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17th IEEE International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

作者： Wen, Huaiyu Li, Sufang Ul Haq, Amin Li, Jian Ping Kumar, Rajesh Saboor, Abdus Jiang, Tao Chengdu Univ Coll Comp Sci Chengdu 616000 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China

ISBN: (纸本)9780738142593

A heart disease diagnosis method has been proposed for effective heart disease diagnosis. In the proposed method machine learning (ML) classifiers have been used for detection of heart disease. Chi square feature selection algorithm has been used for related feature selection to improve the prediction performance of machine learning models. Cross validation, method Hold out has been employed for model hyper parameters tuning and best model selection. Furthermore, performance evaluation metrics, such as classification accuracy, specificity, sensitivity, Matthews' correlation coefficient and execution time have been used for model performance evaluation. The Cleveland heart disease data set has been used for testing of the proposed method. The experimental results demonstrated that proposed method has achieved high performance as compared to state of the art methods. Furthermore, the proposed method performance has been compared with deep learning model. Thus, the proposed method will support the medical professional to diagnosis heart disease efficiently and could easily incorporated in healthcare for diagnosis of heart disease.

关键词： machine learning algorithms Heart disease diagnosis accuracy Feature selection

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Detection of fraudulent transactions using SAS Viya machine learning algorithms

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Procedia Computer Science 2021年 190卷 204-209页

作者： Jenny Domashova Olga Zabelina National Research Nuclear University "MEPhI" 31 Kashirskoe shosse Moscow 115409 Russia

The article presents the results of applying machine learning techniques to detect fraudulent banking transactions. The market of antifraud systems was studied. Ensemble methods for solving classification problem as well as dimensionality reduction techniques were examined. The proposed analysis procedure is based on the selection of the best machine learning model and the identification of the most significant features for detecting fraud. Results-based recommendations can be used in financial institutions as well as in other organizations, where it is required to identify and prevent entities’ fraudulent actions that pose a threat to the functioning of business processes and electronic systems. The proposed fraud detection methodology was implemented on the cloud-based analytical platform Statistical Analysis System (SAS) Viya.

关键词： fraud detection payment transactions machine learning algorithms data preprocessing classification logistic regression autoencoder gradient boosting.

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Predicting and Analyzing Absenteeism at Workplace Using machine learning algorithms 43

Predicting and Analyzing Absenteeism at Workplace Using Mach...

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43rd International Convention on Information, Communication and Electronic Technology (MIPRO)

作者： Rista, Amarildo Ajdari, Jaumin Zenuni, Xhemal SEE Univ Fac Contemporary Sci & Technol Tetovo North Macedonia

ISBN: (纸本)9789532330991

Absenteeism is the usual or recurrent absence from work is continuously causing disruption in the smooth running of business, affecting the organizational performance and productivity and impacting on the employees' morale. The Oil Refinery in Albania (ARMO), employing 1200 employees is facing high rate of absences. If necessary measures are not being serious dealt with, the issue of absenteeism may jeopardize the operation and production. Prediction of absenteeism is too complex influenced by many factors. Usage of data mining and machine learning algorithms is a good solution to predict and analyze it. The aim of this paper is to identify and evaluate the appropriate ML algorithms to predict and analyses absenteeism at workplace. The dataset taken into account consists of some attributes such as: age, education, employment category, day, month, length of service ect, and 125000 records are considered. Analysis and comparison of various algorithms in terms of accuracy, precision and sensitivity are done in Weka tool.

关键词： data mining absenteeism machine learning algorithms prediction

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machine learning algorithms for Forecasting COVID 19 Confirmed Cases in America

Machine Learning Algorithms for Forecasting COVID 19 Confirm...

