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Lashgari M, Choudhury RP, Banerjee A. Patient-specific in silico 3D coronary model in cardiac catheterisation laboratories. Front Cardiovasc Med 2024; 11:1398290. [PMID: 39036504 PMCID: PMC11257904 DOI: 10.3389/fcvm.2024.1398290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Accepted: 06/06/2024] [Indexed: 07/23/2024] Open
Abstract
Coronary artery disease is caused by the buildup of atherosclerotic plaque in the coronary arteries, affecting the blood supply to the heart, one of the leading causes of death around the world. X-ray coronary angiography is the most common procedure for diagnosing coronary artery disease, which uses contrast material and x-rays to observe vascular lesions. With this type of procedure, blood flow in coronary arteries is viewed in real-time, making it possible to detect stenoses precisely and control percutaneous coronary interventions and stent insertions. Angiograms of coronary arteries are used to plan the necessary revascularisation procedures based on the calculation of occlusions and the affected segments. However, their interpretation in cardiac catheterisation laboratories presently relies on sequentially evaluating multiple 2D image projections, which limits measuring lesion severity, identifying the true shape of vessels, and analysing quantitative data. In silico modelling, which involves computational simulations of patient-specific data, can revolutionise interventional cardiology by providing valuable insights and optimising treatment methods. This paper explores the challenges and future directions associated with applying patient-specific in silico models in catheterisation laboratories. We discuss the implications of the lack of patient-specific in silico models and how their absence hinders the ability to accurately predict and assess the behaviour of individual patients during interventional procedures. Then, we introduce the different components of a typical patient-specific in silico model and explore the potential future directions to bridge this gap and promote the development and utilisation of patient-specific in silico models in the catheterisation laboratories.
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Affiliation(s)
- Mojtaba Lashgari
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Robin P. Choudhury
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Abhirup Banerjee
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
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2
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Czmil A. Comparative Study of Fuzzy Rule-Based Classifiers for Medical Applications. SENSORS (BASEL, SWITZERLAND) 2023; 23:992. [PMID: 36679786 PMCID: PMC9864287 DOI: 10.3390/s23020992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/20/2022] [Accepted: 01/13/2023] [Indexed: 06/17/2023]
Abstract
The use of machine learning in medical decision support systems can improve diagnostic accuracy and objectivity for clinical experts. In this study, we conducted a comparison of 16 different fuzzy rule-based algorithms applied to 12 medical datasets and real-world data. The results of this comparison showed that the best performing algorithms in terms of average results of Matthews correlation coefficient (MCC), area under the curve (AUC), and accuracy (ACC) was a classifier based on fuzzy logic and gene expression programming (GPR), repeated incremental pruning to produce error reduction (Ripper), and ordered incremental genetic algorithm (OIGA), respectively. We also analyzed the number and size of the rules generated by each algorithm and provided examples to objectively evaluate the utility of each algorithm in clinical decision support. The shortest and most interpretable rules were generated by 1R, GPR, and C45Rules-C. Our research suggests that GPR is capable of generating concise and interpretable rules while maintaining good classification performance, and it may be a valuable algorithm for generating rules from medical data.
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Affiliation(s)
- Anna Czmil
- The Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, Powstancow Warszawy 12, 35-959 Rzeszow, Poland
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3
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Classification of Covid-19 misinformation on social media based on neuro-fuzzy and neural network: A systematic review. Neural Comput Appl 2023; 35:699-717. [PMID: 36159189 PMCID: PMC9488884 DOI: 10.1007/s00521-022-07797-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 09/06/2022] [Indexed: 01/11/2023]
Abstract
The spread of Covid-19 misinformation on social media had significant real-world consequences, and it raised fears among internet users since the pandemic has begun. Researchers from all over the world have shown an interest in developing deception classification methods to reduce the issue. Despite numerous obstacles that can thwart the efforts, the researchers aim to create an automated, stable, accurate, and effective mechanism for misinformation classification. In this paper, a systematic literature review is conducted to analyse the state-of-the-art related to the classification of misinformation on social media. IEEE Xplore, SpringerLink, ScienceDirect, Scopus, Taylor & Francis, Wiley, Google Scholar are used as databases to find relevant papers since 2018-2021. Firstly, the study begins by reviewing the history of the issues surrounding Covid-19 misinformation and its effects on social media users. Secondly, various neuro-fuzzy and neural network classification methods are identified. Thirdly, the strength, limitations, and challenges of neuro-fuzzy and neural network approaches are verified for the classification misinformation specially in case of Covid-19. Finally, the most efficient hybrid method of neuro-fuzzy and neural networks in terms of performance accuracy is discovered. This study is wrapped up by suggesting a hybrid ANFIS-DNN model for improving Covid-19 misinformation classification. The results of this study can be served as a roadmap for future research on misinformation classification.
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4
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SASD Modeling Using an ANFIS to Prevent the Collision of MASS in Restricted Areas. JOURNAL OF MARINE SCIENCE AND ENGINEERING 2022. [DOI: 10.3390/jmse10070961] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
This study aimed to present the criteria for the collision avoidance of maritime autonomous surface ships (MASS) in restricted areas. The criteria were presented via ship domains, and a seafarers’ awareness-based ship domain (SASD) that dynamically changes the existing SASD according to the external environment was used to develop a new domain model. For application, we have converged an adaptive neuro-fuzzy inference system with the existing model. The proposed SASD was applied to an actual marine restricted area and compared with automatic identification system (AIS) based ship domain models. Consequently, although there was a slight difference depending on the restricted area and domain design method applied, the SASD, (i.e., the minor axis of the elliptical domain) and the AIS-based ship domain were similar. Cross-validation of the seafarers’ awareness-based ship domain and the AIS-based ship domain could propose a reference lateral side passing distance for MASS in restricted areas. However, the major axis of the ellipse should be further studied since the domain design method differs between models.
