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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

共 1866 条记录，以下是51-60 订阅

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Boosting Algorithms Empowering Heart Disease Prediction for Enhanced Medical Accuracy

Boosting Algorithms Empowering Heart Disease Prediction for ...

引用

international Symposium on Advanced Computing and Communication (ISACC)

作者： Urvashi Sharma Kanak Saxena Computer Science & Engineering Department Samrat Ashok Technological Institute Vidisha Head Computer Science & Engineering Department Samrat Ashok Technological Institute Vidisha India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Heart disease remains a leading cause of mortality worldwide, necessitating accurate and reliable predictive models to aid early diagnosis and treatment. Traditional machine learning methods like LR and DT Classifiers offer reasonable recall rates but suffer from lower precision and accuracy. To address this challenge, we employed advanced boosting algorithms, specifically GradientBoostingClassifier and XGBClassifier, to enhance predictive accuracy in heart disease detection. Our study shows that these algorithms achieve accuracy rates above 93%, with precision exceeding 52%, and a significantly low False Positive Rate below 0.5%. However, the trade-off involves lower recall rates, suggesting some true cases remain undetected. Boosting algorithms thus provide a robust approach for heart disease prediction, though attention to false negatives is essential for comprehensive application.

关键词： Heart Accuracy machine learning algorithms Predictive models Boosting Prediction algorithms Classification algorithms Reliability Medical diagnostic imaging Diseases

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Social Media Sentiment Analysis Using machine learning

Social Media Sentiment Analysis Using Machine Learning

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Disruptive Technologies (ICDT), international conference on

作者： Om Rajendra Deokar Tushar Raju Gaikwad Kaushal Ramesh Gawali Piyush Dyaneshwar Ghanghav Sandip Ashok Shivarkar Ajit Ashok Muzumdar Department of Computer Engineering Sanjivani College of Engineering Kopargaon India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

In recent years, social media has emerged as a crucial platform for public expression, enabling individuals to share opinions and sentiments on a wide range of topics. This study explores the application of machine learning techniques-Support Vector machines (SVM), Decision Trees, Naive Bayes, and Random Forest for sentiment analysis of social media content. By leveraging a diverse dataset of user-generated posts, we aim to classify sentiments into positive, negative, and neutral categories. We evaluate the performance of each algorithm based on accuracy, providing a comparative analysis of their effectiveness. Our findings reveal that SVM significantly outperforms the other methods in terms of accuracy establishing it as the most effective model for sentiment classification in this domain. The superior performance of SVM is attributed to its capability to manage high-dimensional data and create optimal hyperplanes for classification. These results emphasize the critical importance of selecting appropriate machine learning models for sentiment analysis, especially in the dynamic landscape of social media.

关键词： Support vector machines Sentiment analysis Analytical models Accuracy Social networking (online) High dimensional data Vectors Real-time systems Bayes methods Decision trees

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Optimizing machine learning Algorithms for Accurate Heart Disease Detection

Optimizing Machine Learning Algorithms for Accurate Heart Di...

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Device Intelligence, Computing and Communication Technologies, (DICCT), international conference on

作者： SM Faizanut Tauhid Safdar Tanweer Md. Tabrez Nafis Mohd Abdul Ahad Syed Mohd Faisal Malik Syed Sibtain Khalid Department of Computer Science and Engineering Jamia Hamdard Delhi India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

Heart disease is a leading global health challenge, emphasizing the need for effective diagnostic solutions. This study evaluated the accuracy of machine learning algorithms in heart disease diagnosis with the aim of finding the most reliable method. The algorithms examined contain Random Forest, Support Vector Classifier, Gradient Boosting, AdaBoost, Optimized Logistic Regression, SVM, Naive Bayes, K-Nearest Neighbors (KNN), Decision Trees, and SGD Classifier. Among them, optimized logistic regression and naive Bayes have the highest accuracy of up to 88.52%, demonstrating their predictive capabilities. The result of this work is optimized logistic regression and naive Bayes are capable of early detection of cardiovascular disease. Offering major perspective for integration into clinical practice. Future work will explore the refinement and real-world application of these models to further improve healthcare outcomes.

关键词： Heart Support vector machines Logistic regression Accuracy machine learning algorithms Translation Nearest neighbor methods Vectors Bayes methods Random forests

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An Intelligent machine learning Approach for Prediction of Road Accident Severity

An Intelligent Machine Learning Approach for Prediction of R...

