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检索条件"主题词=Machine Learning Algorithms"

共 26022 条记录，以下是4791-4800 订阅

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Integrating machine learning and Visualization for Detection of Healthcare Insurance Fraud

Integrating Machine Learning and Visualization for Detection...

引用

International Conference on Parallel Distributed and Grid Computing (PDGC)

作者： A. Kannammal Ann Baby Rachitha K Suwetha G Coimbatore Institute of Technology Coimbatore Tamil Nadu Rajagiri College of Social Sciences Kalamassery Kerala

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

Healthcare fraud is a complex issue, causing billions in losses annually, undermining trust, and complicating care delivery across the system. This study develops a robust fraud detection system that integrates advanced machine learning algorithms with real-time data visualization tools, specifically targeting the healthcare insurance sector. The research involves comprehensive data acquisition, preprocessing, and exploratory data analysis, followed by the application of various machine learning models, including Logistic Regression, AdaBoost, and XGBoost. Among the models evaluated, Logistic Regression demonstrated the highest accuracy of 91.87 percent with a specificity of 99.08 percent, though sensitivity was lower at 24.76 percent. Additionally, the study highlights the importance of real-time monitoring through Power BI dashboards, enhancing decision-making capabilities. By addressing limitations such as class imbalance and focusing on continuous learning, the proposed system aims to provide innovative solutions for proactive fraud detection and improved operational efficiency in healthcare insurance.

关键词： Logistic regression Data analysis Sensitivity machine learning algorithms Insurance Data visualization Medical services machine learning Real-time systems Fraud

来源：评论

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Breast Cancer Diagnosis with Histopathology Images using Adaptive Feature Extraction and machine learning Model

Breast Cancer Diagnosis with Histopathology Images using Ada...

引用

Data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： Sridurgesh R A. Robert Singh Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Tamil Nadu India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

Histopathology photos provide essential data for researchers investigating cancer biology and formulating innovative tactics and analysis. Through the analysis of extensive databases of histopathological pictures, researchers can identify biomarkers, comprehend their classification patterns, and uncover prospective targets for pharmacological development. This research use a combination of feature extraction techniques and machine learning algorithms to categorize breast histopathology pictures. The uneven dataset is rendered balanced through augmentation. A sequence of preparation steps, including grayscale image conversion and picture sharpening using a Wiener filter, is implemented. The local binary patterns (LBP) extract features from photos. machine learning models such as logistic regression, linear discriminant analysis (LDA), random forest, XGBoost, and support vector machine (SVM) utilize the retrieved features. Logistic regression surpasses the other classifiers, achieving an accuracy of 98.88.

关键词： Support vector machines Logistic regression Wiener filters machine learning algorithms Histopathology Biological system modeling Gray-scale Biomarkers Feature extraction Random forests

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Uterine Corpus Endometrial Carcinoma Prediction from Genomic Analysis with machine learning

Uterine Corpus Endometrial Carcinoma Prediction from Genomic...

引用

International Conference on Development and Application Systems

作者： Marina Adriana Mercioni Stefan Holban Politehnica University Timisoara Timisoara Romania

ISBN: (数字)9798350349290

ISBN: (纸本)9798350349306

Endometrial carcinoma (EC) is a common uterine malignancy that still contributes significantly to cancer-related morbidity and mortality. EC identified in an advanced stage has a poor treatment response. In the United States, the most frequent cancer of the female reproductive organs is endometrial carcinoma. The American Cancer Society’s estimations for uterine cancer in the United States in 2024 are approximately 67,880 new cases of uterine cancer will be diagnosed and approximately 13,250 women will die from uterine cancer. EC is the second most common gynecological malignancy in the world and the top in continental Europe. The clinically used EC diagnostic techniques are expensive, time-consuming, and not available to all patients. The fast expansion of computational biology has sparked significant research interest from both data scientists and oncologists, resulting in the creation of quick and cost-effective computer-aided malignancy monitoring systems. machine learning (ML), a subset of artificial intelligence, enables drug development, early disease detection, successful therapy, and treatment modality selection. The use of ML methods in EC diagnosis, treatment, and prediction may be very important. This review provides a summary of EC, as well as risk factors and diagnostic procedures, before delving into a complete genetic investigation of prospective ML modalities for EC prevention, screening, detection, and prognosis.

关键词： machine learning algorithms Reviews Medical treatment Genomics Europe machine learning Prediction algorithms

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A Comprehensive Analysis of Weather Prediction Using machine learning

A Comprehensive Analysis of Weather Prediction Using Machine...

