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International Conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Felix, A. Yovan Sharmila, V. Devi, S. Nandhini Deena, S. Yadav, Ajay Singh Jeyalakshmi, K. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering R.M.D. Engineering College Tamil Nadu Kavaraipettai India Department of Computer Science and Engineering VSB Engineering College Tamil Nadu Karur India Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Tamil Nadu Chennai India Department of Mathematics SRM Institute of Science and Technology Delhi-NCR Campus Modinagar Uttar Pradesh Ghaziabad India Department of Physics PSNA College of Engineering and Technology Tamil Nadu Dindigul India

ISBN: (纸本)9781032669663

The Internet of Things (IoT) connects systems, software, cloud computing, activities and could provide new access points for cyber-attacks. Virus attacks and unauthorized downloading are the two main risks to IoT security at the moment. These dangers run the risk of obtaining private information, damaging their reputation and their finances. A frame is employed in this paper’s deep learning to identify threat avoidance in IoT. The cyber security detection systems following database has to be built, after which the benchmark variables are chosen and assessed in order to establish a cyber-security alert system in a large data set. The deep learning strategy to identifying threat avoidance was described for the simulation of this scenario. From the Mailing database, malware samples have been gathered for analysis. The research results demonstrate that the proposed method for assessing risks to cyber security in IoT provides highest accuracy to current approaches. Because of this, Deep Convolution Neural Network (DCNN) is utilized in IoT to detect attack avoidance. © 2024 The Author(s).

关键词： Internet of things

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Influence of porosity distribution on nonlinear free vibration and transient responses of porous functionally graded skew plates

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Defence Technology（防务技术） 2021年第6期17卷 1918-1935页

作者： Naveen Kumar H S Subhaschandra Kattimani TNguyen-Thoi Department of Mechanical Engineering National Institute of Technology KarnatakaSurathkal575025India Division of Computational Mathematics and Engineering Institute for Computational ScienceTon Duc Thang UniversityHo Chi Minh CityViet Nam Faculty of Civil Engineering Ton Duc Thang University Ho Chi Minh CityViet Nam

This article deals with the investigation of the effects of porosity distributions on nonlinear free vibration and transient analysis of porous functionally graded skew(PFGS)*** effective material properties of the PFGS plates are obtained from the modified power-law equations in which gradation varies through the thickness of the PFGS plate.A nonlinear finite element(FE)formulation for the overall PFGS plate is derived by adopting first-order shear deformation theory(FSDT)in conjunction with von Karman’s nonlinear strain displacement *** governing equations of the PFGS plate are derived using the principle of virtual *** direct iterative method and Newmark’s integration technique are espoused to solve nonlinear mathematical *** influences of the porosity distributions and porosity parameter indices on the nonlinear frequency responses of the PFGS plate for different skew angles are studied in various *** effects of volume fraction grading index and skew angle on the plate’s nonlinear dynamic responses for various porosity distributions are illustrated in detail.

关键词： Porous functionally graded skew plates Porosity distributions Nonlinear transient response Nonlinear frequency ratio Nonlinear frequency parameter

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CNN-LSTM Based on Crow Search Optimization Algorithm for Product Review Classification

CNN-LSTM Based on Crow Search Optimization Algorithm for Pro...

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2023 International Conference on Evolutionary Algorithms and Soft computing Techniques, EASCT 2023

作者： Yamsani, Nagendar Jothi, B. Srinivasan, N. Dineshkumar, R. Ramachandra, A.C. Sr University School of Computer Science and Artificial Intelligence Warangal India Srm Institute of Science and Technology Kattankulathur Department of Computational Intelligence Chennai India School of Computing Sathyabama Institute of Science and Technology Department of Cse Chennai India Saveetha School of Engineering Sriperumbudur Department of Ece Thandalam India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

Product reviews means an everyone's opinion or views towards particular products or services that delivered to a specific company. In modern global, reviews of online product play a vital part in the estimation process of product in a company and their customers. The extracted features are selected by using the Chi-Squared technique and for the classification of product review proposed a model by utilizing the combined deep learning techniques like Convolutional Neural Network (CNN) and Long- Short Term Memory (LSTM) and it optimized by Crow Search Algorithm (CSA). CNN is used for capturing the local information and LSTM neural network is used for the long-distance dependencies. By utilizing the benefits of CNN and LSTM and it is optimized by CSA for the effective classification of product review. The proposed model performance is estimated by using the performance metrics like Accuracy, Recall, Specificity, Sensitivity and F1-score are 98.35%, 93.20%, 97.57%, 94.82% and 95.78% respectively. The proposed model is compared with other existing model in terms of performance metrics which shows the better performance results. © 2023 IEEE.

