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检索条件"主题词=Machine Learning Algorithms"

共 26022 条记录，以下是1291-1300 订阅

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Classification of Terahertz Reflection Spectra using machine learning algorithms

Classification of Terahertz Reflection Spectra using Machine...

引用

International Conference on Infrared and Millimeter Waves

作者： Mathias Hedegaard Kristensen Paweł Piotr Cielecki Esben Skovsen Department of Materials and Production Section for Physics and Mechanics Aalborg University Skjernvej 4A Aalborg East DK Denmark

ISBN: (数字)9781728194271

ISBN: (纸本)9781728194288

A successful implementation of terahertz screening systems requires a development of reliable and efficient identification algorithms. Dimensionality reduction methods are applied to lower the dimensionality of multivariate data while retaining most of the information. Here, we focus on Principal component analysis (PCA) and linear discriminant analysis (LDA) for analysis and classification of terahertz reflection spectra. The complete data set consists of more than 5000 reflection spectra of six active materials. We found that LDA is better for grouping the spectra resulting in highly accurate classification of terahertz spectra. Furthermore, we compare the classification of referenced and non-referenced reflection spectra eligible for real-world applications of terahertz spectroscopy.

关键词： Dimensionality reduction Spectroscopy machine learning algorithms Reflection Linear discriminant analysis Classification algorithms Reliability

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Multi-Criteria Decision Making for machine learning algorithms Using AHP-VIKOR techniques: Case Study Adult Autism Diagnosis

Multi-Criteria Decision Making for Machine Learning Algorith...

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International Conference on Engineering Technology and their Applications (IICETA)

作者： Mustafa Mohammed Jassim Manar Hassan Ali Amer S. Elamer Mustafa Musa Jaber Mohammed Q. Mohammed Ahmed Alkhayyat Department of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad Iraq Iraqi Commission for Computers and Informatics Informatics Institute for Postgraduate Studies Baghdad Iraq Department of Computer Science Dijlah University Collage Baghdad Department of Information Technology Al-Esraa University College Baghdad College of Technical Engineering The Islamic University Najaf Iraq

ISBN: (数字)9781665472159

ISBN: (纸本)9781665472166

autism spectrum disorder is known as a neurodevelopmental disorder with sensory problems. Many infected adults die frequently due to the psychological conditions they live in. machine learning helps doctors detect cases of autism spectrum disorders in adults. It has proven very successful in this field. In this study, after pre-processing and solving the data class imbalance problem the data we proposed to use MCDM methods to know prioritization and the best method to diagnose autism between four algorithms (SVM, Random Forest, Neural Network, and KNN), after obtaining the classification results we utilized AHP method to assign weights to each classification performance metrics based on expert assessment then he used both the obtained weights and the classification results to enter them into the VIKOR method to make a decision based on the weighted table where we would get the ranking of the algorithms. The random Forest method scores the best in this classification for diagnosing autism, with an accuracy rate of 0.9957 and sensitivity 0.9841.

关键词： Support vector machines Autism machine learning algorithms Sensitivity Neural networks Psychology Medical services

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Sleep Stress Level Classification through machine learning algorithms

Sleep Stress Level Classification through Machine Learning A...

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Information Technology (OCIT), OITS International Conference on

作者： Abhyudaya Batabyal Vinayak Singh Mahendra Kumar Gourisaria Himansu Das School of Computer Engineering KIIT Deemed to be University Bhubaneswar Odisha India

Nowadays, chronic insomnia is a critical problem of homo-sapiens. An increase in workload and tension in life led to the development of sleep stress. Sleep stress can damage human beings in a physical, psychological, and social manner. Sickness in the stomach, tension, and frayed nerves while sleeping are the most frequent symptoms of sleep stress. Sleep stress can lead to cardiac infarction, depression, senile psychosis, gastrointestinal problems, diabetes, obesity, and emphysematous. This paper primarily focuses on the classification of sleep stress levels using standard machine learning algorithms like Decision Tree (DT), Logistic Regression (LR), Radial basis function Supported-Vector Classifier (RBF-SVC), K-Nearest Neighbor (KNN), Random Forest (RF), Extreme Gradient Boosting (XGB), Linear Support-Vector Classifier (L-SVC), Naive Bayes (NB), Support-Vector Classifier (SVC), on the scaled dataset using Standard Scaling. LR, KNN, and SVC outperformed all the other machine learning classifiers in terms of performance metrics.

