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检索条件"机构=Dept. of Computer Science and Engineering Amrita School of Computing"

共 1510 条记录，以下是1-10 订阅

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Nova: Revolutionizing Agile Project Management with Scrum and Kanban Integration for Enhanced Team Collaboration and Productivity 5

Nova: Revolutionizing Agile Project Management with Scrum an...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Manaswini, Poluri Shaik, Sajjad Ashwani, Siddhant Balakrishnan, Roshni M. Rajagopal, Shinu M. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

In the realm of software project management, Agile methodologies such as Scrum and Kanban have become quintessential for fostering adaptability and efficiency. "Nova,"a novel project management tool, embodies these methodologies to offer a user-friendly interface that supports the dynamic needs of modern software development teams. This paper explores Nova's unique approach that integrates the foundational principles of Agile which are Scrum, and Kanban, thereby facilitating a seamless and effective management platform. Through a comparative analysis with traditional project management tools, this study evaluates how Nova enhances team collaboration, project tracking, and outcome predictability. Emphasizing its ease of use and adaptability, the research highlights Nova's potential to improve project delivery times and overall team productivity. © 2024 IEEE.

关键词： Lean production

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LiDAR-Based Elderly Fall Detection System for Indoor Environments Using Neural Network Algorithms 4

LiDAR-Based Elderly Fall Detection System for Indoor Environ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Vignesh, Puthucode Ganesh Nithin Kumar, S. Senthil Kumar, T. Jeyakumar, G. Amrita School of Computing Dept. of Computer Science and Engineering Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798331523923

Fall detection systems are critical in elderly care and healthcare monitoring, but traditional camera-based solutions raise significant privacy concerns. This research presents a novel approach utilizing LiDAR (Light Detection and Ranging) sensors for fall detection in indoor environments, addressing privacy issues while maintaining high accuracy. We introduce LiFall, a new dataset captured using a Velodyne VLP 16 Puck LiDAR sensor, comprising 12 scenes and over 500 frames simulating various fall scenarios (including back falls, bed falls, and walking falls), contributing to the scarcity of LiDAR-based fall detection datasets. Our methodology employs two advanced deep learning architectures: Annularly Convolutional Neural Networks (ACNN) and Dynamic Graph Convolutional Neural Networks (DGCNN). Evaluating these models on both the ModelNet40 dataset and our custom LiFall dataset, results show DGCNN outperforms ACNN, achieving an accuracy of 84% on the ModelNet40 dataset with a 70:30 train-test split. On the LiFall dataset, despite challenges with dataset imbalance and achieving lower detection accuracy even after data augmentation, our approach demonstrates the potential of LiDAR-based fall detection systems. Compared to other fall detection methods, our LiDAR-based system offers significant advantages beyond privacy preservation, such as robust performance in low-light conditions and the ability to capture detailed 3D spatial information. While our current implementation is limited to laboratory settings, future work will focus on real-world deployments and further addressing dataset imbalance issues to enhance overall system performance. This research contributes to the field by providing a privacy-preserving, high-performance alternative to camera-based fall detection systems, paving the way for more acceptable monitoring solutions in sensitive indoor environments. © 2025 IEEE.

关键词： Fall detection

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Advancements in Cricket Shot Detection: Integrating Human Pose Estimation and Deep Learning for Automated Analysis 15

Advancements in Cricket Shot Detection: Integrating Human Po...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Datta, K.S. Sreekar Naidu, G. Narasimha Dasari, Rahul Jayakumar, T.V. Dept. Of Computer Science and Engineering Amrita School Of Computing Amrita Vishwa Vidyapeetham Amritapuri India Dept. Of Computer Science and Applications Amrita School Of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350370249

Cricket, a sport loved worldwide, often presents challenges for aspiring players due to limited access to infrastructure and coaching resources. Cricket’s automated learning solutions are still largely unexplored, despite its popularity. By concentrating on the application of cricket shot detection, a crucial component of the game, this research fills this gap. One essential component of computer vision that becomes apparent in deciphering the subtleties of cricket shots is human stance estimate. Pose estimate provides a detailed examination of batting skills and footwork by tracking important areas on the batsman’s body, such as joints and limbs. MoveNet is a significant framework in this domain that has garnered accolades for its real-time performance and lightweight architecture, which enable accurate identification of human poses, which is a crucial requirement in the dynamic world of cricket. This study explores the combination of deep learning techniques and human pose estimate for cricket shot recognition. More precise and reliable cricket shot identification and classification are made possible by the model’s ability to extract complex patterns and features from the input data by utilizing deep learning techniques. Our goal is to automatically identify and classify cricket shots based on players’ minor motions through theoretical investigation, empirical validation, and real-world applications. Through the integration of cutting-edge technologies, such as deep learning, with traditional cricket analysis, our goal is to gain a deeper understanding of the game and discover new avenues for sports analytics, coaching, and broadcasting. ©2024 IEEE.

