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Person Re-Identification: A Retrospective Study, Recent Trends and Future Scope 1

Person Re-Identification: A Retrospective Study, Recent Tren...

引用

1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Soni, Shashank Kumar Kumar, Gautam National Institute of Technology Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350365016

Person re-identification (Re-ID) searches several non-overlapping cameras for a person of interest. The development of deep neural networks combined with the increasing need for intelligent video surveillance has led to a huge surge in interest in the field of computer vision. This article highlights the state-of-the-art techniques used for person re-identification (reID), including the latest advancements and challenges. It outlines the different person re-identification datasets that are available in the literature, evaluates the effectiveness of the best techniques across a range of datasets, and clarifies the important restrictions on person re-identification, including the effects of changing environmental conditions and data limitations. Furthermore, the article points out the future research direction, focusing on creating more realistic datasets and designing robust algorithms that can effectively handle real-world conditions in order to drive the growth and development of re- ID technology. © 2024 IEEE.

关键词： Deep neural networks

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IoT based Wireless Alert System for Individuals with Impaired Hearing 3

IoT based Wireless Alert System for Individuals with Impaire...

引用

3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Susan Jessica, V. Hannah Jael, A. Dishad Emeth Xavio, J. Premsai, I. Thiyagu, T.M. Karunya Institute of Technology and Sciences Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350396157

Humans' relationships with the environment are revolutionized by the Internet of Things (IoT), especially with disadvantaged groups like the deaf population. Individuals with hearing impairments are frequently not alerted by conventional auditory alerts, leaving them vulnerable to risks and disconnected from vital information. The alarm system suggested in this study is IoT-based and warns non-hearing users by valuing their safety with its greatest priority. The technology uses multiple vibration frequencies to notify the user and smart wearables like smart watches to display live footage of the outside environment while it is being recorded by a camera. This novel strategy promises to close the current communication gap and improve the deaf community's standard of living. The IoT ecosystem's interconnected gadgets provide advantages like improved communication, increased independence, and greater inclusion. The revolutionary potential of an Internet of Things-based alert system for the deaf community emphasizes how crucial technology is to promoting accessibility, safety, and equitable participation for everyone. © 2024 IEEE.

关键词： Internet of things

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Classifier Retraining with Gaussian-distributed Prototypes for Class-Imbalanced Federated Medical Image Classification

Classifier Retraining with Gaussian-distributed Prototypes f...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Pan, Haiwei Chen, Chunling Zhang, Kejia Li, Zhe Yu, Fengming Harbin Engineering University College of Computer Science and Technology Harbin China

ISBN: (纸本)9798350386226

Federated learning enables collaborative learning across distributed medical institutions without centralizing data. However, existing studies often overlook class imbalance in medical images, which can degrade model performance, especially for minority classes. In this paper, we propose FedCRGP, a method that uses Gaussian-distributed prototypes to address class imbalance in federated medical image classification. These prototypes incorporate class variance to enhance representation learning. Class-aware Gaussian Prototype Learning (CGPL) is introduced to improve intra-class similarity and reduce interclass similarity by aligning class features around the Gaussian-distributed prototypes. Local and global features sampled from these prototypes are fused to retrain the classifier, mitigating bias caused by class imbalance. Experiments on two medical image classification datasets demonstrate its superior performance, particularly in multi-class scenarios. © 2024 IEEE.

关键词： Federated learning

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Detection of Thyroid Diseases Using Deep Learning 2

Detection of Thyroid Diseases Using Deep Learning

引用

2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Archana, P. Suhana Anjum, S. Keerthana, S.M. Srusti, P.D. Department of Computer Science and Engineering Adichunchanagiri Institute of Technology Chikkamagaluru India

ISBN: (纸本)9798350383867

Thyroid diseases, including hypothyroidism, hyperthyroidism, thyroiditis, nodules, cancer, and euthyroid disease, are serious health problems that require accurate and timely diagnosis and treatment. This article presents a new method that specifically uses deep learning in convolutional neural networks (CNN) to successfully classify thyroid disease based on X-ray and ultrasound images. Our method, which uses CNN principles and data such as x-ray and ultrasound containing multiple thyroid images, is effective in classifying diseases. Thanks to a careful pre-processing, optimization and elimination process, our CNN model achieved an accuracy of 97.92%, demonstrating its potential as an important diagnostic tool. Early diagnosis and treatment of thyroid disease. © 2024 IEEE.

