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检索条件"主题词=Naive Bayes Algorithm"

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Synchrophasor-based Data Mining for Power System Fault Analysis

Synchrophasor-based Data Mining for Power System Fault Analy...

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3rd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies Europe (ISGT Europe)

作者： Al Karim, Miftah Chenine, Moustafa Zhu, Kun Nordstroem, Lars KTH Royal Inst Technol Sch Elect Engn Dept Ind Informat & Control Syst SE-10044 Stockholm Sweden

ISBN: (纸本)9781467325950;9781467325967

Phasor measurement units can provide high resolution and synchronized power system data, which can be effectively utilized for the implementation of data mining techniques. Data mining, based on pattern recognition algorithms can be of significant help for power system analysis, as high definition data is often complex to comprehend. In this paper three pattern recognition algorithms are applied to perform the data mining tasks. The deployment is carried out firstly for fault data classification, secondly for checking which faults are occurring more frequently and thirdly for identifying the root cause of a fault by clustering the parameters behind each scenario. For such purposes three algorithms are chosen, k-Nearest Neighbor, naive bayes and the k-means Clustering.

关键词： Clustering algorithm k-Nearest Neighbor algorithm naive bayes algorithm Phasor measurement Unit pattern recognition power system faults

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Countries Condition of Forestation and Trees Percentage using Machine learning 8

Countries Condition of Forestation and Trees Percentage usin...

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8th International Conference on System Modeling and Advancement in Research Trends (SMART)

作者： Abdullha, Abir Habib, Yeasin Masum, Md Raisul Islam Rabby, A. K. M. Shahariar Azad Daffodil Int Univ Dept CSE Dhaka Bangladesh Apurba Technol Dhaka Bangladesh

ISBN: (纸本)9781728132457

Most countries are now in a dangerous place for forestation and some are in developed forestation. So forestation and trees percentage prediction are to predict the condition of the countries about their condition of forestation and tress percentage. The paper is about a machine learning model to predict the countries condition. We used logistic regression, SVM AND naive bayes to predict the condition also for matrix. we also find the accuracy of logistic regression, SVM, Nave bayes, Ada boosting classifier, Decision tree, ANN, Linear Discriminant Analysis, Gradient Boosting Classifier, MLP Classifier to find our best accuracy and compare with them with our data. we give details of selected algorithms. We collected some previous data and present data and comparing them to predict the condition of the country. we use some conditions and logic for machine learning. By logistic regression, SVM and Nave bayes will show us the prediction and condition of those chosen countries.

关键词： Logistics algorithm SVM algorithm naive bayes algorithm Details About algorithms Compare algorithm Accuracy Condition of Countries in Forestation

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Action-based scheduling technique for 802.15.4/ZigBee wireless body area networks

Action-based scheduling technique for 802.15.4/ZigBee wirele...

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22nd IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Abouzar, Pooyan Shafiee, Kaveh Michelson, David G. Leung, Victor C. M. Univ British Columbia Dept Elect & Comp Engn Vancouver BC V5Z 1M9 Canada

ISBN: (纸本)9781457713484

Energy-efficient communication protocols in resource-constrained networks and specifically wireless body area networks (WBANs) has been of significant importance since they emerged nearly last decade. In this work, we use the periodic nature of body actions to propose an action-based scheduling technique in which time-slot allocations are adapted to the periodic connectedness of on-body links. In other words, the periodicity of on-body links is employed to predict the future behaviours of links to help develop energy-efficient communications between on-body nodes, thereby elongating the network lifetime. Analysis and measurement with 2.4GHz IEEE 802.15.4/ZigBee compliant micaZ motes in a fitness environment serve as our tool to do action recognition and subsequently scheduling. The proposed technique helps us reach within less than 7% of power consumption lower bound while it does not have complexity of most channel prediction algorithms that can result in excessive process power consumption.

关键词： Body area networks 802.15.4/ZigBee action classification feature extraction naive bayes algorithm connectivity periodicity scheduling technique

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Exploration of Autism Spectrum Disorder using Classification algorithms 2nd

Exploration of Autism Spectrum Disorder using Classification...