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5
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Biomedical data analysis using neuro-fuzzy model with post-feature reduction. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2022. [DOI: 10.1016/j.jksuci.2020.01.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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6
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Outcomes of Industry–University Collaboration in Open Innovation: An Exploratory Investigation of Their Antecedents’ Impact Based on a PLS-SEM and Soft Computing Approach. MATHEMATICS 2022. [DOI: 10.3390/math10060931] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
The outcomes of industry–university collaboration, in an open innovation context, may be of great support to firms, in their response to the challenges of today’s highly competitive environment. However, there is no empirical evidence on how these outcomes are influenced by their antecedents. Aiming to fill this gap, a research model to investigate the impact of the major antecedents, identified in the literature as motives, barriers and knowledge transfer channels on the beneficial outcomes and drawbacks of open innovation between the two organizations was developed in this study. The research model was empirically assessed, using a dual-stage predictive approach, based on PLS-SEM and soft computing constituents (artificial neural networks and adaptive neuro-fuzzy inference systems). PLS-SEM was successfully used to test the hypotheses of the research model, while the soft computing approach was employed to predict the complex dependencies between the outcomes and their antecedents. Insights into the relative importance of the antecedents, in shaping the open innovation outcomes, were provided through the importance–performance map analysis. The findings revealed the antecedents that had a significant positive impact on both the beneficial outcomes and drawbacks of industry–university collaboration, in open innovation. The results also highlighted the predictor importance in the research model, as well as the relative importance of the antecedent constructs, based on their effects on the two analyzed outcomes.
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7
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Intuitionistic fuzzy inference system with weighted comprehensive evaluation considering standard deviation-cosine entropy: a fused forecasting model. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07082-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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8
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Efficient NFS Model for Risk Estimation in a Risk-Based Access Control Model. SENSORS 2022; 22:s22052005. [PMID: 35271151 PMCID: PMC8914835 DOI: 10.3390/s22052005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 03/01/2022] [Accepted: 03/02/2022] [Indexed: 11/17/2022]
Abstract
Providing a dynamic access control model that uses real-time features to make access decisions for IoT applications is one of the research gaps that many researchers are trying to tackle. This is because existing access control models are built using static and predefined policies that always give the same result in different situations and cannot adapt to changing and unpredicted situations. One of the dynamic models that utilize real-time and contextual features to make access decisions is the risk-based access control model. This model performs a risk analysis on each access request to permit or deny access dynamically based on the estimated risk value. However, the major issue associated with building this model is providing a dynamic, reliable, and accurate risk estimation technique, especially when there is no available dataset to describe risk likelihood and impact. Therefore, this paper proposes a Neuro-Fuzzy System (NFS) model to estimate the security risk value associated with each access request. The proposed NFS model was trained using three learning algorithms: Levenberg-Marquardt (LM), Conjugate Gradient with Fletcher-Reeves (CGF), and Scaled Conjugate Gradient (SCG). The results demonstrated that the LM algorithm is the optimal learning algorithm to implement the NFS model for risk estimation. The results also demonstrated that the proposed NFS model provides a short and efficient processing time, which can provide timeliness risk estimation technique for various IoT applications. The proposed NFS model was evaluated against access control scenarios of a children's hospital, and the results demonstrated that the proposed model can be applied to provide dynamic and contextual-aware access decisions based on real-time features.
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Mahdevari S, Khodabakhshi MB. A hierarchical local-model tree for predicting roof displacement in longwall tailgates. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06127-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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10
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Application of a Fuzzy Inference System for Optimization of an Amplifier Design. MATHEMATICS 2021. [DOI: 10.3390/math9172168] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Simulation programs are widely used in the design of analog electronic circuits to analyze their behavior and to predict the response of a circuit to variations in the circuit components. A fuzzy inference system (FIS) in combination with these simulation tools can be applied to identify both the main and interaction effects of circuit parameters on the response variables, which can help to optimize them. This paper describes an application of fuzzy inference systems to modeling the behavior of analog electronic circuits for further optimization. First, a Monte Carlo analysis, generated from the tolerances of the circuit components, is performed. Once the Monte Carlo results are obtained for each of the response variables, the fuzzy inference systems are generated and then optimized using a particle swarm optimization (PSO) algorithm. These fuzzy inference systems are used to determine the influence of the circuit components on the response variables and to select them to optimize the amplifier design. The methodology proposed in this study can be used as the basis for optimizing the design of similar analog electronic circuits.
