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jaya-Optimized Fuzzy Reinforcement Learning Classifier for COVID-19

IETE JOURNAL OF RESEARCH 2023年第10期69卷 7314-7325页

作者： Kukker, Amit Sharma, Rajneesh Bharati Vidyapeeth Deemed Univ Coll Engn Elect Engn Dept Pune 411043 Maharashtra India NSUT ICE Dept New Delhi 110078 India

In the proposed work, Reinforcement Learning has been used to identify Coronavirus disease (COVID-19) amongst viral pneumonia and normal conditions using chest X-ray images. Here, the jaya-Optimized Fuzzy Reinforcement Learning algorithm, in conjunction with Wavelet Transform-based feature extraction and Principal Component Analysis-based feature reduction technique, has been applied to construct the proposed classifier. Predication accuracy achieved with the proposed approach for COVID-19, viral pneumonia, and normal cases is 87.75%, 95.63%, and 96.89%, respectively. The average classification accuracy is 93.43%. The results show that the jaya-Optimized Fuzzy Reinforcement Learning approach attains high accuracy and is superior to other contemporary classifiers, i.e. Support Vector Machine, Artificial Neural Network, Tree algorithm, and K-Nearest Neighbor. The work aims to develop a classifier that can segregate COVID-19 cases from normal and viral pneumonia cases based on the chest X-ray images and is online, adaptive, accurate, and fast. The classifier could serve as a diagnostic tool for healthcare professionals in the current pandemic.

关键词： COVID-19 jaya algorithm optimized fuzzy reinforcement learning viral pneumonia wavelet transform

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Minimally parametrized segmentation framework with dual metaheuristic optimisation algorithms and FCM for detection of anomalies in MR brain images

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2022年 78卷

作者： Natarajan, Senthilkumar Govindaraj, Vishnuvarthanan Zhang, Yudong Murugan, Pallikonda Rajasekaran Balasubramanian, Kannapiran Kandasamy, Karunanithi Ejaz, Khurram Kalasalingam Univ Kalasalingam Acad Res & Educ Dept ECE Srivilliputtur India Kalasalingam Univ Kalasalingam Acad Res & Educ Dept BME Srivilliputtur India Univ Leicester Sch Informat Leicester LE1 7RH England Dr Mahalingam Coll Engn & Technol Dept Elect & Commun Engn Pollachi Tamil Nadu India Vel Tech Rangarajan Dr Sagunthala R&D Inst Science Dept EEE Avadi India Univ Teknol Malaysia Dept Comp Sci Johor Baharu Malaysia

Background: Early prognosis of a brain tumour may offer better life expectancy. Magnetic Resonance Imaging (MRI) coupled with an efficient machine learning segmentation technique has proven to be a reliable way of assessing tumours. In addition to the segmentation, the image is needed to optimise to achieve the desired results. In many cases, single-stage optimisation could not complete the search target owing to algorithm-specific limitations. To overcome this hindrance, the dual metaheuristic optimisation technique is widely used to detect tumour affected tissues. Aim: This research emphasises brain tumour region detection using Fuzzy C-Means (FCM) clustering techniques and the segmented output enhancement using two different optimisation techniques, namely, Artificial Bee Colony (ABC) and the jaya algorithm. Methods: This methodology first deploys the FCM clustering technique to segment the tumour region in the MRI. Then, the initial stage of optimisation is done using the ABC algorithm with the help of texture features extracted from the segmented image through the Gray Level Co-occurrence Matrix (GLCM) technique. Lastly, a novel jaya algorithm is deployed for the second stage of optimisation to provide precise segmentation with the support of global and local best solutions. Result and Conclusion: The proposed framework delivers high accuracy in tumour detection. Besides, it has been proven by renowned evaluation metrics, such as Tanimoto Coefficient Index, and Dice Coefficient Index, which are up to 70.12% and 82.56%, respectively, competing with the contemporary methods used for the evaluations of MR brain images.

