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Improved deep convolutional neural networks using chimp optimization algorithm for Covid19 diagnosis from the X-ray images

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第PartC期213卷 119206页

作者： Cai, Chengfeng Gou, Bingchen Khishe, Mohammad Mohammadi, Mokhtar Rashidi, Shima Moradpour, Reza Mirjalili, Seyedali Northwestern Polytech Univ Sch Mech Engn Xian 710072 Peoples R China Imam Khomeini Marine Sci Univ Dept Elect Engn Nowshahr Iran Lebanese French Univ Coll Engn & Comp Sci Dept Informat Technol Erbil Kurdistan Iraq Univ Human Dev Coll Sci & Technol Dept Comp Sci Sulaymaniyah Kurdistan Iraq Torrens Univ Ctr Artificial Intelligence Res & Optimizat Adelaide Australia Obuda Univ Univ Res & Innovat Ctr H-1034 Budapest Hungary

Applying Deep Learning (DL) in radiological images (i.e., chest X-rays) is emerging because of the necessity of having accurate and fast COVID-19 detectors. Deep Convolutional Neural Networks (DCNN) have been typically used as robust COVID-19 positive case detectors in these approaches. Such DCCNs tend to utilize Gradient Descent-Based (GDB) algorithms as the last fully-connected layers' trainers. Although GDB training algorithms have simple structures and fast convergence rates for cases with large training samples, they suffer from the manual tuning of numerous parameters, getting stuck in local minima, large training samples set requirements, and inherently sequential procedures. It is exceedingly challenging to parallelize them with Graphics Processing Units (GPU). Consequently, the chimp optimization algorithm (ChOA) is presented for training the DCNN's fully connected layers in light of the scarcity of a big COVID-19 training dataset and for the purpose of developing a fast COVID-19 detector with the capability of parallel implementation. In addition, two publicly accessible datasets termed COVID-Xray-5 k and COVIDetectioNet are used to benchmark the proposed detector known as DCCN-chimp. In order to make a fair comparison, two structures are proposed: i-6c-2 s-12c-2 s and i-8c-2 s-16c2 s, all of which have had their hyperparameters fine-tuned. The outcomes are evaluated in comparison to standard DCNN, Hybrid DCNN plus Genetic algorithm (DCNN-GA), and Matched Subspace classifier with Adaptive Dictionaries (MSAD). Due to the large variation in results, we employ a weighted average of the ensemble of ten trained DCNN-ChOA, with the validation accuracy of the weights being used to determine the final weights. The validation accuracy for the mixed ensemble DCNN-ChOA is 99.11%. LeNet-5 DCNN's ensemble detection accuracy on COVID-19 is 84.58%. Comparatively, the suggested DCNN-ChOA yields over 99.11% accurate detection with a false alarm rate of less than 0.89%. The

关键词： chimp optimization algorithm COVID-19 diagnosis Deep learning Convolutional neural networks Chest X-rays Machine learning

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Binary chimp optimization algorithm with ML Based Intrusion Detection for Secure IoT-Assisted Wireless Sensor Networks

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SENSORS 2023年第8期23卷 4073页

作者： Aljebreen, Mohammed Alohali, Manal Abdullah Saeed, Muhammad Kashif Mohsen, Heba Al Duhayyim, Mesfer Abdelmageed, Amgad Atta Drar, Suhanda Abdelbagi, Sitelbanat King Saud Univ Community Coll Dept Comp Sci POB 28095 Riyadh 11437 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia King Khalid Univ Appl Coll Dept Comp Sci Muhayil 63311 Saudi Arabia Future Univ Egypt Fac Comp & Informat Technol Dept Comp Sci New Cairo 11835 Egypt Prince Sattam Bin Abdulaziz Univ Coll Comp Engn & Sci Dept Comp Sci Al Kharj 16273 Saudi Arabia Prince Sattam Bin Abdulaziz Univ Dept Comp & Self Dev Preparatory Year Deanship Al Kharj 16278 Saudi Arabia