关键词： Chi-Squared Convolutional Neural Network Crow Search Algorithm Long-Short Term Memory and Product Review

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Optimizing Diagnostic Accuracy in Ultrasound Imaging Through Cloud-Enabled Deep Learning Techniques 4

Optimizing Diagnostic Accuracy in Ultrasound Imaging Through...

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4th International Conference on Soft computing for Security Applications, ICSCSA 2024

作者： Kalpana, V. Girija, M. Tharageswari, K. Yobu, D. Revathi, J. Aruna, R. Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Jeppiaar University School of Engineering and Technology Chennai India Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore India Computer Science and Engineering Cyber Security J.J College of Engineering and Technology Tricy India Department of Mathematics Kongu Engineering College Erode India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9798331515720

This research aims to study predicting and optimizing the diagnostic accuracy by cloud-enabled deep learning techniques in ultrasound imaging for kidney disease diagnosis. The main findings are that CNN gives 92% accurate outcomes in terms of sensitivity, precision, specificity, AUC-ROC, and other terms used for predictive outcomes in the current research. The other outcomes are 88% accuracy and 91% specificity in predicting kidney diseases by using the RNN model. The other two models used for learning are transfer learning and federated learning which also provide good outcomes which are 94% accurate and 91% accurately in diagnosing with the above-said models. These learning approaches optimize or increase the accuracy rates such as 93% by the transfer learning approach and 90% accuracy outcomes by the federated learning mechanism. These above-given models have been taken based on advanced technologies that are used to predict or forecast the diseases through learning processes. The success of these learning mechanisms is explained by the reliable processes used in the learning of the models that are possible by the neural network used in the learning mechanisms. The high accuracy rates of the models show the successfulness of such clinics, who are in rural areas and are unable to meet their necessary outcome-oriented goals, then measures can be taken for running tele-clinics using the above models of learning techniques to diagnose the diseases at the correct time by 'n' number of people in rural areas. Mine is one of them working as a health worker for which measures need to be taken for finding an accurate solution that operates on cloud-enabled deep learning. This model help run family life and be able to diagnose certain conditions at desired rural clinics out of the above learning models. These tools of learning are studied on cloud-based deep learning techniques and performed well in diagnosing kidney diseases on rural tele-medicine and these learning techni

关键词： Ultrasonic imaging

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QoS-Aware and Routing-Flexible Network Slicing for Service-Oriented Networks

arXiv

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arXiv 2024年

作者： Chen, Wei-Kun Liu, Ya-Feng Dai, Yu-Hong Luo, Zhi-Quan The School of Mathematics and Statistics Beijing Key Laboratory on MCAACI Beijing Institute of Technology Beijing100081 China The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China

In this paper, we consider the network slicing (NS) problem which attempts to map multiple customized virtual network requests (also called services) to a common shared network infrastructure and manage network resources to meet diverse quality of service (QoS) requirements. We propose a mixed-integer nonlinear programming (MINLP) formulation for the considered NS problem that can flexibly route the traffic flow of the services on multiple paths and provide end-to-end delay and reliability guarantees for all services. To overcome the computational difficulty due to the intrinsic nonlinearity in the MINLP formulation, we transform the MINLP formulation into an equivalent mixed-integer linear programming (MILP) formulation and further show that their continuous relaxations are equivalent. In sharp contrast to the continuous relaxation of the MINLP formulation which is a nonconvex nonlinear programming problem, the continuous relaxation of the MILP formulation is a polynomial-time solvable linear programming problem, which significantly facilitates the algorithmic design. Based on the newly proposed MILP formulation, we develop a customized column generation (cCG) algorithm for solving the NS problem. The proposed cCG algorithm is a decomposition-based algorithm and is particularly suitable for solving large-scale NS problems. Numerical results demonstrate the efficacy of the proposed formulations and the proposed cCG algorithm. Copyright © 2024, The Authors. All rights reserved.

