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An Intelligent Deep Learning Based Xception Model for Hyperspectral Image Analysis and Classification

Computers, Materials & Continua 2021年第5期67卷 2393-2407页

作者： J.Banumathi A.Muthumari S.Dhanasekaran S.Rajasekaran Irina V.Pustokhina Denis A.Pustokhin k.shankar Department of Information Technology University College of EngineeringNagercoil629004India Department of Computer Science and Engineering University College of EngineeringRamanathapuram623513India Department of Computer Science and Engineering Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Electrical Engineering PSN College of EngineeringTirunelveli627152India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Computer Applications Alagappa UniversityKaraikudi630001India

Due to the advancements in remote sensing technologies,the generation of hyperspectral imagery(HSI)gets significantly *** classification of HSI becomes a critical process in the domain of hyperspectral data *** massive availability of spectral and spatial details of HSI has offered a great opportunity to efficiently illustrate and recognize ground ***,deep learning(DL)models particularly,convolutional neural networks(CNNs)become useful for HSI classification owing to the effective feature representation and high *** this view,this paper introduces a new DL based Xception model for HSI analysis and classification,called Xcep-HSIC ***,the presented model utilizes a feature relation map learning(FRML)to identify the relationship among the hyperspectral features and explore many features for improved classifier ***,the DL based Xception model is applied as a feature extractor to derive a useful set of features from the FRML *** addition,kernel extreme learning machine(kELM)optimized by quantum-behaved particle swarm optimization(QPSO)is employed as a classification model,to identify the different set of class *** extensive set of simulations takes place on two benchmarks HSI dataset,namely Indian Pines and Pavia University *** obtained results ensured the effective performance of the XcepHSIC technique over the existing methods by attaining a maximum accuracy of 94.32%and 92.67%on the applied India Pines and Pavia University dataset respectively.

关键词： Hyperspectral imagery deep learning xception kernel extreme learning map parameter tuning

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Hyperparameter Tuning Bidirectional Gated Recurrent Unit Model for Oral Cancer Classification

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Computers, Materials & Continua 2022年第12期73卷 4541-4557页

作者： k.shankar E.Laxmi Lydia Sachin kumar Ali S.Abosinne Ahmed alkhayyat A.H.Abbas Sarmad Nozad Mahmood Department of Computer Science South Ural State UniversityChelyabinsk454080Russia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Computer Technical Engineering Department College of Technical Engineeringthe Islamic UniversityNajaf54001Iraq College of Technical Engineering the Islamic UniversityNajafIraq College of Information Technology Imam Ja’afar Al-Sadiq UniversityAl-Muthanna66002Iraq Computer Technology Engineering College of Engineering TechnologyAl-Kitab UniversityIraq

Oral Squamous Cell Carcinoma(OSCC)is a type of Head and Neck Squamous Cell Carcinoma(HNSCC)and it should be diagnosed at early stages to accomplish efficient treatment,increase the survival rate,and reduce death *** imaging is a wide-spread standard used for OSCC ***,it is a cumbersome process and demands expert’s ***,there is a need exists for automated detection ofOSCC using Artificial Intelligence(AI)and Computer Vision(CV)*** this background,the current research article introduces Improved Slime Mould Algorithm with Artificial Intelligence Driven Oral Cancer Classification(ISMA-AIOCC)model on Histopathological images(HIs).The presented ISMA-AIOCC model is aimed at identification and categorization of oral cancer using *** the initial stage,linear smoothing filter is applied to eradicate the noise from ***,MobileNet model is employed to generate a useful set of feature ***,Bidirectional Gated Recurrent Unit(BGRU)model is exploited for classification *** the end,ISMA algorithm is utilized to fine tune the parameters involved in BGRU ***,ISMA algorithm is created by integrating traditional SMA and ChaoticOppositional Based Learning(COBL).The proposed ISMA-AIOCC model was validated for performance using benchmark dataset and the results pointed out the supremacy of ISMA-AIOCC model over other recent approaches.

