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Simulating online and offline tasks using hybrid cheetah optimization algorithm for patients affected by neurodegenerative diseases

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SCIENTIFIC REPORTS 2025年第1期15卷 1-22页

作者： Sivasakthivel, Ramkumar Rajagopal, Manikandan Anitha, G. Loganathan, K. Abbas, Mohamed Ksibi, Amel Rao, Koppula Srinivas Christ Univ Sch Sci Dept Comp Sci Bengaluru Karnataka India Christ Univ Sch Business & Management Dept Lean Operat & Syst Bengaluru Karnataka India Amrita Vishwa Vidyapeetham Amrita Sch Comp Dept Comp Sci & Engn Chennai Tamil Nadu India Manipal Univ Jaipur Dept Math & Stat Jaipur 303007 Rajasthan India King Khalid Univ Cent Labs POB 960 Abha Saudi Arabia King Khalid Univ Coll Engn Elect Engn Dept Abha 61421 Saudi Arabia Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst Riyadh Saudi Arabia MLR Inst Technol Dept Comp Sci & Engn Hyderabad Telangana India

Brain-Computer Interface (BCI) is a versatile technique to offer better communication system for people affected by the locked-in syndrome (LIS).In the current decade, there has been a growing demand for improved care and services for individuals with neurodegenerative diseases. To address this barrier, the current work is designed with four states of BCI for paralyzed persons using Welch Power Spectral Density (W-PSD). The features extracted from the signals were trained with a hybrid Feed Forward Neural Network cheetah optimization algorithm (FFNNCOA) in both offline and online modes. Totally, eighteen subjects were involved in this study. The study proved that the offline analysis phase outperformed than the online phase in the real-time. The experiment was achieved the accuracies of 95.56% and 93.88% for men and female respectively. Furthermore, the study confirms that the subject's performance in the offline can manage the task more easily than in online mode.

关键词： Brain-Computer interface Welch power spectral density Upper motor neuron cheetah optimization algorithm Lower motor neuron Radial basis function Common Spatial pattern

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A novel approach to intrusion detection system using hybrid flower pollination and cheetah optimization algorithm

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SCIENTIFIC REPORTS 2025年第1期15卷 1-21页

作者： Kumari, Deepshikha Pranav, Prashant Sinha, Abhinav Dutta, Sandip Birla Inst Technol Dept Comp Sci & Engn Mesra Jharkhand India

The study aims to address critical challenges in network security, particularly the limitations of traditional intrusion detection systems (IDS) in terms of adaptability, detection precision, and high false positive rates in dynamic network environments. A novel hybrid IDS model integrating the Flower Pollination algorithm (FPA), cheetah optimization algorithm (COA), and Artificial Neural Networks (ANN) is proposed to enhance detection accuracy, reduce false positives, and optimize feature selection, anomaly detection, and rule adaptation. The hybrid FPA-COA-ANN model combines the optimization capabilities of FPA and COA with the predictive power of ANN. The model was evaluated using five benchmark datasets-CICIDS-2017, TII-SSRC, Lu-flow, NSL-KDD, and WSN-DS. Key performance metrics were analysed to assess the model's effectiveness in detecting malicious activities in complex network traffic patterns. The hybrid model demonstrated superior performance compared to existing IDS approaches. It achieved accuracy rates of 0.99 on CICIDS-2017, 1.00 on TII-SSRC, 1.00 on Lu-flow, 0.99 on NSL-KDD, and 0.93 on WSN-DS. The results highlight significant improvements in detection precision and adaptability, alongside a reduction in false positive rates, showcasing the model's robustness and scalability for real-time threat detection. The proposed hybrid FPA-COA-ANN model effectively mitigates the limitations of traditional IDS by offering a robust, scalable, and efficient solution for real-time network threat detection. Its high accuracy and adaptability across diverse benchmark datasets underscore its potential as a critical tool for enhancing cybersecurity defences in dynamic and complex environments.

关键词： IDS Flower pollination algorithm cheetah optimization algorithm Artificial neural network

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Optimal Allocation of Renewable Distributed Energy Resources in Distribution System using cheetah optimization algorithm

Optimal Allocation of Renewable Distributed Energy Resources...

