifier for melanoma detection using publically available MED-NODE data set. AMDS goes through four stages. The preprocessing stage is for noise removal; the segmentation stage… More >

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Sultan Mesfer Aldossary^*
Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2199-2212, 2023, DOI:10.32604/iasc.2023.038243
Abstract The challenge of encrypting sensitive information of a medical image in a healthcare system is still one that requires a high level of computing complexity, despite the ongoing development of cryptography. After looking through the previous research, it has become clear that the security issues still need to be looked into further because there is room for expansion in the research field. Recently, neural networks have emerged as a cost-effective and effective optimization strategy in terms of providing security for images. This revelation came about as a result of current developments. Nevertheless, such an implementation is a technique that is… More >

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Yuchen Li¹, Baohong Ling^1,2,*, Donghui Hu¹, Shuli Zheng¹, Guoan Zhang³
Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2213-2225, 2023, DOI:10.32604/iasc.2023.029983
Abstract The goal of steganalysis is to detect whether the cover carries the secret information which is embedded by steganographic algorithms. The traditional steganalysis detector is trained on the stego images created by a certain type of steganographic algorithm, whose detection performance drops rapidly when it is applied to detect another type of steganographic algorithm. This phenomenon is called as steganographic algorithm mismatch in steganalysis. To resolve this problem, we propose a deep learning driven feature-based approach. An advanced steganalysis neural network is used to extract steganographic features, different pairs of training images embedded with steganographic algorithms can obtain diverse features… More >

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Qiang Luo¹, Wenbin Zheng^1,2,*
Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2227-2246, 2023, DOI:10.32604/iasc.2023.040195
（This article belongs to the Special Issue: Computer Vision and Machine Learning for Real-Time Applications)
Abstract In the field of traffic sign recognition, traffic signs usually occupy very small areas in the input image. Most object detection algorithms directly reduce the original image to a specific size for the input model during the detection process, which leads to the loss of small object information. Additionally, classification tasks are more sensitive to information loss than localization tasks. This paper proposes a novel traffic sign recognition approach, in which a lightweight pre-locator network and a refined classification network are incorporated. The pre-locator network locates the sub-regions of the traffic signs from the original image, and the refined classification… More >

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288

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Amani Yahyaoui¹, Jawad Rasheed^2,*, Shtwai Alsubai³, Raed M. Shubair⁴, Abdullah Alqahtani⁵, Buket Isler⁶, Rana Zeeshan Haider⁷
Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2247-2261, 2023, DOI:10.32604/iasc.2023.036840
（This article belongs to the Special Issue: Artificial Intelligence based Healthcare Systems)
Abstract The coronavirus (COVID-19) is a disease declared a global pandemic that threatens the whole world. Since then, research has accelerated and varied to find practical solutions for the early detection and correct identification of this disease. Several researchers have focused on using the potential of Artificial Intelligence (AI) techniques in disease diagnosis to diagnose and detect the coronavirus. This paper developed deep learning (DL) and machine learning (ML) -based models using laboratory findings to diagnose COVID-19. Six different methods are used in this study: K-nearest neighbor (KNN), Decision Tree (DT) and Naive Bayes (NB) as a machine learning method, and… More >

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Jiaao Cui¹, Sixian Chan^1,2,*, Pan Mu¹, Tinglong Tang², Xiaolong Zhou³
Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2263-2277, 2023, DOI:10.32604/iasc.2023.039894
（This article belongs to the Special Issue: Computer Vision and Machine Learning for Real-Time Applications)
Abstract Cross-modality pedestrian re-identification has important applications in the field of surveillance. Due to variations in posture, camera perspective, and camera modality, some salient pedestrian features are difficult to provide effective retrieval cues. Therefore, it becomes a challenge to design an effective strategy to extract more discriminative pedestrian detail. Although many effective methods for detailed feature extraction are proposed, there are still some shortcomings in filtering background and modality noise. To further purify the features, a pure detail feature extraction network (PDFENet) is proposed for VI-ReID. PDFENet includes three modules, adaptive detail mask generation module (ADMG), inter-detail interaction module (IDI) and… More >

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Mohammad S. Al-kahtani¹, Zahid Mehmood^2,3,*, Tariq Sadad⁴, Islam Zada⁵, Gauhar Ali⁶, Mohammed ElAffendi⁶
Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2279-2290, 2023, DOI:10.32604/iasc.2023.037673
Abstract Cybersecurity threats are increasing rapidly as hackers use advanced techniques. As a result, cybersecurity has now a significant factor in protecting organizational limits. Intrusion detection systems (IDSs) are used in networks to flag serious issues during network management, including identifying malicious traffic, which is a challenge. It remains an open contest over how to learn features in IDS since current approaches use deep learning methods. Hybrid learning, which combines swarm intelligence and evolution, is gaining attention for further improvement against cyber threats. In this study, we employed a PSO-GA (fusion of particle swarm optimization (PSO) and genetic algorithm (GA)) for… More >

