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检索条件"机构=Department of artificial intelligence and Machine Learning"

共 2191 条记录，以下是41-50 订阅

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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...

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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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Optimizing Marketing Strategies: Predicting Customer Personality using Advanced machine learning Models 2

Optimizing Marketing Strategies: Predicting Customer Persona...

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2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Aryan, Adla Sagar, Sada Preetham Varma, Nadimpalli Madana Kailash Manikanta, Rangisetti Rajashekar, Ramojula Arora, Gagan Deep Department of Artificial Intelligence & Machine Learning Vardhaman College of Engineering Telangana Hyderabad501218 India

ISBN: (纸本)9798331541217

In the competition of modern marketing, it is highly important to foresee the correct personality profiles of customers because this may further improve the result of marketing campaigns. Therefore, in this research, three major models of machine learning are used, including Random Forest Classifier, Support Vector machine (SVM), and Gradient Boosting Classifier, to discuss the challenges of predicting customer personalities. This study is helpful in finding unique customer segments and appropriate marketing strategies. These algorithms are valid to use for classifying customer personalities, and then determine the efficiency of it using the context also proves that through the more complex applications of such approaches, the efficacy of targeted marketing might be enhanced appropriately. Therefore, even more advanced deep learning techniques would be used at the next stage of research to further improve the forecasting accuracy and applicability in various marketing scenarios. © 2024 IEEE.

关键词： Adversarial machine learning

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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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SkillSync: Evaluating Candidates for Communication and Problem-Solving Proficiency 15

SkillSync: Evaluating Candidates for Communication and Probl...

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

作者： Varma, Nadimpalli Madana Kailash Mattaparty, Sri Harsh Nikitha, Durgam Numan, Mohammed Talaha Vardhaman College of Engineering Department of Artificial Intelligence & Machine Learning Telanagana Hyderabad501286 India

ISBN: (纸本)9798350370249

In contemporary work environments, effective communication, collaborative problem-solving, and positive attitude are pivotal for success. However, conventional hiring approaches often struggle to accurately evaluate these essential competencies. SkillSync presents a groundbreaking solution to this challenge by introducing an innovative assessment platform tailored to measure candidate's communication skills, problem-solving abilities, and positive demeanor. This project investigates the development of a comprehensive assessment tool designed to analyze an individual's disposition and adaptability across diverse workplace situations. The tool will leverage a user-friendly interface and a novel algorithmic approach to evaluate an individual's inherent characteristics. The algorithm will focus on analyzing the relevance and efficacy of candidate responses, with the primary objective of furnishing employers with valuable insights into a candidate's content writing proficiency and their capacity to approach workplace challenges with a positive outlook © 2024 IEEE.

关键词： Communication keyword matching Positive attitude Problem-solving Relevance Sentiment analysis Skillsync Traditional hiring

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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 ...

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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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Optimizing Crop Yields with Multilayer Perceptrons: A Data-Driven Approach 8

Optimizing Crop Yields with Multilayer Perceptrons: A Data-D...

引用

8th International Conference on Inventive Systems and Control, ICISC 202

作者： Nivetha, N. Usharani, S. Department of Computer Science and Engineering Villupuram India Department of Artificial Intelligence and Machine Learning Villupuram India

