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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Iyapparaja, M. Karunanithi, Isvarya Sahana Bhat, U. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

Authentication in modern era, has evolved significantly to address the increasing complexity and security challenges of our digital world. Traditional methods of authentication, such as passwords and PINS have proven to be vulnerable to various forms of attacks. As a result there is a growing need for innovative and robust authentication techniques. Keystroke dynamics involves the analysis of user typing patterns, including the time interval between keystrokes and the key pressed duration, to establish a unique biometric profile for each individual. Unlike traditional authentication methods keystroke dynamics offer several advantages, such as continuous authentication without the need for additional hardware and the ability to adapt to a user's changing typing behavior. This model will additionally analyze users typing patterns have the capability to identify individuals using the system by considering their typing speed. The first premise involves developing a system that can capture the keystroke dynamics of a user during a training phase and create a unique profile for that user. The second premise involves implementing Mahalanobis distance for Dynamic keystroke Authentication with various machine learning algorithms. This model can maximize the strengths of both methods to improve the accuracy and robustness of the authentication system. Authentication using keystroke dynamics is secured and convenient method and can be used wherever there is requirement of authentication such as baking, e-commerce and secured access control system. From the experiment performed below we can attained that the model functions exceptionally well using Isolation Forest with an accuracy of 90%. © 2024 IEEE.

关键词： Support vector machines

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CE-EEN-B0:Contour Extraction Based Extended EfficientNet-B0 for Brain Tumor Classification Using MRI Images

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computers, Materials & Continua 2023年第3期74卷 5967-5982页

作者： Abishek Mahesh Deeptimaan Banerjee Ahona Saha Manas Ranjan Prusty A.Balasundaram School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Centre for Cyber-Physical Systems Vellore Institute of TechnologyChennai600127India

A brain tumor is the uncharacteristic progression of tissues in the *** are very deadly,and if it is not diagnosed at an early stage,it might shorten the affected patient’s life ***,their classification and detection play a critical role in *** Brain tumor detection is done by biopsy which is quite *** is usually not preferred at an early stage of the *** detection involvesMagneticResonance Imaging(MRI),which is essential for evaluating the *** paper aims to identify and detect brain tumors based on their location in the *** order to achieve this,the paper proposes a model that uses an extended deep Convolutional Neural Network(CNN)named Contour Extraction based Extended EfficientNet-B0(CE-EEN-B0)which is a feed-forward neural network with the efficient net layers;three convolutional layers and max-pooling layers;and finally,the global average pooling *** site of tumors in the brain is one feature that determines its effect on the functioning of an ***,this CNN architecture classifies brain tumors into four categories:No tumor,Pituitary tumor,Meningioma tumor,andGlioma *** network provides an accuracy of 97.24%,a precision of 96.65%,and an F1 score of 96.86%which is better than already existing pre-trained networks and aims to help health professionals to cross-diagnose an MRI *** model will undoubtedly reduce the complications in detection and aid radiologists without taking invasive steps.

关键词： Brain tumor image preprocessing contour extraction disease classification transfer learning

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An Accuracy Analysis Of Classical And Quantum Machine Learning Algorithms Using Different Datasets 2

An Accuracy Analysis Of Classical And Quantum Machine Learni...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Bhaskaran, P. Prasanna, S. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350365269

Classical machine learning has practical significant advancements and extensive acceptance across various domains, enabling the growth of precise predictive models. The purpose of this work is to examine the accuracy gained by various machine learning algorithms, both classical and quantum. In addition, the study delves into the fundamental ideas and principles that form the backbone of quantum machine learning algorithms, such as quantum data representation, quantum feature mapping, and quantum optimization strategies. Experiments use classical machine learning models trained and tested on traditional hardware to measure evaluation metrics like classification accuracy, precision, recall, and F1-score for quantum machine learning models executed on quantum computing platforms or simulators. In quantum SVM, the seed dataset obtained 100% accuracy, and the supermarket dataset obtained 69% accuracy when compared to classical SVM. The findings imply that, under some conditions, quantum machine learning algorithms show the capacity to beat their conventional counterparts in terms of accuracy. © 2024 IEEE.

