Home / Journals / IASC / Vol.29, No.3, 2021
  • Open Access

    ARTICLE

    COVID-19 Diagnosis Using Transfer-Learning Techniques

    Mohammed Faisal1,*, Fahad Albogamy2, Hebah ElGibreen3, Mohammed Algabri3, Syed Ahad M. Alvi1, Mansour Alsulaiman3
    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 649-667, 2021, DOI:10.32604/iasc.2021.017898
    (This article belongs to this Special Issue: Computational Intelligence for Internet of Medical Things and Big Data Analytics)
    Abstract COVID-19 was first discovered in Wuhan, China, in December 2019 and has since spread worldwide. An automated and fast diagnosis system needs to be developed for early and effective COVID-19 diagnosis. Hence, we propose two- and three-classifier diagnosis systems for classifying COVID-19 cases using transfer-learning techniques. These systems can classify X-ray images into three categories: healthy, COVID-19, and pneumonia cases. We used two X-ray image datasets (DATASET-1 and DATASET-2) collected from state-of-the-art studies and train the systems using deep learning architectures, such as VGG-19, NASNet, and MobileNet2, on these datasets. According to the validation and testing results, our proposed diagnosis… More >

  • Open Access

    ARTICLE

    Feature Selection Using Artificial Immune Network: An Approach for Software Defect Prediction

    Bushra Mumtaz1, Summrina Kanwal2,*, Sultan Alamri2, Faiza Khan1
    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 669-684, 2021, DOI:10.32604/iasc.2021.018405
    Abstract Software Defect Prediction (SDP) is a dynamic research field in the software industry. A quality software product results in customer satisfaction. However, the higher the number of user requirements, the more complex will be the software, with a correspondingly higher probability of failure. SDP is a challenging task requiring smart algorithms that can estimate the quality of a software component before it is handed over to the end-user. In this paper, we propose a hybrid approach to address this particular issue. Our approach combines the feature selection capability of the Optimized Artificial Immune Networks (Opt-aiNet) algorithm with benchmark machine-learning classifiers… More >

  • Open Access

    ARTICLE

    A Novel Automatic Meal Delivery System

    Jhe-Wei Lin1, Cheng-Yan Siao1, Ting-Hsuan Chien2,*, Rong-Guey Chang1
    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 685-695, 2021, DOI:10.32604/iasc.2021.018254
    (This article belongs to this Special Issue: Machine Learning and Deep Learning for Transportation)
    Abstract Since the rapid growth of the Fourth Industrial Revolution (or Industry 4.0), robots have been widely used in many applications. In the catering industry, robots are used to replace people to do routine jobs. Because meal is an important part of the catering industry, we aim to design and develop a robot to deliver meals for saving cost and improving a restaurant’s performance in this paper. However, for the existing meal delivery system, the guests must make their meals by themselves. To let the food delivery system become more user-friendly, we integrate an automatic guided vehicle (AGV) and a robotic… More >

  • Open Access

    ARTICLE

    Machine Learning Based Framework for Maintaining Privacy of Healthcare Data

    Adil Hussain Seh1, Jehad F. Al-Amri2, Ahmad F. Subahi3, Alka Agrawal1, Rajeev Kumar4,*, Raees Ahmad Khan1
    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 697-712, 2021, DOI:10.32604/iasc.2021.018048
    Abstract The Adoption of Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), cloud services, web-based software systems, and other wireless sensor devices in the healthcare infrastructure have led to phenomenal improvements and benefits in the healthcare sector. Digital healthcare has ensured early diagnosis of the diseases, greater accessibility, and mass outreach in terms of treatment. Despite this unprecedented success, the privacy and confidentiality of the healthcare data have become a major concern for all the stakeholders. Data breach reports reveal that the healthcare data industry is one of the key targets of cyber invaders. In fact the last few… More >

  • Open Access

    ARTICLE

    Blockchain-Based Decision Tree Classification in Distributed Networks

    Jianping Yu1,2,3, Zhuqing Qiao1, Wensheng Tang1,2,3,*, Danni Wang1, Xiaojun Cao4
    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 713-728, 2021, DOI:10.32604/iasc.2021.017154
    Abstract In a distributed system such as Internet of things, the data volume from each node may be limited. Such limited data volume may constrain the performance of the machine learning classification model. How to effectively improve the performance of the classification in a distributed system has been a challenging problem in the field of data mining. Sharing data in the distributed network can enlarge the training data volume and improve the machine learning classification model’s accuracy. In this work, we take data sharing and the quality of shared data into consideration and propose an efficient Blockchain-based ID3 Decision Tree Classification… More >