ISBN: (纸本)9798350386578

Precision agriculture has become a major change in crop farming. It utilises cutting-edge technologies to maximise field-level management. Precision agriculture has completely transformed crop production by leveraging the latest developments to maximize field-level management. Predicting crop yields with accuracy helps farmers reduce their environmental impact, increase productivity, and make well-informed decisions. Accurate and timely insights are frequently lacking in traditional agricultural yield prediction approaches. The study offers a deep learning method for precisely predicting agricultural yields. Accurate crop yield forecasts assist farmers in minimizing their negative environmental effects, boosting productivity, and making educated choices. However, there are many obstacles because conventional agricultural yield prediction methods frequently need more timely and precise insights. Despite their success, several challenges still exist. These include handling heterogeneous data, dealing with missing values, and the complexity of capturing non-linear relationships in the data. To determine whether decision trees or Multi-Layer Perceptrons (MLP) are ideal in crop yield prediction, these models are compared with each other. Multi-layer perceptrons (MLP) are prominent among these techniques. Even though the MLP model was more accurate, decision trees also are relevant to the prediction process. This means have the capability of understanding multi-layer intra-data intricacies through their structure whereas decision trees may overfit on noisy data or grow too deep hence leading to many splits also known as being bushy unless they are pruned to reduce this bushiness. The study suggests a novel method for predicting agricultural productivity using a machine learning model Decision Tree and Multi-Layer Perceptrons (MLP). A web interface is also created to enable smooth communication with the prediction model. Through the usage of this interface, farmers and agr

关键词： Prediction models

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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...

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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

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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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Fake Social Media Detection using machine learning 15

Fake Social Media Detection using Machine Learning

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

作者： Thangamani, R. Vimaladevi, M. Mohammed Rifath, J. Harini, G. Hari, S. Kongu Engineering College Department of Artificial Intelligence Tamil Nadu India Kongu Engineering College Artificial Intelligence And Machine Learning Perundurai India

ISBN: (纸本)9798350370249

Social media platforms such as Facebook, Twitter, Instagram and others have become an integral part of our lives, facilitating global connectivity. But the proliferation of fake profiles on these platforms poses serious risks, including the spread of false information, phishing attacks and identity theft. This research project focuses on identifying fake Twitter profiles using machine learning techniques to distinguish them from genuine accounts. In the dataset used in this study, Twitter profiles are classified into genuine accounts (TFP and E13) and fake accounts (INT, TWT and FSF).. The main objective is to evaluate the effectiveness of the ensemble learning technique Random Forest algorithm in classifying social media profiles. In-depth discussions include algorithm architecture, hyperparameters, and selection of critical features such as follower and friend counts, status changes, and other relevant features. Additionally, alternative classifiers including neural networks, LSTM, XG Boost and others are briefly discussed in the summary. The Random Forest algorithm has a commendable 95.54% accuracy after rigorous training. This high accuracy underlines its ability to effectively distinguish between real and fake Twitter profiles. By assigning a value of 0 to genuine profiles and 1 to fake profiles, the algorithm provides a practical solution to detect and mitigate fake accounts, thereby solving the cyber security issues associated with social media. This research contributes to ongoing efforts to create a safer online environment by identifying and removing fake profiles. © 2024 IEEE.

关键词： Fake News LSTM Profiles ROC Social Media

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Personalized Adaptive learning Platform Empowered by artificial intelligence

Personalized Adaptive Learning Platform Empowered by Artific...

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2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Pradeep, K.R. Manish, A.S. Adithiyaa, A.S. Sahana, N. Abhishek, S.T. Bms Institute of Technology and Management Department of Artificial Intelligence and Machine Learning Karnataka Bengaluru India

ISBN: (纸本)9798350359688

In the current educational landscape, the integration of artificial intelligence is transforming the way students learn and teachers instruct. The 'Personalized Adaptive learning Platform Empowered by artificial intelligence' is an innovative project aimed at revolutionizing the educational landscape by leveraging artificial intelligence (AI) to provide a personalized and interactive learning experience. In this paper we present an educational technology platform which harnesses the capabilities of artificial intelligence (AI) to emulate the role of a human teacher in delivering lectures, offering personalized explanations through visual animations, and closely monitoring student engagement via webcam-based interactions. The purpose of this endeavor is multifaceted. Firstly, it aims to narrow the divide between conventional teaching methods and the changing requirements of modern learners by introducing an AI-driven, personalized approach. Secondly, the integration of webcam monitoring ensures active student participation and attentiveness during lectures. © 2024 IEEE.

关键词： Contrastive learning

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