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Haq AU, Li JP, Khan I, Agbley BLY, Ahmad S, Uddin MI, Zhou W, Khan S, Alam I. DEBCM: Deep Learning-Based Enhanced Breast Invasive Ductal Carcinoma Classification Model in IoMT Healthcare Systems. IEEE J Biomed Health Inform 2024; 28:1207-1217. [PMID: 37015704 DOI: 10.1109/jbhi.2022.3228577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Accurate breast cancer (BC) diagnosis is a difficult task that is critical for the proper treatment of BC in IoMT (Internet of Medical Things) healthcare systems. This paper proposes a convolutional neural network (CNN)-based diagnosis method for detecting early-stage breast cancer. In developing the proposed method, we incorporated the CNN model for the invasive ductal carcinoma (IDC) classification using breast histology image data. We have incorporated transfer learning (TL) and data augmentation (DA) mechanisms to improve the CNN model's predictive outcomes. For the fine-tuning process, the CNN model was trained with breast histology image data. Furthermore, the held-out cross-validation method for best model selection and hyper-parameter tuning was incorporated. In addition, various performance evaluation metrics for model performance assessment were computed. The experimental results confirmed that the proposed model outperformed the baseline models across all evaluation metrics, achieving 99.04% accuracy. We recommend the proposed method for early recognition of BC in IoMT healthcare systems due to its high performance.
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Ramachandran RA, Barão VAR, Ozevin D, Sukotjo C, Srinivasa PP, Mathew M. Early Predicting Tribocorrosion Rate of Dental Implant Titanium Materials Using Random Forest Machine Learning Models. TRIBOLOGY INTERNATIONAL 2023; 187:108735. [PMID: 37720691 PMCID: PMC10503681 DOI: 10.1016/j.triboint.2023.108735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/19/2023]
Abstract
Early detection and prediction of bio-tribocorrosion can avert unexpected damage that may lead to secondary revision surgery and associated risks of implantable devices. Therefore, this study sought to develop a state-of-the-art prediction technique leveraging machine learning(ML) models to classify and predict the possibility of mechanical degradation in dental implant materials. Key features considered in the study involving pure titanium and titanium-zirconium (zirconium = 5, 10, and 15 in wt%) alloys include corrosion potential, acoustic emission(AE) absolute energy, hardness, and weight-loss estimates. ML prototype models deployed confirms its suitability in tribocorrosion prediction with an accuracy above 90%. Proposed system can evolve as a continuous structural-health monitoring as well as a reliable predictive modeling technique for dental implant monitoring.
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Affiliation(s)
| | - Valentim A R Barão
- Department of Prosthodontics and Periodontology, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, São Paulo, Brazil
| | - Didem Ozevin
- Department of Civil, Materials, and Environmental Engineering, University of Illinois at Chicago, IL, USA
| | - Cortino Sukotjo
- Department of Restorative Dentistry, College of Dentistry, University of Illinois at Chicago, IL, USA
| | - Pai P Srinivasa
- Department of Mechanical Engineering, NMAM IT, Nitte, Karnataka, India
| | - Mathew Mathew
- Department of Biomedical Engineering, University of Illinois at Chicago, IL, USA
- Department of Restorative Dentistry, College of Dentistry, University of Illinois at Chicago, IL, USA
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He J, Wang J, Han Z, Li B, Lv M, Shi Y. Cancer detection for small-size and ambiguous tumors based on semantic FPN and transformer. PLoS One 2023; 18:e0275194. [PMID: 36795663 PMCID: PMC9934456 DOI: 10.1371/journal.pone.0275194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 09/12/2022] [Indexed: 02/17/2023] Open
Abstract
Early detection of tumors has great significance for formative detection and determination of treatment plans. However, cancer detection remains a challenging task due to the interference of diseased tissue, the diversity of mass scales, and the ambiguity of tumor boundaries. It is difficult to extract the features of small-sized tumors and tumor boundaries, so semantic information of high-level feature maps is needed to enrich the regional features and local attention features of tumors. To solve the problems of small tumor objects and lack of contextual features, this paper proposes a novel Semantic Pyramid Network with a Transformer Self-attention, named SPN-TS, for tumor detection. Specifically, the paper first designs a new Feature Pyramid Network in the feature extraction stage. It changes the traditional cross-layer connection scheme and focuses on enriching the features of small-sized tumor regions. Then, we introduce the transformer attention mechanism into the framework to learn the local feature of tumor boundaries. Extensive experimental evaluations were performed on the publicly available CBIS-DDSM dataset, which is a Curated Breast Imaging Subset of the Digital Database for Screening Mammography. The proposed method achieved better performance in these models, achieving 93.26% sensitivity, 95.26% specificity, 96.78% accuracy, and 87.27% Matthews Correlation Coefficient (MCC) value, respectively. The method can achieve the best detection performance by effectively solving the difficulties of small objects and boundaries ambiguity. The algorithm can further promote the detection of other diseases in the future, and also provide algorithmic references for the general object detection field.
