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Jixun Gao^1,2, Bingyi Hu², Jialei Liu^3,4,*, Huaichen Wang⁵, Quanzhen Huang¹, Yuanyuan Zhao⁶
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1-16, 2023, DOI:10.32604/iasc.2023.036890
Abstract Mobile Edge Computing (MEC) is proposed to solve the needs of Internet of Things (IoT) users for high resource utilization, high reliability and low latency of service requests. However, the backup virtual machine is idle when its primary virtual machine is running normally, which will waste resources. Overbooking the backup virtual machine under the above circumstances can effectively improve resource utilization. First, these virtual machines are deployed into slots randomly, and then some tasks with cooperative relationship are offloaded to virtual machines for processing. Different deployment locations have different resource utilization and average service response time. We want to find… More >

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Aoqi Xu¹, Khalid A. Alattas², Nasreen Kausar³, Ardashir Mohammadzadeh⁴, Ebru Ozbilge^5,*, Tonguc Cagin⁵
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 17-32, 2023, DOI:10.32604/iasc.2023.036623
（This article belongs to this Special Issue: Neutrosophic Theories in Intelligent Decision Making, Management and Engineering)
Abstract In many problems, to analyze the process/metabolism behavior, a model of the system is identified. The main gap is the weakness of current methods vs. noisy environments. The primary objective of this study is to present a more robust method against uncertainties. This paper proposes a new deep learning scheme for modeling and identification applications. The suggested approach is based on non-singleton type-3 fuzzy logic systems (NT3-FLSs) that can support measurement errors and high-level uncertainties. Besides the rule optimization, the antecedent parameters and the level of secondary memberships are also adjusted by the suggested square root cubature Kalman filter (SCKF).… More >

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Bo Li¹, Zhaolong Ling¹, Yiwen Zhang^1,*, Yong Zhou¹, Yimin Hu², Haifeng Ling³
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 33-48, 2023, DOI:10.32604/iasc.2023.037267
Abstract Learning the Markov blanket (MB) of a given variable has received increasing attention in recent years because the MB of a variable predicts its local causal relationship with other variables. Online MB Learning can learn MB for a given variable on the fly. However, in some application scenarios, such as image analysis and spam filtering, features may arrive by groups. Existing online MB learning algorithms evaluate features individually, ignoring group structure. Motivated by this, we formulate the group MB learning with streaming features problem, and propose an Online MB learning with Group Structure algorithm, OMBGS, to identify the MB of… More >

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Sait Can Yucebas^*
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 49-71, 2023, DOI:10.32604/iasc.2023.036871
Abstract The number of studies in the literature that diagnose cancer with machine learning using genome data is quite limited. These studies focus on the prediction performance, and the extraction of genomic factors that cause disease is often overlooked. However, finding underlying genetic causes is very important in terms of early diagnosis, development of diagnostic kits, preventive medicine, etc. The motivation of our study was to diagnose bladder cancer (BCa) based on genetic data and to reveal underlying genetic factors by using machine-learning models. In addition, conducting hyper-parameter optimization to get the best performance from different models, which is overlooked in… More >

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488

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Asma Qaiser¹, Saman Hina¹, Abdul Karim Kazi^1,*, Saad Ahmed², Raheela Asif³
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 73-90, 2023, DOI:10.32604/iasc.2023.036784
Abstract In the past few years, social media and online news platforms have played an essential role in distributing news content rapidly. Consequently. verification of the authenticity of news has become a major challenge. During the COVID-19 outbreak, misinformation and fake news were major sources of confusion and insecurity among the general public. In the first quarter of the year 2020, around 800 people died due to fake news relevant to COVID-19. The major goal of this research was to discover the best learning model for achieving high accuracy and performance. A novel case study of the Fake News Classification using… More >

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Linxuan Wang^1,*, Xiangwei Kong¹, Hongzhe Xu², Hong Li¹
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 91-110, 2023, DOI:10.32604/iasc.2023.035876
Abstract With the rapid development of autopilot technology, a variety of engineering applications require higher and higher requirements for navigation and positioning accuracy, as well as the error range should reach centimeter level. Single navigation systems such as the inertial navigation system (INS) and the global navigation satellite system (GNSS) cannot meet the navigation requirements in many cases of high mobility and complex environments. For the purpose of improving the accuracy of INS-GNSS integrated navigation system, an INSGNSS integrated navigation algorithm based on TransGAN is proposed. First of all, the GNSS data in the actual test process is applied to establish… More >

