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Hend Alshede^1,2,*, Laila Nassef¹, Nahed Alowidi¹, Etimad Fadel¹
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3257-3278, 2023, DOI:10.32604/iasc.2023.035874
Abstract Advanced Metering Infrastructure (AMI) is the metering network of the smart grid that enables bidirectional communications between each consumer’s premises and the provider’s control center. The massive amount of data collected supports the real-time decision-making required for diverse applications. The communication infrastructure relies on different network types, including the Internet. This makes the infrastructure vulnerable to various attacks, which could compromise security or have devastating effects. However, traditional machine learning solutions cannot adapt to the increasing complexity and diversity of attacks. The objective of this paper is to develop an Anomaly Detection System (ADS) based on deep learning using the… More >

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Ke Xu^1,2, Peng Liu^1,*, Hua Gong^1,2
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3279-3294, 2023, DOI:10.32604/iasc.2023.036007
Abstract A two-agent production and transportation coordinated scheduling problem in a single-machine environment is suggested to compete for one machine from different downstream production links or various consumers. The jobs of two agents compete for the processing position on a machine, and after the processed, they compete for the transport position on a transport vehicle to be transported to two agents. The two agents have different objective functions. The objective function of the first agent is the sum of the makespan and the total transportation time, whereas the objective function of the second agent is the sum of the total completion… More >

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Leilei Li¹, Yansheng Fu², Dongjie Zhu^2,*, Xiaofang Li³, Yundong Sun², Jianrui Ding², Mingrui Wu², Ning Cao^4,*, Russell Higgs⁵
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3295-3307, 2023, DOI:10.32604/iasc.2023.035257
Abstract The knowledge graph with relational abundant information has been widely used as the basic data support for the retrieval platforms. Image and text descriptions added to the knowledge graph enrich the node information, which accounts for the advantage of the multi-modal knowledge graph. In the field of cross-modal retrieval platforms, multi-modal knowledge graphs can help to improve retrieval accuracy and efficiency because of the abundant relational information provided by knowledge graphs. The representation learning method is significant to the application of multi-modal knowledge graphs. This paper proposes a distributed collaborative vector retrieval platform (DCRL-KG) using the multimodal knowledge graph VisualSem… More >

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373

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Senquan Yang¹, Fan Ding¹, Jianjun Liu¹, Pu Li^1,2, Songxi Hu^1,2,*
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3309-3323, 2023, DOI:10.32604/iasc.2023.035051
Abstract Audio signal separation is an open and challenging issue in the classical “Cocktail Party Problem”. Especially in a reverberation environment, the separation of mixed signals is more difficult separated due to the influence of reverberation and echo. To solve the problem, we propose a determined reverberant blind source separation algorithm. The main innovation of the algorithm focuses on the estimation of the mixing matrix. A new cost function is built to obtain the accurate demixing matrix, which shows the gap between the prediction and the actual data. Then, the update rule of the demixing matrix is derived using Newton gradient… More >

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Fadwa Alrowais¹, Radwa Marzouk^2,3, Fahd N. Al-Wesabi^4,*, Anwer Mustafa Hilal⁵
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3325-3342, 2023, DOI:10.32604/iasc.2023.036354
Abstract Sign language recognition can be treated as one of the efficient solutions for disabled people to communicate with others. It helps them to convey the required data by the use of sign language with no issues. The latest developments in computer vision and image processing techniques can be accurately utilized for the sign recognition process by disabled people. American Sign Language (ASL) detection was challenging because of the enhancing intraclass similarity and higher complexity. This article develops a new Bayesian Optimization with Deep Learning-Driven Hand Gesture Recognition Based Sign Language Communication (BODL-HGRSLC) for Disabled People. The BODL-HGRSLC technique aims to… More >

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367

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Turki M. Alanazi^*
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3343-3354, 2023, DOI:10.32604/iasc.2023.036411
Abstract Detecting and recognizing text from natural scene images presents a challenge because the image quality depends on the conditions in which the image is captured, such as viewing angles, blurring, sensor noise, etc. However, in this paper, a prototype for text detection and recognition from natural scene images is proposed. This prototype is based on the Raspberry Pi 4 and the Universal Serial Bus (USB) camera and embedded our text detection and recognition model, which was developed using the Python language. Our model is based on the deep learning text detector model through the Efficient and Accurate Scene Text Detector… More >

