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检索条件"主题词=Machine Learning Algorithms"

共 26130 条记录，以下是1981-1990 订阅

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Comparison of machine learning algorithms for breast cancer

Comparison of machine learning algorithms for breast cancer

引用

International Conference on Inventive Computation Technologies (ICICT)

作者： Palli Suryachandra P Venkata Subba Reddy CSSE Department SVEC Tirupathi Andhra Pradesh CSE Department SVUCE SV University Tirupati Andhra Pradesh

ISBN: (纸本)9781509012862

machine learning algorithms are computer programs that try to predict cancer type based on the past data. The eventual goal of machine learning algorithms in cancer diagnosis is to have a trained machine learning algorithm that gives the gene expression levels from cancer patient, can accurately predict what type and severity of cancer they have, aiding the doctor in treating it. The existing technology compares three different machine learning algorithms are Decision Tree, Support Vector machine, Bayesian Belief Network. The main drawback of these algorithms is unusual because the number of features (gene expressions) far exceeds the number of cases (samples taken from patients). Performance efficiency can be achieved by comparing two more algorithms are Random Forest and Naïve Bayes algorithms. Because Random forest and Naïve Bayes are used as feature selection method, Random Forest is used to rank the feature importance and applied for relevant feedback. The requirements are weka tool, Java and Relational Database.

关键词： Breast cancer machine learning algorithms Prediction algorithms Algorithm design and analysis Classification algorithms Training

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A Python Framework for Exhaustive machine learning algorithms and Features Evaluations

A Python Framework for Exhaustive Machine Learning Algorithm...

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International Conference on Advanced Information Networking and Applications (AINA)

作者： Fabien Dubosson Stefano Bromuri Michael Schumacher AISLab HES-SO Valais//Wallis Management Science and Technology Open University Netherlands

ISBN: (纸本)9781509018598

machine learning domain has grown quickly the last few years, in particular in the mobile eHealth domain. In the context of the DINAMO project, we aimed to detect hypoglycemia on Type 1 diabetes patients by using their ECG, recorded with a sport-like chest belt. In order to know if the data contain enough information for this classification task, we needed to apply and evaluate machine learning algorithms on several kinds of features. We have built a Python toolbox for this reason. It is built on top of the scikit-learn toolbox and it allows evaluating a defined set of machine learning algorithms on a defined set of features extractors, taking care of applying good machine learning techniques such as cross-validation or parameters grid-search. The resulting framework can be used as a first analysis toolbox to investigate the potential of the data. It can also be used to fine-tune parameters of machine learning algorithms or parameters of features extractors. In this paper we explain the motivation of such a framework, we present its structure and we show a case study presenting negative results that we could quickly spot using our toolbox.

关键词： Feature extraction machine learning algorithms Biomedical monitoring Electrocardiography Diabetes Data mining

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Dot-product engine as computing memory to accelerate machine learning algorithms

Dot-product engine as computing memory to accelerate machine...

引用

IEEE International Symposium on Quality Electronic Design

作者： Miao Hu John Paul Strachan Zhiyong Li R. Stanley Williams Hewlett Packard Labs Palo Alto CA USA

Currently, intense work is underway to develop memristor crossbar arrays for high density, nonvolatile memory applications. However, another capability of memristor crossbars - natural dot-product operation for vectors and matrices - holds even greater potential for next-generation computing, including accelerators, neuromorphic computing, and heterogeneous computing. In this paper, we present a dot-product engine (DPE) based on memristor crossbars optimized for dense matrix computation, which is dominated in most machine learning algorithms. We explored multiple methods to enhance DPE's dot-product computing accuracy. Moreover, instead of training crossbars, we try to directly use existing software-trained weight matrices on DPEs so no heroic effort is needed to innovate learning algorithms for new hardware. Our results show that computations utilizing DPEs can achieve 1000 ~ 10000 times better speed-efficiency product comparing to a state-of-art ASIC [1]. And machine learning algorithm utilizing DPEs can easily achieve software-level accuracy on testing. Both experimental demonstrations and data-calibrated circuit simulations are presented to demonstrate the realistic implementation of a memristor crossbar DPE.

关键词： Memristors Resistance Wires Programming machine learning algorithms Algorithm design and analysis Degradation

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A quick view on current techniques and machine learning algorithms for big data analytics

A quick view on current techniques and machine learning algo...

