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Significant genomic introgression from grey junglefowl(Gallus sonneratii)to domestic chickens(Gallus gallus domesticus)
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作者 Xiurong Zhao Junhui Wen +10 位作者 Xinye Zhang Jinxin Zhang Tao Zhu Huie Wang Weifang Yang Guomin Cao Wenjie Xiong Yong Liu Changqing Qu Zhonghua Ning Lujiang Qu 《Journal of Animal Science and Biotechnology》 SCIE CAS CSCD 2024年第4期1482-1493,共12页
Background Chicken is one of the most numerous and widely distributed species around the world,and many studies support the multiple ancestral origins of domestic chickens.The research regarding the yellow skin phenot... Background Chicken is one of the most numerous and widely distributed species around the world,and many studies support the multiple ancestral origins of domestic chickens.The research regarding the yellow skin phenotype in domestic chickens(regulated by BCO2)likely originating from the grey junglefowl serves as crucial evidence for demonstrating the multiple origins of chickens.However,beyond the BCO2 gene region,much remains unknown about the introgression from the grey junglefowl into domestic chickens.Therefore,in this study,based on wholegenome data of 149 samples including 4 species of wild junglefowls and 13 local domestic chicken breeds,we explored the introgression events from the grey junglefowl to domestic chickens.Results We successfully detected introgression regions besides BCO2,including two associated with growth trait(IGFBP2 and TKT),one associated with angiogenesis(TIMP3)and two members of the heat shock protein family(HSPB2 and CRYAB).Our findings suggest that the introgression from the grey junglefowl may impact the growth performance of chickens.Furthermore,we revealed introgression events from grey junglefowl at the BCO2 region in multiple domestic chicken breeds,indicating a phenomenon where the yellow skin phenotype likely underwent strong selection and was retained.Additionally,our haplotype analysis shed light on BCO2 introgression event from different sources of grey junglefowl into domestic chickens,possibly suggesting multiple genetic flows between the grey junglefowl and domestic chickens.Conclusions In summary,our findings provide evidences of the grey junglefowl contributing to the genetic diversity of domestic chickens,laying the foundation for a deeper understanding of the genetic composition within domestic chickens,and offering new perspectives on the impact of introgression on domestic chickens. 展开更多
关键词 BCO2 Domestic chickens grey junglefowl inTROGRESSION
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Case Retrieval Strategy of Turning Process Based on Grey Relational Analysis
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作者 Jianfeng Zhao Yunliang Huo +3 位作者 Ji Xiong Junbo Liu Zhixing Guo Qingxian Li 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第2期1663-1678,共16页
To solve the problem of long response time when users obtain suitable cutting parameters through the Internet based platform,a case-based reasoning framework is proposed.Specifically,a Hamming distance and Euclidean d... To solve the problem of long response time when users obtain suitable cutting parameters through the Internet based platform,a case-based reasoning framework is proposed.Specifically,a Hamming distance and Euclidean distance combined method is designed to measure the similarity of case features which have both numeric and category properties.In addition,AHP(Analytic Hierarchy Process)and entropy weight method are integrated to provide features weight,where both user preferences and comprehensive impact of the index have been concerned.Grey relation analysis is used to obtain the similarity of a new problem and alternative cases.Finally,a platform is also developed on Visual Studio 2015,and a case study is demonstrated to verify the practicality and efficiency of the proposed method.This method can obtain cutting parameters which is suitable without iterative calculation.Compared with the traditional PSO(Particle swarm optimization algorithm)and GA(Genetic algorithm),it can obtain faster response speed.This method can provide ideas for selecting processing parameters in industrial production.While guaranteeing the characteristic information is similar,this approach can select processing parameters which is the most appropriate for the production process and a lot of time can be saved. 展开更多
关键词 CBR turning process grey relation AHP entropy weight
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A Hybrid Optimization Approach of Single Point Incremental Sheet Forming of AISI 316L Stainless Steel Using Grey Relation Analysis Coupled with Principal Component Analysiss
