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11th International Conference on Computational Collective Intelligence (ICCCI)

作者： Mansurova, Madina Barakhnin, Vladimir Khibatkhanuly, Yerzhan Pastushkov, Ilya Al Farabi Kazakh Natl Univ Alma Ata 050040 Kazakhstan Inst Computat Technol SB RAS Novosibirsk Russia Novosibirsk State Univ Novosibirsk Russia

ISBN: (纸本)9783030283742;9783030283735

The modern society have been witnessed that intensive development of Internet technologies had followed to information explosion during last decades. This explosion had been expressing by an exponential growth of data volume among the low-quality information. This paper is designed to provide detailed information about some intellectual tools which are support decision taking by automatic knowledge extraction. In the first part of paper, we considered a preprocessing contains morphological analysis of texts. Then we had considered the model of text documents in the form of a hypergraph and implementation of the random walk method to extract semantically close word's pairs, in other words, pairs that often appears together. Result of calculations is matrix with word affinity coefficients corresponding to each other component of vocabulary vector. In the second part we describe training of neural network for linguistic constructions extraction. These ones include possible values of text named entities descriptors. The neural network enables to retrieve information on one preselected descriptor, for example, location, in the form of the final result of the name of geographical objects. In a general case, the neural network can retrieve information on several descriptors simultaneously.

关键词： Entity extraction Semi-structured data machine learning algorithms Random walk method Neural networks

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Recent innovation in benchmark rates (BMR):evidence from influential factors on Turkish Lira Overnight Reference Interest Rate with machine learning algorithms

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Financial Innovation 2021年第1期7卷 942-961页

作者： Öer Depren Mustafa Tevfik Kartal Serpil KılıçDepren YapıKredi Bank IstanbulTurkey Borsaİstanbul Financial Reporting and Subsidiaries Directorate Reşitpaşa Mahallesi Borsaİstanbul CaddesiNo:434467 IstanbulTurkey Department of Statistics Yildiz Technical UniversityIstanbulTurkey

Some countries have announced national benchmark rates,while others have been working on the recent trend in which the London Interbank Offered Rate will be retired at the end of *** that Turkey announced the Turkish Lira Overnight Reference Interest Rate(TLREF),this study examines the determinants of *** this context,three global determinants,five country-level macroeconomic determinants,and the COVID-19 pandemic are considered by using daily data between December 28,2018,and December 31,2020,by performing machine learning algorithms and Ordinary Least *** empirical results show that(1)the most significant determinant is the amount of securities bought by Central Banks;(2)country-level macroeconomic factors have a higher impact whereas global factors are less important,and the pandemic does not have a significant effect;(3)Random Forest is the most accurate prediction *** action by considering the study’s findings can help support economic growth by achieving low-level benchmark rates.

关键词： Benchmark rate Determinants machine learning algorithms Turkey

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A Predictive Tool For Grid Data Analysis Using machine learning algorithms 10

A Predictive Tool For Grid Data Analysis Using Machine Learn...

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10th Annual Computing and Communication Workshop and Conference (CCWC)

作者： Penn, David Subburaj, Vinitha Hannah Subburaj, Anitha Sarah Harral, Mark West Texas A&M Univ Sch Engn Comp Sci & Math Canyon TX 79016 USA Grp NIRE Lubbock TX USA

ISBN: (纸本)9781728137834

Power and energy sectors are collecting vast amount of data from different sources and trying to use computational tools to analyze and identify useful patterns in the data collected. Some of challenges observed with such big data are high volume, heterogeneous, and rapidly growing data. To efficiently handle such big data, machine learning algorithms are used. In this paper, such machine learning algorithms are used to predict patterns in the grid data collected from the Distributed Energy Resources (DER) at a local electrical engineering company. Predictive framework developed to preprocess the big data, classify the test and training datasets, and the application of different machine learning algorithms is discussed in this paper. The results obtained after analyzing the big data with different machine learning algorithms are also discussed in this paper.

关键词： Bigdata Grid Data Distributed Energy Resources (DER) machine learning algorithms

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A SURVEY OF machine learning algorithms FOR BIG DATA ANALYTICS

A SURVEY OF MACHINE LEARNING ALGORITHMS FOR BIG DATA ANALYTI...

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International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)

作者： Athmaja, S. Hanumanthappa, M. Kavitha, Vasantha Bangalore Univ Dept Comp Sci Bengaluru India Maharani Lakshmi Ammanni Coll Women Dept Comp Sci Bengaluru India

ISBN: (纸本)9781509032945

Big data analytics is a booming research area in computer science and many other industries all over the world It has gained great success in vast and varied application sectors. This includes social media, economy, finance, healthcare, agriculture, etc. Several intelligent machine learning techniques were designed and used to provide big data predictive analytics solutions. A literature survey of different machine learning techniques is provided in this paper. Also a study on commonly used machine learning algorithms for big data analytics is done and presented in this paper.

