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

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YouMatter: A Conversational AI Powered Mental Health Chatbot 15

YouMatter: A Conversational AI Powered Mental Health Chatbot

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

作者： Nazareth, Pradeep Nikhil, G.B. Chirag, G. Prathik, N.R. Alva's Institute of Engineering and Technology Department of Artificial Intelligence and Machine Learning Karnataka Moodbidri India

ISBN: (纸本)9798350370249

In many regions globally, the shortage of trained mental health professionals leaves countless individuals without the support they desperately need. This deficiency, highlighted by the world health organization, underscores the pressing need for innovative solutions to bridge this gap. Chatbots emerged as a scalable intervention, offering an interactive platform for mental health solutions. These systems, adept in spoken, written, and visual languages, hold significant potential, particularly for individuals hesitant to seek traditional mental health services. Even though there are some chatbots already available, like SERMO and Aapka Chikitsak, they still have some problems such as the absence of analyzed reports and graphical data. This paper proposed a new chatbot referred to as YouMatter, to tackle these challenges. Developed on a foundation of advanced natural language processing, and Large Language Model (LLM), YouMatter serves as a mental health care chatbot, extending support and resources to those navigating mental well-being. Leveraging WebSockets, YouMatter facilitates real-time interactions, ensuring assistance is available around the clock. With the capability to analyze users' mental health trends and present graphical insights, YouMatter empowers individuals to track their progress and receive tailored support. Through personalized email tips and accessible chatbot assistance, YouMatter strives to democratize mental health care, removing barriers and fostering happier, healthier lives for all. © 2024 IEEE.

关键词： analyzed reports behavioral health graphical data LLM Model Mental health care chatbots natural language processing real-time interactions

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End-to-End Personalized Medical Recommendation Framework for Disease Prediction 4

End-to-End Personalized Medical Recommendation Framework for...

引用

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

作者： George, Justin M Joy, Emmanuel Evangeline, Suganthi Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Name Coimbatore India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798331523923

This paper addresses major challenges in medical recommendation systems, including low prediction accuracy, inadequate symptom coverage, and a lack of personalized healthcare suggestions. Existing systems often suffer from insufficient datasets and the reliance on basic machine learning algorithms, resulting in suboptimal performance and limited applicability. Therefore, this work aims to overcome these issues by developing an end-to-end medical recommendation system to predict diseases based on individual user-input symptoms and provide healthcare advice. It provides a complete report with disease descriptions, medicine, precautions, dietary advice, and exercise ideas. The system employs multi-class classification algorithms, including Support Vector Classifiers and Random Forests, trained on a structured dataset containing 133 features representing symptoms and disease outcomes. A large, integrated database combines symptoms and present conditions, as pharmaceutical, and lifestyle information to enable effective healthcare recommendations. A web-based interface facilitates user interaction, supported by a robust backend for efficient data management and user experience. Performance was evaluated using confusion matrices, which provided metrics such as accuracy, precision, recall, and F1-score to assess the model's effectiveness. The system achieves good performance in disease prediction, addressing issues of unreliable diagnostics. This paper demonstrates how artificial intelligence can address challenges in healthcare by delivering accurate, timely, and actionable insights for proactive health management and improved patient outcomes, using machine learning and comprehensive data for personalized care. © 2025 IEEE.

关键词： Personalized medicine

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DocAssist: Signature Perception System using Deep learning

DocAssist: Signature Perception System using Deep Learning

引用

2024 International Conference on Computational intelligence and Computing Applications, ICCICA 2024

作者： Sharma, Pooja Miglani, Ishika Sharma, Lucky Panipat Institute Of Engineering And Technology Samalkha India Panipat Institute Of Engineering And Technology Artificial Intelligence And Machine Learning Samalkha India

ISBN: (纸本)9798350306446

All In the realm of communication, individuals employ two primary modes: written and spoken language. Handwriting, in particular, serves as a powerful tool for conveying information and emotions across various contexts, including healthcare. Despite its widespread use, deciphering handwritten text remains a challenge due to variations in individual writing styles and the lack of standardization. This challenge is particularly pronounced in the medical field, where handwritten prescriptions can lead to serious consequences if misinterpreted. Our research addresses this issue by proposing DocAssist, a novel model that utilizes Convolutional Neural Networks to achieve an impressive 81% accuracy in recognizing doctors' handwriting. By leveraging machine learning and image recognition technologies, DocAssist aims to enhance prescription legibility, mitigate medication errors, and ultimately improve patient safety and healthcare outcomes. Through rigorous experimentation with the EMNIST dataset, our study demonstrates the effectiveness of DocAssist in accurately interpreting handwritten prescriptions, highlighting its potential to revolutionize prescription processing and alleviate the burden on healthcare professionals and pharmacists. © 2024 IEEE.