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作者 A Visagan P Ganesh 《Journal of Wuhan University of Technology(Materials Science)》 SCIE EI CAS CSCD 2024年第1期160-166,共7页
We investigated the parametric optimization on incremental sheet forming of stainless steel using Grey Relational Analysis(GRA) coupled with Principal Component Analysis(PCA). AISI 316L stainless steel sheets were use... We investigated the parametric optimization on incremental sheet forming of stainless steel using Grey Relational Analysis(GRA) coupled with Principal Component Analysis(PCA). AISI 316L stainless steel sheets were used to develop double wall angle pyramid with aid of tungsten carbide tool. GRA coupled with PCA was used to plan the experiment conditions. Control factors such as Tool Diameter(TD), Step Depth(SD), Bottom Wall Angle(BWA), Feed Rate(FR) and Spindle Speed(SS) on Top Wall Angle(TWA) and Top Wall Angle Surface Roughness(TWASR) have been studied. Wall angle increases with increasing tool diameter due to large contact area between tool and workpiece. As the step depth, feed rate and spindle speed increase,TWASR decreases with increasing tool diameter. As the step depth increasing, the hydrostatic stress is raised causing severe cracks in the deformed surface. Hence it was concluded that the proposed hybrid method was suitable for optimizing the factors and response. 展开更多
关键词 single point incremental forming AISI 316L taguchi grey relation analysis principal component analysis surface roughness scanning electron microscopy
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Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer
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作者 Hongliang Zhang Yi Chen +1 位作者 Yuteng Zhang Gongjie Xu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第8期1459-1483,共25页
The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worke... The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality. 展开更多
关键词 Distributed flexible job shop scheduling problem dual resource constraints energy-saving scheduling multi-objective grey wolf optimizer Q-LEARNinG
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Enhancing Hyper-Spectral Image Classification with Reinforcement Learning and Advanced Multi-Objective Binary Grey Wolf Optimization
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作者 Mehrdad Shoeibi Mohammad Mehdi Sharifi Nevisi +3 位作者 Reza Salehi Diego Martín Zahra Halimi Sahba Baniasadi 《Computers, Materials & Continua》 SCIE EI 2024年第6期3469-3493,共25页
Hyperspectral(HS)image classification plays a crucial role in numerous areas including remote sensing(RS),agriculture,and the monitoring of the environment.Optimal band selection in HS images is crucial for improving ... Hyperspectral(HS)image classification plays a crucial role in numerous areas including remote sensing(RS),agriculture,and the monitoring of the environment.Optimal band selection in HS images is crucial for improving the efficiency and accuracy of image classification.This process involves selecting the most informative spectral bands,which leads to a reduction in data volume.Focusing on these key bands also enhances the accuracy of classification algorithms,as redundant or irrelevant bands,which can introduce noise and lower model performance,are excluded.In this paper,we propose an approach for HS image classification using deep Q learning(DQL)and a novel multi-objective binary grey wolf optimizer(MOBGWO).We investigate the MOBGWO for optimal band selection to further enhance the accuracy of HS image classification.In the suggested MOBGWO,a new sigmoid function is introduced as a transfer function to modify the wolves’position.The primary objective of this classification is to reduce the number of bands while maximizing classification accuracy.To evaluate the effectiveness of our approach,we conducted experiments on publicly available HS image datasets,including Pavia University,Washington Mall,and Indian Pines datasets.We compared the performance of our proposed method with several state-of-the-art deep learning(DL)and machine learning(ML)algorithms,including long short-term memory(LSTM),deep neural network(DNN),recurrent neural network(RNN),support vector machine(SVM),and random forest(RF).Our experimental results demonstrate that the Hybrid MOBGWO-DQL significantly improves classification accuracy compared to traditional optimization and DL techniques.MOBGWO-DQL shows greater accuracy in classifying most categories in both datasets used.For the Indian Pine dataset,the MOBGWO-DQL architecture achieved a kappa coefficient(KC)of 97.68%and an overall accuracy(OA)of 94.32%.This was accompanied by the lowest root mean square error(RMSE)of 0.94,indicating very precise predictions with minimal error.In the case of the Pavia University dataset,the MOBGWO-DQL model demonstrated outstanding performance with the highest KC of 98.72%and an impressive OA of 96.01%.It also recorded the lowest RMSE at 0.63,reinforcing its accuracy in predictions.The results clearly demonstrate that the proposed MOBGWO-DQL architecture not only reaches a highly accurate model more quickly but also maintains superior performance throughout the training process. 展开更多
