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Comparative Analysis of Deep Learning Methods for Lesion Segmentation in Gastrointestinal Endoscopic Images 1

Comparative Analysis of Deep Learning Methods for Lesion Seg...

引用

1st International Conference for Women in Computing, InCoWoCo 2024

作者： Sai Chandana Reddy, K. Dheeraj Reddy, K. Sree Sai Praneeth Goud, L.E. Singh, Tripty Afnaan, K. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331518943

Gastrointestinal (GI) disorders present diagnostic obstacles, necessitating accurate imaging procedures. Conventional techniques for evaluating endoscopic images are mostly laborious and arbitrary. An innovative method is proposed to improve GI endoscopic image evaluation using three cuttingedge deep learning models, including Seg Net, U-Net, and Deep Lab, for lesion segmentation. Exploiting the recently published Kvasir-SEG dataset, which comprises segmentation masks and accompanied gastrointestinal polyp images, this work attempts to further the field of polyp analysis and detection research. The study aims to enhance diagnostic precision and optimize the analytical process by utilizing deep learning approaches and datasets that are annotated. It will eventually contribute for enhanced patient outcomes as well as healthcare productivity. © 2024 IEEE.

关键词： Endoscopy

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Cloud-Infused AWS Services: Automobile Database Management System 2

Cloud-Infused AWS Services: Automobile Database Management S...

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2nd International Conference on Networking, Embedded and Wireless Systems, ICNEWS 2024

作者： Uthej, K. Keerthan, Nagandla Krishna Sai Musunuru, Nikhil Kumar Beena, B.M. Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350367461

A Cloud-based Automobile Database Management System serves as a specialized software application dedicated to efficiently managing databases hosted on cloud infrastructure, encompassing both system hard drives and networked servers. Various types of DBMS are intricately designed to oversee and regulate databases tailored for specific purposes. In the context of organizing comprehensive information on automobiles, leveraging cloud technology is not merely beneficial but imperative. As part of our strategic initiative, we are implementing a cutting-edge approach that involves the utilization of diverse cloud services, including S3 (Simple Storage Service), EC2 (Elastic Compute Cloud), and IAM (Identity and Access Management), Cognito, Cloud Watch, Simple Notification System (SNS), AWS RDS (Relational Database Service). Our proactive strategy involves the development of an intuitive user interface hosted on the cloud. This interface empowers customers to seamlessly select and explore their preferred vehicles based on individual usage criteria. The innovative integration of cloud services, such as S3 for robust storage, EC2 for scalable computing power, and IAM for secure access control, ensures scalability, accessibility, and smooth functionality, RDS optimizes database management, SNS enables real-time communication, and CloudWatch ensures proactive monitoring. Cognito enhances user authentication and authorization. This comprehensive approach not only enhances the overall user experience but also simplifies the process of selecting a dream vehicle for our customers. A comparative analysis has been sustained for AWS Servies, MySQL of 38% more reliable in retrieval time with Google Cloud Platform (GCP), MySQL. © 2024 IEEE.

关键词： Cloud platforms

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Enhancing Biomedical Literature Analysis to Predict Drug Interactions Using Multi Criteria Decision Making and LSTM-CNN Model

Enhancing Biomedical Literature Analysis to Predict Drug Int...

引用

2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Sankaran, A. Sathiyamurthy, K. Puducherry Technological University Dept. of Computer Science and Engineering India Puducherry Technological University Faculty of Computer Science and Engineering India

ISBN: (纸本)9798350315127

The extraction of information from unstructured text has become a crucial task in the biomedical literature field. Specifically, the analysis of various relationships such as Drug Food, Drug Disease, Drug non-prescription and Drug Drug interactions has gained significant importance. To accomplish this, a combination of Multi Criteria Decision Making (MCDM) and machine learning techniques has been employed. A dataset has been curated and utilized to train a model based on Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN). The trained model enables accurate predictions and identification of relevant information pertaining to drug interactions in biomedical literature. This approach enhances the understanding and knowledge extraction from vast amounts of unstructured text data, leading to improved insights and potential advancements in the field of biomedical research. © 2023 IEEE.