关键词： Automatic anomaly detection FCM GLCM Metaheuristic optimisation ABC algorithm jaya algorithm

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Exemplary approach using tool rotation-assisted μ-ECDM for CFRP composites machining

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MATERIALS AND MANUFACTURING PROCESSES 2023年第3期38卷 271-283页

作者： Bhargav, K. V. J. Balaji, P. S. Sahu, Ranjeet Kumar Katiyar, Jitendra Kumar Natl Inst Technol Rourkela Dept Mech Engn Rourkela 769008 India Natl Inst Technol Karnataka Dept Mech Engn Mangalore India SRM Inst Sci & Technol Dept Mech Engn Chennai Tamil Nadu India

Carbon fiber-reinforced polymer (CFRP) composites are an advanced composite material class due to their remarkable properties such as high load-carrying capacity and low density. CFRP composites have enormous applications in aerospace, biomedical, automobile, etc. Machining the CFRP composite is need of the day, but issues like delamination, fiber pullouts, workpiece damage, etc. have made it difficult. These limitations can be surpassed by the micro-electrochemical corona discharge machining (mu-ECDM) process. Although the process has showcased high process capability and great versatility in machining conducting and non-conducting materials, the process has limitations in machining holes deeper than 300 mu m because of insufficient electrolyte supply at the machining zone. Aiding assistance to the process can overcome the limitation by enhancing electrolyte availability. Therefore, an experimental analysis is carried out by generating through holes on the CFRP composite using a tailor-made rotating tool-assisted micro-electrochemical corona discharge machining (RT-mu-ECDM) system. The process parameters, voltage, concentration, duty factor, and tool rotation rate are taken at three levels. The materials removal rate and overcut as machining characteristics were analyzed. The multi-response optimization using jaya algorithm and R-method is used to obtain the optimal process parameters. The experimental investigation suggests RT-mu-ECDM system can machine through holes on CFRP composite.

关键词： ECDM micromachining CFRP tool-rotation optimization jaya algorithm R-method hybrid machining process polymer composite

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Techno socio-economic analysis and stratified assessment of hybrid renewable energy systems for electrification of rural community

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2023年 55卷

作者： Kumar, Nishant Namrata, Kumari Samadhiya, Akshit Natl Inst Technol Elect Engn Jamshedpur 831015 Jharkhand India

This article proposes a fractional-order conceptual based upgradation of the classical jaya algorithm to emphasize the benefits of rapid renewable technologies. An intelligent framework have been suggested to ascertain the impacts of solar unpredictability, assessing biomass planning profile, scrutinize stochastic reliability, and acquire optimal-economic decision in order to develop expandable, robust decision-making tool for the techno-socio-economic planning by utilizing the proposed meta-heuristic algorithm to address a bi-level programming interface. In the early-phase, nine different solar probabilistic classes were trained using a random forest technique, are used to quantify the implications of seasonal with climatic fluctuations of solar radiation. Finally, competency of the FO-jaya algorithm in terms of exploitation, exploration, convergence and local-minima 20 traditional and four global benchmark functions are considered. Further-ahead, the bi-level archetype has investigated to acquire optimal techno-economic solution of hybrid renewable energy (HRE) system to electrify villages of Eastern-India. Statistical analysis of the proposed FO-jaya optimization after 15 runs observes, the diluted cost of energy (COE) of the system with best as Rs. 3.933/kWh, and worst as Rs. 4.201/kWh, average as Rs. 4.222/kWh and standard deviation as 0.277 in contrast to COE obtained through class-topper optimization (CTO) as Rs. 4.120555094/kWh and jaya algorithm as Rs. 4.232949783/kWh.

关键词： jaya algorithm Fractional order CTO Techno-socio-economical assessment HRE system Village electrification

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Novel Hybrid Optimization Technique for Solar Photovoltaic Output Prediction Using Improved Hippopotamus algorithm

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APPLIED SCIENCES-BASEL 2024年第17期14卷

作者： Wang, Hongbin Mansor, Nurulafiqah Nadzirah Binti Bin Mokhlis, Hazlie Univ Malaya Fac Engn Dept Elect Engn Kuala Lumpur 50603 Malaysia