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11
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A seasonal-trend decomposition-based dendritic neuron model for financial time series prediction. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107488] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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12
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Torre-Bastida AI, Díaz-de-Arcaya J, Osaba E, Muhammad K, Camacho D, Del Ser J. Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions. Neural Comput Appl 2021:1-31. [PMID: 34366573 PMCID: PMC8329000 DOI: 10.1007/s00521-021-06332-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Accepted: 07/12/2021] [Indexed: 02/07/2023]
Abstract
This overview gravitates on research achievements that have recently emerged from the confluence between Big Data technologies and bio-inspired computation. A manifold of reasons can be identified for the profitable synergy between these two paradigms, all rooted on the adaptability, intelligence and robustness that biologically inspired principles can provide to technologies aimed to manage, retrieve, fuse and process Big Data efficiently. We delve into this research field by first analyzing in depth the existing literature, with a focus on advances reported in the last few years. This prior literature analysis is complemented by an identification of the new trends and open challenges in Big Data that remain unsolved to date, and that can be effectively addressed by bio-inspired algorithms. As a second contribution, this work elaborates on how bio-inspired algorithms need to be adapted for their use in a Big Data context, in which data fusion becomes crucial as a previous step to allow processing and mining several and potentially heterogeneous data sources. This analysis allows exploring and comparing the scope and efficiency of existing approaches across different problems and domains, with the purpose of identifying new potential applications and research niches. Finally, this survey highlights open issues that remain unsolved to date in this research avenue, alongside a prescription of recommendations for future research.
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Affiliation(s)
| | - Josu Díaz-de-Arcaya
- TECNALIA, Basque Research and Technology Alliance (BRTA), 48160 Derio, Spain
| | - Eneko Osaba
- TECNALIA, Basque Research and Technology Alliance (BRTA), 48160 Derio, Spain
| | - Khan Muhammad
- Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), Department of Software, Sejong University, Seoul, 143-747 Republic of Korea
| | - David Camacho
- Universidad Politécnica de Madrid, 28031 Madrid, Spain
| | - Javier Del Ser
- University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain
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13
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Dehghani MH, Gholami S, Karri RR, Lima EC, Mahvi AH, Nazmara S, Fazlzadeh M. Process modeling, characterization, optimization, and mechanisms of fluoride adsorption using magnetic agro-based adsorbent. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2021; 286:112173. [PMID: 33618321 DOI: 10.1016/j.jenvman.2021.112173] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 01/08/2021] [Accepted: 02/08/2021] [Indexed: 12/07/2022]
Abstract
In this study, fluoride removal from polluted potable water using magnetic carbon-based adsorbents derived from agricultural biomass was thoroughly investigated. An experimental matrix is designed considering the interactive effects of independent process variables (pH, adsorbent dose, contact time, and initial fluoride concentration) on the removal efficiency. Isotherms and kinetics studies, as well as anions interactions, were also investigated to understand the adsorption mechanisms further. The model parameters of isotherms and kinetics are estimated using nonlinear differential evolution optimization (DEO). Approaches like adaptive neuro-fuzzy inference system (ANFIS) and response surface methodology (RSM) are implemented to predict the fluoride removal and identify the optimal process values. The optimum removal efficiency of GAC-Fe3O4 (89.34%) was found to be higher than that of PAC-Fe3O4 (85.14%). Kinetics experiments indicated that they follow the intraparticle diffusion model, and adsorption isotherms indicated that they follow Langmuir and Freundlich models. Both PAC-Fe3O4 and GAC-Fe3O4 adsorbents have shown an adsorption capacity of 1.20 and 2.74 mg/g, respectively. The model predictions from ANFIS have a strong correlation with experimental results and superior to RSM predictions. The shape of the contours depicts the nonlinearity of the interactive effects and the mechanisms in the adsorption process.
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Affiliation(s)
- Mohammad Hadi Dehghani
- Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran; Institute for Environmental Research, Center for Solid Waste Research, Tehran University of Medical Sciences, Tehran, Iran.
| | - Solmaz Gholami
- Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
| | - Rama Rao Karri
- Petroleum and Chemical Engineering, Faculty of Engineering, Universiti Teknologi Brunei, Brunei Darussalam.
| | - Eder C Lima
- Laboratory of Environmental Technology and Analytical Chemistry (Latama), Institute of Chemistry, Federal University of Rio Grande do Sul (UFRGS), Av. Bento Gonçalves 9500, Postal Box 15003, 91501-970, Porto Alegre, RS, Brazil
| | - Amir Hossein Mahvi
- Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran; Institute for Environmental Research, Center for Solid Waste Research, Tehran University of Medical Sciences, Tehran, Iran
| | - Shahrokh Nazmara
- Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Mehdi Fazlzadeh
- Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
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14
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Teoh Yi Zhe I, Keikhosrokiani P. Knowledge workers mental workload prediction using optimised ELANFIS. APPL INTELL 2021. [DOI: 10.1007/s10489-020-01928-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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15
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Vélez-Guerrero MA, Callejas-Cuervo M, Mazzoleni S. Artificial Intelligence-Based Wearable Robotic Exoskeletons for Upper Limb Rehabilitation: A Review. SENSORS 2021; 21:s21062146. [PMID: 33803911 PMCID: PMC8003246 DOI: 10.3390/s21062146] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 03/04/2021] [Accepted: 03/06/2021] [Indexed: 12/14/2022]
Abstract
Processing and control systems based on artificial intelligence (AI) have progressively improved mobile robotic exoskeletons used in upper-limb motor rehabilitation. This systematic review presents the advances and trends of those technologies. A literature search was performed in Scopus, IEEE Xplore, Web of Science, and PubMed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology with three main inclusion criteria: (a) motor or neuromotor rehabilitation for upper limbs, (b) mobile robotic exoskeletons, and (c) AI. The period under investigation spanned from 2016 to 2020, resulting in 30 articles that met the criteria. The literature showed the use of artificial neural networks (40%), adaptive algorithms (20%), and other mixed AI techniques (40%). Additionally, it was found that in only 16% of the articles, developments focused on neuromotor rehabilitation. The main trend in the research is the development of wearable robotic exoskeletons (53%) and the fusion of data collected from multiple sensors that enrich the training of intelligent algorithms. There is a latent need to develop more reliable systems through clinical validation and improvement of technical characteristics, such as weight/dimensions of devices, in order to have positive impacts on the rehabilitation process and improve the interactions among patients, teams of health professionals, and technology.