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Affiliation(s)
- Jingzhen He
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - Jing Wang
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Zeyu Han
- School of Mathematics and Statistics, Shandong University, WeiHai, China
| | - Baojun Li
- College of Vocational Education, Dezhou University, Dezhou, China
| | - Mei Lv
- School of Physical Education Department, Shandong Women’s University, Jinan, China
| | - Yunfeng Shi
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
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Afrash MR, Bayani A, Shanbehzadeh M, Bahadori M, Kazemi-Arpanahi H. Developing the breast cancer risk prediction system using hybrid machine learning algorithms. JOURNAL OF EDUCATION AND HEALTH PROMOTION 2022; 11:272. [PMID: 36325225 PMCID: PMC9621357 DOI: 10.4103/jehp.jehp_42_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 02/12/2022] [Accepted: 02/21/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Breast cancer (BC) is the most common cause of cancer-related deaths in women globally. Currently, many machine learning (ML)-based predictive models have been established to assist clinicians in decision making for the prediction of BC. However, preventing risk factor formation even with having healthy lifestyle behaviors or preventing disease at early stages can significantly lead to optimal population-wide BC health. Thus, we aimed to develop a prediction model by using a genetic algorithm (GA) incorporating several ML algorithms for the prediction and early warning of BC. MATERIAL AND METHODS The data of 3168 healthy individuals and 1742 patient case records in the BC Registry Database in Ayatollah Taleghani hospital, Abadan, Iran were analyzed. First, a modified hybrid GA was used to perform feature selection and optimization of selected features. Then, with the use of selected features, several ML algorithms were trained to predict BC. Afterward, the performance of each model was measured in terms of accuracy, precision, sensitivity, specificity, and receiver operating characteristic (ROC) curve metrics. Finally, a clinical decision support system based on the best model was developed. RESULTS After performing feature selection, age, consumption of dairy products, BC family history, breast biopsy, chest X-ray, hormone therapy, alcohol consumption, being overweight, having children, and education statuses were selected as the most important features for prediction of BC. The experimental results showed that the decision tree yielded a superior performance than other ML models, with values of 99.3%, 99.5%, 98.26% for accuracy, specificity, and sensitivity, respectively. CONCLUSION The developed predictive system can accurately identify persons who are at elevated risk for BC and can be used as an essential clinical screening tool for the early prevention of BC and serve as an important tool for developing preventive health strategies.
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Affiliation(s)
- Mohammad R. Afrash
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Azadeh Bayani
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mostafa Shanbehzadeh
- Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
| | - Mohammadkarim Bahadori
- Health Management Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Hadi Kazemi-Arpanahi
- Department of Health Information Management and Technology, Abadan University of Medical Sciences, Abadan, Iran
- Student Research Committee, Abadan University of Medical Sciences, Abadan, Iran
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Ashokkumar N, Meera S, Anandan P, Murthy MYB, Kalaivani KS, Alahmadi TA, Alharbi SA, Raghavan SS, Jayadhas SA. Deep Learning Mechanism for Predicting the Axillary Lymph Node Metastasis in Patients with Primary Breast Cancer. BIOMED RESEARCH INTERNATIONAL 2022; 2022:8616535. [PMID: 35993045 PMCID: PMC9385356 DOI: 10.1155/2022/8616535] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 06/29/2022] [Accepted: 07/15/2022] [Indexed: 11/30/2022]
Abstract
The second largest cause of mortality worldwide is breast cancer, and it mostly occurs in women. Early diagnosis has improved further treatments and reduced the level of mortality. A unique deep learning algorithm is presented for predicting breast cancer in its early stages. This method utilizes numerous layers to retrieve significantly greater amounts of information from the source inputs. It could perform automatic quantitative evaluation of complicated image properties in the medical field and give greater precision and reliability during the diagnosis. The dataset of axillary lymph nodes from the breast cancer patients was collected from Erasmus Medical Center. A total of 1050 images were studied from the 850 patients during the years 2018 to 2021. For the independent test, data samples were collected for 100 images from 95 patients at national cancer institute. The existence of axillary lymph nodes was confirmed by pathologic examination. The feed forward, radial basis function, and Kohonen self-organizing are the artificial neural networks (ANNs) which are used to train 84% of the Erasmus Medical Center dataset and test the remaining 16% of the independent dataset. The proposed model performance was determined in terms of accuracy (Ac), sensitivity (Sn), specificity (Sf), and the outcome of the receiver operating curve (Roc), which was compared to the other four radiologists' mechanism. The result of the study shows that the proposed mechanism achieves 95% sensitivity, 96% specificity, and 98% accuracy, which is higher than the radiologists' models (90% sensitivity, 92% specificity, and 94% accuracy). Deep learning algorithms could accurately predict the clinical negativity of axillary lymph node metastases by utilizing images of initial breast cancer patients. This method provides an earlier diagnostic technique for axillary lymph node metastases in patients with medically negative changes in axillary lymph nodes.