关键词： Measurement Stomach Obesity machine learning algorithms Static VAr compensators Psychology Human factors

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Applying Predictive machine learning algorithms to Petroleum Refining Processes as Part of Intelligent Automation

Applying Predictive Machine Learning Algorithms to Petroleum...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Ivan S. Nekrasov Vadim S. Tynchenko Vladimir B. Bukhtoyarov Vera A. Kachaeva Kirill A. Bashmur Aleksandra E. Sinitskaya Laboratory of Biofuel Composition Siberian Federal Univercity Krasnoyarsk Russian Federation dept. of Technological Machines and Equipment of Oil and Gas Complex Siberian Federal Univercity Krasnoyarsk Russian Federation Information-Control Systems Department Institute of Computer Science and Telecommunications Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russian Federation

ISBN: (数字)9781665498043

ISBN: (纸本)9781665498050

Intelligent automation is a term that can be applied to the more complex field of workflow automation, consisting of robotic workplace automation, robotic process automation, machine learning, and artificial intelligence. Depending on the type of business, companies often use one or more types of automation to improve efficiency or effectiveness. As you move from process-driven automation to more flexible data-driven automation, additional costs arise in the form of training datasets, technical development, infrastructure, and expertise. But the potential benefits in terms of new ideas and financial development can increase significantly. The development of mechanized oil production in recent years has been accompanied by significant achievements in the field of digitalization. machine learning, as an important element of digitalization, can successfully solve many production problems. The paper describes the application of some machine learning algorithms for solving the problem of classifying and predicting failures of hydrocracking process equipment that occur during oil refining and diesel fuel production. The application of random forest, principal component analysis and hyperparameter tuning methods is considered. The effectiveness of their application is compared.

关键词： Radio frequency Intelligent automation machine learning algorithms Biological system modeling Oils Refining Production

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Comparative Analysis of machine learning algorithms to Predict Solar Irradiance

Comparative Analysis of Machine Learning Algorithms to Predi...

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Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON), International Conference on

作者： Anirudh Balan Thanya Ramanathan Syama. S Lekshmi. S Department of Electrical and Electronics Engineering Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham India Department of Mechanical Engineering Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham India Electrical and Electronics Engineering Amrita School of Engineering Bangalore Amrita Vishwa Vidyapeetham India

In recent times, there has been a rapid shift from non-renewable to renewable sources of energy. Recently there has been a lot of development in photovoltaic systems that use solar irradiance energy as a source. Solar energy is a clean source of energy that can be tapped to meet the demands of the load/grid. Prediction of the amount of solar irradiance at a location can be beneficial for optimum production. This study compares the accuracy of several machine learning algorithms, including SVM and LSTM, for the prediction of solar irradiance at specified latitudinal and longitudinal coordinates using historic data from NASA POWER. It is observed that solar irradiance is influenced by factors like temperature, precipitation, humidity among others.

关键词： Support vector machines Photovoltaic systems Renewable energy sources machine learning algorithms Temperature Precipitation Production

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An Improved and Efficient Technique for detecting Bengali Fake News using machine learning algorithms

An Improved and Efficient Technique for detecting Bengali Fa...

引用

International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Md. Akkas Ali Md. Liakot Matubber Vandana Sharma B. Balamurugan School of Computing Science and Engineering Galgotias University Greater Noida U.P India Dept. of CSE Bangabandhu Sheikh Mujibur Rahman Science and Technology University Gopalganj Bangladesh

ISBN: (纸本)9781665484527

Fake news is appearing in the news a number of Business, Communities, Political and others reasons and being common in the air world. Humans can be infected it is easy with these false stories of the words which the product has the largest result in a non-Internet environment. Thus, interest in research in this area arose. Extensive research done for the discovery of false stories for English News but few in the Bangla language. We have done a job showing the feasibility study of Bangla's false news from the social media still needs lot of attention. We have used a variety of feature extraction methods and machine learning algorithms for detecting Bangla News which are treated as Fake. We have implemented our proposed system by using the Python programming. Finally, we have gained the best result that is 98.08% of accuracy.