This paper introduces a novel hybrid optimization technique aimed at improving the prediction accuracy of solar photovoltaic (PV) outputs using an Improved Hippopotamus Optimization algorithm (IHO). The IHO enhances the traditional Hippopotamus Optimization (HO) algorithm by addressing its limitations in search efficiency, convergence speed, and global exploration. The IHO algorithm used Latin hypercube sampling (LHS) for population initialization, significantly enhancing the diversity and global search potential of the optimization process. The integration of the jaya algorithm further refines solution quality and accelerates convergence. Additionally, a combination of unordered dimensional sampling, random crossover, and sequential mutation is employed to enhance the optimization process. The effectiveness of the proposed IHO is demonstrated through the optimization of weights and neuron thresholds in the extreme learning machine (ELM), a model known for its rapid learning capabilities but often affected by the randomness of initial parameters. The IHO-optimized ELM (IHO-ELM) is tested against benchmark algorithms, including BP, the traditional ELM, the HO-ELM, LCN, and LSTM, showing significant improvements in prediction accuracy and stability. Moreover, the IHO-ELM model is validated in a different region to assess its generalization ability for solar PV output prediction. The results confirm that the proposed hybrid approach not only improves prediction accuracy but also demonstrates robust generalization capabilities, making it a promising tool for predictive modeling in solar energy systems.

关键词： hippopotamus optimization algorithm latin hypercube sampling jaya algorithm extreme learning machine solar photovoltaic output prediction

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OPTIMIZATION ENABLED DEEP LEARNING FOR STROKE DISEASE PREDICTION FROM MULTIMODALITIES

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JOURNAL OF MECHANICS IN MEDICINE AND BIOLOGY 2024年第5期24卷

作者： Saranya, K. Sumathi, S. Bharathiyar Inst Engn Women Dept Comp Sci & Engn Deviyakurichi Tamil Nadu India Mahendra Engn Coll Dept EEE Namakkal Tamil Nadu India

Stroke is a disease that is caused due to the blockage and burst in the blood vessels of the brain, thus resulting in abrupt brain dysfunction, like sensory or motor disorders, unconsciousness, limb paralysis, and pronunciation disorders. The existing stroke prediction algorithms have some limitations because of the lengthy testing procedures and hefty testing expenses. The main goal of this study is to develop and implement the proposed fusion-based, optimized deep learning model for stroke disease prediction using multimodalities. For that, this research considers the Computed Tomography (CT) and electroencephalogram (EEG) signals as input, and all of these inputs are processed separately to predict the stroke disease. While predicting the stroke disease with a CT image, the bilateral filter performs the pre-processing and the disease prediction is done with the DenseNet model, which is tuned by the proposed jaya Fractional Reptile Search algorithm (jaya FRSA). Similar to how the proposed FRSA does CNN-LSTM training, the Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) predicts the stroke disease using the EEG data as an input after the Gaussian filter removes signal noise. Additionally, the CT image and the EEG signal are processed independently from the image and signal properties. Additionally, the CNN-LSTM model and DenseNet model results are combined using the overlap coefficient to get the final disease prediction. According to the experimental study, the suggested method achieved the maximum image accuracy, sensitivity, and specificity of 0.924, 0.930, and 0.935.

关键词： Reptile search algorithm fractional calculus convolutional neural network long short term memory jaya algorithm

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A new optimization algorithm for solving complex constrained design optimization problems

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ENGINEERING OPTIMIZATION 2017年第1期49卷 60-83页

作者： Rao, R. Venkata Waghmare, G. G. SV Natl Inst Technol Dept Mech Engn Surat India

This article presents the performance of a very recently proposed jaya algorithm on a class of constrained design optimization problems. The distinct feature of this algorithm is that it does not have any algorithm-specific control parameters and hence the burden of tuning the control parameters is minimized. The performance of the proposed jaya algorithm is tested on 21 benchmark problems related to constrained design optimization. In addition to the 21 benchmark problems, the performance of the algorithm is investigated on four constrained mechanical design problems, i.e. robot gripper, multiple disc clutch brake, hydrostatic thrust bearing and rolling element bearing. The computational results reveal that the jaya algorithm is superior to or competitive with other optimization algorithms for the problems considered.

关键词： Constrained design optimization benchmark problems jaya algorithm

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Mass ratio factor in control performance of optimum tuned liquid dampers

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STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS 2023年第18期32卷

作者： Ocak, Ayla Bekdas, Gebrail Nigdeli, Sinan Melih Istanbul Univ Cerrahpasa Dept Civil Engn Istanbul Turkiye Istanbul Univ Cerrahpasa Dept Civil Engn TR-34320 Istanbul Turkiye

