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Applied Mathematics and Mechanics(English Edition) 2020年第11期41卷 1651-1670页

作者： R.kumar R.kumar s.A.sHEHZAD A.J.CHAMKHA Department of Mathematics Central University of Himachal PradeshDharamshala 273001India Department of Mathematics COMSATS University IslamabadSahiwal 57000Pakistan Institute of Research and Development Duy Tan UniversityDa Nang 550000Vietnam Institute of Theoretical and Applied Research(ITAR) Duy Tan UniversityHanoi 100000Vietnam

A comparative three-dimensional(3D)analysis for Casson-nanofluid and Carreau-nanofluid flows due to a flat body in a magnetohydrodynamic(MHD)stratified environment is *** is estimated to be suspended in a Darcy-Forchheimer *** and Dufour responses are also accommodated in the flow field.A moving(rotating)coordinate system is exercised to examine the bidirectionally stretched flow fields(flow,heat transfer,and mass transfer).Nanofluid is compounded by taking ethylene glycol/sodium alginate as base fluid and ferric-oxide(Fe3O4)as *** equations are handled by the application of optimal homotopy asymptotic method(OHAM),where convergence parameters are optimized through the classical least square *** novel mechanism(hidden physics)due to appearing parameters is explored with the assistance of tabular and graphical *** reveal the double behavior state for temperature field with thermal stratification/Dufour number,and for concentration field with soret number due to the presence of turning points.

关键词： thermal-solutal stratification soret-Dufour effect Casson-Carreau nanofluid model porous matrix

D2 analysis for estimating genetic divergence in different clones of Dalbergia sissoo

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Journal of Forestry Research 2016年第5期27卷 1085-1097页

作者： Ashok kumar s. Ravichandran shivani Dobhal Vijay kumar Forest Research Institute (Indian Council of Forestry Research and Education) Dehradun India Directorate of Rice Research Rajendra Nagar HyderabadAndhra Pradesh India Research Circle Punjab Forest Department HoshiarpurPunjab India

Genetic divergence was studied in selected 36 genotypes of Dalbergia sissoo Roxb. on the basis of seven morphological parameters. The divergence among genotypes was estimated by Mahalanobis method and genotypes were grouped into clusters by Tocher＇s method. All the genotypes were classified into seven distinct clusters on the basis of seven morphological traits. Cluster 1 was the largest with 25 genotypes followed by Cluster 2 （four genotypes）. Cluster 3, 5, 6 and 7 were the divergent clusters. The D2 analysis revealed that D2 value （39.42） between clone 5040 and clone 201 was recorded maximum. The intra-cluster distance ranged from 0.00 （Cluster 3, 5, 6 and 7） to 3.89 （Cluster 1）, the Cluster 3 （clone 33） was the most divergent cluster with maximum inter cluster distance （13.97） with the Cluster 7. By the divergence analysis, the parents for hybridization from diverse clusters could be selected for heterotic hybrids.

关键词： Dalbergia sissoo Roxb. Genetic divergence Hybridization D2 analysis Heterotic

Detection of Phishing in Internet-of-Things Using Hybrid Deep Belief Network

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Intelligent Automation & soft Computing 2023年第6期36卷 3043-3056页

作者： s.Ashwini s.Magesh kumar Department of Computer science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences(Deemed to be University)ChennaiTamilnaduIndia

Increase in the use of internet of things owned devices is one of the *** publicly available network many kinds of phishing attacks are able to enter into the mobile devices and corrupt the existing *** Phishing is the slow and resilient attack stacking techniques probe the *** proposed model is focused on detecting phishing attacks in internet of things enabled devices through a robust algorithm called Novel Watch and Trap Algorithm(NWAT).Though Predictive mapping,Predictive Validation and Predictive analysis mechanism is *** the test purpose Canadian Institute of cyber security(CIC)dataset is used for creating a robust prediction *** attack generates a resilience corruption works that slowly gathers the credential information from the *** proposed Predictive analysis model(PAM)enabled NWAT algorithm is used to predict the phishing probes in the form of suspicious process happening in the IoT *** prediction system considers the peer-to-peer communication window open for the established communication,the suspicious process and its pattern is identified by the new *** proposed model is validated by finding thepredictionaccuracy,Precision,recallsF1score,errorrate,Mathew’sCorre-lationCoefficient(MCC)andBalancedDetectionRate(BDR).Thepresented approach is comparatively analyzed with the state-of-the-art approach of existing system related to various types of Phishing probes.

