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检索条件"主题词=Machine Learning Algorithms"

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Classificatin of Brain Tumors by machine learning algorithms

Classificatin of Brain Tumors by Machine Learning Algorithms

引用

International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

作者： Gökalp Çınarer Bülent Gürsel Emiroğlu Department Of Conputer Technologies Bozok Univercity Yozgat Turkey Department Of Conputer Engineering Kırıkkale Univercity Kırıkkale Turkey

Brain tumors are one of the most important causes of death among cancer types. Early and accurate diagnosis of brain tumor plays a key role in the successful implementation of the treatment. Nowadays, new technologies that increase the success rate of neurosurgery and prevent complications continue to develop. Magnetic resonance (MRI) technique is one of the most popular methods used to examine brain tumor images. There are many possible techniques and algorithms for the classification of images. The main purpose of machine learning and classification algorithms is to learn automatically from training and finally make a wise decision with high accuracy. In this study, the performances of tumor classification methods for the classification of MR brain image features as n/a, multifocal, multicentric and gliomatosis were analyzed. In the classification process, the statistical properties of the input images were analyzed and the data were systematically divided into various categories. These data were tested with KNN (k nearest neighbor), RF(random forest), SVM(support vector machines) and LDA(linear discriminant analysis) machine learning algorithms. SVM (support vector machines) algorithm with 90% accuracy rate was found to be better compared to other algorithms.

关键词： Support vector machines Tumors Classification algorithms machine learning algorithms Radio frequency Cancer Magnetic resonance imaging

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SMS Spam Filtering using Supervised machine learning algorithms 8

SMS Spam Filtering using Supervised Machine Learning Algorit...

引用

8th IEEE International Conference on Cloud Computing, Data Science and Engineering (Confluence) / Global Technology, Innovation and Enterpreneurship Summit

作者： Navaney, Pavas Dubey, Gaurav Rana, Ajay Amity Univ Noida Uttar Pradesh India

ISBN: (纸本)9781538617199

This paper presents detection of Spain and ham messages using various supervised machine learning algorithms like naive Bayes Algorithm, support vector machines algorithm, and the maximum entropy algorithm and compares their performance in filtering the Ham and Spam messages. As people indulge more in Web-based activities, and with rising sharing of private data by companies, SMS spam is very common. SMS spam filter inherits much functionality from E-mail Spam Filtering. Comparing the performance of various supervised learning algorithms we find the support vector machine algorithm gives us the most accurate result.

关键词： Entropy Support vector machines Classification algorithms Sparse matrices machine learning algorithms Cloud computing Mathematical model"

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Facial Recognition using machine learning algorithms on Raspberry Pi

Facial Recognition using Machine Learning Algorithms on Rasp...

引用

International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT)

作者： Seema Singh R. Ramya V. Sushma S.R. Roshini R. Pavithra BMS Institute of Technology and Management Bangalore Kamataka India

ISBN: (数字)9781728132617

ISBN: (纸本)9781728132624

Facial recognition is a non-invasive method of biometric authentication and useful for numerous applications. The real time implementation of the algorithm with adequate accuracy is required, with hardware timing into consideration. This paper deals with the implementation of machine learning algorithm for real time facial image recognition. Two dominant methods out of many facial recognition methods are discussed, simulated and implemented using Raspberry Pi. A rigorous comparative analysis is presented considering various limitations which may be the case required for innumerable application which utilize facial recognition. The drawbacks and different use cases of each method is highlighted. The facial recognition software uses algorithms to compare a digital image captured through a camera, to the stored face print so as to authenticate a person's identity. The Haar-Cascade method was one of the first methods developed for facial recognition. The HOG (Histogram of Oriented Gradients) method has worked very effectively for object recognition and thus suitable for facial recognition also. Both the methods are compared with Eigen feature-based face recognition algorithm. Various important features are experimented like speed of operation, lighting condition, frontal face profile, side profiles, distance of image, size of image etc. The facial recognition model is implemented to detect and recognize faces in real-time by means of Raspberry Pi and Pi camera for the user defined database in addition to the available databases.

关键词： Face recognition Face Histograms Feature extraction machine learning algorithms Databases Lighting

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Classification of Rheumatoid Arthritis using machine learning algorithms 4

Classification of Rheumatoid Arthritis using Machine Learnin...

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4th IEEE International Symposium in Robotics and Manufacturing Automation (ROMA)

作者： Ho, Sharon Elamvazuthi, I. Lu, C. K. Univ Teknol PETRONAS Dept Elect & Elect Engn Smart Assist & Rehabilitat Technol SMART Res Grp Perak Malaysia

ISBN: (纸本)9781728103747

Rheumatoid Arthritis (RA) is a persistent provocative ailment that effects and decimates the joints of wrist, finger, and feet. If left untreated, one can lose their ability to lead a normal life. RA is the most typical fiery joint inflammation, influencing around 1-2% of the total populace. Throughout the years, soft computing played an important part in helping ailment analysis in doctor's decision process. The main aim of this study is to investigate the possibility of applying machine learning techniques to the analysis of RA characteristics. As a preliminary work, a credible database has been identified to be used for this research. The database has outputs of array temperature values from thermal imaging for the joints of hand. Furthermore, this database which consists of 8 attributes and 32 instances, are used to determine the performance in terms of accuracy for the classification of different algorithms. In this preliminary work, algorithms such as bagging, J48 and AdaBoost classifiers were trained and tested using the assessment criteria such as accuracy, specificity and sensitivity using Weka tool. From the preliminary finding of this paper, it can be concluded that bagging has better classification accuracy over the others for rheumatoid arthritis dataset.

