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

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Comparative Analysis of Legal Outcome Prediction with Detailed and Summarized Text

Comparative Analysis of Legal Outcome Prediction with Detail...

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International Conference for Convergence of Technology (I2CT)

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

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Legal Outcome Prediction(LOP) is the process of predicting the possible outcome of a court case based on the contents in the case *** aim of this work is to perform a comparative analysis to assess the effectiveness of popular language models for outcome prediction with the case file as well as its summary *** models of BERT, LegalBERT, DistilBERT and RoBERTa are enhanced with transfer learning on the legal data files and the models are evaluated with accuracy and F-score. It is observed that RoBERTa performs best in LOP for both complete case file text as well as for summary text.

关键词： Measurement Deep learning Analytical models Law Transfer learning Predictive models Data models

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Adaptability of Denoising Models for Low Dose CT Images

Adaptability of Denoising Models for Low Dose CT Images

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International Conference on computing and Networking Technology (ICCNT)

作者： Bharathi D Tarun Ramaswamy Anjali Chullimadam Ganesan Bijjala Lohith Chandra Kolli Ajay Paul Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Computed Tomography (CT) is a fundamental diagnostic imaging modality that utilizes various technologies. A CT scan involves capturing multiple X -ray images from various angles around the body and then utilizing computer algorithms to generate detailed cross-sectional images, or slices, depicting the blood vessels, tissues and bones of the patient. The imperative for enhanced imaging often necessitates exposing individuals to substantial doses of ionizing radiation, particularly in instances involving repeated CT scans or elevated radiation doses. In response to radiation-related concerns, Low Dose CT (LDCT) has emerged as a key focus of research, reducing patient exposure to merely 25 percent of the radiation associated with conventional Full Dose CT (FDCT) scans. Despite its evident advantages in radiation reduction, LDCT images tends to yield images characterized by heightened random noise and artifacts. The intrinsic challenges in LDCT images compromise their clinical utility, affecting diagnostic accuracy. Present denoising methodologies often prioritize quantitative metrics, potentially resulting in over-denoised and blurred CT images, neglecting realistic image preservation. This study evaluates contemporary general image denoising techniques alongside deep learning denoising methods specifically tailored for low-dose computed tomography, focusing on their ability to preserve high-frequency details and the realism of the resulting denoised images. The objective is highlight the state of current deep learning denoising models and find models that are able to achieve both improved metrics and produce realistic denoised image outputs.

关键词： Measurement Deep learning Adaptation models Visualization Analytical models PSNR Computed tomography Computational modeling Noise reduction Medical diagnostic imaging

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A Next-Gen Approach to Streamlined Canteen Management Systems using Mesa Framework

A Next-Gen Approach to Streamlined Canteen Management System...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Advik Narendran Anantha Hothri Inuguri Manyam Yashaswini Srinidhi Sundaram Manju Khanna Department of Computer Science and Engineering Amrita School of Computing Bangalore Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

A next-generation canteen management system is proposed that employs intelligent software agents to streamline various tasks, such as order processing and customer service, within canteens. The system automates verification through ID card scanning and OTP authentication, ensuring secure and efficient operations. By integrating agents to manage orders and provide real-time updates, the system enhances the dining experience by reducing manual effort and improving service speed. This smart solution, designed to cater to institutional needs, provides seamless control over canteen processes while enhancing user satisfaction through a modern, technology-driven approach.

关键词： Hands Customer services Authentication Process control Manuals Software agents Real-time systems Computational efficiency Information technology Next generation networking

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Detecting AI-generated images with CNN and Interpretation using Explainable AI

Detecting AI-generated images with CNN and Interpretation us...

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Contemporary computing and Communications (InC4), IEEE International Conference on

作者： Bharathi Mohan G Prasanna Kumar Rangarajan Akilesh rao S Mandava Sukesh Abinandhini D M Jaikanth Y Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350383652

ISBN: (纸本)9798350383669

The rapid proliferation of digitally manipulated images in various media outlets has necessitated the development of robust and reliable methods for distinguishing between authentic and synthetic visual content. In response to this imperative, this research leverages state-of-the-art deep learning techniques and an extensive dataset of ‘real’ and ‘fake’ images to create an AI model for image identification. The study encompasses a meticulous data collection process, preprocessing steps, the selection of the DenseNet121 model architecture, model training, and evaluation of the model's performance through a range of metrics, including accuracy (93.42), precision, recall, and the Area Under the ROC Curve (AUC). To enhance the transparency and interpretability of the model, employed the Gradient-weighted Class Activation Mapping (Grad-CAM) technique, enabling to visualize the regions within images that influenced the model's decision-making. The research provides valuable insights into the development and evaluation of AI systems for image identification, particularly in the context of distinguishing between real and fake content. Furthermore, the interpretability techniques shed light on the model's decision-making process. This research contributes to the field of image authentication and paves the way for enhanced image classification in the era of synthetic media.