关键词： Image enhancement

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Addressing Data Imbalance in machine learning: Challenges and Approaches 6

Addressing Data Imbalance in Machine Learning: Challenges an...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Langote, Meher Zade, Nikita Gundewar, Swapnil Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra Sawangi India Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra Sawangi India

ISBN: (纸本)9798331522667

A key difficulty of dealing with imbalanced datasets is that machine learning models are trained to favor the majority class and therefore perform poorly when it comes to a minority class classification. In this paper, we consider the following methods of tackling the issue of the over-representation of a class;random oversampling, undersampling, SMOTE, cost-sensitive learning, and ensemble methods. Comparing them, we assess the performance of each of these techniques within different classifiers. Techniques including SMOTE together with combined methods including Random Forests also show higher capacity in minority class predictions. However, problems such as overfitting and the increase in the computational load are not escapable yet. We briefly recap the strengths and weaknesses of each approach and propose potential refinements including a combination of both methods and dynamic resampling methods to serve as the next steps toward improving the model. Thereby, our results draw attention to the necessity of accurate methods for handling the class imbalance, which will further yield more accurate and fair machine learning solutions across a variety of applications. © 2025 IEEE.

关键词： Adversarial machine learning

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A Review of machine learning Algorithms for Detection of Breast Cancer 4

A Review of Machine Learning Algorithms for Detection of Bre...

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

作者： Tale, Abhay Barhate, Aditya Verma, Prateek Gourshettiwar, Palash Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331529635

Breast cancer is still a global health concern, chiefly affecting women, as it is one of the major causes of cancer mortality. For the therapy to be effective, early diagnosis of cancer is critical to raising the survival level and increasing the effectiveness of the procedure. The advancement in the applied technique of machine learning (ML) has brought further opportunities for early and effective diagnosis of breast cancer. This review paper provides insights into how different ML algorithms including the Support Vector machine (SVM), Convolutional Neural Network (CNN), Random Forests, and many more have contributed to the detection of breast cancer. These algorithms have proved effective in providing solutions to various medical data and imaging including early diagnosis and treatment. The paper also looks at the use of ML with precision medicine to show that these technologies will help minimize false diagnoses and offer better treatment options. This review consolidates recent works and advancements to emphasize the prospect of ML in breast cancer management and calls for more studies to enhance its implementation in practice © 2024 IEEE.

关键词： Convolutional neural networks

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Stock Price Prediction using Transformer and Bidirectional Long Short-term Memory 3

Stock Price Prediction using Transformer and Bidirectional L...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Murugan, Vigneshwaran Moorthy, Rajalakshmi Shenbaga Sri Ramachandra Institute of Higher Education and Research Sri Ramachandra Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798350369083

Predicting stock prices has always been a challenging problem since it would be tough for algorithms to find complex patterns in stock datasets. This article proposes a new method TRAN-BiLSTM. This method consists of a hybrid architecture of transformers and BiLSTM together. Since the stock dataset is huge and time series, the prices must be noted from the start, so it requires long-term and short-term dependencies. The encoder of the transformer contains multi-head-self-attention-mechanism to get a long and short-term dependency. Transformer's ability to capture the sequence data is weak. Bidirectional Long Short-term Memory (BiLSTM) is utilized to get sequential information as it can able to handle the data in and from directions. Stock datasets taken from Yahoo Finance API have been used in this research. Three different algorithms TRAN-BiLSTM, LSTM, and BiLSTM have been used and compared to propose which algorithm is best suited for the stock price prediction. Experimentations revealed that the TRAN-BiLSTM method outperforms the other two algorithms. © 2024 IEEE.

关键词： Costs

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AI in Gaming: From Simple Algorithms to Complex Agents 2

AI in Gaming: From Simple Algorithms to Complex Agents

引用

2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Waghale, Atharva Potdukhe, Nayan Rewatkar, Rajendra Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

In it, they contended that with the rise of artificial intelligence (AI) in the gaming field, the later has become more than mere algorithms. This review categorizes the milestones in the development of AI from basic algorithms and moving up to the current movement of machine learning and neural networks where AI agents can learn, reason on their own, and respond to the actions of the player. In this paper, seminal points in gaming history, technology progress, and essential artificial intelligence technologies constituting modern gaming are described. It combines the possible ethical issues related to artificial intelligence applications in the gaming industry. Measuring experiences and competitions that are thrilling and exciting but still fair. By outlining the current state and future possibility of artificial intelligence, this article explains how it has brought the change in gaming and has made it more real. © 2024 IEEE.