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S. Sivasubramaniam^*, S. P. Balamurugan
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 111-126, 2023, DOI:10.32604/iasc.2023.035199
Abstract Tongue image analysis is an efficient and non-invasive technique to determine the internal organ condition of a patient in oriental medicine, for example, traditional Chinese medicine (TCM), Japanese traditional herbal medicine, and traditional Korean medicine (TKM). The diagnosis procedure is mainly based on the expert's knowledge depending upon the visual inspection comprising color, substance, coating, form, and motion of the tongue. But conventional tongue diagnosis has limitations since the procedure is inconsistent and subjective. Therefore, computer-aided tongue analyses have a greater potential to present objective and more consistent health assessments. This manuscript introduces a novel Simulated Annealing with Transfer Learning… More >

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Ashvini Kulkarni^1,2, P. Augusta Sophy Beulet^1,*
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 127-146, 2023, DOI:10.32604/iasc.2023.035144
Abstract It is widely acknowledged that navigation is a significant source of between sites. The Global Positioning System (GPS) has numerous navigational advancements, and hence it is used widely. GPS navigation can be compromised at any level between position, location, and estimation, to the detriment of the user. Consequently, a navigation system requires the precise location and underpinning tracking of an object without signal loss. The objective of a hybrid environment prediction system is to foresee the location of the user and their territory by employing a variety of sensors for position estimation and monitoring navigation. This article presents a state… More >

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381

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Tohid Adibi¹, Shams Forruque Ahmed^2,*, Omid Adibi³, Hassan Athari⁴, Irfan Anjum Badruddin⁵, Syed Javed⁵
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 147-163, 2023, DOI:10.32604/iasc.2023.035392
（This article belongs to this Special Issue: Optimization Algorithm for Intelligent Computing Application)
Abstract Heat exchangers are utilized extensively in different industries and technologies. Consequently, optimizing heat exchangers has been a major concern among researchers. Although various studies have been conducted to improve the heat transfer rate, the use of a wavy wall in the presence of different types of heat transfer mechanisms has not been investigated. This study thus investigates the mixed heat transmission behavior of fluid in a horizontal channel with a cavity and a hot, wavy wall. The fluid flow in the channel is considered laminar, and the governing equations including continuity, momentum, and energy are all solved numerically. The numerical… More >

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Amer Alsaraira^1,*, Samer Alabed¹, Eyad Hamad¹, Omar Saraereh²
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 165-177, 2023, DOI:10.32604/iasc.2023.036950
Abstract Alzheimer’s disease (AD) is a kind of progressive dementia that is frequently accompanied by brain shrinkage. With the use of the morphological characteristics of MRI brain scans, this paper proposed a method for diagnosing moderate cognitive impairment (MCI) and AD. The anatomical features of 818 subjects were calculated using the FreeSurfer software, and the data were taken from the ADNI dataset. These features were first removed from the dataset after being preprocessed with an age correction algorithm using linear regression to estimate the effects of normal aging. With these preprocessed characteristics, the extreme learning machine served as a classifier for… More >

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Meilin Wu^1,2, Lianggui Tang^1,2,*, Qingda Zhang^1,2, Ke Yan^1,2
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 179-198, 2023, DOI:10.32604/iasc.2023.036684
Abstract As COVID-19 poses a major threat to people’s health and economy, there is an urgent need for forecasting methodologies that can anticipate its trajectory efficiently. In non-stationary time series forecasting jobs, there is frequently a hysteresis in the anticipated values relative to the real values. The multilayer deep-time convolutional network and a feature fusion network are combined in this paper’s proposal of an enhanced Multilayer Deep Time Convolutional Neural Network (MDTCNet) for COVID-19 prediction to address this problem. In particular, it is possible to record the deep features and temporal dependencies in uncertain time series, and the features may then… More >

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Anu^*, Anita Singhrova
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 199-219, 2023, DOI:10.32604/iasc.2023.035389
Abstract Fog computing is an emergent and powerful computing paradigm to serve latency-sensitive applications by executing internet of things (IoT) applications in the proximity of the network. Fog computing offers computational and storage services between cloud and terminal devices. However, an efficient resource allocation to execute the IoT applications in a fog environment is still challenging due to limited resource availability and low delay requirement of services. A large number of heterogeneous shareable resources makes fog computing a complex environment. In the sight of these issues, this paper has proposed an efficient levy flight firefly-based resource allocation technique. The levy flight… More >