关键词： Quantum entanglement

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Intelligent Connected Vehicle Intrusion Detection and Mitigation: An Analysis of Explainable AI 4

Intelligent Connected Vehicle Intrusion Detection and Mitiga...

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4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Vishnukumar, Ravula Padma, Adla Ramaiah, Mangayarkarasi School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350387063

IoT and AI created a Transportation Management System, resulting in the Internet of Vehicles. Intelligent vehicles are combined with contemporary communication technologies (5G) to achieve automated driving and adequate mobility. IoV faces security issues in the next five (5) areas: data safety, V2X communication safety, platform safety, Intermediate Commercial Vehicles (ICV) safety, and intelligent device safety. Numerous types of AI models have been created to reduce the outcome infiltration risks on ICVs. The need to integrate confidence, transparency, and repeatability into the creation of Artificial Intelligence (AI) for the safety of ICV and to deliver harmless transport systems, on the other hand, has led to an increase in explainable AI (XAI). Therefore, the space of this analysis protected the XAI models employed in ICV intrusion detection systems (IDSs), their taxonomies, and available research concerns. The study's findings demonstrate that, despite its relatively recent submission to ICV, XAI is a potential explore area for those looking to increase the net effect of ICVs. The paper also demonstrates that XAI's greater transparency will help it gain acceptance in the vehicle industry. © 2024 IEEE.

关键词： Network intrusion

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Vortex Ring and Dynamic Lift Generated by Downstroke of Elastic Butterfly Wing 1

Vortex Ring and Dynamic Lift Generated by Downstroke of Elas...

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1st International Conference on Robotics, engineering, science, and Technology, RESTCON 2024

作者： Haishi, Sei Fuchiwaki, Masaki Kyushu Institute of Technology Graduate School of Computer Science and Systems Engineering Iizuka Japan

ISBN: (纸本)9798350348842

The shape of airfoil contributes to the performance of the airfoil itself. Elastic deformation during unsteady motion not only generates vortices with larger vorticity, but also enables the generation of sustained dynamic lift than rigid moving airfoil. Membrane airfoils exhibit the high performance by elastic deformation with unsteady motion. Butterfly wing, which is an example of application of membrane airfoil, have been shown to generate a pair of vortex rings and dynamic lift through flapping motion. However, it has not been clarified sufficiently how the wing shape responds to changes in dynamic lift and vortex rings. In this study, authors also visualized the vortex ring, simultaneously measured the dynamic lift, and measured the wing shape. When the wing pass through the horizontal position (when the flapping angle φ = 0 deg.), the dynamic lift is at its maximum, and the vortex ring becomes more developed. As a result, the wing chord direction gradually curves, and when the flapping angle φ =-30 deg., the entire wing chord becomes camber shape. Since no deformation occurred in the wingspan direction at any flapping angle in this measurement range, it can be said that camber in the wing chord direction is necessary to generate the dynamic lift and vortex ring. © 2024 IEEE.

关键词： Elastic deformation

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FedRec:Trusted rank-based recommender scheme for service provisioning in federated cloud environment

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Digital Communications and Networks 2023年第1期9卷 33-46页

作者： Ashwin Verma Pronaya Bhattacharya Umesh Bodkhe Deepti Saraswat Sudeep Tanwar Kapal Dev Department of Computer Science and Engineering Institute of TechnologyNirma UniversityAhmedabadGujarat382481India Department of Institute of Intelligent Systems University of JohannesburgPO Box 524South Africa

