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

共 36232 条记录，以下是251-260 订阅

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Sentiment Analysis on Telugu Text Translated from English using NLP and ML 3

Sentiment Analysis on Telugu Text Translated from English us...

引用

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

作者： Karthik, A.H.N. Aneesh, Ch. Saumik, Gembali Varun, K.V.V. Kavitha, C.R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

This research study mainly uses Natural Language Processing (NLP) and Machine Learning. The general approach of this tool is to apply sentiment analysis in Telugu Text translated from English. The Technique contains many preprocessing steps, for example, tokenization of texts, normalization and noise removal for translating English text into Telugu which then will use machine translation services. It is followed by sentiment analysis of the statement using NLP models and machine learning approaches and classified them as;positive, negative, or neutral. The main objective of this study is to enhance the accuracy of sentiment identification with non-English language. Environments and also to develop rigorous sentiment analysis tools optimized for Telugu, which are designed as to the language and context-related difficulties to be taken into account. Additionally, the research examines analyses the impact of bilingual datasets on increasing translation accuracy and sentiment classification, while also tackling the obstacles given by idiomatic idioms and context-specific interpretations in Telugu Language. © 2025 IEEE.

关键词： Machine translation

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IoT Environmental Monitoring with Anomaly Detection 3

IoT Environmental Monitoring with Anomaly Detection

引用

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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Advanced Adaptive Traffic Management System for Urban Environments 3

Advanced Adaptive Traffic Management System for Urban Enviro...

引用

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

作者： Sai Srujan Reddy, P. Naveen Raj, B.U. Jose, Nikhil Tom Belwal, Meena Department of Computer Science and Engineering Amrita School of Computing Amrita Viswa Vidhyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Urban traffic congestion is still a major problem that causes delays, collisions, and wasteful use of resources. Conventional fixed-schedule systems, which frequently exacerbate problems at junctions, U-turns, and pedestrian crossings, are insufficient for dynamic traffic situations. The Smart Dynamic Traffic Management System described in this work makes use of servo motors, proximity sensors and ultrasonic sensors, Arduino microcontrollers, and AI-powered cameras. To improve safety and streamline traffic flow, the system prioritizes emergency vehicles, controls U-turns, dynamically modifies traffic signals based on real-time data, and monitors road conditions. Scalability and adaptability for a range of urban scenarios are ensured by the integration of IoT technology and intelligent intercommunication between crossings. A visualization tool based on Pygame was used to simulate and assess the suggested solution. The findings showed that traffic flow had significantly improved, with an average increase in the exit-to-entry ratio of 30.71% for the three roadways. More specifically, traffic on Road A decreased by 15.28%, on Road B decreased by 31.25% and on Road C decreased by 45.61%. The outcomes confirm just how well the system works for reducing traffic and giving priorities to critical traffic situations, such as emergency response and turning. This system uniquely integrates real-time adaptive traffic control algorithms with IoT and machine learning to dynamically prioritize traffic flow and emergency vehicles, overcoming the limitations of existing static systems. © 2025 IEEE.

关键词： Traffic congestion

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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...

引用

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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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...

引用

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

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

引用

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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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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Smart Water Ball Launcher: An Arduino-based Fire Extinguishing System 3

Smart Water Ball Launcher: An Arduino-based Fire Extinguishi...

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

作者： Sridhar, Govindu L.V.N. Janakiram, M. Vishnu Ram Sai, M. Surya Narayana Reddy, G. Belwal, Meena Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The proposed work offers the production of an Arduino controlled Fire-RoboCar equipped with a water ball launcher. In a world dominated by human-machine interaction in industry, the efficiency of any task to be done is promoted by this proposed work and delivers solutions to needs like fighting fires through the provision of a productive method. At a time when increased pollution and the expense of fossil fuel are becoming very high, this alternative is environment-friendly. The Fire-RoboCar is a moving robot that can accomplish fire extinguishing tasking in an industrial complex, proving that the system may easily transport water to desired locations. This proposed work advances robotic vehicles’ capabilities and provides innovative solutions to modern industrial challenges. © 2025 IEEE.

关键词： Fire extinguishers

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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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Explainable Diabetic Retinopathy Detection Using EnlightenGAN Images with Deep Learning Technique 3

Explainable Diabetic Retinopathy Detection Using EnlightenGA...

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3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024

作者： Kannan, M. Nikitha, S. Baig, Thouqeer Akshay, S. Mukhopadhyay, Adwitiya Department of Computer Science School of Computing Amrita Vishwa Vidyapeetham Mysuru Campus Karnataka India

Diabetic Retinopathy (DR) is considered one of the most serious causes of visual impairment and permanent blindness in the world, causing damage to the optic nerve head without any prior clinical sign. Colored retinal images are the most often utilized imaging data in the medical sector for screening and diagnosing eye disorders, and fundus cameras are commonly employed in clinical research to collect these images. Due to a number of reasons, such as exposure discomfort, incorrect equipment parameter setting, improper operation, and a lack of proficiency on the part of various medical professionals during the image-acquisition process, there are many poor-quality retinal scans in the current database. The main purpose of this research is to perform early detection of DR by examining different levels of Proliferative Diabetic Retinopathy (PDR) and Nonproliferative Retinopathy (NPDR) images using deep learning algorithms. This work recommends employing EiGAN (EnlightenGenerative Adversarial Network) to enhance the quality of the fundus images. The ROI-based segmentation and subsequent classification of the quality-enhanced fundus images are accomplished with the help of the proposed Convolutional Transfer Learning Neural Network (CTLNN). The dataset was obtained from Precious Optics, Mysuru, India. The image dataset contains 1920 samples of PDR and NPDR fundus retinal images. Among these, there are multiple classes under NPDR, such as level-1 (healthy), level-2 (mild), level-3 (moderate), and level-4(severe), respectively. This research shows that the methods such as transfer learning and Efficient Net-B7 design to achieve robustness and efficiency. The methodology observes a significant increase across a range of performance indicators through a review of pre and post-enhancement findings. © 2025 The Author(s).

关键词： Clinical research

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