关键词： Long short-term memory

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Real-time Urban Parking Space Management using Fog Computing 1

Real-time Urban Parking Space Management using Fog Computing

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Thangam, S. Snehitha, Munaga Sai Kumar, Kotyada Mohan Kiran Reddy, Koti Leela Sai Praneeth Deepthi, C. Amrita School of Computing Dept. of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350361780

The rise in the number of private automobiles, especially in populated zones leads to many issues like traffic congestion, waste of time and fuel as they spend more time finding a parking spot. Vehicles release toxic gases, as they are ideal in traffic thus the number of pollutants gets increased. Smart parking systems help with these problems. They reduce traffic congestion which leads to less environmental pollution and saves fuel. A cloud-based parking system has been proposed, but it requires high latency and high bandwidth. Fog Computing gives us a better solution than cloud computing. With the help of fog computing smart car parking systems, the proposed model can clear all these issues. The driver gets information about the parking spots with minimal latency and energy consumption. By guiding the drivers where the parking spot is free it uses the space optimally. It also reduces the need to expand the parking area since land acquisition in urban areas is tough. A latency-reducing approach was developed using iFogSim and metrics like latency, total energy consumed, and total time taken were considered for a comparative analysis between the fog environment and the cloud environment. The developed fog-based system demonstrated better results than the cloud-based approach. © 2024 IEEE.

关键词： Fog computing

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Development of a Comprehensive Learning Management System using Full Stack Technology: A Modern Approach to Educational Platforms 5

Development of a Comprehensive Learning Management System us...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Supriya, M. Rathinavel, Keshav Radhakrishnan, Divya Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350389609

Using the advantages of modern web technologies, this project utilizes powerful data modelling characteristics of Prisma and dynamic front-end capacities of *** to design a Learning Management System (LMS). The aim of this project is to create a comprehensive platform that would effectively facilitate the organization of educational resources and assignments, as well as student cooperation, ensuring scalability and performance at the same time. Many software engineering practices are utilized during this project for code maintainability, security, and performance. The full cycle of software development consists of testing and iterative adaptation of the developed systems based on the opinions of end-users. Ultimately, the LMS is improved through thorough testing and feedback from the users in an iterative and progressive process. The findings of this research are pertinent to the future developments of educational technology that will seek to implement modern, full-featured, and user-friendly LMS. © 2024 IEEE.

关键词： Software testing

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Enhancing Blind Communication With Voice-Assisted Email 4

Enhancing Blind Communication With Voice-Assisted Email

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4th International Conference on Expert Clouds and applications, ICOECA 2024

作者： Sasikala, C. Rupalakshmi, M. Sameer Ahammad, S. Meghana, U. Harshad Ali, M. Srinivasa Ramanujan Institute of Technology Dept. Computer Science and Engineering Anantapur India Anantapur India

ISBN: (纸本)9798350385793

Considering the key role of the internet in daily life, accessibility becomes crucial, especially for visually impaired individuals who heavily rely on it for accessing information. However, a significant portion of text materials on the internet remains inaccessible to blind users, limiting their opportunities for development and inclusion in various communities worldwide. Leveraging audio virtual environments, predominantly through screen reader technology, offers a notable advantage for blind individuals in accessing online applications like emails. This study presents a new design aimed at facilitating the email retrieval for blind users with the utilization of speech-to-text and text-to-speech technologies. This approach is particularly suitable for individuals with average human capabilities and a limited level of education, as it relies solely on interactive voice response for input, ensuring effective functionality for the proposed system. © 2024 IEEE.

关键词： Application programming interfaces (API)

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Exploring and Predicting Top streamed spotify songs using Lazy learners

Exploring and Predicting Top streamed spotify songs using La...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Asmitha, M. Kavitha, C.R. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (数字)9798350361537

ISBN: (纸本)9798350361537

Stream prediction holds great significance for analysing and recommending the song based on the viewer or most played category. Prediction involves instances that can be recommended based on playlist presence high viewer count, adding complexity to the prediction process. The paper mainly helps to unfold the convergence of machine learning, song prediction, and multimedia analysis within the context of the Spotify dataset. This paper focuses on the application of data exploration, ML and Data science techniques to unravel patterns and insights within the Spotify dataset. Leveraging the rich and diverse data provided by Spotify, encompassing user interactions, audio features, and textual information, the study aims to develop robust classification models for music-related tasks. The project explores various machine learning algorithms like lazy classifier and lazy regressor a combination of seventy algorithms trained into multiple classes to discern intricate patterns in the data and enhance the accuracy of genre classification. In addition to EDA analysis of features, the project also endeavors to predict Artist Highlights, Playlist Dynamics, Regional Variations. The anticipated outcomes include the development of accurate models. Evaluation metrics such as precision, R-Score, R Sq Score, recall, mean-R Sq Score are employed to assess the performance of the models in handling prediction data associated with diverse music genres. © 2024 IEEE.

