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optimization Based Rice Leaf Disease Classification in Federated Learning

MULTIMEDIA TOOLS AND APPLICATIONS 2024年第29期83卷 72491-72517页

作者： Tripathy, Ramamani Mandala, Jyothi Pappu, Sasibhushana Rao Gopisetty, Guru Kesava Dasu Chitkara Univ Sch Engn Baddi Himachal Prades India Christ Dept Comp Sci & Engn Bangalore India GITAM Dept Comp Sci & Engn Visakhapatnam Campus Visakhapatnam Andhra Pradesh India JNTUK Kakinada KKR & KSR Inst Technol & Sci Dept Informat Technol Guntur India

Numerous farmers worldwide are impacted by diseases connected to rice leaves that frequently endanger the sustainability of the rice industry. Diseases that affect the leaves of rice plants severely limit their ability to produce rice, and they are typically brought on by bacteria, viruses, or fungi. This paper proposes an innovative classification scheme for rice leaf diseases based on Federated Learning (FL). Here, FL framework comprises two entities, namely nodes and servers. Every node does initial local training using local data. Moreover, produced local model is then updated on server. Model aggregation is done at the server since several nodes update their local models and send them to it. The nodes download the global model that server has generated as a result. The nodes update their training using transferred global model and local model. The following series of actions are taken in the training model. The input image is mainly obtained from a database and pre-processed with a Kalman filter to eliminate noise. Then, numerous operations for data augmentation are applied. In addition, feature extraction is done and generated features are used in LeNet for rice leaf diseases classification. LeNet is trained using the Spotted Hyena archimedes Optimizer (SHAO). The developed method shows better precision of 91.3%, recall of 92.2%, f-measure of 91.7%, loss function of 3.3%, Mean Square Error (MSE) of 7.3%, and Root MSE of 27.1%.

关键词： Federated learning archimedes optimization algorithm Spotted hyena optimization Kalman filter LeNet

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Optimal algorithm for color medical encryption and compression images based on DNA coding and a hyperchaotic system in the moments

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2024年 50卷

作者： Bencherqui, Ahmed Tahiri, Mohamed Amine Karmouni, Hicham Alfidi, Mohammed Motahhir, Saad Abouhawwash, Mohamed Askar, S. S. Wen, Shuhuan Qjidaa, Hassan Sayyouri, Mhamed Sidi Mohamed Ben Abdellah Fez Univ Natl Sch Appl Sci Engn Syst & Applicat Lab Fes Morocco Sidi Mohamed Ben Abdellah Fez Univ Fac Sci Elect Signals & Informat Syst Lab Fes Morocco Cadi Ayyad Univ Natl Sch Appl Sci Marrakech Morocco Michigan State Univ Coll Engn Dept Computat Mat Sci & Engn CMSE E Lansing MI 48824 USA Mansoura Univ Fac Sci Dept Math Mansoura 35516 Egypt King Saud Univ Coll Sci Dept Stat & Operat Res POB 2455 Riyadh 11451 Saudi Arabia Yanshan Univ Yanshan Peoples R China

Currently, visual data security plays a significant role in various fields, especially in medical imaging. Addressing the challenges associated with limited key space and vulnerability to different types of attacks within current encryption schemes, this work proposes an optimal compression-encryption scheme for large medical images that incorporates elements of archimedes' optimization algorithm, discrete orthogonal Hahn moments, chaotic systems, and DNA coding. The primary aim of this study is to develop an optimal and exceptionally resilient compression-encryption scheme capable of countering various attack types effectively. This approach is structured into three principal phases: a compression phase harnessing the efficiencies of Hahn's discrete orthogonal moments (HMs) in signal and image representation, coupled with the archimedes optimization algorithm (AO) to ensure optimal tuning of polynomial parameters (a, b) for superior image reconstruction quality. The encryption phase is performed on the compressed image, using hyperchaotic memristive 4-D (HCM-4D), adapted logistics map (ALM) and DNA coding. Initially, the adapted logistics map is responsible for generating random sequences linked to the compressed image. Subsequently, chaotic sequences originating from the hyperchaotic 4D memristive system govern both random sequences and DNA processes. The optimization phase, facilitated by the AO algorithm, focuses on minimizing the value of the objective function (correlation) on the compressed and encrypted images. Ultimately, the image with the lowest correlation value is designated as the optimal compressed-encrypted representation. The simulation results clearly illustrate the resilience of the AO algorithm when juxtaposed with other optimization algorithms, especially with respect to convergence speed and computational efficiency. On the other hand, the proposed compression approach demonstrated exceptional efficiency in compressing medical images, offering

