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检索条件"主题词=Machine Learning Algorithms"

共 26022 条记录，以下是1331-1340 订阅

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Diabetes Detection Using machine learning algorithms

Diabetes Detection Using Machine Learning Algorithms

引用

IEEE Bombay Section Signature Conference (IBSSC)

作者： Nicole D'Souza Kunjal Shah Pranav Singh MPSTME NMIMS Mumbai India

ISBN: (纸本)9781665492928

Diabetes is a serious illness. Predicting this disease in a timely manner is necessary to avoid severe side effects. Current medical practise dictates that a patient undergoes a battery of tests in order to obtain the information necessary for diagnosis, after which treatment is administered based on the diagnosis. However, in many cases, the early stages go undetected, and it is quite difficult for physicians to diagnose due to the interdependence of numerous factors. A single parameter is commonly inadequate for the accurate diagnosis of diabetes and may lead to erroneous decisions. To accurately forecast diabetes at an early stage, multiple criteria must be combined. This study proposes the development of an early diabetes detection model. The model will not only be more accurate than humans, but it will also reduce the workload of medical professionals.

关键词： machine learning algorithms IEEE Sections Medical services Diabetes Batteries Medical diagnostic imaging Diseases

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Comparative Analysis of machine learning algorithms using GANs through Credit Card Fraud Detection

Comparative Analysis of Machine Learning Algorithms using GA...

引用

Computing, Networking, Telecommunications & Engineering Sciences Applications (CoNTESA), International Conference on

作者： Emilija Strelcenia Simant Prakoonwit Department of Creative Technology Bournemouth University Bournemouth United Kingdom

ISBN: (纸本)9798350398021

In more recent years, credit card fraudulent transactions became a major problem. These fraudulent transactions not only incur huge monetary losses to commercial banks and financial institutions, but also stress and trouble to the lives of customers. Furthermore, with the passage of time this issue is increasing and the monetary loss is expected to increase significantly. However, efficient fraud detecting and prevention measures can trim down the monetary loss due to financial fraud activities. Credit card fraud detection has gained much interest from academia. Generative Adversarial Networks (GANs) are an effective class of generative approaches that has been able to generate synthetic data to assist with the classification of credit card fraudulent activities. In this research study we’re going to compare architectures of various GAN models which demonstrate the evolution of these models. It was observed that GANs have received much attention from researchers and also attained promising results in the field of credit card fraud detection.

关键词： Training machine learning algorithms Training data Credit cards Generative adversarial networks Solids Loss measurement

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Classification of Water Potability Using machine learning algorithms

Classification of Water Potability Using Machine Learning Al...

引用

Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Husain Yusuf Salman Alhaddad Salman Yusuf Nabil Hewahi MSc. in Big Data Science and Analytics College of Science University of Bahrain Department of Computer Science College of Information Technology University of Bahrain

ISBN: (纸本)9781665490597

Water is the most important resource for sustaining life. Everyone has the right to have access to pollution-free water. The achievement of safe drinking water leads to tangible benefits to health. According to the World Health organization (WHO), the number of global deaths due to water diseases is about two and a half million deaths per year. Therefore, it is important to study new applications for analyzing and classifying safe water quality. In this research, the classification algorithms of K Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Artificial Neural Network (ANN), Logistic Regression (LR), and Support Vector machine (SVM) are used to classify the potability of the drinking water. RF and DT achieved the highest performance with an accuracy of 83.78% and 74.98% respectively. The lowest classifier accuracy is LR with 48.74%. In addition, the most important features of the RF model are pH, followed by Hardness, followed by Sulfate. On other hand, the least important feature is Turbidity.

关键词： Support vector machines Industries machine learning algorithms Artificial neural networks Water quality Organizations Water pollution

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IoT Anomaly Detection Using a Multitude of machine learning algorithms

IoT Anomaly Detection Using a Multitude of Machine Learning ...

引用

Applied Imagery Pattern Recognition Workshop (AIPR)

作者： Maria Balega Waleed Farag Soundararajan Ezekiel Xin-Wen Wu Alicia Deak Zaryn Good Dept. of Math and Computer Science Indiana University of Pennsylvania Indiana PA U.S.A Dept. of Computer Science University of Mary Washington Fredericksburg VA U.S.A

The security of IoT (Internet of Things) systems is crucial yet challenging. Anomaly detection can help assess and improve the security of these devices and systems. The detection of anomalous traffic can be performed with the use of machine learning algorithms. Gradient Boosting is a machine learning (ML) technique that handles both regression and classification problems and uses decision tree algorithms to produce a prediction model. eXtreme Gradient Boosting (XGBoost) is a unique implementation of Gradient Boosting that has shown very good performance and outcomes in various problems. In this paper, XGBoost’s classification abilities are examined when applied to the adopted IoT-23 dataset to see how well anomalies can be identified and what type of anomaly exists in IoT systems. Moreover, the results obtained from XGBoost are compared to other ML methods including Support Vector machines (SVM) and Deep Convolutional Neural Networks (DCNN). The classification results were assessed based on accuracy, precision, recall, and various other performance metrics. Our experimental results showed that XGBoost outperformed both SVM and DCNN achieving accuracies up to 99.98%. In addition, XGBoost proved to be the most efficient method with respect to execution time.