  • Open Access

    ARTICLE

    Grey Wolf Optimizer-Based Fractional MPPT for Thermoelectric Generator

    A. M. Abdullah1, Hegazy Rezk2,3,*, Abdelrahman Elbloye1, Mohamed K. Hassan1,4, A. F. Mohamed1,5
    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 729-740, 2021, DOI:10.32604/iasc.2021.018595
    Abstract The energy harvested from a thermoelectric generator (TEG) relies mostly on the difference in temperature between the hot side and cold side of the TEG along with the connected load. Hence, a reliable maximum power point tracker is needed to force the TEG to operate close to the maximum power point (MPP) with any variation during the operation. In the current work, an optimized fractional maximum power point tracker (OFMPPT) is proposed to improve the performance of the TEG. The proposed tracker is based on fractional control. The optimal parameters of the OFMPPT have been determined using the grey wolf… More >

  • Open Access

    ARTICLE

    Inversion of Temperature and Humidity Profile of Microwave Radiometer Based on BP Network

    Tao Li1, Ning Peng Li1, Qi Qian1, Wen Duo Xu1, Yong Jun Ren2,*, Jin Yue Xia3
    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 741-755, 2021, DOI:10.32604/iasc.2021.018496
    Abstract In this paper, the inversion method of atmospheric temperature and humidity profiles via ground-based microwave radiometer is studied. Using the three-layer BP neural network inversion algorithm, four BP neural network models (temperature and humidity models with and without cloud information) are established using L-band radiosonde data obtained from the Atmospheric Exploration base of the China Meteorological Administration from July 2018 to June 2019. Microwave radiometer level 1 data and cloud radar data from July to September 2019 are used to evaluate the model. The four models are compared with the measured sounding data, and the inversion accuracy and the influence… More >

  • Open Access

    ARTICLE

    Prediction of the Corrosion Rate of Al–Si Alloys Using Optimal Regression Methods

    D. Saber1,*, Ibrahim B. M. Taha2, Kh. Abd El-Aziz3
    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 757-769, 2021, DOI:10.32604/iasc.2021.018516
    Abstract In this study, optimal regression learner methods were used to predict the corrosion behavior of aluminum–silicon alloys (Al–Si) with various Si ratios in different media. Al–Si alloys with 0, 1%, 8%, 11.2%, and 15% Si were tested in different media with different pH values at different stirring speeds (0, 300, 600, 750, 900, 1050, and 1200 rpm). Corrosion behavior was evaluated via electrochemical potentiodynamic test. The corrosion rates (CRs) obtained from the corrosion tests were utilized in the formation of datasets of various machine regression learner optimization (MRLO) methods, namely, decision tree, support vector machine, Gaussian process regression, and ensemble… More >

  • Open Access

    ARTICLE

    Trust Management-Based Service Recovery and Attack Prevention in MANET

    V. Nivedita1,*, N. Nandhagopal2
    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 771-786, 2021, DOI:10.32604/iasc.2021.017547
    Abstract The mobile ad-hoc network (MANET) output is critically impaired by the versatility and resource constraint of nodes. Node mobility affects connection reliability, and node resource constraints can lead to congestion, which makes the design of a routing MANET protocol with quality of service (QoS) very difficult. An adaptive clustering reputation model (ACRM) method is proposed to improve energy efficiency with a cluster-based framework. The proposed framework is employed to overcome the problems of data protection, privacy, and policy. The proposed ACRM-MRT approach that includes direct and indirect node trust computation is introduced along with the master recovery timer (MRT) for… More >

  • Open Access

    ARTICLE

    A Hypergraph-Embedded Convolutional Neural Network for Ice Crystal Particle Habit Classification

    Mengyuan Liao1, Jing Duan2,3,*, Rong Zhang2,3, Xu Zhou2,3, Xi Wu1, Xin Wang4, Jinrong Hu1
    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 787-801, 2021, DOI:10.32604/iasc.2021.018190
    Abstract In the field of weather modification, it is important to accurately identify the ice crystal particles in ice clouds. When ice crystal habits are correctly identified, cloud structure can be further understood and cloud seeding and other methods of weather modification can be used to change the microstructure of the cloud. Consequently, weather phenomena can be changed at an appropriate time to support human production and quality of life. However, ice crystal morphology is varied. Traditional ice crystal particle classification methods are based on expert experience, which is subjective and unreliable for the identification of the categories by threshold setting.… More >