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2023 International Conference on Evolutionary algorithms and Soft Computing Techniques, EASCT 2023

作者： Pandey, Aditya Soren, Nirmala Sankar, Matta Mani B.I.T Sindri Department of Electrical Engineering Dhanbad India

ISBN: (纸本)9798350313413

The rapid adoption of loads, including commercial, industrial, and residential sectors, imposes significant challenges on distribution networks, necessitating proactive planning and optimal distributed energy resource (DER) allocation. This study proposes an approach that optimally accommodates DER units, specifically wind turbines and solar photovoltaic systems, are appropriately integrated into the distribution network while effectively managing ambiguities related to wind and solar power generation. This integration aims to harness the potential of these renewable sources while mitigating the challenges posed by their intermittency. The cheetah optimizer (CO) algorithm is utilized to determine the optimal locations and ratings of renewable DER, with the primary objectives being the minimization of energy loss, node voltage deviation and the enhancement of the voltage stability index within the distribution system. To empirically validate the proposed methodology, a comprehensive evaluation is conducted using a widely recognized 33-bus benchmark distribution network. This rigorous analysis provides an evidence-based substantiation of the effectiveness of the adopted cheetah optimization (CO) algorithm. The CO algorithm showcases superior performance with a remarkable reduction of 68.697% in energy loss. © 2023 IEEE.

关键词： cheetah optimization algorithm minimization of energy loss Optimal accommodation of DER time varying loads

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Non-uniformly spaced linear antenna array design by means of PEEC approach applying cheetah optimization algorithm

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INTERNATIONAL JOURNAL OF APPLIED ELECTROMAGNETICS AND MECHANICS 2019年第Sup1期60卷 S15-S24页

作者： Moreno, Sinvaldo R. Klein, Carlos E. Mariani, Viviana C. Coelho, Leandro dos S. Alotto, Piergiorgio Torchio, Riccardo Bauernfeind, Thomas Baumgartner, Paul Univ Fed Parana Dept Elect Engn Curitiba Parana Brazil Pontificia Univ Catolica Parana Ind Syst Engn Grad Program Curitiba Parana Brazil Pontificia Univ Catolica Parana Dept Mech Engn Curitiba Parana Brazil Univ Padua Dipartimento Ingn Ind Padua Italy Graz Univ Technol Inst Fundamentals & Theory Elect Engn Graz Austria

This paper presents the application of the Partial Element Equivalent Circuit (PEEC) approach, which is a full wave electromagnetic modelling technique for conductors embedded in arbitrary dielectrics based on equivalent circuits, to the optimal design of antennas with non-uniform spacing between the array elements. The design optimization problem is solved by means of the new nature-inspired cheetah metaheuristic. The main aim of this paper is to introduce the cheetah optimization algorithm to the electromagnetics and antenna community. The results are compared to two well-known optimization algorithms and to show the effectiveness of the proposed algorithm on a realistic benchmark problem.

关键词： Antenna array cheetah optimization algorithm genetic algorithm partial element equivalent circuit particle swarm optimization algorithm

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Non-uniformly spaced linear antenna array design by means of PEEC approach applying cheetah optimization algorithm

Non-uniformly spaced linear antenna array design by means of...

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15th Biennial International Workshop on optimization and Inverse Problems in Electromagnetism (OIPE)

关键词： Antenna array cheetah optimization algorithm genetic algorithm partial element equivalent circuit particle swarm optimization algorithm

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Artificial neural network infused quasi oppositional learning partial reinforcement algorithm for structural design optimization of vehicle suspension components

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MATERIALS TESTING 2024年第11期66卷 1855-1863页

作者： Sait, Sadiq M. Mehta, Pranav Pholdee, Nantiwat Yildiz, Betul Sultan Yildiz, Ali Riza Bursa Uludag Univ Dept Mech Engn TR-16059 Gorukle Bursa Turkiye King Fahd Univ Petr & Minerals Dhahran Saudi Arabia Dharmsinh Desai Univ Dept Mech Engn Nadiad 387001 Gujarat India Khon Kaen Univ Dept Mech Engn Khon Kaen 40002 Thailand Bursa Uludag Univ Deaprtment Mech Engn TR-16059 Bursa Turkiye