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Affiliation(s)
- Manuel Andrés Vélez-Guerrero
- Software Research Group, Universidad Pedagógica y Tecnológica de Colombia, Tunja 150002, Colombia
- Correspondence: ; Tel.: +57-320-820-6832
| | - Mauro Callejas-Cuervo
- School of Computer Science, Universidad Pedagógica y Tecnológica de Colombia, Tunja 150002, Colombia;
| | - Stefano Mazzoleni
- Department of Electrical and Information Engineering, Polytechnic University of Bari, 70126 Bari, Italy;
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16
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Lu X, Li H, Li X, Xu J. Intelligent rail lubrication system based on fuzzy group analysis. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-201398] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The security of a train becomes a more critical issue as the train’s speed and the complexity of the railway conditions increases. It is especially true when the train runs on a curved radius rail when the lateral force between the train and the rail is less stable. The rail’s side grinding is a significant problem that affects the train’s safety, especially when the train passes through small radial sections in mountainous areas. The intelligent rail lubrication system is critical to enhancing rails’ safety and efficiency and reducing grease pollution along rail lines. This system is modeled with a force analysis of train curve motion and numerical simulation of wear power. The lubrication system is constructed with hardware and software. Based on fuzzy group analysis, this system and the adaptive Proportional Integration Differential (PID) controller is presented to improve the lubricative effects. The system test results show that the quality of lubrication control using this system is efficacious; the control convergence is more reliable than the conventional PID controller.
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Affiliation(s)
- Xiangyang Lu
- School of Electrical and Information Engineering, Zhongyuan University of Technology, Zhengzhou, China
| | - Hengyi Li
- Graduate School of Science and Engineering, Ritsumeikan University, 1-1-1 Noji-higashi, Kusatsu, Shiga 525-8577, Japan
| | - Xiaoquan Li
- School of Electrical and Information Engineering, Zhongyuan University of Technology, Zhengzhou, China
| | - Juncai Xu
- Department of Civil Engineering, Case Western Reserve University, Cleveland, OH, USA
- Key Laboratory of Hydraulic and Waterway Engineering of the Ministry of Education, Chongqing Jiaotong University, Chongqing, China
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17
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Choubey S, Karmakar GP. Artificial intelligence techniques and their application in oil and gas industry. Artif Intell Rev 2020. [DOI: 10.1007/s10462-020-09935-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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18
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Lau YJ, Karri RR, Mubarak NM, Lau SY, Chua HB, Khalid M, Jagadish P, Abdullah EC. Removal of dye using peroxidase-immobilized Buckypaper/polyvinyl alcohol membrane in a multi-stage filtration column via RSM and ANFIS. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2020; 27:40121-40134. [PMID: 32656753 DOI: 10.1007/s11356-020-10045-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 07/06/2020] [Indexed: 06/11/2023]
Abstract
The feasibility and performance of Jicama peroxidase (JP) immobilized Buckypaper/polyvinyl alcohol (BP/PVA) membrane for methylene blue (MB) dye removal was investigated in a customized multi-stage filtration column under batch recycle mode. The effect of independent variables, such as influent flow rate, ratio of H2O2/MB dye concentration, and contact time on the dye removal efficiency, were investigated using response surface methodology (RSM). To capture the inherent characteristics and better predict the removal efficiency, a data-driven adaptive neuro-fuzzy inference system (ANFIS) is implemented. Results indicated that the optimum dye removal efficiency of 99.7% was achieved at a flow rate of 2 mL/min, 75:1 ratio of H2O2/dye concentration with contact time of 183 min. The model predictions of ANFIS are significantly good compared with RSM, thus resulting in R2 values of 0.9912 and 0.9775, respectively. The enzymatic kinetic parameters, Km and Vmax, were evaluated, which are 1.98 mg/L and 0.0219 mg/L/min, respectively. Results showed that JP-immobilized BP/PVA nanocomposite membrane can be promising and cost-effective biotechnology for the practical application in the treatment of industrial dye effluents.
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Affiliation(s)
- Yien Jun Lau
- Department of Chemical Engineering, Faculty of Engineering and Science, Curtin University, 98009, Miri, Sarawak, Malaysia
| | - Rama Rao Karri
- Petroleum and Chemical Engineering, Faculty of Engineering, Universiti Teknologi Brunei (UTB), Gadong, Brunei Darussalam
| | - Nabisab Mujawar Mubarak
- Department of Chemical Engineering, Faculty of Engineering and Science, Curtin University, 98009, Miri, Sarawak, Malaysia.
