1
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Rahman A, Debnath T, Kundu D, Khan MSI, Aishi AA, Sazzad S, Sayduzzaman M, Band SS. Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities. AIMS Public Health 2024; 11:58-109. [PMID: 38617415 PMCID: PMC11007421 DOI: 10.3934/publichealth.2024004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Accepted: 12/18/2023] [Indexed: 04/16/2024] Open
Abstract
In recent years, machine learning (ML) and deep learning (DL) have been the leading approaches to solving various challenges, such as disease predictions, drug discovery, medical image analysis, etc., in intelligent healthcare applications. Further, given the current progress in the fields of ML and DL, there exists the promising potential for both to provide support in the realm of healthcare. This study offered an exhaustive survey on ML and DL for the healthcare system, concentrating on vital state of the art features, integration benefits, applications, prospects and future guidelines. To conduct the research, we found the most prominent journal and conference databases using distinct keywords to discover scholarly consequences. First, we furnished the most current along with cutting-edge progress in ML-DL-based analysis in smart healthcare in a compendious manner. Next, we integrated the advancement of various services for ML and DL, including ML-healthcare, DL-healthcare, and ML-DL-healthcare. We then offered ML and DL-based applications in the healthcare industry. Eventually, we emphasized the research disputes and recommendations for further studies based on our observations.
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Affiliation(s)
- Anichur Rahman
- Department of CSE, National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka-1350
- Department of CSE, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Tanoy Debnath
- Department of CSE, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
- Department of CSE, Green University of Bangladesh, 220/D, Begum Rokeya Sarani, Dhaka -1207, Bangladesh
| | - Dipanjali Kundu
- Department of CSE, National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka-1350
| | - Md. Saikat Islam Khan
- Department of CSE, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Airin Afroj Aishi
- Department of Computing and Information System, Daffodil International University, Savar, Dhaka, Bangladesh
| | - Sadia Sazzad
- Department of CSE, National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka-1350
| | - Mohammad Sayduzzaman
- Department of CSE, National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka-1350
| | - Shahab S. Band
- Department of Information Management, International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Taiwan
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2
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Ameri R, Hsu CC, Band SS, Zamani M, Shu CM, Khorsandroo S. Forecasting PM 2.5 concentration based on integrating of CEEMDAN decomposition method with SVM and LSTM. Ecotoxicol Environ Saf 2023; 266:115572. [PMID: 37837695 DOI: 10.1016/j.ecoenv.2023.115572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 09/28/2023] [Accepted: 10/09/2023] [Indexed: 10/16/2023]
Abstract
With urbanization and increasing consumption, there is a growing need to prioritize sustainable development across various industries. Particularly, sustainable development is hindered by air pollution, which poses a threat to both living organisms and the environment. The emission of combustion gases containing particulate matter (PM 2.5) during human and social activities is a major cause of air pollution. To mitigate health risks, it is crucial to have accurate and reliable methods for forecasting PM 2.5 levels. In this study, we propose a novel approach that combines support vector machine (SVM) and long short-term memory (LSTM) with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to forecast PM 2.5 concentrations. The methodology involves extracting Intrinsic mode function (IMF) components through CEEMDAN and subsequently applying different regression models (SVM and LSTM) to forecast each component. The Naive Evolution algorithm is employed to determine the optimal parameters for combining CEEMDAN, SVM, and LSTM. Daily PM 2.5 concentrations in Kaohsiung, Taiwan from 2019 to 2021 were collected to train models and evaluate their performance. The performance of the proposed model is evaluated using metrics such as mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and coefficient of determination (R2) for each district. Overall, our proposed model demonstrates superior performance in terms of MAE (1.858), MSE (7.2449), RMSE (2.6682), and (0.9169) values compared to other methods for 1-day ahead PM 2.5 forecasting. Furthermore, our proposed model also achieves the best performance in forecasting PM 2.5 for 3- and 7-day ahead predictions.
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Affiliation(s)
- Rasoul Ameri
- Department of Information Management, National Yunlin University of Science and Technology, Douliou, Taiwan
| | - Chung-Chian Hsu
- Department of Information Management, International Graduate Institute of Artificial Intelligence, National Yunlin University of Science and Technology, Douliou, Taiwan.
| | - Shahab S Band
- Department of Information Management, International Graduate Institute of Artificial Intelligence, National Yunlin University of Science and Technology, Douliou, Taiwan; Future Technology Research Center, National Yunlin University of Science and Technology, Douliou, Taiwan.
| | - Mazdak Zamani
- Department of Computer Science, New York University, 251 Mercer, New York, NY 10012, USA
| | - Chi-Min Shu
- Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, Yunlin, 64002, Taiwan
| | - Sajad Khorsandroo
- Department of Computer Science, North Carolina A&T State University, Greensboro, NC 27411, USA
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3
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Badshah A, Jalal A, Farooq U, Rehman GU, Band SS, Iwendi C. Service Level Agreement Monitoring as a Service: An Independent Monitoring Service for Service Level Agreements in Clouds. Big Data 2023; 11:339-354. [PMID: 35076283 DOI: 10.1089/big.2021.0274] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The cloud network is rapidly growing due to a massive increase in interconnected devices and the emergence of different technologies such as the Internet of things, fog computing, and artificial intelligence. In response, cloud computing needs reliable dealings among the service providers, brokers, and consumers. The existing cloud monitoring frameworks such as Amazon Cloud Watch, Paraleap Azure Watch, and Rack Space Cloud Kick work under the control of service providers. They work fine; however, this may create dissatisfaction among customers over Service Level Agreement (SLA) violations. Customers' dissatisfaction may drastically reduce the businesses of service providers. To cope with the earlier mentioned issue and get in line with cloud philosophy, Monitoring as a Service (MaaS), completely independent in nature, is needed for observing and regulating the cloud businesses. However, the existing MaaS frameworks do not address the comprehensive SLA for customer satisfaction and penalties management. This article proposes a reliable framework for monitoring the provider's services by adopting third-party monitoring services with clearcut SLA and penalties management. Since this framework monitors SLA as a cloud monitoring service, it is named as SLA-MaaS. On violations, it penalizes those who are found in breach of terms and condition enlisted in SLA. Simulation results confirmed that the proposed framework adequately satisfies the customers (as well as service providers). This helps in developing a trustworthy relationship among cloud partners and increases customer attention and retention.
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Affiliation(s)
- Afzal Badshah
- Department of Computer Science and Software Engineering, International Islamic University, Islamabad, Pakistan
| | - Ateeqa Jalal
- Department of Computer Science, University of Science and Technology, Bannu, Pakistan
| | - Umar Farooq
- Department of Computer Science, University of Science and Technology, Bannu, Pakistan
| | - Ghani-Ur Rehman
- Department of Computer Science and Bioinformatics, Khushal Khan Khattak University, Karak, Pakistan
| | - Shahab S Band
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Douliou, Taiwan
| | - Celestine Iwendi
- School of Creative Technologies University of Bolton, Bolton, United Kingdom
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4
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Asnaashari S, Shateri M, Hemmati-Sarapardeh A, Band SS. Modeling of the Sintered Density in Cu-Al Alloy Using Machine Learning Approaches. ACS Omega 2023; 8:28036-28051. [PMID: 37576653 PMCID: PMC10413372 DOI: 10.1021/acsomega.2c07278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Accepted: 06/21/2023] [Indexed: 08/15/2023]
Abstract
In powder metallurgy materials, sintered density in Cu-Al alloy plays a critical role in detecting mechanical properties. Experimental measurement of this property is costly and time-consuming. In this study, adaptive boosting decision tree, support vector regression, k-nearest neighbors, extreme gradient boosting, and four multilayer perceptron (MLP) models tuned by resilient backpropagation, Levenberg-Marquardt (LM), scaled conjugate gradient, and Bayesian regularization were employed for predicting powder densification through sintering. Yield strength, Young's modulus, volume variation caused by the phase transformation, hardness, liquid volume, liquidus temperature, the solubility ratio among the liquid phase and the solid phase, sintered temperature, solidus temperature, sintered atmosphere, holding time, compaction pressure, particle size, and specific shape factor were regarded as the input parameters of the suggested models. The cross plot, error distribution curve, and cumulative frequency diagram as graphical tools and average percent relative error (APRE), average absolute percent relative error (AAPRE), root mean square error (RMSE), standard deviation (SD), and coefficient of correlation (R) as the statistical evaluations were utilized to estimate the models' accuracy. All of the developed models were compared with preexisting approaches, and the results exhibited that the developed models in the present work are more precise and valid than the existing ones. The designed MLP-LM model was found to be the most precise approach with AAPRE = 1.292%, APRE = -0.032%, SD = 0.020, RMSE = 0.016, and R = 0.989. Lately, outlier detection was applied performing the leverage technique to detect the suspected data points. The outlier detection discovered that few points are located out of the applicability domain of the proposed MLP-LM model.