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393

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Onder Alparslan^*, Omer Cetin
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3355-3370, 2023, DOI:10.32604/iasc.2023.035689
Abstract Unknown closed spaces are a big challenge for the navigation of robots since there are no global and pre-defined positioning options in the area. One of the simplest and most efficient algorithms, the artificial potential field algorithm (APF), may provide real-time navigation in those places but fall into local minimum in some cases. To overcome this problem and to present alternative escape routes for a robot, possible crossing points in buildings may be detected by using object detection and included in the path planning algorithm. This study utilized a proposed sensor fusion method and an improved object classification method for… More >

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Narit Hnoohom¹, Sakorn Mekruksavanich², Anuchit Jitpattanakul^3,4,*
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3371-3385, 2023, DOI:10.32604/iasc.2023.036551
Abstract Falls are the contributing factor to both fatal and nonfatal injuries in the elderly. Therefore, pre-impact fall detection, which identifies a fall before the body collides with the floor, would be essential. Recently, researchers have turned their attention from post-impact fall detection to pre-impact fall detection. Pre-impact fall detection solutions typically use either a threshold-based or machine learning-based approach, although the threshold value would be difficult to accurately determine in threshold-based methods. Moreover, while additional features could sometimes assist in categorizing falls and non-falls more precisely, the estimated determination of the significant features would be too time-intensive, thus using a… More >

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459

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Sultan Mesfer Aldossary^*
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3387-3404, 2023, DOI:10.32604/iasc.2023.035249
Abstract With the emergence of cloud technologies, the services of healthcare systems have grown. Simultaneously, machine learning systems have become important tools for developing matured and decision-making computer applications. Both cloud computing and machine learning technologies have contributed significantly to the success of healthcare services. However, in some areas, these technologies are needed to provide and decide the next course of action for patients suffering from diabetic kidney disease (DKD) while ensuring privacy preservation of the medical data. To address the cloud data privacy problem, we proposed a DKD prediction module in a framework using cloud computing services and a data… More >

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319

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Richard Cruz¹, Johnson Kinyua^1,*, Charles Mutigwe²
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3405-3423, 2023, DOI:10.32604/iasc.2023.035906
Abstract The massive increase in the volume of data generated by individuals on social media microblog platforms such as Twitter and Reddit every day offers researchers unique opportunities to analyze financial markets from new perspectives. The meme stock mania of 2021 brought together stock traders and investors that were also active on social media. This mania was in good part driven by retail investors’ discussions on investment strategies that occurred on social media platforms such as Reddit during the COVID-19 lockdowns. The stock trades by these retail investors were then executed using services like Robinhood. In this paper, machine learning models… More >

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542

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Ayman Amer¹, Firas M. Makahleh², Jafar Ababneh³, Hani Attar⁴, Ahmed Amin Ahmed Solyman⁵, Mehrdad Ahmadi Kamarposhti^6,*, Phatiphat Thounthong⁷
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3425-3446, 2023, DOI:10.32604/iasc.2023.034854
Abstract With the daily expansion of global energy consumption, developing the power grids is of uttermost importance. However, building a new transmission line is costly and time-consuming, so utilizing the same lines with possible higher transmission capacity is very cost-effective. In this regard, to increase the capacity of the transmission lines, the flexible alternating current transmission system (FACTS) has been widely used in power grids in recent years by industrialized countries. One of the essential topics in electrical power systems is the reactive power compensation, and the FACTS plays a significant role in controlling the reactive power current in the power… More >

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Teng Zong^1,2,*, Fengsi Wang³, Xin Wei², Yibo Liu¹
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3447-3468, 2023, DOI:10.32604/iasc.2023.035456
Abstract With the expanding enrollments in higher education, the quality of college education and the learning gains of students have attracted much attention. It is important to study the influencing factors and mechanisms of individual students’ acquisition of learning gains to improve the quality of talent cultivation in colleges. However, in the context of information security, the original data of learning situation surveys in various universities involve the security of educational evaluation data and daily privacy of teachers and students. To protect the original data, data feature mining and correlation analyses were performed at the model level. This study selected 12,181… More >