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Affiliation(s)
- N. Ashokkumar
- Department of Electronics and communication Engineering, Sree Vidyanikethan Engineering College, Tirupati, Andra Pradesh 517102, India
| | - S. Meera
- Department of Computer Science and Engineering, Agni College of Technology, Chennai, 600130 Tamil Nadu, India
| | - P. Anandan
- Department of Electronics and communication Engineering, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu, India
| | | | - K. S. Kalaivani
- Department of Computer Science and Engineering, Kongu Engineering College, Erode, Tamil Nadu 638060, India
| | - Tahani Awad Alahmadi
- Department of Pediatrics, College of Medicine and King Khalid University Hospital, King Saud University, Medical City, PO Box-2925, Riyadh 11461, Saudi Arabia
| | - Sulaiman Ali Alharbi
- Department of Botany and Microbiology, College of Science, King Saud University, PO Box-2455, Riyadh 11451, Saudi Arabia
| | - S. S. Raghavan
- Department of Botany, University of Texas Health and Science Center at Tyler, Tyler, 75703 TX, USA
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Bhuyan HK, Ravi V, Bramha B, Kamila NK. Disease analysis using machine learning approaches in healthcare system. HEALTH AND TECHNOLOGY 2022. [DOI: 10.1007/s12553-022-00687-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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7
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Segmentation and classification of breast cancer using novel deep learning architecture. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07230-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Rashid J, Batool S, Kim J, Wasif Nisar M, Hussain A, Juneja S, Kushwaha R. An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction. Front Public Health 2022; 10:860396. [PMID: 35433587 PMCID: PMC9008324 DOI: 10.3389/fpubh.2022.860396] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 02/22/2022] [Indexed: 12/23/2022] Open
Abstract
Chronic diseases are increasing in prevalence and mortality worldwide. Early diagnosis has therefore become an important research area to enhance patient survival rates. Several research studies have reported classification approaches for specific disease prediction. In this paper, we propose a novel augmented artificial intelligence approach using an artificial neural network (ANN) with particle swarm optimization (PSO) to predict five prevalent chronic diseases including breast cancer, diabetes, heart attack, hepatitis, and kidney disease. Seven classification algorithms are compared to evaluate the proposed model's prediction performance. The ANN prediction model constructed with a PSO based feature extraction approach outperforms other state-of-the-art classification approaches when evaluated with accuracy. Our proposed approach gave the highest accuracy of 99.67%, with the PSO. However, the classification model's performance is found to depend on the attributes of data used for classification. Our results are compared with various chronic disease datasets and shown to outperform other benchmark approaches. In addition, our optimized ANN processing is shown to require less time compared to random forest (RF), deep learning and support vector machine (SVM) based methods. Our study could play a role for early diagnosis of chronic diseases in hospitals, including through development of online diagnosis systems.