关键词： Cricket (Sports)

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Reinforcement Learning-Based Autonomous Landing of AirSim Simulated Quadcopter in Unreal Engine 15

Reinforcement Learning-Based Autonomous Landing of AirSim Si...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Paladugula, Pruthvi Don, S. Vemulapalli, Nehith Sai Prabhat, Guggilam Sai Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Dept. of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350370249

This paper presents a novel approach for landing an autonomous quadcopter using reinforcement learning (RL) in the AirSim simulation environment running on Unreal Engine. The complicated dynamics and unpredictability of quadcopter landing procedures make them difficult to execute and frequently reduce the efficacy of conventional control techniques. In order to overcome these difficulties, the suggested RL model interacts with an incredibly lifelike simulation to learn the best landing techniques, which faithfully replicates physical principles. Impressing with notable improvements in landing precision and resilience in many scenarios, this model holds potential to improve unmanned aerial vehicle operations. By utilizing reinforcement learning within a practical simulation setting, this study advances the creation of more effective and flexible autonomous landing systems for unmanned aerial vehicles (UAVs), potentially leading to benefits in practical uses. ©2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Real-Time Traffic Prediction And Optimization using Machine Learning 3

Real-Time Traffic Prediction And Optimization using Machine ...

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

作者： Vineetha, Kundrapu Tharun Reddy, M. Jaffer Ali, Md. Prathima, Narisetty Rajagopal, Shinu M. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The Real-Time Traffic Prediction and Optimization System may be described as enriched, spacious, algorithm-oriented, and designed to help contribute to the anti-symptomatic deprievement of increasingly congested urban traffic streams. The system with the help of integrating real time data of numerous sources like GPS tracker, traffic sensor as well as weather API, the system using advanced machine learning algorithm in order to predict traffic accurately and optimize routes based on it. This predictive capability then greatly improves the flow of vehicles, the time taken, and the occurrence of choke points. The structure of the system is fully based in the cloud, which makes it easy to scale and very compatible with other systems to amalgamate a vast array of data inputs with high dependability. Fully customizable and intuitive control panel gives live view of traffic, offers actionable stats, and instant control mechanisms to traffic operators to make correct decisions quickly. Congestion in urban areas has an influence on time taken to cover a certain distance, fuel consumption and emission to the atmosphere. On the basis of the above analysis, this work extends a Real-Time Traffic Prediction and Optimization System (RTPOPS) using machine learning approach to predict the traffic density and to further optimize traffic signals and routes. The used system demonstrated successful management and reduction of vehicle density by 30%, and increasing speed limits by 18% and proved that Intelligent Urban Mobility has the potential to be achieved. © 2025 IEEE.

关键词： Traffic congestion

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A Comprehensive End-to-End Solution for Web Security with Cryptography, Multi-Factor Authentication, and Secure Communication 3

A Comprehensive End-to-End Solution for Web Security with Cr...

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

作者： Kumar, Kanderi Johith Sai, Kandlapalli Aravind Reddy, Adapala Dharshan Reddy, Chinthamreddy Pranay Daiwik Rajagopal, Shinu M. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

In today’s digital landscape, securing sensitive information has become an essential priority in the modern digital era for both individuals and organizations. This project introduces a comprehensive web application that integrates advanced encryption and decryption functionalities alongside multifactor authentication (MFA) and secure payment processing. The application supports various cryptographic algorithms, including AES, DES, Triple DES, Blowfish, etc., allowing users to securely encrypt and decrypt data through dedicated routes accessed over secure HTTPS connections. To further enhance security, robust multifactor authentication mechanisms, such as OTP-based challenge-response and QR-code-enabled 2FA, have been implemented to prevent unauthorized access to sensitive operations. The platform also incorporates PGP encryption powered by Keybase, enabling users to securely exchange encrypted emails. Additionally, a secure payment gateway is integrated using Stripe, ensuring sensitive payment data is encrypted and handled safely. With a strong focus on data integrity, user privacy, and modern web security standards, this project delivers an all-encompassing solution for safeguarding sensitive information and facilitating secure communication. By addressing common vulnerabilities and security risks, the platform stands as a reliable and scalable tool for data protection. © 2025 IEEE.