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Abdul Quawi^1,*, Y. Mohamed Shuaib¹, M. Manikandan²
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3469-3486, 2023, DOI:10.32604/iasc.2023.035001
Abstract In this work, an Artificial Neural Network (ANN) based technique is suggested for classifying the faults which occur in hybrid power distribution systems. Power, which is generated by the solar and wind energy-based hybrid system, is given to the grid at the Point of Common Coupling (PCC). A boost converter along with perturb and observe (P&O) algorithm is utilized in this system to obtain a constant link voltage. In contrast, the link voltage of the wind energy conversion system (WECS) is retained with the assistance of a Proportional Integral (PI) controller. The grid synchronization is tainted with the assistance of… More >

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438

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Samah Alhazmi^1,*, Shahnawaz Khan², Mohammad Haider Syed¹
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3487-3499, 2023, DOI:10.32604/iasc.2023.036297
Abstract Quality education is one of the primary objectives of any nation-building strategy and is one of the seventeen Sustainable Development Goals (SDGs) by the United Nations. To provide quality education, delivering top-quality content is not enough. However, understanding the learners’ emotions during the learning process is equally important. However, most of this research work uses general data accessed from Twitter or other publicly available databases. These databases are generally not an ideal representation of the actual learning process and the learners’ sentiments about the learning process. This research has collected real data from the learners, mainly undergraduate university students of… More >

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399

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ARTICLE

Yeferson Torres-Berru^1,2,*, Vivian F. Lopez-Batista¹, Lorena Conde Zhingre³
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3501-3516, 2023, DOI:10.32604/iasc.2023.035367
Abstract In a public procurement process, corruption can occur at each stage, favoring a participant with a previous agreement, which can result in over-pricing and purchases of substandard products, as well as gender discrimination. This paper’s aim is to detect biased purchases using a Spanish Language corpus, analyzing text from the questions and answers registry platform by applicants in a public procurement process in Ecuador. Additionally, gender bias is detected, promoting both men and women to participate under the same conditions. In order to detect gender bias and favoritism towards certain providers by contracting entities, the study proposes a unique hybrid… More >

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Kashif Saleem¹, Rashid Naseem¹, Khalil Khan^1,2, Siraj Muhammad³, Ikram Syed^4,*, Jaehyuk Choi^4,*
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3517-3533, 2023, DOI:10.32604/iasc.2023.036356
Abstract Automation software need to be continuously updated by addressing software bugs contained in their repositories. However, bugs have different levels of importance; hence, it is essential to prioritize bug reports based on their severity and importance. Manually managing the deluge of incoming bug reports faces time and resource constraints from the development team and delays the resolution of critical bugs. Therefore, bug report prioritization is vital. This study proposes a new model for bug prioritization based on average one dependence estimator; it prioritizes bug reports based on severity, which is determined by the number of attributes. The more the number… More >

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290

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Hongwei Guan¹, Lingjian Ye^2,3,*, Yurun Wang², Feifan Shen⁴, Yuchen He³
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3535-3550, 2023, DOI:10.32604/iasc.2023.036399
Abstract The absorber is the key unit in the post-combustion monoethanolamine (MEA)-based carbon dioxide (CO₂) capture process. A rate-based dynamic model for the absorber is developed and validated using steady-state experimental data reported in open literature. Sensitivity analysis is performed with respect to important model parameters associated with the reaction, mass transport and physical property relationships. Then, a singular value decomposition (SVD)-based subspace parameter estimation method is proposed to improve the model accuracy. Finally, dynamic simulations are carried out to investigate the effects of the feed rate of lean MEA solution and the flue inlet conditions. Simulation results indicate that the… More >

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361

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V. Jayanthi^*, S. Thenmalar
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3551-3564, 2023, DOI:10.32604/iasc.2023.036599
Abstract In this paper, Modified Multi-scale Segmentation Network (MMU-SNet) method is proposed for Tamil text recognition. Handwritten texts from digital writing pad notes are used for text recognition. Handwritten words recognition for texts written from digital writing pad through text file conversion are challenging due to stylus pressure, writing on glass frictionless surfaces, and being less skilled in short writing, alphabet size, style, carved symbols, and orientation angle variations. Stylus pressure on the pad changes the words in the Tamil language alphabet because the Tamil alphabets have a smaller number of lines, angles, curves, and bends. The small change in dots,… More >

