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

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

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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 disease.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

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Using Susceptible-Exposed-Infectious-Recovered Model to Forecast Coronavirus Outbreak

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Computers, Materials & Continua 2021年第5期67卷 1595-1612页

作者： Debabrata Dansana Raghvendra Kumar Arupa Parida Rohit Sharma Janmejoy Das Adhikari Hiep Van Le Binh Thai Pham krishna kant singh Biswajeet Pradhan Department of Computer Science and Engineering GIET UniversityGunupurOdishaIndia Department of Electronics&Communication Engineering Faculty of Engineering and TechnologySRM Institute of Science and TechnologyNCR CampusDelhi-NCR CampusModinagarGhaziabadIndia Institute of Research and Development Duy Tan UniversityDa Nang550000Viet Nam University of Transport Technology Hanoi100000Viet Nam Department of ECE KIET Group of InstitutionsGhaziabadIndia Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) University of Technology SydneySydneyNSW2007Australia Department of Energy and Mineral Resources Engineering Sejong UniversitySeoul05006Korea Center of Excellence for Climate Change Research King Abdulaziz UniversityJeddah21589Saudi Arabia Earth Observation Center Institute of Climate ChangeUniversiti Kebangsaan Malaysia43600SelangorMalaysia

The Coronavirus disease 2019(COVID-19)outbreak was rst discovered in Wuhan,China,and it has since spread to more than 200 countries.The World Health Organization proclaimed COVID-19 a public health emergency of international concern on January 30,2020.Normally,a quickly spreading infection that could jeopardize the well-being of countless individuals requires prompt action to forestall the malady in a timely manner.COVID19 is a major threat worldwide due to its ability to rapidly spread.No vaccines are yet available for COVID-19.The objective of this paper is to examine the worldwide COVID-19 pandemic,specically studying Hubei Province,China;Taiwan;South Korea;Japan;and Italy,in terms of exposed,infected,recovered/deceased,original conrmed cases,and predict conrmed cases in specic countries by using the susceptible-exposed-infectious-recovered model to predict the future outbreak of COVID-19.We applied four differential equations to calculate the number of conrmed cases in each country,plotted them on a graph,and then applied polynomial regression with the logic of multiple linear regression to predict the further spread of the pandemic.We also compared the calculated and predicted cases of conrmed population and plotted them in the graph,where we could see that the lines of calculated and predicted cases do intersect with each other to give the perfect true results for the future spread of the virus.This study considered the cases from 22 January 2020 to 25 April 2020.

关键词： COVID-19 SEIR forecasting global pandemic predict conrmed case

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Call for Papers Special Issue on Deep Learning and Evolutionary Computation for Satellite Imagery

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Big Data Mining and Analytics 2022年第1期5卷 79-79页

作者： krishna kant singh Soumyabrata Dev Akansha singh Seungmin Rho Jain(Deemed to be University) BangaloreIndia University College Dublin Ireland Bennett University Greater NoidaIndia Sejong University Korea

Satellite images are humungous sources of data that require efficient methods for knowledge discovery.The increased availability of earth data from satellite images has immense opportunities in various fields.However,the volume and heterogeneity of data poses serious computational challenges.The development of efficient techniques has the potential of discovering hidden information from these images.This knowledge can be used in various activities related to planning,monitoring,and managing the earth resources.Deep learning are being widely used for image analysis and processing.Deep learning based models can be effectively used for mining and knowledge discovery from satellite images.

关键词： has Deep knowledge

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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 world.Importantly,agricultural diversion,illegal encroachment,pollution,and invasive species could cause these lakes to dry up completely or partially by 2025.Illegal saltpan encroachment is causing Sambhar,India’s largest saline lake,to shrink by 4.23%every decade.This study aims to characterize the soil parameters where halophytes are growing.A literature survey was conducted for halophytes and soil characteristics.The study area was divided into four zones for stratified random sampling.Soil sampling was conducted in February 2021.The soil indicators for halophyte selected were pH,electrical conductivity,moisture,salinity,organic carbon,and organic matter.The obtained results were interpolated in the geospatial platform for soil characteristic mapping.It is found that no research is conducted on halophytes of the lake.Studies on soil are also inconsistent and only six common parameters could be identified.Results 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 desiccating.During 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

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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 applications.Whereas,in Three Dimensional applications the task is complex and there are large variations in the altitude levels.In these 3D environments,the sensors are placed in mountains for tracking and deployed in air for monitoring pollution level.For such applications,2D localization models are not reliable.Due to this,the design of 3D localization systems in WSNs faces new challenges.In this paper,in order to find unknown nodes in Three-Dimensional environment,only single anchor node is used.In 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 nodes.Thesemobile target nodes are heterogeneous and deployed in an anisotropic environment having an Irregularity(Degree of Irregularity(DOI))value set to 0.01.The 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

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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 accuracy.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

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Behavior of Delivery Robot in Human-Robot Collaborative Spaces During Navigation

