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Brain Image Classification Using Time Frequency Extraction with Histogram Intensity Similarity

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Computer Systems Science & Engineering 2022年第5期41卷 645-660页

作者： Thangavel Renukadevi Kuppusamy Saraswathi p.prabu K.Venkatachalam Department of Computer Technology-UG Kongu Engineering CollegeErodeIndia Department of Computer Science CHRIST(Deemed to be University)BengaluruIndia Department of Computer Science and Engineering CHRIST(Deemed to be University)BengaluruIndia

Brain medical image classification is an essential procedure in Computer-Aided Diagnosis(CAD)*** methods depend specifically on the local or global *** fusion methods have also been developed,most of which are problem-distinct and have shown to be highly favorable in medical ***,intensity-specific images are not *** recent deep learning methods ensure an efficient means to design an end-to-end model that produces final classification accuracy with brain medical images,compromising *** solve these classification problems,in this paper,Histogram and Time-frequency Differential Deep(HTF-DD)method for medical image classification using Brain Magnetic Resonance Image(MRI)is *** construction of the proposed method involves the following ***,a deep Convolutional Neural Network(CNN)is trained as a pooled feature mapping in a supervised manner and the result that it obtains are standardized intensified pre-processed features for ***,a set of time-frequency features are extracted based on time signal and frequency signal of medical images to obtain time-frequency ***,an efficient model that is based on Differential Deep Learning is designed for obtaining different *** proposed model is evaluated using National Biomedical Imaging Archive(NBIA)images and validation of computational time,computational overhead and classification accuracy for varied Brain MRI has been done.

关键词： Histogram differential deep learning convolutional neural network

Kernel Granulometric Texture Analysis and Light RES-ASpp-UNET Classification for Covid-19 Detection

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Computers, Materials & Continua 2022年第4期71卷 651-666页

作者： A.Devipriya p.prabu K.Venkatachalam Ahmed Zohair Ibrahim Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Computer Science CHRIST(Deemed to be University)Bangalore560029India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic Computer Sciences Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhKSA.P.o.Box:84428Postal Code:11671

This research article proposes an automatic frame work for detectingCOVID -19 at the early stage using chest X-ray image. It is an undeniable factthat coronovirus is a serious disease but the early detection of the virus presentin human bodies can save lives. In recent times, there are so many research solutions that have been presented for early detection, but there is still a lack in needof right and even rich technology for its early detection. The proposed deeplearning model analysis the pixels of every image and adjudges the presence ofvirus. The classifier is designed in such a way so that, it automatically detectsthe virus present in lungs using chest image. This approach uses an imagetexture analysis technique called granulometric mathematical model. Selectedfeatures are heuristically processed for optimization using novel multi scaling deep learning called light weight residual–atrous spatial pyramid pooling(LightRES-ASpp-Unet) Unet model. The proposed deep LightRES-ASppUnet technique has a higher level of contracting solution by extracting majorlevel of image features. Moreover, the corona virus has been detected usinghigh resolution output. In the framework, atrous spatial pyramid pooling(ASpp) method is employed at its bottom level for incorporating the deepmulti scale features in to the discriminative mode. The architectural workingstarts from the selecting the features from the image using granulometricmathematical model and the selected features are optimized using LightRESASpp-Unet. ASpp in the analysis of images has performed better than theexisting Unet model. The proposed algorithm has achieved 99.6% of accuracyin detecting the virus at its early stage.

关键词： Deep residual learning convolutional neural network COVID-19 X-ray principal component analysis granulo metrics texture analysis

Integrated Approach of Brain Disorder Analysis by Using Deep Learning Based on DNA Sequence

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Computer Systems Science & Engineering 2023年第6期45卷 2447-2460页

作者： Ahmed Zohair Ibrahim p.prakash V.Sakthivel p.prabu Computer Sciences Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhP.O.Box 8442811671KSA School of Computer Scence and Engineering Vellore Institute of TechnologyChennai600127India Department of Computer Science CHRIST(Deemed to be University)Bangalore560029India

In order to research brain problems using MRI,pET,and CT neuroimaging,a correct understanding of brain function is *** has been considered in earlier times with the support of traditional *** learning process has also been widely considered in these genomics data processing *** this research,brain disorder illness incliding Alzheimer’s disease,Schizophrenia and parkinson’s diseaseis is analyzed owing to misdetection of disorders in neuroimaging data examined by means fo traditional ***,deep learning approach is incorporated here for classification purpose of brain disorder with the aid of Deep Belief Networks(DBN).Images are stored in a secured manner by using DNA sequence based on JpEG Zig Zag Encryption algorithm(DBNJZZ)*** suggested approach is executed and tested by using the performance metric measure such as accuracy,root mean square error,Mean absolute error and mean absolute percentage *** DBNJZZ gives better performance than previously available methods.

