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A machine learning based on arithmetic optimization algorithm select the significant genes 48

A machine learning based on arithmetic optimization algorith...

48th Annual IEEE International Computers, Software, and Applications Conference (COMPSAC) - Digital Development for a Better Future

作者： Lin, Yu-Da Lin, Jun-Han Hsu, Chiung-Wen Moi, Sin-Hua Natl Penghu Univ Sci & Technol Dept Comp Sci & Informat Engn Penghu Taiwan Natl Kaohsiung Univ Sci & Technol Dept Intelligent Commerce Kaohsiung 80778 Taiwan Kaohsiung Med Univ Grad Inst Clin Med Coll Med Kaohsiung Taiwan

ISBN: (纸本)9798350376975;9798350376968

There is substantial evidence that common genes based on single nucleotide polymorphisms (SNPs) are associated with cancer risk. Identifying disease-causing SNPs has, therefore, become an essential area of research. In this study, we used an arithmetic optimization algorithm (AOA) to select the significant genes using interactions between SNPs in breast cancer. We simultaneously analyzed several independent SNPs of genes for different combinations of interactions and their associated genotypes and calculated the variance between the case and control groups. The optimal combination of interactions was identified by determining the most significant difference in co-occurrence between the case and control groups. We also used the odds ratio (OR) to assess the effect of each combination of SNPs. Our research used a simulated breast cancer SNP dataset, which included 19 SNPs in 372 control participants and 398 breast cancer cases. The estimated OR of the best predicted SNP combination for breast cancer varied from 1.77 to 4.97 (confidence interval (CI): 1.22-13.47;p < 0.05) for specific combinations of two to six SNPs compared with their non-SNP combinations. Our AOA method accurately predicted high-risk SNP-SNP interactions for breast cancer. The proposed algorithm can potentially be adapted to study SNP-SNP interactions in various diseases and cancers.

关键词： arithmetic optimization algorithm epistasis odds ratio

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Estimation of Power System Harmonics Using Least Square Based arithmetic optimization algorithm 20

Estimation of Power System Harmonics Using Least Square Base...

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20th International Bhurban Conference on Applied Sciences and Technology (IBCAST)

作者： Asghar, Aftab Ashraf, Muhammad Mansoor Univ Engn & Technol Taxila Elect Engn Dept Rawalpindi Punjab Pakistan

ISBN: (纸本)9798350308266;9798350308259

Harmonic distortion stands out as a significant power quality issue in contemporary power systems that experience extensive utilization of renewable energy sources. This concern has garnered increased focus in recent years due to the growing integration of power electronic devices and nonlinear loads within the power systems. For electric utilities and users, accurate estimation of harmonic-parameters is an important reference to evaluate power quality. The detrimental impacts of harmonics components on power systems can be effectively mitigated by Active Filters (AFs). The performance of AFs heavily relies on the development of robust and accurate estimator, which plays a vital role in supplying the necessary reference harmonic values. This paper presents a Least Square based arithmetic optimization algorithm (LS-AOA) to solve the harmonics estimation problem. The AOA leverages the distribution characteristic of main arithmetic operators, such as division, subtraction, multiplication and addition in mathematics. AOA is mathematically formulated and implemented to efficiently optimize search processes across diverse ranges. The proposed LS-AOA framework is tested and validated on benchmark test signals with different noise levels and on practically measured voltage waveform of AFPMG. The experimental results demonstrated that the proposed harmonic estimator provides more accurate estimations with significantly lower computational time compared to existing techniques proposed in literature.

关键词： Harmonic Estimation Power Quality arithmetic optimization algorithm Harmonic Extraction

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Optimal Color Image Watermarking Based on DWT-SVD Using an arithmetic optimization algorithm 2nd

Optimal Color Image Watermarking Based on DWT-SVD Using an A...