关键词： Cyber security internet of things phishing attacks fault-tolerant devices smart devices cyber security attacks

Novel Vegetation Mapping Through Remote sensing Images Using Deep Meta Fusion Model

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Intelligent Automation & soft Computing 2023年第6期36卷 2915-2931页

作者： s.Vijayalakshmi s.Magesh kumar Department of Computer science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennaiTamilnaduIndia

Preserving biodiversity and maintaining ecological balance is essential in current environmental *** is challenging to determine vegetation using traditional map classification *** primary issue in detecting vegetation pattern is that it appears with complex spatial structures and similar spectral *** is more demandable to determine the multiple spectral ana-lyses for improving the accuracy of vegetation mapping through remotely sensed *** proposed framework is developed with the idea of ensembling three effective strategies to produce a robust architecture for vegetation *** architecture comprises three approaches,feature-based approach,region-based approach,and texture-based approach for classifying the vegetation *** novel Deep Meta fusion model(DMFM)is created with a unique fusion frame-work of residual stacking of convolution layers with Unique covariate features(UCF),Intensity features(IF),and Colour features(CF).The overhead issues in GPU utilization during Convolution neural network(CNN)models are reduced here with a lightweight *** system considers detailing feature areas to improve classification accuracy and reduce processing *** proposed DMFM model achieved 99%accuracy,with a maximum processing time of 130 *** training,testing,and validation losses are degraded to a significant level that shows the performance quality with the DMFM *** system acts as a standard analysis platform for dynamic datasets since all three different fea-tures,such as Unique covariate features(UCF),Intensity features(IF),and Colour features(CF),are considered very well.

关键词： Vegetation mapping deep learning machine learning remote sensing data image processing

Generation of VLF Mode Instability by Generalized Distribution Function in the Presence of Parallel AC Electric Field in Uranus

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Plasma science and Technology 2010年第4期12卷 421-425页

作者： R.s.PANDEY M.sINGH Pramod kumar K.M.sINGH s.kumar Department of Applied Physics Amity School of Science and TechnologyAmity University Noida UP Department of Physics J. L. N. College Chakradharpur Ranchi University Ranch Department of Physics Veer Kunwar Singh University Department of Physics Nalanda college Biharsharif Nalanda MU Bodh Gaya Bihar

VLF （very low frequency） mode instability with parallel AC electric field was studied for generalized loss-cone distribution with an index j, which is reducible to bi-Maxwellian, loss- cone and delta function for j = 0, 1 and co. The particle trajectories and dispersion relation are obtained through a kinetic approach and method of characteristic solutions. The calculations are compared with the observations of low frequency waves of Voyager 2. The growth rate of plasma parameters suited to magnetosphere of Uranus is obtained. It is inferred that the magnitude as well as frequency of AC （alternating current） field increases the growth rate and widen the band width significantly. In addition to temperature anisotropy, particles in plasma having generalized loss-cone distribution provide an additional source of energy.

关键词： generation of VLF mode instability in Uranus

soft Computing Based Discriminator Model for Glaucoma Diagnosis

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Computer systems science & Engineering 2022年第9期42卷 867-880页

作者： Anisha Rebinth s.Mohan kumar Department of Information science and Engineering NHCEVisvesvaraya Technological UniversityBangaloreIndia Department of Computer science and Engineering Nagarjuna College of Engineering and TechnologyBangaloreIndia

In this study, a Discriminator Model for Glaucoma Diagnosis (DMGD)using soft computing techniques is presented. As the biomedical images such asfundus images are often acquired in high resolution, the Region of Interest (ROI)for glaucoma diagnosis must be selected at first to reduce the complexity of anysystem. The DMGD system uses a series of pre-processing;initial cropping by thegreen channel’s intensity, spatially Weighted Fuzzy C Means (sWFCM), bloodvessel detection and removal by Gaussian Derivative Filters (GDF) and inpaintingalgorithms. Once the ROI has been selected, the numerical features such as colour, spatial domain features from Local Binary Pattern (LBP) and frequencydomain features from LAWs are generated from the corresponding ROI forfurther classification using kernel based support Vector Machine (sVM). TheDMGD system performances are validated using four fundus image databases;ORIGA, RIM-ONE, DRIsHTI-Gs1, and HRF with four different kernels;LinearKernel (LK), Polynomial Kernel (PK), Radial Basis Function (RBFK) kernel,Quadratic Kernel (QK) based sVM classifiers. Results show that the DMGD system classifies the fundus images accurately using the multiple features and kernelbased classifies from the properly segmented ROI.

关键词： Glaucoma support vector classification clustering technique spatial domain and frequency domain features

Optimal and Effective Resource Management in Edge Computing

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Computer systems science & Engineering 2023年第2期44卷 1201-1217页

作者： Darpan Majumder s.Mohan kumar Department of Information science and Engineering New Horizon College of Engineering Research CentreVisvesvaraya Technological University(VTU)BangaloreIndia Department of CsE Nagarjuna College of Engineering and TechnologyBangaloreIndia

Edge computing is a cloud computing extension where physical compu-ters are installed closer to the device to minimize *** task of edge data cen-ters is to include a growing abundance of applications with a small capability in comparison to conventional data *** this framework,Federated Learning was suggested to offer distributed data training strategies by the coordination of many mobile devices for the training of a popular Artiﬁcial Intelligence(AI)model without actually revealing the underlying data,which is signiﬁcantly enhanced in terms of *** learning(FL)is a recently developed decentralized profound learning methodology,where customers train their localized neural network models independently using private data,and then combine a global model on the core server *** models on the edge server use very little time since the edge server is highly *** the amount of time it takes to download data from smartphone users on the edge server has a signiﬁcant impact on the time it takes to complete a single cycle of FL operations.A machine learning strategic planning system that uses FL in conjunction to minimise model training time and total time utilisation,while recognising mobile appliance energy restrictions,is the focus of this *** further speed up integration and reduce the amount of data,it implements an optimization agent for the establishment of optimal aggregation policy and asylum architecture with several employees’shared *** main solutions and lessons learnt along with the prospects are *** show that our method is superior in terms of the effective and elastic use of resources.