The emergence of on-demand service provisioning by Federated Cloud Providers(FCPs)to Cloud Users(CU)has fuelled significant innovations in cloud provisioning *** to the massive traffic,massive CU resource requests are sent to FCPs,and appropriate service recommendations are sent by ***,the FourthGeneration(4G)-Long Term Evolution(LTE)network faces bottlenecks that affect end-user throughput and ***,the data is exchanged among heterogeneous stakeholders,and thus trust is a prime *** address these limitations,the paper proposes a Blockchain(BC)-leveraged rank-based recommender scheme,FedRec,to expedite secure and trusted Cloud Service Provisioning(CSP)to the CU through the FCP at the backdrop of base 5G communication *** scheme operates in three *** the first phase,a BCintegrated request-response broker model is formulated between the CU,Cloud Brokers(BR),and the FCP,where a CU service request is forwarded through the BR to different *** service requests,Anything-as-aService(XaaS)is supported by 5G-enhanced Mobile Broadband(eMBB)*** the next phase,a weighted matching recommender model is proposed at the FCP sites based on a novel Ranking-Based Recommender(RBR)model based on the CU *** the final phase,based on the matching recommendations between the CU and the FCP,Smart Contracts(SC)are executed,and resource provisioning data is stored in the Interplanetary File systems(IPFS)that expedite the block *** proposed scheme FedRec is compared in terms of SC evaluation and formal *** simulation,FedRec achieves a reduction of 27.55%in chain storage and a transaction throughput of 43.5074 Mbps at 150 *** the IPFS,we have achieved a bandwidth improvement of 17.91%.In the RBR models,the maximum obtained hit ratio is 0.9314 at 200 million CU requests,showing an improvement of 1.2%in average servicing latency over non-RBR models and a maximization trade-off of QoE index of 2.76

关键词： Blockchain 5G-enhanced mobile broadband Federated clouds Rank-based recommender model Smart contracts

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ABES: attention bi-directional ensemble SVM for early detection of brain tumors

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Neural Computing and Applications 2024年第26期36卷 16179-16193页

作者： Subramaniam, Erana Veerappa Dinesh Krishnasamy, Valarmathi Department of Computer Science and Business Systems Ramco Institute of Technology Rajapalayam India Department of Electronics and Communication Engineering P.S.R. Engineering College Sivakasi India

Brain tumor is the most serious and deadly disease, and it is formed due to abnormal cell production. There are two different sorts of tumors including benign (non-cancerous) and malignant (cancerous), and the third level of a brain tumor is cancerous, which is a highly deadly form of cancer. Detecting brain tumors early is crucial for accurate diagnosis and efficient treatment planning. Although numerous techniques are established, the prediction techniques are time-consuming, have higher computational complexity, prone to overfitting, and have limited accuracy. Thus we proposed an Attention Bi-directional Gated Recurrent Unit Ensemble Support Vector Machine (ABES model) for detecting brain tumors. Here, the Bi-GRU layer learns the most important context information for every image. The attention layer also uses the attention mechanism to assign weights to each Bi-GRU layer output. The ensemble SVM classifier performs the categorization of brain tumors. Then, the training set is used to train the Attention BiGRU model and Ensemble SVM classifiers. The ABES classifier weights are optimized by utilizing the improved whale optimization algorithm. To demonstrate the efficiency of our proposed method, we compare it to four existing methods including Fully Automatic Heterogeneous Segmentation-based Support Vector Machine, Whale Harris Hawks Optimization-based Deep Learning, Machine learning-based back propagation neural networks, and Deep Convolutional Neural Network. Also, the ABES model is validated using the MRI dataset and the Figshare brain tumor dataset. Accuracy, precision, specificity, and sensitivity are the performance metrics used to evaluate classification performance, which achieves 97.2% accuracy, 98.61% Precision, 96.48% Specificity, and 97.34% Sensitivity, respectively. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Support vector machines

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Resource Based Automatic Calibration System (RBACS) Using Kubernetes Framework

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Intelligent Automation & Soft Computing 2023年第1期35卷 1165-1179页

作者： Tahir Alyas Nadia Tabassum Muhammad Waseem Iqbal Abdullah S.Alshahrani Ahmed Alghamdi Syed Khuram Shahzad Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Computer Science&IT Virtual University of PakistanLahore54000Pakistan Department of Software Engineering The Superior UniversityLahore54000Pakistan Department of Computer Science&Artificial Intelligence College of Computer Science&EngineeringUniversity of Jeddah21493Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Informatics&Systems University of Management&TechnologyLahore54000Pakistan