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Intelligent Automation & Soft Computing 2023年第1期35卷 795-810页

作者： Kiran Jot singh Divneet singh Kapoor Mohamed Abouhawwash Jehad F.Al-Amri Shubham Mahajan Amit kant Pandit Embedded Systems&Robotics Research Group Chandigarh UniversityMohali140413PunjabIndia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia School of Electronics and Communication Shri Mata Vaishno Devi UniversityKatra182320India

Navigation is an essential skill for robots.It becomes a cumbersome task for the robot in a human-populated environment,and Industry 5.0 is an emerging trend that focuses on the interaction between humans and robots.Robot behavior in a social setting is the key to human acceptance while ensuring human comfort and safety.With the advancement in robotics technology,the true use cases of robots in the tourism and hospitality industry are expanding in number.There are very few experimental studies focusing on how people perceive the navigation behavior of a delivery robot.A robotic platform named“PI”has been designed,which incorporates proximity and vision sensors.The robot utilizes a real-time object recognition algorithm based on the You Only Look Once(YOLO)algorithm to detect objects and humans during navigation.This study is aimed towards evaluating human experience,for which we conducted a study among 36 participants to explore the perceived social presence,role,and perception of a delivery robot exhibiting different behavior conditions while navigating in a hotel corridor.The participants’responses were collected and compared for different behavior conditions demonstrated by the robot and results show that humans prefer an assistant role of a robot enabled with audio and visual aids exhibiting social behavior.Further,this study can be useful for developers to gain insight into the expected behavior of a delivery robot.

关键词： Human-robot interaction robot navigation robot behavior collaborative spaces industrial IoT industry 5.0

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Drainage of pancreatic fluid collections in acute pancreatitis:A comprehensive overview

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World Journal of Clinical Cases 2022年第20期10卷 6769-6783页

作者： Akash Bansal Pankaj Gupta Anupam K singh Jimil Shah Jayanta Samanta Harshal S Mandavdhare Vishal Sharma Saroj kant Sinha Usha Dutta Manavjit singh Sandhu Rakesh Kochhar Department of Radiodiagnosis Postgraduate Institute of Medical Education and ResearchChandigarh 160012India Department of Gastroenterology Postgraduate Institute of Medical Education and ResearchChandigarh 160012India

Moderately severe and severe acute pancreatitis is characterized by local and systemic complications.Systemic complications predominate the early phase of acute pancreatitis while local complications are important in the late phase of the disease.Necrotic fluid collections represent the most important local complication.Drainage of these collections is indicated in the setting of infection,persistent or new onset organ failure,compressive or pressure symptoms,and intraabdominal hypertension.Percutaneous,endoscopic,and minimally invasive surgical drainage represents the various methods of drainage with each having its own advantages and disadvantages.These methods are often complementary.In this minireview,we discuss the indications,timing,and techniques of drainage of pancreatic fluid collections with focus on percutaneous catheter drainage.We also discuss the novel methods and techniques to improve the outcomes of percutaneous catheter drainage.

关键词： Pancreatitis,Acute necrotizing Drainage Catheters Stents Therapeutic irrigation Debridement Collections

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Optimization of Cognitive Radio System Using Self-Learning Salp Swarm Algorithm

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Computers, Materials & Continua 2022年第2期70卷 3821-3835页

作者： Nitin Mittal Harbinder singh Vikas Mittal Shubham Mahajan Amit kant Pandit Mehedi Masud Mohammed Baz Mohamed Abouhawwash Department of Electronics&Communication Engineering Chandigarh UniversityMohali140413India School of VLSI Design and Embedded Systems National Institute of TechnologyKurukshetra136119India School of Electronics&Communication Engineering Shri Mata Vaishno Devi UniversityKatra182320India Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering College of Computer and Information TechnologyTaif UniversityTaif21994Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

CognitiveRadio(CR)has been developed as an enabling technology that allows the unused or underused spectrum to be used dynamically to increase spectral efficiency.To improve the overall performance of the CR systemit is extremely important to adapt or reconfigure the systemparameters.The Decision Engine is a major module in the CR-based system that not only includes radio monitoring and cognition functions but also responsible for parameter adaptation.As meta-heuristic algorithms offer numerous advantages compared to traditional mathematical approaches,the performance of these algorithms is investigated in order to design an efficient CR system that is able to adapt the transmitting parameters to effectively reduce power consumption,bit error rate and adjacent interference of the channel,while maximized secondary user throughput.Self-Learning Salp Swarm Algorithm(SLSSA)is a recent meta-heuristic algorithm that is the enhanced version of SSA inspired by the swarming behavior of salps.In this work,the parametric adaption of CR system is performed by SLSSA and the simulation results show that SLSSA has high accuracy,stability and outperforms other competitive algorithms formaximizing the throughput of secondary users.The results obtained with SLSSA are also shown to be extremely satisfactory and need fewer iterations to converge compared to the competitive methods.

关键词： Cognitive radio meta-heuristic algorithm cognitive decision engine salp swarm algorithm

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