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N. Kanagaraj^1,*, Obaid Aldosari¹, M. Ramasamy², M. Vijayakumar²
Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2291-2306, 2023, DOI:10.32604/iasc.2023.039101
（This article belongs to the Special Issue: Fuzzy Soft Computing for Real-time Complex Applications)
Abstract The generation of electricity, considering environmental and economic factors is one of the most important challenges of recent years. In this article, a thermoelectric generator (TEG) is proposed to use the thermal energy of an electric water heater (EWH) to generate electricity independently. To improve the energy conversion efficiency of the TEG, a fuzzy logic controller (FLC)-based perturb & observe (P&O) type maximum power point tracking (MPPT) control algorithm is used in this study. An EWH is one of the major electricity consuming household appliances which causes a higher electricity price for consumers. Also, a significant amount of thermal energy… More >

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Jaime Ortiz Córdoba, Alejandro Molina Zarca^*, Antonio Skármeta
Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2307-2333, 2023, DOI:10.32604/iasc.2023.039607
（This article belongs to the Special Issue: Advanced Achievements of Intelligent and Secure Systems for the Next Generation Computing)
Abstract 5G/Beyond 5G (B5G) networks provide connectivity to many heterogeneous devices, raising significant security and operational issues and making traditional infrastructure management increasingly complex. In this regard, new frameworks such as Anastacia-H2020 or INSPIRE-5GPlus automate the management of next-generation infrastructures, especially regarding policy-based security, abstraction, flexibility, and extensibility. This paper presents the design, workflow, and implementation of a security solution based on Unmanned Aerial Vehicles (UAVs), able to extend 5G/B5G security framework capabilities with UAV features like dynamic service provisioning in specific geographic areas. The proposed solution allows enforcing UAV security policies in proactive and reactive ways to automate UAV dynamic… More >

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Bingcheng Jiang, Qian He^*, Zhongyi Zhai, Hang Su
Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2335-2353, 2023, DOI:10.32604/iasc.2023.039989
（This article belongs to the Special Issue: Advanced Achievements of Intelligent and Secure Systems for the Next Generation Computing)
Abstract Software-defined networking (SDN) enables the separation of control and data planes, allowing for centralized control and management of the network. Without adequate access control methods, the risk of unauthorized access to the network and its resources increases significantly. This can result in various security breaches. In addition, if authorized devices are attacked or controlled by hackers, they may turn into malicious devices, which can cause severe damage to the network if their abnormal behaviour goes undetected and their access privileges are not promptly restricted. To solve those problems, an anomaly detection and access control mechanism based on SDN and neural… More >

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Ali Ahmed^1,*, Alaa Omran Almagrabi², Omar M. Barukab³
Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2355-2370, 2023, DOI:10.32604/iasc.2023.039847
Abstract Content-based medical image retrieval (CBMIR) is a technique for retrieving medical images based on automatically derived image features. There are many applications of CBMIR, such as teaching, research, diagnosis and electronic patient records. Several methods are applied to enhance the retrieval performance of CBMIR systems. Developing new and effective similarity measure and features fusion methods are two of the most powerful and effective strategies for improving these systems. This study proposes the relative difference-based similarity measure (RDBSM) for CBMIR. The new measure was first used in the similarity calculation stage for the CBMIR using an unweighted fusion method of traditional… More >

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Min-Jeong Kim¹, Byeong-Uk Jeon¹, Hyun Yoo², Kyungyong Chung^3,*
Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2371-2386, 2023, DOI:10.32604/iasc.2023.040310
Abstract With the increasing number of digital devices generating a vast amount of video data, the recognition of abnormal image patterns has become more important. Accordingly, it is necessary to develop a method that achieves this task using object and behavior information within video data. Existing methods for detecting abnormal behaviors only focus on simple motions, therefore they cannot determine the overall behavior occurring throughout a video. In this study, an abnormal behavior detection method that uses deep learning (DL)-based video-data structuring is proposed. Objects and motions are first extracted from continuous images by combining existing DL-based image analysis models. The… More >

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Mingyong Li, Zheng Jiang^*, Zongwei Zhao, Longfei Ma
Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2387-2406, 2023, DOI:10.32604/iasc.2023.037900
（This article belongs to the Special Issue: Cognitive Granular Computing Methods for Big Data Analysis)
Abstract As an essential category of public event management and control, sentiment analysis of online public opinion text plays a vital role in public opinion early warning, network rumor management, and netizens’ personality portraits under massive public opinion data. The traditional sentiment analysis model is not sensitive to the location information of words, it is difficult to solve the problem of polysemy, and the learning representation ability of long and short sentences is very different, which leads to the low accuracy of sentiment classification. This paper proposes a sentiment analysis model PERT-BiLSTM-Att for public opinion text based on the pre-training model… More >