关键词： Deep belief networks zig zag deep learning mean absolute percentage error mean absolute error root mean square error DNA genomics

Improved Dragonfly Optimizer for Intrusion Detection Using Deep Clustering CNN-pSO Classifier

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Computers, Materials & Continua 2022年第3期70卷 5949-5965页

作者： K.S.Bhuvaneshwari K.Venkatachalam S.Hubalovsky p.Trojovsky p.prabu Department of Computer Sceince and Engineering Karpagam College of EngineeringCoimbatore641032India Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Mathematics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Computer Sceince CHRIST(Deemed to be University)Bangalore560074India

With the rapid growth of internet based services and the data generated on these services are attracted by the attackers to intrude the networking services and *** on the characteristics of these intruders,many researchers attempted to aim to detect the intrusion with the help of automating ***,the large volume of data is generated and transferred through network,the security and performance are remained an ***(Intrusion Detection System)was developed to detect and prevent the intruders and secure the network *** performance and loss are still an issue because of the features space grows while detecting the *** this paper,deep clustering based CNN have been used to detect the intruders with the help of Meta heuristic algorithms for feature selection and *** proposed system includes three phases such as preprocessing,feature selection and *** the first phase,KDD dataset is preprocessed by using Binning normalization and Eigen-pCA based discretization *** second phase,feature selection is performed by using Information Gain based Dragonfly Optimizer(IGDFO).Finally,Deep clustering based Convolutional Neural Network(CCNN)classifier optimized with particle Swarm Optimization(pSO)identifies intrusion attacks *** clustering loss and network loss can be reduced with the optimization *** evaluate the proposed IDS model with the NSL-KDD dataset in terms of evaluation *** experimental results show that proposed system achieves better performance compared with the existing system in terms of accuracy,precision,recall,f-measure and false detection rate.

关键词： Intrusion detection system binning normalization deep clustering convolutional neural network information gain dragonfly optimizer

Machine Learning Technique to Detect Radiations in the Brain

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Computer Systems Science & Engineering 2022年第7期42卷 149-163页

作者： E.Gothai A.Baseera p.prabu K.Venkatachalam K.Saravanan S.SathishKumar Department of Computer Science and Engineering Kongu Engineering CollegeErode638060India School of Computing Science and Engineering VIT Bhopal UniversityBhopal466114India Department of Computer Science CHRIST(Deemed to be University)Bangalore560029India Department of Computer Science and Engineering CHRIST(Deemed to be University)BangaloreIndia Department of Computer Science and Engineering Erode Sengunthar Engineering CollegeThudupathi638057 Department of EEE M.Kumarasamy College of EngineeringKarur639113TamilnaduIndia

The brain of humans and other organisms is affected in various ways through the electromagneticﬁeld(EMF)radiations generated by mobile phones and cell phone *** variations in the brain are caused by the neurological changes due to the revelation of *** level analysis is used to measure and detect the effect of mobile radiations,but its utilization seems very expensive,and it is a tedious process,where its analysis requires the preparation of cell *** this regard,this research article proposes optimal broadcast-ing learning to detect changes in brain morphology due to the revelation of ***,Drosophila melanogaster acts as a specimen under the revelation of *** segmentation is performed for the brain to attain the microscopic images from the prejudicial geometrical characteristics that are removed to detect the effect of revelation of *** geometrical characteristics of the brain image of that is microscopic segmented are *** results reveal the occur-rence of several prejudicial characteristics that can be processed by machine learn-ing *** important prejudicial characteristics are given to four varieties of classiﬁers such as naïve Bayes,artiﬁcial neural network,support vector machine,and unsystematic forest for the classiﬁcation of open or nonopen micro-scopic image of *** *** results are attained through various experimental evaluations,and the said classiﬁers perform well by achieving 96.44%using the prejudicial characteristics chosen by the feature selection *** proposed system is an optimal approach that automatically identiﬁes the effect of revelation of EMF with minimal time complexity,where the machine learning techniques produce an effective framework for image processing.