Kubernetes,a container orchestrator for cloud-deployed applications,allows the application provider to scale automatically to match thefluctuating intensity of processing *** cluster technology is used to encapsulate,isolate,and deploy applications,addressing the issue of low system reliability due to interlocking ***-based platforms usually entail users define application resource supplies for eco container *** is a constant problem of over-service in data centers for cloud service *** operating costs and incompetent resource utilization can occur in a waste of *** revolutionized the orchestration of the container in the cloud-native *** can adaptively manage resources and schedule containers,which provide real-time status of the cluster at runtime without the user’s *** clusters face unpredictable traffic,and the cluster performs manual expansion configuration by the *** to operational delays,the system will become unstable,and the service will be *** work proposed an RBACS that vigorously amended the distribution of containers operating in the entire Kubernetes *** allocation pattern is analyzed with the Kubernetes *** estimate the overall cost of RBACS,we use several scientific benchmarks comparing the accomplishment of container to remote node migration and on-site *** experiments ran on the simulations to show the method’s effectiveness yielded high precision in the real-time deployment of resources in eco *** to the default baseline,Kubernetes results in much fewer dropped requests with only slightly more supplied resources.

关键词： Docker container virtualization cloud resource kubernetes

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ViF-SD2E: a robust weakly-supervised framework for neural decoding

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Neural Computing and Applications 2025年第9期37卷 6645-6661页

作者： Feng, Jingyi Luo, Yong Song, Shuang Hu, Han National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China School of Information and Electronics Beijing Institute of Technology Beijing100081 China

Neural decoding plays a vital role in the interaction between the brain and the outside world. Our task in this paper is to decode the movement track of a finger directly based on the neural data. Existing neural decoding solutions primarily perform some preprocessing operations on neural data before feeding them into existing models (such as LSTM) for decoding. However, these solutions either are prone to overfitting or cannot well exploit the spatial and temporal information. In our previous observations, there is a symmetrical phenomenon between the unsupervised decoded trajectory and the ground truth trajectory within the activity space. This precisely motivates us to propose (or derive) a robust weakly-supervised framework (or model structure), called ViF-SD2E, for neural decoding. In particular, it consists of a space-division (SD) module and an exploration–exploitation (2E) strategy, to effectively exploit both the spatial information of the outside world and the temporal information of neural activity, where the SD2E output is analogized with the weak 0/1 vision feedback (ViF) label for training. Extensive experiments demonstrate the effectiveness of our method, which can sometimes be comparable to supervised counterparts. Therefore, we redirect our attention to the information (hidden in data) ViF-SD2E conveys to us. In other words, we believe that the advantage of ViF-SD2E lies in the fact that its processing steps are objectively determined by the inherent attributes (i.e., symmetry) of the neural data, or rather, the model structure is fixed. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Space division multiple access

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Single-level Discrete Two Dimensional Wavelet Transform Based Multiscale Deep Learning Framework for Two-Wheeler Helmet Classification

EAI Endorsed Transactions on Industrial Networks and Intelli...

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EAI Endorsed Transactions on Industrial Networks and Intelligent systems 2025年第2期12卷 1-13页

作者： Annadurai, Amrutha Prusty, Manas Ranjan Pandey, Trilok Nath Patra, Subhra Rani School of Computer Science and Engineering Vellore Institute of Technology Chennai India Centre for Cyber Physical Systems Vellore Institute of Technology Chennai India Information Systems and Operations Management University of Texas at Arlington Texas 76019 United States

INTRODUCTION: A robust method is proposed in this paper to detect helmet usage in two-wheeler riders to enhance road safety. OBJECTIVES: This involves a custom made dataset that contains 1000 images captured under diverse real-world scenarios, including variations in helmet size, colour, and lighting conditions. This dataset has two classes namely with helmet and without helmet. METHODS: The proposed helmet classification approach utilizes the Multi-Scale Deep Convolutional Neural Network (CNN) framework cascaded with Long Short-Term Memory (LSTM) network. Initially the Multi-Scale Deep CNN extracts modes by applying Single-level Discrete 2D Wavelet Transform (dwt2) to decompose the original images. In particular, four different modes are used for segmenting a single image namely approximation, horizontal detail, vertical detail and diagonal detail. After feeding the segmented images into a Multi-Scale Deep CNN model, it is cascaded with an LSTM network. RESULTS: The proposed model achieved accuracies of 99.20% and 95.99% using both 5-Fold Cross-Validation (CV) and Hold-out CV methods, respectively. CONCLUSION: This result was better than the CNN-LSTM, dwt2-LSTM and a tailor made CNN model. © 2025 A. Annadurai et al.

关键词： Convolutional neural networks

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