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| 1 | J Photoch Photobiol B显示文摘 | Jangir D K Charak S Mehrotra R Kundu S | 2011 | 105(2):1432011，105，2： | 1 |
| 2 | J Mol Struct显示文摘 | Charak S Jangir D K Tyagi G Mehrotra R | 2011 | 1000(1-3):1502011，1000，13： | 1 |
| 3 | Lens regeneration in mice under the influencee of vitamin A显示文摘 | Shekhawat D S Jangir O P Prakash A | 2001 | J Biosci2001，26，5： | 1 |
| 4 | A propionitrile-induced nitrilase of Rhodococcus sp. NDB 1165 and its application in nicotinic acid synthesis显示文摘 | Shreenath Prasad Anurag Misra Ved Prakash Jangir Abhishek Awasthi Jog Raj Tek Chand Bhalla | 2007 | World Journal of Microbiology and Biotechnology2007，，3： | 1 |
| 5 | Analysis of ovarian tumor pathology by Fourier Transform Infrared Speetroscopy显示文摘 | Mehrotra R Tyagi G Jangir DK | 2010 | Jour- nal Of Ovarian Research2010，3，： | 1 |
| 6 | Modulation of diabetes-mellitus-in- duced male reproductive dysfunctions in experimental ani- mal models with medicinal plants 显示文摘 | Jain GC Jangir RN | 2014 | Pharmacogn Rev2014，8，16： | 1 |
| 7 | Reservation Policy and Indian Constitu- tion in India显示文摘 | Sunil Kumar Jangir | 2013 | American International Journal of Research in Humanities Arts and Social Sciences2013，，： | 1 |
| 8 | A New Metaheuristic Optimization Algorithms for Brushless Direct Current Wheel Motor Design Problem显示文摘The Equilibrium Optimizer(EO),Grey Wolf Optimizer(GWO),and Whale Optimizer(WO)algorithms are being recently developed for engineering optimization problems.In this paper,the EO,GWO,and WO algorithms are applied individually for a brushless direct current(BLDC)design optimization problem.The EO algorithm is inspired by the models utilized to find the system’s dynamic state and equilibrium state.The GWO and WO algorithms are inspired by the hunting behavior of the wolf and the whale,respectively.The primary purpose of any optimization technique is to find the optimal configuration by maximizing motor efficiency and/or minimizing the total mass.Therefore,two objective functions are being used to achieve these objectives.The first refers to a design with high power output and efficiency.The second is a constraint imposed by the reality that the motor is built into the wheel of the vehicle and,therefore,a lightweight is needed.The EO,GWO,and WOA algorithms are then utilized to optimize the BLDC motor’s design variables to minimize the motor’s total mass or maximize the motor efficiency by simultaneously satisfying the six inequality constraints.The simulation is carried out using MATLAB simulation software,and the simulation results prove the dominance of the proposed algorithms.This paper also suggests an efficient method from the proposed three methods for the BLDC motor design optimization problem. | M.Premkumar R.Sowmya Pradeep Jangir Kottakkaran Sooppy Nisar Mujahed Aldhaifallah | 2021 | Computers, Materials & Continua2021，，5： | 0 |
| 9 | IRKO:An Improved Runge-Kutta Optimization Algorithm for Global Optimization Problems显示文摘Optimization is a key technique for maximizing or minimizing functions and achieving optimal cost,gains,energy,mass,and so on.In order to solve optimization problems,metaheuristic algorithms are essential.Most of these techniques are influenced by collective knowledge and natural foraging.There is no such thing as the best or worst algorithm;instead,there are more effective algorithms for certain problems.Therefore,in this paper,a new improved variant of a recently proposed metaphorless Runge-Kutta Optimization(RKO)algorithm,called Improved Runge-Kutta Optimization(IRKO)algorithm,is suggested for solving optimization problems.The IRKO is formulated using the basic RKO and local escaping operator to enhance the diversification and intensification capability of the basic RKO version.The performance of the proposed IRKO algorithm is validated on 23 standard benchmark functions and three engineering constrained optimization problems.The outcomes of IRKO are compared with seven state-of-the-art algorithms,including the basic RKO algorithm.Compared to other algorithms,the recommended IRKO algorithm is superior in discovering the optimal results for all selected optimization problems.The runtime of IRKO is less than 0.5 s for most of the 23 benchmark problems and stands first for most of the selected problems,including real-world optimization problems. | R.Manjula Devi M.Premkumar Pradeep Jangir Mohamed Abdelghany Elkotb Rajvikram Madurai Elavarasan Kottakkaran Sooppy Nisar | 2022 | Computers, Materials & Continua2022，，3： | 0 |
