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Intelligent classification of lung malignancies using deep learning techniques

International Journal of Intelligent Computing and Cybernetics 2022年第3期15卷 345-362页

作者： Priyanka Yadlapalli D.Bhavana suryanarayana gunnam Department of Electronics and Communication Engineering Koneru Lakshmaiah Education FoundationGunturIndia Department of Electronics and Communications Gokaraju Rangaraju Institute of Engineering and TechnologyHyderabadIndia Department of Electronics and Communications VRSiddhartha Engineering CollegeVijayawadaIndia Shanghai Jiao Tong University-Minhang Campus ShanghaiChina

Purpose-Computed tomography(CT)scan can provide valuable information in the diagnosis of lung *** detect the location of the cancerous lung nodules,this work uses novel deep learning *** majority of the early investigations used CT,magnetic resonance and mammography *** appropriate procedures,the professional doctor in this sector analyses these images to discover and diagnose the various degrees of lung *** of the methods used to discover and detect cancer illnesses are time-consuming,expensive and stressful for the *** address all of these issues,appropriate deep learning approaches for analyzing these medical images,which included CT scan images,were ***/methodology/approach-Radiologists currently employ chest CT scans to detect lung cancer at an early *** certain situations,radiologists’perception plays a critical role in identifying lung melanoma which is incorrectly *** learning is a new,capable and influential approach for predicting medical *** this paper,the authors employed deep transfer learning algorithms for intelligent classification of lung *** neural networks(VGG16,VGG19,MobileNet and DenseNet169)are used to constrain the input and output layers of a chest CT scan image ***-The collection includes normal chest CT scan pictures as well as images from two kinds of lung cancer,squamous and adenocarcinoma impacted chest CT scan *** to the confusion matrix results,the VGG16 transfer learning technique has the highest accuracy in lung cancer classification with 91.28% accuracy,followed by VGG19 with 89.39%,MobileNet with 85.60% and DenseNet169 with 83.71% accuracy,which is analyzed using Google ***/value-The proposed approach using VGG16 maximizes the classification accuracy when compared to VGG19,MobileNet and *** results are validated by computing the confusion matrix for each network type.

关键词： Computed tomography scans Convolution neural networks Transfer learning Intelligent classification State-of-the-art(SOTA)accuracy

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Deep Learned Singular Residual Network for Super Resolution Reconstruction

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Computers, Materials & Continua 2023年第1期74卷 1123-1137页

作者： gunnam.suryanarayana D.Bhavana P.E.S.N.Krishna Prasad M.M.K.Narasimha Reddy Md Zia Ur Rahman Department of Electronics and Communications V R Siddhartha Engineering CollegeVijayawada520007India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education FoundationVaddeswaramGuntur522502India Department of CSE Anil Neerukonda Institute of Technology&SciencesSangivalasaVisakhapatnam531162India

Single image super resolution(SISR)techniques produce images of high resolution(HR)as output from input images of low resolution(LR).Motivated by the effectiveness of deep learning methods,we provide a framework based on deep learning to achieve super resolution(SR)by utilizing deep singular-residual neural network(DSRNN)in training *** are obtained from the difference between HR and LR images to generate LR-residual example *** value decomposition(SVD)is applied to each LR-residual image pair to decompose into subbands of low and high frequency ***,DSRNN is trained on these subbands through input and output channels by optimizing the weights and biases of the *** fewer layers in DSRNN,the influence of exploding gradients is *** speeds up the learning process and also improves accuracy by using skip *** trained DSRNN parameters yield residuals to recover the HR subbands in the testing *** analysis shows that the proposed method results in superior performance to existingmethods in terms of subjective *** testing results on popular benchmark datasets such as set5,set14,and urban100 for a scaling factor of 4 show the effectiveness of the proposed method across different qualitative evaluation metrics.

关键词： Deep learning image reconstruction residual network singular values super resolution

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Edge preserving super-resolution algorithm using multi-stage cascaded joint bilateral filter

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International Journal of Modeling, Simulation, and Scientific Computing 2017年第1期8卷 37-51页

作者： gunnam.suryanarayana Ravindra Dhuli School of Electronics Engineering VIT University Vellore-632014TamilnaduIndia

Super-resolution(SR)algorithms address the inabilities of poor imaging devices,there by producing high quality images with enhanced *** propose a new SR approach which produces sharp high resolution(HR)image using its low resolution(LR)*** proposed method uses geometric duality for spatially adapting covariance-based interpolation(CBI).To preserve edge information,a multi-stage cascaded joint bilateral filter(MSCJBF)is proposed as an intermediary *** edges are incorporated in the high frequency subbands obtained by the stationary wavelet transform(SWT),through nearest neighbor interpolation(NNI)*** to the NNI process,the high frequency subbands undergo two-lobed lanczos interpolation to achieve the desired resolution *** quantitative and qualitative analysis for various test images prove the superiority of our method.

关键词： Joint bilateral filter image super-resolution covariance based interpolation stationary wavelet transform

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Do ICMP Security Attacks Have Same Impact on Servers?

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Journal of Information Security 2017年第3期8卷 274-283页

作者： Ganesh Reddy gunnam Sanjeev Kumar Department of Electrical and Computer Engineering The University of Texas-RGV Edinburg USA

There are different types of Cyber Security Attacks that are based on ICMP protocols. Many ICMP protocols are very similar, which may lead security managers to think they may have same impact on victim computer systems or servers. In this paper, we investigate impact of different ICMP based security attacks on two popular server systems namely Microsoft’s Windows Server and Apple’s Mac Server OS running on same hardware platform, and compare their performance under different types of ICMP based security attacks.

关键词： DDoS Security Attacks ICMP Based Cyber Attacks Mac Server OS Windows Server OS

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