| | - Sie Yon Lau
- Department of Chemical Engineering, Faculty of Engineering and Science, Curtin University, 98009, Miri, Sarawak, Malaysia
| | - Han Bing Chua
- Department of Chemical Engineering, Faculty of Engineering and Science, Curtin University, 98009, Miri, Sarawak, Malaysia
| | - Mohammad Khalid
- Graphene & Advanced 2D Materials Research Group (GAMRG), School of Science and Technology, Sunway University, No. 5, Jalan Universiti, Bandar Sunway, 47500, Subang Jaya, Selangor, Malaysia
| | - Priyanka Jagadish
- Graphene & Advanced 2D Materials Research Group (GAMRG), School of Science and Technology, Sunway University, No. 5, Jalan Universiti, Bandar Sunway, 47500, Subang Jaya, Selangor, Malaysia
| | - Ezzat Chan Abdullah
- Department of Chemical Process Engineering, Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia (UTM), Jalan Sultan Yahya Petra, 54100, Kuala Lumpur, Malaysia
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Fan Z, Chiong R, Hu Z, Lin Y. A multi-layer fuzzy model based on fuzzy-rule clustering for prediction tasks. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.04.031] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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20
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Gerami Seresht N, Fayek AR. Neuro-fuzzy system dynamics technique for modeling construction systems. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106400] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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21
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Ali AR, Li J, Kanwal S, Yang G, Hussain A, Jane O'Shea S. A Novel Fuzzy Multilayer Perceptron (F-MLP) for the Detection of Irregularity in Skin Lesion Border Using Dermoscopic Images. Front Med (Lausanne) 2020; 7:297. [PMID: 32733903 PMCID: PMC7359554 DOI: 10.3389/fmed.2020.00297] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Accepted: 05/26/2020] [Indexed: 11/18/2022] Open
Abstract
Skin lesion border irregularity, which represents the B feature in the ABCD rule, is considered one of the most significant factors in melanoma diagnosis. Since signs that clinicians rely on in melanoma diagnosis involve subjective judgment including visual signs such as border irregularity, this deems it necessary to develop an objective approach to finding border irregularity. Increased research in neural networks has been carried out in recent years mainly driven by the advances of deep learning. Artificial neural networks (ANNs) or multilayer perceptrons have been shown to perform well in supervised learning tasks. However, such networks usually don't incorporate information pertaining the ambiguity of the inputs when training the network, which in turn could affect how the weights are being updated in the learning process and eventually degrading the performance of the network when applied on test data. In this paper, we propose a fuzzy multilayer perceptron (F-MLP) that takes the ambiguity of the inputs into consideration and subsequently reduces the effects of ambiguous inputs on the learning process. A new optimization function, the fuzzy gradient descent, has been proposed to reflect those changes. Moreover, a type-II fuzzy sigmoid activation function has also been proposed which enables finding the range of performance the fuzzy neural network is able to attain. The fuzzy neural network was used to predict the skin lesion border irregularity, where the lesion was firstly segmented from the skin, the lesion border extracted, border irregularity measured using a proposed measure vector, and using the extracted border irregularity measures to train the neural network. The proposed approach outperformed most of the state-of-the-art classification methods in general and its standard neural network counterpart in particular. However, the proposed fuzzy neural network was more time-consuming when training the network.
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Affiliation(s)
- Abder-Rahman Ali
- Faculty of Natural Sciences, Computing Science and Mathematics, University of Stirling, Stirling, United Kingdom
| | - Jingpeng Li
- Faculty of Natural Sciences, Computing Science and Mathematics, University of Stirling, Stirling, United Kingdom
| | - Summrina Kanwal
- Department of Computing and Informatics, Saudi Electronic University, Al-Dammam, Saudi Arabia
| | - Guang Yang
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Amir Hussain
- Cognitive Big Data and Cybersecurity Research Lab, Edinburgh Napier University, Edinburgh, United Kingdom
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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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23
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Neuro-Fuzzy-Based Model Predictive Energy Management for Grid Connected Microgrids. ELECTRONICS 2020. [DOI: 10.3390/electronics9060900] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
With constant population growth and the rise in technology use, the demand for electrical energy has increased significantly. Increasing fossil-fuel-based electricity generation has serious impacts on environment. As a result, interest in renewable resources has risen, as they are environmentally friendly and may prove to be economical in the long run. However, the intermittent character of renewable energy sources is a major disadvantage. It is important to integrate them with the rest of the grid so that their benefits can be reaped while their negative impacts can be mitigated. In this article, an energy management algorithm is recommended for a grid-connected microgrid consisting of loads, a photovoltaic (PV) system and a battery for efficient use of energy. A model predictive control-inspired approach for energy management is developed using the PV power and consumption estimation obtained from daylight solar irradiation and temperature estimation of the same area. An energy management algorithm, which is based on a neuro-fuzzy inference system, is designed by determining the possible operating states of the system. The proposed system is compared with a rule-based control strategy. Results show that the developed control algorithm ensures that microgrid is supplied with reliable energy while the renewable energy use is maximized.
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Cortés-Antonio P, Batyrshin I, Martínez-Cruz A, Villa-Vargas LA, Ramírez-Salinas MA, Rudas I, Castillo O, Molina-Lozano H. Learning rules for Sugeno ANFIS with parametric conjunction operations. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106095] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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25
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Kaur R, Kaur K, Khamparia A, Anand D. An Improved and Adaptive Approach in ANFIS to Predict Knee Diseases. INTERNATIONAL JOURNAL OF HEALTHCARE INFORMATION SYSTEMS AND INFORMATICS 2020. [DOI: 10.4018/ijhisi.2020040102] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Artificial intelligence is emerging as a persuasive tool in the field of medical science. This research work also primarily focuses on the development of a tool to automate the diagnosis of inflammatory diseases of the knee joint. The tool will also assist the physicians and medical practitioners for diagnosis. The diseases considered for this research under inflammatory category are osteoarthritis, rheumatoid arthritis and osteonecrosis. A five-layer adaptive neuro-fuzzy (ANFIS) architecture was used to model the system. The ANFIS system works by mapping input parameters to the input membership functions, input membership functions are mapped to the rules generated by the ANFIS model which are further mapped to the output membership function. A comparative performance analysis of fuzzy system and ANFIS system is also done and results generated shows that the ANFIS system outperformed fuzzy system in terms of testing accuracy, sensitivity and specificity.