引用

International Conference on Science Technology Engineering and Management (ICONSTEM)

作者： K. Bala Maheswari S. Gomathi Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

Predicting the weather using machine learning and modern technologies is becoming increasingly commonplace. The ability to accurately predict the climate's long-term temperature is crucial. Multiple prediction works, including flood prediction, storm detection, etc., have made use of decision tree algorithms and support vector machine algorithms. Using the Decision Tree algorithm and Support Vector machines algorithms to calculate the best accuracy result, paper proposes a straightforward method for making long-term weather forecasts based on the analysis of historical data. Predictions of weather conditions like perspective, humidity, rainfall, temperature, floods, and storms can be made with high precision using machine learning algorithms

关键词： Support vector machines machine learning algorithms Accuracy Storms Weather forecasting machine learning Humidity

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Estimating the Privacy Budget for Differential Privacy by machine learning

Estimating the Privacy Budget for Differential Privacy by Ma...

引用

IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Ying-Hsuan Wang Shih-Hsuan Yang Yu-Chi Chen Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

Differential privacy has been proposed as a defense resort for the membership inference attack. Although privacy budget is an important parameter in differential privacy, few literatures have realized a systematic approach for choosing the appropriate privacy budget. This paper explores using machine-learning models to select appropriate privacy budgets based on a constraint on privacy leakage. Five machine-learning models, Decision Tree, Convolutional Neural Network, Logistic Regression, Random Forest and K Nearest Neighbors, are considered in this study. Among the investigated methods, the K Nearest Neighbor algorithm provides the best performance on the model accuracy and the error of privacy leakage.

关键词： Privacy Differential privacy Logistic regression Systematics machine learning algorithms Accuracy Decision trees

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machine learning for Disease Prediction: An Overview

Machine Learning for Disease Prediction: An Overview

引用

Smart Electronics and Communication (ICOSEC), International Conference on

作者： S Shunmuga Priya D Amuthaguka Department of Computer Applications Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Through the use of computers and communication devices, information and communication technology (ICT), has recently had a major influence on the healthcare sector. Smart healthcare has emerged as a result of this technological development. The application of ICT in the healthcare industry has led to significant developments in clinical care and remarkable cost savings in operating and administrative expenses. Data protection and preservation are the additional uses of ICT. Perfect disease diagnosis is critical in the healthcare industry, as it has the probability to save lives. Early disease identification helps in preventing the disease, enables patients to undergo aggressive and successful treatment, and ultimately saves lives. Numerous methods for predicting diseases have been developed by researchers; of these, machine learning (ML) based methods are particularly dependable and effective. This paper offers a thorough and systematic review of the literature on machine learning-based disease prediction.

关键词： Support vector machines Industries machine learning algorithms Sensitivity machine learning Predictive models Prediction algorithms Information and communication technology Regression tree analysis Diseases

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Automatic Running Notes Generation from Audio Lecture using NLP for Comprehensive learning

Automatic Running Notes Generation from Audio Lecture using ...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： A Madhavi Anuraag Chilakamarri Chaithra Jupudi Srinidhi Madanaboina Suraj Sriram Department of Computer Science Engineering VNRVJIET Telangana India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Taking notes is vital for students as it provide a condensed what they learn during classes sessions. Good notetaking helps students understand better and remember more. Notes help students engage more deeply with the material. However, note-taking is difficult because it requires listening, understanding, and writing simultaneousely, especially when lectures are fast-paced. Students also need to learn how to decide what information is important to note down. Automatic note generation solves these problems by using technology to make note-taking easier. It means students don’t have to write everything down as they listen and lets them focus more on understanding the lecture. Thus, the proposed work presents a comprehensive solution for automatic notes generation from audio lectures, facilitating access to summarized content in both English and Telugu languages. Leveraging advanced techniques including Google’s Web Speech API employing a combination of Hidden Markov Models (HMMs) and Deep Neural Networks (DNNs), system accurately transcribes spoken content with a Word Error Rate (WER) of 8% and a Word Accuracy Rate (WAR) of ${9 2 \%}$. Subsequently, employing the TextRank algorithm, the system extracts key information and generates structured notes with precision of 0.82, recall of 0.9649, and an F1 score of 0.886. Furthermore, employing a neural machine translation (NMT) approach, the system achieves a BLEU score of 0.5789, enabling seamless translation of the generated notes into Telugu. The system provides users with the flexibility to download both English and Telugu notes in Microsoft Word format, thus offering versatile tool for accessible knowledge dissemination from audio lectures.