关键词： Ultrasonic imaging

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PlantLDNet : Disease Detection in Leaf Images Using Deep Learning Models 5

PlantLDNet : Disease Detection in Leaf Images Using Deep Lea...

引用

5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Nikhileswar, Gurram Sai Pampana, Surya Sai Jani Anbarasi, L. Bhavana, Chintalapudi Likhitha Nanditha, Rayanki Lakshmi Krishna, Kodela Chaithanya School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350385649

Automated disease diagnosis in the agricultural field has benefited from the recent advances in big data, deep learning, and computer vision across a wide range of applications. However, intelligent disease prediction is crucial for safely maintaining a good agricultural production. Moreover, unclear disease prognosis is caused by the degree of leaf disease inhomogeneity, the complexity of the background-which includes the low contrast with the surrounding area and the potential for shadows with a similar intensity. This research provides a custom Convolutional Neural Network (CNN) model called PlantLDNet that has hyperparameters configured optimum to distinguish between leaf images showing disease and those without it, hence avoiding the problems associated with vanishing gradient. A comparative study of the proposed custom CNN model PlantLDNet and earlier research works was conducted using the following metrics: precision, recall, accuracy, F1 score, loss accuracy, receiver operating characteristic (ROC) curves, and Area Under Curve (AUC) measure. The comparison findings show that the proposed PlantLDNet model has the highest classification prediction effectiveness (99.85%) with less computational complexity. It also has an elevated high F1-score of 93% when trained for Early Bright, 98% for Healthy, and 92% for Late Bright. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Learning Approach on Multimodal Data for Myocardial Infarction Prediction 2

Deep Learning Approach on Multimodal Data for Myocardial Inf...

引用

2nd International Conference on Computational Intelligence and Network Systems, CINS 2024

作者： Parvathi, R. Pavithra, S. Pattabiraman, V. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798331504106

ISBN: (纸本)9798331504106

This research introduces a novel machine learning methodology for classifying electrocardiogram (ECG) images, integrating deep learning models like VGG16, Inception V3, and a custom CNN, alongside ensemble methods such as XGBoost and AdaBoost with RandomForest. The primary objective is to enhance the accuracy of automated ECG analysis, crucial for early detection of cardiovascular diseases (CVDs). By employing comprehensive data preprocessing and augmentation techniques, the models are trained on a diverse dataset encompassing various cardiac rhythm categories. Preliminary results indicate superior performance of the custom CNN model, underscoring the potential of machine learning in improving cardiac diagnostic processes and patient outcomes. Furthermore, patient analysis using a comprehensive dataset containing clinical attributes relevant to heart health supplements the research, aiming to identify key risk factors associated with heart disease and explore their predictive value in diagnosing CVDs. This integrated approach highlights the transformative potential of machine learning in cardiovascular healthcare, promising early detection and personalized treatment strategies for improved patient care. ©20XX IEEE.

关键词： Clinical research

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Performance Improvement of Model View Controller based Applications through Linda's-Key

Performance Improvement of Model View Controller based Appli...

引用

2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2023

作者： Suhas Reddy, P. Anala, Jayanth Krishnamurthy, Vallidevi Balasubramanian, Surendiran Sujithra Kanmani, R. School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India National Institute of Technology Puducherry Department of Computer Science and Engineering India

ISBN: (纸本)9798350312126

The Model-View-Controller (MVC) design pattern is widely used in software engineering for developing user interfaces. While MVC offers many benefits, handling data in a way that is efficient and effective can be a challenge. One approach to optimising the performance of MVC applications is converting lists to dictionaries. This paper discusses the benefits and drawbacks of this approach and presents the findings of recent research on this topic. The main advantage of converting lists to dictionaries is that it can improve the performance of MVC applications by offering faster access times and making code easier to read and maintain. However, there are drawbacks to this approach, such as increased memory usage and slower performance for certain operations. Several studies have been conducted on the performance of MVC applications when using lists versus dictionaries, with varying results. This paper overviews this research and highlights the implications for MVC development. © 2023 IEEE.

关键词： Data handling

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State-of-the-Art of Voice Assistance technology, Mitigating Replay Attacks: A Comprehensive Discussion 5

State-of-the-Art of Voice Assistance Technology, Mitigating ...

