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Diagnosis of COVID-19 from Chest X-Ray Images Using Wavelets-Based Depthwise Convolution Network

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Big Data Mining and Analytics 2021年第2期4卷 84-93页

作者： Krishna kant singh Akansha singh Department of ECE KIET Group of InstitutionsDelhi-NCRGhaziabad 201206India Department of CSE ASETAmity University Uttar PradeshNoida 201310India

Coronavirus disease 2019 also known as COVID-19 has become a pandemic. The disease is caused by a beta coronavirus called Severe Acute Respiratory Syndrome Coronavirus 2(SARS-Co V-2). The severity of the disease can be understood by the massive number of deaths and affected patients globally. If the diagnosis is fast-paced, the disease can be controlled in a better manner. Laboratory tests are available for diagnosis, but they are bounded by available testing kits and time. The use of radiological examinations that comprise Computed Tomography(CT) can be used for the diagnosis of the disease. Specifically, chest X-Ray images can be analysed to identify the presence of COVID-19 in a patient. In this paper, an automated method for the diagnosis of COVID-19 from the chest X-Ray images is proposed. The method presents an improved depthwise convolution neural network for analysing the chest X-Ray images. Wavelet decomposition is applied to integrate multiresolution analysis in the network. The frequency sub-bands obtained from the input images are fed in the network for identifying the *** network is designed to predict the class of the input image as normal, viral pneumonia, and COVID-19. The predicted output from the model is combined with Grad-CAM visualization for diagnosis. A comparative study with the existing methods is also performed. The metrics like accuracy, sensitivity, and F1-measure are calculated for performance evaluation. The performance of the proposed method is better than the existing methodologies and thus can be used for the effective diagnosis of the disease.

关键词： coronavirus COVID-19 deep learning convolution neural network X-Ray images

Characterization of saline soil for the halophytes of largest inland saline wetland of India using geospatial technology

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Journal of Oceanology and Limnology 2023年第4期41卷 1277-1291页

作者： Naik RAJASHREE Sharma LAXMI kant singh AVINASH Department of Environmental Science School of Earth SciencesCentral University of RajasthanBandarsindriAjmer 305817India

About 23%of the surface area and 44%of the volume of all the lakes are occupied by saline lakes in the ***,agricultural diversion,illegal encroachment,pollution,and invasive species could cause these lakes to dry up completely or partially by *** saltpan encroachment is causing Sambhar,India’s largest saline lake,to shrink by 4.23%every *** study aims to characterize the soil parameters where halophytes are growing.A literature survey was conducted for halophytes and soil *** study area was divided into four zones for stratified random *** sampling was conducted in February *** soil indicators for halophyte selected were pH,electrical conductivity,moisture,salinity,organic carbon,and organic *** obtained results were interpolated in the geospatial platform for soil characteristic *** is found that no research is conducted on halophytes of the *** on soil are also inconsistent and only six common parameters could be *** show that the pH ranged 9.37-7.66,electrical conductivity was 16.1-0.38,moisture 23.37%-1.2%,organic carbon 3.29%-0.15%,organic matter 5.6%-0.2%,and salinity 8.86%-0.72%.Though these results show improved condition as compared to last few years,in long term,the lake is *** the UN Decade of Ecosystem Restoration(2021-2030),if these causes are not addressed,the ecosystem may completely dry up.

关键词： halophytes inland lakes saline wetlands soil geospatial mapping interpolation

Electron Acceleration by a radially polarised cosh-Gaussian laser beam in vacuum

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Communications in Theoretical Physics 2021年第9期73卷 124-128页

作者： Jitender singh Jyoti Rajput Harjit singh Ghotra Niti kant Department of Physics Lovely Professional UniversityG.T.RoadPhagwara(144411)PunjabIndia Extreme Light Infrastructure-Nuclear Physics(ELI-NP) Horia Hulubei NIPNE30 Reactorului Street077125 MagureleJud.IifovRomania

In this paper,a radially polarised cosh-Gaussian laser beam(CGLB)is used to study the electron acceleration produced in vacuum.A highly energetic electron beam can be achieved by a CGLB,even with comparatively low-powered *** properties of a CGLB cause it to focus earlier,over a shorter duration than a Gaussian laser beam,which makes it suitable for obtaining high energies over small *** is found that the energy gained by the electrons strongly depends upon the decentering parameter of the laser *** is also observed that for a fixed value of energy gain,if the decentering parameter is increased,then the intensity of the laser field *** dependence of the energy gained by electrons on the laser intensity and the laserspot size is also studied.

