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Sudhagar D, Arokia Renjit J. An IoT and Fuzzy aware e-Healthcare system using feature optimization tuned T-CNN with high dimensional data. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-220629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Many real-time applications, including some emerging ones, rely on high-dimensional feature datasets. For simplifying the high-dimensional data, the various models are available by using the different feature optimization techniques, clustering and classification techniques. Even though the high-dimensional data is not handled effectively due to the increase in the number of features and the huge volume of data availability. In particular, the high-dimensional medical data needs to be handled effectively to predict diseases quickly. For this purpose, we propose a new Internet of Things and Fuzzy-aware e-healthcare system for predicting various diseases such as heart, diabetes, and cancer diseases effectively. The proposed system uses a newly proposed Intelligent Mahalanobis distance aware Fuzzy Weighted K-Means Clustering Algorithm (IMFWKCA) for grouping the high dimensional data and also applies a newly proposed Moth-Flame Optimization Tuned Temporal Convolutional Neural Network (MFO-TCNN) for predicting the diseases effectively. The experiments have been done by using the UCI Repository Machine Learning datasets and live streaming patient records for evaluating the proposed e-healthcare system and have proved as better than others by achieving better performance in terms of precision, recall, f-measure, and prediction accuracy.
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Affiliation(s)
- D. Sudhagar
- Department of Information Technology, Jerusalem College of Engineering, Chennai, India
| | - J. Arokia Renjit
- Department of Computer Science and Engineering, Jeppiaar Engineering College, Chennai, India
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Sustainable Food Production: An Intelligent Fault Diagnosis Framework for Analyzing the Risk of Critical Processes. SUSTAINABILITY 2022. [DOI: 10.3390/su14031083] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Fault diagnosis and prognosis methods are the most useful tools for risk and reliability analysis in food processing systems. Proactive diagnosis techniques such as failure mode and effect analysis (FMEA) are important for detecting all probable failures and facilitating the risk analysis process. However, significant uncertainties exist in the classical-FMEA when it comes to ranking the risk priority numbers (RPNs) of failure modes. Such uncertainties may have an impact on the food sector’s operational safety and maintenance decisions. To address these issues, this research provides a unique FMEA framework for risk analysis within an edible oil purification facility that is based on certain well-known intelligent models. Fuzzy inference systems (FIS), adaptive neuro-fuzzy inference systems (ANFIS), and support vector machine (SVM) models are among those used. The findings of the comparison of the proposed FMEA framework with the classical model revealed that intelligent strategies were more effective in ranking the RPNs of failure modes. Based on the performance criteria, it was discovered that the SVM algorithm classifies the failure modes more accurately and with fewer errors., e.g., RMSE = 7.30 and MAPE = 13.19 with that of other intelligent techniques. Hence, a sensitivity FMEA analysis based on the SVM algorithm was performed to put forward suitable maintenance actions to upgrade the reliability and safety within food processing lines.
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Dinh VB, Tran NT, Dao TP. An integration framework of topology method, enhanced adaptive neuro-fuzzy inference system, water cycle algorithm with evaporation rate for design optimization for a flexure gripper. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06374-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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Cloud- and IoT-based deep learning technique-incorporated secured health monitoring system for dead diseases. Soft comput 2021. [DOI: 10.1007/s00500-021-05866-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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Selvakumar K, SaiRamesh L, Ayyasamy A, Archana M. Intelligent energy-aware multiple quality of service restraints based secured optimal routing protocol with dynamic mobility estimation for wireless sensor networks. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-190050] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
This research work confronts a sender-based responsive and novel protocol named “Intelligent Energy-Aware Multiple restraints Secured Optimal Routing (IEAMSOR)” protocol for WSNs. In order to deal with the various emerges like packet routing, node mobility, and energy optimization as well as energy balancing in WSNs. The proposed protocol accounts for the basic QoS restraints such as Delay, HopCount and Energy Level for each link of ‘n’ number of routes and predicts the best optimal path among these in-between sender and receiver nodes throughout the route discovery process. It also assures the energy level of each node existing on the route during the route reply process. It incorporates the modified mobility prediction approach in order to estimate the stableness of link failure time for every link of each path during the route reply process. The main objective of this work to achieve the energy balancing among the nodes is achieved through fuzzy rules based node’s trust classification is introduced and based on this energy weight of each node is adjusted according to their trustworthiness. It accomplishes the path sustainment process when the link among the two nodes goes down. Moreover, the proposed model has been given careful attention for selecting additional substitute routes throughout link failure. The experimental results have seemed that the IEAMSOR protocol performs better than the existing traditional protocols.
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Affiliation(s)
- K. Selvakumar
- SDepartment of Computer Applications, NIT, Trichy, India
| | - L. SaiRamesh
- Department of Information Science & Technology, CEG Campus, Anna University, Chennai, India
| | - A. Ayyasamy
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, Annamalai University, Chidambaram, Tamilnadu, India
| | - M. Archana
- Department of Information Technology, Faculty of Engineering and Technology, Annamalai University, Chidambaram, Tamilnadu, India
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Abstract
In many engineering problems, the systems dynamics are uncertain, and then, the accurate dynamic modeling is required. Type-2 fuzzy neural networks (T2F-NNs) are extensively used in system identification problems, because of their strong estimation capability. In this paper, the application of T2F-NNs is reviewed and classified. First, an introduction to the principles of system identification, including how to extract data from a system, persistency of excitation, preprocessing of information and data, removal of outlier data, and sorting of data to learn the T2F-NNs, is presented. Then, various learning methods for structure and parameters of the T2F-NNs are reviewed and analyzed. A number of different T2F-NNs that have been used to system identification are reviewed, and their disadvantages and advantages are described. Also, their efficiency in different applications is reviewed. Finally, we will look at the horizon ahead in this issue and analyze its challenges.