关键词： machine learning algorithms Social networking (online) Text categorization Natural languages Process control machine learning Programming

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Diabetes Diagnosis Using machine learning algorithms: Methods and Techniques

Diabetes Diagnosis Using Machine Learning Algorithms: Method...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Shagun Preet Kour Vishal Bharti Department. of Computer Science and Engineering Chandigarh University Mohali India

Diabetes is a sickness with no clear solution, thus early detection is essential. During our study, we employed data mining, machine learning techniques to forecast diabetes. The data is on 768 participants and their different characteristics. On the dataset, we applied four machine learning algorithms for the prediction of diabetic condition. The implemented algorithms were examined for the robustness and consistency and compared using accuracy and F-1 score methods. It was found that the Support Vector machine prototype works well for prediction of diabetic condition with an accuracy of 79% accuracy. This research strives for suggesting a suitable method to help the doctors and health professionals for early detection of diabetes.

关键词： Support vector machines Radio frequency machine learning algorithms Computational modeling Prototypes Medical services Prediction algorithms

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A Comparative Study of Mainstream machine learning algorithms for Spine Health Prediction

A Comparative Study of Mainstream Machine Learning Algorithm...

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IEEE/ICME International Conference on Complex Medical Engineering, CME

作者： Shengyuan Shi Lin Ye Hongying Xiao Tieqing Luo School of Informatics Hunan University of Traditional Chinese Medicine Changsha China School of Foreign Studies South China Normal University Guangzhou China

Spine sub-health and spine-related diseases are common among modern people. The diagnosis and treatment of spinal diseases require doctors with extensive clinical experience, while machine learning can effectively and in large quantities predict spine health, thus assisting doctors in making decisions and reducing the burden of medical staff. In this study, we used seven mainstream machine learning methods - Logistic Regression (LR), K-Nearest Neighbor (KNN), Support Vector machine (SVM), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), to construct classification models for spine health prediction, and compared the advantages and disadvantages of the seven models using multiple evaluation metrics in order to select the appropriate model for the issue of concern in practice. The results show that among the seven machine learning methods, Random Forest and XGBoost perform more outstandingly in each evaluation metric (accuracy and precision are higher than 0.9), while the K-Nearest Neighbor algorithm demonstrate superior performance (0.92) when AUC was used as the evaluation metric. These results suggest that the use of machine learning methods for spine health prediction has good prospects and that the most suitable algorithm can be selected according to our concerns.

关键词： Measurement Support vector machines machine learning algorithms Spine Predictive models Prediction algorithms Data models

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Alcohol Consumption Rate Prediction using machine learning algorithms

Alcohol Consumption Rate Prediction using Machine Learning A...

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Information Technology (OCIT), OITS International Conference on

作者： Advait Singh Vinayak Singh Mahendra Kumar Gourisaria Ashish Sharma School of Computer Engineering KIIT Deemed to be University Bhubaneswar Odisha Department of Computer Engineering and Applications GLA University Mathura UP

Consumption of alcohol among students, mainly college or university students, has risen immensely over the past couple of years. It has been determined that students experiment with alcohol during their college years and around 80% of students consume alcohol in some manner or degree and 50% are involved in binge drinking. This is mainly due to students wanting to explore their newfound independence and freedom which they didn't have during their school years. In this paper, we have analyzed students belonging to two courses of a Secondary School-Maths and Portuguese Language Course. We have applied Feature Scaling along with various machine learning classification models to determine higher alcohol consumption where the Random Forest Model outperformed all other models that have been applied such as Linear, Ridge, and Lasso Regression, Decision Tree, k-NN, XG Boost, Support Vector machine, ADA Boosting Regressor and Gradient Boosting Regressor for analysis of alcohol consumption among secondary school students.

关键词： Support vector machines Analytical models machine learning algorithms Forestry Alcoholic beverages Boosting Student experiments

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Detection of Credit Card Fraud using machine learning algorithms

Detection of Credit Card Fraud using Machine Learning Algori...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Akhilesh Kumar Singh Computer Engineering and Applications GLA University Mathura India

ISBN: (纸本)9781665487351

The use of online transactions in regular life has increased over the past ten years due to continuous development in technology and network connectivity. Online transactions are easy, simple, and user-friendly is the main reason for new users to constantly join the vast population benefitting them from the system. The main demerit of the online transaction system is a misuse of the system is defined as theft or one's credit card information which is used for self-profit without the permission of the cardholder. In this paper we have uses different ML algorithms such as regression, random forest classifier, and decision tree to control the heavily imbalanced dataset. Finally, in this research, we will calculate the accuracy, precision, recall, f1 score, and confusion matrix.

关键词： machine learning algorithms Sociology Forestry Credit cards Market research Classification algorithms Fraud

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