关键词： Mixed-integer linear programming

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Brain-O-Vision: Brain Tumor Detection 1st

Brain-O-Vision: Brain Tumor Detection

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1st International Research Conference on computing Technologies for Sustainable Development, IRCCTSD 2024

作者： Angelin Claret, S.P. Datta, Swati Kwatra, Ish Department of Computational Intelligence Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chengalpattu India

ISBN: (纸本)9783031823886

Sophisticated methods utilizing deep learning neural networks have surfaced as viable options for precise and automated brain tumor identification. This study, which employed use of MobileNetV2, EfficientNet, ResNet50 and VGG16 for the comparitive analysis and select the one which gives highest accuracy. We address the need for more advanced diagnostic techniques by automatically extracting pertinent characteristics from MRI scans and making accurate predictions using deep learning and Grad-Cam segmentation. Our approach entails preprocessing MRI data, CNN architecture design, and model training and assessment with dataset. Our methodology is successful, as seen by the accuracies of MobileNetV2 at 86%, EfficientNet at 84%, ResNet50 at 80%, VGG16 at 88%. The Grad-Cam segmentation performed at the final stage helps in identifying the presence of tumor. All things considered, this study advances the area of medical image analysis and emphasizes how critical it is to use MRI and deep learning to combat brain cancers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Fingerprint Agreement Using Enhanced Kerberos Authentication Protocol on M-Health

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Computer Systems science & engineering 2022年第11期43卷 833-847页

作者： A.S.Anakath S.Ambika S.Rajakumar R.Kannadasan K.S.Sendhil Kumar School of Computing E.G.S.Pillay Engineering CollegeNagapattinam611002India Department of Computer Science and Engineering University College of Engineering AriyalurAriyalur621705India Department of Mathematics University College of Engineering AriyalurAriyalur621705India Department of Software Systems School of Computer Science and Engineering(SCOPE)Vellore Institute of Technology UniversityVellore632014India Department of IoT School of Computer Science and Engineering(SCOPE)Vellore Institute of Technology UniversityVellore632014India

Cloud computing becomes an important application development platform for processing user data with high *** providers are accustomed to providing storage centers outside the trusted location preferred by the data ***,ensuring the security and confidentiality of the data while processing in the centralized network is very *** secured key transmission between the sender and the receiver in the network is a huge challenge in managing most of the sensitive data transmission among the cloud *** are very active over the network like real authenticated user to hack the personal sensitive data,such as bank balance,health data,personal data,and confidential documents over the cloud *** this research,a secured key agreement between the sender and the receiver using Kerberos authentication protocol with fingerprint is proposed to ensure security in *** of patients are monitored using wireless sensor devices and are then transferred to the *** protocol helps in avoiding unnecessary communication of authenticated data over the cloud *** security process is a procedure with the best security in most of the authentication *** node is responsible in carrying data packets from the sender to the receiver in the cloud *** Kerberos protocol is used in trust node to ensure *** communication between the local health center and the healthcare server is ensured by using a fingerprint feature called minutiae form,which refers to the fingerprint image of both sender and *** computational and communicational cost of the proposed system is lesser when compared with other existing authentication methods.

关键词： Protocol security m-health cloud computing biometric fingerprint kerberos protocol

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Quantum Teleportation and EntanglementBased Quantum Blind Signature Protocol for Quantum Secure Communication in Security Service Bases 5

Quantum Teleportation and EntanglementBased Quantum Blind Si...

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5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Sumathy, G. Suresh, A. Udendhran, R. Maheshwari, A. Arunprasath, S. Department of Computational Intelligence Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Networking and Communications Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Chengalpattu Tamil NaduKattankulathur Chennai603203 India

ISBN: (纸本)9798400709418

Quantum teleportation and entanglement-based quantum blind signature protocol were promising new technologies for secure communication between security service bases. They were secure against a variety of attacks, including eavesdropping, signatory disavowal, verifier denial, and forgery. The protocol worked by using quantum entanglement to create a shared secret state between the security service bases. This shared secret state was then used to generate a quantum signature for each message sent between the bases. The signature was then verified by the receiving base using the shared secret state. Compared to classical blind signature schemes, the main advantages of the quantum entanglement-based quantum blind signature protocol were its information-theoretic security and reduced complexity and noise. However, this protocol also faced some challenges, such as preparing and storing entangled states, performing quantum operations and measurements, and ensuring the trustworthiness of the third party. Despite these challenges, the quantum entanglement-based quantum blind signature protocol was a promising new technology for secure communication between security service like military bases. It had the potential to revolutionize the way that military bases communicated with each other, making it possible to send secure messages without revealing the content or identity of the messages. © 2023 ACM.