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3rd International Conference on Digital Technologies and Applications

作者： Bencherqui, Ahmed Tamimi, Mohammed Tahiri, Mohamed Amine Karmouni, Hicham Alfidi, Mohammed Jamil, Mohammed Ouazzani Qjidaa, Hassan Sayyouri, Mhamed Sidi Mohamed Ben Abdellah Univ Natl Sch Appl Sci Engn Syst & Applicat Lab Fes Morocco Sidi Mohamed Ben Abdellah Fez Univ Dhar El Mahrez Fac Sci Lab Elect Signals & Syst Informat LESSI CED STSTIC Fes Morocco Private Univ Fez UPF Fac Engn Sci FSI Syst & Sustainable Environm Lab See Fes Morocco Cadi Ayyad Univ Natl Sch Appl Sci Marrakech Morocco

ISBN: (纸本)9783031298592;9783031298608

Digital watermarking is now recognized as an effective method to ensure authorized access, facilitate content verification, and prevent unauthorized copying, which helps to increase the security of image sharing, transfer, and indexing. In this paper, we propose an optimal watermarking algorithm for color images based on discrete wavelet transform (DWT), singular value decomposition (SVD), and arithmetic optimization algorithm (AOA). The host image is initially divided into subbands via the proposed watermarking process, and each subband singular value is then altered by a scaling factor (alpha) to achieve the highest robustness possible. Finding the optimal value of alpha is a challenging problem. To determine this optimal value automatically, we use the AOA algorithm, which allows us to achieve high robustness without reducing transparency. The simulation results clearly show the effectiveness of our proposed watermarking algorithm in terms of image quality and confidentiality.

关键词： Image Watermarking arithmetic optimization algorithm Security DWT SVD

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Computer Aided Breast Cancer Diagnosis using arithmetic optimization algorithm with Fuzzy Logic on Histopathological Images

Computer Aided Breast Cancer Diagnosis using Arithmetic Opti...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Gurumoorthy, R. Kamarasan, M. Annamalai University Department of Computer and Information Science Annamalai Nagar India

ISBN: (纸本)9798350313987

Computer-aided breast cancer (BC) detection is a medical application that utilizes machine learning approaches and computer algorithms to support doctors in the diagnosis and detection of BC. The primary target is to enhance the efficiency and accuracy of BC diagnosis and screening. BC detection on histopathological images (HPI) is the process of analyzing tissue samples collected during surgery or biopsy to identify cancer cells. By examining these tissue samples under a microscope HPI is acquired and used for the BC grading and diagnosis. This study presents a new Computer Aided Breast Cancer Diagnosis by employing an arithmetic optimization algorithm with Machine Learning (CABCD-AOAML) approach on Histopathological Images. The presented CABCD-AOAML approach intends to detect and classify the occurrence of BC. In the initial phase, the CABCD-AOAML technique applies the CLAHE model for the contrast enhancement process. Besides, the CABCD-AOAML technique uses an ensemble feature fusion process, comprising residual network (ResNet) and DenseNet model. To adjust the hyperparameters of the DL models, the AOA is used to improve the performance. Lastly, the adaptive neuro-fuzzy inference system (ANFIS) approach is employed for the recognition and classification of BC. The investigational output of the CABCD-AOAML technique is assessed on the BreakHis database. The attained outputs portray the better achievement of the CABCD-AOAML technique over recent methods. © 2023 IEEE.

关键词： Adaptive neuro-fuzzy inference system arithmetic optimization algorithm Breast cancer Computer-aided diagnosis Deep learning Fuzzy logic Histopathological images Residual network

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Automated Facial Emotion Detection using arithmetic optimization algorithm with Deep Convolutional Neural Network for Autonomous Vehicle Drivers 2

Automated Facial Emotion Detection using Arithmetic Optimiza...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Saranya, R. Vijayaragavan, M. Ismail Kalilulah, S. Satyanarayana, K.N.V. Suresh, M. Srimathi, S. CSE Department R.M.K. College of Engineering and Technology India Department of EEE Mailam Engineering college Tindivanam T.kVillupuram Dist Mailam India Computer Science and Engineering Dr. MGR Educational and Research Institute Periyar E.V.R. High Road Maduravoyal Chennai600095 India Dept of ECE S.R.K.R.ENGINEERING COLLEGE Chinaamiram A.P. Bhimavaram India Department of ECE Rajarajeswari College of Engineering Bangalore560074 India Department of Bio Technology Saveetha School of Engineering SIMATS Tamilnadu Chennai India