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Affiliation(s)
- Saleh Asnaashari
- School
of Metallurgy and Materials Engineering, University College of Engineering, University of Tehran, Tehran 7761968875, Iran
| | - Mohammadhadi Shateri
- Department
of System Engineering, École de Technologie
Supérieur, Montreal, QC H3C 1K3, Canada
| | | | - Shahab S. Band
- Future
Technology Research Center, College of Future, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan, ROC
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5
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Hsu CC, Ameri R, Lin CW, He JS, Biyari M, Yarahmadi A, Band SS, Lin TK, Fan WL. A robust approach for endotracheal tube localization in chest radiographs. Front Artif Intell 2023; 6:1181812. [PMID: 37251274 PMCID: PMC10219610 DOI: 10.3389/frai.2023.1181812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 04/13/2023] [Indexed: 05/31/2023] Open
Abstract
Precise detection and localization of the Endotracheal tube (ETT) is essential for patients receiving chest radiographs. A robust deep learning model based on U-Net++ architecture is presented for accurate segmentation and localization of the ETT. Different types of loss functions related to distribution and region-based loss functions are evaluated in this paper. Then, various integrations of distribution and region-based loss functions (compound loss function) have been applied to obtain the best intersection over union (IOU) for ETT segmentation. The main purpose of the presented study is to maximize IOU for ETT segmentation, and also minimize the error range that needs to be considered during calculation of distance between the real and predicted ETT by obtaining the best integration of the distribution and region loss functions (compound loss function) for training the U-Net++ model. We analyzed the performance of our model using chest radiograph from the Dalin Tzu Chi Hospital in Taiwan. The results of applying the integration of distribution-based and region-based loss functions on the Dalin Tzu Chi Hospital dataset show enhanced segmentation performance compared to other single loss functions. Moreover, according to the obtained results, the combination of Matthews Correlation Coefficient (MCC) and Tversky loss functions, which is a hybrid loss function, has shown the best performance on ETT segmentation based on its ground truth with an IOU value of 0.8683.
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Affiliation(s)
- Chung-Chian Hsu
- Department of Information Management, International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Douliu, Taiwan
| | - Rasoul Ameri
- Department of Information Management, National Yunlin University of Science and Technology, Douliu, Taiwan
| | - Chih-Wen Lin
- Buddhist Dalin Tzu Chi Hospital, Chiayi, Taiwan
- School of Medicine, Tzu Chi University, Hualien, Taiwan
| | | | - Meghdad Biyari
- Department of Information Management, National Yunlin University of Science and Technology, Douliu, Taiwan
| | - Atefeh Yarahmadi
- Department of Information Management, National Yunlin University of Science and Technology, Douliu, Taiwan
| | - Shahab S. Band
- Future Technology Research Center, National Yunlin University of Science and Technology, Douliu, Taiwan
- International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Douliu, Taiwan
| | - Tin-Kwang Lin
- Buddhist Dalin Tzu Chi Hospital, Chiayi, Taiwan
- School of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Wen-Lin Fan
- Buddhist Dalin Tzu Chi Hospital, Chiayi, Taiwan
- School of Medicine, Tzu Chi University, Hualien, Taiwan
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6
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Singh R, Tiwari P, Band SS, Rehman AU, Mahajan S, Ding Y, Liu X, Pandit AK. Impact of quarantine on fractional order dynamical model of Covid-19. Comput Biol Med 2022; 151:106266. [PMID: 36395591 PMCID: PMC9660264 DOI: 10.1016/j.compbiomed.2022.106266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 10/12/2022] [Accepted: 10/30/2022] [Indexed: 11/11/2022]
Abstract
In this paper, a Covid-19 dynamical transmission model of a coupled non-linear fractional differential equation in the Atangana-Baleanu Caputo sense is proposed. The basic dynamical transmission features of the proposed system are briefly discussed. The qualitative as well as quantitative results on the existence and uniqueness of the solutions are evaluated through the fixed point theorem. The Ulam-Hyers stability analysis of the suggested system is established. The two-step Adams-Bashforth-Moulton (ABM) numerical method is employed to find its numerical solution. The numerical simulation is performed to accesses the impact of various biological parameters on the dynamics of Covid-19 disease.
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Affiliation(s)
- Ram Singh
- Baba Ghulam Shah Badshah University Rajouri, 185234, India,Corresponding authors
| | - Prayag Tiwari
- School of Information Technology, Halmstad University, Sweden,Corresponding authors
| | - Shahab S. Band
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Douliou, Taiwan, ROC,Corresponding authors
| | | | - Shubham Mahajan
- School of Electronic and Communication, Shri Mata Vaishno Devi University, Katra, 182320, India,Ajeenka D Y University, Pune, Maharashtra, India,iNurture Education Solutions Pvt. Ltd., Bangalore, India
| | - Yijie Ding
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, 324000, China
| | - Xiaobin Liu
- Department of Nephrology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, 214023, Wuxi, China,Corresponding authors
| | - Amit Kant Pandit
- School of Electronic and Communication, Shri Mata Vaishno Devi University, Katra, 182320, India
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7
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Mumtaz N, Ejaz N, Habib S, Mohsin SM, Tiwari P, Band SS, Kumar N. An overview of violence detection techniques: current challenges and future directions. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10285-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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8
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Kumar S, Singh SK, Aggarwal N, Gupta BB, Alhalabi W, Band SS. An efficient hardware supported and parallelization architecture for intelligent systems to overcome speculative overheads. INT J INTELL SYST 2022. [DOI: 10.1002/int.23062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Sudhakar Kumar
- Department of Computer Science and Engineering, Chandigarh College of Engineering and Technology Panjab University Chandigarh India
| | - Sunil K. Singh
- Department of Computer Science and Engineering Chandigarh College of Engineering and Technology Chandigarh India
| | - Naveen Aggarwal
- Department of Computer Science and Engineering University Institute of Engineering and Technology Chandigarh India
| | - Brij B. Gupta
- International Center for AI and Cyber Security Research and Innovations & Department of Computer Science and Information Engineering, Asia University Taichung Taiwan
- Lebanese American University Beirut Lebanon
- Department of Computer Science King Abdulaziz University Jeddah Saudi Arabia
- Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India
| | - Wadee Alhalabi
- Department of Computer Science King Abdulaziz University Jeddah Saudi Arabia
| | - Shahab S. Band
- Department of Computer Science National Yunlin University of Science and Technology Douliou Taiwan
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9
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Rahman A, Hossain MS, Muhammad G, Kundu D, Debnath T, Rahman M, Khan MSI, Tiwari P, Band SS. Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues. Cluster Comput 2022; 26:1-41. [PMID: 35996680 PMCID: PMC9385101 DOI: 10.1007/s10586-022-03658-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 05/10/2022] [Accepted: 06/17/2022] [Indexed: 06/15/2023]
Abstract
Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare field. Traditionally, the healthcare system works based on centralized agents sharing their raw data. Therefore, huge vulnerabilities and challenges are still existing in this system. However, integrating with AI, the system would be multiple agent collaborators who are capable of communicating with their desired host efficiently. Again, FL is another interesting feature, which works decentralized manner; it maintains the communication based on a model in the preferred system without transferring the raw data. The combination of FL, AI, and XAI techniques can be capable of minimizing several limitations and challenges in the healthcare system. This paper presents a complete analysis of FL using AI for smart healthcare applications. Initially, we discuss contemporary concepts of emerging technologies such as FL, AI, XAI, and the healthcare system. We integrate and classify the FL-AI with healthcare technologies in different domains. Further, we address the existing problems, including security, privacy, stability, and reliability in the healthcare field. In addition, we guide the readers to solving strategies of healthcare using FL and AI. Finally, we address extensive research areas as well as future potential prospects regarding FL-based AI research in the healthcare management system.
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Affiliation(s)
- Anichur Rahman
- Present Address: Department of Computer Science and Engineering, National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka Bangladesh
- Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Md. Sazzad Hossain
- Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Ghulam Muhammad
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Dipanjali Kundu
- Present Address: Department of Computer Science and Engineering, National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka Bangladesh
| | - Tanoy Debnath
- Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Muaz Rahman
- Present Address: Department of Computer Science and Engineering, National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka Bangladesh
| | - Md. Saikat Islam Khan
- Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Prayag Tiwari
- Department of Computer Science, Aalto University, Espoo, Finland
| | - Shahab S. Band
- Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin, 64002 Taiwan
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10
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Saberi-Movahed F, Mohammadifard M, Mehrpooya A, Rezaei-Ravari M, Berahmand K, Rostami M, Karami S, Najafzadeh M, Hajinezhad D, Jamshidi M, Abedi F, Mohammadifard M, Farbod E, Safavi F, Dorvash M, Mottaghi-Dastjerdi N, Vahedi S, Eftekhari M, Saberi-Movahed F, Alinejad-Rokny H, Band SS, Tavassoly I. Decoding clinical biomarker space of COVID-19: Exploring matrix factorization-based feature selection methods. Comput Biol Med 2022; 146:105426. [PMID: 35569336 PMCID: PMC8979841 DOI: 10.1016/j.compbiomed.2022.105426] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 03/01/2022] [Accepted: 03/18/2022] [Indexed: 02/06/2023]
Abstract
One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients' characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O2 Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases.