An Internet of Things (IoT)-assisted Wireless Sensor Network (WSNs) is a system where WSN nodes and IoT devices together work to share, collect, and process data. This incorporation aims to enhance the effectiveness and efficiency of data analysis and collection, resulting in automation and improved decision-making. Security in WSN-assisted IoT can be referred to as the measures initiated for protecting WSN linked to the IoT. This article presents a Binary chimp optimization algorithm with Machine Learning based Intrusion Detection (BCOA-MLID) technique for secure IoT-WSN. The presented BCOA-MLID technique intends to effectively discriminate different types of attacks to secure the IoT-WSN. In the presented BCOA-MLID technique, data normalization is initially carried out. The BCOA is designed for the optimal selection of features to improve intrusion detection efficacy. To detect intrusions in the IoT-WSN, the BCOA-MLID technique employs a class-specific cost regulation extreme learning machine classification model with a sine cosine algorithm as a parameter optimization approach. The experimental result of the BCOA-MLID technique is tested on the Kaggle intrusion dataset, and the results showcase the significant outcomes of the BCOA-MLID technique with a maximum accuracy of 99.36%, whereas the XGBoost and KNN-AOA models obtained a reduced accuracy of 96.83% and 97.20%, respectively.

关键词： intrusion detection system wireless sensor networks machine learning chimp optimization algorithm feature selection

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Distributed power generation planning for distribution network using chimp optimization algorithm in order to reliability improvement

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ELECTRIC POWER SYSTEMS RESEARCH 2023年第1期217卷

作者： Borousan, Farzaneh Hamidan, Mohammad-Ali Islamic Azad Univ IAU Dept Elect Engn Ahvaz Branch Ahvaz Iran

The most important challenges facing system designers in radial distribution networks include low voltage profiles, high current values, very high power losses and voltage drops. Therefore, the use of Distributed Gen-erations (DGs) improves the technical and economic performance of distribution systems. In this article, in order to identify the optimal location of DGs, increase voltage stability and improve reliability in 119-bus distribution network, a comparison is made of the results of chimp optimization algorithm (ChOA) and other algorithms. The objective functions implemented in ChOA include the minimization of investment and operation costs to avoid economic losses and the minimization of Energy Not-Supplied (ENS) to prevent network blackouts. The ChOA method performs better than algorithms such as the Whale optimization algorithm (WOA), the Gray Wolf optimization (GWO) algorithm, Social Spider algorithm (SSA), and Imperialist Competitive algorithm (ICA). The simulation results show that by considering DGs using ChOA in 119-bus distribution networks, the value of reliability improvement is reduced by 61.70%. The results show that the value of ENS using ChOA compared to WOA, GWO, SSA and ICA algorithms is improved by 2.60%, 4.63%, 6.37% and 9.67%, respectively. The pro-posed model shows that reliability improvement is better than other algorithms in distribution networks.

关键词： Distributed generations Radial distribution networks Reliability improvement chimp optimization algorithm

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Dual interactive Wasserstein generative adversarial networks optimized with hybrid Archimedes optimization and chimp optimization algorithm-based channel estimation in OFDM

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2024年第7期37卷 e5720-e5720页

作者： Mydhili, S. K. KGiSL Inst Technol Dept Elect & Commun Engn Coimbatore India KGiSL Inst Technol Dept Elect & Commun Engn Coimbatore Tamil Nadu India

Dual interactive Wasserstein generative adversarial networks optimized with hybrid Archimedes optimization and chimp optimization algorithm-based channel estimation in OFDM (DiWGAN-Hyb AOA-COA-MIMO-OFDM) is proposed in this manuscript. In OFDM, there is a non-stationary channel physical appearance during channel estimation (CE). Therefore in this work, Hyb AOA-COA is employed to enhance the DiWGAN weight parameters. The proposed DiWGAN-Hyb AOA-COA-MIMO-OFDM technique is executed in network simulator (NS2) tool. The proposed technique attains lower computational cost 99.67%, 92.34%, and 97.45%;lesser bit error rate 98.33%, 83.12%, and 88.96%;and lesser mean square error 93.15%, 79.90%, and 92.88% compared with existing methods, like MIMO-OFDM system using deep neural network and MN-based improved AMO model (DNN-IAMO-MIMO-OFDM), MIMO-OFDM systems using the deep learning and optimization (RBFNN-PSO-MIMO-OFDM), and MIMO-OFDM systems using hybrid neural network (HNN-CSI-MIMO-OFDM) respectively. Dual interactive Wasserstein generative adversarial networks optimized with hybrid Archimedes optimization and chimp optimization algorithm-based channel estimation in OFDM (DiWGAN-Hyb-AOA-COA-MIMO-OFDM) is proposed in this manuscript. During channel estimation in OFDM, there is a non-stationary channel physical appearance, problems arise to overcome this issue, and DiWGAN is used. Therefore in this work, hybrid Archimedes optimization algorithm and chimp optimization algorithm (Hyb AOA-COA) are employed to enhance the DiWGAN weight parameters. The proposed is compared with existing methods, like DNN-IAMO-MIMO-OFDM, MIMO OFDM RBFNN-PSO-MIMO-OFDM, and HNN-CSI-MIMO-OFDM, respectively. image