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Affiliation(s)
- Junaid Rashid
- Department of Computer Science and Engineering, Kongju National University, Cheonan, South Korea
| | - Saba Batool
- Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan
| | - Jungeun Kim
- Department of Computer Science and Engineering, Kongju National University, Cheonan, South Korea
- *Correspondence: Jungeun Kim
| | - Muhammad Wasif Nisar
- Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan
| | - Amir Hussain
- Data Science and Cyber Analytics Research Group, Edinburgh Napier University, Edinburgh, United Kingdom
| | - Sapna Juneja
- Department of Computer Science, KIET Group of Institutions, Ghaziabad, India
| | - Riti Kushwaha
- Department of Computer Science, Bennett University, Greater Noida, India
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Ul Haq A, Li JP, Wali S, Ahmad S, Ali Z, Khan J, Khan A, Ali A. StackBC: Deep learning and transfer learning techniques based stacking approach for accurate Invasive Ductal Carcinoma classification using histology images. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-212240] [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
Artificial intelligence (AI) based computer-aided diagnostic (CAD) systems can effectively diagnose critical disease. AI-based detection of breast cancer (BC) through images data is more efficient and accurate than professional radiologists. However, the existing AI-based BC diagnosis methods have complexity in low prediction accuracy and high computation time. Due to these reasons, medical professionals are not employing the current proposed techniques in E-Healthcare to effectively diagnose the BC. To diagnose the breast cancer effectively need to incorporate advanced AI techniques based methods in diagnosis process. In this work, we proposed a deep learning based diagnosis method (StackBC) to detect breast cancer in the early stage for effective treatment and recovery. In particular, we have incorporated deep learning models including Convolutional neural network (CNN), Long short term memory (LSTM), and Gated recurrent unit (GRU) for the classification of Invasive Ductal Carcinoma (IDC). Additionally, data augmentation and transfer learning techniques have been incorporated for data set balancing and for effective training the model. To further improve the predictive performance of model we used stacking technique. Among the three base classifiers (CNN, LSTM, GRU) the predictive performance of GRU are better as compared to individual model. The GRU is selected as a meta classifier to distinguish between Non-IDC and IDC breast images. The method Hold-Out has been incorporated and the data set is split into 90% and 10% for training and testing of the model, respectively. Model evaluation metrics have been computed for model performance evaluation. To analyze the efficacy of the model, we have used breast histology images data set. Our experimental results demonstrated that the proposed StackBC method achieved improved performance by gaining 99.02% accuracy and 100% area under the receiver operating characteristics curve (AUC-ROC) compared to state-of-the-art methods. Due to the high performance of the proposed method, we recommend it for early recognition of breast cancer in E-Healthcare.
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Affiliation(s)
- Amin Ul Haq
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Jian Ping Li
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Samad Wali
- Department of Mathematics, Namal Institute, Mianwali, Pakistan
| | - Sultan Ahmad
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia
| | - Zafar Ali
- School of Computer Science and Engineering Southeast University, Nanjing, China
| | - Jalaluddin Khan
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Ajab Khan
- Director Oric, Abbottabad University of Science and Technology, Abbottabad, KPk, Pakistan
| | - Amjad Ali
- Department of Computer Science and Software Technology, University of Swat, KPK, Pakistan
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Shaikh TA, Ali R. An automated machine learning tool for breast cancer diagnosis for healthcare professionals. Health Syst (Basingstoke) 2021; 11:303-333. [DOI: 10.1080/20476965.2021.1966324] [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] Open
Affiliation(s)
- Tawseef Ayoub Shaikh
- Department Of Computer Science & Engineering, Baba Ghulam Shah Badshah University Rajouri, Rajouri, J&K, India
| | - Rashid Ali
- Department Of Computer Engineering, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
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ElOuassif B, Idri A, Hosni M, Abran A. Classification techniques in breast cancer diagnosis: A systematic literature review. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2021. [DOI: 10.1080/21681163.2020.1811159] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Bouchra ElOuassif
- Department of Web and Mobile Engineering, Software Project Management Research Team, ENSIAS, Mohammed V University, Rabat, Morocco
| | - Ali Idri
