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检索条件"主题词=Machine Learning Algorithms"

共 25782 条记录，以下是481-490 订阅

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machine learning algorithms for the Prediction of Daily Mean Solar Power

Machine Learning Algorithms for the Prediction of Daily Mean...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Rashmi Bachulkar Rohan Bhatkande Akshata Patil Bibi Fatima Telgi IT Executive Global Energy Pvt Ltd AITM Belagavi

ISBN: (纸本)9781665435253

Daily Mean Solar Power is the main factor that is taken into consideration while a solar panel is being installed. The average solar irradiance data which is collected from the NASA satellite is being used as input to the model. The results obtained from the model showed the amount of solar energy that can be generated before any installation of solar panel at a given location by using the machine learning(ML) algorithms. To predict the mean solar irradiance three regression algorithms are being used along with its parameters that are linked to solar irradiance. In the given work there is representation of a comparative analysis among these machine learning algorithms. Furthermore, the data is divided into four parts i.e. prediction of solar irradiance for one week, one month, one year and fifteen years data. This data is applied for three algorithms namely Decision Tree Regression, Linear Regression, Support Vector Regression. And the algorithm which produces least root mean square error is selected for prediction.

关键词： Support vector machines machine learning algorithms Satellites Linear regression Solar energy Prediction algorithms Solar panels

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machine learning algorithms for Binary Classification of Liver Disease

Machine Learning Algorithms for Binary Classification of Liv...

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International Scientific-Practical Conference Problems of Infocommunications Science and Technology

作者： Anton Sokoliuk Galyna Kondratenko Ievgen Sidenko Yuriy Kondratenko Anatoly Khomchenko Igor Atamanyuk Petro Mohyla Black Sea National University Mykolaiv Ukraine Mykolayiv National Agrarian University Mykolaiv Ukraine

The number of patients with liver diseases has been continuously increasing because of excessive consumption of alcohol, inhale of harmful gases, intake of contaminated food, pickles, and drugs. Early diagnosis of liver problems will increase patients' survival rates. Liver disease can be diagnosed by analyzing the levels of enzymes in the blood. Creating automatic classification tools may reduce the burden on doctors. To achieve this numerous classification algorithm (Decision Tree, Random Forest, SVM, Neural Net, Naive Bayes, and others) from different machine learning libraries (Scikit-learn, ***, Keras) are tested against existing liver patients' dataset, considering appropriate for each algorithm preliminary data processing. These algorithms evaluated based on three criteria: accuracy, sensitivity, specificity.

关键词： Training Support vector machines machine learning algorithms Liver diseases Neural networks Medical services Vegetation

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machine learning algorithms in Smart Antenna and Arrays for Internet of Things Applications

Machine Learning Algorithms in Smart Antenna and Arrays for ...

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Artificial Intelligence and Smart Systems (ICAIS), International Conference on

作者： Abhishek Javali Anindita Sahoo Jagrati Gupta CMR Institute of Technology Bengaluru India

ISBN: (数字)9781728195377

ISBN: (纸本)9781728195384

machine learning algorithms (MLA) have revolutionized many eras in the recent past. MLA has started to simplify the data world with its revolutionary methodologies. Use of MLA for Smart Antenna Arrays (SAA) has been the need of the hour to enhance the system performance of SAA. The combination of MLA and SAA can have diverse usefulness in the Internet of Things (IoT) applications. This paper has provided a brief survey on the use of MLA for SAA for IoT applications in many fields. As the highly interconnected world of IoT needs many optimization, stringent analysis and advanced prediction methods MLA for SAA can be a good solution to counter the challenges faced by SAA of the IoT.

关键词： machine learning algorithms System performance Prediction methods Real-time systems Internet of Things Arrays Antenna arrays

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machine learning algorithms for Emotion Detection using EEG Signals

Machine Learning Algorithms for Emotion Detection using EEG ...

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Computing, Applications and Systems (COMPAS), IEEE International Conference on

作者： Md Sajjad Hossain Saroj Kr. Biswas Dalton Meitei Thounaojam Atiya Khan Kazy Noor e Alam Siddiquee CSE Dept. NIT Slichar Assam India Faculty of Engineering Multimedia University Cyberjaya Malaysia