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So-Eun Jeon, Sun-Jin Lee, Il-Gu Lee^*
Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2407-2419, 2023, DOI:10.32604/iasc.2023.039937
（This article belongs to the Special Issue: Advanced Achievements of Intelligent and Secure Systems for the Next Generation Computing)
Abstract With the development of the 5th generation of mobile communication (5G) networks and artificial intelligence (AI) technologies, the use of the Internet of Things (IoT) has expanded throughout industry. Although IoT networks have improved industrial productivity and convenience, they are highly dependent on nonstandard protocol stacks and open-source-based, poorly validated software, resulting in several security vulnerabilities. However, conventional AI-based software vulnerability discovery technologies cannot be applied to IoT because they require excessive memory and computing power. This study developed a technique for optimizing training data size to detect software vulnerabilities rapidly while maintaining learning accuracy. Experimental results using a software… More >

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Junru Liu, Yonggui Lv^*
Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2421-2439, 2023, DOI:10.32604/iasc.2023.040262
（This article belongs to the Special Issue: Artificial Intelligence Algorithm for Industrial Operation Application)
Abstract Numerous clothing enterprises in the market have a relatively low efficiency of assembly line planning due to insufficient optimization of bottleneck stations. As a result, the production efficiency of the enterprise is not high, and the production organization is not up to expectations. Aiming at the problem of flexible process route planning in garment workshops, a multi-object genetic algorithm is proposed to solve the assembly line balance optimization problem and minimize the machine adjustment path. The encoding method adopts the object-oriented path representation method, and the initial population is generated by random topology sorting based on an in-degree selection mechanism.… More >

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Nur Fairuz Mohamed Yusof¹, Dahaman Ishak², Muhammad Ammirrul Atiqi Mohd Zainuri^3,*, Muhammad Najwan Hamidi², Zuhair Muhammed Alaas⁴, Mohamed Mostafa Ramadan Ahmed⁵
Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2441-2457, 2023, DOI:10.32604/iasc.2023.037778
Abstract Grid-connected reactive-load compensation and harmonic control are becoming a central topic as photovoltaic (PV) grid-connected systems diversified. This research aims to produce a high-performance inverter with a fast dynamic response for accurate reference tracking and a low total harmonic distortion (THD) even under nonlinear load applications by improving its control scheme. The proposed system is expected to operate in both stand-alone mode and grid-connected mode. In stand-alone mode, the proposed controller supplies power to critical loads, alternatively during grid-connected mode provide excess energy to the utility. A modified variable step incremental conductance (VS-InCond) algorithm is designed to extract maximum power… More >

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M. R. Vimala Devi, S. Kalaivani^*
Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2459-2476, 2023, DOI:10.32604/iasc.2023.038183
Abstract Spectral unmixing helps to identify different components present in the spectral mixtures which occur in the uppermost layer of the area owing to the low spatial resolution of hyperspectral images. Most spectral unmixing methods are globally based and do not consider the spectral variability among its endmembers that occur due to illumination, atmospheric, and environmental conditions. Here, endmember bundle extraction plays a major role in overcoming the above-mentioned limitations leading to more accurate abundance fractions. Accordingly, a two-stage approach is proposed to extract endmembers through endmember bundles in hyperspectral images. The divide and conquer method is applied as the first… More >

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Dong Chen¹, Tanping Zhou^1,2,3,*, Xu An Wang^1,2, Zichao Song¹, Yujie Ding¹, Xiaoyuan Yang^1,2
Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2477-2497, 2023, DOI:10.32604/iasc.2023.039238
Abstract Smart grid (SG) brings convenience to users while facing great challenges in protecting personal private data. Data aggregation plays a key role in protecting personal privacy by aggregating all personal data into a single value, preventing the leakage of personal data while ensuring its availability. Recently, a flexible subset data aggregation (FSDA) scheme based on the Paillier homomorphic encryption was first proposed by Zhang et al. Their scheme can dynamically adjust the size of each subset and obtain the aggregated data in the corresponding subset. In this paper, firstly, an efficient attack with both theorems proving and experimentative verification is… More >

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Ammar Almomani^1,2,*, Iman Akour³, Ahmed M. Manasrah^4,5, Omar Almomani⁶, Mohammad Alauthman⁷, Esra’a Abdullah¹, Amaal Al Shwait¹, Razan Al Sharaa¹
Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2499-2517, 2023, DOI:10.32604/iasc.2023.039687
Abstract The exponential growth of Internet and network usage has necessitated heightened security measures to protect against data and network breaches. Intrusions, executed through network packets, pose a significant challenge for firewalls to detect and prevent due to the similarity between legitimate and intrusion traffic. The vast network traffic volume also complicates most network monitoring systems and algorithms. Several intrusion detection methods have been proposed, with machine learning techniques regarded as promising for dealing with these incidents. This study presents an Intrusion Detection System Based on Stacking Ensemble Learning base (Random Forest, Decision Tree, and k-Nearest-Neighbors). The proposed system employs pre-processing… More >

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