关键词： Support vector machines Performance evaluation machine learning algorithms Predictive models Boosting Prediction algorithms Security

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Performance Comparison of machine learning algorithms in Groundwater Potability Prediction

Performance Comparison of Machine Learning Algorithms in Gro...

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Recent Advances and Innovations in Engineering (ICRAIE)

作者： Elisuba Kuruvilla Subrahmanya Kundapura Dept. of Water Resources and Ocean Engineering National Institute of Technology Karnataka Mangalore India

Rising global water demand has resulted in the overuse of groundwater resources and a decline in groundwater quality. Physical and chemical characteristics significantly impacted by geological formations and human activities determine how groundwater quality varies. An accurate and reliable assessment of groundwater resource information is the key element for effective management and enhancement of groundwater quality. The utilization of modern machine learning (ML) techniques in groundwater quality assessment provides insights for policymakers in suggesting remedies and management approaches for groundwater quality issues. machine learning models outperform other simulation models, using input and output datasets without considering the intricate relationship of the model to be analyzed and decreasing computational efforts. Comparison of various ML techniques, including Ensemble, Nonlinear, and Linear models for the prediction of groundwater potability is the main objective of this study. The presence of potable groundwater suggests that the water is fit for human consumption. The proposed approach makes use of eight ML algorithms i.e. Gradient Boosting Classifier (GB), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Support Vector machine (SVM), Linear Regression (LR) and Stochastic Gradient Descent (SGD) algorithm. According to the results, the Ensemble ML models outperformed well followed by the Nonlinear models, and Linear classification ML models have comparatively less accuracy and reliability.

关键词： Support vector machines Analytical models machine learning algorithms Computational modeling Predictive models Prediction algorithms Boosting

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Detection of Covid-19 Virus using Supervised machine learning algorithms

Detection of Covid-19 Virus using Supervised Machine Learnin...

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Information Technology (ACIT)

作者： Ahmad T. Al-Taani Batool Al-Rababaah Department of Computer Sciences Yarmouk University Irbid Jordan

ISBN: (数字)9798350320244

ISBN: (纸本)9798350320251

Due to the continuous increase of Covid-19 infections as a global pandemic, it became necessary to detect it to avoid the damage caused by the spread of the infection. Artificial Intelligence (AI) techniques such as machine learning and deep learning have an important and effective role in the medical field applications like the classification of medical images and the detection of many diseases. In this article, we propose the use of several supervised machine learning classifiers for Covid-19 virus detection using chest x-ray (CXR) images. Five supervised classifiers are used: Support Vector machines (SVM), Naive Bayes (NB), K-Nearest Neighbors (KNN), Logistic Regression (LR) and Artificial Neural Network (ANN). A standard dataset of 1824 CXR images are used for training and testing; 70% for training and 30% for testing. Four image embedders including Vgg16, Vgg19, SqueezeNet, and Inception-v3 are used in the experiments. Experiment results showed that most of these models achieved promising accuracy, precision, recall, and F1-scores. KNN, ANN, and LR classifiers have achieved highest classification accuracies using SqueezeNet image embedder.

关键词： COVID-19 Training Support vector machines machine learning algorithms Pandemics Artificial neural networks X-ray imaging

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Beat-by-Beat Classification of ECG Signals Using machine learning algorithms to Detect PVC Beats for Real-time Predictive Cardiac Health Monitoring

Beat-by-Beat Classification of ECG Signals Using Machine Lea...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： I Hua Tsai Bashir I. Morshed Department of Computer Science Texas Tech University Lubbock TX USA

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

A premature ventricular contraction (PVC) disrupt the normal heart rhythm and indicate underlying cardiac disease. We aim to detect these PVC beats from electrocardiogram (ECG/EKG) data by automatically classifying these ECG beats with high accuracy in real-time. In this study, we used MIT BIH Long-Term Electrocardiogram Database (ltdb) dataset from the PhysioNet database. We extract signal-specific features and signal-independent features and combine them for feature ranking. We use principal component analysis (PCA), elastic net regularization (ENR), univariate filter of constant, quasi constant and duplicate feature removal (CQCDFR) and analysis of variance test (ANOVA) for feature selection. We take the top 10 features for four methods and classify them separately. The machine learning model explored is the random forest classifier. In our analysis, elastic net regularization performed best in terms of accuracy in cardiac patients. We further use the feature with the best accuracy in four algorithms to test sensitivity, specificity, accuracy, precision, f1-score to evaluate statistics. The overall accuracy of elastic net regularization for classifying the highest first 8 feature data is 97.8%. The sensitivity was 94.7% and the specificity was 99.6%. The accuracy rate is 99.6%, and the F1 score is 97.1%. The method can accurately detect ECG beats and analyze categories for real-time cardiac monitoring for feedback to the use patient. Efficient feature selection minimizes the number of features used and reduces the power consumption of the monitoring device.