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393

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Xin Wang^*, Shuang Liu, Chang-Cai Zhou
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 221-234, 2023, DOI:10.32604/iasc.2023.036766
Abstract Knee osteoarthritis (OA) is a common disease that impairs knee function and causes pain. Currently, studies on the detection of knee OA mainly focus on X-ray images, but X-ray images are insensitive to the changes in knee OA in the early stage. Since magnetic resonance (MR) imaging can observe the early features of knee OA, the knee OA detection algorithm based on MR image is innovatively proposed to judge whether knee OA is suffered. Firstly, the knee MR images are preprocessed before training, including a region of interest clipping, slice selection, and data augmentation. Then the data set was divided… More >

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Abdullah Mujawib Alashjaee^*
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 235-254, 2023, DOI:10.32604/iasc.2023.036247
Abstract With the recent increase in network attacks by threats, malware, and other sources, machine learning techniques have gained special attention for intrusion detection due to their ability to classify hundreds of features into normal system behavior or an attack attempt. However, feature selection is a vital preprocessing stage in machine learning approaches. This paper presents a novel feature selection-based approach, Remora Optimization Algorithm-Levy Flight (ROA-LF), to improve intrusion detection by boosting the ROA performance with LF. The developed ROA-LF is assessed using several evaluation measures on five publicly available datasets for intrusion detection: Knowledge discovery and data mining tools competition,… More >

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Baoling Qin^*
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 255-267, 2023, DOI:10.32604/iasc.2023.037556
Abstract Although the Internet of Things has been widely applied, the problems of cloud computing in the application of digital smart medical Big Data collection, processing, analysis, and storage remain, especially the low efficiency of medical diagnosis. And with the wide application of the Internet of Things and Big Data in the medical field, medical Big Data is increasing in geometric magnitude resulting in cloud service overload, insufficient storage, communication delay, and network congestion. In order to solve these medical and network problems, a medical big-data-oriented fog computing architecture and BP algorithm application are proposed, and its structural advantages and characteristics… More >

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416

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Zainab Ali Abbood^1,2,*, Doğu Çağdaş Atilla^3,4, Çağatay Aydin⁵
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 269-281, 2023, DOI:10.32604/iasc.2023.035276
Abstract Deep learning (DL) is a subdivision of machine learning (ML) that employs numerous algorithms, each of which provides various explanations of the data it consumes; mobile ad-hoc networks (MANET) are growing in prominence. For reasons including node mobility, due to MANET’s potential to provide small-cost solutions for real-world contact challenges, decentralized management, and restricted bandwidth, MANETs are more vulnerable to security threats. When protecting MANETs from attack, encryption and authentication schemes have their limits. However, deep learning (DL) approaches in intrusion detection systems (IDS) can adapt to the changing environment of MANETs and allow a system to make intrusion decisions… More >

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32

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Hala J. Alshahrani¹, Abdulbaset Gaddah², Ehab S. Alnuzaili³, Mesfer Al Duhayyim^4,*, Heba Mohsen⁵, Ishfaq Yaseen⁶, Amgad Atta Abdelmageed⁶, Gouse Pasha Mohammed⁶
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 283-300, 2023, DOI:10.32604/iasc.2023.035334
Abstract Sentiment analysis (SA) is a growing field at the intersection of computer science and computational linguistics that endeavors to automatically identify the sentiment presented in text. Computational linguistics aims to describe the fundamental methods utilized in the formation of computer methods for understanding natural language. Sentiment is classified as a negative or positive assessment articulated through language. SA can be commonly used for the movie review classification that involves the automatic determination that a review posted online (of a movie) can be negative or positive toward the thing that has been reviewed. Deep learning (DL) is becoming a powerful machine… More >

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521

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Vaishnavi Jayaraman¹, Arun Raj Lakshminarayanan^1,*, Saravanan Parthasarathy¹, A. Suganthy²
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 301-320, 2023, DOI:10.32604/iasc.2023.037823
Abstract Waste production rises in tandem with population growth and increased utilization. The indecorous disposal of waste paves the way for huge disaster named as climate change. The National Environment Agency (NEA) of Singapore oversees the sustainable management of waste across the country. The three main contributors to the solid waste of Singapore are paper and cardboard (P&C), plastic, and food scraps. Besides, they have a negligible rate of recycling. In this study, Machine Learning techniques were utilized to forecast the amount of garbage also known as waste audits. The waste audit would aid the authorities to plan their waste infrastructure.… More >