关键词 Hyperspectral image classification reinforcement learning multi-objective binary grey wolf optimizer band selection
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Optimization of Agricultural Industrial Structure in Changping District of Beijing Based on Grey Relational Analysis
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作者 Haosong LI Rao CHEN 《Asian Agricultural Research》 2024年第4期3-7,共5页
In the economic development of Beijing,although the share of the total amount of agricultural industry in the overall economy is relatively low,it has an important impact on the daily life of residents,social stabilit... In the economic development of Beijing,although the share of the total amount of agricultural industry in the overall economy is relatively low,it has an important impact on the daily life of residents,social stability and the development of other industries.Changping District,as an important agricultural production base of Beijing,its agricultural development has an indispensable strategic significance for the stability and growth of the entire regional economy.Therefore,it is very important to study the structure of agricultural industry in Changping District.Based on the detailed analysis of the agricultural industrial structure of Changping District,this paper uses the grey relation theory to analyze the different industries in the agricultural industrial structure of Changping District,including planting,forestry,animal husbandry,fishery and agricultural,forestry,service industries,in order to reveal the impact of these industries on the agricultural industrial structure of Changping District.Through this study,it comes up with specific and feasible suggestions for the optimization of agricultural industrial structure in Changping District,and provides valuable reference for the agricultural development of other areas in Beijing. 展开更多
关键词 grey RELATION theory Changping DISTRICT AGRICULTURAL inDUSTRIAL structure
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Management of Strawberry Grey Mold Disease Using Biocontrol Agents and Plant Extracts
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作者 P. Sakthi Priya Srushtideep Angidi +2 位作者 Uday Kumar Thera S. V. Nandeesha Thangaswamy Rajesh 《American Journal of Plant Sciences》 CAS 2024年第7期538-551,共14页
Strawberry (Fragaria × ananassa Duch.) is a significant global soft fruit crop, prized for its nutrient content and pleasant flavor. However, diseases, particularly grey mold caused by Botrytis cinerea Pers. Fr. ... Strawberry (Fragaria × ananassa Duch.) is a significant global soft fruit crop, prized for its nutrient content and pleasant flavor. However, diseases, particularly grey mold caused by Botrytis cinerea Pers. Fr. poses major constraints to strawberry production and productivity. Grey mold severely impacts fruit quality and quantity, diminishing market value. This study evaluated five B. cinerea isolates from various locations in the Ri-Bhoi district of Meghalaya. All isolates were pathogenic, with isolate SGM 2 identified as highly virulent. Host range studies showed the pathogen-producing symptoms in the fava bean pods, marigold, gerbera, and chrysanthemum flowers and in the fava bean, gerbera, and lettuce leaves. In vitro tests revealed that neem extract (15% w/v) achieved the highest mycelial growth inhibition at 76.66%, while black turmeric extract (5% w/v) had the lowest inhibition at 9.62%. Dual culture methods with bio-control agents indicated that Bacillus subtilis recorded the highest mean inhibition at 77.03%, while Pseudomonas fluorescens had the lowest at 20.36% against the two virulent isolates. Pot evaluations demonstrated that B. subtilis resulted in the lowest percent disease index at 20.59%, followed by neem extract at 23.31%, with the highest disease index in the control group at 42.51%. Additionally, B. subtilis significantly improved plant growth, yielding an average of 0.32 kg compared to 0.14 kg in the control. The promising results of B. subtilis and neem leaf extract from this study suggest their potential for eco-friendly managing grey mold in strawberries under field conditions. 展开更多