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Affiliation(s)
| | | | - Adel Mehrpooya
- School of Mathematical Sciences, Science and Engineering Faculty, Queensland University of Technology (QUT), Brisbane, Australia
| | | | - Kamal Berahmand
- School of Computer Science, Faculty of Science, Queensland University of Technology (QUT), Brisbane, Australia
| | - Mehrdad Rostami
- Center for Machine Vision and Signal Analysis (CMVS), University of Oulu, Oulu, Finland
| | - Saeed Karami
- Department of Mathematics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, 45137-66731, Iran
| | - Mohammad Najafzadeh
- Department of Applied Mathematics, Faculty of Sciences and Modern Technologies, Graduate University of Advanced Technology, Kerman, Iran
| | | | - Mina Jamshidi
- Department of Applied Mathematics, Faculty of Sciences and Modern Technologies, Graduate University of Advanced Technology, Kerman, Iran
| | - Farshid Abedi
- Infectious Diseases Research Center, Birjand University of Medical Sciences, Birjand, Iran
| | | | - Elnaz Farbod
- Baruch College, City University of New York, New York, USA
| | - Farinaz Safavi
- Neuroimmunology and Neurovirology Branch, National Institute of Neurological Disorders and Stroke, National Institute of Health, Bethesda, MD, USA
| | - Mohammadreza Dorvash
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Viewbank, VIC, Australia
| | - Negar Mottaghi-Dastjerdi
- Department of Pharmacognosy and Pharmaceutical Biotechnology, School of Pharmacy, Iran University of Medical Sciences, Tehran, Iran
| | | | - Mahdi Eftekhari
- Department of Computer Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Farid Saberi-Movahed
- Department of Applied Mathematics, Faculty of Sciences and Modern Technologies, Graduate University of Advanced Technology, Kerman, Iran,Corresponding author
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia
| | - Shahab S. Band
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin, 64002, Taiwan
| | - Iman Tavassoly
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY10029, USA,Corresponding author
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11
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Band SS, Ardabili S, Yarahmadi A, Pahlevanzadeh B, Kiani AK, Beheshti A, Alinejad-Rokny H, Dehzangi I, Chang A, Mosavi A, Moslehpour M. A Survey on Machine Learning and Internet of Medical Things-Based Approaches for Handling COVID-19: Meta-Analysis. Front Public Health 2022; 10:869238. [PMID: 35812486 PMCID: PMC9260273 DOI: 10.3389/fpubh.2022.869238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 04/20/2022] [Indexed: 11/13/2022] Open
Abstract
Early diagnosis, prioritization, screening, clustering, and tracking of patients with COVID-19, and production of drugs and vaccines are some of the applications that have made it necessary to use a new style of technology to involve, manage, and deal with this epidemic. Strategies backed by artificial intelligence (A.I.) and the Internet of Things (IoT) have been undeniably effective to understand how the virus works and prevent it from spreading. Accordingly, the main aim of this survey is to critically review the ML, IoT, and the integration of IoT and ML-based techniques in the applications related to COVID-19, from the diagnosis of the disease to the prediction of its outbreak. According to the main findings, IoT provided a prompt and efficient approach to tracking the disease spread. On the other hand, most of the studies developed by ML-based techniques aimed at the detection and handling of challenges associated with the COVID-19 pandemic. Among different approaches, Convolutional Neural Network (CNN), Support Vector Machine, Genetic CNN, and pre-trained CNN, followed by ResNet have demonstrated the best performances compared to other methods.
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Affiliation(s)
- Shahab S. Band
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Douliou, Taiwan
| | - Sina Ardabili
- Department of Informatics, J. Selye University, Komárom, Slovakia
| | - Atefeh Yarahmadi
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Douliou, Taiwan
| | - Bahareh Pahlevanzadeh
- Department of Design and System Operations, Regional Information Center for Science and Technology (R.I.C.E.S.T.), Shiraz, Iran
| | - Adiqa Kausar Kiani
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Douliou, Taiwan
| | - Amin Beheshti
- Department of Computing, Macquarie University, Sydney, NSW, Australia
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, U.N.S.W. Sydney, Sydney, NSW, Australia
- U.N.S.W. Data Science Hub, The University of New South Wales (U.N.S.W. Sydney), Sydney, NSW, Australia
- Health Data Analytics Program, AI-enabled Processes (A.I.P.) Research Centre, Macquarie University, Sydney, NSW, Australia
| | - Iman Dehzangi
- Department of Computer Science, Rutgers University, Camden, NJ, United States
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, United States
| | - Arthur Chang
- Bachelor Program in Interdisciplinary Studies, National Yunlin University of Science and Technology, Douliu, Taiwan
| | - Amir Mosavi
- John von Neumann Faculty of Informatics, Obuda University, Budapest, Hungary
- Institute of Information Engineering, Automation and Mathematics, Slovak University of Technology in Bratislava, Bratislava, Slovakia
| | - Massoud Moslehpour
- Department of Business Administration, College of Management, Asia University, Taichung, Taiwan
- Department of Management, California State University, San Bernardino, CA, United States
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12
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Sharifonnasabi F, Jhanjhi NZ, John J, Obeidy P, Band SS, Alinejad-Rokny H, Baz M. Hybrid HCNN-KNN Model Enhances Age Estimation Accuracy in Orthopantomography. Front Public Health 2022; 10:879418. [PMID: 35712286 PMCID: PMC9197238 DOI: 10.3389/fpubh.2022.879418] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Accepted: 04/22/2022] [Indexed: 11/17/2022] Open
Abstract
Age estimation in dental radiographs Orthopantomography (OPG) is a medical imaging technique that physicians and pathologists utilize for disease identification and legal matters. For example, for estimating post-mortem interval, detecting child abuse, drug trafficking, and identifying an unknown body. Recent development in automated image processing models improved the age estimation's limited precision to an approximate range of +/- 1 year. While this estimation is often accepted as accurate measurement, age estimation should be as precise as possible in most serious matters, such as homicide. Current age estimation techniques are highly dependent on manual and time-consuming image processing. Age estimation is often a time-sensitive matter in which the image processing time is vital. Recent development in Machine learning-based data processing methods has decreased the imaging time processing; however, the accuracy of these techniques remains to be further improved. We proposed an ensemble method of image classifiers to enhance the accuracy of age estimation using OPGs from 1 year to a couple of months (1-3-6). This hybrid model is based on convolutional neural networks (CNN) and K nearest neighbors (KNN). The hybrid (HCNN-KNN) model was used to investigate 1,922 panoramic dental radiographs of patients aged 15 to 23. These OPGs were obtained from the various teaching institutes and private dental clinics in Malaysia. To minimize the chance of overfitting in our model, we used the principal component analysis (PCA) algorithm and eliminated the features with high correlation. To further enhance the performance of our hybrid model, we performed systematic image pre-processing. We applied a series of classifications to train our model. We have successfully demonstrated that combining these innovative approaches has improved the classification and segmentation and thus the age-estimation outcome of the model. Our findings suggest that our innovative model, for the first time, to the best of our knowledge, successfully estimated the age in classified studies of 1 year old, 6 months, 3 months and 1-month-old cases with accuracies of 99.98, 99.96, 99.87, and 98.78 respectively.
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Affiliation(s)
- Fatemeh Sharifonnasabi
- Department of Computer Science & Engineering, School of Computing & IT (SoCIT), Taylor's University, Subang Jaya, Malaysia
| | - Noor Zaman Jhanjhi
- Department of Computer Science & Engineering, School of Computing & IT (SoCIT), Taylor's University, Subang Jaya, Malaysia
| | - Jacob John
- Department of Restorative Dentistry, Faculty of Dentistry, University of Malaya, Kuala Lumpur, Malaysia
| | - Peyman Obeidy
- Charles Perkins Centre, Faculty of Medicine and Health, University of Sydney, Darlington, NSW, Australia
| | - Shahab S Band
- Future Technology Research Centre, College of Future, National Yunlin University of Science and Technology, Yunlin, Taiwan
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, University of New South Wales (UNSW) Sydney, Kensington, NSW, Australia.,UNSW Data Science Hub, The University of New South Wales, UNSW Sydney, Kensington, NSW, Australia.,Health Data Analytics Program, AI-enabled Processes (AIP) Research Centre, Macquarie University, Macquarie Park, NSW, Australia
| | - Mohammed Baz
- Department of Computer Engineering, College of Computer and Information Technology, Taif University, Taif, Saudi Arabia
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13
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Tandon R, Agrawal S, Chang A, Band SS. VCNet: Hybrid Deep Learning Model for Detection and Classification of Lung Carcinoma Using Chest Radiographs. Front Public Health 2022; 10:894920. [PMID: 35795700 PMCID: PMC9251197 DOI: 10.3389/fpubh.2022.894920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 04/22/2022] [Indexed: 11/25/2022] Open
Abstract
Detection of malignant lung nodules from Computed Tomography (CT) images is a significant task for radiologists. But, it is time-consuming in nature. Despite numerous breakthroughs in studies on the application of deep learning models for the identification of lung cancer, researchers and doctors still face challenges when trying to deploy the model in clinical settings to achieve improved accuracy and sensitivity on huge datasets. In most situations, deep convolutional neural networks are used for detecting the region of the main nodule of the lung exclusive of considering the neighboring tissues of the nodule. Although the accuracy achieved through CNN is good enough but this models performance degrades when there are variations in image characteristics like: rotation, tiling, and other abnormal image orientations. CNN does not store relative spatial relationships among features in scanned images. As CT scans have high spatial resolution and are sensitive to misalignments during the scanning process, there is a requirement of a technique which helps in considering spatial information of image features also. In this paper, a hybrid model named VCNet is proposed by combining the features of VGG-16 and capsule network (CapsNet). VGG-16 model is used for object recognition and classification. CapsNet is used to address the shortcomings of convolutional neural networks for image rotation, tiling, and other abnormal image orientations. The performance of VCNeT is verified on the Lung Image Database Consortium (LIDC) image collection dataset. It achieves higher testing accuracy of 99.49% which is significantly better than MobileNet, Xception, and VGG-16 that has achieved an accuracy of 98, 97.97, and 96.95%, respectively. Therefore, the proposed hybrid VCNet framework can be used for the clinical purpose for nodule detection in lung carcinoma detection.
