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Fard HS, Parvin H, Mahmoudi M. Improving predictions of rock tunnel squeezing with ensemble Q-learning and online Markov chain. Sci Rep 2024; 14:22885. [PMID: 39358373 PMCID: PMC11447021 DOI: 10.1038/s41598-024-72998-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Accepted: 09/12/2024] [Indexed: 10/04/2024] Open
Abstract
Predicting rock tunnel squeezing in underground projects is challenging due to its intricate and unpredictable nature. This study proposes an innovative approach to enhance the accuracy and reliability of tunnel squeezing prediction. The proposed method combines ensemble learning techniques with Q-learning and online Markov chain integration. A deep learning model is trained on a comprehensive database comprising tunnel parameters including diameter (D), burial depth (H), support stiffness (K), and tunneling quality index (Q). Multiple deep learning models are trained concurrently, leveraging ensemble learning to capture diverse patterns and improve prediction performance. Integration of the Q-learning-Online Markov Chain further refines predictions. The online Markov chain analyzes historical sequences of tunnel parameters and squeezing class transitions, establishing transition probabilities between different squeezing classes. The Q-learning algorithm optimizes decision-making by learning the optimal policy for transitioning between tunnel states. The proposed model is evaluated using a dataset from various tunnel construction projects, assessing performance through metrics like accuracy, precision, recall, and F1-score. Results demonstrate the efficiency of the ensemble deep learning model combined with Q-learning-Online Markov Chain in predicting surrounding rock tunnel squeezing. This approach offers insights into parameter interrelationships and dynamic squeezing characteristics, enabling proactive planning and support measures implementation to mitigate tunnel squeezing hazards and ensure underground structure safety. Experimental results show the model achieves a prediction accuracy of 98.11%, surpassing individual CNN and RNN models, with an AUC value of 0.98.
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Affiliation(s)
| | - Hamid Parvin
- Department of Computer Engineering, Nourabad Mamasani Branch, Islamic Azad university, Nourabad Mamasani, Fars, Iran.
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2
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Noori S, Al‐A'araji N, Al‐Shamery E. Construction of dynamic protein interaction network based on gene expression data and quartile one principle. Proteins 2022; 90:1219-1228. [DOI: 10.1002/prot.26304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 01/12/2022] [Accepted: 01/13/2022] [Indexed: 11/09/2022]
Affiliation(s)
- Soheir Noori
- Software Department University of Babylon Hillah Babylon Iraq
- Computer Science Department University of Kerbala Karbala Iraq
| | | | - Eman Al‐Shamery
- Software Department University of Babylon Hillah Babylon Iraq
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3
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Mohammadi M, Mobarakeh MI. An integrated clustering algorithm based on firefly algorithm and self-organized neural network. PROGRESS IN ARTIFICIAL INTELLIGENCE 2022. [DOI: 10.1007/s13748-022-00275-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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4
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A multi-level consensus function clustering ensemble. Soft comput 2021. [DOI: 10.1007/s00500-021-06092-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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5
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Multi-objective whale optimization algorithm and multi-objective grey wolf optimizer for solving next release problem with developing fairness and uncertainty quality indicators. APPL INTELL 2021. [DOI: 10.1007/s10489-020-02018-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
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6
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Dabighi K, Nazari A, Saryazdi S. A step edge detector based on bilinear transformation. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-191229] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Nowadays, Canny edge detector is considered to be one of the best edge detection approaches for the images with step form. Various overgeneralized versions of these edge detectors have been offered up to now, e.g. Saryazdi edge detector. This paper proposes a new discrete version of edge detection which is obtained from Shen-Castan and Saryazdi filters by using bilinear transformation. Different experimentations are conducted to decide the suitable parameters of the proposed edge detector and to examine its validity. To evaluate the strength of the proposed model, the results are compared to Canny, Sobel, Prewitt, LOG and Saryazdi methods. Finally, by calculation of mean square error (MSE) and peak signal-to-noise ratio (PSNR), the value of PSNR is always equal to or greater than the PSNR value of suggested methods. Moreover, by calculation of Baddeley’s error metric (BEM) on ten test images from the Berkeley Segmentation DataSet (BSDS), we show that the proposed method outperforms the other methods. Therefore, visual and quantitative comparison shows the efficiency and strength of proposed method.
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Affiliation(s)
- Korosh Dabighi
- Department of Mathematics, Kerman Branch, Islamic Azad University, Kerman, Iran
| | - Akbar Nazari
- Department of Pure Mathematics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Saeid Saryazdi
- Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
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7
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Kejia S, Parvin H, Qasem SN, Tuan BA, Pho KH. A classification model based on svm and fuzzy rough set for network intrusion detection. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-191621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Intrusion Detection Systems (IDS) are designed to provide security into computer networks. Different classification models such as Support Vector Machine (SVM) has been successfully applied on the network data. Meanwhile, the extension or improvement of the current models using prototype selection simultaneous with their training phase is crucial due to the serious inefficacies during training (i.e. learning overhead). This paper introduces an improved model for prototype selection. Applying proposed prototype selection along with SVM classification model increases attack discovery rate. In this article, we use fuzzy rough sets theory (FRST) for prototype selection to enhance SVM in intrusion detection. Testing and evaluation of the proposed IDS have been mainly performed on NSL-KDD dataset as a refined version of KDD-CUP99. Experimentations indicate that the proposed IDS outperforms the basic and simple IDSs and modern IDSs in terms of precision, recall, and accuracy rate.