关键词 Strawberry grey Mold BCA Plant Extracts Botrytis cinerea
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Optimizing Grey Wolf Optimization: A Novel Agents’ Positions Updating Technique for Enhanced Efficiency and Performance
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作者 Mahmoud Khatab Mohamed El-Gamel +2 位作者 Ahmed I. Saleh Asmaa H. Rabie Atallah El-Shenawy 《Open Journal of Optimization》 2024年第1期21-30,共10页
Grey Wolf Optimization (GWO) is a nature-inspired metaheuristic algorithm that has gained popularity for solving optimization problems. In GWO, the success of the algorithm heavily relies on the efficient updating of ... Grey Wolf Optimization (GWO) is a nature-inspired metaheuristic algorithm that has gained popularity for solving optimization problems. In GWO, the success of the algorithm heavily relies on the efficient updating of the agents’ positions relative to the leader wolves. In this paper, we provide a brief overview of the Grey Wolf Optimization technique and its significance in solving complex optimization problems. Building upon the foundation of GWO, we introduce a novel technique for updating agents’ positions, which aims to enhance the algorithm’s effectiveness and efficiency. To evaluate the performance of our proposed approach, we conduct comprehensive experiments and compare the results with the original Grey Wolf Optimization technique. Our comparative analysis demonstrates that the proposed technique achieves superior optimization outcomes. These findings underscore the potential of our approach in addressing optimization challenges effectively and efficiently, making it a valuable contribution to the field of optimization algorithms. 展开更多
关键词 grey Wolf Optimization (GWO) Metaheuristic Algorithm Optimization Problems Agents’ Positions Leader Wolves Optimal Fitness Values Optimization Challenges
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Wind farm active power dispatching algorithm based on Grey Incidence 被引量:2
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作者 Binbin Zhang Mengxin Jia +2 位作者 Chaobo Chen Kun Wang Jichao Li 《Global Energy Interconnection》 EI CAS CSCD 2023年第2期175-183,共9页
This study proposes a wind farm active power dispatching(WFAPD) algorithm based on the grey incidence method, which does not rely on an accurate mathematical model of wind turbines. Based on the wind turbine start-sto... This study proposes a wind farm active power dispatching(WFAPD) algorithm based on the grey incidence method, which does not rely on an accurate mathematical model of wind turbines. Based on the wind turbine start-stop data at different wind speeds, the weighting coefficients, which are the participation degrees of a variable speed system and a variable pitch system in power regulation, are obtained using the grey incidence method. The incidence coefficient curve is fitted by the B-spline function at a full range of wind speeds, and the power regulation capacity of all wind turbines is obtained. Finally, the WFAPD algorithm, which is based on the regulating capacity of each wind turbine, is compared with the wind speed weighting power dispatching(WSWPD) algorithm in MATLAB. The simulation results show that the active power fluctuation of the wind farm is smaller, the rotating speed of wind turbines is smoother, and the fatigue load of highspeed turbines is effectively reduced. 展开更多
关键词 Wind farm Active power dispatching grey incidence B-spline function
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Deformation critical threshold estimation of Xiaowan ultrahigh arch dam with time-varying grey model 被引量:1
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作者 Er-feng Zhao Bo Li +1 位作者 Hao Chen Bing-bing Nie 《Water Science and Engineering》 EI CAS CSCD 2023年第3期302-312,共11页
The structural behavior of the Xiaowan ultrahigh arch dam is primarily influenced by external loads and time-varying characteristics of dam concrete and foundation rock mass during long-term operation. According to ov... The structural behavior of the Xiaowan ultrahigh arch dam is primarily influenced by external loads and time-varying characteristics of dam concrete and foundation rock mass during long-term operation. According to overload testing with a geological model and the measured time series of installed perpendicular lines, the space and time evolution characteristics of the arch dam structure were analyzed, and its mechanical performance was evaluated. Subsequently, the deformation centroid of the deflective curve was suggested to indicate the magnitude and unique distribution