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Affiliation(s)
- Ritu Tandon
- Institute of Advance Computing, SAGE University, Indore, India
- *Correspondence: Ritu Tandon
| | - Shweta Agrawal
- Institute of Advance Computing, SAGE University, Indore, India
- Shweta Agrawal
| | - Arthur Chang
- Bachelor Program in Interdisciplinary Studies, National Yunlin University of Science and Technology, Douliu, Taiwan
- Arthur Chang
| | - Shahab S. Band
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Douliu, Taiwan
- Shahab S. Band
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14
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A A, M P, Bourouis S, Band SS, Mosavi A, Agrawal S, Hamdi M. Meta-Heuristic Algorithm-Tuned Neural Network for Breast Cancer Diagnosis Using Ultrasound Images. Front Oncol 2022; 12:834028. [PMID: 35769710 PMCID: PMC9234296 DOI: 10.3389/fonc.2022.834028] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Accepted: 03/14/2022] [Indexed: 11/18/2022] Open
Abstract
Breast cancer is the most menacing cancer among all types of cancer in women around the globe. Early diagnosis is the only way to increase the treatment options which then decreases the death rate and increases the chance of survival in patients. However, it is a challenging task to differentiate abnormal breast tissues from normal tissues because of their structure and unclear boundaries. Therefore, early and accurate diagnosis and classification of breast lesions into malignant or benign lesions is an active domain of research. Over the decade, numerous artificial neural network (ANN)-based techniques were adopted in order to diagnose and classify breast cancer due to the unique characteristics of learning key features from complex data via a training process. However, these schemes have limitations like slow convergence and longer training time. To address the above mentioned issues, this paper employs a meta-heuristic algorithm for tuning the parameters of the neural network. The main novelty of this work is the computer-aided diagnosis scheme for detecting abnormalities in breast ultrasound images by integrating a wavelet neural network (WNN) and the grey wolf optimization (GWO) algorithm. Here, breast ultrasound (US) images are preprocessed with a sigmoid filter followed by interference-based despeckling and then by anisotropic diffusion. The automatic segmentation algorithm is adopted to extract the region of interest, and subsequently morphological and texture features are computed. Finally, the GWO-tuned WNN is exploited to accomplish the classification task. The classification performance of the proposed scheme is validated on 346 ultrasound images. Efficiency of the proposed methodology is evaluated by computing the confusion matrix and receiver operating characteristic (ROC) curve. Numerical analysis revealed that the proposed work can yield higher classification accuracy when compared to the prevailing methods and thereby proves its potential in effective breast tumor detection and classification. The proposed GWO-WNN method (98%) gives better accuracy than other methods like SOM-SVM (87.5), LOFA-SVM (93.62%), MBA-RF (96.85%), and BAS-BPNN (96.3%)
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Affiliation(s)
- Ahila A
- Department of Electronics and Communication Engineering, Sethu Institute of Technology, Kariapatti, India
- *Correspondence: Ahila A, ; Poongodi M., ; Shahab S. Band, ; Amir Mosavi,
| | - Poongodi M
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
- *Correspondence: Ahila A, ; Poongodi M., ; Shahab S. Band, ; Amir Mosavi,
| | - Sami Bourouis
- Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
| | - Shahab S. Band
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Douliou, Taiwan
- *Correspondence: Ahila A, ; Poongodi M., ; Shahab S. Band, ; Amir Mosavi,
| | - Amir Mosavi
- John von Neumann Faculty of Informatics, Obuda University, Budapest, Hungary
- *Correspondence: Ahila A, ; Poongodi M., ; Shahab S. Band, ; Amir Mosavi,
| | | | - Mounir Hamdi
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
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15
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Venkatesh C, Ramana K, Lakkisetty SY, Band SS, Agarwal S, Mosavi A. A Neural Network and Optimization Based Lung Cancer Detection System in CT Images. Front Public Health 2022; 10:769692. [PMID: 35747775 PMCID: PMC9210805 DOI: 10.3389/fpubh.2022.769692] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 01/20/2022] [Indexed: 11/20/2022] Open
Abstract
One of the most common causes of death from cancer for both women and men is lung cancer. Lung nodules are critical for the screening of cancer and early recognition permits treatment and enhances the rate of rehabilitation in patients. Although a lot of work is being done in this area, an increase in accuracy is still required to swell patient persistence rate. However, traditional systems do not segment cancer cells of different forms accurately and no system attained greater reliability. An effective screening procedure is proposed in this work to not only identify lung cancer lesions rapidly but to increase accuracy. In this procedure, Otsu thresholding segmentation is utilized to accomplish perfect isolation of the selected area, and the cuckoo search algorithm is utilized to define the best characteristics for partitioning cancer nodules. By using a local binary pattern, the relevant features of the lesion are retrieved. The CNN classifier is designed to spot whether a lung lesion is malicious or non-malicious based on the retrieved features. The proposed framework achieves an accuracy of 96.97% percent. The recommended study reveals that accuracy is improved, and the results are compiled using Particle swarm optimization and genetic algorithms.
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Affiliation(s)
- Chapala Venkatesh
- Department of ECE, Annamacharya Institute of Technology and Sciences, Rajampet, India
| | - Kadiyala Ramana
- Department of IT, Chaitanya Bharathi Institute of Technology, Hyderabad, India
- Kadiyala Ramana
| | | | - Shahab S. Band
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Douliou, Taiwan
- *Correspondence: Shahab S. Band
| | | | - Amir Mosavi
- John von Neumann Faculty of Informatics, Obuda University, Budapest, Hungary
- Faculty of Civil Engineering, TU-Dresden, Dresden, Germany
- Institute of Information Engineering, Automation and Mathematics, Slovak University of Technology in Bratislava, Bratislava, Slovakia
- Amir Mosavi
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16
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Safaei-Farouji M, Band SS, Mosavi A. Oil Family Typing Using a Hybrid Model of Self-Organizing Maps and Artificial Neural Networks. ACS Omega 2022; 7:11578-11586. [PMID: 35449927 PMCID: PMC9017107 DOI: 10.1021/acsomega.1c05811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 01/24/2022] [Indexed: 06/14/2023]
Abstract
Identifying the number of oil families in petroleum basins provides practical and valuable information in petroleum geochemistry studies from exploration to development. Oil family grouping helps us track migration pathways, identify the number of active source rock(s), and examine the reservoir continuity. To date, almost in all oil family typing studies, common statistical methods such as principal component analysis (PCA) and hierarchical clustering analysis (HCA) have been used. However, there is no publication regarding using artificial neural networks (ANNs) for examining the oil families in petroleum basins. Hence, oil family typing requires novel and not overused and common techniques. This paper is the first report of oil family typing using ANNs as robust computational methods. To this end, a self-organization map (SOM) neural network associated with three clustering validity indexes was employed on oil samples belonging to the Iranian part of the Persian Gulf oilfields. For the SOM network, at first, 10 default clusters were selected. Afterward, three effective clustering validity coefficients, namely, Calinski-Harabasz (CH), Silhouette (SH), and Davies-Bouldin (DB), were studied to find the optimum number of clusters. Accordingly, among 10 default clusters, the maximum CH (62) and SH (0.58) were acquired for 4 clusters. Similarly, the lowest DB (0.8) was obtained for four clusters. Thus, all three validation coefficients introduced four clusters as the optimum number of clusters or oil families. According to the geochemical parameters, it can be deduced that the corresponding source rocks of four oil families have been deposited in a marine carbonate depositional environment under dysoxic-anoxic conditions. However, oil families show some differences based on geochemical data. The number of oil families identified in the present report is consistent with those previously reported by other researchers in the same study area. However, the techniques used in the present paper, which have not been implemented so far, can be introduced as more straightforward for clustering purposes in oil family typing than those of common and overused methods of PCA and HCA.