关键词： Moments Image compression-encryption Chaotic system archimedes optimization algorithm

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The implementation of a regularized radial basis function model for predicting California bearing capacity

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第3期7卷 2359-2380页

作者： Zhan, Weiwei Chongqing Vocat Coll Transportat Sch Rd Bridge & Architecture Chongqing 402247 Peoples R China

The characterization of soil resistance properties is of paramount importance in the realm of pavement design. Indices such as dynamic modulus, resilient modulus, and California Bearing Ratio (CBR) are commonly employed to ascertain these properties. The CBR test is an experimental methodology implemented in laboratory or field settings to assess the subgrade's shear strength and stiffness modulus. Due to the exorbitant expenses linked with these assessments, it is deemed essential to implement distinct methodologies to predict their results. The advancement of artificial intelligence (AI) methodologies has enabled the production of innovative models and algorithms. Implementing these techniques enables research professionals to choose predictive methodologies over empirical ones. This study employs an AI methodology to assess the mechanical properties of the CBR index. The radial basis function (RBF) technique was used as a problem-solving methodology to create a machine-learning model. The methodology entails the utilization of sampling experiments to tackle a precisely defined problem. Three different meta-heuristic optimisation techniques were used in this study, namely, the archimedes optimization algorithm (AOA), bald eagle search optimization (BESO), and Ebola optimization search (EOS), to achieve optimal outcomes. Furthermore, the entire procedure is subjected to evaluation by employing evaluators. According to the results, RBEO obtained the most suitable values in presented metrics, such as R2 and RMSE, equal to 0.9969 and 0.629, respectively. In general, EOS indicated the most desirable accuracy when coupled with RBF compared to BESO and AOA.

关键词： California bearing ratio Radial basis function archimedes optimization algorithm Bald eagle search optimization Ebola optimization search

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Prediction of pile settlement using hybrid support vector regressor

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第3期7卷 2103-2120页

作者： Li, Yinan Li, Tianzhai Xinyang Agr & Forestry Univ Coll Landscape Architecture & Art Xinyang 464000 Henan Peoples R China Xinyang Vocat & Tech Coll Architectural Engn Inst Xinyang 464000 Henan Peoples R China

Pile settlement is the downward displacement of a foundational pile in construction, used to support structures by transferring loads to stable soil or rock layers beneath the surface. Accurately predicting pile settlement is critical for successful foundation design in civil engineering. In recent years, machine learning techniques have been utilized to predict pile settlement with improved accuracy compared to traditional analytical methods. This study has considered three different optimization algorithms combined with Support vector regression (SVR) to enhance its predictive capabilities further. These algorithms include archimedes optimization (AO), Marine Predators algorithm (MPA), and Augmented Grey Wolf Optimizer (AGWO). The proposed SVR-based models are trained and tested using field data from several projects with varying soil conditions. The input parameters include pile diameter, length, soil properties, and load characteristics. In addition, various statistical measures are used to evaluate each model's performance. In conclusion, the proposed SVR models, including optimization algorithms, particularly AGWO, MPA, and AO, provide a robust and accurate prediction of pile settlement. In comparing the models with each other, SVAO was able to obtain the most appropriate values compared to the other two models, with R-2 = 0.997 and RMSE = 0.201. In general, these models can be used as a reliable tool in foundation design for predicting pile settlement and ensuring the safety and stability of structures.