关键词： big data analytics machine learning algorithms technique prediction model

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Trust in the European Central Bank: Using Data Science and predictive machine learning algorithms 10

Trust in the European Central Bank: Using Data Science and p...

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10th International Conference on Advanced Computer Information Technologies (ACIT)

作者： Skirka, Andrii Adamyk, Bogdan Adamyk, Oksana Valytska, Mariana Ternopil Natl Econ Univ Dept Banking Ternopol Ukraine Ternopil Natl Econ Univ Dept Econ & Audit Ternopol Ukraine Natl Pedag Univ Ternopil Volodymyr Hnatiuk Ternopol Ukraine

ISBN: (纸本)9781728167602

Purpose: This empirical scientific research project aims to apply data science and machine learning tools to determine the influence of different factors on the level of trust in the European Central Bank. This research based on the data from the European Commission's Eurobarometer Survey 89. The paper also aims to represent some predictive analytics techniques to anticipate the level of confidence towards cental bank. Besides that, we build a couple of data visualizing plots, in order to show the main significant impact on the dependent variable. We created the ECB TrustMap Plot, correlation heatmap matrix and Alluvial diagram. Using this plots, we represented changes in network structure over people responses and decision making. Methodology: to calculate the index of trust in the central bank we used Logistic Regressioin, Decision Tree, Random Forrest and Neural Network models. Verify the output and results by using the VIF of the Logistic Model, Cross-validation, Confusion matrix, ROC-curves and accuracy estimations. Main Findings: trust in one-single currency, inflation problems, expectations about the future of EU, indicator of happiness and other indicators has a significant impact on the the level of trust in the central bank.

关键词： Data Science machine learning algorithms data visualization predictive analytics trust central bank

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Evaluating the Efficiency of Several machine learning algorithms for Fall Detection 1

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13th International Conference on Intelligent Human Computer Interaction (IHCI)

作者： Banda, Parimala Mohammadian, Masoud Reddy, Gudur Raghavendra Univ Canberra Sch IT & Syst Canberra ACT Australia Univ Canberra Sch Design & Built Environm Canberra ACT Australia

ISBN: (数字)9783030984045

ISBN: (纸本)9783030984045;9783030984038

Elderly falls are a growing phenomenon observed within the world. According to World Health Organization (WHO), it is the second leading cause of unintentional or accidental deaths among the elderly. Thus, the need for research regarding the development of fall detection systems is imperative. Researchers have utilized various approaches to develop fall detection systems, significant number of which have employed machine Leaning (ML) algorithms for fall detection. In this study, we evaluated the efficiency of six ML algorithms on a public fall detection dataset. A robust deep neural network for fall detection (FD-DNN) is identified to be the current state-of-the-art, it detects falls by using a self-built sensor that consumes low power. By evaluating the efficiency of six machine learning algorithms on a publicly available joint fall detection dataset, the accuracy of the fall detection was increased from 99.17% to 99.88% by using the K-nearest Neighbor indicating that common machine learning algorithms can achieve identical or higher accuracy rendering the complex and expensive deep neural network-based fall detection systems inefficient.

关键词： Fall detection Elderly fall detection machine learning algorithms Wearable fall detection system SisFall dataset MobiFall dataset K-nearest neighbors KNN Support vector machines SVM Decision tree Random forest Logistic regression Naive Bayes

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Detecting and profiling sedentary young men using machine learning algorithms 5

Detecting and profiling sedentary young men using machine le...

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IEEE Symposium on Computational Intelligence and Data Mining (CIDM)

作者： Siirtola, Pekka Pyky, Riitta Ahola, Riikka Koskimaki, Heli Jamsa, Timo Korpelainen, Raija Roning, Juha Univ Oulu Comp Sci & Engn Dept POB 4500 FI-90014 Oulu Finland Oulu Deaconess Inst Dept Sports & Exercise Med Oulu Finland Oulu Univ Hosp Med Res Ctr Oulu Oulu Finland Univ Oulu SF-90100 Oulu Finland Univ Oulu Fac Med Inst Hlth Sci Oulu Finland Univ Oulu Fac Med Dept Med Technol SF-90100 Oulu Finland

ISBN: (纸本)9781479945184

Many governments and institutions have guidelines for health-enhancing physical activity. Additionally, according to recent studies, the amount of time spent on sitting is a highly important determinant of health and wellbeing. In fact, sedentary lifestyle can lead to many diseases and, what is more, it is even found to be associated with increased mortality. In this study, a data set consisting of self-reported questionnaire, medical diagnoses and fitness tests was studied to detect sedentary young men from a large population and to create a profile of a sedentary person. The data set was collected from 595 young men and contained altogether 678 features. Most of these are answers to multi-choice close-ended questions. More precisely, features were mostly integers with a scale from 1 to 5 or from 1 to 2, and therefore, there was only a little variability in the values of features. In order to detect and profile a sedentary young man, machine learning algorithms were applied to the data set. The performance of five algorithms is compared (quadratic discriminant analysis (QDA), linear discriminant analysis (LDA), C4.5, random forests, and k nearest neighbours (kNN)) to find the most accurate algorithm. The results of this study show that when the aim is to detect a sedentary person based on medical records and fitness tests, LDA performs better than the other algorithms, but still the accuracy is not high. In the second part of the study the differences between highly sedentary and non-sedentary young men are searched, recognition can be obtained with high accuracy with each algorithm.