关键词： Computer aided diagnosis deep learning BGRU biomedical imaging oral cancer histopathological images

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A Novel Deep Neural Network for Intracranial Haemorrhage Detection and Classification

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Computers, Materials & Continua 2021年第9期68卷 2877-2893页

作者： D.Venugopal T.Jayasankar Mohamed Yacin Sikkandar Mohamed Ibrahim Waly Irina V.Pustokhina Denis A.Pustokhin k.shankar Department of ECE KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Electronics and Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Computer Applications Alagappa UniversityKaraikudi630001India

Data fusion is one of the challenging issues,the healthcare sector is facing in the recent *** diagnosis from digital imagery and treatment are deemed to be the right *** Haemorrhage(ICH),a condition characterized by injury of blood vessels in brain tissues,is one of the important reasons for *** generated by X-rays and Computed Tomography(CT)are widely used for estimating the size and location of *** use manual planimetry,a time-consuming process for segmenting CT scan *** Learning(DL)is the most preferred method to increase the efficiency of diagnosing *** this paper,the researcher presents a unique multi-modal data fusion-based feature extraction technique with Deep Learning(DL)model,abbreviated as FFE-DL for Intracranial Haemorrhage Detection and Classification,also known as *** proposed FFEDL-ICH model has four stages namely,preprocessing,image segmentation,feature extraction,and *** input image is first preprocessed using the Gaussian Filtering(GF)technique to remove ***,the Density-based Fuzzy C-Means(DFCM)algorithm is used to segment the ***,the Fusion-based Feature Extraction model is implemented with handcrafted feature(Local Binary Patterns)and deep features(Residual Network-152)to extract useful ***,Deep Neural Network(DNN)is implemented as a classification technique to differentiate multiple classes of *** researchers,in the current study,used benchmark Intracranial Haemorrhage dataset and simulated the FFEDL-ICH model to assess its diagnostic *** findings of the study revealed that the proposed FFEDL-ICH model has the ability to outperform existing models as there is a significant improvement in its *** future researches,the researcher recommends the performance improvement of FFEDL-ICH model using learning rate scheduling techniques for DNN.

关键词： Intracerebral hemorrhage fusion model feature extraction deep features classification

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An Optimal Classification Model for Rice Plant Disease Detection

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Computers, Materials & Continua 2021年第8期68卷 1751-1767页

作者： R.Sowmyalakshmi T.Jayasankar V.Ayyem PiIllai kamalraj Subramaniyan Irina V.Pustokhina Denis A.Pustokhin k.shankar Department of Electronics and Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India Department of Electronics and Communication Engineering Gokaraju Rangaraju Institute of Engineering TechnologyHyderabad500090India Department of Electronics and Communication Engineering Karpagam Academy of Higher EducationCoimbatore641021India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Computer Applications Alagappa UniversityKaraikudi630001India

Internet of Things(IoT)paves a new direction in the domain of smart farming and precision *** farming is an upgraded version of agriculture which is aimed at improving the cultivation practices and yield to a certain *** smart farming,IoT devices are linked among one another with new technologies to improve the agricultural *** farming makes use of IoT devices and contributes in effective decision *** is the major food source in most of the ***,it becomes inevitable to detect rice plant diseases during early stages with the help of automated tools and IoT *** development and application of Deep Learning(DL)models in agriculture offers a way for early detection of rice diseases and increase the yield and *** study presents a new Convolutional Neural Network-based inception with ResNset v2 model and Optimal Weighted Extreme Learning Machine(CNNIR-OWELM)-based rice plant disease diagnosis and classification model in smart farming *** proposed CNNIR-OWELM method involves a set of IoT devices which capture the images of rice plants and transmit it to cloud server via *** CNNIROWELM method uses histogram segmentation technique to determine the affected regions in rice plant *** addition,a DL-based inception with ResNet v2 model is engaged to extract the ***,in OWELM,the Weighted Extreme Learning Machine(WELM),optimized by Flower Pollination Algorithm(FPA),is employed for classification *** FPA is incorporated into WELM to determine the optimal parameters such as regularization coefficient C and kernelγ.The outcome of the presented model was validated against a benchmark image dataset and the results were compared with one *** simulation results inferred that the presented model effectively diagnosed the disease with high sensitivity of 0.905,specificity of 0.961,and accuracy of 0.942.

