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检索条件"主题词=Machine Learning Algorithms"

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Performance Analysis of Adaptive Filter and machine learning algorithms for Heart Rate Estimation Using PPG Signal

Performance Analysis of Adaptive Filter and Machine Learning...

引用

Information and Communication Technology for Development for Africa (ICT4DA), International Conference on

作者： Tsion Yigzaw Fikreselam Gared Amare Kassaw Bahir Dar Institute of Technology Bahir Dar University

ISBN: (纸本)9781665436670

Photoplethysmography (PPG) signal provide advanced and simple ways for estimating heart rate (HR) information as an unremarkable system on wearable devices. In this paper, we analyze the performance of adaptive filter and machine learning (ML) algorithms for estimation of HR during physical activity. Three cascades recursive least square (RLS) and cascades normalized least mean square (NLMS) adaptive filters are developed and combined using convex combination scheme to reduce motion artifacts (MA) from the recorded PPG signal. Then, ML based spectral tracking algorithms is applied, to locate the spectral peak corresponding to HR. Four different supervised ML algorithms (Support Vector machine, Decision Tree, K- Nearest Neighbor and Logistic Regression) are examined to track the spectral peaks and the decision tree out performs all three algorithms with an accuracy of 98.96%. Experimental results on the PPG datasets including 23 subjects used in the 2015 IEEE signal processing cup showed that the proposed approach has a very good performance by achieving an average absolute error (AAE) of 1.98 beats per minute (BPM) and the personal correlation coefficient of 0.9899. AAE result proved that the proposed method provides accurate HR estimation performance in comparison with other existing works.

关键词： Heart rate Support vector machines Maximum likelihood estimation machine learning algorithms Tracking Wearable computers Signal processing algorithms

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An Empirical Study of machine learning algorithms for Social Media Bot Detection

An Empirical Study of Machine learning Algorithms for Social...

引用

IOT, Electronics and Mechatronics Conference (IEMTRONICS), IEEE International

作者： Maryam Heidari James H Jr Jones Ozlem Uzuner George Mason University

Social media bots can change society's perspective in different aspects of life. This paper analyzes sentiment features and their effect on the accuracy of machine learning models for social media bot detection. Social bots can use tweet sentiment to create a backfire effect and confirmation bias to create a fake trend or change public opinion. We analyze bot detection problems based on sentiment features inspired by the work by Micheal Workman [1] and create new features based on textual information of online comments. We offer a quantitative approach to create new features and compare machine learning models for bot detection. This work is based on psychological and social effects inherent in tweets' text content based on the work by [1]. The new set of sentiment features are extracted from a tweet's text and used to train bot detection models. Also, we implement the new model for the Dutch language and achieve more than 87% accuracy for the Dutch tweets based on new sentiment features. Considering new sentiment features based on psychological and social factors for a tweet's text will open a potential research area for social media bot detection.

关键词： Electric potential Mechatronics machine learning algorithms Social networking (online) Conferences Psychology machine learning

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Detection of Breast Cancer using machine learning algorithms – A Survey

Detection of Breast Cancer using Machine Learning Algorithms...

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International Conference on Computing Methodologies and Communication (ICCMC)

作者： M. S. Harinishree C. R. Aditya D. N. Sachin Vidyavardhaka College of Engineering Mysuru India

A common disease affecting millions of women in today's world is breast cancer. To minimize the high number of superfluous breast biopsies, a few computer supported frameworks have been proposed somewhat recently. machine learning provides potentially large opportunities for computer-aided diagnosis of a disease. machine learning gives possibly huge freedoms to computer helped finding of an illness. This paper investigates the informational indexes accessible to prepare the machine learning [ML] models and give a far reaching correlation of various kinds of models applied to anticipate breast cancer.

关键词： Training machine learning algorithms Image analysis Computational modeling Training data machine learning Breast cancer

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Performance Comparison of machine learning algorithms for Prediction of Students’ Social Engagement

Performance Comparison of Machine Learning Algorithms for Pr...