ISBN: (纸本)9798350340235

Facial Emotion Recognition (FER) utilizing Deep Learning (DL) is indeed employed in the context of autonomous vehicle drivers. By investigating the facial expressions of drivers, the vehicle system is gain insights into the emotional state of drivers. FER is given to whole safety by identifying critical emotions like fear, anger, or surprise. When the system identifies these emotions, it responds accordingly by altering the driving performance or taking preventive measures for avoiding potential risks or aggressive maneuvers. The training of the DL algorithms namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are done on the preprocessed data. These approaches learn to extract appropriate features in facial images and forecast the same emotions. This manuscript offers the design of Automated Facial Emotion Detection using arithmetic optimization algorithm with Deep Convolutional Neural Network (AFED-AOADCNN) technique for Autonomous Vehicle Drivers. The purpose of the AFED-AOADCNN technique is to detect various kinds of facial emotions in autonomous vehicle drivers. In this introduced AFED-AOADCNN technique, DCNN method is applied for procedure of feature extraction. Next, the AOA is employed for optimum hyperparameter tuning of the DCNN method. At last, quantized neural network (QNN) approach is exploited for the identification and classification of different kinds of facial emotions. The investigational evaluation of the AFED-AOADCNN method is assessed on facial emotion dataset. The experimental validation stated the improved outputs of the AFED-AOADCNN method over recent approaches. © 2023 IEEE.

关键词： arithmetic optimization algorithm Autonomous vehicle drivers Computer vision Deep convolutional neural network Facial emotion recognition

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FPGA-based hardware implementation of chaotic opposition-based arithmetic optimization algorithm

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APPLIED SOFT COMPUTING 2024年 154卷

作者： Zermani, Mohamed Aymen Manita, Ghaith Chhabra, Amit Feki, Elyes Mami, Abdelkader Tunis El Manar Univ Dept Phys LAPER Fac Sci Campus Univ El Manar Tunis 2092 Tunisia Univ Manouba ESEN Manouba Tunisia Univ Sousse Lab MARS ISITCom LR17ES05 Sousse Tunisia Guru Nanak Dev Univ Dept Comp Engn & Technol Amritsar India

The hardware implementation of optimization algorithms has gained significant attention due to its potential for augmenting performance and efficiency. This paper presents the hardware implementation of a Chaotic Opposition-Based arithmetic optimization algorithm (COAOA). COAOA is inspired by integrating chaotic dynamics and opposition-based learning (OBL) principles in the original AOA algorithm. Inspired by chaotic maps, COAOA amplifies exploration and exploitation capabilities, while OBL enhances the search process by considering both positive and negative solution counterparts. Our proposed hardware implementation of COAOA harnesses Field-Programmable Gate Arrays (FPGAs) to accelerate optimization processes. Leveraging FPGA's parallel processing capabilities, the COAOA algorithm is efficiently parallelized and mapped onto the hardware architecture, streamlining arithmetic operations and governing algorithmic execution flow. To assess its efficacy, a comparison was made between the performance of the suggested structure and five alternative algorithms: opposition-based learning AOA (OAOA), Chaotic AOA, the original AOA, PSO and GA. Statistical analyses and tests were performed using benchmark functions like Rosenbrock, Rastrigin, Six-Hump CamelBack, and Zirilli functions. The empirical findings indicate the substantial acceleration achieved through the hardware implementation of COAOA compared to the original AOA algorithm and its variants. This compelling outcome positions COAOA as a viable solution for real-time optimization challenges, signifying its potential to significantly expedite optimization tasks in practical applications.

