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检索条件"机构=Artificial Intelligence and Data Engineering"

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Assistance for Visually Impaired individuals using artificial intelligence and Natural Language Processing

Assistance for Visually Impaired individuals using Artificia...

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2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Sathvig, Sri K. Bharathi.ln, Priyanka Santhosh.m Ramya, J. St. Joseph's College of Engineering Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331529833

People with Visual Impairments are vulnerable to various challenges in their everyday lives ranging from Access to Information, Travelling to places, Recognizing their surroundings, Isolated Lifestyle and much more. The Proposed solution aims to address these socially inclusive problems through the use of artificial intelligence, Natural Language Processing, Machine Learning and Computer Vision. The feature index involves using Object Detection models like Yolo v5 for Obstacle avoidance, Integration of Google Map API for Seamless Navigation and NLP based Optical Character Recognition (OCR) for Text Recognition. An average accuracy of 86 percentage was achieved across all models. This solution aims to provide Better Lifestyle, Increased accessibility and Reduced Dependability for the Community of Visually Impaired Individuals. © 2025 IEEE.

关键词： artificial intelligence Computer Vision Machine Learning Natural Language Processing

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Mental Health Assistance and Early Detection of Alzheimer and Parkinson Diseases using Deep Learning

Mental Health Assistance and Early Detection of Alzheimer an...

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2024 International Conference on Computer engineering, Network and Intelligent Multimedia, CENIM 2024

作者： Muthusenthil, B. Jeyakani, P. Dhanakoti, V. Bharathi, B Aditya SRM Valliammai Engineering College Department of Artificial Intelligence and Data science Chengalpattu India

ISBN: (纸本)9798350368802

Parkinson's and Alzheimer's disease are neurological challenges with significant impacts in patients. Early detection is crucial for effective management. We propose a pioneering system that integrates validated tests with a mental health chatbot powered by deep learning to assist people with mentioned motor diseases and assist in mental health wellness. The chatbot engages users in mental health discussions and assists in interpreting screening tests for PD and AD, offering tailored recommendations based on user responses. By fine-tuning a pre-trained FLAN-T5 model, we enhance the chatbot's natural language understanding, enabling it to address mental health issues concisely. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Aerial Lifesavers Enhancing Rescue Missions With Drone 4

Aerial Lifesavers Enhancing Rescue Missions With Drone

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Kalaiselvi, P. Harini, R. Egadarshan, S. Dinesh, B. Vignesh, R. Sri Sairam Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

Failure to recognize the number of persons living in affected regions of a disaster can hinder efforts to rescue and make it difficult to know where help is most needed or to respond faster enough to those under greatest threats. Such delay increases casualties and poorly allocates and coordinates resources, wasting chances of saving lives. This study presents an innovative technique for using drones fitted with cameras that can produce real time heat maps during emergencies. The main objectives are to accurately estimate human density using a pre-trained model as well as prioritizing rescue operations effectively. The conventional approaches used in surveying areas affected by disasters are often slow and could be inaccurate thereby delaying critical interventions. Integrating drone imagery with real-time data visualization tools like Folium provides precise, timely information, which enables better emergency responses. These heat maps depict the changing extent of human overcrowding in terms of its levels that facilitate prompt identification of rescue sites. Drone Technology offers extensive coverage in large-scale remote areas leading to quickened data gathering. © 2024 IEEE.

关键词： Drones

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Enhanced Intrusion Detection based on Advanced Deep Scaled Multi Perceptron Neural Network with Enhanced Particle Swarm intelligence 8

Enhanced Intrusion Detection based on Advanced Deep Scaled M...

引用

8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Nathiya, N. Mohanarengan, S. Diwakar, S. Chinnamannan, K. Pugazhraj, D. Elango, S.T. Paavai College Of Engineering Artificial Intelligence and Data Science Tamil Nadu Namakkal India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376425

Network communication is an incredible service for sharing the information among all over the world. Among the communication, the data breaches are occurring during transmission is increasing due to hackers plays to attack the service. By the intrusion created by the attackers during the commutation, through cyber-attack become more powerful to create various attacks like Distributed service attacks, Sybil attacks, vampire attack, defense data drop attacks, decoder attack and soon. So, the development of intrusion detection is important through artificial intelligence is crucial to prevent the attacks. Mort of exiting model failed to analyze the nature and behavior of feature properties to identify the interrupts due to non-relational communication feature creates more dimension. The data shift problems degrade the poor accuracy in precision and recall rate. To resolve this problem, to propose an advanced Deep scaled Multi perceptron neural network (DSMPNN) classifier based on Enhanced particle swarm intelligence (EPSI) is used for optimal feature selection to improve the classification IDS. Initially the communication logs are collected as to take DarkNet-IDS dataset and preprocessing is carried put to normalize the dataset based on Min-Max normalization. The Traffic intensive defect rate (TIDM) is estimated by observing the feature defect scaling measure. Then the feature section is carried out by EPSI to identify the relation mutual defect feature list and classified with Deep scaled multi perceptron neural network. Finally, the DSMPNN identifies the intrusion classed based on class by reference effectively. The proposed system improves the classification accuracy ass well in higher precision rate, Fl1 rate with higher positive feature limits related to class with redundant false rate compared to the other exiting system. © 2024 IEEE.