rules for a typical dam section using the measured deformation values at multi-monitoring points. The ellipse equations of the critical ellipsoid for the centroid were derived from the historical measured time series. Hydrostatic and seasonal components were extracted from the measured deformation values with a traditional statistical model, and residuals were adopted as a grey component. A time-varying grey model was developed to accurately predict the evolution of the deformation behavior of the ultrahigh arch dam during future operation. In the developed model, constant coefficients were modified so as to be time-dependent functions, and the prediction accuracy was significantly improved through introduction of a forgetting factor. Finally, the critical threshold was estimated, and predicted ellipsoids were derived for the Xiaowan arch dam. The findings of this study can provide technical support for safety evaluation of the actual operation of ultrahigh arch dams and help to provide early warning of abnormal changes. 展开更多
关键词 Arch dam Deformation behavior EVOLUTION Critical threshold grey model
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Enhancing rock fragmentation prediction in mining operations:A hybrid GWO-RF model with SHAP interpretability 被引量:1
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作者 ZHANG Yu-lin QIU Yin-gui +2 位作者 ARMAGHANI Danial Jahed MONJEZI Masoud ZHOU Jian 《Journal of Central South University》 SCIE EI CAS CSCD 2024年第8期2916-2929,共14页
In the mining industry,precise forecasting of rock fragmentation is critical for optimising blasting processes.In this study,we address the challenge of enhancing rock fragmentation assessment by developing a novel hy... In the mining industry,precise forecasting of rock fragmentation is critical for optimising blasting processes.In this study,we address the challenge of enhancing rock fragmentation assessment by developing a novel hybrid predictive model named GWO-RF.This model combines the grey wolf optimization(GWO)algorithm with the random forest(RF)technique to predict the D_(80)value,a critical parameter in evaluating rock fragmentation quality.The study is conducted using a dataset from Sarcheshmeh Copper Mine,employing six different swarm sizes for the GWO-RF hybrid model construction.The GWO-RF model’s hyperparameters are systematically optimized within established bounds,and its performance is rigorously evaluated using multiple evaluation metrics.The results show that the GWO-RF hybrid model has higher predictive skills,exceeding traditional models in terms of accuracy.Furthermore,the interpretability of the GWO-RF model is enhanced through the utilization of SHapley Additive exPlanations(SHAP)values.The insights gained from this research contribute to optimizing blasting operations and rock fragmentation outcomes in the mining industry. 展开更多
关键词 BLASTinG rock fragmentation random forest grey wolf optimization hybrid tree-based technique
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Three-Dimensional Sound Source Location Algorithm for Subsea Leakage Using Hydrophone 被引量:1
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作者 LI Hao-jie CAI Bao-ping +6 位作者 YUAN Xiao-bing KONG Xiang-di LIU Yong-hong Javed Akbar KHAN CHU Zheng-de YANG Chao TANG An-bang 《China Ocean Engineering》 SCIE EI CSCD 2024年第2期326-337,共12页
Leakages from subsea oil and gas equipment cause substantial economic losses and damage to marine ecosystem,so it is essential to locate the source of the leak.However,due to the complexity and variability of the mari... Leakages from subsea oil and gas equipment cause substantial economic losses and damage to marine ecosystem,so it is essential to locate the source of the leak.However,due to the complexity and variability of the marine environment,the signals collected by hydrophone contain a variety of noises,which makes it challenging to extract useful signals for localization.To solve this problem,a hydrophone denoising algorithm is proposed based on variational modal decomposition(VMD)with grey wolf optimization.First,the average envelope entropy is used as the fitness function of the grey wolf optimizer to find the optimal solution for the parameters K andα.Afterward,the VMD algorithm decomposes the original signal parameters to obtain the intrinsic mode functions(IMFs).Subsequently,the number of interrelationships between each IMF and the original signal was calculated,the threshold value was set,and the noise signal was removed to calculate the time difference using the valid signal obtained by reconstruction.Finally,the arrival time difference is used to locate the origin of the leak.The localization accuracy of the method in finding leaks is investigated experimentally by constructing a simulated leak test rig,and the effectiveness and feasibility of the method are verified. 展开更多