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Affiliation(s)
- Majid Safaei-Farouji
- School
of Geology, College of Science, University
of Tehran 1417935840 Tehran, Iran
| | - Shahab S. Band
- Future
Technology Research Center, College of Future, National Yunlin University of Science and Technology, 123 10 University Road, Section
3, Douliou, Yunlin 64002, Taiwan, ROC
| | - Amir Mosavi
- John
von Neumann Faculty of Informatics, Obuda
University, 1034 Budapest, Hungary
- Institute
of Information Society, University of Public
Service, 1083 Budapest, Hungary
- Institute
of Information Engineering, Automation and Mathematics, Slovak University of Technology, 812 37 Bratislava, Slovakia
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17
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Akhtar SM, Nazir M, Saleem K, Ahmad RZ, Javed AR, S. Band S, Mosavi A. A Multi-Agent Formalism Based on Contextual Defeasible Logic for Healthcare Systems. Front Public Health 2022; 10:849185. [PMID: 35309219 PMCID: PMC8927623 DOI: 10.3389/fpubh.2022.849185] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 01/24/2022] [Indexed: 12/05/2022] Open
Abstract
In the last decade, smart computing has garnered much attention, particularly in ubiquitous environments, thus increasing the ease of everyday human life. Users can dynamically interact with the systems using different modalities in a smart computing environment. The literature discussed multiple mechanisms to enhance the modalities for communication using different knowledge sources. Among others, Multi-context System (MCS) has been proven quite significant to interlink various context domains dynamically to a distributed environment. MCS is a collection of different contexts (independent knowledge sources), and every context contains its own set of defined rules and facts and inference systems. These contexts are interlinked via bridge rules. However, the interaction among knowledge sources could have the consequences such as bringing out inconsistent results. These issues may report situations such as the system being unable to reach a conclusion or communication in different contexts becoming asynchronous. There is a need for a suitable framework to resolve inconsistencies. In this article, we provide a framework based on contextual defeasible reasoning and a formalism of multi-agent environment is to handle the issue of inconsistent information in MCS. Additionally, in this work, a prototypal simulation is designed using a simulation tool called NetLogo, and a formalism about a Parkinson's disease patient's case study is also developed. Both of these show the validity of the framework.
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Affiliation(s)
- Salwa Muhammad Akhtar
- Computer Science Department, Faculty of Computer Science and IT, University of Lahore, Lahore, Pakistan
| | - Makia Nazir
- Computer Science Department, Faculty of Computer Science and IT, University of Lahore, Lahore, Pakistan
| | - Kiran Saleem
- School of Software, Dalian University of Technology, Dalian, China
| | - Rana Zeeshan Ahmad
- Department of Information Technology, University of Sialkot, Sialkot, Pakistan
| | - Abdul Rehman Javed
- Department of Cyber Security, Air University, Islamabad, Pakistan
- *Correspondence: Abdul Rehman Javed
| | - Shahab S. Band
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Douliou, Taiwan
- Shahab S. Band
| | - Amir Mosavi
- Institute of Information Society, University of Public Service, Budapest, Hungary
- John von Neumann Faculty of Informatics, Obuda University, Budapest, Hungary
- Institute of Information Engineering, Automation and Mathematics, Slovak University of Technology in Bratislava, Bratislava, Slovakia
- Amir Mosavi
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18
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Mani V, Kavitha C, Band SS, Mosavi A, Hollins P, Palanisamy S. A Recommendation System Based on AI for Storing Block Data in the Electronic Health Repository. Front Public Health 2022; 9:831404. [PMID: 35127632 PMCID: PMC8814315 DOI: 10.3389/fpubh.2021.831404] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 12/20/2021] [Indexed: 11/16/2022] Open
Abstract
The proliferation of wearable sensors that record physiological signals has resulted in an exponential growth of data on digital health. To select the appropriate repository for the increasing amount of collected data, intelligent procedures are becoming increasingly necessary. However, allocating storage space is a nuanced process. Generally, patients have some input in choosing which repository to use, although they are not always responsible for this decision. Patients are likely to have idiosyncratic storage preferences based on their unique circumstances. The purpose of the current study is to develop a new predictive model of health data storage to meet the needs of patients while ensuring rapid storage decisions, even when data is streaming from wearable devices. To create the machine learning classifier, we used a training set synthesized from small samples of experts who exhibited correlations between health data and storage features. The results confirm the validity of the machine learning methodology.
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Affiliation(s)
- Vinodhini Mani
- Department of Computer Science and Engineering, School of Computing, Sathyabama Institute of Science and Technology, Chennai, India
- *Correspondence: Vinodhini Mani
| | - C. Kavitha
- Department of Computer Science and Engineering, School of Computing, Sathyabama Institute of Science and Technology, Chennai, India
| | - Shahab S. Band
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Yunlin, Taiwan
- Shahab S. Band
| | - Amir Mosavi
- Faculty of Civil Engineering, TU-Dresden, Dresden, Germany
- Institute of Information Society, University of Public Service, Budapest, Hungary
- John von Neumann Faculty of Informatics, Obuda University, Budapest, Hungary
- Amir Mosavi
| | - Paul Hollins
- Cultural Research Development School of Arts, Institute of Management, University of Bolton, Bolton, United Kingdom
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19
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Sharifrazi D, Alizadehsani R, Joloudari JH, Band SS, Hussain S, Sani ZA, Hasanzadeh F, Shoeibi A, Dehzangi A, Sookhak M, Alinejad-Rokny H. CNN-KCL: Automatic myocarditis diagnosis using convolutional neural network combined with k-means clustering. Math Biosci Eng 2022; 19:2381-2402. [PMID: 35240789 DOI: 10.3934/mbe.2022110] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Myocarditis is the form of an inflammation of the middle layer of the heart wall which is caused by a viral infection and can affect the heart muscle and its electrical system. It has remained one of the most challenging diagnoses in cardiology. Myocardial is the prime cause of unexpected death in approximately 20% of adults less than 40 years of age. Cardiac MRI (CMR) has been considered a noninvasive and golden standard diagnostic tool for suspected myocarditis and plays an indispensable role in diagnosing various cardiac diseases. However, the performance of CMR depends heavily on the clinical presentation and features such as chest pain, arrhythmia, and heart failure. Besides, other imaging factors like artifacts, technical errors, pulse sequence, acquisition parameters, contrast agent dose, and more importantly qualitatively visual interpretation can affect the result of the diagnosis. This paper introduces a new deep learning-based model called Convolutional Neural Network-Clustering (CNN-KCL) to diagnose Myocarditis. In this study, we used 47 subjects with a total number of 98,898 images to diagnose myocarditis disease. Our results demonstrate that the proposed method achieves an accuracy of 97.41% based on 10 fold-cross validation technique with 4 clusters for diagnosis of Myocarditis. To the best of our knowledge, this research is the first to use deep learning algorithms for the diagnosis of myocarditis.
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Affiliation(s)
- Danial Sharifrazi
- Department of Computer Engineering, School of Technical and Engineering, Shiraz Branch, Islamic Azad University, Shiraz, IR
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, VIC 3216, AU
| | | | - Shahab S Band
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology 123 University Road, Section 3, Douliou, Yunlin 64002, TW
| | - Sadiq Hussain
- System Administrator, Dibrugarh University, Assam 786004, IN
| | - Zahra Alizadeh Sani
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
- Omid hospital, Iran University of Medical Sciences, Tehran, IR
| | | | - Afshin Shoeibi
- FPGA Laboratory, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, IR
| | - Abdollah Dehzangi
- Department of Computer Science, Rutgers University, Camden, NJ 08102, USA
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA
| | - Mehdi Sookhak
- Department of Computer Science, Texas A & M University at Corpus Christi, Corpus Christi, TX 78412, USA
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, AU
- Health Data Analytics Program, AI-enabled Processes (AIP) Research Centre, Macquarie University, Sydney 2109, AU
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20
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Rahman A, Chakraborty C, Anwar A, Karim MR, Islam MJ, Kundu D, Rahman Z, Band SS. SDN-IoT empowered intelligent framework for industry 4.0 applications during COVID-19 pandemic. Cluster Comput 2022; 25:2351-2368. [PMID: 34341656 PMCID: PMC8318841 DOI: 10.1007/s10586-021-03367-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 06/29/2021] [Accepted: 07/18/2021] [Indexed: 05/09/2023]
Abstract
The industrial ecosystem has been unprecedentedly affected by the COVID-19 pandemic because of its immense contact restrictions. Therefore, the manufacturing and socio-economic operations that require human involvement have significantly intervened since the beginning of the outbreak. As experienced, the social-distancing lesson in the potential new-normal world seems to force stakeholders to encourage the deployment of contactless Industry 4.0 architecture. Thus, human-less or less-human operations to keep these IoT-enabled ecosystems running without interruptions have motivated us to design and demonstrate an intelligent automated framework. In this research, we have proposed "EdgeSDN-I4COVID" architecture for intelligent and efficient management during COVID-19 of the smart industry considering the IoT networks. Moreover, the article presents the SDN-enabled layer, such as data, control, and application, to effectively and automatically monitor the IoT data from a remote location. In addition, the proposed convergence between SDN and NFV provides an efficient control mechanism for managing the IoT sensor data. Besides, it offers robust data integration on the surface and the devices required for Industry 4.0 during the COVID-19 pandemic. Finally, the article justified the above contributions through particular performance evaluations upon appropriate simulation setup and environment.
