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lionRank: lion algorithm-based metasearch engines for re-ranking of webpages

Science China(Information Sciences) 2018年第12期61卷 65-80页

作者： P.VIJAYA Satish CHANDER Waljat College of Applied Sciences

Due to the rapid growth of the web, the process of collecting the relevant web pages based on the user query is one of the major challenging tasks in recent days. Hence, it is very complicated for the users to know the most relevant information even though various search engines are widely employed. To deal with users' trouble in identifying the relevant information from the web, we have proposed a meta-lion search engine to capture and analyze the ranking scores of various search engines and thereby, generate the re-ranked score results. Accordingly, lionRank, a lion algorithm-based meta-search engine is proposed for the re-ranking of the web pages. Here, different features like text based, factor based, rank based and classifier based features are used by the underlying search engines. In classifier based feature extraction,we have used the fuzzy integrated extended nearest neighbor(FENN) classifier to include the semantics in feature extraction. Moreover, an intelligent re-ranking process is proposed based on the lion algorithm to fuse the features scores optimally. Finally, the results of the proposed lionRank is analyzed with the web page database collected through four benchmark queries, and the quantitative performance are analyzed using precision, recall, and F-score. From the results, we proved that the proposed lionRank obtained the maximum F-score of 81% as compared with that of existing search engines like QuadRank, Outrank, Google,Yahoo, and Bing.

关键词： web technology meta-search engine feature extraction lion algorithm FENN classifier

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Sustainable Investment Forecasting of Power Grids Based on theDeep Restricted Boltzmann Machine Optimized by the lion algorithm

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Computer Modeling in Engineering & Sciences 2022年第1期130卷 269-286页

作者： Qian Wang Xiaolong Yang Di Pu Yingying Fan School of Economics andManagement Jilin Institute of Chemical TechnologyJilin132022China School of Economics andManagement Northeast Electric Power UniversityJilin132012China Digital Grid Research Institute China Southern Power GridGuangzhou510000China School of Art Hebei GEO UniversityShijiazhuang050031China

This paper proposes a new power grid investment prediction model based on the deep restricted Boltzmann machine(DRBM)optimized by the lion algorithm(LA).Firstly,two factors including transmission and distribution price reform(TDPR)and 5G station construction were comprehensively incorporated into the consideration of influencing factors,and the fuzzy threshold method was used to screen out critical influencing ***,the LA was used to optimize the parameters of the DRBM model to improve the model’s prediction accuracy,and the model was trained with the selected influencing factors and ***,the LA-DRBM model was used to predict the investment of a power grid enterprise,and the final prediction result was obtained by modifying the initial result with the modifying *** LA-DRBMmodel compensates for the deficiency of the singlemodel,and greatly improves the investment prediction accuracy of the power *** this study,a power grid enterprise was taken as an example to carry out an empirical analysis to prove the validity of the model,and a comparison with the RBM,support vector machine(SVM),back propagation neural network(BPNN),and regression model was conducted to verify the superiority of the *** conclusion indicates that the proposed model has a strong generalization ability and good robustness,is able to abstract the combination of low-level features into high-level features,and can improve the efficiency of the model’s calculations for investment prediction of power grid enterprises.

关键词： lion algorithm deep restricted boltzmann machine fuzzy threshold method power grid investment forecasting

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A new energy efficient management approach for wireless sensor networks in target tracking

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Defence Technology（防务技术） 2021年第3期17卷 932-947页

作者： Ce Pang Gong-guo Xu Gan-lin Shan Yun-pu Zhang Army Engineering University Hebei Shijiazhuang050003China

This paper mainly studied the problem of energy conserving in wireless sensor networks for target tracking in defensing combats. Firstly, the structures of wireless sensor nodes and networks were illustrated;Secondly, the analysis of existing energy consuming in the sensing layer and its calculation method were provided to build the energy conserving objective function;What’s more, the other two indicators in target tracking, including target detection probability and tracking accuracy, were combined to be regarded as the constraints of the energy conserving objective function. Fourthly, the three energy conserving approaches, containing optimizing the management scheme, prolonging the time interval between two adjacent observations, and transmitting the observations selectively, were introduced;In addition, the improved lion algorithm combined with the Logistic chaos sequence was proposed to obtain sensor management schemes. Finally, simulations had been made to prove the effectiveness of the proposed methods and algorithm.

关键词： Wireless sensor networks Target searching Target tracking Energy efficiency lion algorithm

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Bank transaction data modeling by optimized hybrid machine learning merged with ARIMA

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Journal of Management Analytics 2020年第4期7卷 624-648页

作者： A.Kullaya Swamy B.Sarojamma S.G.Govt.Degree&PG College PilerIndia Department of Statistics Sri Venkateswara UniversityTirupatiIndia

The bank transactions are needed to be modeled to predict the future transactions of the banks based on the previous *** order to achieve efficient modeling of bank data transactions,Deep Belief Network(DBN)and Neural network(NN)classifiers are used in this ***,the bank transaction data such as transaction count and amount are subjected to feature extraction to extract the statistical ***,the extracted data are modeled using the combination of DBN and NN models,where the average modeled output from both the network is considered as the final *** above procedure is utilized for the two prediction models such as transaction count and transaction ***,the transaction count from prediction model 1 is subjected to the Auto-Regressive Integrated Moving Average(ARIMA)model to compute the relationship between the transition count and transition ***,as the main contribution,the number of hidden neurons in both DBN and NN are optimized or tuned accurately using the hybridized optimization models with lion algorithm(LA),and Artificial Bee Colony(ABC)named L-ABC *** average of entire transactional amounts,*** modeled outputs are matched with the actual data to validate the performance of the implemented model.

关键词： bank data transaction time series modeling Deep Belief Network Neural Network lion algorithm Artificial Bee Colony algorithm

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Quantitative and qualitative correlation analysis of optimal route discovery for vehicular ad-hoc networks

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Journal of Central South University 2018年第7期25卷 1732-1745页

作者： MUKUND B.Wagh GOMATHI N. Department of CSE VEL-TECH Dr.RR & Dr.SR Technical University

Vehicular ad-hoc networks （VANETs） are a significant field in the intelligent transportation system （ITS） for improving road security. The interaction among the vehicles is enclosed under VANETs. Many experiments have been performed in the region of VANET improvement. A familiar challenge that occurs is obtaining various constrained quality of service （QoS） metrics. For resolving this issue, this study obtains a cost design for the vehicle routing issue by focusing on the QoS metrics such as collision, travel cost, awareness, and congestion. The awareness of QoS is fuzzified into a price design that comprises the entire cost of routing. As the genetic algorithm （GA） endures from the most significant challenges such as complexity, unassisted issues in mutation, detecting slow convergence, global maxima, multifaceted features under genetic coding, and better fitting, the currently established lion algorithm （LA） is employed. The computation is analyzed by deploying three well-known studies such as cost analysis, convergence analysis, and complexity investigations. A numerical analysis with quantitative outcome has also been studied based on the obtained correlation analysis among various cost functions. It is found that LA performs better than GA with a reduction in complexity and routing cost.

关键词： vehicular ad-hoc network lion algorithm fuzzy quality of service routing

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