引用

International Conference on Computing Methodologies and Communication (ICCMC)

作者： Jigna B Prajapati Savan K Patel Acharya Motibhai Patel Institute of Computer Studies Ganpat University Gujarat India

The various techniques and algorithms of ML & DL are becoming popular for prediction with different level of accuracy. This paper includes performance comparison of few machine learning algorithms in the reference of student social engagement during covid-19 pandemic period. In this study, the comparison of Naïve Bayes, J48 tree, REPTree and Random forest algorithm is carried on structured dataset of 1200+ instance. In this paper, study proposes & scrutinizes commonly used social app & platform. Further, it compares them with the various ML approach. The objective of this study is to foreseeing the correlation between student social engagement for one the most popular social engagement platform during covid-19 pandemic. This paper focusses on accuracy, F-measure and time to summarize comparison result. The findings of the study and dynamic analysis indicate ML/Deep learning algorithm can lead better accuracy and other factor for preprocessed student social engagement dataset. The finding can predict engagement of students for most popular social media platform with performance comparison of ML algorithm.

关键词： COVID-19 Freeware machine learning algorithms Social networking (online) Pandemics Heuristic algorithms Multimedia Web sites

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A Forecast of Coronary Heart Disease using Proficient machine learning algorithms

A Forecast of Coronary Heart Disease using Proficient Machin...

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International Conference on Image Information Processing

作者： Shivani Gaba Alankrita Aggarwal Shally Nagpal Deepak Kumar Pardeep Singh Computer Science and Engineering Panipat Institute of Engineering and Technology Panipat Haryana India Cybernatics School of Computer Science University of Petroleum and Energy Studies Dehradun

ISBN: (纸本)9781665433624

Coronary Heart Diseases (CHDs) are a fundamental explanation of enormous deaths on earth in the last decades and are a dangerous disease in India and worldwide and Coronary Heart Disease has developed as one of the most unmistakable and uninformed reasons for death all around the globe. Thus, a dependable, precise & achievable framework for analyzing these maladies for appropriate therapy. Artificial Intelligence evaluations & systems are being used to restore data collections to robotize investigation within enormous & uneasy information. Numerous scientists, as of late, have been utilizing a few Artificial Intelligence methods to facilitate well-being for industry & professionals analysis of coronary-disease infections. This work intends to make use of chronological medical data to forecast CHD using machine learning. The work introduces machine learning techniques of different models dependent on calculations, procedures, and analyzes exhibition. Also, in this paper three supervised learning methods: Linear Regression using stochastic gradient descent and Decision Tree to find out the relationship in CHD data to improve prediction rate.

关键词： Heart machine learning algorithms Service robots Supervised learning Linear regression Stochastic processes Medical treatment

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Using machine learning algorithms in Predictive Psychodiagnostics Transportation Case

Using Machine Learning Algorithms in Predictive Psychodiagno...

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Wave Electronics and its Application in Information and Telecommunication Systems (WECONF)

作者： P. G. Menshih I. V. Niesov S. D. Erokhin A. A. Evsyukov M. G. Gorodnichev Moscow Technical University of Communications and Informatics Moscow Russia

Currently, unmanned vehicles cannot yet be introduced on public roads, and the person behind the wheel is not predictable and behavior is a multi-parameter task. The article describes a methodology for calculating and evaluating various characteristics of a respondent's psychodiagnostic test using the integrated use of typical driver behavior uncertainty and assessing his condition through machine learning (ML) algorithms. The application of machine learning allows you to reduce the number of test questions and increase the processing speed, since the developed algorithm finds hidden patterns well. A large number of factors affect the driver's condition, and this article is devoted to determining the driver's condition in real time, which reduces the likelihood of an emergency. Based on the developed algorithm, it is possible to conduct an interview and pre-trip control. On the one hand, this makes it possible to improve the quality of the recruited personnel, and on the other, to improve the quality of logistics and, most importantly, to increase road safety.

关键词： Measurement Training machine learning algorithms Uncertainty Clustering algorithms Psychology Predictive models

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Comparison of Different machine learning algorithms for Detecting Bankruptcy

Comparison of Different Machine Learning Algorithms for Dete...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Maria Sultana Keya Himu Akter Md. Atiqur Rahman Md. Mahbobur Rahman Minhaz Uddin Emon Md. Sabab Zulfiker Daffodil International University Dhaka Bangladesh

ISBN: (数字)9781728185019

ISBN: (纸本)9781728185026

There has been severe experiments from academics and merchandisers concerning models for Predicting bankruptcy. The paper propounds an extensive rethink of work done during 5 years in the petition of intellectual strategy to accomplish bankruptcy prediction problems. Several machine learning directions are being used in this research paper for Predicting bankruptcy. Some algorithms: AdaBoost, Decision tree, J48, Bagging, Random Forest are used in this paper. By traditional models, machine learning models offer enhancing bankruptcy prediction accuracy. Different types of models are tested using several evaluation metrics. The five years Bagging accuracy range is 95% within 97% among another model. Here include kfold cross-validation(k=10) to measure our accuracy. Bagging accuracy is high in this paper. Confusion matrix is used to recount the perfection of a classification model that gives true values for knowing.