关键词： arithmetic optimization algorithm Chaotic random Opposition -based method Field-programmable gate arrays Hardware implementation

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Deep Convolutional Spiking Neural Network optimized with arithmetic optimization algorithm for lung disease detection using chest X-ray images

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2023年第Part2期79卷 000-000页

作者： Rajagopal, R. Karthick, R. Meenalochini, P. Kalaichelvi, T. Francis Xavier Engn Coll Dept Elect & Elect Engn Tirunelveli 627003 Tamilnadu India KLN Coll Engn Dept Comp Sci & Engn Sivagangai 630612 Tamilnadu India Sethu Inst Technol Dept Elect & Elect Engn Virudunagar 626115 Tamil Nadu India Panimalar Engn Coll Dept Artificial Intelligence & Data Sci AI &DS Chennai 600123 India

Lung disease is a most common disease all over the world. A numerous feature extraction with classification models were discussed previously about the lung disease, but those methods having high over fitting problem, consequently, decrease the accuracy of detection. To overwhelm this issue, a Deep Convolutional Spiking Neural Network optimized with arithmetic optimization algorithm is proposed in this manuscript for Lung Disease Detection using Chest X-ray Images as COVID-19, normal and viral pneumonia. Initially, NIH chest X-ray image dataset is taken from Kaggle repository for detecting lung disease. Then, the chest X-ray images are pre-processed using the Anisotropic Diffusion Filter Based Unsharp Masking and crispening scheme for removing noise and enhancing the image quality. These pre-processed outputs are fed to feature extraction. In feature extraction process, the empirical wavelet transform method is used. These extracted features are given into Deep Convolutional Spiking Neural Network classifier (DCSNN) for detecting lung diseases. Here, the weight with bias parameter of DCSNN is enhanced based upon arithmetic optimization algorithm (AOA), which improves detection accuracy. The simulation is executed in MATLAB. The proposed LDC-DCSNN-AOA technique attains higher accuracy, higher Precision, higher F-Score analyzed with the existing techniques, like Lung disease detection using Support Vector Machines optimized with Social Mimic optimization (LDC-SVM-SMO), Lung disease detection using eXtreme Gradient Boosting optimized by particle swarm optimization (LDC-XGBoost-PSO), Lung disease detection using neuro-fuzzy classifier optimized with multi-objective genetic algorithm (LDC-NFC-MOGA), Lung disease detection using convolutional neural network optimized with Bayesian optimization LDC -CNN-BOA respectively.

关键词： Chest X-ray images Anisotropic Diffusion Filter Based Unsharp Masking and crispening Empirical wavelet transform Lung Disease detection Deep Convolutional Spiking Neural Network classifier arithmetic optimization algorithm

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Probability distribution arithmetic optimization algorithm based on variable order penalty functions to solve combined economic emission dispatch problem

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APPLIED ENERGY 2022年 316卷

作者： Hao, Wen-Kuo Wang, Jie-Sheng Li, Xu-Dong Song, Hao-Ming Bao, Yin-Yin Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114044 Peoples R China

The optimization objective of combined economic emission dispatch (CEED) problem is to minimize the total cost of power generation and the emission of harmful gases under the premise of satisfying the load demand and operation constraints. In today's world, energy and environmental problems are becoming more and more serious. Solving CEED problems can reduce costs, save energy, and reduce environmental pollution. By adopting the maximum/maximum price penalty factor, the multi-objective CEED optimization problem is transformed into a single objective optimization problem, and a probability distribution arithmetic optimization algorithm (AOA) based on variable order penalty function was proposed to solve the CEED problem. The energy attenuation strategy was proposed to replace the variation trend of Math optimizer accelerated (MOA) in the original AOA, which better balances the exploration and exploitation of AOA. Five probability distribution functions, including exponential distribution, uniform distribution, normal distribution, beta distribution and gamma distribution, are used to replace a control parameter mu, so as to enhance its searching ability, improve the convergence speed, and enhance the ability to jump out of the local optimal. Based on the CEC-BC-2017 benchmark functions, the original AOA is compared with the five improved AOA to select the best improved strategy, and the effectiveness of the improved strategy is verified by simulation comparison with other intelligent optimization algorithms. In dealing with the constraint of CEED problem, five kinds of penalty functions with varying order were proposed, which are sine function, hyperbolic tangent function, arctangent function, V type function and hyperbolic secant function, respectively, to replace the traditional fixed penalty function. The CEED problem with 6 units are selected and solved under the total demand of 150 MW, 175 MW, 200 MW and 225 MW respectively. The experimental results show that th

关键词： arithmetic optimization algorithm Combined economic emission dispatch Probability distribution Variable order penalty function

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An enhanced hybrid arithmetic optimization algorithm for engineering applications

