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检索条件"机构=School of Computing Engineering and Science"

共 32939 条记录，以下是71-80 订阅

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Blockchain based File Storage and File Sharing System with Time-Restricted Access using InterPlanetary File System 3

Blockchain based File Storage and File Sharing System with T...

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Venkat Prashanth, M. Hemesh Verma, K. Chauhan, Jaykishor Prasad Rajagopal, Shinu M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bangalore India

ISBN: (纸本)9798331527549

This paper presents a file storage and sharing system implemented using IPFS Pinata, *** and Hardhat which offer decentralized, effective, and secure access to digital assets. This system also enables the ability to upload files to the IPFS network for distributed storage and the ability to share them with peers . It also implements a time based access control system using smart contracts to easily control access to the files uploaded. This innovation improves security and facilitates timely exchange of information while also eradicating the necessity of having to manage access. This is due to the versatility of blockchain’s unalterable nature together with IPFS distributed file-sharing system which provides a secure file-sharing method while keeping costs low and helps maintain adaptability. © 2025 IEEE.

关键词： Blockchain decentralized IPFS secure access Time Based Access Conrol

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Predicting and Evaluating Anomaly Detection and Traffic Analysis on Software Defined Networks Using a Hybrid Machine Learning Approach 5th

Predicting and Evaluating Anomaly Detection and Traffic Anal...

引用

5th International Conference on computing and Network Communications, CoCoNet 2023

作者： Darshan, S. Radhika, N. Radhika, G. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9789819745395

In the rapidly evolving landscape of Software Defined Networks (SDNs), network security is of paramount importance. This research paper presents a comprehensive study on anomaly detection and traffic analysis in SDNs using machine learning algorithms. The study addresses a critical research gap by proposing an effective methodology for detecting network anomalies and analyzing traffic patterns. Leveraging a carefully selected dataset, we evaluate the performance of Logistic Regression, Decision Tree, Random Forest, Neural Networks and Hybrid Models. The results demonstrate the superior performance of the hybrid model built using Decision Tree and Neural Networks and the competitive accuracy of Neural Networks in SDN anomaly detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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DeepUIR-Net: Underwater Image Restoration Using Residual-UNet with Optimized Efficient Channel Attention Network Integration 2nd

DeepUIR-Net: Underwater Image Restoration Using Residual-UNe...

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2nd International Conference on Applied Algorithms, ICAA 2025

作者： Rayvanth, N. Amruth, S. Jaya Suryaa, E. Resmi, S. Singh, Rimjhim Padam Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9783031845420

The underwater image restoration task targets retaining the true colors of the underwater scenarios captured in the images by rectifying the distorted colors to critically analyze the ocean scenarios and resources. This area has been explored using different deep learning techniques such as Convolutional neural networks, Residual Networks, and various other models that made use of post-processing techniques for the final enhanced outputs. Hence, this study proposes a novel approach by fine-tuning the Residual-UNet architecture and combining it with an Efficient Channel Attention network in an optimized architecture for the task of underwater image restoration. Channel attention mechanisms have a great ability to reduce model complexity by making use of fewer parameters and increasing the performance of the model. One such mechanism, called Efficient Channel Attention, has been incorporated into the Residual-UNet model to extract features efficiently and obtain an enhanced underwater image that is true to the ground truth image by making use of the HardSwish activation function. The dataset used to train the Residual-UNet model is LSUI (Large Scale Underwater Images), a benchmark dataset consisting of various real underwater images. The proposed model obtained SSIM and PSNR values of 0.9998 and 74.851 dB respectively and outperformed other models by a margin of 4%. To determine the superiority of the Efficient Channel Attention network optimally integrated into the Residual-UNet model, various base models without this attention module have also been trained and evaluated. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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IoT Environmental Monitoring with Anomaly Detection 3

IoT Environmental Monitoring with Anomaly Detection

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Abhiram, D. Dhanush Reddy, K. Koushik Reddy, P. Rajagopal, Shinu Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bangalore India

ISBN: (纸本)9798331527549

This project proposes an IoT-based environmental evaluation system taking into consideration its surroundings through a microcontroller ESP8266 and sensor DHT11. The system reads the current temperature and humidity in addition to providing alerts for changes as well as threshold and sensor integrity issues. It contains context awareness and adaptive monitoring. It connects to the Arduino IoT Cloud, which allows users to monitor data in real-time, or receive notifications, or set up thresholds right from the mobile or web application. The features such as real-time monitoring, real-time anomaly detection, and remote access make this project practical as the key application for smart homes, farming and industries applications. © 2025 IEEE.

关键词： Microcontrollers

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Federated Learning based Gender Classification in Heterogeneous and Distributed Data having Concept Drift 4

Federated Learning based Gender Classification in Heterogene...

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4th International Conference on Evolutionary computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Sharma, Vishwash Reddy Challa, Venkatahemant Kumar Pranavi, Pasupuleti Singh, Rimjhim Padam Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

Federated Learning is gaining significant traction in recent times, due to its ability to protect privacy while giving comparable results across various use cases. However, it often falls short in delivering good results in scenarios where the client devices at different places store huge and heterogeneous data. The studies explore the effectiveness of federated learning, when utilized for model training on heterogeneous data having concept drift nature and being placed on different client devices. The paper proposes a novel combination of EfficientNet-B0 model fine-tuned with adaptive and weighted normalization layers in a federated learning setup for gender classification in heterogeneous Fairface dataset having human images from different races. Several other Convolutional Neural Network (CNN) models namely, ResNet50, DenseNet121, GoogLeNet, AlexNet etc. have also been trained in different traditional and federated setups. Experiments revealed that EfficientNet-B0 outperformed the other models trained in all the scenarios with an outperforming accuracy of 88.18% in the traditional federated setup and 90.56% when fine-tuned using normalization methods and evaluated in a federated setup with four client devices. These findings highlight the potential of normalization techniques to improve the effectiveness of federated learning, especially when dealing with concept drift data. © 2025 The Authors. Published by Elsevier B.V.