关键词： Heating systems Training Visualization Accuracy Decision making Data models Stability analysis

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Real-Time Speech-to-Text Holographic Communication for the Deaf Children and Elderly

Real-Time Speech-to-Text Holographic Communication for the D...

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Multimedia Processing, Communication & Information Technology (MPCIT), International Conference on

作者： Sreevathsa Sree Charan Vemula Srihitha Suja Palaniswamy Savarala Chethana Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350375466

ISBN: (纸本)9798350375473

In the field of assistive technologies, innovative solutions that cater to the needs of individual with disabilities are paramount for fostering inclusive communication environments. The proposed work introduces an innovative real-time speech-to-text conversion system using holographic display technology to aid communication for the deaf and hard-of-hearing (DHH). The system incorporates two complementary speech recognition approaches as each contribute uniquely to improve the system's overall performance and accuracy in different ways and also reduce the latency. First approach is a conventional speech recognition module and another is an enhanced version of the Wav2vec2 model, which has been retrained to improve its efficacy with diverse speech patterns. Hosted on a Raspberry Pi and interfaced with an USB microphone, the system captures spoken language effectively. Upon recognition, the text is instantly displayed on a holographic screen made up of LED matrices and set against an acrylic glass surface, creating a visually engaging representation of floating text. The integration of these two recognition methods enhances the system's accuracy and reliability in processing real-time speech. By providing a visual output of spoken words, this system not only bridges communication gaps but also enriches the interaction experience within various social and professional settings. The application of holographic technology in this context is pioneering, offering substantial promise for developing accessible communication tools that are both functional and inclusive. This work underscores the potential of advanced display and speech processing technologies to transform interactions for individuals with hearing impairments, fostering greater inclusivity and engagement in everyday communications.

关键词： Visualization Accuracy Text recognition Transforms Speech enhancement Universal Serial Bus Real-time systems Reliability Surface treatment Speech to text

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Exploring Network Paradigms: A QoS Analysis Perspective

Exploring Network Paradigms: A QoS Analysis Perspective

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International Conference on computing and Networking Technology (ICCNT)

作者： S. Nishal S.Dhakshin Kumar T. Jaswanth Shinu M. Rajagopal Sreebha Bhaskaran Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Peer-to-Peer and client-server network models have been investigated as data transfer methods, the literature has not yet completely documented their advantages and disadvantages, especially for stability. The primary objective of this study is stability restrictions in network systems, and it compares the Peer-to-Peer (P2P) and Client-Server architectures in great detail. By comparing the performance in important measures while maintaining stability limitations, the goal is to determine which design is better. In particular, the research examines the two designs bandwidth, throughput, packet loss rate, and retransmission rate. It shows that the Client-Server design performs better in terms of throughput, bandwidth, and retransmission rates, demonstrating reliable data handling and effective delivery within specified intervals. Peer-to-peer architecture, on the other hand, shows an advantage with a reduced packet loss rate, indicating potential resistance against interruptions during data delivery. The study’s final results helps in making accurate choices in choosing an architecture for network systems that best satisfies with stability criteria.

关键词： Resistance Stability criteria Packet loss computer architecture Bandwidth Quality of service Throughput Reliability engineering Peer-to-peer computing Network systems

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A Personalized Visual Communication App for Children with Autism

A Personalized Visual Communication App for Children with Au...

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North Karnataka Subsection Flagship International Conference (NKCon), IEEE

作者： Ankit Karki Kalakunnath Namitha Kunal Singh Ronak Agrawal Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350364569

ISBN: (纸本)9798350364576

Autism Spectrum Disorder (ASD) is a condition that impacts interaction, communication and behavior usually showing signs, around the age of two. This paper presents a novel mobile application developed using Flutter to help children with ASD improve cognitive and communication skills through personalized, AI-powered learning modules, interactive quizzes, and visual aids. The proposed app aims to empower individuals with autism, facilitate learning and social interactions and equip parents with tools for monitoring progress. = The application includes communication features, tailored educational modules and progress monitoring capabilities. It leverages Flutter for platform functionality ensuring accessibility on both Android and iOS devices. The user friendly interface, symbols, for communication and secure parent dashboard enhance usability. Initial assessments indicate increased user engagement improved communication skills and greater independence. With a technology that offers interactive forms of communication, personalized learning modules, and dynamic assessments to match a student’s progress with their performance. This app is available for Android and iOS devices through Flutter, a technology that enables it to work in both platforms. The app is easily accessible by users due to its user-friendly layout, visual communication symbols, and secure parent dashboard. Preliminary evaluations indicate increased user participation, enhanced communication, and higher self-reliance. Compared to others apps, this one adopts a more broad approach, emphasizing communication, education, and life skills.