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Affiliation(s)
- Anichur Rahman
- National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka, Bangladesh
| | - Chinmay Chakraborty
- Electronics and Communication Engineering, Birla Institute of Technology, Mesra, Jharkhand India
| | - Adnan Anwar
- Centre for Cyber Security Resaerch and Innovation (CSRI), Deakin University, Melbourne, VIC 3220 Australia
| | - Md. Razaul Karim
- Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | | | - Dipanjali Kundu
- National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka, Bangladesh
| | - Ziaur Rahman
- Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Shahab S. Band
- National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliu, Taiwan
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21
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Habibi H, Rasoolzadegan A, Mashmool A, Band SS, Chronopoulos AT, Mosavi A. SaaSRec+: a new context-aware recommendation method for SaaS services. Math Biosci Eng 2022; 19:1471-1495. [PMID: 35135213 DOI: 10.3934/mbe.2022068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Cloud computing is an attractive model that provides users with a variety of services. Thus, the number of cloud services on the market is growing rapidly. Therefore, choosing the proper cloud service is an important challenge. Another major challenge is the availability of diverse cloud services with similar performance, which makes it difficult for users to choose the cloud service that suits their needs. Therefore, the existing service selection approaches is not able to solve the problem, and cloud service recommendation has become an essential and important need. In this paper, we present a new way for context-aware cloud service recommendation. Our proposed method seeks to solve the weakness in user clustering, which itself is due to reasons such as 1) lack of full use of contextual information such as cloud service placement, and 2) inaccurate method of determining the similarity of two vectors. The evaluation conducted by the WSDream dataset indicates a reduction in the cloud service recommendation process error rate. The volume of data used in the evaluation of this paper is 5 times that of the basic method. Also, according to the T-test, the service recommendation performance in the proposed method is significant.
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Affiliation(s)
- Hossein Habibi
- Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Abbas Rasoolzadegan
- Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Amir Mashmool
- Department of Computer Engineering, University of Birjand, Birjand, Iran
| | - Shahab S Band
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan
| | - Anthony Theodore Chronopoulos
- Department of Computer Science, University of Texas, San Antonio TX 78249, USA
- Department of Computer Engineering & Informatics, University of Patras, Rio 26500, Greece
| | - Amir Mosavi
- Faculty of Civil Engineering, Technische Universitat Dresden, Dresden 01069, Germany
- Institute of Software Design and Development, Obuda University, Budapest 1034, Hungary
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22
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Rahman A, Rahman M, Kundu D, Karim MR, Band SS, Sookhak M. Study on IoT for SARS-CoV-2 with healthcare: present and future perspective. Math Biosci Eng 2021; 18:9697-9726. [PMID: 34814364 DOI: 10.3934/mbe.2021475] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The ever-evolving and contagious nature of the Coronavirus (COVID-19) has immobilized the world around us. As the daily number of infected cases increases, the containment of the spread of this virus is proving to be an overwhelming task. Healthcare facilities around the world are overburdened with an ominous responsibility to combat an ever-worsening scenario. To aid the healthcare system, Internet of Things (IoT) technology provides a better solution-tracing, testing of COVID patients efficiently is gaining rapid pace. This study discusses the role of IoT technology in healthcare during the SARS-CoV-2 pandemics. The study overviews different research, platforms, services, products where IoT is used to combat the COVID-19 pandemic. Further, we intelligently integrate IoT and healthcare for COVID-19 related applications. Again, we focus on a wide range of IoT applications in regards to SARS-CoV-2 tracing, testing, and treatment. Finally, we effectively consider further challenges, issues, and some direction regarding IoT in order to uplift the healthcare system during COVID-19 and future pandemics.
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Affiliation(s)
- Anichur Rahman
- Department of Computer Science and Engineering, National Institute of Textile Engineering and Research (NITER), Constituent Institute of Dhaka University, Savar, Dhaka-1350, Bangladesh
- Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Muaz Rahman
- Department of Electrical and Electronic Engineering, National Institute of Textile Engineering and Research (NITER), Constituent Institute of Dhaka University, Savar, Dhaka-1350, Bangladesh
| | - Dipanjali Kundu
- Department of Computer Science and Engineering, National Institute of Textile Engineering and Research (NITER), Constituent Institute of Dhaka University, Savar, Dhaka-1350, Bangladesh
| | - Md Razaul Karim
- Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Shahab S Band
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan
| | - Mehdi Sookhak
- Dept. of Computer Science, Texas A & M University-Corpus Christi, 6300 Ocean Drive, Corpus Christi, Texas, USA, 78412
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23
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Deif MA, Solyman AAA, Kamarposhti MA, Band SS, Hammam RE. A deep bidirectional recurrent neural network for identification of SARS-CoV-2 from viral genome sequences. Math Biosci Eng 2021; 18:8933-8950. [PMID: 34814329 DOI: 10.3934/mbe.2021440] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
In this work, Deep Bidirectional Recurrent Neural Networks (BRNNs) models were implemented based on both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) cells in order to distinguish between genome sequence of SARS-CoV-2 and other Corona Virus strains such as SARS-CoV and MERS-CoV, Common Cold and other Acute Respiratory Infection (ARI) viruses. An investigation of the hyper-parameters including the optimizer type and the number of unit cells, was also performed to attain the best performance of the BRNN models. Results showed that the GRU BRNNs model was able to discriminate between SARS-CoV-2 and other classes of viruses with a higher overall classification accuracy of 96.8% as compared to that of the LSTM BRNNs model having a 95.8% overall classification accuracy. The best hyper-parameters producing the highest performance for both models was obtained when applying the SGD optimizer and an optimum number of unit cells of 80 in both models. This study proved that the proposed GRU BRNN model has a better classification ability for SARS-CoV-2 thus providing an efficient tool to help in containing the disease and achieving better clinical decisions with high precision.
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Affiliation(s)
- Mohanad A Deif
- Department of Bioelectronics, Modern University of Technology and Information (MTI) University, Cairo 11571, Egypt
| | - Ahmed A A Solyman
- Department of Electrical and Electronics Engineering, Istanbul Gelisim University, Avcılar 34310, Turkey
| | | | - Shahab S Band
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, 123 University Road, Yunlin 64002, Taiwan
| | - Rania E Hammam
- Department of Bioelectronics, Modern University of Technology and Information (MTI) University, Cairo 11571, Egypt
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24
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Kumar RL, Khan F, Din S, Band SS, Mosavi A, Ibeke E. Recurrent Neural Network and Reinforcement Learning Model for COVID-19 Prediction. Front Public Health 2021; 9:744100. [PMID: 34671588 PMCID: PMC8521000 DOI: 10.3389/fpubh.2021.744100] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 09/02/2021] [Indexed: 01/11/2023] Open
Abstract
Detection and prediction of the novel Coronavirus present new challenges for the medical research community due to its widespread across the globe. Methods driven by Artificial Intelligence can help predict specific parameters, hazards, and outcomes of such a pandemic. Recently, deep learning-based approaches have proven a novel opportunity to determine various difficulties in prediction. In this work, two learning algorithms, namely deep learning and reinforcement learning, were developed to forecast COVID-19. This article constructs a model using Recurrent Neural Networks (RNN), particularly the Modified Long Short-Term Memory (MLSTM) model, to forecast the count of newly affected individuals, losses, and cures in the following few days. This study also suggests deep learning reinforcement to optimize COVID-19's predictive outcome based on symptoms. Real-world data was utilized to analyze the success of the suggested system. The findings show that the established approach promises prognosticating outcomes concerning the current COVID-19 pandemic and outperformed the Long Short-Term Memory (LSTM) model and the Machine Learning model, Logistic Regresion (LR) in terms of error rate.
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Affiliation(s)
- R. Lakshmana Kumar
- Department of Computer Applications, Hindusthan College of Engineering and Technology, Coimbatore, India
| | - Firoz Khan
- Dubai Men's College, Higher Colleges of Technology, Dubai, United Arab Emirates
| | - Sadia Din
- Department of Information and Communication Engineering, Yeung University, Gyeongsan, South Korea
| | - Shahab S. Band
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Douliu, Taiwan
| | - Amir Mosavi
- Faculty of Civil Engineering, Technische Universität Dresden, Dresden, Germany
- John von Neumann Faculty of Informatics, Obuda University, Budapest, Hungary
| | - Ebuka Ibeke
- School of Creative and Cultural Business, Robert Gordon University, Aberdeen, United Kingdom
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25
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Chuang HM, Band SS, Sookhak M, Pinandhito K. The value co-creation behavior in learning communities: Comparing conventional learning and e-learning. Math Biosci Eng 2021; 18:7239-7268. [PMID: 34814247 DOI: 10.3934/mbe.2021358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
With the rapid development of ICT, the present world is experiencing rapid changes in the field of education. Implementation of e-learning and ICT in the education system could allow teachers to upgrade and improve their lectures. However, from the perspective of value co-creation behavior in learning communities, conventional learning and e-learning classrooms may encounter different opportunities and challenges. Thus, a more in-depth investigation would be needed. Based on the S-O-R framework, this study identifies self-directed learning as a stimulus, perceived benefits as the organism, and value co-creation behavior as the response. By applying the multi-criteria decision-making techniques of DEMATEL, ANP, and VIKOR, this study explores the causal effects, influential weights, and performance ranking of the primary constructs in the framework as criteria. This study's theoretical and practical implications are discussed, and ways of improving learning performance are suggested.