关键词： Federated learning

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Scheduling Policies in Edge computing for Real-Time Video Analytics 3

Scheduling Policies in Edge Computing for Real-Time Video An...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Shreevatsan, S.S. Annambhotla, Yasasree Lasya Reji, Anaswara Rajagopal, Shinu M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

This study compares different scheduling algorithms used in edge computing for real-time video processing. Instead of sending video data to cloud servers, the video is processed directly at the edge, reducing delays and saving bandwidth. The focus is on how different scheduling methods manage resources, prioritize tasks, and reduce the time it takes to analyze video. The algorithms are tested in simulated environments to understand how they impact system efficiency and performance. The goal of this work is to find the best scheduling policies for real-time video processing at the edge, improving overall system performance and scalability. © 2025 IEEE.

关键词： Scheduling algorithms

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Leveraging Sequential Deep Learning Models for Robust Compiler Bug Detection in Software Logs 3

Leveraging Sequential Deep Learning Models for Robust Compil...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Mishra, Sahil Tiwari, Utkarsh Kavitha, C.R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Compiler bugs critically impact the correctness of software applications, making their detection and resolution vital for improving software quality. This paper proposes a novel approach leveraging sequential deep learning models, including Long short-term memory, Gated recurrent unit, Recurrent neural network, and a stacked ensemble model, to enhance compiler bug identification. By combining the strengths of these architectures, the proposed method improves predictive accuracy and generalization. To ensure model interpretability, we employ LIME (Local Interpretable Model-agnostic Explanations) to identify the features influencing bug detection decisions. Experimental results demonstrate that the stacked ensemble outperforms individual models in terms of precision, recall, F1-score, and ROC-AUC. This work advances the state of compiler log analysis and contributes significantly to software quality assurance by integrating robust deep learning techniques with explainable AI. © 2025 IEEE.

关键词： Application programs

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From Pixels to Truth: Unveiling Deepfakes with a Meso-5 and XceptionNet Fusion Network 5th

From Pixels to Truth: Unveiling Deepfakes with a Meso-5 and ...

引用

5th International Conference on computing, Communications, and Cyber Security, IC4S 2023

作者： Abhinandhan, S. Sreedevi, A.G. Saranya, G. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9789819773701

The growth of deepfakes, or hyper-realistic synthetic media, demands effective detection algorithms to ensure the validity of visual information. This study presents a unique deepfake detection model based on a hybrid architecture that takes advantage of the characteristics of both Meso-5 and XceptionNet. Meso-5, which is well-known for its capacity to efficiently capture mesoscopic picture characteristics, is carefully paired with Xception-Net’s outstanding feature extraction capabilities. This novel strategy tries to achieve higher deepfake detection accuracy than previous approaches. We assess the proposed model’s performance using the publicly available Deepfake Detection Challenge (DFDC) dataset, which was published by Facebook in 2018. Our results show that this hybrid technique works well, with an impressive accuracy of 81.24% in detecting deepfakes. These findings demonstrate our model’s ability to mitigate the deepfake danger and build a more dependable future for visual information. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deepfake detection DFDC dataset Hybrid architecture Hyper-realistic synthetic visuals Image detection Meso-5 Visual threat XceptionNet

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Enabling Cursor Control Through Eye Movement Using Hidden Markov Model 2nd

Enabling Cursor Control Through Eye Movement Using Hidden Ma...

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2nd International Conference on Data science and Network engineering, ICDSNE 2024

作者： Tanusha, G. Havirbhavi, P. Ashwini, K. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9789819783359

First and foremost, we present a novel scheme for controlling the cursor based on eye movement. Other strategies rely on physical items like touchpads or a mouse and place strict requirements on people with motor handicaps. Our solution connects digital interaction with human visual attention through eye-tracking technology. We examine the nuanced gaze patterns that develop, allowing for cursor control while also developing a sense of interaction context. Gaze data is transformed by our method into the motion of a cursor. It is highly accurate and adjustable. Cursor motions are generated based on the gaze movement pattern, making the interface more dynamically accurate and more tailored to individual behaviors. The context-aware interface maximizes efficiency through its adaptability as it learns and adopts the behaviors. In addition, our research introduces a novel scheme through the use of software tools to offer an eye-controlled mouse system. This system accurately recognizes facial features and interprets eye movements through image processing techniques. The system enables intuitive cursor control by interpreting observable eye movements. Transforming eye motions into a cursor's actions, it enhances user accessibility and hands-free interaction;in other words, its performance is captured in the experimental analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Eye movements

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Predicting Judgement Outcomes from Legal Case File Summaries with Explainable Approach 27th

Predicting Judgement Outcomes from Legal Case File Summaries...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Surisetty, Hima Varshini Gottimukkala, Sarayu Varma Prabhakar, Priyanka Pati, Peeta Basa Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9783031781063

Legal Outcome Prediction (LOP) is the process of predicting the possible outcome of a court case based on the case file. The aim of this work is to assess the performance of four deep learning models namely BERT, LegalBERT, DistilBERT and RoBERTa. These models were trained and tested with complete case files and their summaries. The models were then evaluated using metrics like train-test accuracy, loss plots and F-score. Based on these evaluation metrics, RoBERTa was chosen as the model with the best performance. In order to understand the predictions made by the deep learning models, Local Interpretable Model-Agnostic Explanations (LIME) was used. LIME is an eXplainable AI (XAI) tool that highlights the words in a case file which were used in order to predict the outcome of that case. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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