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Diabetic retinopathy detection by optimized deep learning model

MULTIMEDIA TOOLS AND APPLICATIONS 2023年第18期82卷 27949-27971页

作者： Rachapudi, Venubabu Rao, K. Subba Rao, T. Subha Mastan Dileep, P. Deepika Roy, T. L. Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram Guntur Andhra Pradesh India B V Raju Inst Technol Dept Comp Sci & Engn Medak Telangana India CMR Tech Campus Dept Comp Sci & Engn Hyderabad Telangana India Malla Reddy Coll Engn & Technol Dept Comp Sci & Engn Kompally Hyderabad Telangana India CMR Tech Campus Dept Comp Sci & Engn AI&ML Hyderabad Telangana India

In the medical image analysis, the diagnosis of diabetic retinopathy (DR) from fundus images are identified as an open challenge and requires possible solutions. The major stages of the proposed DR are Pre-processing, Segmentation, Feature Extraction, and Classification. In Pre-processing, the retinal fundus images are RGB images, among them the G-channel is selected. Following that, histogram equalization and contrast limited adaptive histogram equalization (HE and CLAHE) are applied. Then the next stage is removing the optic disc (OD) and it is done by Circle Hough Transform (CHT). Then, the Gray Level thresholding is used for removing the blood vessels. Then the Exudates are segmented by the Modified Expectation Maximization (MEM) algorithm. Then Gray Level Co-occurrence Matrix (GLCM) is used for feature extraction. At last, features are classified by the Deep Neural Network with a butterfly optimization algorithm (DNN-BOA) classifier which is used for classifying the several stages of DR. The proposed scheme is implemented on MATLAB 2021a. The performance of the implemented of the proposed scheme is compared with the other approaches with some measures like precision, accuracy, sensitivity, F-score and specificity on the DIARETDB1 and MESSIDOR datasets. The accuracy of the proposed scheme is 0.983 and 0.989 on the two datasets respectively. The accuracy of the proposed scheme is 25.9%, 23.29%, 14.5% and 16.6% better than the approaches like KNN, SVM, DNN and DBN on the MESSIDOR dataset.

关键词： Diabetic retinopathy (DR) butterfly optimization algorithm Deep neural network (DNN) Histogram equalization (HE) Modified expectation maximization (MEM) algorithm Fundus image

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optimization of weight and cost of cantilever retaining wall by a hybrid metaheuristic algorithm

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ENGINEERING WITH COMPUTERS 2022年第4期38卷 2897-2923页

作者： Sharma, Sushmita Saha, Apu Kumar Lohar, Geetanjali Natl Inst Technol Agartala India

A retaining wall is a structure used to resist the lateral pressure of soil or any backfill material. Cantilever retaining walls provide resistance to overturning and sliding by using backfill weight. In this paper, the weight and cost of the cantilever retaining wall have been minimized using a hybrid metaheuristic optimization technique, namely, h-BOASOS. The algorithm has been developed by the ensemble of two popular metaheuristics, butterfly optimization algorithm (BOA) and symbiosis organism search (SOS) algorithm. BOA's exploratory intensity is coupled with SOS's greater exploitative capacity to find the superior algorithm h-BOASOS. The newly developed algorithm has been tested with a suite of 35 classical benchmark functions, and the results are compared with several state-of-the-art metaheuristic algorithms. The results are evaluated statistically by the Friedman rank test, and convergence curves measure the convergence speed of the algorithm. It is observed in both cases that h-BOASOS is superior to other algorithms. The suggested approach is then used to solve four real-world engineering design problems to examine the problem-solving capacity of the proposed algorithm, and the results are contrasted with a wide range of algorithms. The proposed h-BOASOS is considered to be the winner on each occasion. Finally, the newly suggested algorithm is applied to find the cost and weight of the cantilever retaining wall problems of two different heights, 3.2 m and 6.3 m. The obtained results are compared with the component algorithms and found that the new algorithm works better than the compared algorithms.