关键词： Pile settlement Support vector regression archimedes optimization algorithm Marine predators' algorithm Augmented grey wolf optimizer

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Development of lightweight intrusion model in Industrial Internet of Things using deep learning technique

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DISCOVER APPLIED SCIENCES 2024年第7期6卷 346页

作者： Sinha, Raj Thakur, Padmanabh Gupta, Sandeep Shukla, Anand Lovely Profess Univ Sch Comp Applicat Phagwara Punjab India Graph Era Deemed Be Univ Dept Elect Engn Dehra Dun 248002 Uttarakhand India Wollega Univ Nekemte Ethiopia

Nowadays, the IDS is being used in conjunction with the IIoT system to reduce the security risk, but on the other hand, the false rate of the IDS is very high. Therefore, in this work, a pre-training method, making use of both a deep neural network and a deep auto-encoder, has been proposed for the quick prediction of assaults with increased accuracy and a reduced false rate. The replicas were expanded using hyperparameter optimization (HPO) techniques. The proposed model delivers an alternative to deep learning construction replicas through an HPO procedure incorporating the archimedes optimization algorithm. This optimization technique can be used to determine the hyperparameter value and the ideal categorical hyperparameter combination for improved detection performance. The DS2OS dataset is used alongside numerous other indicators to evaluate the efficacy of the developed model. The various existing techniques of assault detection have also been considered to show the effectiveness of the proposed model. Through the comparative evaluation of the outcomes, it is shown that the developed model provides better performance than the other existing models. Eventually, it is discovered that the suggested security paradigm is successful in fending off a variety of internal and external threats. A hybridization of DNN and DAE is considered for accurate assaults detection. archimedes optimization algorithm has been used to improv the detection performance. The percentage accuracy and precision of the developed model are 96.57 and 98.57. The percentage recall and F-measure of the developed hybrid model are 98.69 and 98.35.

关键词： Industrial Internet of Things Intrusion detection organization archimedes optimization algorithm Cyber-attacks

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archimedes Optimizer: Theory, Analysis, Improvements, and Applications

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ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING 2023年第4期30卷 2543-2578页

作者： Dhal, Krishna Gopal Ray, Swarnajit Rai, Rebika Das, Arunita Midnapore Coll Autonomous Dept Comp Sci & Applicat Midnapore W Bengal India Maulana Abul Kalam Azad Univ Technol Dept Comp Sci & Engn Kolkata W Bengal India Sikkim Univ Dept Comp Applicat Gangtok Sikkim India

The intricacy of the real-world numerical optimization tribulations has full-fledged and diversely amplified necessitating proficient yet ingenious optimization algorithms. In the domain wherein the classical approaches fall short, the predicament resolving nature-inspired optimization algorithms (NIOA) tend to hit upon an excellent solution to unbendable optimization problems consuming sensible computation time. Nevertheless, in the last few years approaches anchored in nonlinear physics have been anticipated, announced, and flourished. The process based on non-linear physics modeled in the form of optimization algorithms and as a subset of NIOA, in countless cases, has successfully surpassed the existing optimization methods with their effectual exploration knack thus formulating utterly fresh search practices. archimedes optimization algorithm (AOA) is one of the recent and most promising physics optimization algorithms that use meta-heuristics phenomenon to solve real-world problems by either maximizing or minimizing a variety of measurable variables such as performance, profit, and quality. In this paper, archimedes optimization algorithm (AOA) has been discussed in great detail, and also its performance was examined for Multi-Level Thresholding (MLT) based image segmentation domain by considering t-entropy and Tsallis entropy as objective functions. The experimental results showed that among recent Physics Inspired optimization algorithms (PIOA), the archimedes optimization algorithm (AOA) produces very promising outcomes with Tsallis entropy rather than with t-entropy in both color standard images and medical pathology images.