- Department of Web and Mobile Engineering, Software Project Management Research Team, ENSIAS, Mohammed V University, Rabat, Morocco
| | - Mohamed Hosni
- Department of Web and Mobile Engineering, Software Project Management Research Team, ENSIAS, Mohammed V University, Rabat, Morocco
| | - Alain Abran
- Department of Software Engineering and Information Technology, Ecole De Technologie Supérieure, –university of Québec, Montreal, Canada
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12
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Development and Evaluation of the Combined Machine Learning Models for the Prediction of Dam Inflow. WATER 2020. [DOI: 10.3390/w12102927] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Predicting dam inflow is necessary for effective water management. This study created machine learning algorithms to predict the amount of inflow into the Soyang River Dam in South Korea, using weather and dam inflow data for 40 years. A total of six algorithms were used, as follows: decision tree (DT), multilayer perceptron (MLP), random forest (RF), gradient boosting (GB), recurrent neural network–long short-term memory (RNN–LSTM), and convolutional neural network–LSTM (CNN–LSTM). Among these models, the multilayer perceptron model showed the best results in predicting dam inflow, with the Nash–Sutcliffe efficiency (NSE) value of 0.812, root mean squared errors (RMSE) of 77.218 m3/s, mean absolute error (MAE) of 29.034 m3/s, correlation coefficient (R) of 0.924, and determination coefficient (R2) of 0.817. However, when the amount of dam inflow is below 100 m3/s, the ensemble models (random forest and gradient boosting models) performed better than MLP for the prediction of dam inflow. Therefore, two combined machine learning (CombML) models (RF_MLP and GB_MLP) were developed for the prediction of the dam inflow using the ensemble methods (RF and GB) at precipitation below 16 mm, and the MLP at precipitation above 16 mm. The precipitation of 16 mm is the average daily precipitation at the inflow of 100 m3/s or more. The results show the accuracy verification results of NSE 0.857, RMSE 68.417 m3/s, MAE 18.063 m3/s, R 0.927, and R2 0.859 in RF_MLP, and NSE 0.829, RMSE 73.918 m3/s, MAE 18.093 m3/s, R 0.912, and R2 0.831 in GB_MLP, which infers that the combination of the models predicts the dam inflow the most accurately. CombML algorithms showed that it is possible to predict inflow through inflow learning, considering flow characteristics such as flow regimes, by combining several machine learning algorithms.
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Bian K, Zhou M, Hu F, Lai W. RF-PCA: A New Solution for Rapid Identification of Breast Cancer Categorical Data Based on Attribute Selection and Feature Extraction. Front Genet 2020; 11:566057. [PMID: 33033496 PMCID: PMC7510777 DOI: 10.3389/fgene.2020.566057] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 08/19/2020] [Indexed: 11/29/2022] Open
Abstract
Breast cancer is one of the most common cancer diseases in women. The rapid and accurate diagnosis of breast cancer is of great significance for the treatment of cancer. Artificial intelligence and machine learning algorithms are used to identify breast malignant tumors, which can effectively solve the problems of insufficient recognition accuracy and long time-consuming in traditional breast cancer diagnosis methods. To solve these problems, we proposed a method of attribute selection and feature extraction based on random forest (RF) combined with principal component analysis (PCA) for rapid and accurate diagnosis of breast cancer. Firstly, RF was used to reduce 30 attributes of breast cancer categorical data. According to the average importance of attributes and out of bag error, 21 relatively important attribute data were selected for feature extraction based on PCA. The seven features extracted from PCA were used to establish an extreme learning machine (ELM) classification model with different activation functions. By comparing the classification accuracy and training time of these different models, the activation function of the hidden layer was determined as the sigmoid function. When the number of neurons in the hidden layer was 27, the accuracy of the test set was 98.75%, the accuracy of the training set was 99.06%, and the training time was only 0.0022 s. Finally, in order to verify the superiority of this method in breast cancer diagnosis, we compared with the ELM model based on the original breast cancer data and other intelligent classification algorithm models. The algorithm used in this article has a faster recognition time and a higher recognition accuracy than other algorithms. We also used the breast cancer data of breast tissue reactance features to verify the reliability of this method, and ideal results were obtained. The experimental results show that RF-PCA combined with ELM can significantly reduce the time required for the diagnosis of breast cancer, which has the ability of rapid and accurate identification of breast cancer and provides a theoretical basis for the intelligent diagnosis of breast cancer.