关键词： Classification Rheumatoid Arthritis machine learning algorithms

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Major earthquake event prediction using various machine learning algorithms

Major earthquake event prediction using various machine lear...

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International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)

作者： Roxane Mallouhy Chady Abou Jaoude Christophe Guyeux Abdallah Makhoul College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al Khobar Kingdom of Saudi Arabia Ticket Lab Antonine University Lebanon FEMTO-ST Institute UMR 6174 CNRS University of Bourgogne Franche-Comté Belfort France

ISBN: (数字)9781728149202

ISBN: (纸本)9781728149219

At least two basic categories of earthquake prediction exist: Short-term predictions and forecast ones. Short term earthquake predictions are made hours or days in advance, while forecasts are predicted months to years in advance. The majority of studies are done on forecast, taking into consideration the history of earthquakes in specific countries and areas. In this context, the core idea of this work is to predict whereas an event is classified as negative or positive major earthquake by applying different machine learning algorithms. Eight different algorithms have been applied on a real earthquake dataset, namely: Random Forest, Naive Bayes, Logistic Regression, MultiLayer Perceptron, AdaBoost, K-nearest neighbors, Support Vector machine, and Classification and Regression Trees. For each selected model, various hyperparameters have been selected, and obtained prediction results have been fairly compared using various metrics, leading to a reliable prediction of major events for 3 of them.

关键词： Earthquakes Vegetation machine learning algorithms Prediction algorithms machine learning Probability Logistics

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Analysis of machine learning algorithms for Predicting Personality: Brief Survey and Experimentation

Analysis of Machine Learning Algorithms for Predicting Perso...

引用

Global Conference for Advancement in Technology (GCAT)

作者： Aditi V. Kunte Suja Panicker School of computer science and engineering Dr.Vishwanath Karad MIT World Peace University Pune India

ISBN: (纸本)9781728136950

Use of social media is increasing day by day. A huge amount of textual data, as well as images, continue to explode to the web daily. Personality prediction is an evergreen area of research. Predicting personality with the help of data through social media is a promising approach as this method does not require any questionnaires to be filled by users thus reducing time and increasing credibility. In the current work we have fetched real-time Twitter data through a standard API and thereby created a sample experimental dataset. We have employed several popular machine learning algorithms such as kNN, Naive Bayes and SVM. It was observed that Naive Bayes yielded the highest accuracy of 71.67% as compared to the other classifiers.

关键词： application program interfaces learning (artificial intelligence) pattern classification social networking (online) support vector machines Application programming interfaces Support Vector Network machine learning algorithms Pattern recognition Social Media experimentation Personality Bayes

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COMPARISON OF machine learning algorithms AND WATER CLOUD MODEL FOR LEAF AREA INDEX ESTIMATION OVER CORN FIELDS

COMPARISON OF MACHINE LEARNING ALGORITHMS AND WATER CLOUD MO...

引用

IEEE International Geoscience and Remote Sensing Symposium

作者： Mehdi Hosseini Heather McNairn Scott Mitchell Andrew Davidson Laura Dingle Robertson Geomatics and Landscape Ecology Laboratory Carleton University Ottawa Canada Agriculture and Agri-Food Canada Ottawa Research and Development Centre Ottawa Canada

The Water Cloud Model (WCM) has been widely used for estimation of Leaf Area Index (LAI) from Synthetic Aperture Radar (SAR). In different studies, it was demonstrated that this model performs well if it is calibrated well. However, calibration of this model requires access to both LAI and soil moisture for the calibration points. An alternative, if the soil moisture data are not available, is machine learning (ML) algorithms. However, ML methods are highly dependent on the number of calibration points. In this study, 6 different ML algorithms including Neural Network (NN), Support Vector machine (SVM), Ensemble of Trees (ET), Regression Tree (RT), Radial Basis Function (RBF) and Gaussian Process Model (GPM) are used and compared with the WCM model for estimation of LAI over corn fields. This comparison was done using different numbers of calibration points. The results demonstrated that when a lower number of calibration points are used, WCM outperformed some of the ML algorithms including NN, SVM and ET algorithms. But with more calibration points, all machine learning algorithms outperformed the WCM. The highest accuracies were from the GPM model with a correlation coefficient (R) of 0.93, Root Mean Square (RMSE) of 0.56 m~2m~(-2) and Mean Absolute Error (MAE) of 0.38 m~2m~(-2). Theses results were derived using the data collected during the SMAP Validation Experiment 2012 (SMAPVEX12) that was conducted in Manitoba, Canada. Further testing and comparison of the ML algorithms and WCM model using data from other Joint Experiment for Crop Assessment and Monitoring (JECAM) sites are ongoing.