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IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

作者： Mario Fernando Jojoa Acosta Begoña Garcia-Zapirain University of Deusto

ISBN: (纸本)9781665415897;9781665446839

This paper presents a Multilayer Perceptron and Support Vector machine algorithms approach to predict the number of COVID19 infections in different countries of America. It intends to serve as a tool for decision-making and tackling the pandemic that the world is currently facing. The models were trained and tested using open data from the European Union repository where a time series of confirmed contagious cases was modeled until May 25, 2020. The hyperparameters as number of neurons per layer were set up using a tabu list algorithm. The countries selected to carry out the study were Brazil, Chile, Colombia, Mexico, Peru and the United States. The metrics used are Pearson's correlation coefficient (CP), Mean Absolute Error (MAE), and Mean Percentage Error (MPE). For the testing stage we obtained the following results: Brazil, CP=0.65, MAE=2508 and MPE=17%; Chile, CP=0.64, MAE=504, MPE=16%; Colombia, CP=0.83, MAE=76, MPE=9%; Mexico, CP=0.77, MAE=231, MPE=9%; Peru, CP=0.76, MAE=686, MPE=18% and the United States of America, CP=0.93, MAE=799, MPE=4%. This resulted in powerful machine learning tools although it is necessary to use specific algorithms depending on the data and the stage of the country's pandemic.

关键词： Support vector machines Measurement COVID-19 machine learning algorithms Pandemics Signal processing algorithms Tools

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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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Analysis of performance metrics of heart failured patients using Python and machine learning algorithms

Global Transitions Proceedings

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Global Transitions Proceedings 2021年第2期2卷 233-237页

作者： Rachana R Sanni H.S. Guruprasad Department of ISE BMSCE Bangalore- 560019 India ***

Heart is one of the most important organs in Human's body. In life, some changes may happen that may bring various diseases like, blood pressure, sugar, etc. Similarly, heart failure is also a dreadful disease. Heart failure is a serious condition and there is no cure for this disease. It is a situation in which the patient's heart is not pumping the blood well as the normal heart pumps. Heart Failure prediction is a complex task in the medical field. The rates of heart failure have been increasing day by day as the rate of population is also increasing day by day. This paper aims at analyzing the machine learning algorithms based on the percentage of various performance metrics (such as, Accuracy, Precision and Recall). The machine learning methodology is proposed. The most suitable algorithm for each metrics is predicted. It is analyzed using the specific variables in the dataset by using the python programming as well as different supervised machine learning algorithms which include, Decision Tree, Logistic Regression, KNN and Random Forest. Anaconda jupyter notebook is used for implementing python scripting.

关键词： Analyzing Disease Heart Failure machine learning algorithms Patients Python Programming Python Scripting

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machine learning algorithms approach for Gastrointestinal Polyps classification

Machine Learning algorithms approach for Gastrointestinal Po...

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IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA)

作者： Kristijan Cincar Ioan Sima Faculty of Mathematics and Informatics West University Timisoara Romania Faculty of Mathematics and Computer Science Babes-Bolyai University Cluj-Napoca Romania

ISBN: (数字)9781728167992

ISBN: (纸本)9781728168005

In this paper we applied machine learning techniques for Gastrointestinal Polyps classification from colonoscopy video clips and compared our results to other methods and with the results of clinicians with different levels of experience. machine learning technology allows us to classify tissues that can reduce the waiting time for patients' results. We tested four machine learning algorithms (Support Vector machine, Random Forest, Random Subspace and Extra-Trees) for classification of the polyps in hyperplastic, serrated and adenoma lesions. We used a dataset in which there are 152 instances with the three types of lesions, for 76 polyps. The best results were obtained by Random Forest algorithm with the accuracy of 87%, and the worst results were obtained by Support Vector machine with the accuracy of between 63% and 73%.

关键词： Cancer Image color analysis Support vector machines Vegetation machine learning algorithms Lesions Shape

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machine learning algorithms for air pollutants forecasting

Machine Learning algorithms for air pollutants forecasting

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International Symposium for Design and Technology of Electronics Packages (SIITME)

作者： Marius Dobrea Andreea Bădicu Marina Barbu Oana Subea Mihaela Bălănescu Geroge Suciu Andrei Bîrdici Oana Orza Ciprian Dobre BEIA Consult International Bucharest Romania University POLITEHNICA Bucharest Romania Bucharest Romania

ISBN: (数字)9781728175065

ISBN: (纸本)9781728175072

Air pollution represents an issue that raises many concerns nowadays, as it has various negative effects on the environment and the economy worldwide. Because of the rapid urbanization, cities are suffering from polluted air, so it is important to predict future air quality. For this purpose, new applications of artificial intelligence should be employed. In this paper, we will present several machine learning algorithms, the possible software that can be used for them and the applications used in the field of air quality. Based on the research in the field, we propose SVR, ARIMA and LSTM, 3 machine learning models, which can be used to predict air pollution. These algorithms have been tested using time-series for PM 10 and PM 2.5 particles. The results showed that SVR and ARIMA algorithms are the most suitable in forecasting air pollutant concentrations.