Tuned liquid dampers provide structure control with the help of the liquid mass in tanks that are attached to the structure. The mass ratio affects the optimum tuned liquid damper (TLD) parameters. This study examines the effect of mass ratio on the control performance of TLD devices in providing seismic control of structures with different damping ratios. For this purpose, TLD devices with different mass ratios were placed on two different single-story steel and reinforced concrete structure models, and their performance under earthquake excitation was investigated. TLD parameters for obtaining the optimum displacement level in the 0.5- and 1.0-s structure natural period for both structure types were optimized with the jaya algorithm (JA), which is a metaheuristic algorithm. By using the optimum TLD parameters, the structural displacement and total acceleration values were obtained by the critical earthquake analysis. When the results are examined, it is understood that TLD mass increase from a 20% mass ratio for both structure types and selected structure periods does not have a significant effect on TLD control performance.

关键词： damping jaya algorithm mass ratio optimization structural control Tuned liquid damper (TLD)

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Novel Mode Adaptive Artificial Neural Network for Dynamic Learning: Application in Renewable Energy Sources Power Generation Prediction

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ENERGIES 2020年第23期13卷

作者： Zamee, Muhammad Ahsan Won, Dongjun Inha Univ Dept Elect & Comp Engn 100 Inha Ro Incheon 22212 South Korea

A reasonable dataset, which is an essential factor of renewable energy forecasting model development, sometimes is not directly available. Waiting for a substantial amount of training data creates a delay for a model to participate in the electricity market. Also, inappropriate selection of dataset size may lead to inaccurate modeling. Besides, in a multivariate environment, the impact of different variables on the output is often neglected or not adequately addressed. Therefore, in this work, a novel Mode Adaptive Artificial Neural Network (MAANN) algorithm has been proposed using Spearman's rank-order correlation, Artificial Neural Network (ANN), and population-based algorithms for the dynamic learning of renewable energy sources power generation forecasting model. The proposed algorithm has been trained and compared with three population-based algorithms: Advanced Particle Swarm Optimization (APSO), jaya algorithm, and Fine-Tuning Metaheuristic algorithm (FTMA). Also, the gradient descent algorithm is considered as a base case for comparing with the population-based algorithms. The proposed algorithm has been applied in predicting the power output of a Solar Photovoltaic (PV) and Wind Turbine Energy System (WTES). Using the proposed methodology with FTMA, the error was reduced by 71.261% and 80.514% compared to the conventional fixed-sized dataset gradient descent-based training approach for Solar PV and WTES, respectively.

关键词： dynamic learning advanced particle swarm optimization jaya algorithm fine-tuning metaheuristic algorithm renewable energy power forecasting spearman’ s rank-order correlation artificial neural network

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Optimal Location for FACTS Devices: Hybrid algorithm to Improve Power Flow and System Security

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CYBERNETICS AND SYSTEMS 2023年第6期54卷 938-960页

作者： Alluri, Amarendra Lanka, Ravi Sreenivas Rayapudi, Srinivasa Rao Gudlavalleru Engn Coll Vijayawada Andhra Pradesh India JNTUK Coll Engn Kakinada Andhra Pradesh India

In this study, an efficient and reliable algorithm known as jaya algorithm-enabled Flower Pollination algorithm (JA-FPA) is introduced for enhancing the Optimal Power Flow (OPF) and system security by a multi objective model. The multi-objective model is first derived based on entropy of individual objectives such as the Voltage Stability Index (VSI), Power Loss Index (PLI), Line Collapse Proximity Indexes (LCPI), severity and TCSC cost. Multi- objectives are achieved by optimizing the location as well as size of Flexible Alternating Current Transmission System (FACTS) devices in the test bus system. Consequently, the most popular and ubiquitous component of the FACTS equipment category, the Thyristor Controlled Series Capacitor (TCSC), is utilized in this study. The best solution with optimal sizing and allocation of TCSC devices for achieving the aforesaid objectives is chosen from the JA-FPA optimized outputs. The proposed model is performed on two benchmark test bus systems. The adopted model is distinguished with conventional models, and the results are obtained to analyze its effectiveness. In particular, the computational time of proposed model is is 1.74%, 2.52%, 1.13%, 0.63%, 50.15%, 2.36%, 3.83%, 6.00%, 2.79%, and 7.57% superior to existing GWO, WOA, DA, FPA, JA, PSO + GSA, PSO, Henry gas, GA, and TLBO approaches.

关键词： FACTS devices Flower Pollination algorithm jaya algorithm location sizing tranmission system

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