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Affiliation(s)
- Ranjit Kaur
- Lovely Professional University, Phagwara, India
| | | | | | - Divya Anand
- Lovely Professional University, Phagwara, India
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26
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Usage and implementation of neuro-fuzzy systems for classification and prediction in the diagnosis of different types of medical disorders: a decade review. Artif Intell Rev 2020. [DOI: 10.1007/s10462-020-09804-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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27
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Das H, Naik B, Behera H. Medical disease analysis using Neuro-Fuzzy with Feature Extraction Model for classification. INFORMATICS IN MEDICINE UNLOCKED 2020. [DOI: 10.1016/j.imu.2019.100288] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
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28
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Salmanpour MR, Shamsaei M, Saberi A, Klyuzhin IS, Tang J, Sossi V, Rahmim A. Machine learning methods for optimal prediction of motor outcome in Parkinson’s disease. Phys Med 2020; 69:233-240. [DOI: 10.1016/j.ejmp.2019.12.022] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 12/11/2019] [Accepted: 12/23/2019] [Indexed: 11/24/2022] Open
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Kumar S, Dhanabalan S, Narayanan CS. Application of ANFIS for the Selection of Optimal Wire-EDM Parameters While Machining Ti-6Al-4V Alloy and Multi-Parametric Optimization Using GRA Method. INTERNATIONAL JOURNAL OF DECISION SUPPORT SYSTEM TECHNOLOGY 2019. [DOI: 10.4018/ijdsst.2019100105] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The applications of artificial intelligence (AI) are becoming more popular and relevant research have been conducted in every field of engineering and science by using these AI techniques. Therefore, this research aims to examine the influence of wire electric-discharge machining (WEDM) parameters on performance parameters to improve the productivity with a higher surface finish of titanium alloy (Ti-6Al-4V) by using the artificial intelligent technique. In this experimental analysis, the Adaptive Network Based fuzzy Inference System (ANFIS) model has been highly-developed and the multi-parametric optimization has been done to find the optimal solution for the machining of the titanium superalloy. The peak current (Ip), taper angle, pulse on time (Ton), pulse of time (Toff) and the dielectric fluid flow rate were selected as operation constraints to conduct experimental trials. The surface roughness (SR) and MRR were considered as output responses. The influence on machining performance has been analyzed by an ANFIS model and the developed model was validated with the full factorial regression models. The developed models showed the minimum mean percentage error and the optimized parameters by the GRA method showed the considerable improvement in the process.
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Affiliation(s)
- Sandeep Kumar
- Department of Mechanical Engineering, M. Kumarasamy College of Eng., Karur, India
| | - S. Dhanabalan
- Department of Mechanical Engineering, M. Kumarasamy College of Eng., Karur, India
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30
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Karaboga D, Kaya E. Estimation of number of foreign visitors with ANFIS by using ABC algorithm. Soft comput 2019. [DOI: 10.1007/s00500-019-04386-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
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31
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Nakasima-López S, Castro JR, Sanchez MA, Mendoza O, Rodríguez-Díaz A. An approach on the implementation of full batch, online and mini-batch learning on a Mamdani based neuro-fuzzy system with center-of-sets defuzzification: Analysis and evaluation about its functionality, performance, and behavior. PLoS One 2019; 14:e0221369. [PMID: 31487293 PMCID: PMC6728050 DOI: 10.1371/journal.pone.0221369] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Accepted: 08/05/2019] [Indexed: 01/31/2023] Open
Abstract
Due to the rapid technological evolution and communications accessibility, data generated from different sources of information show an exponential growth behavior. That is, volume of data samples that need to be analyzed are getting larger, so the methods for its processing have to adapt to this condition, focusing mainly on ensuring the computation is efficient, especially when the analysis tools are based on computational intelligence techniques. As we know, if you do not have a good control of the handling of the volume of the data, some techniques that are based on learning iterative processes could represent an excessive load of computation and could take a prohibitive time in trying to find a solution that could not come close to desired. There are learning methods known as full batch, online and mini-batch, and they represent a good strategy to this problem since they are oriented to the processing of data according to the size or volume of available data samples that require analysis. In this first approach, synthetic datasets with a small and medium volume were used, since the main objective is to define its implementation and in experimentation phase through regression analysis obtain information that allows us to assess the performance and behavior of different learning methods under distinct conditions. To carry out this study, a Mamdani based neuro-fuzzy system with center-of-sets defuzzification with support of multiple inputs and outputs was designed and implemented that had the flexibility to use any of the three learning methods, which were implemented within the training process. Finally, results show that the learning method with best performances was Mini-Batch when compared to full batch and online learning methods. The results obtained by mini-batch learning method are as follows; mean correlation coefficient R¯ with 0.8268 and coefficient of determination R2¯ with 0.7444, and is also the method with better control of the dispersion between the results obtained from the 30 experiments executed per each dataset processed.