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502

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Hamida Litimein¹, Zhen-You Huang¹, Muhammad Shamrooz Aslam^2,*
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 321-341, 2023, DOI:10.32604/iasc.2023.036786
Abstract In this paper, we study the circular formation problem for the second-order multi-agent systems in a plane, in which the agents maintain a circular formation based on a probabilistic position. A distributed hybrid control protocol based on a probabilistic position is designed to achieve circular formation stabilization and consensus. In the current framework, the mobile agents follow the following rules: 1) the agent must follow a circular trajectory; 2) all the agents in the same circular trajectory must have the same direction. The formation control objective includes two parts: 1) drive all the agents to the circular formation; 2) avoid… More >

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370

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E. Jenifer Sweetlin^*, S. Saudia
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 343-367, 2023, DOI:10.32604/iasc.2023.036742
Abstract This paper proposes a hybrid feature selection sequence complemented with filter and wrapper concepts to improve the accuracy of Machine Learning (ML) based supervised classifiers for classifying the survivability of breast cancer patients into classes, living and deceased using METABRIC and Surveillance, Epidemiology and End Results (SEER) datasets. The ML-based classifiers used in the analysis are: Multiple Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine and Multilayer Perceptron. The workflow of the proposed ML algorithm sequence comprises the following stages: data cleaning, data balancing, feature selection via a filter and wrapper sequence, cross validation-based training, testing and… More >

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Jun Lee¹, Hyun Kwon^2,*
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 369-384, 2023, DOI:10.32604/iasc.2023.036052
Abstract Enhancing the sense of presence of participants is an important issue in terms of security awareness for remote collaboration in extended reality. However, conventional methods are insufficient to be aware of remote situations and to search for and control remote workspaces. This study proposes a spatial multi-presence system that simultaneously provides multiple spaces while rapidly exploring these spaces as users perform collaborative work in an extended reality environment. The proposed system provides methods for arranging and manipulating remote and personal spaces by creating an annular screen that is invisible to the user. The user can freely arrange remote participants and… More >

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Xudong Wang^1,2, Xueshuai Qin¹, Huiyan Zhang^2,*, Luis Ismael Minchala³
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 385-400, 2023, DOI:10.32604/iasc.2023.036633
Abstract Unmanned vehicles are currently facing many difficulties and challenges in improving safety performance when running in complex urban road traffic environments, such as low intelligence and poor comfort performance in the driving process. The real-time performance of vehicles and the comfort requirements of passengers in path planning and tracking control of unmanned vehicles have attracted more and more attentions. In this paper, in order to improve the real-time performance of the autonomous vehicle planning module and the comfort requirements of passengers that a local granular-based path planning method and tracking control based on multi-segment Bezier curve splicing and model predictive… More >

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Kha Tu Huynh¹, Thi Phuong Linh Le¹, Muhammad Arif², Thien Khai Tran^3,*
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 401-418, 2023, DOI:10.32604/iasc.2023.036981
Abstract To pursue the ideal of a safe high-tech society in a time when traffic accidents are frequent, the traffic signs detection system has become one of the necessary topics in recent years and in the future. The ultimate goal of this research is to identify and classify the types of traffic signs in a panoramic image. To accomplish this goal, the paper proposes a new model for traffic sign detection based on the Convolutional Neural Network for comprehensive traffic sign classification and Mask Region-based Convolutional Neural Networks (R-CNN) implementation for identifying and extracting signs in panoramic images. Data augmentation and… More >

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Ebu Yusuf GÜVEN, Zeynep GÜRKAŞ-AYDIN^*
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 419-437, 2023, DOI:10.32604/iasc.2023.038043
Abstract The Internet of Things (IoT) has aided in the development of new products and services. Due to the heterogeneity of IoT items and networks, traditional techniques cannot identify network risks. Rule-based solutions make it challenging to secure and manage IoT devices and services due to their diversity. While the use of artificial intelligence eliminates the need to define rules, the training and retraining processes require additional processing power. This study proposes a methodology for analyzing constrained devices in IoT environments. We examined the relationship between different sized samples from the Kitsune dataset to simulate the Mirai attack on IoT devices.… More >

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Muhammad Aasim Qureshi^1,*, Muhammad Asif², Saira Anwar³
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 439-453, 2023, DOI:10.32604/iasc.2023.036102
Abstract Learning programming and using programming languages are the essential aspects of computer science education. Students use programming languages to write their programs. These computer programs (students or practitioners written) make computers artificially intelligent and perform the tasks needed by the users. Without these programs, the computer may be visioned as a pointless machine. As the premise of writing programs is situated with specific programming languages, enormous efforts have been made to develop and create programming languages. However, each programming language is domain-specific and has its nuances, syntax and semantics, with specific pros and cons. These language-specific details, including syntax and… More >