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Affiliation(s)
- Kai Bian
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, China
| | - Mengran Zhou
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, China.,State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science and Technology, Huainan, China
| | - Feng Hu
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, China
| | - Wenhao Lai
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, China
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14
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Islam MM, Haque MR, Iqbal H, Hasan MM, Hasan M, Kabir MN. Breast Cancer Prediction: A Comparative Study Using Machine Learning Techniques. ACTA ACUST UNITED AC 2020. [DOI: 10.1007/s42979-020-00305-w] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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15
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Yin PN, Kc K, Wei S, Yu Q, Li R, Haake AR, Miyamoto H, Cui F. Histopathological distinction of non-invasive and invasive bladder cancers using machine learning approaches. BMC Med Inform Decis Mak 2020; 20:162. [PMID: 32680493 PMCID: PMC7367328 DOI: 10.1186/s12911-020-01185-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Accepted: 07/13/2020] [Indexed: 01/18/2023] Open
Abstract
Background One of the most challenging tasks for bladder cancer diagnosis is to histologically differentiate two early stages, non-invasive Ta and superficially invasive T1, the latter of which is associated with a significantly higher risk of disease progression. Indeed, in a considerable number of cases, Ta and T1 tumors look very similar under microscope, making the distinction very difficult even for experienced pathologists. Thus, there is an urgent need for a favoring system based on machine learning (ML) to distinguish between the two stages of bladder cancer. Methods A total of 1177 images of bladder tumor tissues stained by hematoxylin and eosin were collected by pathologists at University of Rochester Medical Center, which included 460 non-invasive (stage Ta) and 717 invasive (stage T1) tumors. Automatic pipelines were developed to extract features for three invasive patterns characteristic to the T1 stage bladder cancer (i.e., desmoplastic reaction, retraction artifact, and abundant pinker cytoplasm), using imaging processing software ImageJ and CellProfiler. Features extracted from the images were analyzed by a suite of machine learning approaches. Results We extracted nearly 700 features from the Ta and T1 tumor images. Unsupervised clustering analysis failed to distinguish hematoxylin and eosin images of Ta vs. T1 tumors. With a reduced set of features, we successfully distinguished 1177 Ta or T1 images with an accuracy of 91–96% by six supervised learning methods. By contrast, convolutional neural network (CNN) models that automatically extract features from images produced an accuracy of 84%, indicating that feature extraction driven by domain knowledge outperforms CNN-based automatic feature extraction. Further analysis revealed that desmoplastic reaction was more important than the other two patterns, and the number and size of nuclei of tumor cells were the most predictive features. Conclusions We provide a ML-empowered, feature-centered, and interpretable diagnostic system to facilitate the accurate staging of Ta and T1 diseases, which has a potential to apply to other types of cancer.
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Affiliation(s)
- Peng-Nien Yin
- Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, 1 Lomb Memorial Drive, Rochester, NY, 14623, USA
| | - Kishan Kc
- Golisano College of Computing and Information Sciences, Rochester Institute of Technology, 20 Lomb Memorial Drive, Rochester, NY, 14623, USA
| | - Shishi Wei
- Golisano College of Computing and Information Sciences, Rochester Institute of Technology, 20 Lomb Memorial Drive, Rochester, NY, 14623, USA
| | - Qi Yu
- Golisano College of Computing and Information Sciences, Rochester Institute of Technology, 20 Lomb Memorial Drive, Rochester, NY, 14623, USA
| | - Rui Li
- Golisano College of Computing and Information Sciences, Rochester Institute of Technology, 20 Lomb Memorial Drive, Rochester, NY, 14623, USA
| | - Anne R Haake
- Golisano College of Computing and Information Sciences, Rochester Institute of Technology, 20 Lomb Memorial Drive, Rochester, NY, 14623, USA
| | - Hiroshi Miyamoto
- Department of Pathology and Laboratory Medicine, University of Rochester Medical Center, 601 Elmwood Avenue, Rochester, NY, 14642, USA.
| | - Feng Cui
- Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, 1 Lomb Memorial Drive, Rochester, NY, 14623, USA.
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16
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Ul Haq A, Li J, Memon MH, Khan J, Ud Din S. A novel integrated diagnosis method for breast cancer detection. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-191461] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Amin Ul Haq
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - JianPing Li
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Muhammad Hammad Memon
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Jalaluddin Khan
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Salah Ud Din
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
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17
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Development of automated hybrid intelligent system for herbs plant classification and early herbs plant disease detection. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04634-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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18
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Zhao Q, Zhang Y. Ensemble Method of Feature Selection and Reverse Construction of Gene Logical Network Based on Information Entropy. INT J PATTERN RECOGN 2019. [DOI: 10.1142/s0218001420590041] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In this paper, we propose a novel ensemble gene selection method to obtain a gene subset. Then we provide a reverse construction method of gene network derived from expression profile data of the gene subset. The uncertainty coefficient based on information entropy are used to define the existence of logical relations among these genes. If the uncertainty coefficient between some genes exceeds predefined thresholds, the gene nodes will be connected by directed edges. Thus, a gene network is generated, which we define as gene logical network. This method is applied to the breast cancer data including control group and experimental group, with comparisons of the 2nd-order logic type distribution, average degree as well as average path length of the networks. It is found that these structures with different networks are quite distinct. By the comparison of the degree difference between control group and experimental group, the key genes are picked up. By defining the dynamics evolution rules of state transition based on the logical regulation among the key genes in the network, the dynamic behaviors for normal breast cells and cells with cancer of different stages are simulated numerically. Some of them are highly related to the development of breast cancer through literature inquiry. The study may provide a useful revelation to the biological mechanism in the formation and development of cancer.