关键词： Network intrusion

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Multimodal Content Generation: A Project for AI Creativity 2

Multimodal Content Generation: A Project for AI Creativity

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Geetha, M.P. Hariharan, S. Jeevanprasanth, T. Sanjay, L. Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

21st-century interactions involve the consumption of content, whether in the form of text, audio, images, or any other medium. This paper presents a multimodal content generation (MCG) system that seeks to ease the content production process by providing integrated solutions for video, image, audio and text content production among others. Large language models (LLMs) have shown a great promise in enhancing the creativity process in various fields such as media and education where mediums of communication interweave. The system takes into account the current data, situation, user needs, and composes an appropriate and suitable content for them. At the same time, conventional AI models, such as transformers, are combined with multimodal models enhanced with optimization strategies such as transfer learning and fine-tuning. This strategy greatly augments the content generation potential and scalability of the system and thus, is of great importance in the design of systems for the content production process. The quantum- inspired elements of the hybrid model are the ones targeted towards making changes in decision and feature selection within the creative processes. The effectiveness of MCG in this research was compared to other content creation systems, and the results of the research revealed that MCG is more creative, efficient, and satisfying for the users than the existing systems;therefore, it has a great potential for changing the practices of producing content in a multimedia environment. © 2024 IEEE.

关键词： Distribution transformers

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Multi-Disease Prediction and Classifier: A Comprehensive Approach for Healthcare Decision Support 5

Multi-Disease Prediction and Classifier: A Comprehensive App...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Sudha, S.V. Priya, Kamali S. Krishnapriya, P. Prathiksha, P.N. Department of Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Coimbatore India

ISBN: (纸本)9798350361155

The speedy identification and early detection of diseases that are deadly are greatly important when it comes to human life saving. On the other hand, the existence of poor medical system alongside the lack of equal distribution of healthcare presents a barrier towards early disease detection leaving death as the only option. One of the ways to tackle these problems is the utilization of machine learning in detection and analysis of diseases risk is machine learning promising. Utilizing predictive analytics in medicine, practitioners can make accurate and relevant medical insights promptly, relying on the information they have. The work focused on generating prediction models to identify seven most severe illnesses: diabetes, pneumonia, malaria, heart disease, kidney disease, breast cancer, and liver disease, using an algorithm of Random Forest Classifier. Alongside this, the Convolutional Neural Networks were utilized for the detection of malaria and pneumonia. Users can get better knowledge about their health problems and at the same time reduce the access constraints to healthcare by using machine learning. This research is also about designing a web app with the Flask framework and will give its consumers an easy-to-use tool for the simultaneous prediction of all seven diseases. Such a user interface grants better healthcare access and results in the use of proactive measures, as people can take a more active role in their wellness. © 2024 IEEE.

关键词： Diseases

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Gesture Based Sign Language Recognition for Specially Challenged Using Yolov5

Gesture Based Sign Language Recognition for Specially Challe...

引用

2024 International Conference on Science, Technology, engineering and Management, ICSTEM 2024

作者： Geetha, M.P. Swetha, S. Subitsha, M. Visnupriya, K.V. Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore India

ISBN: (纸本)9798350376913

Sign language is a vital mode of communication for individuals with hearing and speech impairments, enabling them to interact with the world around them. This paper presents a comprehensive study and implementation of a Gesture-Based Sign Language Recognition system designed to empower specially challenged individuals by providing them with an effective means of *** proposed system leverages computer vision,machine learning and deep learning techniques to recognize and interpret gestures, converting them into text or *** system has the potential to be integrated into various assistive devices, mobile applications, and educational platforms, making sign language communication more accessible and empowering individuals with hearing and speech impairments. © 2024 IEEE.

关键词： Computer vision

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StealthWatch: artificial intelligence based Shoplifting Detection [Retail Theft] 4

StealthWatch: Artificial Intelligence based Shoplifting Dete...