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Salah Alzghoul^1,*, Othman Smadi¹, Ali Al Bataineh², Mamon Hatmal³, Ahmad Alamm⁴
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 455-470, 2023, DOI:10.32604/iasc.2023.038338
Abstract Currently, the decision of aortic valve replacement surgery time for asymptomatic patients with moderate-to-severe aortic stenosis (AS) is made by healthcare professionals based on the patient’s clinical biometric records. A delay in surgical aortic valve replacement (SAVR) can potentially affect patients’ quality of life. By using ML algorithms, this study aims to predict the optimal SAVR timing and determine the enhancement in moderate-to-severe AS patient survival following surgery. This study represents a novel approach that has the potential to improve decision-making and, ultimately, improve patient outcomes. We analyze data from 176 patients with moderate-to-severe aortic stenosis who had undergone or… More >

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K. Chanthirasekaran, Raghu Gundaala^*
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 471-489, 2023, DOI:10.32604/iasc.2023.036130
Abstract When it comes to decreasing margins and increasing energy efficiency in near-threshold and sub-threshold processors, timing error resilience may be viewed as a potentially lucrative alternative to examine. On the other hand, the currently employed approaches have certain restrictions, including high levels of design complexity, severe time constraints on error consolidation and propagation, and uncontaminated architectural registers (ARs). The design of near-threshold circuits, often known as NT circuits, is becoming the approach of choice for the construction of energy-efficient digital circuits. As a result of the exponentially decreased driving current, there was a reduction in performance, which was one of… More >

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Zimeng Yang, Qiulan Wu, Feng Zhang^*, Xuefei Chen, Weiqiang Wang, Xueshen Zhang
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 491-506, 2023, DOI:10.32604/iasc.2023.037558
Abstract Semantic segmentation of remote sensing images is one of the core tasks of remote sensing image interpretation. With the continuous development of artificial intelligence technology, the use of deep learning methods for interpreting remote-sensing images has matured. Existing neural networks disregard the spatial relationship between two targets in remote sensing images. Semantic segmentation models that combine convolutional neural networks (CNNs) and graph convolutional neural networks (GCNs) cause a lack of feature boundaries, which leads to the unsatisfactory segmentation of various target feature boundaries. In this paper, we propose a new semantic segmentation model for remote sensing images (called DGCN hereinafter),… More >

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Lei Shen^*, Ting Huang, Nishui Cai, Hao Wu
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 507-524, 2023, DOI:10.32604/iasc.2023.037640
Abstract Through the use of the internet and cloud computing, users may access their data as well as the programmes they have installed. It is now more challenging than ever before to choose which cloud service providers to take advantage of. When it comes to the dependability of the cloud infrastructure service, those who supply cloud services, as well as those who seek cloud services, have an equal responsibility to exercise utmost care. Because of this, further caution is required to ensure that the appropriate values are reached in light of the ever-increasing need for correct decision-making. The purpose of this… More >

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Jianbo Lu^1,2,*, Ruxin Shi², Jin Tong³, Wenqi Cheng⁴, Xiaoya Ma^1,3, Xiaobin Liu²
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 525-544, 2023, DOI:10.32604/iasc.2023.038287
Abstract In the deep learning approach for identifying plant diseases, the high complexity of the network model, the large number of parameters, and great computational effort make it challenging to deploy the model on terminal devices with limited computational resources. In this study, a lightweight method for plant diseases identification that is an improved version of the ShuffleNetV2 model is proposed. In the proposed model, the depthwise convolution in the basic module of ShuffleNetV2 is replaced with mixed depthwise convolution to capture crop pest images with different resolutions; the efficient channel attention module is added into the ShuffleNetV2 model network structure… More >

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Cherifa Nakkach^*, Amira Zrelli, Tahar Ezzedine
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 545-560, 2023, DOI:10.32604/iasc.2023.036385
Abstract Due to the development of diversified and flexible building energy resources, the balancing energy supply and demand especially in smart buildings caused an increasing problem. Energy forecasting is necessary to address building energy issues and comfort challenges that drive urbanization and consequent increases in energy consumption. Recently, their management has a great significance as resources become scarcer and their emissions increase. In this article, we propose an intelligent energy forecasting method based on hybrid deep learning, in which the data collected by the smart home through meters is put into the pre-evaluation step. Next, the refined data is the input… More >