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Affiliation(s)
- Qingfeng Zhao
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong 266590, P. R. China
- Shandong Province Key Laboratory of Wisdom Mine Information Technology, Shandong University of Science and Technology, Qingdao 266590, P. R. China
| | - Yulin Zhang
- College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, Shandong 266590, P. R. China
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19
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Mohanty F, Rup S, Dash B, Majhi B, Swamy MNS. Digital mammogram classification using 2D-BDWT and GLCM features with FOA-based feature selection approach. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04186-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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20
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Chicco D, Rovelli C. Computational prediction of diagnosis and feature selection on mesothelioma patient health records. PLoS One 2019; 14:e0208737. [PMID: 30629589 PMCID: PMC6328132 DOI: 10.1371/journal.pone.0208737] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Accepted: 11/22/2018] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Mesothelioma is a lung cancer that kills thousands of people worldwide annually, especially those with exposure to asbestos. Diagnosis of mesothelioma in patients often requires time-consuming imaging techniques and biopsies. Machine learning can provide for a more effective, cheaper, and faster patient diagnosis and feature selection from clinical data in patient records. METHODS AND FINDINGS We analyzed a dataset of health records of 324 patients having mesothelioma symptoms from Turkey. The patients had prior asbestos exposure and displayed symptoms consistent with mesothelioma. We compared probabilistic neural network, perceptron-based neural network, random forest, one rule, and decision tree classifiers to predict diagnosis of the patient records. We measured classifiers' performance through standard confusion matrix scores such as Matthews correlation coefficient (MCC). Random forest outperformed all models tried, obtaining MCC = +0.37 on the complete imbalanced dataset and MCC = +0.64 on the under-sampled balanced dataset. We then employed random forest feature selection to identify the two most relevant dataset traits associated with mesothelioma: lung side and platelet count. These two risk factors resulted so predictive, that decision tree focusing on them achieved the second top accuracy on the complete dataset diagnosis prediction (MCC = +0.28), outperforming all other methods and even decision tree itself applied to all features. CONCLUSIONS Our results show that machine learning can predict diagnoses of patients having mesothelioma symptoms with high accuracy, sensitivity, and specificity, in few minutes. Additionally, random forest can efficiently select the most important features of this clinical dataset (lung side and platelet count) in few seconds. The importance of pleural plaques in lung sides and blood platelets in mesothelioma diagnosis indicates that physicians should focus on these two features when reading records of patients with mesothelioma symptoms. Moreover, doctors can exploit our machinery to predict patient diagnosis when only lung side and platelet data are available.
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Affiliation(s)
- Davide Chicco
- Peter Munk Cardiac Centre, Toronto, Ontario, Canada
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Cristina Rovelli
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
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21
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Lung boundary detection for chest X-ray images classification based on GLCM and probabilistic neural networks. ACTA ACUST UNITED AC 2019. [DOI: 10.1016/j.procs.2019.09.314] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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22
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23
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Yassin NIR, Omran S, El Houby EMF, Allam H. Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 156:25-45. [PMID: 29428074 DOI: 10.1016/j.cmpb.2017.12.012] [Citation(s) in RCA: 120] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Revised: 11/26/2017] [Accepted: 12/11/2017] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE The high incidence of breast cancer in women has increased significantly in the recent years. Physician experience of diagnosing and detecting breast cancer can be assisted by using some computerized features extraction and classification algorithms. This paper presents the conduction and results of a systematic review (SR) that aims to investigate the state of the art regarding the computer aided diagnosis/detection (CAD) systems for breast cancer. METHODS The SR was conducted using a comprehensive selection of scientific databases as reference sources, allowing access to diverse publications in the field. The scientific databases used are Springer Link (SL), Science Direct (SD), IEEE Xplore Digital Library, and PubMed. Inclusion and exclusion criteria were defined and applied to each retrieved work to select those of interest. From 320 studies retrieved, 154 studies were included. However, the scope of this research is limited to scientific and academic works and excludes commercial interests. RESULTS This survey provides a general analysis of the current status of CAD systems according to the used image modalities and the machine learning based classifiers. Potential research studies have been discussed to create a more objective and efficient CAD systems.