关键词： Agriculture internet of things smart farming deep learning rice plant diseases

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Implementation of Legendre Neural Network to Solve Time-Varying Singular Bilinear Systems

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Computers, Materials & Continua 2021年第12期69卷 3685-3692页

作者： V.Murugesh B.Saravana Balaji Habib Sano Aliy J.Bhuvana P.Saranya Andino Maseleno k.shankar A.Sasikala Department of Computer Science College of InformaticsBule Hora UniversityPO Box 144Ethiopia Department of Information Technology Lebanese French UniversityErbil44001Iraq Deptartment of Logistics and Supply Chain Management Arba Minch UniversitySawla CampusPO Box 13Ethiopia Department of MCA School of Computer Science and ITJain(Deemed to be)UniversityBangalore560069India Department of Computer Science and Engineering SRM Institute of Science and TechnologyChennai603203India Department of Information Systems STMIK PringsewuLampungIndonesia Department of Computer Applications Alagappa UniversityKaraikudi630003India Department of EEE Sri Sairam Institute of TechnologyChennai600044India

Bilinear singular systems can be used in the investigation of different types of engineering *** the past decade,considerable attention has been paid to analyzing and synthesizing singular bilinear *** importance lies in their real world application such as economic,ecological,and socioeconomic *** are also applied in several biological processes,such as population dynamics of biological species,water balance,temperature regulation in the human body,carbon dioxide control in lungs,blood pressure,immune system,cardiac regulation,*** singular systems naturally represent different physical processes such as the fundamental law of mass action,the DC motor,the induction motor drives,the mechanical brake systems,aerial combat between two aircraft,the missile intercept problem,modeling and control of small furnaces and hydraulic rotary multimotor *** current research work discusses the Legendre Neural Network’s implementation to evaluate time-varying singular bilinear systems for finding the exact *** results were obtained from two methods namely the Rk-Butcher algorithm and the Runge kutta Arithmetic Mean(RkAM)*** with the results attained from Legendre Neural Network Method for time-varying singular bilinear systems,the output proved to be *** such,this research article established that the proposed Legendre Neural Network could be easily implemented in *** can obtain the solution for any length of time from this method in time-varying singular bilinear systems.

关键词： Time-varying singular bilinear systems Rk-butcher algorithm legendre neural network method

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Optimal Confidential Mechanisms in Smart City Healthcare

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Computers, Materials & Continua 2022年第3期70卷 4883-4896页

作者： R.Gopi P.Muthusamy P.Suresh C.G.Gabriel Santhosh kumar Irina V.Pustokhina Denis A.Pustokhin k.shankar Department of Computing Science and Engineering Dhanalakshmi Srinivasan Engineering CollegePerambalur621212India School of Computing Science and Engineering Galgotias UniversityNCRDelhi203201India Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Electrical and Electronics Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Computer Applications Alagappa UniversityKaraikudi630001India

Smart City Healthcare(SHC2)system is applied in monitoring the patient at home while it is also expected to react to their needs in a timely *** system also concedes the freedom of a *** is a part of this system and it helps in providing care to the *** healthcare devices are trustworthy since it almost certainly recognizes the potential intensifications at very early stage and alerts the patients and medical experts to such an extent that they are provided with immediate *** methodologies exhibit few shortcomings in terms of computational complexity,cost and data ***,the current research article examines SHC2 security through LightWeight Cipher(LWC)with Optimal S-Box model in PRESENT *** procedure aims at changing the sub bytes in which a single function is connected with several bytes’information to upgrade the security level through Swam *** key contribution of this research article is the development of a secure healthcare model for smart city using SHC2 security via LWC and Optimal S-Box *** study used a nonlinear layer and single 4-bit S box for round configuration after verifying SHC2 information,constrained by Mutual Authentication(MA).The security challenges,in healthcare information systems,emphasize the need for a methodology that immovably concretes the *** methodology should act practically,be an effective healthcare framework that depends on solidarity and adapts to the developing *** service providers integrated the IoT applications and medical services to offer individuals,a seamless technology-supported healthcare *** proposed SHC^(2) was implemented to demonstrate its security levels in terms of time and access *** model was tested under different parameters such as encryption time,decryption time,access time and response time inminimum ***,the level of the model and throughput were analyzed by maximum value i.e.,50Mbp

