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8th International Conference on Mathematics, Information Technologies, Education (MoMLeT and DS)

作者： Zdebskyi, Petro Vysotska, Victoria Peleshchak, Roman Peleshchak, Ivan Demchuk, Andriy Krylyshyn, Maksym Lviv Polytech Natl Univ Lvov Ukraine Ivan Franko Drohobych State Pedag Univ Drogobych Ukraine

The purpose of this article is to develop an application for recognizing the user's point of view in order to control the mouse pointer. In the course of the task, an analysis of the subject area and technologies for implementation of the application was conducted. The algorithms of machine learning for solving the problem were considered. The input data of system are 50 coordinates placed on the face, which include the contours of the face, eyebrows, eyes, nose and coordinates of pupils. Finding the required coordinates occur without use of special devices, but only with webcam used for recognition. The application is implemented in the form of two modules, one of which is responsible for training system for the recognition of the view;the other one is responsible for controlling the mouse cursor with a view. The product can be operated on any operational system: the main requirement is the presence of an interpreter for the Python programming language, which can be downloaded for free from the official site.

关键词： Control the Mouse Pointer User's Point machine learning Neural Network Python eye tracking mouse pointer human eye third party device artificial neural network data analyzation real time user perspective machine learning algorithm sequence action response web camera trademark office mouse cursor system component functional requirement eye recognition image recognition image processing convolutional neural network component called viewpointdata data stream mining demand knowledge facial feature weight coefficient open source

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Line-to-Line Faults Detection for Photovoltaic Arrays Based on I-V Curve Using Pattern Recognition 46

Line-to-Line Faults Detection for Photovoltaic Arrays Based ...

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IEEE 46th Photovoltaic Specialists Conference (PVSC)

作者： Eskandari, Aref Milimonfared, Jafar Aghaei, Mohammadreza Vidal de Oliveira, Aline Kirsten Ruether, Ricardo Amirkabir Univ Technol Tehran 158754413 Iran Albert Ludwigs Univ Freiburg D-79110 Freiburg Germany Univ Fed Santa Catarina BR-88040900 Florianopolis SC Brazil

ISBN: (纸本)9781728104942

Fault detection plays a crucial role in reliability and safety of photovoltaic systems. However, the fault detection by the conventional protection devices is always difficult due to non-linear characteristics of PV systems, Maximum Power Point Tracking (MPPT), low irradiation, and high fault impedance. In addition, it may lead to the power losses, efficiency reduction and even fire hazard. This paper proposes an innovative fault detection method based on the pattern recognition techniques and extraction of the essential features from the current-voltage (I-V) characteristics. The main benefit of this method is using less data to detect faults while improving accuracy. The primary results demonstrate that the proposed method is accurate, effective and reliable for detecting line-line faults in PV systems.

关键词： Fault Detection machine learning algorithm Photovoltaic System Line-Line Fault Pattern Recognition

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machine learning-based self-powered acoustic sensor for speaker recognition

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NANO ENERGY 2018年 53卷 658-665页

作者： Han, Jae Hyun Bae, Kang Min Hong, Seong Kwang Park, Hyunsin Kwak, Jun-Hyuk Wang, Hee Seung Joe, Daniel Juhyung Park, Jung Hwan Jung, Young Hoon Hur, Shin Yoo, Chang D. Lee, Keon Jae Korea Adv Inst Sci & Technol Dept Mat Sci & Engn 291 Daehak Ro Daejeon 34141 South Korea Korea Adv Inst Sci & Technol Dept Elect Engn 291 Daehak Ro Daejeon 34141 South Korea KIMM Dept Nat Inspired Nanoconvergence Syst 156 Gajeongbuk Ro Daejeon 34103 South Korea CAMM 156 Gajeongbuk Ro Daejeon 34103 South Korea

Herein, we report a new platform of machine learning-based speaker recognition via the flexible piezoelectric acoustic sensor (f-PAS) with a highly sensitive multi-resonant frequency band. The resonant self-powered f-PAS was fabricated by mimicking the operating mechanism of the basilar membrane in the human cochlear. The f-PAS acquired abundant voice information from the multi-channel sound inputs. The standard TIDIGITS dataset were recorded by the f-PAS and converted to frequency components by using a Fast Fourier Transform (FFT) and a Short-Time Fourier Transform (STFT). The machine learning based Gaussian Mixture Model (GMM) was designed by utilizing the most highest and second highest sensitivity data among multi-channel outputs, exhibiting outstanding speaker recognition rate of 97.5% with error rate reduction of 75% compared to that of the reference MEMS microphone.