关键词 grey wolf optimizer variational modal decomposition mean envelope entropy correlation coefficient time difference of arrival
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Multi-Objective Optimization of Fused Deposition Modeling for Mechanical Properties of Biopolymer Parts Using the Grey-Taguchi Method
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作者 Kapil Kumar Hari Singh 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2023年第1期51-64,共14页
The urgent need to develop customized functional products only possible by 3D printing had realized when faced with the unavailability of medical devices like surgical instruments during the coronavirus-19 disease and... The urgent need to develop customized functional products only possible by 3D printing had realized when faced with the unavailability of medical devices like surgical instruments during the coronavirus-19 disease and the ondemand necessity to perform surgery during space missions.Biopolymers have recently been the most appropriate option for fabricating surgical instruments via 3D printing in terms of cheaper and faster processing.Among all 3D printing techniques,fused deposition modelling(FDM)is a low-cost and more rapid printing technique.This article proposes the fabrication of surgical instruments,namely,forceps and hemostat using the fused deposition modeling(FDM)process.Excellent mechanical properties are the only indicator to judge the quality of the functional parts.The mechanical properties of FDM-processed parts depend on various process parameters.These parameters are layer height,infill pattern,top/bottom pattern,number of top/bottom layers,infill density,flow,number of shells,printing temperature,build plate temperature,printing speed,and fan speed.Tensile strength and modulus of elasticity are chosen as evaluation indexes to ascertain the mechanical properties of polylactic acid(PLA)parts printed by FDM.The experiments have performed through Taguchi’s L27orthogonal array(OA).Variance analysis(ANOVA)ascertains the significance of the process parameters and their percent contributions to the evaluation indexes.Finally,as a multiobjective optimization technique,grey relational analysis(GRA)obtains an optimal set of FDM process parameters to fabricate the best parts with comprehensive mechanical properties.Scanning electron microscopy(SEM)examines the types of defects and strong bonding between rasters.The proposed research ensures the successful fabrication of functional surgical tools with substantial ultimate tensile strength(42.6 MPa)and modulus of elasticity(3274 MPa). 展开更多
关键词 Fused deposition modeling Mechanical properties Taguchi method ANOVA grey relational analysis SEM
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Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection 被引量:1
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作者 Hala AlShamlan Halah AlMazrua 《Computers, Materials & Continua》 SCIE EI 2024年第4期675-694,共20页
In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selec... In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment. 展开更多
关键词 Bio-inspired algorithms BIOinFORMATICS cancer classification evolutionary algorithm feature selection gene expression grey wolf optimizer harris hawks optimization k-nearest neighbor support vector machine
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A Rule-Based Approach for Grey Hole Attack Prediction in Wireless Sensor Networks
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作者 C.Gowdham S.Nithyanandam 《Intelligent Automation & Soft Computing》 SCIE 2023年第3期3815-3827,共13页
The Wireless Sensor Networks(WSN)are vulnerable to assaults due to the fact that the devices connected to them have a reliable connection to the inter-net.A malicious node acts as the controller and uses a grey hole a... The Wireless Sensor Networks(WSN)are vulnerable to assaults due to the fact that the devices connected to them have a reliable connection to the inter-net.A malicious node acts as the controller and uses a grey hole attack to get the data from all of the other nodes in the network.Additionally,the nodes are dis-carding and modifying the data packets according to the requirements of the sys-tem.The assault modifies the fundamental concept of the WSNs,which is that different devices should communicate with one another.In the proposed system,there is a fuzzy idea offered for the purpose of preventing the grey hole attack from making effective communication among the WSN devices.The