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检索条件"机构=Artificial Intelligence and Machine Learning Engineering"

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An artificial intelligence Approach to Quantifying Exercise Form for Optimal Performance and Injury Prevention 3rd

An Artificial Intelligence Approach to Quantifying Exercise ...

引用

3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Sowmia, K.R. Jayaganeshan, T. Mohammed Abraar Khan, F. Madhesh, S. Kabilesh, S. Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9789819708918

Human body posture estimation remains one of the most challenging problems despite substantial research and improvements in the fields of artificial intelligence and computer vision. Human pose detection has several uses, including assisted living, video surveillance, biometrics, public security, and at-home health monitoring. Nowadays, teenagers between the ages of sixteen and twenty-five are encouraged to engage in rigorous training because they are interested in maintaining a specific degree of physical fitness in their bodies. If they do not maintain good form or posture, this tough training activity could lead to muscular injuries or any other small or serious problems. They should engage a personal trainer or someone who can help them follow the right methods to sustain and prevent injuries if they want to keep up with all of this. This only applies to those who perform these intense exercises in a gym or other public space;at-home exercisers would not be able to afford personal trainers and instructors. One can utilize an AI-trained model to maintain good body posture for intense workouts. The analysis and evaluation of the participants’ exercise movements will be done by the study using a combination of motion sensors, cameras, and machine learning algorithms. The AI-powered system will examine each participant's form, offer real-time feedback, and correct mistakes made while training with various workouts. This AI-trained model serves as a personalized trainer that can be tweaked to the user's specifications. The AI model assigns a preference category to each workout and notifies the user if it detects a posture issue. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Computer vision

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A Deep Insight on Cricket Video to Text Summarization Using Neural Networks 10

A Deep Insight on Cricket Video to Text Summarization Using ...

引用

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Gopal, J. Vijay Prasad, K. Sai Kumar, Dhatrika Kamal Prashanth, Maddhe Sai Kumar, Vuppala Praneeth Teja, I. Nikhil Sri Sai Computer Science Engineering Artificial Intelligence and Machine Learning Mlr Institute of Technology Hyderabad India

ISBN: (纸本)9798350384369

In this modern era, the demand for efficient and automated cricket video summarization techniques is rapidly increasing. This paper introduces an innovative and advance neural network system that transforms the way cricket match videos are summarized. Cricket video-to-text summarization system overcomes the limitations of traditional manual summarization approach by utilizing various deep learning techniques to completely automate the summarization process, our system can extract crucial insights from lengthy cricket match footage and convert them into easily readable text formats. The system employs three-pronged approach which involves extraction of visual features using VGG16 Convolutional Neural Network (CNN), scoreboard information is extracted through Optical Character Recognition (OCR) technology, and Text Summarization performed by Long Short-Term Memory (LSTM) network. Our system revolutionizes the way cricket enthusiasts engage with match videos, providing a game-changing experience for fans worldwide. © 2024 IEEE.

关键词： Convolutional neural networks

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A Comprehensive Multi-Modal and Multi-Model API for Real-Time Sign Language Translation 15

A Comprehensive Multi-Modal and Multi-Model API for Real-Tim...

引用

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

作者： Prianka, R.R. Singh, Tanya Singh, Rishabh Solanki Jaiswal, Divyesh Rohith, M.S. New Horizon College of Engineering Department of Artificial Intelligence & Machine Learning Karnataka Bengaluru India

ISBN: (纸本)9798350370249

This research introduces a novel three-stream, multi-modal and multi-model architecture with a comprehensive API to advance the scalability of sign language translation systems. It integrates a sentence correction model, an emotion detection system, and a sign language recognition model. The sentence correction model ensures translations are both semantically and syntactically accurate. The emotion detection system interprets emotional nuances, which is vital for signs that look similar but have different meanings. The sign language recognition model accurately decodes gestures from video, enhancing the fidelity of translations. Preliminary results indicate a significant improvement in translation accuracy and contextual relevance, with notable reductions in semantic errors and better conveyance of emotional states. This system offers more inclusive and accessible technology, paving the way for broader communication possibilities and greatly benefiting the deaf and hard-ofhearing community. © 2024 IEEE.