关键词： Flexible piezoelectric Self-powered Acoustic sensor machine learning algorithm Speaker recognition

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machine learning-based in-line holographic sensing of unstained malaria-infected red blood cells

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JOURNAL OF BIOPHOTONICS 2018年第9期11卷 e201800101-e201800101页

作者： Go, Taesik Kim, Jun H. Byeon, Hyeokjun Lee, Sang J. Pohang Univ Sci & Technol Dept Mech Engn Pohang 37673 South Korea

Accurate and immediate diagnosis of malaria is important for medication of the infectious disease. Conventional methods for diagnosing malaria are time consuming and rely on the skill of experts. Therefore, an automatic and simple diagnostic modality is essential for healthcare in developing countries that lack the expertise of trained microscopists. In the present study, a new automatic sensing method using digital in-line holographic microscopy (DIHM) combined with machine learning algorithms was proposed to sensitively detect unstained malaria-infected red blood cells (iRBCs). To identify the RBC characteristics, 13 descriptors were extracted from segmented holograms of individual RBCs. Among the 13 descriptors, 10 features were highly statistically different between healthy RBCs (hRBCs) and iRBCs. Six machine learning algorithms were applied to effectively combine the dominant features and to greatly improve the diagnostic capacity of the present method. Among the classification models trained by the 6 tested algorithms, the model trained by the support vector machine (SVM) showed the best accuracy in separating hRBCs and iRBCs for training (n = 280, 96.78%) and testing sets (n = 120, 97.50%). This DIHM-based artificial intelligence methodology is simple and does not require blood staining. Thus, it will be beneficial and valuable in the diagnosis of malaria.

关键词： diagnosis digital holographic microscopy machine learning algorithm malaria

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Obstacle Avoidance algorithm for Indoor Mobility Robot Based on Improved Support Vector machine 2019

Obstacle Avoidance Algorithm for Indoor Mobility Robot Based...

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Proceedings of the 2019 The 2nd International Conference on Robotics, Control and Automation Engineering

作者： Jie Hao Northwest Minzu University School of Electrical Engineering Lanzhou China

ISBN: (纸本)9781450376228

With the development of artificial intelligence, robot will be applied to every aspect of our life. In the domain of robotics, especially indoor robotics, the ability of obstacle avoidance is one of the most important questions. In this paper, first of all, some classical machine learning algorithms are applied for indoor robot obstacle avoidance which is in the unknown environment based on multiple ultrasonic sensors. During the move, obstacles will be detected by sensors on the robot, through the machine learning algorithm, the robot will know how to do when the sensor detect an obstacle. Then, proposes an improved Support Vector machine(SVM) algorithm based on the Grid Search algorithm. This algorithm can accurately help robot find the trajectory for moving. Experimental results also presented to show the algorithm improved the generalization ability and real-time performance

关键词： SVM algorithm ultrasonic sensor obstacle avoidance Indoor mobile robot machine learning algorithm Grid Search

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Textual Content Categorizing Technology Development Based on Ontology 8

Textual Content Categorizing Technology Development Based on...

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8th International Conference on Mathematics, Information Technologies, Education (MoMLeT and DS)

作者： Lytvyn, Vasyl Vysotska, Victoria Rusyn, Bohdan Pohreliuk, Liubomyr Berezin, Pavlo Naum, Oleh Lviv Polytech Natl Univ Lvov Ukraine Silesian Tech Univ Gliwice Poland NAS Ukraine Karpenko Phys Mech Inst Kiev Ukraine Ivan Franko Drohobych State Pedag Univ Drogobych Ukraine

The methods and means of using ontologies within systems for the categorization of textual content were created. Also, a method for optimizing the definition of which rubrics best relate to a certain text content was developed. The intellectual system that will use the methods developed earlier, as well as other research results was implemented. The results will allow users to easily filter their text content. The system developed has intuitive user interface.

关键词： ontology content text categorization text content information technology computer science intellectual system intelligent system text content categorization user interface text document text classification information system machine learning algorithm user unfriendly interface content categorization system

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Performance Evaluation and Practical Use of Supervised Data Mining algorithms for Credit Card Approval

Performance Evaluation and Practical Use of Supervised Data ...

