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

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Breast Cancer Detection using Ultrasound Images based and Convolutional Neural Network 6

Breast Cancer Detection using Ultrasound Images based and Co...

引用

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

作者： Langote, Meher Hiwase, Kunal Wande, Radha Tale, Abhay Kalekar, Harsh Gundewar, Swapnil Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Sawangi India Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Maharashtra Sawangi India

ISBN: (纸本)9798331522667

Breast cancer forms one of the most common types of cancer among women worldwide;therefore, it highlights the importance of having rapid and timely diagnosis methods. Here, we will survey the use of convolutional neural networks (CNNs) for mutually exclusive cancer detection in ultrasound images using detectors. A dataset of benign and malignant breast tumors visualized via ultrasound is taken as the base for the training and testing of the CNN model. The CNN architecture is arranged to select valuable features from the ultrasound pictures only, but not malignant lesions. The performance and the evaluation of the CNN model are done through metrics like accuracy, precision, recall, and F1 score. The findings confirm the efficiency of using the CNN here, as it shows the extent and solidity of reading textures with excellent precision and quick performance in breast cancer screening. This investigation is the advancement of computer-aided diagnostic tools for breast cancer screening. As medical practice sees these benefits, the efficiency of healthcare delivery and patient outcomes are bound to be optimized. © 2025 IEEE.

关键词： Ultrasonic imaging

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AI-Driven Vegetation Monitoring and Fire Risk Mitigation for Wildfire Prevention and Powerline Safety 3

AI-Driven Vegetation Monitoring and Fire Risk Mitigation for...

引用

3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Harini, B. Mp, Gouri Manju, M. Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331523893

Ensuring the safety and reliability of power lines is crucial for preventing forest fires caused by vegetation encroachment. Traditional monitoring methods are labor-intensive, costly, and error-prone, often resulting in delayed identification of potential hazards. ForestGuard, an AI-powered vegetation monitoring and fire risk management system, addresses these challenges by integrating advanced geospatial and machine learning technologies. The system uses high-resolution satellite imagery and YOLOv5 for near-real-time detection and classification of vegetation and potential hazards around power lines. Built with Python and QGIS, ForestGuard automates the creation of Digital Terrain Models (DTM) and Digital Surface Models (DSM) to assess vegetation height and proximity to power lines. Upon identifying hazardous conditions, the system promptly notifies relevant authorities, enabling rapid responses to mitigate risks. By reducing reliance on manual inspections and ensuring timely interventions, ForestGuard minimizes the likelihood of power outages and wildfires. This scalable and efficient solution demonstrates significant potential for enhancing proactive vegetation management and improving the safety and reliability of power line networks. © 2025 IEEE.

关键词： Risk management

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Classification of Brain Autoimmune Diseases using Deep learning: A Predictive Approach 3

Classification of Brain Autoimmune Diseases using Deep Learn...

引用

3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Shivanii, M. Madhumetha, K. Poornima, M. St. Joseph's College of Engineering Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Autoimmune brain conditions such as pituitary, meningioma, glioma are some of the present challenges in early diagnosis due to their complexities and image characteristics. The proposed system, Autoimmune Disease Detection in the Brain- Deep learning (AIDB-DL) is built over four stages: early, intermediate, advanced and late. By using deep learning techniques, AIDB-DL identifies imaging biomarkers and unique patterns to each conditions. AIDB-DL uses data pre-processing techniques to handle issues such as noise and other unwanted characteristics which gave a demonstrated accuracy of 91%, facilitating precise classification and supporting personalized treatment planning thereby having a significant advancement in precise medicine and paving way for global improvements in brain healthcare. © 2025 IEEE.

关键词： Contrastive learning

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Unveiling the Potential of machine learning: Harnessing machine learning for Enhanced Coronary Heart Disease Detection and Intervention 4

Unveiling the Potential of Machine Learning: Harnessing Mach...