关键词： Smart city healthcare security block cipher LWC

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Privacy Preserving Blockchain Technique to Achieve Secure and Reliable Sharing of IoT Data

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Computers, Materials & Continua 2020年第10期65卷 87-107页

作者： Bao Le Nguyen E.Laxmi Lydia Mohamed Elhoseny Irina V.Pustokhina Denis A.Pustokhin Mahmoud Mohamed Selim Gia Nhu Nguyen k.shankar Faculty of Information Technology Duy Tan UniversityDa Nang550000Vietnam Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Faculty of Computers and Information Mansoura UniversityMansoura35516Egypt Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow115093Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Mathematics College of Science&Humanities in AlaflajPrince Sattam bin Abdulaziz UniversityAlaflaj16278Saudi Arabia Graduate School Duy Tan UniversityDa Nang550000Vietnam Department of Computer Applications Alagappa UniversityKaraikudi630003India

In present digital era,an exponential increase in Internet of Things(IoT)devices poses several design issues for business concerning security and *** studies indicate that the blockchain technology is found to be a significant solution to resolve the challenges of data security exist in *** this view,this paper presents a new privacy-preserving Secure Ant Colony optimization with Multi kernel Support Vector Machine(ACOMkSVM)with Elliptical Curve cryptosystem(ECC)for secure and reliable IoT data *** program uses blockchain to ensure protection and integrity of some data while it has the technology to create secure ACOMkSVM training algorithms in partial views of IoT data,collected from various data ***,ECC is used to create effective and accurate privacy that protects ACOMkSVM secure learning *** this study,the authors deployed blockchain technique to create a secure and reliable data exchange platform across multiple data providers,where IoT data is encrypted and recorded in a distributed *** security analysis showed that the specific data ensures confidentiality of critical data from each data provider and protects the parameters of the ACOMkSVM model for data *** examine the performance of the proposed method,it is tested against two benchmark dataset such as Breast Cancer Wisconsin Data Set(BCWD)and Heart Disease Data Set(HDD)from UCI AI *** simulation outcome indicated that the ACOMkSVM model has outperformed all the compared methods under several aspects.

关键词： Blockchain optimization elliptical curve cryptosystem security ant colony optimization multi kernel support vector machine

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Metaheuristic Clustering Protocol for Healthcare DataCollection in MobileWireless Multimedia Sensor Networks

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Computers, Materials & Continua 2021年第3期66卷 3215-3231页

作者： G G.kadiravan P.Sujatha T.Asvany R.Punithavathi Mohamed Elhoseny Irina V.Pustokhina Denis A.Pustokhin k.shankar Department of Computer Science Pondicherry UniversityPuducherry605014India Department of Information Technology M.Kumarasamy College of EngineeringKarur639113India Faculty of Computers and Information Mansoura UniversityEgypt Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Computer Applications Alagappa UniversityKaraikudiIndia

Nowadays,healthcare applications necessitate maximum volume of medical data to be fed to help the physicians,academicians,pathologists,doctors and other healthcare *** in the domain of Wireless Sensor Networks(WSN)andMultimediaWireless Sensor Networks(MWSN)are tremendous.M-WMSN is an advanced form of conventional Wireless Sensor Networks(WSN)to networks that use multimedia *** compared with traditional WSN,the quantity of data transmission in M-WMSN is significantly high due to the presence of multimedia ***,clustering techniques are deployed to achieve low amount of energy *** current research work aims at introducing a new Density Based Clustering(DBC)technique to achieve energy efficiency *** DBC technique is mainly employed for data collection in healthcare environment which primarily depends on three input parameters namely remaining energy level,distance,and node *** addition,two static data collector points called Super Cluster Head(SCH)are placed,which collects the data from normal CHs and forwards it to the Base Station(BS)*** supports multi-hop data transmission that assists in effectively balancing the available *** simulation analysiswas conducted to showcase the superior performance of DBC technique and the results were examined under diverse *** simulation outcomes concluded that the proposed DBC technique improved the network lifetime to a maximum of 16,500 rounds,which is significantly higher compared to existing methods.