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Affiliation(s)
- Nisreen I R Yassin
- Systems & Information Department, Engineering Research Division, National Research Centre, Dokki, Cairo 12311, Egypt.
| | - Shaimaa Omran
- Systems & Information Department, Engineering Research Division, National Research Centre, Dokki, Cairo 12311, Egypt.
| | - Enas M F El Houby
- Systems & Information Department, Engineering Research Division, National Research Centre, Dokki, Cairo 12311, Egypt.
| | - Hemat Allam
- Anaesthesia & Pain, Medical Division, National Research Centre, Dokki, Cairo 12311, Egypt.
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24
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Jothi G, Inbarani HH, Azar AT, Devi KR. Rough set theory with Jaya optimization for acute lymphoblastic leukemia classification. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3359-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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25
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Punitha S, Ravi S, Anousouya Devi M, Vaishnavi J. Particle swarm optimized computer aided diagnosis system for classification of breast masses. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2017. [DOI: 10.3233/jifs-169224] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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26
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Automatic detection of respiratory arrests in OSA patients using PPG and machine learning techniques. Neural Comput Appl 2016. [DOI: 10.1007/s00521-016-2617-9] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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27
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Assessment of prediction ability for reduced probabilistic neural network in data classification problems. Soft comput 2016. [DOI: 10.1007/s00500-016-2382-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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28
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Senthil Kumar S, Hannah Inbarani H, Azar AT, Polat K. Covering-based rough set classification system. Neural Comput Appl 2016. [DOI: 10.1007/s00521-016-2412-7] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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29
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Azar AT, Inbarani HH, Renuga Devi K. Improved dominance rough set-based classification system. Neural Comput Appl 2016. [DOI: 10.1007/s00521-016-2177-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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30
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Kusy M, Zajdel R. Application of Reinforcement Learning Algorithms for the Adaptive Computation of the Smoothing Parameter for Probabilistic Neural Network. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2015; 26:2163-2175. [PMID: 25532211 DOI: 10.1109/tnnls.2014.2376703] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
In this paper, we propose new methods for the choice and adaptation of the smoothing parameter of the probabilistic neural network (PNN). These methods are based on three reinforcement learning algorithms: Q(0)-learning, Q(λ)-learning, and stateless Q-learning. We regard three types of PNN classifiers: the model that uses single smoothing parameter for the whole network, the model that utilizes single smoothing parameter for each data attribute, and the model that possesses the matrix of smoothing parameters different for each data variable and data class. Reinforcement learning is applied as the method of finding such a value of the smoothing parameter, which ensures the maximization of the prediction ability. PNN models with smoothing parameters computed according to the proposed algorithms are tested on eight databases by calculating the test error with the use of the cross validation procedure. The results are compared with state-of-the-art methods for PNN training published in the literature up to date and, additionally, with PNN whose sigma is determined by means of the conjugate gradient approach. The results demonstrate that the proposed approaches can be used as alternative PNN training procedures.
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31
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Mnasser A, Bouani F, Ksouri M. Neural Networks Predictive Controller Using an Adaptive Control Rate. INTERNATIONAL JOURNAL OF SYSTEM DYNAMICS APPLICATIONS 2014. [DOI: 10.4018/ijsda.2014070106] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
A model predictive control design for nonlinear systems based on artificial neural networks is discussed. The Feedforward neural networks are used to describe the unknown nonlinear dynamics of the real system. The backpropagation algorithm is used, offline, to train the neural networks model. The optimal control actions are computed by solving a nonconvex optimization problem with the gradient method. In gradient method, the steepest descent is a sensible factor for convergence. Then, an adaptive variable control rate based on Lyapunov function candidate and asymptotic convergence of the predictive controller are proposed. The stability of the closed loop system based on the neural model is proved. In order to demonstrate the robustness of the proposed predictive controller under set-point and load disturbance, a simulation example is considered. A comparison of the control performance achieved with a Levenberg-Marquardt method is also provided to illustrate the effectiveness of the proposed controller.
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Affiliation(s)
- Ahmed Mnasser
- Faculty of Sciences of Tunis, Tunis El Manar University, Tunis, Tunisia
| | - Faouzi Bouani
- Analysis, Conception and Control of Systems Laboratory, National Engineering School of Tunis, Tunis El Manar University, Tunis, Tunisia
| | - Mekki Ksouri
- Analysis, Conception and Control of Systems Laboratory, National Engineering School of Tunis, Tunis El Manar University, Tunis, Tunisia
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