关键词： Electromagneticﬁeld radiations brain morphology segmentation machine learning image processing

Cross-Layer Hidden Markov Analysis for Intrusion Detection

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Computers, Materials & Continua 2022年第2期70卷 3685-3700页

作者： K.Venkatachalam p.prabu B.Saravana Balaji Byeong-Gwon Kang Yunyoung Nam Mohamed Abouhawwash Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Department of Computer Science CHRIST(Deemed to be University)Bangalore560029India Department of Information Technology Lebanese French UniversityErbil44001KRIraq Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast Lansing48824MIUSA

Ad hoc mobile cloud computing networks are affected by various issues,like delay,energy consumption,flexibility,infrastructure,network lifetime,security,stability,data transition,and link *** the issues above,route failure is prevalent in ad hoc mobile cloud computing networks,which increases energy consumption and delay and reduces *** issues may affect several interconnected nodes in an ad hoc mobile cloud computing *** address these weaknesses,which raise many concerns about privacy and security,this study formulated clustering-based storage and search optimization approaches using cross-layer *** proposed approaches were formed by cross-layer analysis based on intrusion detection ***,the clustering process based on storage and search optimization was formulated for clustering and route maintenance in ad hoc mobile cloud computing ***,delay,energy consumption,network lifetime,and link accomplishment are highly addressed by the proposed *** hidden Markov model is used to maintain the data transition and distributions in the *** data communication network,like ad hoc mobile cloud computing,faces security and confidentiality ***,the main security issues in this article are addressed using the storage and search optimization ***,the new algorithm developed helps detect intruders through intelligent cross layer analysis with theMarkov *** proposed model was simulated in Network Simulator 3,and the outcomes were compared with those of prevailing methods for evaluating parameters,like accuracy,end-to-end delay,energy consumption,network lifetime,packet delivery ratio,and throughput.

关键词： Data transition end-to-end delay energy consumption flexibility hidden Markov model intrusion detection link optimization packet delivery ratio privacy security searching throughput

Covid-19 CT Lung Image Segmentation Using Adaptive Donkey and Smuggler Optimization Algorithm

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Computers, Materials & Continua 2022年第4期71卷 1133-1152页

作者： p.prabu K.Venkatachalam Ala Saleh Alluhaidan Radwa Marzouk Myriam Hadjouni Sahar A.El_Rahman Department of Computer Science CHRIST(Deemed to be University)BengaluruIndia Department of Computer Science and Engineering CHRIST(Deemed to be University)BengaluruIndia Information Systems Department College of Computer and Information SciencePrincess Nourah Bint Abdulrahman UniversityRiyadhKSA 84428Saudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt Computer Sciences Department College of Computer and Information SciencePrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Electrical Engineering Faculty of Engineering-ShoubraBenha UniversityCairoEgypt

COVID’19 has caused the entire universe to be in existential healthcrisis by spreading globally in the year 2020. The lungs infection is detected inComputed Tomography (CT) images which provide the best way to increasethe existing healthcare schemes in preventing the deadly virus. Nevertheless,separating the infected areas in CT images faces various issues such as lowintensity difference among normal and infectious tissue and high changes inthe characteristics of the infection. To resolve these issues, a new inf-Net (LungInfection Segmentation Deep Network) is designed for detecting the affectedareas from the CT images automatically. For the worst segmentation results,the Edge-Attention Representation (EAR) is optimized using AdaptiveDonkey and Smuggler Optimization (ADSO). The edges which are identifiedby the ADSO approach is utilized for calculating dissimilarities. An IFCM(Intuitionistic Fuzzy C-Means) clustering approach is applied for computingthe similarity of the EA component among the generated edge maps andGround-Truth (GT) edge maps. Also, a Semi-Supervised Segmentation(SSS) structure is designed using the Randomly Selected propagation (Rp)technique and Inf-Net, which needs only less number of images and unlabelleddata. Semi-Supervised Multi-Class Segmentation (SSMCS) is designed usinga Bi-LSTM (Bi-Directional Long-Short-Term-memory), acquires all theadvantages of the disease segmentation done using Semi Inf-Net and enhancesthe execution of multi-class disease labelling. The newly designed SSMCSapproach is compared with existing U-Net++, MCS, and *** such as MAE (Mean Absolute Error), Structure measure, Specificity(Spec), Dice Similarity coefficient, Sensitivity (Sen), and Enhance-AlignmentMeasure are considered for evaluation purpose.