| 10 | Multi-Objective Grey Wolf Optimization Algorithm for Solving Real-World BLDC Motor Design Problem显示文摘The first step in the design phase of the Brushless Direct Current(BLDC)motor is the formulation of the mathematical framework and is often used due to its analytical structure.Therefore,the BLDC motor design problem is considered to be an optimization problem.In this paper,the analytical model of the BLDC motor is presented,and it is considered to be a basis for emphasizing the optimization methods.The analytical model used for the experimentation has 78 non-linear equations,two objective functions,five design variables,and six non-linear constraints,so the BLDC motor design problem is considered as highly non-linear in electromagnetic optimization.Multi-objective optimization becomes the forefront of the current research to obtain the global best solution using metaheuristic techniques.The bio-inspired multi-objective grey wolf optimizer(MOGWO)is presented in this paper,and it is formulated based on Pareto optimality,dominance,and archiving external.The performance of theMOGWO is verified on standard multi-objective unconstraint benchmark functions and applied to the BLDC motor design problem.The results proved that the proposedMOGWO algorithm could handle nonlinear constraints in electromagnetic optimization problems.The performance comparison in terms of Generational Distance,inversion GD,Hypervolume-matrix,scattered-matrix,and coverage metrics proves that the MOGWO algorithm can provide the best solution compared to other selected algorithms.The source code of this paper is backed up with extra online support at https://premkumarmanoharan.wixsite.com/mysite and https://www.mathworks.com/matlabcentral/fileexchange/75259-multiobjective-non-sorted-grey-wolf-mogwo-nsgwo. | M.Premkumar Pradeep Jangir B.Santhosh Kumar Mohammad A.Alqudah Kottakkaran Sooppy Nisar | 2022 | Computers, Materials & Continua2022，，2： | 0 |
| 11 | HARTIV:Human Activity Recognition Using Temporal Information in Videos显示文摘Nowadays,the most challenging and important problem of computer vision is to detect human activities and recognize the same with temporal information from video data.The video datasets are generated using cameras available in various devices that can be in a static or dynamic position and are referred to as untrimmed videos.Smarter monitoring is a historical necessity in which commonly occurring,regular,and out-of-the-ordinary activities can be automatically identified using intelligence systems and computer vision technology.In a long video,human activity may be present anywhere in the video.There can be a single ormultiple human activities present in such videos.This paper presents a deep learning-based methodology to identify the locally present human activities in the video sequences captured by a single wide-view camera in a sports environment.The recognition process is split into four parts:firstly,the video is divided into different set of frames,then the human body part in a sequence of frames is identified,next process is to identify the human activity using a convolutional neural network and finally the time information of the observed postures for each activity is determined with the help of a deep learning algorithm.The proposed approach has been tested on two different sports datasets including ActivityNet and THUMOS.Three sports activities like swimming,cricket bowling and high jump have been considered in this paper and classified with the temporal information i.e.,the start and end time for every activity present in the video.The convolutional neural network and long short-term memory are used for feature extraction of temporal action recognition from video data of sports activity.The outcomes show that the proposed method for activity recognition in the sports domain outperforms the existing methods. | Disha Deotale Madhushi Verma P.Suresh Sunil Kumar Jangir Manjit Kaur Sahar Ahmed Idris Hammam Alshazly | 2022 | Computers, Materials & Continua2022，，2： | 0 |
| 12 | BHGSO:Binary Hunger Games Search Optimization Algorithm for Feature Selection Problem显示文摘In machine learning and data mining,feature selection(FS)is a traditional and complicated optimization problem.Since the run time increases exponentially,FS is treated as an NP-hard problem.The researcher’s effort to build a new FS solution was inspired by the ongoing need for an efficient FS framework and the success rates of swarming outcomes in different optimization scenarios.This paper presents two binary variants of a Hunger Games Search Optimization(HGSO)algorithm based on V-and S-shaped transfer functions within a wrapper FS model for choosing the best features from a large dataset.The proposed technique transforms the continuous HGSO into a binary variant using V-and S-shaped transfer functions(BHGSO-V and BHGSO-S).To validate the accuracy,16 famous UCI datasets are considered and compared with different state-of-the-art metaheuristic binary algorithms.The findings demonstrate that BHGSO-V achieves better performance in terms of the selected number of features,classification accuracy,run time,and fitness values than other state-of-the-art algorithms.The results demonstrate that the BHGSO-V algorithm can reduce dimensionality and choose the most helpful features for classification problems.The proposed BHGSO-V achieves 95%average classification accuracy for most of the datasets,and run time is less than 5 sec.for low and medium dimensional datasets and less than 10 sec for high dimensional datasets. | R.Manjula Devi M.Premkumar Pradeep Jangir B.Santhosh Kumar Dalal Alrowaili Kottakkaran Sooppy Nisar | 2022 | Computers, Materials & Continua2022，，1： | 0 |
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