关键词： machine learning algorithms Estimation Calibration Biological system modeling Soil moisture Clouds Agriculture

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Detection of Stroke Disease using machine learning algorithms

Detection of Stroke Disease using Machine Learning Algorithm...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： Tasfia Ismail Shoily Tajul Islam Sumaiya Jannat Sharmin Akter Tanna Taslima Mostafa Alif Romana Rahman Ema North Western University Khulna Bangladesh

A stroke is a medical condition in which poor blood flow to the brain results in cell death. It is now a day a leading cause of death all over the world. Several risk factors believe to be related to the cause of stroke has been found by inspecting the affected individuals. Using these risk factors, a number of works have been carried out for predicting and classifying stroke diseases. Most of the models are based on data mining and machine learning algorithms. In this work, we have used four machine learning algorithms to detect the type of stroke that can possibly occur or occurred form a person's physical state and medical report data. We have collected a good number of entries from the hospitals and use them to solve our problem. The classification result shows that the result is satisfactory and can be used in real time medical report. We believe that machine learning algorithms can help better understanding of diseases and can be a good healthcare companion.

关键词： Stroke (medical condition) Classification algorithms Decision trees machine learning algorithms Hemorrhaging Diseases Random forests

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ESTIMATING SOIL MOISTURE FROM C AND X BAND SAR USING machine learning algorithms AND COMPACT POLARIMETRY 38

ESTIMATING SOIL MOISTURE FROM C AND X BAND SAR USING MACHINE...

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38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Santi, E. Pettinato, S. Paloscia, S. Dabboor, M. Notarnicola, C. Padovano, A. Greifeneder, F. Cuozzo, G. CNR IFAC Via Madonna del Piano 10 I-50019 Florence Italy Govt Canada Environm & Climate Change Canada Sci & Technol Branch Dorval PQ Canada EURAC Res Inst Earth Observat Viale Druso 1 I-39100 Bolzano Italy

ISBN: (纸本)9781538671504

This research aims at exploiting the integration of C- and X-band SAR data for the monitoring of Soil Moisture Content (SMC). Time series of Radarsat2 (RS2) and COSMOSkyMed (CSK) images are collected on two test areas, located in Italy and in Canada. The backscattering sensitivity to SMC measured by in- situ stations is investigated considering the available sensor frequencies and polarizations. In addition, for exploiting the potential of fully polarimetric acquisitions of RS2, simulated Compact Polarimetric (CP) data are computed by using a Radarsat Constellation Mission (RCM) data simulator, and their sensitivity to the target SMC is examined. Based on the experimental findings, two machine learning (ML) approaches to the SMC retrieval, namely Support Vector Regression (SVR) and Artificial Neural Network (ANN) are implemented and tested on the two areas. Looking at the preliminary results, the integration of X- and C- band images does provide valuable information for the retrieval of SMC, while the simulated CP parameters exhibit a certain sensitivity to SMC. On the South Tyrol test area, both SVR and ANN tested with different combinations of RS2 and CSK data were able to retrieve SMC with a RMSE between 2% and 4% of SMC and correlation coefficient R between 0.85 and 0.97, depending on the combination of inputs. The application of the ML algorithms to the other available images on the Mazia test area and the implementation of the ML retrieval algorithm using CP data are still under investigation.

关键词： COSMO-SkyMed Radarsat2 agricultural areas soil moisture retrieval machine learning algorithms RCM compact polarimetry

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Comparisons of machine learning algorithms for Driving Behavior Recognition Using In-Vehicle CAN Bus Data

Comparisons of Machine Learning Algorithms for Driving Behav...

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International Conference on Informatics, Electronics & Vision (ICIEV)

作者： Wen-Hui Chen Yu-Chen Lin Wei-Hao Chen Graduate Institute of Automation Technology National Taipei University of Technology Taipei Taiwan Department of Automatic Control Engineering Feng Chia University Taichung Taiwan

Driving behavior recognition is an active research topic as it has many potential applications, such as fleet management, vehicle anti-theft, and planning of car insurance policies. Nowadays, the most successful approaches to driving behavior recognition are based on machine learning algorithms. Each machine learning algorithm has its pros and cons, and no single algorithm fits all problems. Therefore, how to determine an appropriate algorithm suitable for discovering driving patterns is a critical step in driving behavior recognition. This paper aims to conduct an empirical study for driving behavior recognition and evaluate the recognition performance of popular machine-learning algorithms. The experimental results showed that many sensor values gathered from the CAN bus are either highly correlated with one another or less important attributed to driving behavior identification. Among traditional machine learning approaches, ensemble tree-based algorithms, such as Random Forests and Decision Trees have better performance when compared with other approaches.

关键词： Wheels machine learning algorithms Engines Torque Smart phones Distributed Bragg reflectors machine learning

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