This paper introduces and investigates an enhanced Partial Reinforcement optimization algorithm (E-PROA), a novel evolutionary algorithm inspired by partial reinforcement theory to efficiently solve complex engineering optimization problems. The proposed algorithm combines the Partial Reinforcement optimization algorithm (PROA) with a quasi-oppositional learning approach to improve the performance of the pure PROA. The E-PROA was applied to five distinct engineering design components: speed reducer design, step-cone pulley weight optimization, economic optimization of cantilever beams, coupling with bolted rim optimization, and vehicle suspension arm optimization problems. An artificial neural network as a metamodeling approach is used to obtain equations for shape optimization. Comparative analyses with other benchmark algorithms, such as the ship rescue optimization algorithm, mountain gazelle optimizer, and cheetah optimization algorithm, demonstrated the superior performance of E-PROA in terms of convergence rate, solution quality, and computational efficiency. The results indicate that E-PROA holds excellent promise as a technique for addressing complex engineering optimization problems.

关键词： suspension arm partial reinforcement optimization algorithm ship rescue optimization algorithm mountain gazelle optimizer cheetah optimization algorithm

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Dynamic prediction of carbon prices based on the multi-frequency combined model

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PEERJ COMPUTER SCIENCE 2025年 11卷 e2827-e2827页

作者： Duan, Yonghui Fan, Yingying Wang, Xiang Liu, Kaige Zhang, Xiaotong Henan Univ Technol Sch Civil Engn Zhengzhou Henan Peoples R China Zhengzhou Aviat Ind Management Coll Sch Civil Engn Zhengzhou City Henan Province Peoples R China

As a central participant and important leader in the global climate governance system, China is facing the urgent need to predict and regulate the price of carbon emissions to promote the sound development of its carbon market. In this article, a rolling prediction model based on Least Absolute Shrinkage and Selection Operator-cheetah optimization algorithm-extreme gradient boosting (Lasso-COA-XGBoost) carbon price decomposition integration is proposed to address the defects of low prediction accuracy and insufficient model stability of a single machine learning model in the carbon price prediction problem. During the modeling process, the adaptive Lasso method is first employed to select factors from 15 primary indicators of carbon prices, identifying the most important influencing factors. Next, the COA-XGBoost model is built and the parameters of the XGBoost model are optimized using the COA algorithm. Finally, the complete ensemble empirical Mode Decomposition with adaptive noise (CEEMDAM) method is utilized to decompose the residual sequence of the COA-XGBoost model and reconstruct it into high-frequency and low-frequency components. Appropriate frequency models are applied to achieve error correction, thereby constructing the combined Lasso-COA-XGBoost-CEEMDAN model. To further enhance the predictive accuracy and practicality of the model, a rolling time window is introduced for forecasting in the Hubei and Guangzhou carbon emission trading markets, ensuring that the forecasting model can adapt to market changes in real-time. The experimental results show that, taking the carbon price prediction in Hubei as an example, the proposed hybrid model has a significant improvement in prediction accuracy compared with the comparison model (XGBoost model): the RMSE is improved by 99.9987%, the MAE is improved by 99.9039%, the MAPE is improved by 99.9960%, and the R-2 is improved by 0.2004%, and the advantages of this hybrid model are also verified in other experiments.

关键词： Carbon price forecasting Error correction cheetah optimization algorithm Hybrid prediction

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Utilizing Decision Tree-Based Patterns For Predicting Building Energy Consumption

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JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2025年第7期28卷 1529-1541页

作者： Xiao, Juxian Zhang, Zhentao Shijiazhuang Coll Appl Technol Dept Architectural Engn Shijiazhuang 050081 Peoples R China

Efficiently managing building heating loads (HL) is essential for maximizing energy use. and reducing environmental impact. This study explores the application of decision tree (DT)-based patterns in predicting HL, coupled with two innovative optimizers, the cheetah optimization algorithm (COA) and Smell Agent optimization (SAO). The research leverages the flexibility and interpretability of DT, a machine learning framework, to framework complex relationships between various building parameters and HL. DTs excel at capturing non-linear relationships, making them suitable for such applications. Incorporating the COA and SAO optimizers introduces an element of intelligence into the framework process. Preliminary outcomes indicate that the combination of DT with COA and SAO optimizers significantly improves the accuracy of HL prediction. This enhancement has promising implications for building management systems, allowing for more precise control of heating systems and energy consumption optimization. Significantly, the hybrid DT+SAO (DTSA) framework delivers reliable outcomes for HL prediction, boasting an impressive correlation coefficient (R2) value of 0.996 as well as a low root mean squared error (RMSE) value of 0.657. This study advances the broader field of energy-efficient building regulation by showcasing the potential of machine learning frameworks and intelligent optimization algorithms for accurately forecasting HL.