关键词： Meta-heuristics Hybrid algorithm butterfly optimization algorithm Symbiosis organisms search Cantilever retaining wall

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Diagnosing the spores of tomato fungal diseases using microscopic image processing and machine learning

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第26期83卷 67283-67301页

作者： Javidan, Seyed Mohamad Banakar, Ahmad Vakilian, Keyvan Asefpour Ampatzidis, Yiannis Rahnama, Kamran Tarbiat Modares Univ Dept Biosyst Engn Tehran Iran Gorgan Univ Agr Sci & Nat Resources Dept Biosyst Engn Gorgan Iran Univ Florida Southwest Florida Res & Educ Ctr Agr & Biol Engn Dept 2685 FL-29 Immokalee FL 34142 USA Gorgan Univ Agr Sci & Nat Resources Fac Plant Prod Dept Plant Protect Gorgan Iran

Accurate diagnosis of plant diseases by the assessment of pathogen presence to reduce disease-related production loss is one of the most fundamental issues for farmers and specialists. This will improve product quality, increase productivity, reduce the use of fungicides, and reduce the final cost of agricultural production. Today, new technologies such as image processing, artificial intelligence, and deep learning have provided reliable solutions in various fields of precision agriculture and smart farm management. In this research, microscopic image processing and machine learning have been used to identify the spores of four common tomato fungal diseases. A dataset including 100 microscopic images of spores for each disease was developed, followed by the extraction of the texture, color, and shape features from the images. The classification results using random forest revealed an accuracy higher than 98%. Besides, as a reliable feature selection algorithm, the butterfly optimization algorithm (BOA) was used to detect the effective image features to identify and classify diseases. This algorithm recognized image textural features as the most effective features in the diagnosis and classification of disease spores. Considering only the eight most effective features selected with BOA resulted in an accuracy of 95% in disease detection. To further investigate the performance of the proposed method, its accuracy was compared with the accuracies of convolutional neural networks and EfficientNet as two reliable deep learning algorithms. Not only the prediction accuracy of these methods was not favorable (65 and 83.55%, respectively), they were very time-consuming. According to the findings, the proposed framework has high reliability in disease diagnosis and can help in the management of tomato fungal diseases.

关键词： Artificial intelligence butterfly optimization algorithm Disease diagnosis Microscopic image processing Morphological features Tomato disease spores

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A hybrid butterfly and Newton-Raphson swarm intelligence algorithm based on opposition-based learning

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第10期27卷 14469-14514页

作者： Li, Chuan Zhu, Yanjie Xian Univ Posts & Telecommun Sch Comp Xian 710121 Shaanxi Peoples R China

In response to the issues of local optima entrapment, slow convergence, and low optimization accuracy in butterfly optimization algorithm (BOA), this paper proposes a hybrid butterfly and Newton-Raphson swarm intelligence algorithm based on Opposition-based learning (BOANRBO). Firstly, by Opposition-based learning, the initialization strategy of the butterfly algorithm is improved to accelerate convergence. Secondly, adaptive perception modal factors are introduced into the original butterfly algorithm, controlling the adjustment rate through the adjustment factor alpha to enhance the algorithm's global search capability. Then, the exploration probability p\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p$$\end{document} is dynamically adjusted based on the algorithm's runtime, increasing or decreasing exploration probability by examining changes in fitness to achieve a balance between exploration and exploitation. Finally, the exploration capability of BOA is enhanced by incorporating the Newton-Raphson-based optimizer (NRBO) to help BOA avoid local optima traps. The optimization performance of BOANRBO is evaluated on 65 standard benchmark functions from CEC-2005, CEC-2017, and CEC-2022, and the obtained optimization results are compared with the performance of 17 other well-known algorithms. Simulation results indicate that in the 12 test functions of CEC-2022, the BOANRBO algorithm achieved 8 optimal results (66.7%). In CEC-2017, out of 30 test functions, it obtained 27 optimal results (90%). In CEC-2005, among 23 test functions, it secured 22 optimal results (95.6%). Additionally, experiments have validated the algorithm's practicality and superior performance in 5 engineering design optimization problems and 2 real-world problems.

关键词： butterfly optimization algorithm Newton-Raphson-based optimizer Opposition-based learning Adaptive perception modal factor Dynamic exploration probability

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Considering the uncertainty of hydrothermal wind and solar-based DG

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ALEXANDRIA ENGINEERING JOURNAL 2020年第6期59卷 4211-4236页

作者： Ansari, Muhammad Mohsin Guo, Chuangxin Shaikh, Muhammad Chopra, Nitish Yang, Bo Pan, Jun Zhu, Yishun Huang, Xurui Zhejiang Univ Coll Elect Engn Yuquan Campus Hangzhou Peoples R China Yanshan Univ Inst Elect Engn Qinhuangdao Hebei Peoples R China St Soldier Grp Inst Jalandhar Punjab India Guangzhou Power Supply Bur Integrated Energy Technician Planning & Res Ctr Guangzhou Guangdong Peoples R China

