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检索条件"机构=Computer Science and Engineering Nitte Meenakshi Intitute of Technology"

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Hand Sign Language Detection Using Deep Learning and computer Vision 1

Hand Sign Language Detection Using Deep Learning and Compute...

引用

1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Joshi, Sujata Sharma, Bhaskar Dharmender Tripathi, Divyansh Shah, Akshat Nitte Meenakshi Institute of Technology Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350316469

This work presents an innovative system/app designed to assist individuals with visual or hearing impairments in communicating with the outside world using American Sign Language (ASL). By leveraging machine learning and image processing techniques, the proposed system captures and processes hand gestures, enabling the optimal transliteration of sign language alphabets and numbers into machine-readable English text. The system employs deep learning or random forest algorithms to classify the gestures, followed by mapping them to specific alphabets or numbers. The resulting text is presented audibly, bridging the communication gap and facilitating interactions between ASL users and those unfamiliar with sign language. Through the integration of technology, this work aims to enhance inclusivity and accessibility for individuals having disabilities, fostering greater engagement and interaction with the wider world. © 2023 IEEE.

关键词： American Sign Language Communication Barriers Deep Learning Gesture Recognition Hand Gestures Hearing Impairment Random Forest Sign Language Visual Impairment

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MindCare: Machine Learning based Personalized Mental Health Monitor 4

MindCare: Machine Learning based Personalized Mental Health ...

引用

4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Ushashree, P. Naik, Archana Geethanjali College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331540364

Mental health challenges are growing in recent years, emphasizing the need for effective monitoring and inter- vention systems. This paper presents a comprehensive approach to detect mental unstabilities by analyzing the text entered by the user. Utilizing a combination of sentiment analysis and machine learning techniques, the proposed system preprocesses text data to extract sentiment scores and other key features, which are then used to train models for accurate mental health assessments. The system also offers personalized recommendations based on the user's mental state. Experimental results demonstrate the effectiveness of the approach, with the model achieving accuracy of 82.31% for negative text classification. These results underscore the potential of the system to serve as a valuable tool in the early detection and management of mental health issues. © 2024 IEEE.

关键词： Electronic health record

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Customer Feedback and Sentiment Analysis for Hotel Services 2

Customer Feedback and Sentiment Analysis for Hotel Services

引用

2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Abhyudhay, S.R. Aditya, G.M. Upadya, Achyuta K. Naik, Archana Ushashree, P. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India Geethanjali College of Engineering and Technology Department of Computer Science and Engineering Hydrabad India

ISBN: (纸本)9798350382952

In the era of digital transformation, the hospitality industry faces unique challenges and opportunities. With travellers increasingly relying on online reviews to make informed decisions, the role of sentiment analysis in the hotel sector has never been more critical. This comprehensive research aims to provide a profound understanding of sentiment analysis in the context of hotel reviews and delves into preprocessing and the application of state-of-the-art sentiment analysis techniques. The sentiment analysis model, based on SVM, achieved promising results in categorizing hotel reviews into positive, negative sentiments. The evaluation metrics indicate an accuracy of 82%. Visualizations such as the confusion matrix and ROC curve further demonstrate the model's effectiveness in distinguishing between positive and negative sentiments. The findings of this research shed light on the distribution of sentiments in hotel reviews, providing actionable insights for the hospitality industry. © 2024 IEEE.

关键词： Sentiment analysis

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AI-Driven Health: A Web App for Enhanced Healthcare Queries and Nutrition Analysis 5

AI-Driven Health: A Web App for Enhanced Healthcare Queries ...

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

作者： Ushashree, P. Naik, Archana Sri, P. Amrutha Sai Geethanjali College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331504403

The AI-Driven Health chat assistant is an innovative healthcare solution that seamlessly integrates technology and care, enabling users to have natural language conversations about symptoms, treatments, and general health inquiries. The chatbot functions as a virtual assistant, enabling users to inquire about medical concerns and obtain clear explanations. The nutrition bot utilizes the advanced capabilities of Google Gemini to analyze food images, offering users comprehensive nutritional information. The proposed system undergoes constant refinement through regular testing and user feedback, guaranteeing a smooth and dependable platform for accessible healthcare interactions. The AI-Driven Health chat assistant strives to enhance the accessibility and comprehensibility of healthcare information, ultimately enhancing a healthier and more informed society. © 2024 IEEE.