  • Open Access

    ARTICLE

    Breast Cancer Classification Using Deep Convolution Neural Network with Transfer Learning

    Hanan A. Hosni Mahmoud*, Amal H. Alharbi, Doaa S. Khafga
    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 803-814, 2021, DOI:10.32604/iasc.2021.018607
    Abstract In this paper, we aim to apply deep learning convolution neural network (Deep-CNN) technology to classify breast masses in mammograms. We develop a Deep-CNN combined with multi-feature extraction and transfer learning to detect breast cancer. The Deep-CNN is utilized to extract features from mammograms. A support vector machine (SVM) is then trained on the Deep-CNN features to classify normal, benign, and cancer cases. The scoring features from the Deep-CNN are coupled with texture features and used as inputs to the final classifier. Two texture features are included: texture features of spatial dependency and gradient-based histograms. Both are employed to locate… More >

  • Open Access

    ARTICLE

    Surge Fault Detection of Aeroengines Based on Fusion Neural Network

    Desheng Zheng1, Xiaolan Tang1,*, Xinlong Wu1, Kexin Zhang1, Chao Lu2, Lulu Tian3
    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 815-826, 2021, DOI:10.32604/iasc.2021.017737
    Abstract Aeroengine surge fault is one of the main causes of flight accidents. When a surge occurs, it is hard to detect it in time and take anti-surge measures correctly. Recently, people have been applying detection methods based on mathematical models and expert knowledge. Due to difficult modeling and limited failure-mode coverage of these methods, early surge detection cannot be achieved. To address these problems, firstly, this paper introduced the data of six main sensors related to the aeroengine surge fault, such as, total pressure at compressor (high pressure rotor) outlet (Pt3), high pressure compressor rotor speed (N2), power level angle… More >

  • Open Access

    ARTICLE

    A General Technique for Real-Time Robotic Simulation in Manufacturing System

    Ting-Hsuan Chien1,*, Cheng-Yan Siao2, Rong-Guey Chang2
    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 827-838, 2021, DOI:10.32604/iasc.2021.018256
    (This article belongs to this Special Issue: Machine Learning and Deep Learning for Transportation)
    Abstract This paper describes a real-time simulator that allows the user in the factories to simulate arbitrary interaction between machinery and equipment. We discussed in details not only the general technique for developing such a real-time simulator but also the implementation of the simulator in its actual use. As such, people on the production line could benefit from observing and controlling robots in factories for preventing or reducing the severity of a collision, using the proposed simulator and its related technique. For that purpose, we divided the simulator into two main models: the real-time communication model and the simulation model. For… More >

  • Open Access

    ARTICLE

    Extraction of Opinion Target Using Syntactic Rules in Urdu Text

    Toqir A. Rana1,*, Bahrooz Bakht1, Mehtab Afzal1, Natash Ali Mian2, Muhammad Waseem Iqbal3, Abbas Khalid1, Muhammad Raza Naqvi4
    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 839-853, 2021, DOI:10.32604/iasc.2021.018572
    Abstract Opinion target or aspect extraction is the key task of aspect-based sentiment analysis. This task focuses on the extraction of targeted words or phrases against which a user has expressed his/her opinion. Although, opinion target extraction has been studied extensively in the English language domain, with notable work in other languages such as Chinese, Arabic etc., other regional languages have been neglected. One of the reasons is the lack of resources and available texts for these languages. Urdu is one, with millions of native and non-native speakers across the globe. In this paper, the Urdu language domain is focused on… More >

  • Open Access

    ARTICLE

    Forecast of LSTM-XGBoost in Stock Price Based on Bayesian Optimization

    Tian Liwei1,2,*, Feng Li1, Sun Yu3, Guo Yuankai4
    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 855-868, 2021, DOI:10.32604/iasc.2021.016805
    Abstract The prediction of the “ups and downs” of stock market prices is one of the important undertakings of the financial market. Since accurate prediction helps foster considerable economic benefits, stock market prediction has attracted significant interest by both investors and researchers. Efforts into building an accurate, stable and effective model to predict stock prices’ movements have been proliferating at a fast pace, to meet such a challenge. Firstly, this paper uses a correlation analysis to analyze the attributes of a stock dataset, processing missing values, determining the data attributes to be retained data, then divide it in a training set… More >

