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Mohammed Faisal^1,*, 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 - 01 July 2021
（This article belongs to the 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 More >

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Bushra Mumtaz¹, Summrina Kanwal^2,*, Sultan Alamri², Faiza Khan¹
Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 669-684, 2021, DOI:10.32604/iasc.2021.018405 - 01 July 2021
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) More >

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Jhe-Wei Lin¹, Cheng-Yan Siao¹, Ting-Hsuan Chien^2,*, Rong-Guey Chang¹
Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 685-695, 2021, DOI:10.32604/iasc.2021.018254 - 01 July 2021
（This article belongs to the 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… More >

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Adil Hussain Seh¹, Jehad F. Al-Amri², Ahmad F. Subahi³, Alka Agrawal¹, Rajeev Kumar^4,*, Raees Ahmad Khan¹
Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 697-712, 2021, DOI:10.32604/iasc.2021.018048 - 01 July 2021
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.… More >

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Jianping Yu^1,2,3, Zhuqing Qiao¹, Wensheng Tang^1,2,3,*, Danni Wang¹, Xiaojun Cao⁴
Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 713-728, 2021, DOI:10.32604/iasc.2021.017154 - 01 July 2021
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… More >

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A. M. Abdullah¹, Hegazy Rezk^2,3,*, Abdelrahman Elbloye¹, Mohamed K. Hassan^1,4, A. F. Mohamed^1,5
Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 729-740, 2021, DOI:10.32604/iasc.2021.018595 - 01 July 2021
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… More >

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Tao Li¹, Ning Peng Li¹, Qi Qian¹, Wen Duo Xu¹, Yong Jun Ren^2,*, Jin Yue Xia³
Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 741-755, 2021, DOI:10.32604/iasc.2021.018496 - 01 July 2021
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 More >

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D. Saber^1,*, 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 - 01 July 2021
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,… More >

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V. Nivedita^1,*, N. Nandhagopal²
Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 771-786, 2021, DOI:10.32604/iasc.2021.017547 - 01 July 2021
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… More >

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Mengyuan Liao¹, Jing Duan^2,3,*, Rong Zhang^2,3, Xu Zhou^2,3, Xi Wu¹, Xin Wang⁴, Jinrong Hu¹
Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 787-801, 2021, DOI:10.32604/iasc.2021.018190 - 01 July 2021
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 More >

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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 - 01 July 2021
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.… More >

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Desheng Zheng¹, Xiaolan Tang^1,*, 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 - 01 July 2021
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 More >

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Ting-Hsuan Chien^1,*, Cheng-Yan Siao², Rong-Guey Chang²
Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 827-838, 2021, DOI:10.32604/iasc.2021.018256 - 01 July 2021
（This article belongs to the 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 More >

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Toqir A. Rana^1,*, 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 - 01 July 2021
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… More >

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Tian Liwei^1,2,*, Feng Li¹, Sun Yu³, Guo Yuankai⁴
Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 855-868, 2021, DOI:10.32604/iasc.2021.016805 - 01 July 2021
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… More >

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Majid Hussain^1,2, Dileep Kumar¹, Imtiaz Hussain Kalwar³, Tayab Din Memon^4,5, Zubair Ahmed Memon⁶, Kashif Nisar^7,*, Bhawani Shankar Chowdhry¹
Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 869-883, 2021, DOI:10.32604/iasc.2021.017790 - 01 July 2021
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… More >

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Munif Alotaibi^1,*, Bandar Alotaibi²
Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 885-898, 2021, DOI:10.32604/iasc.2021.018350 - 01 July 2021
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… More >

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Lin Xiang¹, Jiaohua Qin^1,*, 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 - 01 July 2021
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 More >

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Xiaoliang Lin^1,*, 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 - 01 July 2021
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 More >

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1130

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Maryam Munawar, Iram Noreen^*
Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 927-937, 2021, DOI:10.32604/iasc.2021.018854 - 01 July 2021
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… More >

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