关键词： learning (artificial intelligence) medical diagnostic computing fitness test health-enhancing physical activity machine learning algorithms medical diagnosis sedentary young men Accuracy Diseases Educational institutions Feature extraction machine learning algorithms Medical diagnostic imaging Sociology machine learning algorithms medical diagnostic computing Medical diagnosis Sociology Educational institutions Feature extraction Fitness Testing maladjustment health diagnostic imaging Disease diagnosis

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Heart Disease Prediction Using machine learning algorithms 1

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18th Control Instrumentation System Conference (CISCON)

作者： Mammen, Rea Pawar, Arti Manipal Acad Higher Educ ICAS Manipal 576104 India

ISBN: (数字)9789811969133

ISBN: (纸本)9789811969157;9789811969133;9789811969126

Heart disease is synonymous with heart attacks and strokes. But, cardiovascular disease also includes maladies like coronary artery disease (CAD), heart arrhythmias, hypertension, congenital heart disease, etc. Heart disease plagues a majority of the population today and is the leading cause of death globally. Efficient prediction systems to diagnose heart diseases are a must in the health care industry. Such systems are already in use but there is scope for improvement and with technological advancement over the years, the accuracy of disease prediction has been improved. machine learning is a branch of artificial intelligence that predicts several naturally occurring events by training a model with some data and then using unseen data to test it. This paper seeks to analyze a few machine learning algorithms and tests their accuracy in predicting heart diseases.

关键词： machine learning algorithms Coronary artery disease Logistic regression SVM Decision tree Naive Bayes

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The use of machine learning algorithms for image recognition

The use of machine learning algorithms for image recognition

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Radioelectronic Systems Conference (RSC)

作者： Matuszewski, Jan Rajkowski, Adam Mil Univ Technol Fac Elect Inst Radioelect 2 Gen Sylwestra Kaliskiego St PL-00908 Warsaw Poland Sandomierz Radoszki JW 35333 Brt Sandomierz Poland

ISBN: (纸本)9781510636729

The article presents a way of using machine learning algorithms to recognize objects in images. To implement this task, an artificial neural network was used, which has a high adaptability and allows work with a very large set of input data. The neural network was described using a program written in the MATLAB simulation environment. The basic problem faced by the designer of objects recognition is to collect a sufficient training set of images to achieve the high probability of correct recognition. The set of learning patterns in the artificial neural networks may contain from several dozen thousands to one million training samples. In this article at the beginning the neural network was pre-trained trained based on the images included in the publicly available CIFAR 100 database, which are characterized by a small size of 32x32 pixels. It contains 70 000 images assigned to 10 basic categories. Then the author's database, consisting from 1000 pedestrians, cars and road signs was used. The article contains a description of applied algorithm, method of supervised learning and correction of weight coefficients, selection of activation function and operation on max pooling filter. The results of proposed solution are presented in the form of screenshots from calculations and in figures depicting results of recognized objects. Attention was also paid to the impact of used database for learning the network on the speed of calculations and recognition efficiency. The proper selection of number and types of layers, number of neurons, activation function and the value of the learning factor is very important in designing the neural network in application to objects recognition contained in the images. The problems occurring in the process of learning the neural networks and suggestions for their further improvement are also presented.

关键词： image recognition deep neural network machine learning algorithms

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Evaluation of machine learning algorithms for Intrusion Detection System 15

Evaluation of Machine Learning Algorithms for Intrusion Dete...

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15th IEEE International Symposium on Intelligent Systems and Informatics (SISY)

作者： Almseidin, Mohammad Alzubi, Maen Kovacs, Szilveszter Alkasassbeh, Mouhammd Univ Miskolc Dept Informat Technol H-3515 Miskolc Hungary Mutah Univ Dept Informat Technol Amman Jordan

ISBN: (纸本)9781538638552

Intrusion detection system (IDS) is one of the implemented solutions against harmful attacks. Furthermore, attackers always keep changing their tools and techniques. However, implementing an accepted IDS system is also a challenging task. In this paper, several experiments have been performed and evaluated to assess various machine learning classifiers based on KDD intrusion dataset. It succeeded to compute several performance metrics in order to evaluate the selected classifiers. The focus was on false negative and false positive performance metrics in order to enhance the detection rate of the intrusion detection system. The implemented experiments demonstrated that the decision table classifier achieved the lowest value of false negative while the random forest classifier has achieved the highest average accuracy rate.

关键词： machine learning algorithms Intrusion detection Vegetation Algorithm design and analysis Prediction algorithms Testing Neural networks

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