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Affiliation(s)
- Huan-Ming Chuang
- Department of Information Management, National Yunlin University of Science and Technology, 123 University Road, Yunlin 64002, Taiwan
| | - Shahab S Band
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, 123 University Road, Yunlin 64002, Taiwan
| | - Mehdi Sookhak
- School of Information Technology, Illinois State University, Illinois, USA
| | - Kenneth Pinandhito
- Department of Information Management, National Yunlin University of Science and Technology, 123 University Road, Yunlin 64002, Taiwan
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26
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Shojaei S, Shojaei S, Band SS, Farizhandi AAK, Ghoroqi M, Mosavi A. Application of Taguchi method and response surface methodology into the removal of malachite green and auramine-O by NaX nanozeolites. Sci Rep 2021; 11:16054. [PMID: 34362984 PMCID: PMC8346513 DOI: 10.1038/s41598-021-95649-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 07/28/2021] [Indexed: 02/07/2023] Open
Abstract
In the present study, the simultaneous removal of malachite green (MG) and auramine-O (AO) dyes from the aqueous solution by NaX nanozeolites in a batch system is investigated. Taguchi method and response surface methodology (RSM) were used to optimize and model dye removal conditions. In order to do so, the effect of various factors (dyes concentration, sonication time, ionic strength, adsorbent dosage, temperature, and pH of the solution) on the amount of dye removal was evaluated by the Taguchi method. Then, the most important factors were chosen and modeled by the RSM method so as to reach the highest percentage of dye removal. The proposed quadratic models to remove both dyes were in good accordance with the actual experimental data. The maximum removal efficiencies of MG and AO dyes in optimal operating conditions were 99.07% and 99.61%, respectively. Also, the coefficients of determination (R2) for test data were 0.9983 and 0.9988 for MG and AO dyes, respectively. The reusability of NaX nanozeolites was evaluated during the adsorption process of MG and AO. The results showed that the adsorption efficiency decreases very little up to five cycles. Moreover, NaX nanozeolites were also applied as adsorbents to remove MG and AO from environmental water samples, and more than 98.1% of both dyes were removed from the solution in optimal conditions.
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Affiliation(s)
- Siroos Shojaei
- Department of Chemistry, Faculty of Sciences, University of Sistan and Baluchestan, Zahedan, 98135-674, Iran.
| | - Saeed Shojaei
- Department of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran, Tehran, Iran
| | - Shahab S Band
- Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, 64002, Yunlin, Taiwan.
| | | | - Milad Ghoroqi
- Department of Civil Engineering, Islamic Azad University, Central Tehran Branch, Tehran, P.O. Box 13185, Iran
| | - Amir Mosavi
- John von Neumann Faculty of Informatics, Obuda University, 1034, Budapest, Hungary
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27
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Mahmoudi MR, Baleanu D, Band SS, Mosavi A. Factor analysis approach to classify COVID-19 datasets in several regions. Results Phys 2021; 25:104071. [PMID: 33777669 PMCID: PMC7982653 DOI: 10.1016/j.rinp.2021.104071] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 03/05/2021] [Accepted: 03/08/2021] [Indexed: 05/23/2023]
Abstract
The aim of this research is to investigate the relationships between the counts of cases with Covid-19 and the deaths due to it in seven countries that are severely affected from this pandemic disease. First, the Pearson's correlation is used to determine the relationships among these countries. Then, the factor analysis is applied to categorize these countries based on their relationships.
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Affiliation(s)
| | - Dumitru Baleanu
- Department of Mathematics, Faculty of Art and Sciences, Cankaya University Balgat 06530, Ankara, Turkey
- Institute of Space Sciences, Magurele-Bucharest, Romania
| | - Shahab S Band
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan, ROC
| | - Amir Mosavi
- John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary
- School of Economics and Business, Norwegian University of Life Sciences, 1430 Ås, Norway
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28
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Saha S, Arabameri A, Saha A, Blaschke T, Ngo PTT, Nhu VH, Band SS. Prediction of landslide susceptibility in Rudraprayag, India using novel ensemble of conditional probability and boosted regression tree-based on cross-validation method. Sci Total Environ 2021; 764:142928. [PMID: 33127137 DOI: 10.1016/j.scitotenv.2020.142928] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 10/02/2020] [Accepted: 10/04/2020] [Indexed: 06/11/2023]
Abstract
The present research examines the landslide susceptibility in Rudraprayag district of Uttarakhand, India using the conditional probability (CP) statistical technique, the boost regression tree (BRT) machine learning algorithm, and the CP-BRT ensemble approach to improve the accuracy of the BRT model. Using the four fold of data, the models' outcomes were cross-checked. The locations of existing landslides were detected by general field surveys and relevant records. 220 previous landslide locations were obtained, presented as an inventory map, and divided into four folds to calibrate and authenticate the models. For modelling the landslide susceptibility, twelve LCFs (landslide conditioning factors) were used. Two statistical methods, i.e. the mean absolute error (MAE) and the root mean square error (RMSE), one statistical test, i.e. the Freidman rank test, as well as the receiver operating characteristic (ROC), efficiency and precision were used for authenticating the produced landslide models. The results of the accuracy measures revealed that all models have good potential to recognize the landslide susceptibility in the Garhwal Himalayan region. Among these models, the ensemble model achieved a higher accuracy (precision: 0.829, efficiency: 0.833, AUC: 89.460, RMSE: 0.069 and MAE: 0.141) than the individual models. According to the outcome of the ensemble simulations, the BRT model's predictive accuracy was enhanced by integrating it with the statistical model (CP). The study showed that the areas of fallow land, plantation fields, and roadsides with elevations of more than 1500 m. with steep slopes of 24° to 87° and eroding hills are highly susceptible to landslides. The findings of this work could help in minimizing the landslides' risk in the Western Himalaya and its adjoining areas with similar landscapes and geological characteristics.
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Affiliation(s)
- Sunil Saha
- Department of Geography, University of Gour Banga, West Bengal 732103, India
| | - Alireza Arabameri
- Department of Geomorphology, Tarbiat Modares University, Tehran 14115-111, Iran.
| | - Anik Saha
- Department of Geography, University of Gour Banga, West Bengal 732103, India
| | - Thomas Blaschke
- Department of Geoinformatics - Z_GIS, University of Salzburg, 5020 Salzburg, Austria.
| | - Phuong Thao Thi Ngo
- Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam; Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City 700000, Viet Nam.
| | - Viet Ha Nhu
- Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Viet Nam.
| | - Shahab S Band
- Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam; Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan, ROC.
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29
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Nosratabadi S, Khazami N, Abdallah MB, Lackner Z, S. Band S, Mosavi A, Mako C. Social Capital Contributions to Food Security: A Comprehensive Literature Review. Foods 2020; 9:foods9111650. [PMID: 33198127 PMCID: PMC7698312 DOI: 10.3390/foods9111650] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Revised: 10/30/2020] [Accepted: 11/10/2020] [Indexed: 01/23/2023] Open
Abstract
Social capital creates a synergy that benefits all members of a community. This review examines how social capital contributes to the food security of communities. A systematic literature review, based on Prisma, is designed to provide a state of the art review on capacity social capital in this realm. The output of this method led to finding 39 related articles. Studying these articles illustrates that social capital improves food security through two mechanisms of knowledge sharing and product sharing (i.e., sharing food products). It reveals that social capital through improving the food security pillars (i.e., food availability, food accessibility, food utilization, and food system stability) affects food security. In other words, the interaction among the community members results in sharing food products and information among community members, which facilitates food availability and access to food. There are many shreds of evidence in the literature that sharing food and food products among the community member decreases household food security and provides healthy nutrition to vulnerable families, and improves the food utilization pillar of food security. It is also disclosed that belonging to the social networks increases the community members’ resilience and decreases the community’s vulnerability that subsequently strengthens the stability of a food system. This study contributes to the common literature on food security and social capital by providing a conceptual model based on the literature. In addition to researchers, policymakers can use this study’s findings to provide solutions to address food insecurity problems.