关键词： Performance evaluation machine learning algorithms Databases Electrocardiography Feature extraction Real-time systems Classification algorithms

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Comparison of machine learning algorithms for EMG based Muscle Function Analysis

Comparison of Machine Learning Algorithms for EMG based Musc...

引用

International Conference on Circuits, Communication, Control and Computing

作者： Praveen Kumar Suma K V Manjunath C Lakkannavar Varun C R Department of Electronics and Communication Ramaiah Institute of Technology Banglore Department of Electronics and Communications Ramaiah Institute of Technology Banglore Department of Aerospace and Engineering Indian Institute of Science Banglore

Electromyography (EMG) is the process of measuring the electrical activity that is produced during the muscle activation and is also called as myoelectric signal. This electrical activity can be recorded to analyze the performance of any muscle. machine learning algorithms can be deployed to predict the presence of abnormality in the muscles. In this study, we have worked on predicting the possibilities of cervicalgia, more commonly known as neck pain, by using distinct time domain features extracted from surface EMG signals recorded from SCM muscle, and performing the comparative analysis of different machine learning binary classifier algorithms like Support Vector machine (SVM), K-Nearest Neighbors (KNN), Random Forest, Decision Tree, Naïve Bayes Classifier and Logistic Regression. The feature selection was made using techniques like PCA and Pearson's Correlation Matrix. Maximum classification accuracy of 91.6% was achieved. If predicted well in advance, they can provide valid information to doctors, physiotherapists, yoga therapists or medical practitioners, who can then adapt their diagnosis and treatment for each patient.

关键词： Support vector machines machine learning algorithms Correlation Muscles Feature extraction Electromyography Time-domain analysis

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Electrical fault classification strategies for maintenance models using machine learning algorithms

Electrical fault classification strategies for maintenance m...

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IEEE International Conference on Automatica (ICA-ACCA)

作者： Luis Felipe Rau Marcela Jamett Pablo Adasme Department of Electrical Engineering University of Santiago de Chile Santiago Chile

ISBN: (纸本)9781665494090

Mining equipment suppliers are investigating new maintenance models for mining truck monitoring and control systems that focus on process efficiency due to the high costs involved. The aim of this article is to generate a predictive maintenance model for electrical faults through machine learning algorithms that predict and classify the severity of faults in harnesses. The study is carried out in the harnesses of the control system of the diesel engine component of a fleet of trucks. Predictive analysis considers two factors: number and trends of failures associated with a proposed classification. Neural networks and decision trees perform better. These results determine a CBM (Condition Based Maintenance) model that makes the maintenance program more efficient.

关键词： Automatic frequency control Schedules machine learning algorithms Costs Heuristic algorithms Diesel engines Maintenance engineering

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Analysis of Crop Yield Prediction using machine learning algorithms

Analysis of Crop Yield Prediction using Machine Learning alg...

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International Conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Vamsi Krishna Thejeswar Reddy Sai Harsha Kaladevi Ramar Shanmugasundaram Hariharan Bhanuprasad A Dept. of Computer Science & Engineering Saveetha Engineering College Affiliation of Anna University Chennai India Department of CSE Vardhaman College of Engineering Hyderabad India

ISBN: (纸本)9781665474177

Agriculture is the backbone of any country. Totally 58% of the population is dependent on agriculture, in 48% is directly dependent on it. India is one of the agricultural countries, its economy is primarily based on agricultural yield growth. Agriculture is largely affected by rainwater and temperature which is highly uncertain in India. Technology proved to be useful for agriculture, increasing the productivity of crops and providing better yields for the farmer. Simple parameters like State, district, temperature, and rainfall are used in this paper so that the farmer can predict the yield for the crop in the year needed. The system follows to do a comparative analysis of K-Nearest Neighbor’s Random Forest and Linear Regression algorithms to obtain this prediction for enhancing the algorithms to give a better prediction. The system follows the prediction based on parameters such as state, crop, temperature, and rainfall. In this model, K-Nearest Neighbor’s algorithm has shown 97% accuracy, the Linear Regression algorithm has shown 54% and the Random Forest algorithm has shown 75%accuracy. From these algorithms, K-Nearest Neighbors has shown the highest accuracy.

关键词： Productivity machine learning algorithms Linear regression Sociology Crops Prediction algorithms Agriculture

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