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Tianshun Xing, Jianjun Chen^*, Taihua Xu, Yan Fan
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 561-581, 2023, DOI:10.32604/iasc.2023.037874
Abstract It is well-known that attribute reduction is a crucial action of rough set. The significant characteristic of attribute reduction is that it can reduce the dimensions of data with clear semantic explanations. Normally, the learning performance of attributes in derived reduct is much more crucial. Since related measures of rough set dominate the whole process of identifying qualified attributes and deriving reduct, those measures may have a direct impact on the performance of selected attributes in reduct. However, most previous researches about attribute reduction take measures related to either supervised perspective or unsupervised perspective, which are insufficient to identify attributes… More >

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Yue Yu¹, Junhua Wu^1,*, Guangshun Li¹, Wangang Wang²
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 583-598, 2023, DOI:10.32604/iasc.2023.037875
Abstract As an emerging hot technology, smart grids (SGs) are being employed in many fields, such as smart homes and smart cities. Moreover, the application of artificial intelligence (AI) in SGs has promoted the development of the power industry. However, as users’ demands for electricity increase, traditional centralized power trading is unable to well meet the user demands and an increasing number of small distributed generators are being employed in trading activities. This not only leads to numerous security risks for the trading data but also has a negative impact on the cost of power generation, electrical security, and other aspects.… More >

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Zhifang Liao¹, Hao Gu¹, Shichao Zhang¹, Ronghui Mo¹, Yan Zhang^2,*
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 599-618, 2023, DOI:10.32604/iasc.2023.036794
Abstract Context-Sensitive Task (CST) is a complex task type in crowdsourcing, such as handwriting recognition, route plan, and audio transcription. The current result inference algorithms can perform well in simple crowdsourcing tasks, but cannot obtain high-quality inference results for CSTs. The conventional method to solve CSTs is to divide a CST into multiple independent simple subtasks for crowdsourcing, but this method ignores the context correlation among subtasks and reduces the quality of result inference. To solve this problem, we propose a result inference algorithm based on the Partially ordered set and Tree augmented naive Bayes Infer (P&T-Inf) for CSTs. Firstly, we… More >

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Kaiyue Wang¹, Jian Gao^1,2,*, Xinyan Lei¹
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 619-638, 2023, DOI:10.32604/iasc.2023.036701
Abstract Traffic characterization (e.g., chat, video) and application identification (e.g., FTP, Facebook) are two of the more crucial jobs in encrypted network traffic classification. These two activities are typically carried out separately by existing systems using separate models, significantly adding to the difficulty of network administration. Convolutional Neural Network (CNN) and Transformer are deep learning-based approaches for network traffic classification. CNN is good at extracting local features while ignoring long-distance information from the network traffic sequence, and Transformer can capture long-distance feature dependencies while ignoring local details. Based on these characteristics, a multi-task learning model that combines Transformer and 1D-CNN for… More >

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Zhengfang He^1,2,*, Cristina E. Dumdumaya², Ivy Kim D. Machica²
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 639-654, 2023, DOI:10.32604/iasc.2023.035352
Abstract Sentiment analysis, commonly called opinion mining or emotion artificial intelligence (AI), employs biometrics, computational linguistics, natural language processing, and text analysis to systematically identify, extract, measure, and investigate affective states and subjective data. Sentiment analysis algorithms include emotion lexicon, traditional machine learning, and deep learning. In the text sentiment analysis algorithm based on a neural network, multi-layer Bi-directional long short-term memory (LSTM) is widely used, but the parameter amount of this model is too huge. Hence, this paper proposes a Bi-directional LSTM with a trapezoidal structure model. The design of the trapezoidal structure is derived from classic neural networks, such… More >

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Jinping Sun^*, Dan Li, Honglin Cheng
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 655-675, 2023, DOI:10.32604/iasc.2023.038016
Abstract Aiming at the problem that a single correlation filter model is sensitive to complex scenes such as background interference and occlusion, a tracking algorithm based on multi-time-space perception and instancespecific proposals is proposed to optimize the mathematical model of the correlation filter (CF). Firstly, according to the consistency of the changes between the object frames and the filter frames, the mask matrix is introduced into the objective function of the filter, so as to extract the spatio-temporal information of the object with background awareness. Secondly, the object function of multi-feature fusion is constructed for the object location, which is optimized… More >