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Industrial Electronics: Developments & applications (ICIDeA), IEEE international conference on-

作者： Prashanth Ragam Namana Sri Rahul Pandi Balaji Sankati Jyothi Pavithra Danaveni School of Computer Science and Engineering VIT-AP University Amaravati Andhra Pradesh India Software Development Cell VIT-AP University Amaravati Andhra Pradesh India Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana India

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

Road accidents are a primary global concern for public safety, with India having a very high death toll. This study presents an intelligent machine learning approach to predict the severity of road accidents, contributing to the development of more effective and preventive measures. We utilized a comprehensive dataset of road accidents in India, employing advanced machine learning algorithms, including Histogram-based Gradient Boosting (HGB), Random Forest (RF), Support Vector machines (SVM), and K-Nearest Neighbors (KNN). Our methodology involved extensive data pre-processing, feature engineering, and model optimization. The Random Forest algorithm demonstrated superior performance, achieving an accuracy of 94.7% and an F1-score of 0.947. This research provides a robust predictive model and information on the key factors influencing the accident's severity. The novelty of our approach lies in the comprehensive feature engineering tailored to the Indian context and the application of ensemble learning techniques to capture complex, nonlinear relationships in accident data. The findings of this study have significant implications for policymakers, urban planners, and law enforcement agencies in their efforts to improve road safety.

关键词： Support vector machines Radio frequency Road accidents machine learning algorithms Predictive models Nearest neighbor methods Prediction algorithms Road safety Public security Random forests

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Fake Trend Detection in Twitter Using machine learning 3rd

Fake Trend Detection in Twitter Using Machine Learning

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3rd international conference on Computing and Communication Networks, ICCCN 2023

作者： Chinnaiah, Valliyammai Dhayanithi, Manikandan Patturaj, Santhosh Ranganathan, Ramanujan Mohan, Vishnu B. A. Department of Computer Technology Madras Institute of Technology Anna University Chennai India

ISBN: (纸本)9789819726707

Social media plays a major part in everyone’s lives in which it aids the people to interact and connect with them. Social media has created large communities to influence what everyone reads, does, or even thinks. The influencing mechanism of Twitter trends is exploited using bots and spam accounts where several tweets on a topic are boosted into trends based in an artificial way. The fake trends are common in the social media domain which is discovered through promoting gambling, information phishing, disinformation campaigns, political slogans, and hate speech. The manipulation of Twitter trends has great implications because Twitter trends get more attention. The broader media channels cover trends through utilizing as a stand-in for what people are talking about. Unfortunately, this stand-in is manipulated, clouding the public’s perception of the topics being discussed. It is important to detect these fake trends and ensure that the public is not exposed to fake trends. The proposed fake trend detection system classifies the Twitter accounts into bot and human accounts. TwiBot-20 benchmark dataset is trained to classify the bot accounts using Random Forest model which achieves 94% accuracy. The metadata features and influence entropies are extracted from bot accounts. The influence entropy is measured by constructing an influence graph using igraph and NetworkX through analyzing the retweet count and user mentions in each tweet of an account. The trending topic of the tweets is analyzed and the authenticity of the trend is categorized using one-dimensional clustering. A web application is developed to facilitate the proposed fake trend detection system for the users without technical knowledge. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Tweets

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Color and Defect Detection in Industry Automation for Real Time Intelligent Product Sorting

Color and Defect Detection in Industry Automation for Real T...

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Device Intelligence, Computing and Communication Technologies, (DICCT), international conference on

作者： Sujatha Rajkumar Shreyas Nirgude Ashwin Anil Kulkarni Sumathi G Hamid Abdi School of Electronics Engineering Vellore Institute of Technology Vellore India Science Engineering and Built Environment Deakin University Waurn Ponds campus Australia

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

This paper presents a real-time object sorting system that classifies objects based on color and detects surface defects in an intelligent manner for multi-purpose application like autonomous vehicles, supply chain, robotics and so on. The proposed system uses state-of-the-art camera module to capture images of objects and processes them through computer vision to classify items based on color and defect status. The captured images are compared against a custom dataset of labeled samples to ensure accuracy. Based on performance metrics, the intelligent CNN algorithm demonstrates superior classification accuracy compared to RFC, especially in complex scenarios. The edge processor module controls sorting mechanism, where servos actuate to direct objects into designated bins based on classification results. The research work illustrates exploring the potential of machine learning algorithm in automated quality assurance, offering scalable, adaptable, and efficient solutions for industrial applications.

关键词： computer vision machine learning algorithms Automation Accuracy Image color analysis Supply chains Real-time systems Surface treatment Sorting Defect detection

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Investigating Salient Object Detection Methods Tailored for Edge Computing Infrastructure 3rd

Investigating Salient Object Detection Methods Tailored for...