引用

4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Vadher, Harshali Hemant Aryan, Adla Vamshi, Kuruva Rajashekar, Ramojula Ashish, Degavath Arora, Gagan Deep Vardhaman College of Engineering Department of Artificial Intelligence & Machine Learning Telangana Hyderabad501218 India

ISBN: (纸本)9798350364729

Coronary heart disease (CHD) remains a critical global health issue, necessitating effective predictive models to enhance early diagnosis and intervention. This study explores the application of various machine learning algorithms to CHD data to identify and predict the risk of developing the disease based on health indicators. We employed several algorithms, including Logistic Regression, Random Forest Classifier, Support Vector machine (SVM), and Recurrent Neural Network (RNN), each with its unique strengths and limitations. The Logistic Regression model achieved an accuracy of approximately 86%, proving effective in binary classification tasks but revealing potential class imbalance issues. The Random Forest Classifier demonstrated robustness with an accuracy of around 85.24%, adeptly handling complex datasets. While specific accuracy metrics for the SVM were not detailed, it was noted for its classification capabilities. The RNN achieved an accuracy of approximately 85.96%, effectively capturing temporal dependencies within the data. Results indicate that although all models performed well, challenges such as class imbalance impacted performance metrics, especially in predicting CHD cases. This highlights the need for further refinement in predictive models to improve precision, recall, and F1-scores. Later research will examine the integration of advanced deep learning methodologies to enhance the precision and dependability of CHD forecasts. © 2024 IEEE.

关键词： Recurrent 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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Nurturhub: A Smart Maternal Health Monitoring Device for Post Partum Depression 8

Nurturhub: A Smart Maternal Health Monitoring Device for Pos...

引用

8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Jeanne Pereira, Kate Angela Acharya, Shreenath Rihan, D.S.A. Bangera, Prajwal Rao, Pratham Kumar St. Joseph Engineering College Department of Artificial Intelligence and Machine Learning Mangalore India

ISBN: (纸本)9798350350593

Wearables integrated with AI and IoT platforms are used to monitor stress, maternal health, and mental illness, offering personalized insights and recommendations. The device uses sophisticated sensors, such as heart rate (HR), blood volume pulse (BVP), and electrodermal activity (EDA) sensors, to gather physiological data from expectant moms. These technologies promise enhanced healthcare outcomes through continuous data-driven interventions. The proposed work of Nurturhub, pioneers a transformative approach to pregnancy care by integrating advanced technology to prioritize the holistic well-being of expectant mothers. Through the integration of machine intelligence and sensor technology, Nurturhub provides a comprehensive solution for prenatal care, enabling individualized health insights and ongoing monitoring. Its use throughout pregnancy may lead to better mother health outcomes, early postpartum depression identification, and general well-being. The methodology employs comprehensive sensor data extraction, preprocessing, and model development, resulting in exceptional accuracy in stress classification and personalized recommendations for maternal health management. By leveraging technology and compassionate support, Nurturhub sets a new standard for comprehensive perinatal care, with future endeavors aimed at expanding to encompass additional health parameters and enhancing data security measures. © 2024 IEEE.

关键词： Obstetrics

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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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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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OptiNLP: Leveraging Natural Language Processing for Optimal Feature Selection in Dimensionality Reduction

OptiNLP: Leveraging Natural Language Processing for Optimal ...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Varma, Nadimpallli Madana Kailash Anjali, Lingampalli Babu, Macharla Vishnu Vardhan Ismail, Shaik Sahel, Mohammed Sharfuddin Arora, Gagandeep Department of Artificial Intelligence &Machine Learning Vardhaman College of Engineering Telangana Hyderabad501286 India

ISBN: (纸本)9798350374933

In today's data-driven world, the exponential growth of data has led to the emergence of high-dimensional datasets, often causing complexity and overload in machine learning models. To address these challenges, dimensionality reduction techniques such as feature selection and feature extraction have become pivotal in enhancing model performance and interpretability. This paper focuses on exploring various feature selection methods to assist researchers in identifying the most suitable technique for sentiment analysis tasks. Utilizing Natural Language Processing (NLP), we conduct a comprehensive evaluation of different feature selection models. Our experimental findings show that, in the context of sentiment analysis, filter method consistently tends to be more effective than other evaluated feature selection techniques. Filter performs better than other methods in terms of precision, effectiveness, and model interpretability, which makes them ideal compared to other feature selection techniques. By leveraging insights from our comparative analysis, researchers can make informed decisions on feature selection techniques. © 2024 IEEE.

关键词： Dimensionality reduction

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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...

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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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