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

共 21727 条记录，以下是381-390 订阅

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Implementing Multinomial Naive Bayes for Employee Empowerment: A Transparent Platform for Task Management and Issue Resolution

Implementing Multinomial Naive Bayes for Employee Empowermen...

引用

2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Sudeesh Kumar, V. Mahendira Kumar, S. Kumar, S. Praveen Akyf Thanish, A. Rithani, M. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350379716

Developing a positive motivational culture among employees in today's changing workplace is achieved through effective communication and problem-solving. This abstract introduces an innovative Employee Empowerment Platform designed to enhance transparency and collaboration within the work-force. Using a text classification algorithm, this work presents an employee authorization system that classifies and analyzes problem reports submitted by employees. Using a multi-step approach to data preprocessing, the system extracts HTML tags, square brackets, and special characters from text data collected from problem reports. Count Vectorization and TF-IDF are two mathematical techniques used for transforming preprocessed text into features. At its heart, the platform uses a polynomial naive base classification to identify positive and negative emotions in problem reports and assign them to the appropriate category. In addition, the system uses data visualization techniques to provide a comprehensive understanding of the model's performance. Through accuracy plots and confusion matrices, you can study the prediction skill of the classifier. By establishing a clear process for finding work and facilitating problem classification, our employee empowerment program helps companies identify common concerns about work and fosters an exchange of ideas between employees creating a workplace environment that nurtures individual growth and collective success. The main objective of the platform is to increase enthusiasm and empowerment in the workplace by addressing challenges quickly. © 2024 IEEE.

关键词： Empowerment of personnel

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Identifying Sentiment in Legal Case Judgments using Random Forest Classifier 9

Identifying Sentiment in Legal Case Judgments using Random F...

引用

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

作者： Pavani, Bhupathi Vishva Mahitha, Desham Prabhakar, Priyanka Pati, Peeta Basa Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

This paper explores sentiment analysis in legal judgments using machine learning techniques. Six machine learning models, including Naïve Bayes, K Nearest Neighbors, Logistic Regression, Support Vector Machine, Random Forest, and Decision Tree, are used along with three data embeddings namely T5, RoBerta, and LegalBert. The study aims to evaluate the effectiveness of these models in sentiment analysis tasks tailored to legal documents. Each model is trained and evaluated based on the unique characteristics of legal texts. Hyperparameter tuning has also been performed for all the models. The focus is on achieving high accuracy, F1 score and understanding the interpretability of the models' predictions. The highest accuracy achieved was by Random Forest of 67.5%. The study aims to provide insights into the applicability of various machine learning algorithms and data embeddings for sentiment analysis in the legal domain. © 2024 IEEE.

关键词： Sentiment analysis

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A Comparative Study of Machine Learning Algorithms and Explainable AI for Fitness Club Attendance Classification 6

A Comparative Study of Machine Learning Algorithms and Expla...

引用

6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Divyasri, S. Gupta, Deepa Amrita School of Computing Department of Computer Science & Engineering Amrita Vishwa Vidyapeetham Bangalore India

ISBN: (纸本)9798350381689

The growing interest in health and fitness has resulted in an increase in the number of people joining fitness clubs. However, maintaining member engagement remains a challenge for fitness club operators. The goal of this study is to create a binary classification model that predicts whether a member is likely to attend a fitness club or not. In this paper, we propose a framework to explain how good the fitness club attendance classification performance will be under various Machine learning models by understanding its metrics. In this study by employing several classification models to determine Fitness Club attendance Classification using machine learning algorithms such as Random Forest, Gradient Boosting, Light GBM, etc. The Random Forest classifier performs with a higher accuracy of 0.87, and Explainable AI has been used to reveal the most significant feature. © 2024 IEEE.

关键词： Adversarial machine learning

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Design and Implementation of a Movie Rental Website Using Automata Theory 5

Design and Implementation of a Movie Rental Website Using Au...