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2nd International Conference on Recent Trends in Advanced Computing, Disruptive Innovation (ICRTAC-DTI)

作者： Deepa, B. Marseline, Jeen K. S. Sri Krishna Arts & Sci Coll Coimbatore 8 Tamil Nadu India

Data mining plays a vital role in identifying the classification algorithm for classification and prediction of disease in medical field. Predicting disease depends on the accuracy of the dataset and mechanism of machine learning algorithms used to classify the dataset. To detect new born with risk of autism spectrum disorder (ASD) which is predominantly diagnosing the behavioral observation of children with above 2 years. Although identifying brain disease using magnetic resonance imaging (MRI) available in the last few years it is time consuming and very expensive. Early stage of predicting autism disorder done with the help of using machine learning algorithms. This paper criticize various classification algorithms then evaluates the accuracy and then selects the best classification algorithm to analyze and classify each individual who gets affect from autism spectrum disorder (ASD) in early stage. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： Data mining naive bayes algorithm Decision table Confusion Matrix Autism Support Vector Machine

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Design of a Novel Wireless Power System Using Machine Learning Techniques for Drone Applications

Design of a Novel Wireless Power System Using Machine Learni...

引用

IEEE Wireless Power Transfer Conference (WPTC)

作者： Jeong, Soyeon Bito, Jo Tentzeris, Manos M. Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

ISBN: (纸本)9781509045853

In this paper, the design of a novel wireless power transfer system utilizing drones with machine learning techniques is presented. Research on drones is currently a fast growing field with a great potential in many ubiquitous applications. The wireless power transfer system with the fixed operation frequency at 13.56MHz is applied to an 1-coil receiver on the drone, with an array of transmitter coils on the ground. This work presents an approach where the data is considered "classified" using machine learning techniques, which allows the accurate prediction of the drone's position, thus enhancing the wireless power transfer efficiency.

关键词： Wireless power transmission magnetic resonant inductive coupling drones machine learning classification naive bayes algorithm

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Text Mining Research of Ancient Medical Records of Dysmenorrhea Based on Syndrome Prediction Modeling 4

Text Mining Research of Ancient Medical Records of Dysmenorr...

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4th International Symposium on Artificial Intelligence for Medicine Science (ISAIMS)

作者： Wang, Jiaqi Wang, Chunyan Wang, Ruixiang Shandong Univ Tradit Chinese Med Inst Chinese Med Literature & Culture 4655 Daxue Rd Jinan 250355 Peoples R China Shandong Univ Tradit Chinese Med Inst Management 4655 Daxue Rd Jinan 250355 Peoples R China

ISBN: (纸本)9798400708138

This study aims to supplement the incomplete descriptions of dysmenorrheal syndrome types in ancient medical records and tap into the tacit knowledge in ancient medical records. Based on the dysmenorrhea medical record using the naive bayesian algorithm to establish the syndrome-type prediction model, its diagnostic accuracy is 73.77%. The model is applied to 20 medical records and found that three predicted syndrome types differ from the expert judgment. After a comprehensive analysis, this difference reflects the tacit knowledge in the text. The results show that the syndrome prediction model can assist experts in studying and understanding ancient medical records, make tacit knowledge explicit, and provide a reference for modern clinical diagnosis and treatment.

关键词： Dysmenorrhea TCM syndrome Ancient medical records Tacit knowledge naive bayes algorithm

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Adaptive Testing Tool for Students with Dyslexia

Adaptive Testing Tool for Students with Dyslexia

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China-Qatar International Workshop on Artificial Intelligence and Applications to Intelligent Manufacturing (AIAIM)

作者： Gupta, Ravisha Coll Engn Pune Pune 411005 Maharashtra India

ISBN: (纸本)9781538664452

Dyslexia is a neurological and lifetime condition that is often inherited. One out of ten people have some or the other traits of dyslexia, and because of this around 20% face difficulties in reading and writing. Appropriate guidance provided to students with dyslexia at an early age can reduce the problems faced by them drastically. With massive strides in technology, a digital solution to this problem can prove to be more effective and efficient. For this purpose, a helping aid in the form of an android application is devised. It will help to fasten the learning process among these students who come across a wide variety of digital tools and are more prone to get attracted to such tools for learning than the standard pen-paper tools. This application will help the user to master writing by practicing alphabets, numbers and words.

关键词： Dyslexia machine learning naive bayes algorithm adaptive learning scoring algorithm

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Anticipation of Winning Probability in Poker Using Data Mining

Anticipation of Winning Probability in Poker Using Data Mini...