关键词： Predictive models Prediction algorithms Data models Air pollution Atmospheric modeling machine learning algorithms Forecasting

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State of Charge Estimation of Lithium-Ion Battery for Electric Vehicles Using machine learning algorithms

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WORLD ELECTRIC VEHICLE JOURNAL 2021年第1期12卷 38页

作者： Chandran, Venkatesan Patil, Chandrashekhar K. Karthick, Alagar Ganeshaperumal, Dharmaraj Rahim, Robbi Ghosh, Aritra KPR Inst Engn & Technol Dept Elect & Commun Engn AvinashiRd Coimbatore 641407 Tamil Nadu India Brahma Valley Coll Engn & Res Inst Dept Mech Engn Nasik 422213 Maharashtra India KPR Inst Engn & Technol Dept Elect & Elect Engn AvinashiRd Coimbatore 641407 Tamil Nadu India Kalasalingam Acad Res & Educ Sch Elect & Elect Technol Krishnankoil 626126 Tamil Nadu India Sekolah Tinggi Ilmu Manajemen Sukma Dept Informat Management Medan 20219 Sumatera Utara Indonesia Univ Exeter Coll Engn Math & Phys Sci Renewable Energy Penryn TR10 9FE Cornwall England

The durability and reliability of battery management systems in electric vehicles to forecast the state of charge (SoC) is a tedious task. As the process of battery degradation is usually non-linear, it is extremely cumbersome work to predict SoC estimation with substantially less degradation. This paper presents the SoC estimation of lithium-ion battery systems using six machine learning algorithms for electric vehicles application. The employed algorithms are artificial neural network (ANN), support vector machine (SVM), linear regression (LR), Gaussian process regression (GPR), ensemble bagging (EBa), and ensemble boosting (EBo). Error analysis of the model is carried out to optimize the battery's performance parameter. Finally, all six algorithms are compared using performance indices. ANN and GPR are found to be the best methods based on MSE and RMSE of (0.0004, 0.00170) and (0.023, 0.04118), respectively.

关键词： lithium-ion battery battery management sustainable energy machine learning algorithms electric vehicles state of charge

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machine learning algorithms Based on Hidden Markov Models in Low-Speed Speech Codecs for Assessing Speech Quality

Machine Learning Algorithms Based on Hidden Markov Models in...

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International Conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Sergey Nikolaevich Kirillov Vladimir Timurovich Dmitriev Sergey Olegovich Aleksenko Ryazan State Radio Engineering University named after V.F. Utkin Ryazan Russia

ISBN: (数字)9781728188409

ISBN: (纸本)9781728181134

A machine learning algorithm for a low-speed primary coding algorithm based on automatic recognition of speech signal fragments (SS) using hidden Markov models (HMM) has been developed. When assessing the quality of SS at transmitter output, spectral dynamics algorithm is used, as well as complex algorithms for assessing speech quality. Reduction of redundancy in SS is provided by using speech recognition and synthesis algorithms. Training with HMM to solve speech recognition problem included the definition of such parameters as: matrix of probabilities of transitions between different states of the model. To solve these problems two iterative algorithms were used: forward-backward algorithm and Baum-Welsh algorithm. As an experiment, using Hidden Markov Model Toolkit (HTK) software model, an independent monophonic speech model of Russian language was built. The model was based on the recognition from a list of phonemes consisting of 52 phonetic units and Russian language dictionary including 500000 words. Well-known algorithms to assess speech quality at low-speed codec output are investigated in the interests of machine learning. It is shown that the use of these algorithms does not provide necessary correlation of objective and subjective algorithms to assess speech quality under the action of acoustic interference. algorithms for objective quality assessment that ensure the correlation of objective and subjective assessments of speech quality with the accuracy of 0.5 points in accordance with GOST R 50840-95 are proposed. The research has shown that the use of these algorithms in the encoder allows increasing speech quality at the output of low-speed codec with its machine learning by 0.5 ... 1 point in accordance with GOST R 50840-95.

关键词： machine learning algorithms Speech coding Heuristic algorithms Software algorithms Hidden Markov models Speech recognition machine learning

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