关键词： Secure communication

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Advancing Optical Tomography Image Analysis: Exploring Convolutional Neural Network Model Variants for Retinal Damage Detection 10

Advancing Optical Tomography Image Analysis: Exploring Convo...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer engineering, UPCON 2023

作者： Sathyan, S. Chanchal, M. Raghul, S. Jeyakumar, G. Amrita Vishwa Vidyapeetham Amrita School of Physical Sciences Department of Mathematics Coimbatore India Cmr Institute of Technology Department of Information Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350382471

Retina is a vital component of the eye;it plays a crucial role in visual perception. Any damage to the retina can lead to vision impairment and severe ocular diseases. Early detection and accurate diagnosis of retinal conditions is crucial. In the domain of medical image analysis, especially within the scope of retinal imaging, convolutional neural network (CNN)-based deep learning models have exhibited promising results. This paper involves the detection of retinal conditions such as Diabetic Macular Edema (DME), Choroidal Neovascularization (CNV) and Drusen. The paper presents a comparative study of CNN models for the detection of these retinal damages using Optical Coherence Tomography (OCT) images. This study focuses on evaluating the performance of popular CNN architectures, like Residual Networks (ResNet), Dense Convolutional Networks (DenseNet), Inception Networks, Visual Geometry Group (VGG). Also, the paper provides a comparative analysis of different depth variants of deep learning models such as ResNet (18, 50 and 101), DenseNet (121, 169, and 201), Inception-V3, Inception-ResNet-V2, and VGG (16 and 19). These models are assessed on a dataset containing OCT images of Normal, DME, CNV and Drusen retinal conditions. The effectiveness of the models are evaluated using evaluation metrics such as accuracy, precision, recall and F1 score. Additionally, visualizations such as confusion matrix, training accuracy versus validation accuracy plots, training loss versus validation loss plots are utilized to provide a better understanding of the model performance. The idea is to explore the impact of depth on the model performance and understand how deeper models demonstrate an enhanced ability to learn complex features and capture intricate details within the retinal data. Exploring these depth variants aims to provide insights into the advantages and potential trade-offs associated with increasing depth of deep learning models for retinal damage detection tasks. © 2

关键词： choroidal neovascularization deep learning DenseNet diabetic macular edema drusen Inception networks medical image analysis ocular diseases optical coherence tomography (OCT) ResNet retina retinal imaging VGG

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Time parallelization for hyperbolic and parabolic problems

arXiv

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arXiv 2025年

作者： Gander, Martin J. Wu, Shu-Lin Zhou, Tao Department of Mathematics University of Geneva CP64 Geneva 41211 Switzerland School of Mathematics and Statistics Northeast Normal University Changchun130024 China Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences China

Time parallelization, also known as PinT (Parallel-in-Time) is a new research direction for the development of algorithms used for solving very large scale evolution problems on highly parallel computing architectures. Despite the fact that interesting theoretical work on PinT appeared as early 1964, it was not until 2004, when processor clock speeds reached their physical limit, that research in PinT took off. A distinctive characteristic of parallelization in time is that information flow only goes forward in time, meaning that time evolution processes seem necessarily to be sequential. Nevertheless, many algorithms have been developed over the last two decades to do PinT computations, and they are often grouped into four basic classes according to how the techniques work and are used: shooting-type methods;waveform relaxation methods based on domain decomposition;multigrid methods in space-time;and direct time parallel methods. However, over the past few years, it has been recognized that highly successful PinT algorithms for parabolic problems struggle when applied to hyperbolic problems. We focus in this survey therefore on this important aspect, by first providing a summary of the fundamental differences between parabolic and hyperbolic problems for time parallelization. We then group PinT algorithms into two basic groups: the first group contains four effective PinT techniques for hyperbolic problems, namely Schwarz Waveform Relaxation with its relation to Tent Pitching;Parallel Integral Deferred Correction;ParaExp;and ParaDiag. While the methods in the first group also work well for parabolic problems, we then present PinT methods especially designed for parabolic problems in the second group: Parareal: the Parallel Full Approximation Scheme in Space-Time;Multigrid Reduction in Time;and Space-Time Multigrid. We complement our analysis with numerical illustrations using four time-dependent PDEs: the heat equation;the advection-diffusion equation;Burgers’ equa

关键词： Wave equations

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