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

Assessment of clinical subjects’ cognitive functions and state is essential to e-healthcare delivery and developing novel human-machine interfaces. With the advancements in brain physiology research, electroencephalogram (EEG)-based emotion detection has emerged as an intriguing and popular study area. The emotion detection model can be implemented in human mental health monitoring, therapeutic support, law enforcement agencies, driver monitoring, etc. Numerous studies have been conducted in the last few decades to use EEG waves to detect emotions. In these research articles, an exhaustive study was not shown to find the best machine learning (ML) algorithms for multi-class emotion detection. Moreover, most of these researchers used a large number of electrodes in the experiment, which creates complexity in real-time setup. Therefore, the primary objective of this study is to identify the most effective ML algorithm that gives the expected accuracy using a single electrode. The proposed research has been done on the SEED-IV dataset to classify four common types of emotions: Happy, Fear, Sad, and Neutral. Different types of standard ML classifiers, including ensemble ML classifiers, have been used in this experiment. The performance of this study has been assessed by splitting and 10-fold cross-validation. Linear Discriminant Analysis (LDA) shows a better classification rate of 72.22% and 72.31% among all the classifiers according to Accuracy and F1 score. This study’s results anticipate opening up new possibilities for neuroscience by demonstrating that single-channel EEG data alone is sufficient for categorizing emotions.

关键词： Electrodes Emotion recognition machine learning algorithms Accuracy machine learning Electroencephalography Real-time systems Object recognition Monitoring Standards

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machine learning algorithms for Classifying Abscessed and Impacted Tooth: Comparison Study

Machine Learning Algorithms for Classifying Abscessed and Im...

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International Conference on Biomedical Engineering (IBIOMED)

作者： Ni’matul ’Abdah Adhiya Fakhriy Igi Ardiyanto Hanung Adi Nugroho Gilang Nugraha Putu Pratama Department of Electrical Engineering and Information Technology Universitas Gadjah Mada Yogyakarta Indonesia Universitas Gadjah Mada Yogyakarta Indonesia

This paper presents a comparative study of machine learning algorithms for classifying normal, abscessed, and impacted tooth based on periapical radiograph images. Those methods are Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Random Forest (RF), Gaussian Naive Bayes (NB), and Support Vector machine (SVM). Haralick texture, Hu's moment invariants, and color histogram are utilized to obtain the feature vector of those images. The accuracy can be calculated with 10-fold cross-validation. We also verify the accuracy of the machine learning algorithms under the various number of training images. We take 30, 45, and 60 images from three classes. Regardless the number of training images, RF keeps outperforming the others in the term of accuracy.

关键词： Teeth Dentistry machine learning algorithms Radio frequency Support vector machines Image color analysis Forestry

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machine learning algorithms for Data Mining and Predictive Analytics in Precision Agriculture

Machine Learning Algorithms for Data Mining and Predictive A...

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Proceedings of the International Convention MIPRO

作者： Diyana Kinaneva Georgi Hristov Georgi Georgiev Plamen Zahariev Department of Telecommunications University of Ruse “Angel Kanchev” Ruse Bulgaria

ISBN: (数字)9798350382501

ISBN: (纸本)9798350382518

Artificial intelligence is a global topic that holds significant importance in recent years. It sparks debates about its usage, but despite the contradictions that arise, there are many benefits to applying it in various areas. In this paper, the authors investigate the possibilities of implementing machine learning algorithms to help optimize crop management for precision agriculture. Meeting current food demands becomes an increasingly challenging task as the population grows. Applying machine learning using IoT data analytics in the agricultural sector will bring forth additional advantages, increasing not only the quantity but also the quality of production from crop fields to meet the rising food demands. The main objective of the paper is to employ various machine learning algorithms for predictive modeling and subsequently develop a robust model capable of making accurate predictions on unseen agricultural data. The research methodology involves preprocessing the acquired dataset, including data cleaning and feature engineering to enhance model performance. The authors systematically apply a range of machine learning algorithms, such as Logistic regression, Decision trees, Naïve Bayes, and k-nearest Neighbor, to identify the most effective approach for crop yield prediction. For better model performance, boosting techniques of machine learning are also implemented. Cross-validation and performance metrics such as accuracy, confusion matrix, precision, recall and F1 score, are included to evaluate the accuracy of the models, aiming to highlight the strengths and weaknesses of each algorithm in the context of precision agriculture.

关键词： Logistic regression machine learning algorithms Accuracy Computational modeling Predictive models Data models Bayes methods

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machine learning algorithms applied in automatic classification of social network users

Machine learning algorithms applied in automatic classificat...

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International Conference on Computational Aspects of Social Networks, CASON

作者： Bruno Vicente Alves de Lima Vinicius Ponte Machado Departamento de Informática e Estatística Laboratório de Inteligência Computacional Teresina PI Brasil

This work shows the results of an analysis of machine learning algorithms applied in automatic classification for the users of the social network called ***. The tests were done using a database with 2000 users. The analysis identifies which algorithm performs better in automatic classification of users within a social network. The algorithms tested were Multilayer Perceptron, Support Vector machine, Kohonen Network and K-means Algorithm.