currently available model is unable to recognise the myriad of different kinds of attacks.The fuzzy engine identified suspicious actions by utilising the rules that were gen-erated to make a prediction about the malicious node that would halt the process.Experiments conducted using simulation are used to determine delay,accuracy,energy consumption,throughput,and the ratio of packets successfully delivered.It stands in contrast to the model that was suggested,as well as the methodologies that are currently being used,and analogue behavioural modelling.In comparison to the existing method,the proposed model achieves an accuracy rate of 45 per-cent,a packet delivery ratio of 79 percent,and a reduction in energy usage of around 35.6 percent.These results from the simulation demonstrate that the fuzzy grey detection technique that was presented has the potential to increase the net-work’s capability of detecting grey hole assaults. 展开更多
关键词 Attack prediction grey hole wireless sensor networks rule-based model grey attack
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Materials Selection of Thermoplastic Matrices of Natural Fibre Composites for Cyclist Helmet Using an Integration of DMAIC Approach in Six Sigma Method Together with Grey Relational Analysis Approach
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作者 N.A.Maidin S.M.Sapuan +1 位作者 M.T.Mastura M.Y.M.Zuhri 《Journal of Renewable Materials》 SCIE EI 2023年第5期2381-2397,共17页
Natural fibre reinforced polymer composite(NFRPC)materials are gaining popularity in the modern world due to their eco-friendliness,lightweight nature,life-cycle superiority,biodegradability,low cost,and noble mechani... Natural fibre reinforced polymer composite(NFRPC)materials are gaining popularity in the modern world due to their eco-friendliness,lightweight nature,life-cycle superiority,biodegradability,low cost,and noble mechanical properties.Due to the wide variety of materials available that have comparable attributes and satisfy the requirements of the product design specification,material selection has become a crucial component of design for engineers.This paper discusses the study’s findings in choosing the suitable thermoplastic matrices of Natural Fibre Composites for Cyclist Helmet utilising the DMAIC,and GRA approaches.The results are based on integrating two decision methods implemented utilising two distinct decision-making approaches:qualitative and quantitative.This study suggested thermoplastic polyethylene as a particularly ideal matrix in composite cyclist helmets during the selection process for the best thermoplastic matrices material using the 6σtechnique,with the decision based on the highest performance,the lightest weight,and the most environmentally friendly criteria.The DMAIC and GRA approach significantly influenced the material selection process by offering different tools for each phase.In the future study,selection technique may have been more exhaustive if more information from other factors had been added. 展开更多
关键词 Material selection DMAIC Six Sigma grey relational analysis thermoplastic matrices natural fibre polymerreinforced composites
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Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection Systems
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作者 Reem Alkanhel Doaa Sami Khafaga +5 位作者 El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Rashid Amin Mostafa Abotaleb B.M.El-den 《Computers, Materials & Continua》 SCIE EI 2023年第2期2695-2709,共15页
The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices remotely.Due to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy... The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices remotely.Due to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing attacks.That’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT networks.The imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network attacks.Therefore,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization algorithms.To prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE balancing.Experimental results showed that the proposed approach outperforms the other approaches and could boost the detection accuracy.In addition,a statistical analysis is performed to study the significance and stability of the proposed approach.The conducted experiments include seven different types of attack cases in the RPL-NIDS17 dataset.Based on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%). 展开更多
关键词 Metaheuristics grey wolf optimization dipper throated optimization dataset balancing locality sensitive hashing SMOTE