关键词： Archimedes optimization algorithm channel estimation algorithm chimp optimization algorithm dual interactive Wasserstein generative adversarial network multiple inputs multiple output and orthogonal frequency division multiplexing

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Breast cancer diagnosis using hybrid AlexNet-ELM and chimp optimization algorithm evolved by Nelder-mead simplex approach

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2023年第1期85卷

作者： Junyue, Cao Zeebaree, Diyar Qader Chen, Qingfeng Zebari, Dilovan Asaad Guangxi Univ Coll Life Sci & Technol Nanning 530004 Peoples R China Duhok Polytech Univ Tech Coll Informat Akre Dept Comp Network & Informat Secur Duhok Iraq Guangxi Univ Sch Comp Elect & Informat Nanning Peoples R China Nawroz Univ Coll Sci Dept Comp Sci Duhok 42001 Kurdistan Regio Iraq

This study proposes a Hybrid AlexNet-Extreme Learning Machine (ELM) approach for breast cancer diagnosis using mammography images. Batch normalization is applied to improve AlexNet's performance, and the chimp optimization algorithm (ChOA) is utilized to avoid sub-optimal solutions in ELM. The Nelder-mead simplex (NEMS) technique is then employed to enhance the convergence behavior of ChOA. The study's main contributions are the proposed hybrid model and the application of ChOA and NEMS techniques to improve the performance of ELM. The proposed model is evaluated using the CBIS-DDSM dataset, with wiener and CALHE filters used as preprocessors. The effectiveness of the classification is examined using five optimization algorithms, and several metrics. The outcomes demonstrate that CALHE filter offered the best performance overall, and AlexNet-BN-ELM-CHOA-NEMS was the most accurate of the five models, with a sensitivity of 96.03 %, a specificity of 94.60 %, and an overall accuracy of 95.32 %. The findings demonstrate the effectiveness of the proposed model in breast cancer diagnosis.

关键词： Brest cancer diagnosis chimp optimization algorithm AlexNet Nelder-mead simplex approach

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Real-time COVID-19 diagnosis from X-Ray images using deep CNN and extreme learning machines stabilized by chimp optimization algorithm

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2021年第0期68卷 102764-102764页

作者： Hu Tianqing Khishe, Mohammad Mohammadi, Mokhtar Parvizi, Gholam-Reza Karim, Sarkhel H. Taher Rashid, Tarik A. Henan Polytech Univ Coll Comp Sci & Technol Jiaozuo Henan Peoples R China Imam Khomeini Marine Sci Univ Dept Elect Engn Nowshahr Iran Lebanese French Univ Dept Informat Technol Erbil Krg Iraq Univ Isfahan Fac Foreign Languages Esfahan Iran Univ Halabja Coll Sci Comp Dept Halabja Iraq Univ Kurdistan Hewler Comp Sci & Engn Dept Erbil Krg Iraq

Real-time detection of COVID-19 using radiological images has gained priority due to the increasing demand for fast diagnosis of COVID-19 cases. This paper introduces a novel two-phase approach for classifying chest X-ray images. Deep Learning (DL) methods fail to cover these aspects since training and fine-tuning the model's parameters consume much time. In this approach, the first phase comes to train a deep CNN working as a feature extractor, and the second phase comes to use Extreme Learning Machines (ELMs) for real-time detection. The main drawback of ELMs is to meet the need of a large number of hidden-layer nodes to gain a reliable and accurate detector in applying image processing since the detective performance remarkably depends on the setting of initial weights and biases. Therefore, this paper uses chimp optimization algorithm (ChOA) to improve results and increase the reliability of the network while maintaining real-time capability. The designed detector is to be benchmarked on the COVID-Xray-5k and COVIDetectioNet datasets, and the results are verified by comparing it with the classic DCNN, Genetic algorithm optimized ELM (GA-ELM), Cuckoo Search optimized ELM (CS-ELM), and Whale optimization algorithm optimized ELM (WOA-ELM). The proposed approach outperforms other comparative benchmarks with 98.25 % and 99.11 % as ultimate accuracy on the COVID-Xray-5k and COVIDetectioNet datasets, respectively, and it led relative error to reduce as the amount of 1.75 % and 1.01 % as compared to a convolutional CNN. More importantly, the time needed for training deep ChOA-ELM is only 0.9474 milliseconds, and the overall testing time for 3100 images is 2.937 s.