引用

5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Sandosh, S. Saxena, Raina Shah, Shashvi Rachiraju, Sai Sahiti School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350385649

In recent years, smart home assistants have become increasingly popular among consumers worldwide. These intelligent devices, powered by artificial intelligence and voice recognition technology, Smart home assistants bring convenience and effectiveness to handling daily activities and overseeing different aspects of our residences. Nevertheless, the expanding use of these assistants raises apprehensions regarding security and privacy due to heightened connectivity and data gathering capabilities. The intensity of security threats associated with these devices is alarming. Specifically, security concerns regarding replay attacks have emerged as a critical challenge. While various strategies have been proposed by researchers in this domain, there is a need for more robust mechanisms. This research paper proposes a novel approach to mitigate replay attacks by combining nonce numbers and timestamps. The proposed use of timestamp and nonce number methods in preventing replay attacks in smart home assistants is highly promising. The proposed solution enhances the security of voice commands transmitted between the user and the smart home assistant. By introducing unique nonce numbers and timestamps in the communication protocol, the system effectively detects and prevents the replaying of previously recorded commands. © 2024 IEEE.

关键词： Authentication

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Two-Pronged Intrusion Detection System for MANET

Two-Pronged Intrusion Detection System for MANET

引用

2024 IEEE International Conference on Emerging Technologies in computer science for Interdisciplinary Applications, ICETCS 2024

作者： Rajan, Nandana Vamsi Kiran, M. Vigneshwari, K. Jaiswal, Sameeksha Nithya, B. National Institute of Technology Department of Computer Science and Engineering Trichy India

ISBN: (纸本)9798350372502

Mobile Ad hoc Networks (MANETs) provide a flexible wireless infrastructure for various applications. Still, their decentralized and dynamic nature makes them susceptible to security threats. The topology of a MANET changes frequently due to node mobility. This dynamic nature can result in frequent network disruptions, packet losses, and increased latency. This paper introduces a Two-Pronged Intrusion Detection System (TP-IDS) designed for MANET, focusing on blackhole, hello flooding, and UDP flooding attacks. These attacks take advantage of the dynamic and self-organizing features of MANETs, making the network perform poorly. A dataset with eight features is created, capturing mobility, received, sent, and dropped packets, forwarding difference, energy, and transmission power. We have used SMOTE to overcome the class imbalance. Hyperparameter Optimization is performed using Bayesian Optimization and Optuna across Decision Tree, Random Forest, and XGBoost classifiers showcasing strengths and weaknesses for optimal selection. We conclude that the XGBoost classifier with Grid-initialized Bayesian hyperparameter optimization provides the best overall performance to create a robust TP-IDS. © 2024 IEEE.

关键词： Intrusion detection

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Detection and Prevention Mechanism for DDoS Attacks in Cloud Computing:The Role of Software-Defined Networking(SDN) 5

Detection and Prevention Mechanism for DDoS Attacks in Cloud...

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5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Shilpa Dahiya, Mamta Virmani, Charu Mriirs Computer Science And Engineering Faridabad India Mriirs Computer Science Engineering School Of Engineering And Technology Faridabad India

ISBN: (纸本)9798350330861

Cloud computing has developed as a vital technology for offering on-demand computing resources that meet the changing requirements of modern applications. The cloud environment, on the other hand, is especially vulnerable to Distributed Denial of Service (DDoS) assaults, which can interrupt services and cause serious financial and reputational harm. The purpose of this research paper is to investigate the detection and prevention techniques for DDoS attacks in a cloud computing environment, with a special emphasis on the function of Software-Defined Networking (SDN).This paper present an overview of cloud infrastructure, its importance in today's world, and the inherent weaknesses that make it an appealing target for attackers. Furthermore, it cover numerous sorts of DDoS attacks that might jeopardize cloud service availability and performance. The paper also explores the advantages SDN offers to cloud security by utilizing the dynamic and centralized control it provides. Through flow monitoring and analysis, behavioral anomaly detection, and machine learning-based approaches, the potential of SDN to improve the detection of DDoS attacks is explored. The SDN-enabled preventative techniques, such as traffic engineering, rate limitation, access control, and authentication procedures, as well as the significance of service provider cooperation in effectively mitigating DDoS assaults, are also discussed. © 2023 IEEE.

关键词： Software defined networking

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