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International Conference on Transparent Optical Networks

作者： Josep Lluis Berral-García Barcelona Supercomputing Center Barcelona Spain

ISBN: (纸本)9781509014682

Big-data is an excellent source of knowledge and information from our systems and clients, but dealing with such amount of data requires automation, and this brings us to data mining and machine learning techniques. In the ICT sector, as in many other sectors of research and industry, platforms and tools are being served and developed in order to help professionals to treat their data and learn from it automatically; most of those platforms coming from big companies like Google or Microsoft, or from incubators at the Apache Foundation. This brief review explains the basics of machine learning with some ICT examples, and enumerates some (but not all) of the most used tools for analyzing and modelling big-data.

关键词： Data mining Clustering algorithms Data models Prediction algorithms Libraries machine learning algorithms Classification algorithms

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Fall detection using machine learning algorithms

Fall detection using machine learning algorithms

引用

International Conference on Software, Telecommunications and Computer Networks (SoftCOM)

作者： Pranesh Vallabh Reza Malekian Ning Ye Dijana Capeska Bogatinoska Department of Electrical Electronic and Computer Engineering University of Pretoria Pretoria South Africa Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing Jiangsu Province China College of Computer Nanjing University of Posts and Telecommunications Nanjing China Faculty of Computer Science and Engineering University of Information Science and Technology “St. Paul the Apostle” Ohrid Republic of Macedonia

ISBN: (纸本)9781509025787

In this paper, the recognition of and the differentiation between fall activities and activities of daily living (ADL) was performed using the MobiFall dataset. A large database was constructed to train and validate the model. Feature selection methods were implemented to reduce dimensionality. Five different classification algorithms were implemented and evaluated based on their accuracy' sensitivity, and specificity achieved. The k-Nearest Neighbors' algorithm obtained an overall accuracy of 87.5% with a sensitivity of 90.70%, and a specificity of 83.78%.

关键词： Feature extraction Accelerometers Sensors Hidden Markov models machine learning algorithms Gyroscopes Acceleration

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Citizen security using machine learning algorithms through open data

Citizen security using machine learning algorithms through o...

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IEEE Latin-American Conference on Communications, LATINCOM

作者： Gusseppe Bravo Rocca Manuel Castillo-Cara Raul Arias Levano Javier Villegas Herrera Luis Orozco-Barbosa Computer Science School-Sciences Faculty Universidad Nacional de Ingeniería Perú Albacete Research Institute of Informatics Campus Universitario s/n Albacete Spain

ISBN: (纸本)9781509051380

The following work is an application proposal based on machine learning algorithms for a possible solution for the public safety problem in a South American city. The aim of this application is to reduce the threat risk of the physical integrity of pedestrians by geolocating, in real-time, safer places to walk. In this context for a city, San Isidro, a business district of Lima, has been established as study case. The district has been divided into map sectors and subsectors, so that by using the GPS location service integrated in mobile devices, it is possible to identify areas that have the highest incidence of different types of incidents. This functionality will allow users to choose safer routes by taking into account the information provided for each sector. The data used in this application has been obtained from an Open Data platform managed by the San Isidro municipality. In this application, we have processed the data enabling the easy and friendly access to the information by the end user. The importance of this work is how we have used the machine learning algorithm for incident rates in real and future time, trying to make predictions that can not only provide safe routes to users, but also predict disasters and allow public authorities to act in advance, thus minimizing the impact of future incidents.

关键词： Automobiles Vehicle crash testing Urban areas machine learning algorithms Correlation Prediction algorithms

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Fall recognition using wearable technologies and machine learning algorithms

Fall recognition using wearable technologies and machine lea...

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IEEE International Conference on Big Data

作者： Austin Harris Hanna True Zhen Hu Jin Cho Nancy Fell Mina Sartipi Department of Computer Science and Engineering University of Tennessee at Chattanooga Chattanooga TN USA Department of Physical Therapy University of Tennessee at Chattanooga Chattanooga TN USA

ISBN: (纸本)9781467390064

Falls are common and dangerous for the elderly or individuals with decreased independence or functional limitations. Fall recognition is extremely important for fallers, healthcare providers, and society. Immediate fall recognition triggers emergency services and potentially decreases individuals time with injury without care. Acute post-fall intervention works to mitigate life threatening fall consequences, decrease fall risk through rehabilitation, and improve quality of life. Extended from our research on real-time fall risk estimation with the functional reach test and Timed Up and Go test built in mStroke, a real-time and automatic mobile health system for post-stroke recovery and rehabilitation, our investigation here is expanded to include fall recognition by taking advantage of wearable technologies and machine learning algorithms. Up to three wearable sensors are employed to acquire raw motion data related to activities of daily living or falls. Feature selection and classification on the basis of machine learning algorithms are explored for fall recognition. The fall recognition performances are presented to justify their accuracy and reliability. Meanwhile, the effects of sensor placement/location and the feature number on the recognition performance are also discussed in this paper.