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Affiliation(s)
- Shen Kejia
- The Second Affiliated Hospital of the Second Military Medical University, Shanghai City, China
| | - Hamid Parvin
- Institute of Research and Development, Duy Tan University, Da Nang, Vietnam
- Faculty of Information Technology, Duy Tan University, Da Nang, Vietnam
- Department of Computer Science, Nourabad Mamasani Branch, Islamic Azad University, Mamasani, Iran
| | - Sultan Noman Qasem
- Computer Science Department, College of Computer and Information Sciences, AI Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
- Computer Science Department, Faculty of Applied Science, Taiz University, Taiz, Yemen
| | - Bui Anh Tuan
- Department of Mathematics Education, Teachers College, Can Tho University, Can Tho City, Vietnam
| | - Kim-Hung Pho
- Fractional Calculus, Optimization and Algebra Research Group, Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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8
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Tianhe Y, Mahmoudi MR, Qasem SN, Tuan BA, Pho KH. Numerical function optimization by conditionalized PSO algorithm. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-191685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
A lot of research has been directed to the new optimizers that can find a suboptimal solution for any optimization problem named as heuristic black-box optimizers. They can find the suboptimal solutions of an optimization problem much faster than the mathematical programming methods (if they find them at all). Particle swarm optimization (PSO) is an example of this type. In this paper, a new modified PSO has been proposed. The proposed PSO incorporates conditional learning behavior among birds into the PSO algorithm. Indeed, the particles, little by little, learn how they should behave in some similar conditions. The proposed method is named Conditionalized Particle Swarm Optimization (CoPSO). The problem space is first divided into a set of subspaces in CoPSO. In CoPSO, any particle inside a subspace will be inclined towards its best experienced location if the particles in its subspace have low diversity; otherwise, it will be inclined towards the global best location. The particles also learn to speed-up in the non-valuable subspaces and to speed-down in the valuable subspaces. The performance of CoPSO has been compared with the state-of-the-art methods on a set of standard benchmark functions.
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Affiliation(s)
- Yin Tianhe
- College of Science, Ningbo University of Technology, Ningbo City, Zhejiang Province, China
| | - Mohammad Reza Mahmoudi
- Institute of Research and Development, Duy Tan University, Da Nang, Vietnam
- Department of Statistics, Faculty of Science, Fasa University, Fasa, Iran
| | - Sultan Noman Qasem
- Computer Science Department, College of Computer and Information Sciences, AI Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
- Computer Science Department, Faculty of Applied Science, Taiz University, Taiz, Yemen
| | - Bui Anh Tuan
- Department of Mathematics Education, Teachers College, Can Tho University, Can Tho City, Vietnam
| | - Kim-Hung Pho
- Fractional Calculus, Optimization and Algebra Research Group, Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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Wang Z, Parvin H, Qasem SN, Tuan BA, Pho KH. Cluster ensemble selection using balanced normalized mutual information. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-191531] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
A bad partition in an ensemble will be removed by a cluster ensemble selection framework from the final ensemble. It is the main idea in cluster ensemble selection to remove these partitions (bad partitions) from the selected ensemble. But still, it is likely that one of them contains some reliable clusters. Therefore, it may be reasonable to apply the selection phase on cluster level. To do this, a cluster evaluation metric is needed. Some of these metrics have been recently introduced; each of them has its limitations. The weak points of each method have been addressed in the paper. Subsequently, a new metric for cluster assessment has been introduced. The new measure is named Balanced Normalized Mutual Information (BNMI) criterion. It balances the deficiency of the traditional NMI-based criteria. Additionally, an innovative cluster ensemble approach has been proposed. To create the consensus partition considering the elected clusters, a set of different aggregation-functions (called also consensus-functions) have been utilized: the ones which are based upon the co-association matrix (CAM), the ones which are based on hyper graph partitioning algorithms, and the ones which are based upon intermediate space. The experimental study indicates that the state-of-the-art cluster ensemble methods are outperformed by the proposed cluster ensemble approach.