关键词： machine learning algorithms Sensitivity Statistical analysis Biological system modeling Predictive models Bankruptcy Bagging

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Performance Comparison of Intrusion Detection System Using Three Different machine learning algorithms

Performance Comparison of Intrusion Detection System Using T...

引用

International Conference on Inventive Computation Technologies (ICICT)

作者： Zena Khalid Ibrahim Mohammed Younis Thanon Collage of Engineering University of Mosul Mosul Iraq

ISBN: (数字)9781728185019

ISBN: (纸本)9781728185026

Intrusion Detection Systems (IDS) are essential for Network Security in order to control the network and also to analyze the incoming network traffic. With the existence of a huge volume of data and network traffic, there is a need to develop modern technologies like big data and the Internet of Things (IoT), and Cloud Computing (CC). In this article, a comparative study is performed for three machine learning algorithms that were implemented on the NSL- KDD dataset for the IDS system. To obtain the optimal accuracy, it is required to select the appropriate set of features in a large dataset. So, the ANOVA F-test and Recursive Feature Elimination (RFE) was used to select important features. The authors have conducted an experiment on IDS that uses three different machine learning algorithms, Random Forest (RF), K Nearest Neighbor (KNN), and Support Vector machine (SVM). The performance of the different models was compared using all the features and the best-selected features were executed using the confusion matrices.

关键词： Radio frequency machine learning algorithms Intrusion detection Telecommunication traffic Feature extraction Internet of Things Analysis of variance

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Comparative Study to Identify the Heart Disease Using machine learning algorithms

Comparative Study to Identify the Heart Disease Using Machin...

引用

Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), International Conference on

作者： Rezaul Karim Wang Chengliang Mohaiminul Islam School of Big Data & Software Engineering Chongqing University Chongqing China School of Intelligent Technology and Engineering Chongqing University of Science and Technology Chongqing China

ISBN: (数字)9781665415408

ISBN: (纸本)9781665447065

Nowadays, heart disease is a common and frequently present disease in the human body and it's also hunted lots of humans from this world. Especially in the USA, every year mass people are affected by this disease after that in India also. Doctor and clinical research said that heart disease is not a suddenly happen disease it's the cause of continuing irregular lifestyle and different body's activity for a long period after then it's appeared in sudden with symptoms. After appearing those symptoms people seek for a treat in hospital for taken different test and therapy but these are a little expensive. So awareness before getting appeared in this disease people can get an idea about the patient condition from this research result. This research collected data from different sources and split that data into two parts like 80% for the training dataset and the rest 20% for the test dataset. Using different classifier algorithms tried to get better accuracy and then summarize that accuracy. These algorithms are namely Random Forest Classifier, Decision Tree Classifier, Support Vector machine, k-nearest neighbor, Logistic Regression, and Naive Bayes. SVM, Logistic Regression, and KNN gave the same and better accuracy as other algorithms. This paper proposes a development that which factor is vulnerable to heart disease given basic prefix like sex, glucose, Blood pressure, Heart rate, etc. The future direction of this paper is using different devices and clinical trials for the real-life experiment.

关键词： Support vector machines machine learning algorithms Vegetation Classification algorithms Regression tree analysis Diseases Logistics

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Comparative Analysis of the Results of EMG Signal Classification Based on machine learning algorithms

Comparative Analysis of the Results of EMG Signal Classifica...

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International Conference on Information Science and Communications Technologies (ICISCT)

作者： Adilbek Turgunov Kudratjon Zohirov Rashid Nasimov Sanjar Mirzakhalilov Information technology Karshi branch of the Tashkent University of Information Technologies named after Mahammad al-Khwarizmi Karshi Uzbekistan Computer Systems Tashkent University of Information Technologies named after Mahammad al-Khwarizmi Tashkent Uzbekistan

ISBN: (纸本)9781665432597

In this paper has been provided information on the structure, modules and features of the hardware and software complex for classifying hand movements. The hardware-software complex is based on a modern BITalino device. The main idea of the paper is to classify more hand movements using fewer sensors. In the future, it will be possible to produce cheap and easy-to-use myoprostheses based on the scientific results presented in this paper.

关键词： Information science machine learning algorithms Pattern classification Electromyography Software Physiology Hardware

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