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288

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Zhenshuai Wan^*, Yu Fu, Chong Liu, Longwang Yue
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3565-3581, 2023, DOI:10.32604/iasc.2023.036601
Abstract Hydraulic servo system plays an important role in industrial fields due to the advantages of high response, small size-to-power ratio and large driving force. However, inherent nonlinear behaviors and modeling uncertainties are the main obstacles for hydraulic servo system to achieve high tracking performance. To deal with these difficulties, this paper presents a backstepping sliding mode controller to improve the dynamic tracking performance and anti-interference ability. For this purpose, the nonlinear dynamic model is firstly established, where the nonlinear behaviors and modeling uncertainties are lumped as one term. Then, the extended state observer is introduced to estimate the lumped disturbance.… More >

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465

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Hongxin Luo, Xiaobing Zhou^*, Haiyan Ding, Liqing Wang
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3583-3593, 2023, DOI:10.32604/iasc.2023.036622
Abstract Online advertising has gained much attention on various platforms as a hugely lucrative market. In promoting content and advertisements in real life, the acquisition of user target actions is usually a multi-step process, such as impression→click→conversion, which means the process from the delivery of the recommended item to the user’s click to the final conversion. Due to data sparsity or sample selection bias, it is difficult for the trained model to achieve the business goal of the target campaign. Multi-task learning, a classical solution to this problem, aims to generalize better on the original task given several related tasks by… More >

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430

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Haijian Shao¹, Edwin Ma², Ming Zhu¹, Xing Deng³, Shengjie Zhai^1,*
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3595-3606, 2023, DOI:10.32604/iasc.2023.036323
Abstract Accurate handwriting recognition has been a challenging computer vision problem, because static feature analysis of the text pictures is often inadequate to account for high variance in handwriting styles across people and poor image quality of the handwritten text. Recently, by introducing machine learning, especially convolutional neural networks (CNNs), the recognition accuracy of various handwriting patterns is steadily improved. In this paper, a deep CNN model is developed to further improve the recognition rate of the MNIST handwritten digit dataset with a fast-converging rate in training. The proposed model comes with a multi-layer deep arrange structure, including 3 convolution and… More >

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432

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Adepu Shravan Kumar, S. Srinivasan^*
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3607-3620, 2023, DOI:10.32604/iasc.2023.036647
Abstract At the present time, the Industrial Internet of Things (IIoT) has swiftly evolved and emerged, and picture data that is collected by terminal devices or IoT nodes are tied to the user's private data. The use of image sensors as an automation tool for the IIoT is increasingly becoming more common. Due to the fact that this organisation transfers an enormous number of photographs at any one time, one of the most significant issues that it has is reducing the total quantity of data that is sent and, as a result, the available bandwidth, without compromising the image quality. Image… More >

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Ierin Babu^1,*, R. MathuSoothana², S. Kumar²
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3621-3636, 2023, DOI:10.32604/iasc.2023.033791
Abstract Educational Data Mining (EDM) is an emergent discipline that concentrates on the design of self-learning and adaptive approaches. Higher education institutions have started to utilize analytical tools to improve students’ grades and retention. Prediction of students’ performance is a difficult process owing to the massive quantity of educational data. Therefore, Artificial Intelligence (AI) techniques can be used for educational data mining in a big data environment. At the same time, in EDM, the feature selection process becomes necessary in creation of feature subsets. Since the feature selection performance affects the predictive performance of any model, it is important to elaborately… More >

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409

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Walaa H. Elashmawi^1,3, Ahmad Akram², Mohammed Yasser², Menna Hisham², Manar Mohammed², Noha Ihab², Ahmed Ali^4,5,*
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3637-3656, 2023, DOI:10.32604/iasc.2023.035605
Abstract Parking space is usually very limited in major cities, especially Cairo, leading to traffic congestion, air pollution, and driver frustration. Existing car parking systems tend to tackle parking issues in a non-digitized manner. These systems require the drivers to search for an empty parking space with no guarantee of finding any wasting time, resources, and causing unnecessary congestion. To address these issues, this paper proposes a digitized parking system with a proof-of-concept implementation that combines multiple technological concepts into one solution with the advantages of using IoT for real-time tracking of parking availability. User authentication and automated payments are handled… More >