关键词： Heating load Decision Tree cheetah optimization algorithm Smell Agent optimization

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Predictive Modeling of Yoga's Impact on Individuals with Venous Chronic Cerebrospinal System

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第5期15卷 1008-1020页

作者： Qiu, Sanjun Guangxi Police Coll Nanning 530028 Guangxi Peoples R China

People leading a modern lifestyle often experience varicose veins, commonly attributed to factors associated with work and diet, such as prolonged periods of standing or excess weight. These disorders include elevated blood pressure in the lower extremities, especially the legs. An often-researched metric associated with these illnesses is the Vascular Clinical Severity Score (VCSS), which is connected to discomfort and skin discolorations. However, yoga appears to be a viable way to prevent and manage these problems, significantly lessening the negative consequences of varicose veins. The investigation of yoga's effect on VCSS in this study uses a novel strategy combining machine learning with the Extra Tree Classification (ETC), which is improved by the cheetah Optimizer (CO) and Black Widow Optimizer (BWO). In this study, the ETC model was combined with previously mentioned optimizers, and two models were amalgamated, referred to as ETBW and ETCO. Through the evaluation of the performance of these models, it was discerned that the accuracy measure for prediction was associated with the ETCO model in the context of VCSS. By revealing subtle correlations between yoga treatments and VCSS results, this multidisciplinary approach seeks to provide a thorough knowledge of preventative and control processes. This research advances the understanding of vascular health by correlating yoga interventions with VCSS outcomes using machine learning and optimization algorithms. By enhancing predictive accuracy, it promotes multidisciplinary collaboration, personalized medicine, and innovation in healthcare, promising improved patient care and outcomes in varicose vein management.

关键词： Yoga varicose veins Extra Tree Classification cheetah optimization algorithm Black Widow Optimizer

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Hybrid neuro-fuzzy models for assessing the optimum moisture content of lime cement-treated soil

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第6期7卷 5217-5234页

作者： Yu, Li Cai, Xiaoling Li, Ji'ming Lanzhou Univ Technol Western Ctr Disaster Mitigat Civil Engn Minist Educ Lanzhou 730050 Gansu Peoples R China Wuxi City Coll Vocat Technol Sch Architecture & Environm Engn Wuxi 214000 Jiangsu Peoples R China

This study explores the application of machine learning (ML) techniques to predict the optimum moisture content (OMC) of soil-stabilizer combinations. OMC represents the moisture level where soil achieves peak compaction and strength in conjunction with a stabilizer, playing a vital role in attaining desired engineering properties in soil stabilization endeavors. Employing the adaptive neuro-fuzzy inference system (ANFIS) as a robust ML tool, this research endeavors to formulate intricate and accurate models. These models forge connections between OMC and many intrinsic soil properties, including particle-size linear shrinkage, plasticity, distribution, and the nature and quantity of stabilizing additives. A diverse dataset is curated to ascertain the responsiveness of OMC to variations in influential factors, encompassing distinct soil types and previously documented results from stabilization tests. In an endeavor to enhance model precision, this study integrates two meta-heuristic algorithms: the cheetah optimization algorithm (CO) and the equilibrium slime mould algorithm (ESM). By synergistically leveraging these algorithms, the accuracy of the models is fortified. Rigorous validation ensues through an analysis of OMC\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$OMC$$\end{document} samples drawn from diverse soil types obtained from historical stabilization test outcomes. The study unveils three notable models: ANCO (ANFIS + CO), ANES (ANFIS + ESM), and an independent ANFIS model. Each of these models furnishes invaluable insights that substantiate the meticulous projection of OMC for soil-stabilizer blends. Noteworthy among them is the ANCO model, exhibiting exceptional performance metrics. The R2 (correlation coefficient) value of 0.996 and an impressively low RMSE of 0.436 indi

关键词： Optimum moisture content Machine learning Adaptive neuro-fuzzy inference system cheetah optimization algorithm Equilibrium slime mould algorithm

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