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Affiliation(s)
- Sukey Nakasima-López
- Faculty of Chemical Sciences and Engineering, Universidad Autónoma de Baja California, Tijuana, Baja California, México
| | - Juan R. Castro
- Faculty of Chemical Sciences and Engineering, Universidad Autónoma de Baja California, Tijuana, Baja California, México
| | - Mauricio A. Sanchez
- Faculty of Chemical Sciences and Engineering, Universidad Autónoma de Baja California, Tijuana, Baja California, México
- * E-mail:
| | - Olivia Mendoza
- Faculty of Chemical Sciences and Engineering, Universidad Autónoma de Baja California, Tijuana, Baja California, México
| | - Antonio Rodríguez-Díaz
- Faculty of Chemical Sciences and Engineering, Universidad Autónoma de Baja California, Tijuana, Baja California, México
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32
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Alsamhan A, Ragab AE, Dabwan A, Nasr MM, Hidri L. Prediction of formation force during single-point incremental sheet metal forming using artificial intelligence techniques. PLoS One 2019; 14:e0221341. [PMID: 31437217 PMCID: PMC6705755 DOI: 10.1371/journal.pone.0221341] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Accepted: 08/05/2019] [Indexed: 11/19/2022] Open
Abstract
Single-point incremental forming (SPIF) is a technology that allows incremental manufacturing of complex parts from a flat sheet using simple tools; further, this technology is flexible and economical. Measuring the forming force using this technology helps in preventing failures, determining the optimal processes, and implementing on-line control. In this paper, an experimental study using SPIF is described. This study focuses on the influence of four different process parameters, namely, step size, tool diameter, sheet thickness, and feed rate, on the maximum forming force. For an efficient force predictive model based on an adaptive neuro-fuzzy inference system (ANFIS), an artificial neural network (ANN) and a regressions model were applied. The predicted forces exhibited relatively good agreement with the experimental results. The results indicate that the performance of the ANFIS model realizes the full potential of the ANN model.
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Affiliation(s)
- Ali Alsamhan
- King Saud University, Industrial Engineering Department, King Saud University, Riyadh, Saudi Arabia
| | - Adham E. Ragab
- King Saud University, Industrial Engineering Department, King Saud University, Riyadh, Saudi Arabia
| | - Abdulmajeed Dabwan
- King Saud University, Industrial Engineering Department, King Saud University, Riyadh, Saudi Arabia
- * E-mail:
| | - Mustafa M. Nasr
- King Saud University, Industrial Engineering Department, King Saud University, Riyadh, Saudi Arabia
| | - Lotfi Hidri
- King Saud University, Industrial Engineering Department, King Saud University, Riyadh, Saudi Arabia
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33
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Hardware Realization of the Pattern Recognition with an Artificial Neuromorphic Device Exhibiting a Short-Term Memory. Molecules 2019; 24:molecules24152738. [PMID: 31357695 PMCID: PMC6696233 DOI: 10.3390/molecules24152738] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 07/24/2019] [Accepted: 07/26/2019] [Indexed: 11/16/2022] Open
Abstract
Materials exhibiting memory or those capable of implementing certain learning schemes are the basic building blocks used in hardware realizations of the neuromorphic computing. One of the common goals within this paradigm assumes the integration of hardware and software solutions, leading to a substantial efficiency enhancement in complex classification tasks. At the same time, the use of unconventional approaches towards signal processing based on information carriers other than electrical carriers seems to be an interesting trend in the design of modern electronics. In this context, the implementation of light-sensitive elements appears particularly attractive. In this work, we combine the abovementioned ideas by using a simple optoelectronic device exhibiting a short-term memory for a rudimentary classification performed on a handwritten digits set extracted from the Modified National Institute of Standards and Technology Database (MNIST)(being one of the standards used for benchmarking of such systems). The input data was encoded into light pulses corresponding to black (ON-state) and white (OFF-state) pixels constituting a digit and used in this form to irradiate a polycrystalline cadmium sulfide electrode. An appropriate selection of time intervals between pulses allows utilization of a complex kinetics of charge trapping/detrapping events, yielding a short-term synaptic-like plasticity which in turn leads to the improvement of data separability. To the best of our knowledge, this contribution presents the simplest hardware realization of a classification system capable of performing neural network tasks without any sophisticated data processing.
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Kumar S, Dhanabalan S, Narayanan CS. Application of ANFIS and GRA for multi-objective optimization of optimal wire-EDM parameters while machining Ti–6Al–4V alloy. SN APPLIED SCIENCES 2019. [DOI: 10.1007/s42452-019-0195-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022] Open
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36
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37
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Abstract
Time series forecasting can be a complicated problem when the underlying process shows high degree of complex nonlinear behavior. In some domains, such as financial data, processing related time-series jointly can have significant benefits. This paper proposes a novel multivariate hybrid neuro-fuzzy model for forecasting tasks, which is based on and generalizes the neuro-fuzzy model with consequent layer multi-variable Gaussian units and its learning algorithm. The model is distinguished by a separate consequent block for each output, which is tuned with respect to the its output error only, but benefits from extracting additional information by processing the whole input vector including lag values of other variables. Numerical experiments show better accuracy and computational performance results than competing models and separate neuro-fuzzy models for each output, and thus an ability to implicitly handle complex cross correlation dependencies between variables.
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38
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Energy Consumption, Economic Growth, and CO2 Emissions in G20 Countries: Application of Adaptive Neuro-Fuzzy Inference System. ENERGIES 2018. [DOI: 10.3390/en11102771] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Understanding the relationships among CO2 emissions, energy consumption, and economic growth helps nations to develop energy sources and formulate energy policies in order to enhance sustainable development. The present research is aimed at developing a novel efficient model for analyzing the relationships amongst the three aforementioned indicators in G20 countries using an adaptive neuro-fuzzy inference system (ANFIS) model in the period from 1962 to 2016. In this regard, the ANFIS model has been used with prediction models using real data to predict CO2 emissions based on two important input indicators, energy consumption and economic growth. This study made use of the fuzzy rules through ANFIS to generalize the relationships of the input and output indicators in order to make a prediction of CO2 emissions. The experimental findings on a real-world dataset of World Development Indicators (WDI) revealed that the proposed model efficiently predicted the CO2 emissions based on energy consumption and economic growth. The direction of the interrelationship is highly important from the economic and energy policy-making perspectives for this international forum, as G20 countries are primarily focused on the governance of the global economy.