关键词： Nature-inspired optimization algorithms Image segmentation optimization Tsallis t-entropy archimedes optimization algorithm Physics inspired optimization algorithms

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An archimedes metaheuristic algorithm based optimum relay coordination in microgrid and combined overhead/cable distribution network

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JOURNAL OF SUPERCOMPUTING 2023年第18期79卷 21166-21184页

作者： Kudkelwar, Sagar B. Sinha, Bam Bahadur Gunturi, Sravan Kumar GH Raisoni Inst Engn & Technol Elect Engn Dept Nagpur India Indian Inst Informat Technol Comp Sci & Engn Ranchi India Deemed Univ Koneru Lakshmaiah Educ Fdn Elect & Commun Engn Dept Hyderabad 500075 Telangana India

The issue of power system protection remains a crucial aspect for power engineers. One of the important protection devices in distribution networks is the overcurrent relay, which requires constant attention to ensure its performance is optimized. This paper applied very first time the archimedes principle-based archimedes optimization algorithm (AOA) on an optimal overcurrent relay coordination problem from the power system protection area. The proposed algorithm is evaluated on two unique case studies, the IEC microgrid benchmark and a combination of overhead/underground cable systems, to verify its efficiency. The uniqueness of these case studies is that in microgrids due to the incorporation of wind turbines bidirectional current flows whereas in overhead/underground system magnitude of current will be different in the various part of the network. Because of this impact of current in both networks, the relay coordination job becomes challenging. The results demonstrate that the proposed AOA technique outperforms other existing algorithms, such as Particle Swarm optimization, Water Cycle algorithm (WCA), Whale optimization algorithm, Crow Search algorithm, and Evaporation Rate WCA, in terms of computational efforts and performance. Furthermore, the proposed algorithm effectively minimizes the total operating time for both networks without violating any constraints. The study confirms that the proposed algorithm provides an efficient solution for the overcurrent relay coordination problem, which can be beneficial for power system protection. The archimedes principle-based archimedes optimization algorithm is demonstrated to be a promising optimization tool that can effectively solve similar optimization problems in the future.

关键词： archimedes optimization algorithm Time multiplier setting Overcurrent relay CTI

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A Blockchain-Based Hybrid Hunger Game Search archimedes optimization Enabled Deep Learning for Multiclass Plant Disease Detection Using Leaf Images

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INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS 2024年第0期

作者： Gajmal, Yogesh Manohar Jagtap, Arvind M. Kale, Kiran Dhanaji Gawade, Jawahar Sambhaji More, Pranav Finolex Acad Management & Technol Dept Informat Technol Ratnagiri 415639 Maharashtra India MIT Art Design & Technol Univ MIT Sch Comp Dept Comp Sci & Engn Pune Maharashtra India Presidency Univ Dept Elect & Commun Engn Bangalore India SVPMs Coll Engn Malegaon BK Dept Informat Technol Baramati Pune India Universal AI Univ Universal AI & Future Technol Sch Karjat Maharashtra India

Plants are susceptible to a wide range of diseases when they are growing. One of the crucial difficulties in agriculture is the earlier finding of plant diseases. If the diseases are not detected at the beginning, it may have an undesirable effect on the entire production. To avoid these issues, a blockchain-based hybrid optimized deep learning (DL) approach is devised in this work. The plant leaf images are stored in the blockchain network and the noise level of the images is minimized by the Kalman filter. In image segmentation, the Deep Joint segmentation technique is employed to segment the disease-affected portion of the image. The position and color augmentation are carried out to enhance the size and clarity of the image. Moreover, the statistical and speeded-up robust features (SURF) are extracted in the feature extraction stage. In the first level classification process, the developed hunger game search archimedes optimization (HGSAO) enabled SpinalNet is employed for classifying the plant type and the second level classification is carried out for multiclass disease identification using the proposed HGSAO optimized SpinalNet. Moreover, the proposed HGSAO with SpinalNet outperformed the accuracy of 0.972, True positive rate (TPR) of 0.963, true negative rate (TNR) of 0.951, false negative rate (FNR) of 0.936 and false positive rate (FPR) of 0. 942.