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Meng X, Zhang Y, Qiao J. An adaptive task-oriented RBF network for key water quality parameters prediction in wastewater treatment process. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05659-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Decision making based on linguistic interval-valued intuitionistic neutrosophic Dombi fuzzy hybrid weighted geometric operator. Soft comput 2020. [DOI: 10.1007/s00500-020-05282-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Munirathinam T, Ganapathy S, Kannan A. Cloud and IoT based privacy preserved e-Healthcare system using secured storage algorithm and deep learning. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-191490] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Rapid introduction of new diseases and the severity improvement of existing dead diseases due to the bad food habits and lacking of awareness over the health conscious food items those are available in the market. The Internet of Things (IoT) gets more attention for reducing the disease severity by knowing the current status of their disease according to the dynamic inputs of human body through IoT devices today. Moreover, the combination of IoT and cloud computing technologies are playing major roles in e-health services. In this scenario, security is a major issue in the process of data storage and communication. For this purpose, we propose a new e-healthcare system for monitoring the dead disease level by using the technologies such as IoT and Cloud with the help of deep learning approach and fuzzy rules with temporal features. In this system, the medical data is retrieved from various located patients who are utilizing the e-healthcare assisting devices. First, the retrieved and encrypted data is stored in cloud by applying a newly proposed secured cloud storage algorithm. Second, the stored data can be retrieved the data as original data by applying the decryption process. Third, a new cloud framework is introduced for predicting the status of heart beat rates and diabetes levels by using the medical data that is created by applying the UCI Repository dataset. In addition, a new deep learning approach which applies the Convolutional Neural Network for predicting the disease severity. The experimental results are obtained by conducting various experiments for the proposed model by using the dataset and the hospital patient records. The proposed model results outperforms the available disease prediction systems in terms of prediction accuracy.
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Affiliation(s)
- T. Munirathinam
- Department of Computer Science and Engineering, CEG Campus, Anna University, Chennai-25, India
| | - Sannasi Ganapathy
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai-127, India
| | - Arputharaj Kannan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
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Antony Rosewelt L, Arokia Renjit J. A content recommendation system for effective e-learning using embedded feature selection and fuzzy DT based CNN. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-191721] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
- L. Antony Rosewelt
- Department of Information Technology, Jerusalem College of Engineering, Chennai, India
| | - J. Arokia Renjit
- Department of Computer Science and Engineering, Jeppiaar Engineering College, Chennai, India
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de Campos Souza PV. Fuzzy neural networks and neuro-fuzzy networks: A review the main techniques and applications used in the literature. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106275] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Pham QB, Afan HA, Mohammadi B, Ahmed AN, Linh NTT, Vo ND, Moazenzadeh R, Yu PS, El-Shafie A. Hybrid model to improve the river streamflow forecasting utilizing multi-layer perceptron-based intelligent water drop optimization algorithm. Soft comput 2020. [DOI: 10.1007/s00500-020-05058-5] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Son LH, Ciaramella A, Thu Huyen DT, Staiano A, Tuan TM, Van Hai P. Predictive reliability and validity of hospital cost analysis with dynamic neural network and genetic algorithm. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-04876-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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Shang Y, Han Z, Qiao Y, Zhou J. Visualization analysis of the journal of intelligent & fuzzy systems (2002–2018). JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-18326] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Yu Shang
- College of Information System and Management, National University of Defense Technology, Changsha, Hunan, China
- Cryogenic Technology Division, Beijing Institute of Aerospace Testing Technology, Beijing, China
| | - Zhiguo Han
- Library, Beijing Sport University, Beijing, China
| | - Yun Qiao
- College of Information System and Management, National University of Defense Technology, Changsha, Hunan, China
- Department of Financial, China Aerospace Science and Technology Corporation, Beijing, China
| | - Jinglun Zhou
- College of Information System and Management, National University of Defense Technology, Changsha, Hunan, China
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Intelligent temporal classification and fuzzy rough set-based feature selection algorithm for intrusion detection system in WSNs. Inf Sci (N Y) 2019. [DOI: 10.1016/j.ins.2019.05.040] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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S.K. L, Mohanty SN, S. SR, Krishnamoorthy S, J. U, Shankar K. Online clinical decision support system using optimal deep neural networks. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.105487] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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18
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Selvaraj S, Sivaraman S. Prediction model for optimized self-compacting concrete with fly ash using response surface method based on fuzzy classification. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3575-1] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Yu M, Qu M, Hu J. A research method to capture design state based on multi-fuzzy cognitive mapping. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2015. [DOI: 10.3233/ifs-151970] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Mingjiu Yu
- Department of Industrial Design, Northwestern Polytechnical University, Xi’an, Shaanxi, China
| | - Min Qu
- Department of Industrial Design, Northwestern Polytechnical University, Xi’an, Shaanxi, China
| | - Jun Hu
- Department of Industrial Design, Eindhoven University of Technology, Eindhoven, The Netherlands
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