引用

4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Kanimozhi, T. Rajamurugan, A. Sudhesh Rajan, M.N. Harihareshwar, D.P. Vanaj Kalyan, S.R. Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529635

Retail industries face significant challenges in preventing shoplifting, which leads to substantial financial losses. 'Stealth Watch' is a comprehensive solution that leverages advanced computer vision techniques to combat this issue. Utilizing the power of YOLO (You Only Look Once) and OpenCV (Open Source Computer Vision Library), the system monitors live video footage in real-time to detect suspicious activities. When a potential shoplifting event is identified, 'Stealth Watch' sends immediate alerts to connected systems and triggers an automatic call to the store's owner or manager, ensuring prompt awareness and action. The system also incorporates facial recognition technology to log shoplifters' identities, storing this information along with the date and time of the incident in a database for future reference. 'Stealth Watch' exemplifies how cutting-edge technology can enhance security in retail environments. By automating the detection and alert process, this system reduces the need for constant human monitoring while increasing the speed and accuracy of responses to theft. Its ability to log repeat offenders also serves as a deterrent, making it a valuable tool for store owners and security personnel. In conclusion, 'Stealth Watch' is a recent advancement in the use of technology to address the persistent challenges of retail security. © 2024 IEEE.

关键词： Sales

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Malayalam Fake News Detection Using Optimized Convolutional Neural Network (OPCNN) 11

Malayalam Fake News Detection Using Optimized Convolutional ...

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11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Mohan, Aiswarya Iqbal, Nafla Mashpher, Manaal Rajagiri School of Engineering and Technology Department of Artificial Intelligence and Data Science Kakkanad India

ISBN: (纸本)9798350379136

These days, a range of issues are being caused by fake news, including satirical pieces, fake news, and intentional government propaganda. Because of the blathering social media discourse, the idea of "fake news"has recently evolved. Wide-ranging consequences of fake news dissemination include the development of distorted opinions and the tampering of election outcomes to benefit specific politicians. Spammers also use captivating news headlines to entice readers to click on their advertisements. These days, it can be quite difficult to tell if the news we hear is accurate. There aren't many methods for assessing authenticity, and the ones that are accessible are all difficult and beyond the capabilities of the average person. A fact-checking platform that gives us that chance is desperately needed. Deep Learning (DL) approaches are suited for more accurately and efficiently detecting false news because of their recent accomplishments in difficult natural language processing tasks. The suggested approach is a multi-layered, optimal CNN model for detecting fake news. The OPCNN and Word embedding are optimized for feature extraction using the hyperopt optimization method in order to produce highly accurate results. In this study, we generated word embeddings using the FastText technique. The purpose of the model is to identify news stories published in Malayalam that are either false or misleading. The model is trained on 80% of a newly compiled Malayalam dataset consisting of news articles from various online sources having 11,686 news items with 5843 false and real news entries apiece and then evaluated on the remaining 20%. The evaluation's findings show that the suggested model works better than current methods and has a high degree of accuracy when it comes to identifying false information in Malayalam. © 2024 IEEE.

关键词： Convolutional neural networks

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Voice-Driven Panoramic Imagery: Real-Time Generative AI for Immersive Experiences 2

Voice-Driven Panoramic Imagery: Real-Time Generative AI for ...

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2nd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Venkatachalam, Nirmala Rayana, Mukul Bala Vignesh, S. Prathamesh, S. Easwari Engineering College Department of Artificial Intelligence and Data Science TamilNadu Chennai India

ISBN: (纸本)9798350327533

This research study introduces an innovative system that aims to synthesize 360-degree panoramic images in Realtime based on vocal prompts from the user, leveraging state-of-The-Art Generative AI with a combination of advanced NLP models. The primary objective of this system is to transform spoken descriptions into immersive and interactive visual scenes, specifically designed to provide users with first-person field views. This cutting-edge technology has the potential to revolutionize the realm of virtual reality (VR) experiences, enabling users to effortlessly create and navigate through personalized environments. The fundamental goal of this system is to enable the generation of real-Time images that are seamlessly compatible with VR headsets, offering a truly immersive and adaptive visual experience. Beyond its technological advancements, this research also highlights its significant potential for creating a positive social impact. One notable application lies in psychological interventions, particularly in the context of phobia treatment and therapeutic settings. Here, patients can safely confront and work through their fears within these synthesized environments, potentially offering new avenues for therapy. Furthermore, the system serves educational and entertainment purposes by bringing users' imaginations to life, providing an unparalleled platform for exploring the boundaries of virtual experiences. Overall, this research represents a promising stride towards a more immersive and adaptable future in VR technology, with the potential to enhance various aspects of human lives, from mental health treatment to entertainment and education. © 2024 IEEE.

关键词： Adaptive Visual Experience First-Person view Generative artificial intelligence (AI) Immersive Visual Scenes Natural Language Processing (NLP) Panoramic Images Personalized Environments Phobia Treatment prompts Psychological Interventions Real-Time Synthesis User Interaction Virtual Experiences Virtual Reality (VR) Voice commands VR Headsets

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