关键词： API Deaf and Hard of Hearing Community Deep learning Emotion Detection Flask Multi-Modal Multi-Model Grammar Correction Python Sign Language Translation

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An Analysis on Transliterated Dravidian Text

An Analysis on Transliterated Dravidian Text

引用

2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Teena, A.J. Acharya, Shreenath Coelho, Reevan Fernandes, Edita Sa, Jane Rachel D Shetty, Nithyananda St. Joseph Engineering College Department of Artificial Intelligence and Machine Learning Mangalore India

ISBN: (纸本)9798331517892

The rapid growth and accessibility of social media platforms have led to an exponential increase in content generation across multilingual code-mixed languages. Sentiment analysis on multilingual inputs presents significant challenges due to the absence of well-developed corpora, standardized script systems, and the widespread use of transliterated text. In language processing, transliteration enables users to interact with original scripts through more familiar scripts, such as the Latin alphabet. However, it introduces complexities like out-of-vocabulary words, dialectal variations, and orthographic inconsistencies. To bridge this gap, we present a comprehensive study on sentiment analysis of transliterated Dravidian languages. Utilizing online discourse platforms, we developed a large-scale corpus consisting of 26,000 examples and constructed two models. The first model is trained to classify text into three categories, while the second model generates descriptive sentiment labels, offering deeper insights into user opinions. The presented work has acquired an accuracy of 89% and has implications for businesses, policymakers, and researchers seeking to analyze and understand public opinion in Kannada-speaking regions. © 2024 IEEE.

关键词： Classification (of information)

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Decoding Sentiments: A Comprehensive Exploration of Real-Time Emotion Recognition from Text Through Deep learning 10

Decoding Sentiments: A Comprehensive Exploration of Real-Tim...

引用

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Sowmya, G. Akhilesh, R. Rohan, Aurangabadkar Rao, Garikapati Kausthub Nayak, Atul Kumar Midhilesh, Koppolu Computer Science Engineering Artificial intelligence and Machine learning Mlr Institute of Technology Hyderabad India

ISBN: (纸本)9798350384369

Emotional state of a person can be found out through various modalities such as using facial expressions, voice of the person, text and so on. Our study is solely based on the emotion extracted through the text. Past research focused on categorizing texts into sentiments like positive, negative, or neutral, our novel approach shifts to predicting specific emotional states like angry, sad or happy. Our study covers the previous researches done in this field which are ranging from deep learning models, natural language processing, and machine learning models. Recurrent neural networks and its variants are mainly studied for recognizing the emotion in text. The integration of emotion detection in the customer feedback enhances organizational insights, enabling proactive measures to elevate customer satisfaction. © 2024 IEEE.

关键词： Contrastive learning

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Lung Cancer Classification Using CNN with Data Augmentation 5

Lung Cancer Classification Using CNN with Data Augmentation

引用

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Karunakaran, V. Akshaya, N. Harismita, B. Atchaya, S. Sri Eshwar College of Engineering Artificial Intelligence and Machine Learning Coimbatore India Sri Eshwar College of Engineering Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798331518097

Lung cancer remains a leading cause of cancer-related deaths globally. This study investigates the application of Convolutional Neural Networks (CNNs) for the classification of lung cancer using the IQ-OTH/NCCD lung cancer dataset. The study compares the performance of CNN models with and without data augmentation techniques. Experimental results demonstrate that CNN models with data augmentation exhibit superior performance, achieving higher accuracy, precision, F1-score, and recall compared to models trained without data augmentation. These findings highlight the importance of data augmentation in improving the accuracy and robustness of deep learning models for lung cancer classification, ultimately contributing to early detection and improved patient outcomes. © 2024 IEEE.

关键词： Lung cancer

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Personality Based Text Generation 15

Personality Based Text Generation

引用

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

作者： Priyanka, S. Santhiya, S. Keerthika, S. Divyadharshini, B. Madan, P. Yaswin, M.S. Kongu Engineering College Department of Artificial Intelligence Erode Perundurai India Kongu Engineering College Department of Artificial Intelligence and Machine Learning Erode Perundurai India

ISBN: (纸本)9798350370249

The rise of chatbots in applications like customer service and companionship highlights the need for personalized experiences. Current limitations often cause user frustration and disengagement. This work proposes a novel approach to enhance chatbot interactions by dynamically adapting text generation to the user's personality using the Big Five personality traits (Openness, Conscientiousness, Extroversion, Agreeableness, Neuroticism). The proposed approach involves multiple stages, starting with data acquisition and preprocessing. A large text dataset annotated for the Big Five traits forms the foundation for training a personality analysis model. Neural network models such as GPT-3, CTRL, and GPT-Neo 2.7B are evaluated for personality-based text generation. Among these, GPT-Neo 2.7B shows the best performance, with a BLEU score of 0.65, in capturing personality nuances. Evaluating the system's effectiveness involves human assessment of the generated text to ensure accurate translation of personality indicators into appropriate communication styles. Additionally, user studies will measure satisfaction and engagement during interactions with the chatbot. The primary goal is to develop a functional chatbot prototype that personalizes responses based on the user's personality, leading to improved engagement and satisfaction, thereby significantly advancing the field of human-computer interaction. © 2024 IEEE.