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39
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Zhang L, Peng Z. An improved genetic algorithm analysis and application study for urban microcellular network planning. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018. [DOI: 10.3233/jifs-169633] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Lu Zhang
- School of Urban Design, Wuhan University Wuhan, Wuhan, China
| | - Zhenghong Peng
- School of Urban Design, Wuhan University Wuhan, Wuhan, China
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40
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Training ANFIS by Using an Adaptive and Hybrid Artificial Bee Colony Algorithm (aABC) for the Identification of Nonlinear Static Systems. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2018. [DOI: 10.1007/s13369-018-3562-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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41
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Ustabas Kaya G, Erkaymaz O, Sarac Z. A new adaptive neuro-fuzzy solution for optimization of the parameters in the digital holography setup. Soft comput 2018. [DOI: 10.1007/s00500-018-3482-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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42
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Singh R, Bhushan B. Real-time control of ball balancer using neural integrated fuzzy controller. Artif Intell Rev 2018. [DOI: 10.1007/s10462-018-9658-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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43
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Mirzaeinejad H. Robust predictive control of wheel slip in antilock braking systems based on radial basis function neural network. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2018.05.043] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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44
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Hussain K, Salleh MNM, Cheng S, Shi Y. On the exploration and exploitation in popular swarm-based metaheuristic algorithms. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3592-0] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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45
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Kim EH, Oh SK, Pedrycz W. Design of double fuzzy clustering-driven context neural networks. Neural Netw 2018; 104:1-14. [PMID: 29689457 DOI: 10.1016/j.neunet.2018.03.018] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2017] [Revised: 03/19/2018] [Accepted: 03/27/2018] [Indexed: 11/16/2022]
Abstract
In this study, we introduce a novel category of double fuzzy clustering-driven context neural networks (DFCCNNs). The study is focused on the development of advanced design methodologies for redesigning the structure of conventional fuzzy clustering-based neural networks. The conventional fuzzy clustering-based neural networks typically focus on dividing the input space into several local spaces (implied by clusters). In contrast, the proposed DFCCNNs take into account two distinct local spaces called context and cluster spaces, respectively. Cluster space refers to the local space positioned in the input space whereas context space concerns a local space formed in the output space. Through partitioning the output space into several local spaces, each context space is used as the desired (target) local output to construct local models. To complete this, the proposed network includes a new context layer for reasoning about context space in the output space. In this sense, Fuzzy C-Means (FCM) clustering is useful to form local spaces in both input and output spaces. The first one is used in order to form clusters and train weights positioned between the input and hidden layer, whereas the other one is applied to the output space to form context spaces. The key features of the proposed DFCCNNs can be enumerated as follows: (i) the parameters between the input layer and hidden layer are built through FCM clustering. The connections (weights) are specified as constant terms being in fact the centers of the clusters. The membership functions (represented through the partition matrix) produced by the FCM are used as activation functions located at the hidden layer of the "conventional" neural networks. (ii) Following the hidden layer, a context layer is formed to approximate the context space of the output variable and each node in context layer means individual local model. The outputs of the context layer are specified as a combination of both weights formed as linear function and the outputs of the hidden layer. The weights are updated using the least square estimation (LSE)-based method. (iii) At the output layer, the outputs of context layer are decoded to produce the corresponding numeric output. At this time, the weighted average is used and the weights are also adjusted with the use of the LSE scheme. From the viewpoint of performance improvement, the proposed design methodologies are discussed and experimented with the aid of benchmark machine learning datasets. Through the experiments, it is shown that the generalization abilities of the proposed DFCCNNs are better than those of the conventional FCNNs reported in the literature.
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Affiliation(s)
- Eun-Hu Kim
- Department of Electrical Engineering, The University of Suwon, San 2-2 Wau-ri, Bongdam-eup, Hwaseong-si, Gyeonggi-do, 445-743, South Korea
| | - Sung-Kwun Oh
- Department of Electrical Engineering, The University of Suwon, San 2-2 Wau-ri, Bongdam-eup, Hwaseong-si, Gyeonggi-do, 445-743, South Korea.
| | - Witold Pedrycz
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2G6, Canada; Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland; Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
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Kalantari A, Kamsin A, Shamshirband S, Gani A, Alinejad-Rokny H, Chronopoulos AT. Computational intelligence approaches for classification of medical data: State-of-the-art, future challenges and research directions. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2017.01.126] [Citation(s) in RCA: 70] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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48
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Rahmanifard H, Plaksina T. Application of artificial intelligence techniques in the petroleum industry: a review. Artif Intell Rev 2018. [DOI: 10.1007/s10462-018-9612-8] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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49
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Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey. Artif Intell Rev 2018. [DOI: 10.1007/s10462-017-9610-2] [Citation(s) in RCA: 118] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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50
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Md Nor N, Hussain MA, Che Hassan CR. Fault diagnosis and classification framework using multi-scale classification based on kernel Fisher discriminant analysis for chemical process system. Appl Soft Comput 2017. [DOI: 10.1016/j.asoc.2017.09.019] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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