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

作者： Saluja, Abhinav Panda, Amitesh Kesavan, Suhas Panda, Niharika Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

This paper proposes a novel approach to designing a movie rental website using concepts from push down automata theory. The objective is to create a user-friendly platform for browsing, selecting, and renting movies while leveraging the computational power of push down automata (PDA) to manage the underlying logic efficiently. The system architecture employs push down automata to model various states and transitions in the movie rental process. Users can interact with the website through a GUI, where they can search for movies, view their details and begin rental transactions. Behind the scenes, push down automata handles the validation of user inputs and enforces business rules such as rental duration and payment processing. Push down automata is utilized to represent the different states of a movie's availability, including available for rent, items in cart, currently rented, and returned. Transitions between these states are triggered by user actions such as renting a movie, returning it, or extending the rental period. By modeling these states and transitions with push down automata, the system ensures consistency and correctness in the rental process. Overall, this project demonstrates the practical application of push down automata theory in the development of a movie rental website, offering a scale-able and efficient solution for managing movie rental operations while providing a seamless user experience. © 2024 IEEE.

关键词： Web Design

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A Comparison of Explainable AI Models on Numeric and Graph-Structured Data 2

A Comparison of Explainable AI Models on Numeric and Graph-S...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Avinash, Alekh Harikumar, Akshay Nair, Adithya Pai, Sreejith Kumara Surendran, Simi George, Livya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

An exponential growth of interest in the healthcare IoT over the past few years has increased the adoption of AI. However, healthcare analytics demands highly accurate and reliable systems that can not afford even a small amount of vulnerability. The black-box nature of AI models raises significant concerns about the computations involved in these predictions, leading to a lack of trust in these models. Explainable Artificial Intelligence (XAI) seeks to resolve this problem by providing a human-understandable explanation for AI decisions, bringing transparency, trust, and fairness to these AI models. This paper evaluates the existing XAI models on numeric and graph-structured healthcare data. The LIME and SHAP models are first used to explain the predictions in the numerical dataset of fetal health prediction. Second, graph-structured data is analyzed using GNN Explainer and PG Explainer. This evaluation suggests that XAI model interpretations benefit healthcare professionals and patients to trust AI predictions. © 2024 Elsevier B.V.. All rights reserved.

关键词： Forecasting

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Implementation and Analysis of TCP CUBIC Congestion Control System in Mininet 9

Implementation and Analysis of TCP CUBIC Congestion Control ...

引用

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

作者： Sunder, S Navin Akathya, Ve Ram Seshadri, Samarth Rajagopal, Shinu M. Bhaskaran, Sreebha Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

In the realm of data transfer dynamics, an intricate comprehension of congestion control is imperative for the seamless optimization of network performance. This study rigorously delves into the complexities, unveiling discernible disparities between sending rates and throughput. Emphasizing the critical role of congestion control, the research highlights escalating retransmissions and congestion as pronounced inefficiencies. The proposed methodology encompasses a thorough examination of packet loss, network utilization, and queuing delays, paired with the implementation of effective congestion control strategies. Post-implementation, the results demonstrate a significant enhancement in network performance, characterized by adaptive sending rates and minimized inefficiencies. This research underscores congestion control's pivotal role in optimizing performance, offering valuable insights for achieving an efficient and responsive network. © 2024 IEEE.

关键词： Data transfer

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IoT Sensor Data Stream Compression with Hybrid Compression Algorithms 15

IoT Sensor Data Stream Compression with Hybrid Compression A...

引用

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

作者： Garikipati, Karthik Muppala, Tejaswi Vinitha Chowdary, A. Sahay, Apurvanand Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

The recent world advancement in technology especially in the use of IoT devices has posed some serious complications in terms of how effectively we can convey data, this is normally a result of the limited bandwidth and power capacity. In this paper, we look into the need for efficient data compression in IoT devices and come up with a new approach to compress the data stream needed for IoT sensors. In this paper, we propose to integrate Dynamic Huffman Coding with Run Length Encoding smoothly as one novel network coding strategy to decrease the load carried by the communication channels and at the same time ensure the originality of the transmitted data. The proposed method is based on the use of RLE to remove a lot of redundant data and dynamic Huffman coding for further data compression. When compared with other central algorithms like dynamic Huffman, static Huffman, RLE, LZW, ZLIB, LZMA, and BZ2 this hybrid approach provided more efficient compression ratios and consumed less power. Compression can be considered an essential paradigm for IoT devices because it can minimize the restrictions associated with the prevention of limited bandwidths and power sources that are characteristic of such devices, which results in improved data sending and a longer lifespan of devices. As substantiated by the findings from our experiments, this approach holds a high degree of effectiveness and, therefore, is suitable for the IoT domain with a scarcity of resources in particular. The contributions of this research support the promotion of compression strategies while considering the IoT-specific needs thus enhancing standard system outcomes and durability. © 2024 IEEE.