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2022年 394卷 1页

作者： Hu, Gang Zhong, Jingyu Du, Bo Wei, Guo Xian Univ Technol Dept Appl Math Xian 710054 Peoples R China Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Univ North Carolina Pembroke Pembroke NC 28372 USA Xian Univ Technol 5 South Jinhua Rd Xian 710048 Peoples R China

arithmetic optimization algorithm (AOA) is a newly well-developed meta-heuristic algorithm that is inspired by the distribution behavior of main arithmetic operators in mathematics. Although the original AOA has shown well competitive performance with popular meta-heuristic algorithms, it still faces the issues of insufficient exploitation ability, ease of falling into local optima and low convergence accuracy in large-scale applications. In order to ameliorate these deficiencies, an enhanced hybrid AOA named CSOAOA, integrated with point set strategy, optimal neighborhood learning strategy and crisscross strategy, is developed in this paper. First, a good point set initialization strategy is added to obtain a higher-quality initial population, which improves the convergence speed of the algorithm. Then, the optimal neighborhood learning strategy is adopted to guide the individual's search behavior and avoid the algorithm falling into the current local optimum, which boosts the search efficiency and calculation accuracy. Finally, by combining AOA with the crisscross optimization algorithm, the exploration and utilization ability of the crisscross algorithm are integrated into the CSOAOA. These strategies collaborate to enhance AOA in accelerating overall performance. The superiority of the proposed CSOAOA is comprehensively verified by comparing with the original AOA, six improved AOA and numerous celebrated and newly developed algorithms on the well-known 23 classical benchmark functions, IEEE Congress on Evolutionary Computation (CEC) 2019 test suite and IEEE CEC 2020 benchmark functions, respectively. Meanwhile, the practicability of CSOAOA is also highlighted by solving eight real-world engineering design problems. Furthermore, the statistical testing of CSOAOA has been conducted to validate its significance. Experimental results and statistical comparisons manifest the superior performance of CSOAOA over the comparison algorithms in terms of precision, converge

关键词： arithmetic optimization algorithm Engineering optimization Good point set Optimal neighborhood Crisscross optimizationalgorithm Benchmark

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Self-attention based progressive generative adversarial network optimized with arithmetic optimization algorithm for kidney stone detection

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第6期35卷 1-1页

作者： Devi, V. Brindha Elma, K. Johny Rooban, S. Shajin, Francis H. Sri Sai Ram Inst Technol Dept Informat Technol Chennai Tamil Nadu India Easwari Engn Coll Dept Informat Technol Chennai Tamil Nadu India Koneru Lakshmaiah Educ Fdn Dept Elect & Commun Engn Guntur Andhra Pradesh India Anna Univ Dept Elect & Commun Engn Chennai Tamil Nadu India

Self-attention based progressive generative adversarial network optimized with arithmetic optimization algorithm (AOA) is proposed in this manuscript for kidney stone detection. Initially, the input kidney stone images are gathered via CT kidney dataset. Then, the input image is preprocessed by utilizing APPDRC filtering approach. Also, the preprocessed images are given to the multi-level thresholding segmentation technique for segmented the image. Then the segmented images are given to the TF-IDF feature extraction method for extracting the features. Then the extracted features are fed to feature selection using Weibull distributive generalized multidimensional scaling methods for selecting the features. Then the selected features are SPGGAN classification method for kidney stone detection. Generally, SPGGAN not reveal any adoption of optimization methods compute optimum parameters for assuring correct kidney stone detection. Thus, AOA is used for optimizing the weight parameters of SPGGAN and it is implemented in python, its performance is examined under certain performances metrics, such as accuracy, precision, sensitivity, specificity, F-measure, computational time, and ROC. The proposed SPGGAN-AOA-KSD method attains 54.78%, 34.89%, and 20.96% higher accuracy and 3.45%, 4.08%, and 5.06% greater AUC compared with existing methods, such as a ANN-OGGA-KSD, HMANN-BPA-KSD, and ANN-CSOA-KSD respectively.

关键词： arithmetic optimization algorithm kidney stone detection multi-level thresholding technique self-attention based progressive generative adversarial network term frequency-inverse document frequency

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