关键词： Bot (Internet)

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Prediction of Breast Cancer Using Convolution Neural Network 8th

Prediction of Breast Cancer Using Convolution Neural Network

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8th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2023

作者： Aditya Pai, H. Pareek, Piyush Kumar Suresh Kumar, A. Guru Prasad, M.S. Bengaluru India Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India Dehradun India

ISBN: (纸本)9789819976218

The Breast cancer is the a type of a carcinogenic cells that is developed in the breast and is more commonly found in women. This type of cancer has been found more fatal amongst women after the lung cancer. The paper proposes a study where it helps in identifying the breast cancer at faster rate using Convolution Neural Network (CNN). The paper uses a technique of identifies and differentiates the mammography pictures into three types, first one to be benign, second one is malignant, and the third one to be normal. This technique helps the medical professional in identifying the type of cancer. The CNN model used uses VGG-16 architecture for cancer detection and classification. The proposed system uses huge dataset of 275,000 RGB image patches. The proposed prediction is a deep learning technique where it’s architecture has two stages, first is image processing and the second is image classification. The result of using this technique reaches the accuracy level of 95.72% in classifying the different types of cancer. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Breast cancer detection Convolution neural network Deep learning VGG16 architecture

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Intelligent Accident Management System

Intelligent Accident Management System

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Communication, Control and Intelligent Systems (CCIS)

作者： Jayashree Jayashree Chandana R Prerana B Hiremath Pavana Kumar Suresh Pawar Computer Science and Engineering Nitte Meenakshi Intitute of Technology Bangalore India

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

With the growing population and the consequent surge in vehicular traffic, there is an urgent need for heightened safety measures. Addressing this challenge requires the integration of advanced systems equipped with highly sensitive sensors to proactively mitigate the risk of accidents. Ensuring vehicle reliability is paramount, leading to the incorporation of microcontrollers within each vehicle. These microcontrollers enable seamless data storage in cloud-based platforms and utilize SIM technology for uninterrupted communication and data transmission. Beyond data aggregation, these systems play a critical role in swiftly guiding individuals to emergency services in the event of an accident. Additionally, they provide crucial information and assistance to both drivers and passengers, both before and after an accident, thereby enhancing overall safety measures. This comprehensive approach not only prioritizes accident prevention but also ensures rapid and effective response mechanisms, thereby reducing potential risks and elevating overall road safety standards.

关键词： Road accidents Microcontrollers Rendering (computer graphics) Road safety Sensor systems Sensors Surges Standards Accidents Vehicles

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Detection and Classification of Multiple Plant Diseases Using CNN 1

Detection and Classification of Multiple Plant Diseases Usin...

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1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Vijaya Shetty, S. Hemantha, N.A. Devaganamath, Anand Ananth, R. Alur, Abhishek M Nitte Meenakshi Institute of Technology Department of Computer Science Engineering Bangalore India

ISBN: (纸本)9798350323795

Plant diseases can have a significant influence on agricultural productivity, quality, and profitability. Early identification and precise diagnosis of plant diseases are essential for successful disease control, which may reduce the use of pesticides and minimize crop losses. In this study, a machine learning-based approach is developed to identify the diseases of various plants using leaf images. Dataset of leaf images of 14 different crops are collected, including apple, pepper, potato, raspberry, blueberry, cherry, peach, soybean, squash, strawberry, corn, grape, orange, and tomato. The images are preprocessed using techniques such as normalization, resizing, and augmentation, to reduce noise and increase the diversity of the dataset. Convolution neural network (CNN) model is used as the back-end and Flask for the front-end to create a user-friendly web application. An interpretability analysis is conducted to understand the features learned by the model, which can provide insights into the underlying causes of the diseases. The study shows the potential of machine learning approaches for early diagnosis and identification of plant diseases, which can assist farmers in taking prompt action and minimizing crop losses. This approach can be extended to other crops and can be integrated with other technologies, such as drones and sensors, for remote and real-time monitoring of crop health. © 2023 IEEE.