关键词： COVID-19 Real-time chimp optimization algorithm Deep convolutional neural networks Chest X-ray images

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Enhancing Global Maximum Power Point of Solar Photovoltaic Strings under Partial Shading Conditions Using chimp optimization algorithm

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ENERGIES 2021年第14期14卷 4086页

作者： Nagadurga, Timmidi Narasimham, Pasumarthi Venkata Ramana Lakshmi Vakula, V. S. Devarapalli, Ramesh Marquez, Fausto Pedro Garcia Lakireddy Bali Reddy Coll Engn Dept Elect & Elect Engn Mylavaram 521230 India Jawaharlal Nehru Technol Univ Univ Coll Engn Dept Elect & Elect Engn Kakinada 533003 India Velagapudi Ramakrishna Siddhartha Engn Coll Dept Elect & Elect Engn Kanuru 520007 India JNTUK Univ Coll Engn Dept Elect & Elect Engn Vizianagaram 535003 India Birsa Inst Technol Sindri Dept Elect Engn Dhanbad 828123 Bihar India Univ Castilla La Mancha Ingenium Res Grp Ciudad Real 13071 Spain

This paper proposes the application of a metaheuristic algorithm inspired by the social behavior of chimps in nature, called chimp optimization algorithm (ChOA), for the maximum power point tracking of solar photovoltaic (PV) strings. In this algorithm, the chimps hunting process is mathematically articulated, and new mechanisms are designed to perform the exploration and exploitation. To evaluate the ChOA, it is applied to some fixed dimension benchmark functions and engineering problem application of tracking maximum power from solar PV systems under partial shading conditions. Partial shading condition is a common problem that appears in the solar PV modules installed in domestic areas. This shading alters the power developed by the solar PV panel, and exhibits multiple peaks on the power variation with voltage (P-V) characteristic curve. The dynamics of the solar PV system have been considered, and the mathematical model of a single objective function has been framed for tuning the optimal control parameter with the suggested algorithm. Implementing various practical shading patterns of solar PV systems with the ChOA algorithm has shown improved solar power point tracking performance compared to other algorithms in the literature.

关键词： solar photovoltaic system maximum power point tracking chimp optimization algorithm grey wolf optimization availability reliability

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Enhancing Robot Path Planning Through a Twin-Reinforced chimp optimization algorithm and Evolutionary Programming algorithm

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IEEE ACCESS 2024年 12卷 170057-170078页

作者： Zhang, Yang Zhang, Hu Beijing AIQI Technol Co LTD Beijing Peoples R China

The importance of efficient path planning (PP) cannot be overstated in the domain of robots, as it involves the utilization of intelligent algorithms to determine the optimal trajectory for robot to navigate between two given points. The main target of PP is to determine potential trajectories for robot operating in a complex environment containing various obstacles. The implementation of these movements should facilitate robot in traversing a path without encountering any collisions, starting from its initial location and reaching the intended destination. In order to address the challenges associated with robot PP, this study applies the chimp optimization algorithm (CHOA) as a local searching (LS) technique and the evolutionary programming algorithm (EPA) to enhance the potential route discovered via a collection of LSs. In order to address CHOA's tendency to converge to local minima, a new updating technique called twin-reinforced (TR) is developed. In order to assess the effectiveness of the TRCHOA, we conducted a comparative analysis with other widely used meta-heuristic algorithms that are typically employed for solving robot PP problems. Additionally, we included the conventional probabilistic roadmap method (PRM) in our evaluation. We evaluated the planning performances of these algorithms on a standardized set of benchmark problems. Our findings indicate that the TRCHOA outperforms the other algorithms in terms of its planning performance. The evaluation of planning effectiveness encompasses several key criteria, namely path length, consistency of scheduled paths, time complexity, and rate of success. The experiments conducted in this study provide evidence of the statistically significant value of the enhancements obtained through the implementation of the proposed method. The findings derived from the TRCHOA provide compelling evidence of its capacity to accurately determine the most optimal route within the specified test map.