关键词： Adversarial machine learning

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Design of Weed Detection Technique Using Yolo Models

Design of Weed Detection Technique Using Yolo Models

引用

2024 IEEE Students Conference on engineering and Systems, SCES 2024

作者： Sreehari, M. Jain, Ankur Vellore Institiute of Technology Artificial intelligence and Machine Learning Department of computer science engineering specialization Bhopal India

ISBN: (纸本)9798350374711

The use of the ADAM (Adaptive Moment Estimation) and SGD (Stochastic Gradient Descent) algorithms to optimize the YOLOv7(You Only Look Once), YOLOv8, and YOLO-NAS models for weed detection in agricultural landscapes is investigated in this study. Offering a thorough comparative study based on mean Average Precision (mAP), recall, and precision measures is the goal. When compared to YOLOv7, YOLOv8 regularly performs better, exhibiting balanced recall and higher precision. With remarkable mAP scores at IOU (Intersection Over Union) 0.50, YOLOv8 ADAM in particular sticks out, demonstrating strong weed identification abilities. The YOLOv7 ADAM exhibits a precision-recall trade-off, with precision being slightly sacrificed for recall. Model superiority is not solely determined by the optimization algorithm used (ADAM vs. SGD);YOLOv8 variations perform admirably. For real-world implementation, practical factors like processing efficiency and deployment viability are even more important than quantitative criteria. This work establishes the foundation for comprehending the complex trade-offs and advantages that are present in every model and algorithm, in addition to helping with the educated selection of the best YOLO models for weed detection. Future research directions in object detection in agriculture, including ensemble techniques and hyperparameter tuning, promise more advancements in this subject as technology develops. © 2024 IEEE.

关键词： Stochastic systems

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SightAssist: A Multi-facility machine learning Approach for Empowering the Visually Impaired 2nd

SightAssist: A Multi-facility Machine Learning Approach for...

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2nd International Conference on Data Science and Network engineering, ICDSNE 2024

作者： Bandopadhyay, Saikat Dutta, Jhalak Das, Smita Datta, Soumyajit Ghosh, Debaditya Dey, Rohit Kumar Nandigrami, Jeet Department of Artificial Intelligence and Machine Learning Netaji Subhash Engineering College Kolkata India Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India Department of Computer Science and Engineering National Institute of Technology Agartala India Department of Artificial Intelligence and Machine Learning Heritage Institute of Technology Kolkata India

ISBN: (纸本)9789819783359

Blindness or visual impairment ranks among the top ten disabilities affecting both men and women, impacting over 35 million individuals of all ages in India. Access to visual information is crucial for enhancing the independence and safety of blind and visually impaired individuals. There exists a compelling imperative to develop assistive technologies that can significantly improve their quality of life. This paper presents SightAssist, a multilingual software solution designed to address the challenges faced by the visually impaired community in India. SightAssist utilizes a camera-based approach to serve as a digital prosthetic eye and virtual assistant, capable of currency detection, facial recognition, and providing various functionalities to assist with daily activities. Initially, a custom-labeled dataset has been prepared using a pre-trained YOLO model. Gradually, dataset pre-processing, normalization, and semantic segmentation are employed to optimize model performance. Additionally, voice-based accessibility features have been integrated using the Web Speech API, enabling users to interact with the application through voice commands. The experimental results show the successful development of SightAssist and its potential to significantly improve the lives of visually impaired people. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Assistive technology

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Enhancing Security in Automobile Edge Computing through Federated learning and Blockchain

Enhancing Security in Automobile Edge Computing through Fede...

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2023 IEEE International Conference on Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security, iQ-CCHESS 2023

作者： Sandosh, S. Doshi, Sujay Joshi, Atharva Vellore Institute of Technology Faculty for Computer Science with Specialization in Artificial Intelligence and Machine Learning Chennai India Vellore Institute of Technology Computer Science with Specialization in Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798350314946

Vehicular networks that consist of interlinked automobiles and transportation network are prone to cyberattacks due to the increased use of software and the presence of wireless interfaces. To counter these threats, intrusion detection systems can be tailored efficiently. In recent years, there has been significant development in detection of malicious attack traffic through the use of machine learning. However, traditional systems require powerful computing devices to continuously train and update complex network models, which can reduce the efficiency and effectiveness of the system due to resource constraints, untimely updates and dealing with large volumes of data. To address this issue, a cooperative intrusion detection mechanism which had been proposed has been improved with a classification based novel approach, that eases the burden by distributing the training model to distributed edge devices such as connected vehicles and roadside units. The previous approach has a slow response time to the increasing network issues. The proposed work offers a new approach through federated learning with the Extra Trees Classification algorithm. The Extra Trees Classifier performs better than other machine learning models in all aspects. The proposed approach provides a faster response to security threats in automobile edge computing. The use of a distributed federated approach reduces the load on the server while maintaining confidentiality by aggregating models using a blockchain. © 2023 IEEE.

关键词： Blockchain

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