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Tianyu He¹, Xiaolong Xu^1,*, Zhichen Hu¹, Qingzhan Zhao², Jianguo Dai², Fei Dai³
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3657-3673, 2023, DOI:10.32604/iasc.2023.036831
Abstract With the rapid development of information technology, the electronification of medical records has gradually become a trend. In China, the population base is huge and the supporting medical institutions are numerous, so this reality drives the conversion of paper medical records to electronic medical records. Electronic medical records are the basis for establishing a smart hospital and an important guarantee for achieving medical intelligence, and the massive amount of electronic medical record data is also an important data set for conducting research in the medical field. However, electronic medical records contain a large amount of private patient information, which must… More >

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412

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V. Gowri^1,*, V. Vijaya Chamundeeswari²
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3675-3690, 2023, DOI:10.32604/iasc.2023.035817
Abstract Decision forest is a well-renowned machine learning technique to address the detection and prediction problems related to clinical data. But, the traditional decision forest (DF) algorithms have lower classification accuracy and cannot handle high-dimensional feature space effectively. In this work, we propose a bootstrap decision forest using penalizing attributes (BFPA) algorithm to predict heart disease with higher accuracy. This work integrates a significance-based attribute selection (SAS) algorithm with the BFPA classifier to improve the performance of the diagnostic system in identifying cardiac illness. The proposed SAS algorithm is used to determine the correlation among attributes and to select the optimum… More >

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312

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U. Sakthivelu, C. N. S. Vinoth Kumar^*
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3691-3707, 2023, DOI:10.32604/iasc.2023.036946
Abstract The detection of cyber threats has recently been a crucial research domain as the internet and data drive people’s livelihood. Several cyber-attacks lead to the compromise of data security. The proposed system offers complete data protection from Advanced Persistent Threat (APT) attacks with attack detection and defence mechanisms. The modified lateral movement detection algorithm detects the APT attacks, while the defence is achieved by the Dynamic Deception system that makes use of the belief update algorithm. Before termination, every cyber-attack undergoes multiple stages, with the most prominent stage being Lateral Movement (LM). The LM uses a Remote Desktop protocol (RDP)… More >

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583

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Moody Alhanaya, Khalil Hamdi Ateyeh Al-Shqeerat^*
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3709-3724, 2023, DOI:10.32604/iasc.2023.036856
Abstract The increasing number of security holes in the Internet of Things (IoT) networks creates a question about the reliability of existing network intrusion detection systems. This problem has led to the developing of a research area focused on improving network-based intrusion detection system (NIDS) technologies. According to the analysis of different businesses, most researchers focus on improving the classification results of NIDS datasets by combining machine learning and feature reduction techniques. However, these techniques are not suitable for every type of network. In light of this, whether the optimal algorithm and feature reduction techniques can be generalized across various datasets… More >

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398

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Byeori Kim¹, Minseong Choi¹, Taek-Young Youn², Jeong Hyun Yi¹, Haehyun Cho^1,*
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3725-3742, 2023, DOI:10.32604/iasc.2023.036574
Abstract Recently, there has been a sudden shift from using traditional office applications to the collaborative cloud-based office suite such as Microsoft Office 365. Such cloud-based systems allow users to work together on the same document stored in a cloud server at once, by which users can effectively collaborate with each other. However, there are security concerns unsolved in using cloud collaboration. One of the major concerns is the security of data stored in cloud servers, which comes from the fact that data that multiple users are working together cannot be stored in encrypted form because of the dynamic characteristic of… More >

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277

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Waleed Akbar, Afaq Muhammad, Wang-Cheol Song^*
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3743-3759, 2023, DOI:10.32604/iasc.2023.036440
Abstract Data is growing quickly due to a significant increase in social media applications. Today, billions of people use an enormous amount of data to access the Internet. The backbone network experiences a substantial load as a result of an increase in users. Users in the same region or company frequently ask for similar material, especially on social media platforms. The subsequent request for the same content can be satisfied from the edge if stored in proximity to the user. Applications that require relatively low latency can use Content Delivery Network (CDN) technology to meet their requirements. An edge and the… More >

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