关键词： Bandwidth Constraints BZ2 Data Compression Dynamic Huffman Coding Hybrid Techniques Internet of Things (IoT) IoT Sensors LZMA LZW Power Constraints RLE Run-Length Encoding Static Huffman Coding ZLIB

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Evaluation of RPL and SDN-WISE in UAV assisted WSN data collection 2

Evaluation of RPL and SDN-WISE in UAV assisted WSN data coll...

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2nd International Conference on computer Vision and Internet of Things, ICCVIoT 2024

作者： Deepankumar, S. Rengaraj, R. Gupta, Boggarapu Mithil Raj Kumar Nalinadevi, K. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9781837242672

This paper evaluates the performance of the Routing Protocol for Low-Power and Lossy Networks (RPL) and the Software-Defined WIreless SEnsor network (SDN-WISE) approach in a IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN) network scenario. In this network scenario a mobile sink acting as an Unmanned Aerial Vehicle (UAV) is used for data collection in Wireless Sensor Networks (WSNs). A novel methodology is presented for generating efficient paths for the mobile UAV sink. It involves leveraging the K means clustering algorithm for finding the centroids which can cover the sensors within the transmission range. The Ant Colony Optimization technique is then used for traversing the centroids to solve the Travelling Salesmen problem (TSP). Key performance metrics such as average end-to-end delay, packet delivery ratio and energy consumption are analysed under varying conditions and sensor node densities. The study provides valuable insights into optimising UAV-assisted WSNs for enhanced performance and longevity in real-world applications. © The Institution of engineering & Technology 2024.

关键词： Ant colony optimization

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Context Aware Recommendation System Using LLM 8

Context Aware Recommendation System Using LLM

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

作者： Madhav, Kagolanu Venkata Chandra Veeramaneni, Asritha Vivek, Vinnakota Sai Belwal, Meena Khare, Sangita Amrita School of computing Dept of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bangalore India

ISBN: (纸本)9798331505462

This paper proposes a unique approach to emotion recognition and employs Vision Transformer (ViT) model for the sake of giving personalised suggestions of music selections and environments. ViT is able to determine the user emotion based on his facial expression detection technology. Those emotions will provide the critical inputs that will allow recommendations to be modified to context-aware standards the mood you're in the time of year, the specifics of the month. Proprietary technology creates music playlists suitable to the mood of the moment as it suggests the best locations for experiences based on an emotional context and other external factors such as the season and time of month. As we move deeper into the future, this merging of emotion detection technology and customized recommendations can deliver an unparalleled user experience by offering a user exactly what they would like in their location of the area they are you engaged in. It just ensures that the location to suggest should not only be ideal to the user mood but must also be practical as well by taking into account the locations weather and local events. The whole concept of this is to create a more rounded and complete approach to curating a fitness routine tailored to the individual, taking into account their mental health as well as external issues. © 2024 IEEE.

关键词： Emotion Recognition

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Malicious URL Detection: Comparative Study of Machine Learning Algorithms 15

Malicious URL Detection: Comparative Study of Machine Learni...

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

作者： Jaswanthi, Thadakaluru Harshitha, Talluri Tanya, Simhadri Belwal, Meena Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350370249

Malicious URLs can be detected by leveraging lexical analysis and machine learning, where the parsed components of the URL are parsed, and its features include domain reputation, URL length, and the presence of suspicious characters. Research on the detection of malicious URLs suffers challenges in keeping pace with the changing structural landscape of URLs. Adaptation to dynamic patterns and addressing the technical issues of efficient processing and scalability become important to stay one step ahead of the evolving threat landscape. This paper concentrates on addressing these challenges with the integration of Machine Learning and Lexical Analysis. Features extracted in this process are then fed into machine learning algorithms for the classification of URLs and are tested on unseen data. Research performance is measured using several metrics including Precision, Recall, F1-score and Accuracy during the Model evaluation. Out of all the models experimented, Extra Trees classifier stands out the best with 92% accuracy, showing effectiveness in accurate detection of malicious URL’s. ©2024 IEEE.

关键词： Adversarial machine learning

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