关键词： Plant leaf disease SpinalNet hunger game search optimization archimedes optimization algorithm Deep Joint segmentation

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Developing support vector regression and radial basis function neural networks in optimized holistic frameworks to estimate hardness properties of concrete

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第5期45卷 8349-8364页

作者： Wang, Chuncha Fujian Chuanzheng Commun Coll Fuzhou Peoples R China

The hardness properties of constructional materials should be investigated as important factors in assessing the performance over the operation period. Two tests are performed to determine the stiffness characteristic, including slump and compressive strength (CS). They must be considered to examine efficiency, durability, and resistance to pressure. Due to the structure's susceptibility and usage in dams, bridges, etc., high-performance concrete must have an appropriate set of these tests. There are two soft-based and laboratory methods for performing these tests. The laboratory method is not economical in terms of cost and time, and artificial intelligence (AI) is used to reduce the aforementioned factors. Models and optimizers use software-based methods to help reduce errors and increase model accuracy. So, The main purpose of this research has been introducing novel ways of coupling an ensemble model with optimizers by adjusting some internal parameters. In this article, two models, the Radial Basis Function Neural network and Support Vector Regression were combined and coupled with General Normal Distribution optimization (GNDO) and archimedes optimization algorithm (AOA) into the two frameworks of SVRRBF-AOA and SVRRBF-GNDO. As a result, the hybrid model of SVRRBF-AOA could perform well by obtaining R-2 and RMSE of 0.9915 and 2.71 for the slump and 0.9845 and 3.34 for CS, respectively.

关键词： High-performance concrete slump compressive strength support vector regression Radial basis function generalized normal distribution optimization archimedes optimization algorithm

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Novel hybrid of AOA-BSA with double adaptive and random spare for global optimization and engineering problems

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ALEXANDRIA ENGINEERING JOURNAL 2023年 73卷 543-577页

作者： Hashim, Fatma A. Abu Khurma, Ruba Albashish, Dheeb Amin, Mohamed Hussien, Abdelazim G. Helwan Univ Fac Engn Helwan Egypt Al Ahliyya Univ Fac Informat Technol Comp Sci Dept Amman Jordan Al Balqa Appl Univ Prince Abdullah Bin Ghazi Fac Informat & Commun Te Comp Sci Dept Salt Jordan Menoufia Univ Fac Sci Al Minufiyah Egypt Linkoping Univ Dept Comp & Informat Sci Linkoping Sweden Fayoum Univ Fac Sci Faiyum Egypt Middle East Univ MEU Res Unit Amman 11831 Jordan

archimedes optimization algorithm (AOA) is a new physics-based optimizer that sim-ulates archimedes principles. AOA has been used in a variety of real-world applications because of potential properties such as a limited number of control parameters, adaptability, and changing the set of solutions to prevent being trapped in local optima. Despite the wide acceptance of AOA, it has some drawbacks, such as the assumption that individuals modify their locations depending on altered densities, volumes, and accelerations. This causes various shortcomings such as stagnation into local optimal regions, low diversity of the population, weakness of exploitation phase, and slow convergence curve. Thus, the exploitation of a specific local region in the conventional AOA may be examined to achieve a balance between exploitation and exploration capabilities in the AOA. The bird Swarm algorithm (BSA) has an efficient exploitation strategy and a strong ability of search process. In this study, a hybrid optimizer called AOA-BSA is proposed to overcome the limitations of AOA by replacing its exploitation phase with a BSA exploitation one. Moreover, a transition operator is used to have a high balance between exploration and exploitation. To test and examine the AOA-BSA performance, in the first experimental series, 29 unconstrained functions from CEC2017 have been used whereas the series of the second experiments use seven constrained engi-neering problems to test the AOA-BSA's ability in handling unconstrained issues. The performance of the suggested algorithm is compared with 10 optimizers. These are the original algorithms and 8 other algorithms. The first experiment's results show the effectiveness of the AOA-BSA in optimiz-ing the functions of the CEC2017 test suite. AOABSA outperforms the other metaheuristic algo-rithms compared with it across 16 functions. The results of AOABSA are statically validated using Wilcoxon Rank sum. The AOABSA shows superior convergence capability.

关键词： archimedes optimization algorithm Bird Swarm algorithm optimization Metaheuristic Engineering problems

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