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Jensen-Renyi’s-Tsallis Fuzzy Divergence Information Measure with its Applications

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Communications in Mathematics and statistics 2022年第3期10卷 451-482页

作者： Ratika Kadian satish kumar Department of Mathematics Maharishi Markandeshwar(Deemed to be)UniversityMullana-Ambala 133207India

In this paper,we have characterized the sum of two general measures associated with two distributions with discrete random *** of these measures is logarithmic,while others contains the power of variables,named as joint representation of Renyi’s-Tsallis divergence ***,we propose a divergence measure based on Jensen-Renyi’s-Tsallis entropy which is known as a Jensen-Renyi’s-Tsallis divergence *** is a generalization of J-divergence information *** of the silent features of this measure is that we can allot the equal weight to each probability *** makes it specifically reasonable for the study of decision problems,where the weights could be the prior ***,the idea has been generalized from probabilistic to fuzzy similarity/dissimilarity *** the validation of the proposed measure,some of its key properties are also ***,the performance of the proposed measure is contrasted with some existing *** last,some illustrative examples are solved in the context of clustering analysis,financial diagnosis and pattern recognition which demonstrate the practicality and adequacy of the proposed measure between two fuzzy sets(Fss).

关键词： Fuzzy sets Divergence measure similarity measure Clustering analysis Financial diagnosis

On a special type of Ma-Minda function

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Applied Mathematics(A Journal of Chinese Universities) 2024年第4期39卷 654-673页

作者： s.sivaprasad kumar shagun Banga Department of Applied Mathematics Delhi Technological UniversityDelhi-110042India

We introduce a primitive class of analytic functions, by specializing in many wellknown classes, classify Ma-Minda functions based on its conditions and their interesting geometrical aspects. Further, study a newly defined subclass of starlike functions involving a special type of Ma-Minda function introduced here for obtaining inclusion and radius results. We also establish some majorization, Bloch function norms, and other related problems for the same class.

关键词： Ma-Minda function typically real function Hankel determinants sharp coefficient bounds radius problems logarithmic function

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Global operational land imager Landsat-8 reflectance-based active fire detection algorithm

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International Journal of Digital Earth 2018年第2期11卷 154-178页

作者： s.s.kumar D.P.Roy Plant and Environmental sciences New Mexico State UniversityLas CrucesNMUSA Geospatial sciences Center of Excellence South Dakota State UniversityBrookingsSDUSA

A global operational land imager(GOLI)Landsat-8 daytime active fire detection algorithm is *** utilizes established contextual active fire detection approaches but takes advantage of the significant increase in fire reflectance in Landsat-8 band 7(2.20μm)relative to band 4(0.66μm).The detection thresholds are fixed and based on a statistical examination of 39 million non-burning Landsat-8 ***-temporal tests based on band 7 reflectance and relative changes in normalized difference vegetation index in the previous six months are used to reduce commissions *** probabilities of active fire detection for the GOLI and two recent Landsat-8 active fire detection algorithms are simulated to provide insights into their performance with respect to the fire size and *** algorithms are applied to 11 Landsat-8 images that encompass a range of burning conditions and *** and omission errors are assessed by visual interpretation of detected active fire locations and by examination of the Landsat-8 images and higher spatial resolution Google Earth *** GOLI algorithm has lower omission and comparable commission errors than the recent Landsat-8 active fire detection *** GOLI algorithm has demonstrable potential for global application and is suitable for implementation with other Landsat-like reflective wavelength sensors.

关键词： Earth observation remote sensing visible/infrared remote sensing

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

Multi-year MODIs active fire type classification over the Brazilian Tropical Moist Forest Biome

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International Journal of Digital Earth 2017年第1期10卷 54-84页

作者： D.P.Roy s.s.kumar Geospatial sciences Center of Excellence South Dakota State UniversityBrookingsSDUSA