Lately, researchers have widely centered around renewable energy assets, for example, wind control and solar units to decrease the utilization of fossil fuels as the fundamental wellspring of ecological natural contaminations. One of the fundamental difficulties for the generation of wind and solar energies is that they frequently including the uncertainty because of the stochastic natures of wind speeds and solar radiation. Along these lines, the current uncertainty to assets the wind and solar units and necessary to assess the arranging strategy of distribution systems for having a dependable execution. To display the uncertainty related along with the wind and solar power, the point estimate method (PEM) is utilized. Weibull and Beta distributions are utilized to deal with uncertain information factors. These fundamental goals present work is to minimize the generation cost of the framework is enhanced dependent on the butterfly optimization algorithm (BOA). Four case test frameworks viewed as it is discovered that the proposed strategy gives better arrangement as far as execution time and normal cost viability. The recreation results demonstrate that the entrance of sustainable energy source builds, the generation cost diminishes. The outcomes acquired along with the butterfly optimization algorithm contrasted and another one understood strategy. Also, the precise distribution of generation cost. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University.

关键词： butterfly optimization algorithm Point estimate method Hydro Thermal Solar power & Wind power plant

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Hybrid BOA-GWO-PSO algorithm for mitigation of congestion by optimal reactive power management

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OPTIMAL CONTROL APPLICATIONS & METHODS 2023年第2期44卷 935-966页

作者： Badi, Manjulata Mahapatra, Sheila Raj, Saurav Alliance Univ Dept Elect & Elect Engn Bangalore Karnataka India Inst Chem Technol Dept Elect Engn Marathwada Campus Jalna India

The generation of power with load optimization, particularly in the current deregulated electricity market conditions, is a very important process for improved planning and operation of the grid. In addition, it is very important for the system not to experience problems due to congestion, have tensile stability, and protection to increase the share of electricity from renewable sources with the current supply system. This article presents load balancing with the butterfly optimization algorithm (BOA) in a hybridized form to minimize and maximize loads when used in pool and hybrid markets. The methods have been designed to prevent the drawbacks of BOA and generate a better trade-off between exploration and exploitation abilities by hybridizing it with particle swarm optimization (PSO) and gray wolf optimizer (GWO). Empirical research on other algorithms shows that proposed hybrid BOA-GWO-PSO algorithm performs better and shows potential in diverse problems. These studies give it a significant advantage over BOA in general, and when it is employed to solve complex optimization problems validated on benchmark IEEE 30 bus system. A comparative analysis has been conducted to validate the potency of the hybrid BOA-GWO-PSO approach with some conventional meta-heuristic algorithms. Analysis of results by mathematical validation on 23 benchmark functions and application in congestion management by optimal reactive power management (RPM) reveal that the proposed technique has the potent to solve real world optimization problems and is competitive with recent methods reported in state-of- art literature.

关键词： active power loss butterfly optimization algorithm congestion management optimization reactive power management

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Innovation network output prediction model under small sample

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WIRELESS NETWORKS 2023年 1-12页

作者： Zhong, Ruiqiong Chen, Weili Wang, Dong Yang, Minqiong Feng, Gege Guangdong Univ Foreign Studies Sch Informat Sci & Technol Guangzhou 510006 Peoples R China Guangzhou Univ Sch Management Guangzhou 510006 Peoples R China

As regards the innovation network output prediction, there are problems of poor generalization ability and low prediction accuracy due to the small sample size and the missing data in Industry-University-Research Cooperation. As a result, the Innovation Network Prediction Model (INPM) is proposed to improve the above-mentioned problems. Firstly, according to butterfly optimization algorithm (BOA), three strategies are proposed including particle swarm optimization, dynamic switching probability, and multiple stages perturbation based on Cauchy-Gaussian Mutation. Secondly, this paper proposes particle dynamic multiple stages perturbation_butterfly optimization algorithm (PDM_BOA);and again, by combining support vector regression (SVR) with PDM_BOA, the INPM is developed. Finally, the simulation results show that the INPM has better generalization ability and higher prediction accuracy. The results also indicate that the optimization accuracy of BOA is dramatically improved by PDM.