关键词： Prediction models

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Time-Series-Based Stock Market Analysis using Machine Learning 15

Time-Series-Based Stock Market Analysis using Machine Learni...

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

作者： Amruth, S. Jaya Nigelesh, T.M. Shruthik, V. Sai Reddy, Valluru Sateesh Venugopalan, Manju Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

The stock market is a very complex and volatile entity. Predicting it is a very challenging task but the emergence of Machine Learning and the availability of large-scale stock market data provide an opportunity to leverage data-driven approaches for improving prediction accuracy. In this paper, a framework for stock market analysis using up-to-date financial data extracted from the Yahoo Finance library and machine learning models is proposed. This paper's approach is to capture the complex relationships and patterns in the data by using machine learning techniques. Specifically, machine learning algorithms like XGBoost, Gradient Boost, and Random Forests are employed. By analyzing diverse data sources from various companies, the aim is to extract valuable insights for stock prediction. The experiments involve processing contemporary stock data, implementing machine learning models on it, and comparing the outcomes. The predominant motives are to enhance stock prediction accuracy, manage inherent risks, enable real-time decision-making, and maximize profitability by providing valuable insights for investors that help optimize their portfolios. However, the results are real-time and the optimal algorithm for a particular stock is decided on a dynamic basis due to the stock market being volatile and unpredictable in nature. © 2024 IEEE.

关键词： Decision Tree Gradient Boost Machine Learning Random Forest Stock Market Analysis XGBoost Yahoo Finance

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Cyberbullying Detection on Multiclass Data Using Machine Learning and A Hybrid CNN-BiLSTM Architecture

Cyberbullying Detection on Multiclass Data Using Machine Lea...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Balaji, Peddi Gowtham Katariya, Priyanka Prakash Sruthi, S. Venugopalan, Manju Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

Cyberbullying has emerged as a significantly concerning online societal issue in the past few years with the proliferation of social media platforms. To address and overcome this pressing communal concern and successfully detect cyberbullying across various categories, this study proposes a novel approach that integrates Convolutional Neural Networks and Bidirectional Long Short-Term Memory Networks, alongside six traditional machine learning techniques, driven by the purpose of enhancing the accuracy and robustness of cyberbullying detection across diverse cultural and textual landscapes. To highlight the multiclass aspect, the model is trained and tested on a dataset containing instances categorized into Age, Religion, Ethnicity, Gender, Other Cyberbullying, and Not Cyberbullying. Through rigorous experimentation, the proposed deep learning model's proficiency results in a 0.83 accuracy and F measure, showcasing its efficacy in capturing the nuances of various categories. Meanwhile, among the six machine learning models employed, Random Forest emerges as the top performer with values 0.94 and 0.93 for the accuracy and F measure respectively. This comprehensive methodology underscores its ability to effectively discern between the different classes and offers a promising solution to combat cyberbullying across diverse demographics and thematic dimensions. © 2024 IEEE.

关键词： Adversarial machine learning

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Adaptive Multi-Level Feedback Round-Robin 15

Adaptive Multi-Level Feedback Round-Robin

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

作者： Prajana, Mamidi Rajkumar, Harsha Sannidhi, Akepati Sai Vidyasri, Kammari Krishnan, Sajitha Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798350370249

Scheduling is vital in operating systems, controlling process resource access. Round Robin stands out among traditional algorithms due to its improved performance, unlike First Come First Serve (FCFS) or Shortest Job First (SJF). While the Priority-based scheduling algorithm executes processes depending on their priority, the proposed algorithm is particularly designed for time-sharing systems. The paper aims to improve system performance, reduce latency, and enhance overall responsiveness in multitasking environments. The time quantum assigned ensures fair allocation of CPU to all the processes getting executed. Based on their burst time and assigned priorities, different processes can be divided within multi-level queues. Multilevel queues adjust task priorities based on the time execution. The use of priority for each process enables prioritization of the important processes in each queue. Decrementing the priority based on the time taken to execute, is an efficient solution to reduce the starvation time of the other processes. It shows a reduction in turn-around time. The implementation will also include comprehensive performance evaluations and benchmarking against traditional scheduling algorithms, demonstrating the superiority and effectiveness of the proposed scheduler in real-world scenarios. © 2024 IEEE.

关键词： Scheduling algorithms

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