关键词： Robots optimization Collision avoidance Navigation Convergence Trajectory Animals Evolutionary computation Path planning chimp optimization algorithm evolutionary programming path planning robot twin-reinforced technique

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Predicting the environmental economic dispatch problem for reducing waste nonrenewable materials via an innovative constraint multi-objective chimp optimization algorithm

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JOURNAL OF CLEANER PRODUCTION 2022年 365卷

作者： Zhu, Lei Ren, Hao Habibi, Mostafa Mohammed, Khidhair Jasim Khadimallah, Mohamed Amine Xian Univ Architecture & Technol Sch Publ Adm Xian 710055 Shaanxi Peoples R China Xijing Univ Sch Humanities & Educ Xian 710123 Shaanxi Peoples R China Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Duy Tan Univ Fac Elect Elect Engn Da Nang 550000 Vietnam Sharif Univ Technol Ctr Excellence Design Robot & Automat Dept Mech Engn Azadi AvePOB 11365-9567 Tehran Iran Al Mustaqbal Univ Coll Air Conditioning & Refrigerat Tech Engn Dept Babylon 51001 Iraq Prince Sattam Bin Abdulaziz Univ Coll Engn Civil Engn Dept Al Kharj 16273 Saudi Arabia Univ Carthage Polytech Sch Tunisia Lab Syst & Appl Mech Tunis Tunisia

The usage of conventional fossil fuels has aided fast economic growth while also having negative consequences, such as increased global warming and the destruction of the ecosystem. This paper proposes a novel swarm-based metaheuristic method called chimp optimization algorithm (ChOA) to tackle the environmental, economic dispatch issue and reducing the waste nonrenewable materials. In this regard, two objective functions named fuel cost function and emission cost function are proposed. Unique constrained handling also solves the challenge of multi-objective optimization. Standard IEEE 30 bus with six generators and a 10-unit system are used to demonstrate the usefulness of ChOA. The result of ChOA is compared with Individual Best Memory Penalty Factor Grey Wolf Optimizer (IBMPF-GWO), Improved Whale Trainer (IWT), Chaotic Biogeography-Based Optimizer (CBBO), Non-Linear Migration BBO (NLBBO), Hybrid Gravitational Search algorithm Particle Swarm optimization (GSAPSO), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Differential Evolution-Crossover Quantum Particle Swarm optimization (DE-CQPSO), Salp Swarm algorithm (SSA), Dragonfly algorithm (DA), and Fuzzy Grasshopper optimization algorithm (FGOA) to confirm its efficiency. For both single -and multi-objective optimization, ChOA's assessment index and convergence rate are superior to other bench-mark algorithms, regardless of whether the goal is to reduce emissions or to reduce fuel costs. The efficacy and robustness of the ChOA in handling environmental economic dispatch problems have been shown by discovering a good compromise value.

关键词： chimp optimization algorithm Carbon emission reduction Multi-objective optimization Environmental economic dispatch

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An efficient multilevel thresholding segmentation method for thermography breast cancer imaging based on improved chimp optimization algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 185卷 115651-115651页

作者： Houssein, Essam H. Emam, Marwa M. Ali, Abdelmgeid A. Minia Univ Fac Comp & Informat Al Minya Egypt

Thermography images are a helpful screening tool that can detect breast cancer by showing the body parts that indicate an abnormal change in temperature. Various segmentation methods are proposed to extract regions of interest from breast cancer images to enhance the classification. Many issues were solved using thresholding. In this paper, a new efficient version of the recent chimp optimization algorithm (ChOA), namely opposition based Levy Flight chimp optimizer (IChOA), was proposed. The original ChOA algorithm can stagnate in local optima and needs varied exploration with an adequate blending of exploitation. Therefore, the convergence is accelerated by improving the initial diversity and good exploitation capability at a later stage of generations. Opposition-based learning (OBL) is applied at the initialization phase of ChOA to boost its population diversity in the search space, and the Levy Flight is used to enhance its exploitation. Moreover, the IChOA is applied to tackle the image segmentation problem using multilevel thresholding. The proposed method tested using Otsu and Kapur methods over a dataset from Mastology Research with Infrared Image (DMR-IR) database during the optimization process. Furthermore, compared against seven other meta-heuristic algorithms, namely Gray wolf optimization (GWO), Moth flame optimization (MFO), Whale optimization algorithm (WOA), Sine-cosine algorithm (SCA), Slap swarm algorithm (SSA), Equilibrium optimization (EO), and original chimp optimization algorithm (ChOA). Results based on the fitness values of obtained best solutions revealed that the IChOA achieved valuable and accurate results in terms of quality, consistency, accuracy, and the evaluation matrices such as PSNR, SSIM, and FSIM. Eventually, IChOA obtained robustness for the segmentation of various positive and negative cases compared to the methods of its counterparts.

关键词： Breast cancer chimp optimization algorithm Image segmentation Kapur's and Otsu's method Multi-level thresholding Thermography images Meta-heuristics

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