The Brazilian Tropical Moist Forest Biome(BTMFB)spans almost 4 million km^(2) and is subject to extensive annual fires that have been categorized into deforestation,maintenance,and forest fire *** on fire types is important as they have different atmospheric emissions and ecological impacts.A supervised classification methodology is presented to classify the fire type of MODerate resolution Imaging spectroradiometer(MODIs)active fire detections using training data defined by consideration of Brazilian government forest monitoring program annual land cover maps,and using predictor variables concerned with fuel flammability,fuel load,fire behavior,fire seasonality,fire annual frequency,proximity to surface transportation,and local *** fire seasonality,local temperature,and fuel flammability were the most influential on the *** fire type results for all 1.6 million MODIs Terra and Aqua BTMFB active fire detections over eight years(2003–2010)are presented with an overall fire type classification accuracy of 90.9%(kappa 0.824).The fire type user’s and producer’s classification accuracies were respectively 92.4%and 94.4%(maintenance fires),88.4%and 87.5%(forest fires),and,88.7%and 75.0%(deforestation fires).The spatial and temporal distribution of the classified fire types are presented and are similar to patterns reported in the available recent literature.

关键词： Earth observation land cover remote sensing

Physics design of 14 MeV neutron generator facility at the Institute for Plasma Research

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Plasma science and Technology 2023年第12期25卷 115-121页

作者： H L sWAMI s VALA M RAJPUT M ABHANGI Ratnesh kumar A sAXENA Rajesh kumar Institute for Plasma Research Gandhinagar 382428India Homi Bhabha National Institute Training School ComplexMumbai 400094India

A high energy and high yield neutron source is a prime requirement for technological studies related to fusion reactor development. It provides a high-energy neutron environment for small-scale fusion reactor components research and testing such as tritium breeding, shielding, plasmafacing materials, reaction cross-section data study for fusion materials, etc. Along with ITER participation, the Institute of Plasma Research, India is developing an accelerator-based 14 MeV neutron source with a yield of 10^(12)n s^(-1). The design of the source is based on the deuterium–tritium fusion reaction. The deuterium beam is accelerated and delivered to the tritium target to generate 14 MeV neutrons. The deuterium beam energy and tritium availability in the tritium target are the base parameters of the accelerator-based neutron source design. The paper gives the physics design of the neutron generator facility of the Institute for Plasma Research. It covers the requirements, design basis, and physics parameters of the neutron generator. As per the analytical results generator can produce more than 1 × 10^(12)n s^(-1)with a 110 keV D^(+) ion beam of 10 mA and a minimum 5 Ci tritium target. However, the detailed simulation with the more realistic conditions of deuteron ion interaction with the tritium titanium target shows that the desired results cannot be achieved with 110 keV. The safe limit of the ion energy should be 300 keV as per the simulation. At 300 keV ion energy and 20 mA current, it reaches 1.6 × 10^(12)n s^(-1). Moreover, it was found that to ensure sufficiently long operation time a tritium target of more than 20 Ci should be used. The scope of the neutron source is not limited to the fusion reactor research studies, it is extended to other areas such as medical radioisotopes research, semiconductor devices irradiations, and many more.

关键词： neutron generator neutronics accelerator physics design nuclear fusion

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Multiobjective optimization of friction welding of UNs s32205 duplex stainless steel

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Defence Technology（防务技术） 2015年第2期11卷 157-165页

作者： P.M.AJITH Birendra kumar BARIK P.sATHIYA s.ARAVINDAN Department of Production Engineering National Institute of Technology Department of Mechanical Engineering Indian Institute of Technology Delhi

The present study is to optimize the process parameters for friction welding of duplex stainless steel(Dss UNs s32205).Experiments were conducted according to central composite *** variables,as inputs of the neural network,included friction pressure,upsetting pressure,speed and burn-off *** strength and microhardness were selected as the outputs of the neural *** weld metals had higher hardness and tensile strength than the base material due to grain refinement which caused failures away from the joint interface during tensile *** to shorter heating time,no secondary phase intermetallic precipitation was observed in the weld joint.A multi-layer perceptron neural network was established for modeling *** various training algorithms,belonging to three classes,namely gradient descent,genetic algorithm and LevenbergeM arquardt,were used to train artificial neural *** optimization was carried out by using particle swarm optimization *** test was carried out by setting the optimized *** conformation test,maximum tensile strength and maximum hardness obtained are 822 MPa and 322 Hv,*** metallurgical investigations revealed that base metal,partially deformed zone and weld zone maintain austenite/ferrite proportion of 50:50.

关键词：多目标优化不锈钢焊接摩擦压力双相人工神经网络 UNs 拉伸强度优化工艺参数

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