关键词： cosh-Gaussian laser beam vacuum electron acceleration decentered parameter

ON APPROXIMATE EFFICIENCY FOR NONSMOOTH ROBUST VECTOR OPTIMIZATION PROBLEMS

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Acta Mathematica Scientia 2020年第3期40卷 887-902页

作者： Tadeusz ANTCZAK Yogendra PANDEY Vinay singh Shashi kant MISHRA Faculty of Mathematics and Computer Science University of LódźBanacha 2290-238 LódźPoland Department of Mathematics Satish Chandra CollegeBallia 277001India Department of Mathematics National Institute of TechnologyAizawl-796012MizoramIndia Department of Mathematics Banaras Hindu UniversityVaranasi-221005India

In this article,we use the robust optimization approach(also called the worst-case approach)for findingε-efficient solutions of the robust multiobjective optimization problem defined as a robust(worst-case)counterpart for the considered nonsmooth multiobjective programming problem with the uncertainty in both the objective and constraint ***,we establish both necessary and sufficient optimality conditions for a feasible solution to be anε-efficient solution(an approximate efficient solution)of the considered robust multiobjective optimization *** also use a scalarizing method in proving these optimality conditions.

关键词： Robust optimization approach robust multiobjective optimization ε-efficient solution ε-optimality conditions scalarization

A new flavone C-glucoside from Glycosmis arborea(Rutaceae)

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Chinese Chemical Letters 2010年第12期21卷 1443-1445页

作者： Rajni kant Sharma Sushma Semwal Devendra singh Negi Department of Chemistry HNB Garhwal Central University SrinagarUttarakhandIndia Department of Chemistry University of Cape TownCape TownRondebosch 7701South Africa

A new C-glycosyl flavone 7,4＇-dihydroxy-5-methoxyflavone-6-C-β-D-glucopyranoside 1 has been isolated and characterized form ethanolic extract of leaves part of Glycosmis *** structure of compound was verified by mea... 详细信息

关键词： Glycosmis arborea Rutaceae Flavone C-glucoside

Satellite Image Classification Using a Hybrid Manta Ray Foraging Optimization Neural Network

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Big Data Mining and Analytics 2023年第1期6卷 44-54页

作者： Amit Kumar Rai Nirupama Mandal Krishna kant singh Ivan Izonin Department of Electronics Engineering Indian Institute of TechnologyDhanbad(ISMDhanbad)Dhanbad 826004India Department of Electronics and Communication Engineering Asansol Engineering CollegeAsansol 713305India Department of CSE ASETAmity UniversityNoida 201301India Department of Artificial Intelligence Lviv Polytechnic National UniversityLviv 79000Ukraine

A semi supervised image classification method for satellite images is proposed in this *** satellite images contain enormous data that can be used in various *** analysis of the data is a tedious task due to the amount of data and the heterogeneity of the ***,in this paper,a Radial Basis Function Neural Network(RBFNN)trained using Manta Ray Foraging Optimization algorithm(MRFO)is *** is a three-layer network comprising of input,output,and hidden layers that can process large *** trained network can discover hidden data patterns in unseen *** learning algorithm and seed selection play a vital role in the performance of the *** seed selection is done using the spectral indices to further improve the performance of the *** manta ray foraging optimization algorithm is inspired by the intelligent behaviour of manta *** emulates three unique foraging behaviours namelys chain,cyclone,and somersault *** satellite images contain enormous amount of data and thus require exploration in large search *** spiral movement of the MRFO algorithm enables it to explore large search spaces *** proposed method is applied on pre and post flooding Landsat 8 Operational Land Imager(OLI)images of New Brunswick *** method was applied to identify and classify the land cover changes in the area induced by *** images are classified using the proposed method and a change map is developed using post classification *** change map shows that a large amount of agricultural area was washed away due to *** measurement of the affected area in square kilometres is also performed for mitigation *** results show that post flooding the area covered by water is increased whereas the vegetated area is *** performance of the proposed method is done with existing state-of-the-art methods.