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作者： Lei Duan College of Engineering Northeastern University

Business analysis is using large volume of data to help people to make *** learning methods are widely used in bank system,especially in credit card *** goal is to use various data mining algorithms to predict whether a new customer credit scores or to detect credit *** will serve as the basis for the design of a new campaign because a transition from manual review to automatic review by computer could dramatically improve the work efficiency and reduce the labor *** tools such as self organizing map and algorithms could shed light on the self-evaluation for credit card applicants,which could raise efficiency.

关键词： credit card approval machine learning algorithm self-evaluation

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Embedding naïve Bayes algorithm data model in predicting student graduation 19

Embedding naïve Bayes algorithm data model in predicting st...

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Proceedings of the 3rd International Conference on Telecommunications and Communication Engineering

作者： Ace C. Lagman Joseph Q. Calleja Corazon G. Fernando Joseph G. Gonzales John Benedict Legaspi John Heland Jasper C. Ortega Ronel F. Ramos Maria Vicky S. Solomo Regina C. Santos FEU Institute of Technology Manila Philippines

ISBN: (纸本)9781450371803

In the Philippines, according to Philippine Authority of Statistics, there is an imbalance between the student enrollment and student graduation. Almost half of the first-time freshmen full time students who began seeking a bachelor's degree do not graduate on time. The study aims to utilize how Naïve Bayes algorithm - a data classification algorithm that is based on probabilistic analysis - can be used in educational data mining specifically in student graduation. The study is focused on the application of the Naïve Bayes algorithm in predicting student graduation by generating a model that could early predict and identify students who are prone of not having graduation on time, so proper remediation and retention policies can be formulated and implemented by institutions.

关键词： data mining machine learning algorithm big data analytics classification algorithm Naïve Bayes prediction student graduation

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Diagnosis of Alzheimer's Disease using machine learning 4

Diagnosis of Alzheimer's Disease using Machine Learning

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4th International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Lodha, Priyanka Talele, Ajay Degaonkar, Kishori Vishwakarma Inst Technol Dept EnTC Pune Maharashtra India

ISBN: (纸本)9781538652572

machine learning is being widely used in various medical fields. Advances in medical technologies have given access to better data for identifying symptoms of various diseases in early stages. Alzheimer's disease is chronic condition that leads to degeneration of brain cells leading at memory enervation. Patients with cognitive mental problems such as confusion and forgetfulness, also other symptoms including behavioral and psychological problems are further suggested having CT, MRI, PET, EEG, and other neuroimaging techniques. The aim of this paper is making use of machine learning algorithms to process this data obtained by neuroimaging technologies for detection of Alzheimer's in its primitive stage.

关键词： Linear Regression machine learning algorithm Alzheimer's disease symptoms Random Forest Gradient Boosting algorithm Neural Network

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Shorten Bipolarity Checklist for the Differentiation of Subtypes of Bipolar Disorder Using machine learning 2018

Shorten Bipolarity Checklist for the Differentiation of Subt...

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6th International Conference on Bioinformatics and Computational Biology (ICBCB)

作者： Feng, Chaonan Gao, Huimin Ling, Xuefeng B. Ji, Jun Ma, Yantao Qingdao Univ China Coll Comp Sci & Technol Qingdao Peoples R China Peking Univ Inst Mental Hlth Beijing Peoples R China Stanford Univ Dept Surg Stanford CA 94305 USA

ISBN: (纸本)9781450363488

The differentiation of type I and type II of bipolar disorder is difficult. In clinical practices, corresponding diagnostic operability is poor since their criterions are similar and do not include past or lifetime characteristics. The aim of this study was to generate the clinical feasible scale by using machine learning algorithms based on the analysis of a Chinese multi-center cohort data. To evaluate the importance of each item of Affective Disorder Evaluation(ADE), a case-control study of Chinese samples including 281 type I of bipolar disorder and 79 type II of bipolar disorder patients conducted from 9 Chinese health facilities participating in CAFE-BD. The novel scale was formed by selected items from ADE according to its importance calculated by mutual information criteria of minimal-redundancy-maximal-relevance(mRMR).

关键词： Bipolar disorder subtype ADE machine learning algorithm mRMR

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