关键词： Adaptive donkey and snuggler optimization.bi-directional long short term memory coronavirus disease 2019 randomly selected propagation semi-supervised learning

Enhanced cytotoxic effect on human lung carcinoma cell line(A549) by gold nanoparticles synthesized from Justicia adhatoda leaf extract

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Asian pacific Journal of Tropical Biomedicine 2018年第11期8卷 540-547页

作者： D.Latha p.prabu C.Arulvasu R.ManikANDan S.Sampurnam V.Narayanan Department of Inorganic Chemistry University of Madras Guindy Campus Department of Zoology Pachaiyappa's College for Men Kanchipuram Department of Zoology University of Madras Guindy Campus Chennai

Objective: To synthesize bio-inspired gold nanoparticles(AuNps) using the leaf extract of Justicia adhatoda and evaluate the anti-cancer activity on human lung cancer cell line(A549).Methods: Synthesis of AuNps was done using an aqueous leaf extract of Justicia adhatoda as a green route. The bio-synthesized AuNps were confirmed and characterized by using various spectral studies such as UV-Vis spectrum, Scanning Electron Microscope with EDAX, Transmission Electron Microscope, Fourier Transmission Infrared Spectroscope analysis and Surface Enhanced Raman Spectroscopy. The cell viability was determined by MTT reduction assay. In addition, cytomorphology and the nuclear morphological study of A549 cell line was observed under fluorescence microscope. Results: UV-Vis spectrum showed surface plasmon resonance peak at 547 nm, scanning electron microscope and transmission electron microscope studies showed the monodispersed spherical shape and its average size in the range of 40.1 nm was noticed. Fourier Transmission Infrared Spectroscope analysis confirmed that the C=O group of amino acids of proteins had strong ability to bind with the surface of nanoparticle. Interestingly, our results also demonstrated inhibited proliferation of A549 cell line by MTT(IC50 value: 80 μg/mL). Cell morphology was observed and cell death was caused by apoptosis as revealed by propidium iodide staining. Conclusions: The current study proves the anticancer potential of bio-synthesized AuNps. Thus, synthesized AuNps can be used for the treatment of human lung cancer cell(A549) and it can be exploited for drug delivery in future.

关键词： Justicia adhatoda Leaf extract AuNps Cytotoxicity A549 cell line

Efficient Communication in Wireless Sensor Networks Using Optimized Energy Efficient Engroove Leach Clustering protocol

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Tsinghua Science and Technology 2024年第4期29卷 985-1001页

作者： N.Meenakshi Sultan Ahmad A.V.prabu J.Nageswara Rao Nashwan Adnan Othman Hikmat A.M.Abdeljaber R.Sekar Jabeen Nazeer SRM Institute of Science and Technology KattankulathurChennai 603203India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj 11942Saudi Arabia University Center for Research and Development(UCRD) Department of Computer Science and EngineeringChandigarh UniversityGharuanMohali 140413India Department of ECE Koneru Lakshmaiah Education FoundationVaddeswaramGuntur 522502India Department of Computer Science and Engineering Lakireddy Balireddy College of Engineering(A)NTR District 521230India Department of Computer Engineering College of EngineeringKnowledge UniversityErbil 44001Iraq Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmman 11937Jordan Department of Electronics and Communication Engineering(ECE) School of EngineeringPresidency UniversityBangalore 834001India

The Wireless Sensor Network(WSN)is a network that is constructed in regions that are inaccessible to human *** widespread deployment of wireless micro sensors will make it possible to conduct accurate environmental monitoring for a use in both civil and military *** make use of these data to monitor and keep track of the physical data of the surrounding environment in order to ensure the sustainability of the *** data have to be picked up by the sensor,and then sent to the sink node where they may be *** nodes of the WSNs are powered by batteries,therefore they eventually run out of *** energy restriction has an effect on the network life span and environmental *** objective of this study is to further improve the Engroove Leach(EL)protocol’s energy efficiency so that the network can operate for a very long time while consuming the least amount of *** lifespan of WSNs is being extended often using clustering and routing *** Meta Inspired Hawks Fragment Optimization(MIHFO)system,which is based on passive clustering,is used in this study to do *** cluster head is chosen based on the nodes’residual energy,distance to neighbors,distance to base station,node degree,and node *** on distance,residual energy,and node degree,an algorithm known as Heuristic Wing Antfly Optimization(HWAFO)selects the optimum path between the cluster head and Base Station(BS).They examine the number of nodes that are active,their energy consumption,and the number of data packets that the BS *** overall experimentation is carried out under the MATLAB *** the analysis,it has been discovered that the suggested approach yields noticeably superior outcomes in terms of throughput,packet delivery and drop ratio,and average energy consumption.

关键词： wireless sensor networks energy efficient engroove leach protocol meta inspired Hawks fragment optimization heuristic wing antfly optimization