关键词： Agreeableness Conscientiousness CTRL EleutherAI's GPT-Neo 2.7B Extroversion GPT-3 Neuroticism Openness

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Secure Photo Sharing Across Platforms Using Blockchain Technology 4

Secure Photo Sharing Across Platforms Using Blockchain Techn...

引用

4th International Conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Nivetha, K. Harini, M. Harshinni, V. Nivetha, N. Tamilselvan, P. M Kumarasamy College of Engineering Department of Artificial Intelligence and Data Science Karur India M Kumarasamy College of Engineering Department of Artificial Intelligence and Machine Learning Karur India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523923

With the rapid expansion of online social networks (OSNs), photo sharing has become increasingly prevalent, raising significant privacy and security concern. Existing approaches, like centralized access control mechanisms and encryption-based privacy solutions, suffer from inconsistent policy enforcement, unauthorized access risk, and lack of transparency. Moreover, traditional OSN platforms afford limited control over the dissemination of content with respect to counteracting illegal reproductions. In order to address these challenges, Photo Chain is designed. The relevance of this name derives from its concept of using a blockchain mechanism to enable photo sharing in social networks securely and in a controlled manner. The system has smart contracts for dynamic access control, PreHash for photo integrity verification, and Gaussian Blur for the protection of sensitive regions. Dissemination control also provides various ways to prevent unauthorized sharing of photos. Ethereum-based smart contracts maintain an immutable log of transactions to guarantee privacy and transparency through user-defined access policies. A functional prototype has also been developed and tested for security, privacy, and controlled dissemination of pictures through OSNs. This prototype has demonstrated the usefulness of Photo Chain. © 2025 IEEE.

关键词： Differential privacy

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Enhancing Career Development: An AI-Driven Mentorship Platform for Student-Mentor Connections

Enhancing Career Development: An AI-Driven Mentorship Platfo...

引用

2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Charanya, P. Nidhish, M. Prajesh, U.R. Saktheeswar, K. Pillai, Sanjana S. Sri Eshwar College of Engineering Intelligence and Machine Learning Department of Artificial Coimbatore India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798331512965

Thunai is an AI-powered mentorship platform that connects users with personalized mentors for career guidance. Key features include automated calendar booking, video communication with privacy-enhancing dynamic emojis, and AI-driven mentor matching aligned with mentee goals. The AI assistant, GURU, supports task management, resource recommendations, and LinkedIn-based evaluations to foster growth. Thunai offers subscription plans and partnerships with educational and mental health organizations, enhancing engagement, leadership development, and networking. Leveraging AI technologies like sentiment analysis and feedback, Thunai improves mentor matching, user satisfaction, and mentee engagement for career and emotional well-being. © 2025 IEEE.

关键词： Professional aspects

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Avoidance of Ship Collision Using Deep Reinforcement learning with Bi-Directional Long Short-Term Memory in Continuous Action Spaces 2

Avoidance of Ship Collision Using Deep Reinforcement Learnin...

引用

2nd IEEE International Conference on Data Science and Network Security, ICDSNS 2024

作者： Krishnamurthy, Revatthy Adnan, Myasar Mundher Sunil Kumar, V. Srinivas, T. Aditya Sai Aluvala, Srinivas Rajalakshmi Engineering College Artificial Intelligence & Machine Learning Department Chennai India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Artificial Intelligence and Machine Learning Jayaprakash Naryan College of Engineering Mahabubnagar India School of Computer Science and Engineering Sr University Warangal India

ISBN: (纸本)9798350373110

Recent days, with the globalization of the world economy the ships count are increasing for marine transportation and the Waterways are growing increasingly overcrowded than previous. This situation may leads to problem of collision of ships which may cause losing of life and damage for property and to nature. Many automatically collision models of ships are implemented but many focused only on the ship-ship encounter situation only. By using a grid sensor which is virtual sensor, agents of Deep Reinforcement learning (DRL) classify approach of a multiple ships. This framework introduces an automatically collision detection algorithm for ships using DRL in continuous action spaces. DRL is used for avoidance of collision with a maximum distance of safe passing between ships. A unique method is developed named inside Obstacle Zone by Target (OZT) used to change learning capability that expands the OZT. Using Bi-directional Long Short-Term Memory (BI-LSTM) cell, network is redesigned and continuous action spaces training is carried out to train a model with longer safe distance of ships. In collision detection model that the bow cross range is effective for COLREGs compliant collision avoidance that is proposed in this model. The propose model also validates a scenario that included more ships and have passed that Imazu problem. The proposed BI-LSTM model achieved 80.43 % of accuracy, 95.67% of precision, 85.89% of recall and 93.54% of f1 score values. © 2024 IEEE.

关键词： Deep reinforcement learning

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