关键词： Authentication Protocol

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Enhanced Image Captioning Using CNN and BLIP Models

Enhanced Image Captioning Using CNN and BLIP Models

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Tejesh, Vambara Supriya, M. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331509828

This research focuses on generating image captions using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models. As deep learning advances, the availability of large datasets and increased computing power make it more feasible to build models capable of creating captions for images. This paper uses CNN and RNN models in Python to achieve this. Image captioning combines image recognition and Natural Language Processing (NLP) to interpret image context and express it in English, drawing on core computer vision principles. This study reviews important concepts in image captioning, including the applications of Keras, NumPy, and Jupyter notebook for development. This paper also explores the use of the Flickr dataset and CNN for image classification. The BLEU score of the proposed models is found to be close to 60%. On further enhancement to the model, it could be achieved to get a better score. © 2025 IEEE.

关键词： Convolutional neural networks

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A Study of Landmark-Augmented and Landmark-Free Approaches: Yoga Pose Recognition 9

A Study of Landmark-Augmented and Landmark-Free Approaches: ...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Marasani, Jayasurya Yerramreddy, Dhanvanth Reddy Karthik, Abburi Sai Karthik, Maddala H. S. M. K. Jayakumar, T.V. Nedungadi, Prema Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science And Engineering Amritapuri India Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science And Applications Amritapuri India Amrita Vishwa Vidyapeetham Amrita School of Computing Amritapuri India

ISBN: (纸本)9798350394474

In the realm of yoga pose recognition, this project delves into a comprehensive investigation of two distinct methodologies, namely landmark-augmented and landmark-free approaches, to enhance the accuracy and efficacy of pose recognition systems. The first approach leverages state-of-the-art pose estimation techniques employing MoveNet, MediaPipe, and YOLO V8 to precisely detect pivotal anatomical landmarks that define yoga postures. Subsequently, these detected landmarks are utilized as foundational cues for the classification process. On the other hand, the landmark-free approach directly employs cutting-edge deep learning architectures, specifically Vision Transformer, ResNet50, and VGG16, to classify yoga poses without the explicit utilization of detected landmarks. By juxtaposing these two methodologies, the project endeavors to uncover the comparative advantages, challenges, and limitations associated with each approach in the domain of yoga pose recognition. Beyond performance evaluation, this study aims to provide valuable insights into the most effective and robust techniques for accurately identifying and classifying yoga poses. The findings are poised to contribute to the development of sophisticated yoga pose recognition systems that hold promise in assisting yoga practitioners in achieving proper alignment and posture during their practice, ultimately promoting their overall well-being and practice effectiveness. © 2024 IEEE.

关键词： Deep learning

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AI based Pentest For EHR and Other Health Monitoring Devices 5

AI based Pentest For EHR and Other Health Monitoring Devices

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Kumaran, U. Gurupriya, M. Chinta, Hitesh Surya Maganti, Akshita Uma Sai Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science Bangalore India Amrita School of Computing Dept. of Computer Science Bangalore India

ISBN: (纸本)9798331504403

The increasing reliance on Electronic Health Records (EHRs) and health monitoring devices has made healthcare systems vulnerable to cyberattacks. This research proposes an AI-based penetration testing framework to address these security concerns. By integrating machine learning techniques, our framework offers a more realistic approach to learning actual attack patterns, improving detection accuracy and response time compared to traditional methods. The framework's ability to minimize false positives and adapt to new threats demonstrates its effectiveness in strengthening healthcare system security. This research highlights the potential of AI-driven penetration testing in safeguarding patient information and protecting the integrity of healthcare systems. By identifying and addressing vulnerabilities, this framework can contribute to a more secure and resilient healthcare environment. © 2024 IEEE.

关键词： Electronic health record

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ConfigMaster: An Interactive Solution for System Management 8

ConfigMaster: An Interactive Solution for System Management

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Ganapathi, Ananya Sivakumar, Parikshith Divya, K.V. Elango, Aditya Gupta, Hardik Amrita School of Computing Dept. of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798331505462

The 'ConfigMaster: An interactive solution for system management' is a simple utility written using Bash scripting. It is designed for Unix-like systems. The tool lets users easily configure system settings and do administrative tasks without having to remember complex commands. The tool minimizes user's work by automating tasks. The tool can be used even by users with very little technical knowledge, as it has a simple menu-based interface to manage things like network settings, software packages, and wireless connections directly from the terminal. By automating common tasks, the tool makes system management more efficient and accessible. By automating common tasks, the tool makes system management more efficient and accessible. It's very useful to simplify system configuration on Unix-like systems using scripting languages like Bash. 'ConfigMaster' aims to offer a smooth user experience with the incorporation of core management principles from an operating system, such as disk scheduling, process synchronization, and memory management etc. Being flexible itself, this tool will bring further improvements to make effective responses to emerging user needs. The framework being flexible, it can be expanded to add new features to configure under different scenarios. By integrating essentials operating system operations, 'ConfigMaster' enables users to efficiently perform system configurations along with promoting ease of use. © 2024 IEEE.

关键词： Wi-Fi

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