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Affiliation(s)
- Saeed Nosratabadi
- Doctoral School of Management and Business Administration, Szent Istvan University, 2100 Godollo, Hungary; (S.N.); (N.K.); (M.B.A.)
| | - Nesrine Khazami
- Doctoral School of Management and Business Administration, Szent Istvan University, 2100 Godollo, Hungary; (S.N.); (N.K.); (M.B.A.)
| | - Marwa Ben Abdallah
- Doctoral School of Management and Business Administration, Szent Istvan University, 2100 Godollo, Hungary; (S.N.); (N.K.); (M.B.A.)
| | - Zoltan Lackner
- Department of Food Economics, Faculty of Food Science, Szent Istvan University, Villanyi str. 29-43, 1118 Budapest, Hungary;
| | - Shahab S. Band
- Future Technology Research Center, National Yunlin University of Science and Technology, Douliou, Yunlin 64002, Taiwan
- Correspondence: (S.S.B.); (A.M.)
| | - Amir Mosavi
- Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany
- Thuringian Institute of Sustainability and Climate Protection, 07743 Jena, Germany
- School of Economics and Business, Norwegian University of Life Sciences, 1430 Ås, Norway
- John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary
- School of the Built Environment, Oxford Brookes University, Oxford OX3 0BP, UK
- Correspondence: (S.S.B.); (A.M.)
| | - Csaba Mako
- Department of Public Management and Information Technology, National University of Public Services, 1083 Budapest, Hungary;
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30
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Ecer F, Ardabili S, Band SS, Mosavi A. Training Multilayer Perceptron with Genetic Algorithms and Particle Swarm Optimization for Modeling Stock Price Index Prediction. Entropy (Basel) 2020; 22:e22111239. [PMID: 33287007 PMCID: PMC7712111 DOI: 10.3390/e22111239] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 10/28/2020] [Accepted: 10/28/2020] [Indexed: 11/16/2022]
Abstract
Predicting stock market (SM) trends is an issue of great interest among researchers, investors and traders since the successful prediction of SMs' direction may promise various benefits. Because of the fairly nonlinear nature of the historical data, accurate estimation of the SM direction is a rather challenging issue. The aim of this study is to present a novel machine learning (ML) model to forecast the movement of the Borsa Istanbul (BIST) 100 index. Modeling was performed by multilayer perceptron-genetic algorithms (MLP-GA) and multilayer perceptron-particle swarm optimization (MLP-PSO) in two scenarios considering Tanh (x) and the default Gaussian function as the output function. The historical financial time series data utilized in this research is from 1996 to 2020, consisting of nine technical indicators. Results are assessed using Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and correlation coefficient values to compare the accuracy and performance of the developed models. Based on the results, the involvement of the Tanh (x) as the output function, improved the accuracy of models compared with the default Gaussian function, significantly. MLP-PSO with population size 125, followed by MLP-GA with population size 50, provided higher accuracy for testing, reporting RMSE of 0.732583 and 0.733063, MAPE of 28.16%, 29.09% and correlation coefficient of 0.694 and 0.695, respectively. According to the results, using the hybrid ML method could successfully improve the prediction accuracy.
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Affiliation(s)
- Fatih Ecer
- Department of Business Administration, Afyon Kocatepe University, Afyonkarahisar 03030, Turkey;
| | - Sina Ardabili
- Biosystem Engineering Department, University of Mohaghegh Ardabili, Ardabil 5619911367, Iran;
- Kando Kalman Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary
| | - Shahab S. Band
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan;
| | - Amir Mosavi
- Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany
- Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
- School of Economics and Business, Norwegian University of Life Sciences, 1430 As, Norway
- Correspondence: or
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31
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Akhoundi B, Nabipour M, Hajami F, Band SS, Mosavi A. Calculating Filament Feed in the Fused Deposition Modeling Process to Correctly Print Continuous Fiber Composites in Curved Paths. Materials (Basel) 2020; 13:ma13204480. [PMID: 33050351 PMCID: PMC7600913 DOI: 10.3390/ma13204480] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 10/01/2020] [Accepted: 10/07/2020] [Indexed: 11/17/2022]
Abstract
Fused deposition modeling (FDM) is a popular additive manufacturing (AM) method that has attracted the attention of various industries due to its simplicity, cheapness, ability to produce complex geometric shapes, and high production speed. One of the effective parameters in this process is the filament feed presented in the production G-code. The filament feed is calculated according to the layer height, the extrusion width, and the length of the printing path. All required motion paths and filling patterns created by commercial software are a set of straight lines or circular arcs placed next to each other at a fixed distance. In special curved paths, the distance of adjacent paths is not equal at different points, and due to the weakness of common commercial software, it is not possible to create curved paths for proper printing. The creation of a special computer code that can be used to make various functions of curved paths was investigated in this study. The filament feed parameter was also studied in detail. Next, by introducing a correction technique, the filament feed was changed on the curved path to uniformly distribute the polymer material. Variable-stiffness composite samples consisting of curved fibers can be produced with the proposed method. The high quality of the printed samples confirms the suggested code and technique.
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Affiliation(s)
- Behnam Akhoundi
- Additive Manufacturing Laboratory, Faculty of Mechanical Engineering, Tarbiat Modares University, Tehran 14115-143, Iran; (B.A.); (M.N.)
| | - Mojtaba Nabipour
- Additive Manufacturing Laboratory, Faculty of Mechanical Engineering, Tarbiat Modares University, Tehran 14115-143, Iran; (B.A.); (M.N.)
| | - Faramarz Hajami
- Department of Mechanical Engineering, Faculty of Mechatronics, Karaj Branch, Islamic Azad University, Karaj 3149968111, Iran;
| | - Shahab S. Band
- Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan
- Correspondence: (S.S.B.); (A.M.)
| | - Amir Mosavi
- Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany
- School of Economics and Business, Norwegian University of Life Sciences, 1430 Ås, Norway
- Kando Kalman Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary
- Correspondence: (S.S.B.); (A.M.)
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32
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Band SS, Janizadeh S, Chandra Pal S, Saha A, Chakrabortty R, Shokri M, Mosavi A. Novel Ensemble Approach of Deep Learning Neural Network (DLNN) Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility. Sensors (Basel) 2020; 20:E5609. [PMID: 33008132 PMCID: PMC7582716 DOI: 10.3390/s20195609] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 09/22/2020] [Accepted: 09/24/2020] [Indexed: 11/16/2022]
Abstract
This study aims to evaluate a new approach in modeling gully erosion susceptibility (GES) based on a deep learning neural network (DLNN) model and an ensemble particle swarm optimization (PSO) algorithm with DLNN (PSO-DLNN), comparing these approaches with common artificial neural network (ANN) and support vector machine (SVM) models in Shirahan watershed, Iran. For this purpose, 13 independent variables affecting GES in the study area, namely, altitude, slope, aspect, plan curvature, profile curvature, drainage density, distance from a river, land use, soil, lithology, rainfall, stream power index (SPI), and topographic wetness index (TWI), were prepared. A total of 132 gully erosion locations were identified during field visits. To implement the proposed model, the dataset was divided into the two categories of training (70%) and testing (30%). The results indicate that the area under the curve (AUC) value from receiver operating characteristic (ROC) considering the testing datasets of PSO-DLNN is 0.89, which indicates superb accuracy. The rest of the models are associated with optimal accuracy and have similar results to the PSO-DLNN model; the AUC values from ROC of DLNN, SVM, and ANN for the testing datasets are 0.87, 0.85, and 0.84, respectively. The efficiency of the proposed model in terms of prediction of GES was increased. Therefore, it can be concluded that the DLNN model and its ensemble with the PSO algorithm can be used as a novel and practical method to predict gully erosion susceptibility, which can help planners and managers to manage and reduce the risk of this phenomenon.
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Affiliation(s)
- Shahab S. Band
- Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
- Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan
| | - Saeid Janizadeh
- Department of Watershed Management Engineering and Sciences, Faculty in Natural Resources and Marine Science, Tarbiat Modares University, 14115-111 Tehran, Iran;
| | - Subodh Chandra Pal
- Department of Geography, The University of Burdwan, West Bengal, Burdwan 713104, India; (S.C.P.); (A.S.); (R.C.)
| | - Asish Saha
- Department of Geography, The University of Burdwan, West Bengal, Burdwan 713104, India; (S.C.P.); (A.S.); (R.C.)
| | - Rabin Chakrabortty
- Department of Geography, The University of Burdwan, West Bengal, Burdwan 713104, India; (S.C.P.); (A.S.); (R.C.)
| | - Manouchehr Shokri
- Institute of Structural Mechanics, Bauhaus Universität Weimar, 99423 Weimar, Germany;
| | - Amirhosein Mosavi
- Environmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc ThangUniversity, Ho Chi Minh City 700000, Vietnam;
- Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
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Mosavi A, Shokri M, Mansor Z, Qasem SN, Band SS, Mohammadzadeh A. Machine Learning for Modeling the Singular Multi-Pantograph Equations. Entropy (Basel) 2020; 22:E1041. [PMID: 33286810 PMCID: PMC7597098 DOI: 10.3390/e22091041] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 08/10/2020] [Accepted: 08/11/2020] [Indexed: 11/16/2022]
Abstract
In this study, a new approach to basis of intelligent systems and machine learning algorithms is introduced for solving singular multi-pantograph differential equations (SMDEs). For the first time, a type-2 fuzzy logic based approach is formulated to find an approximated solution. The rules of the suggested type-2 fuzzy logic system (T2-FLS) are optimized by the square root cubature Kalman filter (SCKF) such that the proposed fineness function to be minimized. Furthermore, the stability and boundedness of the estimation error is proved by novel approach on basis of Lyapunov theorem. The accuracy and robustness of the suggested algorithm is verified by several statistical examinations. It is shown that the suggested method results in an accurate solution with rapid convergence and a lower computational cost.
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Affiliation(s)
- Amirhosein Mosavi
- Environmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam;
- Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam
| | - Manouchehr Shokri
- Faculty of Civil Engineering, Institute of Structural Mechanics (ISM), Bauhaus-Universität Weimar, 99423 Weimar, Germany;
| | - Zulkefli Mansor
- Fakulti Teknologi dan Sains Maklumat, Universiti Kebangsan Malaysia, Bangi 43600, Selangor, Malaysia;
| | - Sultan Noman Qasem
- Computer Science Department, College of Computer and Information Sciences, Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia;
- Computer Science Department, Faculty of Applied Science, Taiz University, Taiz 6803, Yemen
| | - Shahab S. Band
- Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan
- Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
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