  • Open Access

    ARTICLE

    Stator Winding Fault Detection and Classification in Three-Phase Induction Motor

    Majid Hussain1,2, Dileep Kumar Soother1, Imtiaz Hussain Kalwar3, Tayab Din Memon4,5, Zubair Ahmed Memon6, Kashif Nisar7,*, Bhawani Shankar Chowdhry1
    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 869-883, 2021, DOI:10.32604/iasc.2021.017790
    Abstract Induction motors (IMs) are the workhorse of the industry and are subjected to a harsh environment. Due to their operating conditions, they are exposed to different kinds of unavoidable faults that lead to unscheduled downtimes and losses. These faults may be detected early through predictive maintenance (i.e., deployment of condition monitoring systems). Motor current signature analysis (MCSA) is the most widely used technique to detect various faults in industrial motors. The stator winding faults (SWF) are one of the major faults. In this paper, we present an induction motor fault detection and identification system using signal processing techniques such as… More >

  • Open Access

    ARTICLE

    Detection of COVID-19 Using Deep Learning on X-Ray Images

    Munif Alotaibi1,*, Bandar Alotaibi2
    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 885-898, 2021, DOI:10.32604/iasc.2021.018350
    Abstract The novel coronavirus 2019 (COVID-19) is one of a large family of viruses that cause illness, the symptoms of which range from a common cold, fever, coughing, and shortness of breath to more severe symptoms. The virus rapidly and easily spreads from infected people to others through close contact in the absence of protection. Early detection of COVID-19 assists governmental authorities and healthcare specialists in reducing the chain of transmission and flattening the curve of the pandemic. The widespread form of the COVID-19 diagnostic test lacks a high true positive rate and a low false negative rate and needs a… More >

  • Open Access

    ARTICLE

    A Robust Text Coverless Information Hiding Based on Multi-Index Method

    Lin Xiang1, Jiaohua Qin1,*, Xuyu Xiang1, Yun Tan1, Neal N. Xiong2
    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 899-914, 2021, DOI:10.32604/iasc.2021.017720
    Abstract Recently, researchers have shown that coverless information hiding technology can effectively resist the existing steganalysis tools. However, the robustness of existing coverless text information hiding methods is generally poor. To solve this problem, we propose a robust text coverless information hiding method based on multi-index. Firstly, the sender segment the secret information into several keywords. Secondly, we transform keywords into keyword IDs by the word index table and introduce a random increment factor to control. Then, search all texts containing the keyword ID in the big data text, and use the robust text search algorithm to find multiple texts. Finally,… More >

  • Open Access

    ARTICLE

    Container Application Migration Algorithm in Internet of Vehicles

    Xiaoliang Lin1,*, Junxiao Shi1, Yanbo Wang1, Chenyang Liu1, Bin Lu1, Siwen Xu2
    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 915-926, 2021, DOI:10.32604/iasc.2021.018513
    Abstract Internet of Vehicles (IoV) is a popular application scenario that combines edge computing and the Internet of Things. Among them, service migration caused by IoV application mobility is a research hotspot in this field. This paper studies the migration strategy of container applications based on edge computing in the IoV business scenario. In order to solve the difficulty in selecting the target server of the application to be migrated in the crossroads scenario, this paper converts the migration decision to the shortest path problem based on dynamic programming, and obtains the best migration choice at the current time by finding… More >

  • Open Access

    ARTICLE

    Duplicate Frame Video Forgery Detection Using Siamese-based RNN

    Maryam Munawar, Iram Noreen*
    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 927-937, 2021, DOI:10.32604/iasc.2021.018854
    Abstract Video and image data is the most important and widely used format of communication today. It is used as evidence and authenticated proof in different domains such as law enforcement, forensic studies, journalism, and others. With the increase of video applications and data, the problem of forgery in video and images has also originated. Although a lot of work has been done on image forgery, video forensic is still a challenging area. Videos are manipulated in many ways. Frame insertion, deletion, and frame duplication are a few of the major challenges. Moreover, in the perspective of duplicated frames, frame rate… More >

Share Link

WeChat scan