关键词： Accuracy machine learning algorithms Error analysis Reviews Hidden Markov models Neural machine translation User experience Data mining Usability Optimization

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Alzheimer’s Disease Prediction Using machine learning

Alzheimer’s Disease Prediction Using Machine Learning

引用

International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Shashwat Mishra Viplav Sharma G. Ramya Department of Computing Technologies SRM Institute of Science and Technology Chennai India Department of Computing Technologies Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

Alzheimer's disease is a progressive and debilitating neurodegenerative disorder that poses a significant public health challenge worldwide. Early detection and prediction of Alzheimer's disease are critical for effective treatment and care. This research paper presents a novel approach for predicting Alzheimer's disease using machine learning methodologies. Our study leverages a diverse dataset that includes clinical, genetic, and neuroimaging data from individuals at various stages of the disease. Through rigorous preprocessing and feature selection processes, we extract relevant features and apply advanced machine learning algorithms. Our results demonstrate the efficacy of this approach in accurately predicting the onset and progression of Alzheimer's disease. The model's high accuracy, sensitivity, and specificity suggest its potential as a valuable tool for clinicians and researchers. Furthermore, our study highlights key factors contributing to the prediction and offers insights into the underlying biological processes of the disease. This research holds promise for early intervention and personalized patient care, ultimately contributing to improved management of Alzheimer's disease and enhancing the quality of life for affected individuals and their families.

关键词： Neuroimaging Accuracy machine learning algorithms Predictive models Sensitivity and specificity Feature extraction Alzheimer's disease

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Advanced machine learning Techniques for Improved Diagnostic Accuracy in Medical Imaging: A Novel Approach

Advanced Machine Learning Techniques for Improved Diagnostic...

引用

Cybernation and Computation (CYBERCOM), International Conference on

作者： Ravi Krishan Pandey Praveen K Shukla Computer Science & Engineering Babu Banarsi Das University Lucknow India

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

It is in this regard that ML in medical imaging is going to revolutionize diagnostic practices by increasing its accuracy and reducing variability. In the present study, therefore, a new hybrid machine learning model has been designed in which a convolutional neural network is fused with the decision tree algorithm to improvise the detection and classification of medical conditions from imaging data. The features are extracted through a CNN component, and with that, it discovers complicated patterns within the images. Then, after feature extraction, these features pass through a decision tree for the classification of images and allow a high interpretability, showing how the decisions are made. The model was trained on a variety of conditions dataset with 10,000 medical images and was further validated. These results allow for an overall accuracy for the detection of malignancy of 93.4%, sensitivity of 92%, and specificity of 94%. Concretely, this approach decreases by 6.5% the number of false positives compared to a standalone CNN alone, which proves to be very robust and reliable. Results such as those mentioned above underline the huge potential of hybrid ML models for medical diagnostics, which already could ensure a high degree of accuracy and interpretability necessary for clinical application. This work in the future shall address generalization to other imaging modalities and integration with more sophisticated machine learning techniques for improved performance.

关键词： Accuracy Sensitivity machine learning algorithms Computational modeling Predictive models Feature extraction Data models Decision trees Convolutional neural networks Medical diagnostic imaging

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Fake Review Identification Using machine learning

Fake Review Identification Using Machine Learning

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Communication, Computer Sciences and Engineering (IC3SE), International Conference on

作者： Chandrajeet Singh Sarvesh Tanwar Amity Institute of Information Technology Noida

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

The use of false opinions, sometimes known as fake reviews, to mislead customers has become more common in recent times. This is due to the fast growth of online marketing initiatives such as purchasing and selling. Customers can leave comments and evaluations regarding the goods or services they have purchased when they shop online. Prior to making a purchase, prospective buyers typically read the reviews and comments that have been left on the website. The present difficulty, meanwhile, is getting newcomers to discern genuine evaluations from fraudulent ones, which go on to deceive consumers, create losses, and harm the image of companies. This research objective is to create a trained model that can point out fraudulent feedback on e-commerce sites by utilizing sentiment ratings and n-grams of the review text provided by the reviewer. When the outcomes were compared to previous studies that employed the same dataset, proposed methods performed better in terms of accuracy than the equivalent approaches.

关键词： Feedback loop Accuracy Protocols machine learning algorithms Reviews machine learning Predictive models

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