关键词： Radial Basis Function Neural Network(RBFNN) Manta Ray Foraging Optimization algorithm(MRFO) Landsat 8 classification change detection disaster mitigation planning

Three Dimensional Optimum Node Localization in Dynamic Wireless Sensor Networks

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Computers, Materials & Continua 2022年第1期70卷 305-321页

作者： Gagandeep singh Walia Parulpreet singh Manwinder singh Mohamed Abouhawwash Hyung Ju Park Byeong-Gwon Kang Shubham Mahajan Amit kant Pandit Department of Electronics and Communication Engineering Lovely Professional UniversityJalandhar144411PunjabIndia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea School of Electronics&Communication Shri Mata Vaishno Devi UniversityKatra182320India

Location information plays an important role in most of the applications in Wireless Sensor Network(WSN).Recently,many localization techniques have been proposed,while most of these deals with two Dimensional ***,in Three Dimensional applications the task is complex and there are large variations in the altitude *** these 3D environments,the sensors are placed in mountains for tracking and deployed in air for monitoring pollution *** such applications,2D localization models are not *** to this,the design of 3D localization systems in WSNs faces new *** this paper,in order to find unknown nodes in Three-Dimensional environment,only single anchor node is *** the simulation-based environment,the nodes with unknown locations are moving at middle&lower layers whereas the top layer is equipped with single anchor node.A novel soft computing technique namely Adaptive Plant Propagation Algorithm(APPA)is introduced to obtain the optimized locations of these mobile *** target nodes are heterogeneous and deployed in an anisotropic environment having an Irregularity(Degree of Irregularity(DOI))value set to *** simulation results present that proposed APPAalgorithm outperforms as tested among other meta-heuristic optimization techniques in terms of localization error,computational time,and the located sensor nodes.

关键词： Wireless sensor networks localization particle swarm optimization h-best particle swarm optimization biogeography-based optimization grey wolf optimizer firefly algorithm adaptive plant propagation algorithm

Deep Learning Based Automated Detection of Diseases from Apple Leaf Images

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Computers, Materials & Continua 2022年第4期71卷 1849-1866页

作者： Swati singh Isha Gupta Sheifali Gupta Deepika Koundal Sultan Aljahdali Shubham Mahajan Amit kant Pandit Department of Electronics and Communication Engineering University Institute of TechnologyHimachal Pradesh UniversityShimla171005India Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia Department of Systemics School of Computer ScienceUniversity of Petroleum and Energy StudiesDehradunIndia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia School of Electronics&Communication Shri Mata Vaishno Devi UniversityKatra185320India

In Agriculture Sciences, detection of diseases is one of the mostchallenging tasks. The mis-interpretations of plant diseases often lead towrong pesticide selection, resulting in damage of crops. Hence, the automaticrecognition of the diseases at earlier stages is important as well as economicalfor better quality and quantity of fruits. Computer aided detection (CAD)has proven as a supportive tool for disease detection and classification, thusallowing the identification of diseases and reducing the rate of degradationof fruit quality. In this research work, a model based on convolutional neuralnetwork with 19 convolutional layers has been proposed for effective andaccurate classification of Marsonina Coronaria and Apple Scab diseases fromapple leaves. For this, a database of 50,000 images has been acquired bycollecting images of leaves from apple farms of Himachal Pradesh (H.P)and Uttarakhand (India). An augmentation technique has been performedon the dataset to increase the number of images for increasing the *** performance analysis of the proposed model has been compared with thenew two Convolutional Neural Network (CNN) models having 8 and 9 layersrespectively. The proposed model has also been compared with the standardmachine learning classifiers like support vector machine, k-Nearest Neighbour, Random Forest and Logistic Regression models. From experimentalresults, it has been observed that the proposed model has outperformed theother CNN based models and machine learning models with an accuracy of99.2%.

关键词： Deep learning convolutional neural network apple leaves apple scab support vector machine

Gliosarcoma:A rare variant of glioblastoma multiforme in paediatric patient:Case report and review of literature

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World Journal of Clinical Cases 2016年第9期4卷 302-305页

作者： Ugan singh Meena Sumit Sharma Sanjeev Chopra Shashi kant Jain Department of Neurosurgery Swai Man Singh Medical College

Gliosarcoma is rare central nervous system tumour and a variant of glioblastoma multiforme with bimorphic histological pattern of glial and sarcomatous differentiation. It occurs in elderly between 5^(th) and 6^(th) decades of life and extremely rare in children. It is highly aggressive tumour and managed like glioblastoma multiforme. A 12-year-old female child presented with complaints of headache and vomiting from 15d and blurring of vision from 3d. Magnetic resonance imaging of brain shows heterogeneous mass in right parieto-occipital cortex. A right parieto-occipito-temporal craniotomy with complete excision of mass revealed a primary glioblastoma on histopathological investigation. Treatment consists of maximum surgical excision followed by adjuvant radiotherapy. The etiopathogenesis, treatment modalities and prognosis is discussed. The available literature is also reviewed.

关键词： Paediatric gliosarcoma Surgery Glioblastoma multiforme Radiotherapy