关键词： machine learning algorithms Databases Support vector machines Social network services Error analysis Training Clustering algorithms

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machine learning algorithms based Detection and Analysis of Stress - A Review

Machine Learning Algorithms based Detection and Analysis of ...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Ravi Raja A Sistla Jyothirmy Gottam Geethika Seetha Bharath Sai Buddhanti Saiteja V H Prasad Reddy ECE Department Velagapudi Ramakrishna Siddhartha Engineering College Vijayawadaity AP India

Now-a-days stress is one of the major issues in every individual’s life. It may cause many physiological and psychological problems. Many researchers have taken this into account and proposed various stress detection models. This paper mainly focuses on various stress detection models which are published in the latest years. The stress level of an individual is classified using machine learning algorithms like K-Nearest Neighbor (KNN), Naive Bayes (NB), Logistic regression (LR), Support Vector machine (SVM) etc. It is observed that SVM produces a high accuracy when compared with other classifiers. The organization of the paper is as follows, Section I gives a brief introduction on stress and a basic idea about the datasets used by the researchers. Section II provides literature review. Section III contains analysis on collected literature.

关键词： Support vector machines Renewable energy sources machine learning algorithms Bibliographies Psychology Human factors Organizations

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ESEEG: An Efficient Epileptic Seizure Detection using EEG signals based on machine learning algorithms 2

ESEEG: An Efficient Epileptic Seizure Detection using EEG si...

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2nd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2022

作者： Abdelminaam, Diaa Salama Fahmy, Andrew Gamal Ali, Youssef Mohamed El-Din, Omar Ahmed Diaa Aly, Ahmed Raouf Heidar, Mahmoud Misr International University Faculty of Computer Science Cairo Egypt

ISBN: (纸本)9781665466776

Epileptic seizure Disease is a substantial health burden and cause of death worldwide. For epileptic seizure localization and classification,It's critical to be able to detect recorded epileptic seizure activity in EEG segments. Seizure progression is usually a dynamic and non-stationary process involving signals of multiple frequency., therefore visual and traditional frequency-based techniques are restricted in their utility. Researchers are attempting to integrate artificial intelligence (AI) and machine learning (ML) approaches to improve medical practise as a result of advancements in AI and ML methodology. Early disease identification and prediction is a critical goal in healthcare in order to provide timely preventive actions. This is especially true in the case of epilepsy, which is characterised by frequent and unexpected convulsions. We present a comprehensive overview of existing machine learning strategies for early seizure prediction using EEG data. We will highlight the research's shortcomings, difficulties, and pitfalls, as well as make recommendations for future directions. The following findings (accuracy) were obtained: KNN : 94%, Decision Tree : 94%, Random forrest: 96%, and Convolutional Neural Network (CNN) : 96.9% © 2022 IEEE.

关键词： Location awareness Visualization machine learning algorithms Epilepsy machine learning Medical services Ubiquitous computing

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machine learning algorithms for Medicine Text Mining Disease Diagnosis

Machine Learning Algorithms for Medicine Text Mining Disease...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： Mridula Gupta Vignesh T Udita Goyal Shakeela Banu C Monali Ravindra Borade D. Anandhasilambarasan Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Quantum University Research Center Quantum University Roorkee Uttarakhand India School of Mangement PG Management JAIN (Deemed to be University) Bangalore Karnataka AI & Ds Vishwakarma Institute of Technology Pune Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

The proliferation of digital devices like smartphones and the Internet of Things has resulted in a flood of data. Every second, millions of bytes of data (including text, music, images, and videos) are produced. Thus, the need for big data analytics has been sparked by the current situation. Healthcare service records are an example of the large amounts of data created every day by hospitals. Information on patient symptoms, drug use, and the discovery of new illnesses, among other things, may be gleaned from this massive dataset. In this investigation, we use text processing methodology to public hospital patient record text data from 2017–2019 to better understand how diseases manifest in their patients. Preprocessing data from medical records involves using the Nave Bayes Classifier & Random Forest algorithms to categorise illnesses. To narrow down the collection of symptom statements, a list of altered Indonesian stop words was employed. Compared to the Naive Bayes classification technique. Experiments validate the effectiveness and efficiency of our suggested technique for illness classification in medical records.

关键词： Text mining machine learning algorithms Accuracy Hospitals Stroke (medical condition) Classification algorithms Medical diagnostic imaging Random forests Videos Text processing

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