关键词： Performance evaluation Autism Visualization Visual communication Operating systems Symbols Learning (artificial intelligence) Mobile applications Usability Monitoring

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An Explainable AI Approach to Classify Smoker and Drinker Profiles via Medical Parameters

An Explainable AI Approach to Classify Smoker and Drinker Pr...

引用

International Conference for Convergence of Technology (I2CT)

作者： Krishna Nanda Sakshi Maurya Deepa Gupta Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Smoking and consumption of excess alcohol is one of the major rising concerns in our society. In accordance with a WHO report released on July 31, 2023, tobacco use kills over 8 million people annually, of which 1.3 million are non-smokers who are exposed to passive smoking, and three million die from alcohol abuse. It not only affects the physical and mental health of the people who practices it but also affects the people who are around them. This work will play an important role as it helps in identifying these unhealthy habits from medical parameters which can contribute in prevention of the health risks which lie ahead. The results obtained are beneficial for the intended users like health professionals, insurance policy handlers and public health agencies where they will be able to identify the people who show addiction to these habits. We have performed various machine learning classification models which includes Light Gradient Boosting (LGB), Logistic Regression, Extreme Gradient Boosting (XGB), Random Forest, K-Nearest Neighbor etc, on the dataset which contains medical parameters as features which is used to categorize a person as a smoker or not a smoker or someone who used to smoke, and a drinker or not. Also, the explainability tools used helps to understand the key features which contribute to the decision making in the classification

关键词： Logistic regression Explainable AI Addiction Decision making Insurance Mental health Boosting

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Speech and Text-based Depression Detection On Social Media For Early Intervention

Speech and Text-based Depression Detection On Social Media F...

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Global Conference for Advancement in Technology (GCAT)

作者： Gona Sai Tejeshwar Reddy Kalisetty Hari Chandan Karri Venkat Kiran Allu Jaswanth Jasmine Bhaskar Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

Depression is a significant mental health and SDG3 issue with far-reaching social and economic impacts. While early detection and intervention are crucial, traditional screening methods are often not accessible or accurate. This paper presents a method for detecting depression through a combination of voice and text analysis. By using log mel spectrograms and convolutional neural networks (CNNs) for voice analysis, and bi-directional long short-term memory (BiLSTM) networks for text analysis, the model improves detection accuracy and provides insights into mental health and well-being. The model structure, data collection, processing techniques, and training processes for both CNN and Bi-LSTM models are detailed. This study employs the "Suicide and Depression Detection" dataset from Kaggle for text and the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) for voice. A user interface is developed to ensure system accessibility. The experimental results demonstrate the high accuracy of the proposed approach for depression detection. This research highlights the importance of an accessible interface that combines multiple analysis methods and the need for interdisciplinary collaboration to address complex societal and mental health issues.

关键词： Analytical models Accuracy Text analysis Speech analysis Bidirectional long short term memory Mental health User interfaces Depression Data models Convolutional neural networks

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Advancing Supply Chain Management: Cloud-Based Demand Forecasting Solutions

Advancing Supply Chain Management: Cloud-Based Demand Foreca...

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Advancement in Technology (ICONAT), International Conference for

作者： M Spoorthi Subhiksha Suresh Rao Sahil Mishra Shinu M Rajagopal Mrudula Prashanth Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

On-demand information that can be provided throughout all parts of the supply chain network and through cloud-based supply chain management system optimizes resource flow, enhances cooperation, and increases adaptability. The study delves into the amalgamation of AWS (Amazon Web Services) cloud solutions into demand projections for use in supply chain oversight. Inventory levels optimization, production planning streamlining and, thus, global supply chain efficiency tailoring all hinge on one factor: demand forecasting. Adopting AWS cloud services elicits a plethora of benefits; ranging from — but not limited to — scalability, flexibility and cost efficiency. AWS avails an array of instruments and provisions that enable entities to sift through copious historical data seamlessly. They can mix this with extrinsic determinants like market dynamism or even seasonal oscillations so as to arrive at those ever-accurate demand forecasts. On another front altogether is AWS’ ability to handle storage, processing plus visualization capabilities for data silos integration towards seamless real-time updates delivery on forecast reports.

关键词： Supply chain management Web services Scalability Instruments Supply chains Production planning Demand forecasting Real-time systems Oscillators Optimization

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