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Affiliation(s)
- Zecong Wang
- School of Computer Science and Cyberspace Security, Hainan University, China
| | - Hamid Parvin
- Institute of Research and Development, Duy Tan University, Da Nang, Vietnam
- Faculty of Information Technology, Duy Tan University, Da Nang, Vietnam
- Department of Computer Science, Nourabad Mamasani Branch, Islamic Azad University, Mamasani, Iran
| | - Sultan Noman Qasem
- Computer Science Department, College of Computer and Information Sciences, AI Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
- Computer Science Department, Faculty of Applied Science, Taiz University, Taiz, Yemen
| | - Bui Anh Tuan
- Department of Mathematics Education, Teachers College, Can Tho University, Can Tho City, Vietnam
| | - Kim-Hung Pho
- Fractional Calculus, Optimization and Algebra Research Group, Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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Yang Y, Liu H, Guan Z, He X, Liu G. CoHomo: A cluster-attribute correlation aware graph clustering framework. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.06.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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11
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Li G, Mahmoudi MR, Qasem SN, Tuan BA, Pho KH. Cluster ensemble of valid small clusters. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-191530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Guang Li
- Institute of Data Science, City University of Macau, Macau
| | - Mohammad Reza Mahmoudi
- Institute of Research and Development, Duy Tan University, Da Nang, Vietnam
- Department of Statistics, Faculty of Science, Fasa University, Fasa, Iran
| | - Sultan Noman Qasem
- Department of Computer Science, College of Computer and Information Sciences, AI Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
- Department of Computer Science, Faculty of Applied Science, Taiz University, Taiz, Yemen
| | - Bui Anh Tuan
- Department of Mathematics Education, Teachers College, Can Tho University, Can Tho City, Vietnam
| | - Kim-Hung Pho
- Fractional Calculus, Optimization and Algebra Research Group, Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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Modified Immune Evolutionary Algorithm for Medical Data Clustering and Feature Extraction under Cloud Computing Environment. JOURNAL OF HEALTHCARE ENGINEERING 2020; 2020:1051394. [PMID: 32399163 PMCID: PMC7201819 DOI: 10.1155/2020/1051394] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 11/26/2019] [Accepted: 12/10/2019] [Indexed: 11/18/2022]
Abstract
Medical data have the characteristics of particularity and complexity. Big data clustering plays a significant role in the area of medicine. The traditional clustering algorithms are easily falling into local extreme value. It will generate clustering deviation, and the clustering effect is poor. Therefore, we propose a new medical big data clustering algorithm based on the modified immune evolutionary method under cloud computing environment to overcome the above disadvantages in this paper. Firstly, we analyze the big data structure model under cloud computing environment. Secondly, we give the detailed modified immune evolutionary method to cluster medical data including encoding, constructing fitness function, and selecting genetic operators. Finally, the experiments show that this new approach can improve the accuracy of data classification, reduce the error rate, and improve the performance of data mining and feature extraction for medical data clustering.
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Bahrani P, Minaei-Bidgoli B, Parvin H, Mirzarezaee M, Keshavarz A, Alinejad-Rokny H. User and item profile expansion for dealing with cold start problem. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-191225] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Affiliation(s)
- Payam Bahrani
- Department of Computer Engineering, Science and Research branch, Islamic Azad University, Tehran, IR
| | - Behrouz Minaei-Bidgoli
- Department of Computer Engineering, Iran University of Science and Technology, Tehran, IR
| | - Hamid Parvin
- Department of Computer Engineering, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani, IR
- Young Researchers and Elite Club, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani, IR
| | - Mitra Mirzarezaee
- Department of Computer Engineering, Science and Research branch, Islamic Azad University, Tehran, IR
| | - Ahmad Keshavarz
- Department of Electrical Engineering, Persian Gulf University, Bushehr, IR
| | - Hamid Alinejad-Rokny
- The Graduate School of Biomedical Engineering, UNSW Australia, Sydney, AU
- School of Computer Science and Engineering, UNSW Australia, Sydney, AU
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14
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Najafi F, Parvin H, Mirzaie K, Nejatian S, Rezaie V. Dependability‐based cluster weighting in clustering ensemble. Stat Anal Data Min 2020. [DOI: 10.1002/sam.11451] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Affiliation(s)
- Fatemeh Najafi
- Department of Computer Engineering, Maybod BranchIslamic Azad University Maybod Iran
| | - Hamid Parvin
- Department of Computer Engineering, Noorabad Mamasani BranchIslamic Azad University Noorabad Iran
- Young Researchers and Elite Clubs, Noorabad Mamasani BranchIslamic Azad University Noorabad Iran
| | - Kamal Mirzaie
- Department of Computer Engineering, Maybod BranchIslamic Azad University Maybod Iran
| | - Samad Nejatian
- Young Researchers and Elite Clubs, Yasooj BranchIslamic Azad University Yasooj Iran
- Department of Electrical Engineering, Yasooj BranchIslamic Azad University Yasooj Iran
| | - Vahideh Rezaie
- Young Researchers and Elite Clubs, Yasooj BranchIslamic Azad University Yasooj Iran
- Department of Electrical Engineering, Yasooj BranchIslamic Azad University Yasooj Iran
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15
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A comprehensive study of clustering ensemble weighting based on cluster quality and diversity. Pattern Anal Appl 2017. [DOI: 10.1007/s10044-017-0676-x] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
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