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IEEE International Conference on Computer, Communication and Control (IC4)

作者： Ambekar, Gaurav Sheth, Shiben Chikane, Tushar Sable, Abhilasha Ghag, Kranti Shah & Anchor Kutchhi Engn Coll Dept Informat Technol Bombay Maharashtra India

ISBN: (纸本)9781479981632

Poker is one of the world's most popular and widely played card games. In Poker, there is a fixed set of winning conditions and the player with the highest winning condition wins the game. The main part of the game is to bet strategically and in a calculated manner so that there is less chance of risk and the opponents are not able to guess the cards in the hand. To help players understand when and how to bet smartly, this application will be developed. This system provides knowledge to the users about their probability of winning based on the cards available to them. The system which has been developed is lightweight and easy-to-use so that all types of players can use it. The aim of this system is to help gamblers bet better thereby increasing their winnings, addiction to Poker gambling and also generate greater revenue collections for gaming consortiums. The most important point of this paper is to show how we have used data mining and statistical probabilities to formulate an algorithm which gives out correct predictions of the winning hand. We formally define the system and outline the challenges that arose while developing technology to support it. We hope that this paper will encourage more research by the gaming consortiums and the gambling community in this exciting area of winning by probability calculations and card counting.

关键词： Data mining Machine learning Poker Winning Probability naive bayes algorithm

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A Review on Disease Detection from Medical Images using Machine Learning 6

A Review on Disease Detection from Medical Images using Mach...

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6th International Conference on Trends in Electronics and Informatics (ICOEI)

作者： Mim, Tanjima Akhanda Rimi, Tanzina Afroz Daffodil Int Univ Dept Comp Sci & Engn Dhaka Bangladesh

ISBN: (纸本)9781665483285;9781665483292

As a result of developments in imaging and processing, artificial intelligence may be used to perform a variety of radiological imaging tasks, including risk assessment, detection, diagnosis, prognosis, and therapy response, as well as multi-omics illness discovery. After reading this succinct overview of the topic, the reader will be able to recognize the nomenclature, numerous subfields, and components of machine learning, as well as its therapeutic potential. Radiomics is described as the process of transforming pictures into usable data. It is a subset of computer-aided diagnostics. Quantitative radiomics' ultimate purpose is to either give projected image-based illness phenotypes for precision medicine or to provide quantitative image-based phenotypes for data mining in concert with other-omics for discovery (i.e., imaging genomics). Due to the complexity of deep networks, the constant improvement of computer software and hardware, and the difficulty of detecting minute changes in disease states versus variations in common things, well-annotated enormous data sets are required for deep learning to succeed in radiology. With imaging exams performed often in clinical practice, machine learning in radiology is predicted to have a significant clinical influence in the future, offering improved decision support during medical image interpretation. The term "decision support" is crucial since it implies that computers will assist people in making better decisions. Radiologists may be able to use computers in their everyday work to help them work more effectively with colleagues from other medical fields, which could lead to better precision medicine.

关键词： Artificial Neural Networks (ANN) Artificial Intelligence Machine Learning and a variety of machine learning algorithms Including K-Nearest Neighbors Support Vector Machines(SVM) Decision Trees naive bayes algorithm Deep Learning Convolutional Neural Networks (CNN)

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Diagnosis of Coronary Artery Disease Using Cost-Sensitive algorithms

Diagnosis of Coronary Artery Disease Using Cost-Sensitive Al...

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12th IEEE International Conference on Data Mining (ICDM)

作者： Alizadehsani, Roohallah Hosseini, Mohammad Javad Sani, Zahra Alizadeh Ghandeharioun, Asma Boghrati, Reihane Sharif Univ Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9780769549255;9781467351645

One of the main causes of death the world over are cardiovascular diseases, of which coronary artery disease (CAD) is a major type. This disease occurs when the diameter narrowing of one of the left anterior descending, left circumflex, or right coronary arteries is equal to or greater than 50 percent. Angiography is the principal diagnostic modality for the stenosis of heart vessels;however, because of its complications and costs, researchers are looking for alternative methods such as data mining. This study conducts data mining algorithms on the Z-Alizadeh Sani dataset which has been collected from 303 random visitors to Tehran's Shaheed Rajaei Cardiovascular, Medical and Research Center. In this paper, the reason of effectiveness of a preprocessing algorithm on the dataset is investigated. This algorithm which has been merely introduced in our previous works, extracts three new features from the dataset. These features are then used to enrich the primary dataset in order to achieve more accurate results. Moreover, despite the fact that misclassification of diseased patients has more side effects than that of healthy ones, to the best of our knowledge cost-sensitive algorithms have yet to be used in this field. Therefore, in this paper 10-fold cross validation on cost-sensitive algorithms along with base classifiers of naive bayes, Sequential Minimal Optimization (SMO), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and C4.5 were employed. As a result, the SMO algorithm has yield to very high sensitivity (97.22%) and accuracy (92.09%) rates, the likes of which have not been reported simultaneously in the existing literature.

关键词： component naive bayes algorithm Data Mining Coronary Artery Disease C4.5 algorithm Cost Sensitive algorithms Feature Extraction

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