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Grey Wolf Optimizer Based Deep Learning for Pancreatic Nodule Detection
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作者 T.Thanya S.Wilfred Franklin 《Intelligent Automation & Soft Computing》 SCIE 2023年第4期97-112,共16页
At an early point,the diagnosis of pancreatic cancer is mediocre,since the radiologist is skill deficient.Serious threats have been posed due to the above reasons,hence became mandatory for the need of skilled technici... At an early point,the diagnosis of pancreatic cancer is mediocre,since the radiologist is skill deficient.Serious threats have been posed due to the above reasons,hence became mandatory for the need of skilled technicians.However,it also became a time-consuming process.Hence the need for automated diagnosis became mandatory.In order to identify the tumor accurately,this research pro-poses a novel Convolution Neural Network(CNN)based superior image classi-fication technique.The proposed deep learning classification strategy has a precision of 97.7%,allowing for more effective usage of the automatically exe-cuted feature extraction technique to diagnose cancer cells.Comparative analysis with CNN-Grey Wolf Optimization(GWO)is carried based on varied testing and training outcomes.The suggested study is carried out at a rate of 90%–10%,80%–20%,and 70%–30%,indicating the robustness of the proposed research work.Outcomes show that the suggested method is effective.GWO-CNN is reli-able and accurate relative to other detection methods available in the literatures. 展开更多
关键词 Convolution neural network deep learning technique feature extraction grey wolf optimizer
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Recognizing Ancient South Indian Language Using Opposition Based Grey Wolf Optimization
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作者 A.Naresh Kumar G.Geetha 《Intelligent Automation & Soft Computing》 SCIE 2023年第3期2619-2637,共19页
Recognizing signs and fonts of prehistoric language is a fairly difficult job that requires special tools.This stipulation make the dispensation period over-riding,difficult and tiresome to calculate.This paper present ... Recognizing signs and fonts of prehistoric language is a fairly difficult job that requires special tools.This stipulation make the dispensation period over-riding,difficult and tiresome to calculate.This paper present a technique for recognizing ancient south Indian languages by applying Artificial Neural Network(ANN)associated with Opposition based Grey Wolf Optimization Algorithm(OGWA).It identifies the prehistoric language,signs and fonts.It is an apparent from the ANN system that arbitrarily produced weights or neurons linking various layers play a significant role in its performance.For adaptively determining these weights,this paper applies various optimization algorithms such as Opposition based Grey Wolf Optimization,Particle Swarm Optimization and Grey Wolf Opti-mization to the ANN system.Performance results are illustrated that the proposed ANN-OGWO technique achieves superior accuracy over the other techniques.In test case 1,the accuracy value of OGWO is 94.89%and in test case 2,the accu-racy value of OGWO is 92.34%,on average,the accuracy of OGWO achieves 5.8%greater accuracy than ANN-GWO,10.1%greater accuracy than ANN-PSO and 22.1%greater accuracy over conventional ANN technique. 展开更多
关键词 Ancient language symbols CHARACTERS artificial neural network opposition based grey wolf optimization
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Prediction of Total Output Value of Construction Industry in Jiangxi Province Based on Grey Prediction Model
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作者 Le XU Yuangui LIU 《Asian Agricultural Research》 2023年第5期11-13,43,共4页
In order to realize the accurate prediction of the total output value of construction industry in the future,the grey prediction model is used to compare the measured value with the predicted value from 2012 to 2021,a... In order to realize the accurate prediction of the total output value of construction industry in the future,the grey prediction model is used to compare the measured value with the predicted value from 2012 to 2021,and based on the existing data,the total output value of construction industry in Jiangxi Province in the next five years is predicted.The results show that the grey prediction model has a good prediction effect,and the error between the predicted value and the measured value is within 14%,which provides a basis for policy adjustment and resource optimization. 展开更多
关键词 Jiangxi Province grey prediction model Total output value of construction industry FORECAST
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