关键词： Crops

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Wingman- Virtual Assistant for Visually Impaired 3

Wingman- Virtual Assistant for Visually Impaired

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3rd IEEE Mysore Sub Section International Conference, MysuruCon 2023

作者： Neeli, Jyothi Pushpanjali, M.K. Shivam, Ayushman Nikhil Anand, Raja Sengar, Romsha Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India Nitte Meenakshi Institute of Technology Department of EEE Bangalore India

ISBN: (纸本)9798350340358

It's clear to say that assistive technologies have grown more crucial in improving the quality of life of people with disabilities, especially those who are visually impaired, thanks to the wealth of information obtained from careful study of many articles. This research paper proposes the development of a virtual assistant for blind people that uses object detection, image classification, and voice assistant technologies. Primarily the assistant will act as a guiding dog for the visually impaired person. Various tech stacks and logic used to accomplish the final version of virtual assistant has been included in detail in different sections of the paper. © 2023 IEEE.

关键词： Object detection

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A GAIT-Based Three-Level Authentication System for Enhanced Cybersecurity 1

A GAIT-Based Three-Level Authentication System for Enhanced ...

引用

1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Vijaya Shetty, S. Sowmya, P. Shetty J, Shruthi Rautray, Samparna Nitte Meenakshi Institute of Technology Bangalore Department of Computer Science Engineering India

ISBN: (纸本)9798350316469

Although centralized digital organizations offer secure communication between people, services, and technology, the digital revolution poses significant threats. Data can be mined, profiled, and used without the user's knowledge or permission. Additionally, centralized solutions are wasteful, contain security holes, and are difficult for people to use. To ensure the privacy and security of dispersed digital identities, it is essential to authenticate and verify digital identities. Unfortunately, the existing body of knowledge lacks in-depth studies on privacy preservation and securing sensitive information in identity and access management systems, as well as concerns related to unified communications. Blockchain technology offers innovative solutions for digital identity management and verification, which are the most promising applications. Authenticating, distributing, and securing sensitive data requires safer techniques, and blockchain identification system development can help solve problems faced by centralized databases. Physical technology and human action are not enough to protect against cybercrime. Cybercriminals use online tools to commit crimes, but AI technology can help reduce incursions. In this study, password-based authentication, biometric authentication, and AI-powered anomaly detection have been employed for Continuous monitoring and real-time analysis to enhance defence against AI-driven attacks. The developed robust gait motion data acquisition system allows in measuring of human limb acceleration, angular velocity and muscle activity throughout time and helps in determining genuine, active, and passive imposters. The performance of the system is studied in terms of DET curve which shows how well the authentication system operates at different threshold points. © 2023 IEEE.

关键词： AI-based attacks anomaly detection authentication gait patterns

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Fake News Detection Using Neural Network

Fake News Detection Using Neural Network

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Ushashree, P. Naik, Archana Gurav, Siddarth Kumar, Ankit Chethan, S.R. Madhumala, B.S. Nitte Meenakshi Institute of Technology Department of Computer Science & Engineering Bangalore India

ISBN: (纸本)9798350398465

This paper uses capabilities of deep learning algorithms Long Short-Term Memory (LSTM) models for the detection of hoax news. In today's world Fake news has become a major problem, as it can spread quickly and have a significant impact on public opinion. Traditional methods for detecting fake news have relied on fact-checking and manual verification, which are time-consuming and not always effective. With the increasing availability of news articles and social media posts, there is a need for automated methods for detecting fake news. One of the effective ways used to eradicate the fake news is adopting LSTM models that have shown considerable results in a variety of natural language processing tasks, including text classification and sentiment analysis. This paper describes how to use LSTM for fake news detection and evaluates its performance on a news article dataset. © 2023 IEEE.

关键词： Long short-term memory

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