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373

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Xueqing Liao^*
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 677-690, 2023, DOI:10.32604/iasc.2023.037219
Abstract In order to improve the consistency between the recommended retrieval results and user needs, improve the recommendation efficiency, and reduce the average absolute deviation of resource retrieval, a design method of intelligent recommendation retrieval model for Fujian intangible cultural heritage digital archive resources based on knowledge atlas is proposed. The TG-LDA (Tag-granularity LDA) model is proposed on the basis of the standard LDA (Linear Discriminant Analysis) model. The model is used to mine archive resource topics. The Pearson correlation coefficient is used to measure the relevance between topics. Based on the measurement results, the FastText deep learning model is used… More >

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873

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549

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Tusongjiang Kari¹, Zhiyang He¹, Aisikaer Rouzi², Ziwei Zhang³, Xiaojing Ma^1,*, Lin Du¹
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 691-705, 2023, DOI:10.32604/iasc.2023.037617
Abstract Power transformer is one of the most crucial devices in power grid. It is significant to determine incipient faults of power transformers fast and accurately. Input features play critical roles in fault diagnosis accuracy. In order to further improve the fault diagnosis performance of power transformers, a random forest feature selection method coupled with optimized kernel extreme learning machine is presented in this study. Firstly, the random forest feature selection approach is adopted to rank 42 related input features derived from gas concentration, gas ratio and energy-weighted dissolved gas analysis. Afterwards, a kernel extreme learning machine tuned by the Aquila… More >

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714

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554

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V. Swetha^*, G. Vadivu
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 707-726, 2023, DOI:10.32604/iasc.2023.035483
Abstract Every year, the number of women affected by breast tumors is increasing worldwide. Hence, detecting and segmenting the cancer regions in mammogram images is important to prevent death in women patients due to breast cancer. The conventional methods obtained low sensitivity and specificity with cancer region segmentation accuracy. The high-resolution standard mammogram images were supported by conventional methods as one of the main drawbacks. The conventional methods mostly segmented the cancer regions in mammogram images concerning their exterior pixel boundaries. These drawbacks are resolved by the proposed cancer region detection methods stated in this paper. The mammogram images are classified… More >

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566

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354

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S. Balaji^*, S. Karthik
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 727-743, 2023, DOI:10.32604/iasc.2023.037409
Abstract The creation of national energy strategy cannot proceed without accurate projections of future electricity consumption; this is because EC is intimately tied to other forms of energy, such as oil and natural gas. For the purpose of determining and bettering overall energy consumption, there is an urgent requirement for accurate monitoring and calculation of EC at the building level using cutting-edge technology such as data analytics and the internet of things (IoT). Soft computing is a subset of AI that tries to design procedures that are more accurate and reliable, and it has proven to be an effective tool for… More >

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990

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437

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S. R. Deepa¹, M. Subramoniam^2,*, R. Swarnalatha³, S. Poornapushpakala², S. Barani²
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 745-761, 2023, DOI:10.32604/iasc.2023.034211
Abstract The non-invasive evaluation of the heart through EectroCardioGraphy (ECG) has played a key role in detecting heart disease. The analysis of ECG signals requires years of learning and experience to interpret and extract useful information from them. Thus, a computerized system is needed to classify ECG signals with more accurate results effectively. Abnormal heart rhythms are called arrhythmias and cause sudden cardiac deaths. In this work, a Computerized Abnormal Heart Rhythms Detection (CAHRD) system is developed using ECG signals. It consists of four stages; preprocessing, feature extraction, feature optimization and classifier. At first, Pan and Tompkins algorithm is employed to… More >

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678

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402

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Rajesh Bharati^*, Vahida Attar
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 763-775, 2023, DOI:10.32604/iasc.2023.035503
Abstract Online Transaction Processing (OLTP) gets support from data partitioning to achieve better performance and scalability. The primary objective of database and application developers is to provide scalable and reliable database systems. This research presents a novel method for data partitioning and load balancing for scalable transactions. Data is efficiently partitioned using the hybrid graph partitioning method. Optimized load balancing (OLB) approach is applied to calculate the weight factor, average workload, and partition efficiency. The presented approach is appropriate for various online data transaction applications. The quality of the proposed approach is examined using OLTP database benchmark. The performance of the… More >