引用

3rd international conference on Computing, Communication and learning, CoCoLe 2024

作者： Sebastian, Elsa Martin, John Paul Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam Kottayam686635 India

ISBN: (纸本)9783031790409

Salient object detection aims in identifying the most significant parts of a scene as viewed by the human eye and plays a vital role in a variety of applications, including object detection, video compression, and augmented reality. Saliency detection has emerged as a fundamental preprocessing step in many computer vision and image processing tasks. Implementing saliency detection in edge computing platforms is challenging, however it promises enhanced real-time responsiveness, reduced bandwidth usage, and improved privacy. This paper examines the most advanced techniques for adapting saliency detection techniques to edge computing and discusses encountered challenges. The paper also gives insights into the evaluation metrics that can be utilized for performance assessment, investigates primary application domain and proposes potential directions for future research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Comparative Analysis of machine learning Algorithms for Disease Prediction Using Medical Datasets

Comparative Analysis of Machine Learning Algorithms for Dise...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： C. Roopa R.S. Latha M. Dhavamani G. Bommisrilekkhaa P. Angel Shalini R. Dhanyajothi Department of Computer Science and Engineering Kongu Engineering College Erode Tamilnadu Department of Artificial Intelligence Kongu Engineering College Erode Tamilnadu Department of Mathematics Kongu Engineering College Erode Tamilnadu

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

application of machine learning in the analysis of medical data can be said to be one of the current transformations happening within the healthcare fraternity. This paper highlights several ways in which various machine learning techniques can be used for gaining valuable insight into disparate types of medical data, such as patient records or genetic information, toward further clinical decision-making with a view to better prediction of diseases or strategies related to personalized medicine. The study critically evaluated various algorithms by considering their performances, suitability with regard to certain medical datasets they would be set upon to analyze, and to interpret the performance, particularly within the supervised learning frameworks. It talks about methodologies through which feature selection can take place to make sure that there is effective model development. Consequential applications in early disease detection as well as the optimization of their treatments are further emphasized, representing great potential for massive improvements in their recovery outcomes. The results highlight how far reaching data-driven approaches can affect the future of healthcare. It is envisioned that, with the use of such methods, the paper foresees a paradigm shift in disease diagnosis and treatment for personalized medical care and improved health outcomes.

关键词： machine learning algorithms Precision medicine Supervised learning machine learning Predictive models Feature extraction Prediction algorithms Medical diagnostic imaging Optimization Diseases

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A Review on machine learning Algorithms for Real-Time Traffic Management 3rd

A Review on Machine Learning Algorithms for Real-Time Traffi...

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3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

作者： Cholera, Bhavesh Jayantilal Bajeja, Sunil Lalchand Faculty of Computer Application Marwadi University Gujarat Rajkot India

ISBN: (纸本)9783031862984

The use of machine learning algorithms in traffic control has garnered a lot of interest recently. In order to better understand the design and development of prediction algorithms for machine learning-based real-time traffic management, In this paper, we summarize the deep learning and machine learning methods for data processing, like image processing, SVM, KNN, CNN, HOG, big data, ANN, regression, Yolov3 algorithms, LSTM, GRU, etc. We are attempting to identify these methods’ limitations and potential future applications. This study helps to choose the best prediction algorithms. Here, we tried to use congestion theory in a real-time traffic application. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Leveraging Sentinel-2 Multispectral Data and machine learning Algorithms for Land Use Land Cover Mapping in Semi-Arid Regions

Leveraging Sentinel-2 Multispectral Data and Machine Learnin...

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Advancement in Computation & computer Technologies (InCACCT), international conference on

作者： Narayan Vyas Koushik Sundar Amit Sharma Pradeep Shree Adhikari Department of Computer Science and Application Vivekananda Global University Jaipur India Citibank NA Jersey City NJ USA Department of Computer Science and Engineering Vivekananda Global University Jaipur India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Land use and land cover (LULC) classification is a fundamental approach for landscape pattern mapping and monitoring of natural ecosystems and anthropogenic activities and is, therefore, an essential base for resource management to support sustainability. The land cover classes were classified in this study using multispectral data. The study utilizes Sentinel-2 data captured in February 2024, and a median composite image was generated using the Google Earth Engine (GEE) platform. Three different classification algorithms, random forest (RF), classification and regression tree (CART), and naive bayes (NB), were used to produce LULC maps. The algorithms are efficient in capturing different land cover patterns with spectral diversity. The performance with essential measures such as overall accuracy (OA) and Kappa coefficient (KC) has been evaluated to assess classification outcomes. Among these classifiers, the RF classifier with the maximum OA (96.91%) and KC (96.10%) exhibited optimal results and robustness for LULC classification. CART followed this, with OA of 95.88% and KC of 94.81%. NB achieved an OA of 85.57% and KC of 81.93%. It confirms the validity of the suggested method for obtaining LULC maps through Sentinel-2 data and ML algorithms, achieving maximum accuracy in semi-arid regions.

关键词： Earth machine learning algorithms Accuracy Satellites Land surface Classification algorithms Internet Random forests Engines Classification tree analysis

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