关键词： Wearable sensors machine learning algorithms Real-time systems Legged locomotion Data analysis Sternum Robustness

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An approach to improve flexible manufacturing systems with machine learning algorithms

An approach to improve flexible manufacturing systems with m...

引用

Annual Conference of Industrial Electronics Society

作者： Hang Li Institute of Automation and Information System Technische Universität München Garching near Munich Germany

The electricity consumption in the industry occupies considerable ratio in the gross electricity consumption compared with the consumption in other sectors e.g. residential, agriculture etc. One crucial solution to this problem is to optimize the production structure. The grand plan “Industry 4.0” provides a more adaptable and flexible perspective for the smart factory. The complexity of a manufacturing system, on the other hand, has been enhanced. machine learning algorithms are a cluster of excellent approaches to control a complex system and to optimize a stochastic process. In order to improve the performance of a production system, it must be formulated to an executive model at first, then the optional control policies can be selected to cope with it. In this paper, the classification algorithm and the Q-learning algorithm have been implemented to reduce the electricity consumption in an automation system. The simulation results prove that they are capable for manipulating the multi routes transporting system and the system can performance better with the implementation of the machine learning algorithms.

关键词： Belts machine learning algorithms Manufacturing systems Flexible manufacturing systems Energy consumption Industries

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Developing Novel machine learning algorithms to Improve Sedentary Assessment for Youth Health Enhancement

Developing Novel Machine Learning Algorithms to Improve Sede...

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IEEE International Conference on Healthcare Informatics (ICHI)

作者： Gowtham Kumar Golla Jordan A. Carlson Jun Huan Jacqueline Kerr Tarrah Mitchell Kelsey Borner Department of Electrical Engineering and Computer Science University of Kansas Jayhawk Blvd Lawrence KS Center for Children's Healthy Lifestyles and Nutrition Children's Mercy Hospital Department of Family Medicine and Public Health University of California San Diego CA Department of Psychology University of Kansas Lawrence KS

ISBN: (纸本)9781509061181

Sedentary behavior of youth is an important determinant of health. However, better measures are needed to improve understanding of this relationship and the mechanisms at play, as well as to evaluate health promotion interventions. Wearable accelerometers are considered as the standard for assessing physical activity in research, but do not perform well for assessing posture (i.e., sitting vs. standing), a critical component of sedentary behavior. The machine learning algorithms that we propose for assessing sedentary behavior will allow us to re-examine existing accelerometer data to better understand the association between sedentary time and health in various populations. We collected two datasets, a laboratory-controlled dataset and a free-living dataset. We trained machine learning classifiers separately on each dataset and compared performance across datasets. The classifiers predict five postures: sit, stand, sit-stand, stand-sit, and stand\walk. We compared a manually constructed Hidden Markov model (HMM) with an automated HMM from existing software. The manually constructed HMM gave more F1-Macro score on both datasets.

关键词： Hidden Markov models machine learning algorithms Accelerometers Algorithm design and analysis Standards Prediction algorithms Training

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Combining Crop Proportion Phenology Index models with machine learning algorithms for estimating winter wheat areas

Combining Crop Proportion Phenology Index models with machin...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： L. Li Y.Z. Pan Q.C. Xin College of Resources Science and Technology Beijing Normal University Beijing China Department of Geography and Planning Sun Yat-sen University Guangzhou China

Monitoring crop areas is a key issue in remote sensing studies. A Crop Proportion Phenology Index (CPPI) model has previously been developed for estimation of winter wheat areas. Here we test the CPPI model in different areas using remote sensing data for varied kernel functions, including linear regression (LR), Artificial Neural Network (ANN), and Support Vector Regression (SVR). The differences of the model performances among different kernel functions were found to be small for areas with simple planting structure. For areas where multiple crop types have similar phenology cycles, the non-linear model of ANN was found to perform the best. This study indicates that the CPPI model can be applied to map winter wheat distribution in areas with complex planting structures, thus it holds promises for estimating fractional areas of winter wheat areas over large geographic areas.

关键词： Agriculture Remote sensing Kernel Time series analysis MODIS machine learning algorithms Artificial neural networks

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