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Jiajia Yu^*
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 777-797, 2023, DOI:10.32604/iasc.2023.037354
Abstract This study aims to reduce the interference of ambient noise in mobile communication, improve the accuracy and authenticity of information transmitted by sound, and guarantee the accuracy of voice information delivered by mobile communication. First, the principles and techniques of speech enhancement are analyzed, and a fast lateral recursive least square method (FLRLS method) is adopted to process sound data. Then, the convolutional neural networks (CNNs)-based noise recognition CNN (NR-CNN) algorithm and speech enhancement model are proposed. Finally, related experiments are designed to verify the performance of the proposed algorithm and model. The experimental results show that the noise classification… More >

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754

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434

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Shen Guo^*, Wanxing Sheng, Shuaitao Bai, Jichuan Zhang, Peng Wang
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 799-814, 2023, DOI:10.32604/iasc.2023.038330
Abstract With the full development of disk-resident databases (DRDB) in recent years, it is widely used in business and transactional applications. In long-term use, some problems of disk databases are gradually exposed. For applications with high real-time requirements, the performance of using disk database is not satisfactory. In the context of the booming development of the Internet of things, domestic real-time databases have also gradually developed. Still, most of them only support the storage, processing, and analysis of data values with fewer data types, which can not fully meet the current industrial process control system data types, complex sources, fast update… More >

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394

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Xiong Xu¹, Chun Zhou^2,*, Chenggang Wang¹, Xiaoyan Zhang², Hua Meng²
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 815-831, 2023, DOI:10.32604/iasc.2023.034656
Abstract Clustering analysis is one of the main concerns in data mining. A common approach to the clustering process is to bring together points that are close to each other and separate points that are away from each other. Therefore, measuring the distance between sample points is crucial to the effectiveness of clustering. Filtering features by label information and measuring the distance between samples by these features is a common supervised learning method to reconstruct distance metric. However, in many application scenarios, it is very expensive to obtain a large number of labeled samples. In this paper, to solve the clustering… More >

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400

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Ling Wang, Jingchi Jiang^*, Jingwen Song, Jie Liu
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 833-848, 2023, DOI:10.32604/iasc.2023.036402
Abstract It is significant for agricultural intelligent knowledge services using knowledge graph technology to integrate multi-source heterogeneous crop and pest data and fully mine the knowledge hidden in the text. However, only some labeled data for agricultural knowledge graph domain training are available. Furthermore, labeling is costly due to the need for more data openness and standardization. This paper proposes a novel model using knowledge distillation for a weakly supervised entity recognition in ontology construction. Knowledge distillation between the target and source data domain is performed, where Bi-LSTM and CRF models are constructed for entity recognition. The experimental result is shown… More >

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443

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Peichen Zhao¹, Qi Yue^2,*, Zhibin Deng³
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 849-873, 2023, DOI:10.32604/iasc.2023.037090
Abstract In previous research on two-sided matching (TSM) decision, agents’ preferences were often given in the form of exact values of ordinal numbers and linguistic phrase term sets. Nowdays, the matching agent cannot perform the exact evaluation in the TSM situations due to the great fuzziness of human thought and the complexity of reality. Probability hesitant fuzzy sets, however, have grown in popularity due to their advantages in communicating complex information. Therefore, this paper develops a TSM decision-making approach with multi-attribute probability hesitant fuzzy sets and unknown attribute weight information. The agent attribute weight vector should be obtained by using the… More >

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403

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Arfa Siddiqu, Abdul Basit^*, Waheed Noor, Muhammad Asfandyar Khan, M. Saeed H. Kakar, Azam Khan
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 875-885, 2023, DOI:10.32604/iasc.2023.036426
Abstract The optical character recognition for the right to left and cursive languages such as Arabic is challenging and received little attention from researchers in the past compared to the other Latin languages. Moreover, the absence of a standard publicly available dataset for several low-resource languages, including the Pashto language remained a hurdle in the advancement of language processing. Realizing that, a clean dataset is the fundamental and core requirement of character recognition, this research begins with dataset generation and aims at a system capable of complete language understanding. Keeping in view the complete and full autonomous recognition of the cursive… More >

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V. Nivethitha^*, G. Aghila
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 887-904, 2023, DOI:10.32604/iasc.2023.034247
Abstract Cloud computing is considered to facilitate a more cost-effective way to deploy scientific workflows. The individual tasks of a scientific workflow necessitate a diversified number of large states that are spatially located in different datacenters, thereby resulting in huge delays during data transmission. Edge computing minimizes the delays in data transmission and supports the fixed storage strategy for scientific workflow private datasets. Therefore, this fixed storage strategy creates huge amount of bottleneck in its storage capacity. At this juncture, integrating the merits of cloud computing and edge computing during the process of rationalizing the data placement of scientific workflows and… More >

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401