关键词： Smart sensor environment healthcare data multimedia big data processing clustering mobility energy efficiency

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3D and 2D topographic correction to estimated geothermal gradient from the base of gas hydrate stability zone in the Andaman Forearc Basin

Energy Geoscience

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Energy Geoscience 2024年第2期5卷 314-320页

作者： Uma shankar Department of Geophysics Institute of ScienceBHUVaranasiIndia

Methane gas hydrate related bottom-simulating reflectors(BSRs)are imaged based on the in-line and cross-line multi-channel seismic(MCS)data from the Andaman Forearc *** depth of the BSR depends on pressure and temperature and pore water *** these assumptions,the BSR depth can be used to estimate the geothermal gradient(GTG)based on the availability of in-situ temperature *** calculation is done assuming a 1D conductive model based on available in-situ temperature measurement at site NGHP-01-17 in the study ***,in the presence of seafloor topography,the conductive temperature field in the subsurface is affected by lateral refraction of heat,which focuses heat in topographic lows and away from topographic *** 1D estimate of GTG in the Andaman Forearc Basin has been validated by drilling results from the NGHP-01 expedition.2D analytic modeling to estimate the effects of topography is performed earlier along selected seismic profiles in the study *** study extended to estimate the effect of topography in 3D using a numerical *** corrected GTG data allow us to determine GTG values free of topographic *** difference between the estimated GTG and values corrected for the 3D topographic effect varies up to~5℃/*** conclude that the topographic correction is relatively small compared to other uncertainties in the 1D model and that apparent GTG determined with the 1D model captures the major features,although the correction is needed prior to interpreting subtle features of the derived GTG maps.

关键词： Gas hydrate BSR Geothermal gradient 3D and 2D topographic modeling Andaman Forearc Basin

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Comparative measurement of CO_(2),CH_(4)and CO at two traffic interjunctions having inflated vehicular flow in Delhi

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Journal of Environmental Sciences 2024年第7期141卷 314-329页

作者： komal Daya Soni khem Singh shankar G.Aggarwal CSIR-National Physical Laboratory Dr.K.S.Krishnan MargNew Delhi 110012India Academy of Scientific&Innovative Research(AcSIR) Ghaziabad 201002India

Vehicular emissions are considered one of the major anthropogenic sources of greenhouse gases and poor air quality in metropolitan *** study aims to see the correlation of CO_(2),CH_(4),and CO through monitoring over a period from December 2020 to October 2021 covering three seasons’winter,summer,and monsoon at two different traffic locations of Delhi having different traffic volumes,road patterns,and traffic *** annual average morning concentration of CO_(2),CH_(4)and CO was found(533±105),(7.3±3.1),(10.7±3.0)ppm at Najafgarh and(480±70),(5.2±1.8),(7.8±2.8)ppm at Rajendra Place,respectively.A relationship between concentration of all three gases and meteorological parameters such as temperature,humidity,wind speed and wind direction has also been investigated using Pearson correlation coefficient and pollution rose diagram.A comparable pattern in concentration was observed for all three gases in spatial(location)and temporal(diurnal)*** concentration trend of CO_(2)in different seasons is winter>summer>monsoon,while in the case of CH_(4)winter=summer>monsoon but not any seasonal trend was noted in CO *** is observed that CO_(2)has a good relation with CO(a tracer for vehicular emission)in terms of diurnal variation,whereas,CH_(4)does not represent a relation with CO and CO_(2)diurnally,suggesting that vehicles are the source of CO_(2)but not much contributing to other greenhouse gases like CH_(4).

关键词： Vehicular emission Greenhouse gases Harmful pollutants Meteorological parameters

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