关键词： Innovation output Small-sample prediction butterfly optimization algorithm

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Optimal reactive power management through a hybrid BOA-GWO-PSO algorithm for alleviating congestion

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2023年第4期14卷 1437-1456页

作者： Badi, Manjulata Mahapatra, Sheila Alliance Univ ACED EEE Dept Bangalore India

In today's deregulated energy market, improving grid management in generating power with load optimization is a critical challenge. It is also perilous that the system does not have any problems owing to transmission line clogs. The butterfly optimization algorithm is used for load balancing and load optimization in electricity markets. The proposed approach integrates Particle Swarm optimization and Grey Wolf Optimizer, merging them with butterfly optimization algorithm as a hybridised form to enhance exploration and exploitation skills. The benefit of the butterfly optimization algorithm in general, as well as when it is employed to address difficult optimization issues, is validated using the New England 39 bus test system. The amalgamated algorithm approach was compared to other established meta-heuristic algorithms for the reactive power management under variable loading conditions. Using the realistic New England 39 bus system, the suggested algorithm minimizes transmission losses by 6.344% and operating costs by 6.347% with respect to the base case, respectively. The research work reveals that proposed amalgamated algorithm employing butterfly optimization algorithm, Grey Wolf Optimizer, and Particle Swarm optimization performs better and offers more potential in a range of situations. The proposed technique mathematical validation indicated that it has the capacity to tackle complex optimization issues and compete with contemporary peer-reviewed literature solutions.

关键词： Congestion management Reactive power management optimization Real power loss butterfly optimization algorithm Hybridization algorithm

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OTFS-NOMA System Resource Allocation Scheme Based on SADEBOA algorithm 36

OTFS-NOMA System Resource Allocation Scheme Based on SADEBOA...

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36th Chinese Control and Decision Conference (CCDC)

作者： Hao, Kaicheng Yang, Yongli Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan Peoples R China

ISBN: (纸本)9798350387780;9798350387797

This paper investigates the power allocation problem in the downlink of an Orthogonal Time Frequency Space-Non-Orthogonal Multiple Access (OTFS-NOMA) system under high-speed conditions, with the objective of maximizing system capacity and rate. A novel butterfly optimization algorithm based on Differential Evolution (DE) and Simulated Annealing (SA), referred to as SADEBOA, is proposed. In the global search phase, SADEBOA incorporates DE to enhance the global search capability of the butterfly optimization algorithm (BOA), mutating the optimal individual and guiding the population towards the optimal individual's location. In the local search phase, SADEBOA combines SA to accept a local optimal solution as a new solution with a certain probability, introducing a certain bias that is more likely to approach the optimal solution. Comparative simulations are conducted with BOA, MSBOA, DA, and RSO algorithms, as well as traditional power allocation schemes. The simulation results demonstrate that, compared to the baseline BOA algorithm, MSBOA, DA, and RSO algorithms, SADEBOA achieves performance improvements of approximately 15%, 5.3%, 21.2%, and 28.4%, in enhancing OTFS-NOMA system capacity.

关键词： orthogonal time frequency space non-orthogonal multiple access butterfly optimization algorithm resource allocation

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Optimal Allocation for Photovoltaic/ Wind Turbine Applying A Hybrid butterfly Genetic algorithm 21

Optimal Allocation for Photovoltaic/ Wind Turbine Applying A...

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21st International Middle East Power Systems Conference (MEPCON)

作者： Megahed, M. G. Sweif, R. A. Abdel-Salam, T. S. British Univ Egypt Elect & Commun Dept Cairo Egypt Ain Shams Univ Elect Power & Machine Dept Cairo Egypt British Univ Egypt Fac Energy & Environm Engn Cairo Egypt

ISBN: (纸本)9781728152899

In this paper, optimal load flow is required to minimize the power losses in radial distribution systems. Load flow in power systems is used to calculate voltage at each node under different conditions in addition to determining all losses in radial network. Applying Backward/forward sweep load flow method. The objective of the optimal load flow is to reduce system losses by integrating (DGs). Applying Renewable DGs are strongly recommended because of its reliability and safe operations compared to fuels used for generating electricity. The DGs are composed of PV/Wind turbine to compensate the active power while capacitors are utilized to compensate reactive power. Genetic algorithm, and butterfly technique are applied and combined as an optimization technique to decide the optimal sizes/locations of DG-units to be placed in the radial-distribution networks. The results are tested on the "IEEE 69-bus" test networks to validate the proposed algorithms.

关键词： Load flow Distributed generation-optimization power loss reduction optimization genetic algorithm butterfly optimization algorithm Hybridization

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