关键词： Federated learning machine learning edge computing resource management

An Efficient stabbing Based Intrusion Detection Framework for sensor Networks

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Computer systems science & Engineering 2022年第10期43卷 141-157页

作者： A.Arivazhagi s.Raja kumar Department of Computer science and Engineering University College of EngineeringAriyalurTamilnaduIndia Department of Mathematics University College of EngineeringAriyalurTamilnaduIndia

Intelligent Intrusion Detection system(IIDs)for networks provide a resourceful solution to network security than conventional intrusion defence mechanisms like a *** efficiency of IIDs highly relies on the algorithm *** enhancements towards these methods are utilized to enhance the classification accuracy and diminish the testing and training time of these ***,a novel and intelligent learning approach are known as the stabbing of intrusion with learning framework(sILF),is proposed to learn the attack features and reduce the *** also reduces the testing and training time effectively and enhances Linear support Vector Machine(l-sVM).It constructs an auto-encoder method,an efficient learning approach for feature construction unsupervised ***,the inclusive certified signature(ICs)is added to the encoder and decoder to preserve the sensitive data without being harmed by the *** training the samples in the preliminary stage,the selected features are provided into the classifier(lsVM)to enhance the prediction ability for intrusion and classification ***,the model efficiency is learned *** multi-classification is examined and compared with various classifier approaches like conventional sVM,Random Forest(RF),Recurrent Neural Network(RNN),sTL-IDs and game *** outcomes show that the proposed l-sVM has triggered the prediction rate by effectual testing and training and proves that the model is more efficient than the traditional approaches in terms of performance metrics like accuracy,precision,recall,F-measure,pvalue,MCC and so *** proposed sILF enhances network intrusion detection and offers a novel research methodology for intrusion ***,the simulation is done with a MATLAB environment where the proposed model shows a better trade-off compared to prevailing approaches.

关键词： Network security sensor network intrusion detection learning framework linear support vector machine the detection mechanism

Performance analysis of an integrated scheme in optical burst switching high-speed networks

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Chinese Optics Letters 2008年第4期6卷 244-247页

作者： Amit kumar Garg R.s.Kaler school of Electronics and Communication Engineering shri Mata Vaishno Devi University(J and K) India school of Electronics and Communication Engineering Thapar University Patiala PunjabIndia

A new integrated scheme based on resource-reservation and adaptive network flow routing to alleviate contention in optical burst switching networks is proposed. The objective of the proposed scheme is to reduce the overall burst loss in the network and at the same time to avoid the packet out-of-sequence arrival problem. simulations are carried out to assess the feasibility of the proposed scheme. Its performance is compared with that of contention resolution schemes based on conventional routing. Through extensive simulations, it is shown that the proposed scheme not only provides significantly better burst loss performance than the basic equal proportion and hop-length based traffic routing algorithms, but also is void of any packet re-orderings.

关键词： rate node Performance analysis of an integrated scheme in optical burst switching high-speed networks OBs length

Antidiabetic and anti-lipemic effects of Cassia siamea leaves extract in streptozotocin induced diabetic rats

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Asian Pacific Journal of Tropical Medicine 2010年第11期3卷 871-873页

作者： s kumar V kumar Om Prakash Institute of Pharmaceutical sciences Kurukshetra UniversityKurukshetra -136119HaryanaIndia

Objective:To investigate the antidiabetic and anti-lipemic effects of Cassia siamea methanolic leaves ***:The antidiabetic study was performed by measuring blood glucose level with elegance glucometer at weekly intervals i.e.0,7,14 and 21 in normal and streptozotocin induced diabetic *** cholesterol,triglyceride and HDL-cholesterol were determined in normal and streptozotocin induces diabetic rats by *** was used as a reference drug at a dose of 10 mg/***:After the oral administration of extracts at doses of 250 mg/kg and 500 mg/kg for three weeks,blood glucose levels and body weights were significantly improved(Pso resulted in significantly reduction of serum cholesterol and ***-cholesterol level was found to be improved to(Psions:The Cassia siamea leaf extract is useful in controlling blood glucose level as well as improving lipid metabolism and body weight in rats with induced diabetes.

关键词： Cassia siamea Antidiabetic Anti-lipemic Rat streptozotocin