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检索条件"主题词=Machine Learning Algorithms"

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Travel Time Prediction: Comparison of machine learning algorithms in a Case Study 20

Travel Time Prediction: Comparison of Machine Learning Algor...

引用

20th IEEE International Conference on High Performance Computing and Communications (HPCC) / 16th IEEE International Conference on Smart City (SmartCity) / 4th IEEE International Conference on Data Science and Systems (DSS)

作者： Goudarzi, Forough Brunel Univ London Coll Engn Design & Phys Sci London England

ISBN: (纸本)9781538666142

Travel time prediction has important applications within the field of intelligent transportation, such as vehicle routing, congestion and traffic management. A challenging task in travel time prediction is obtaining data that is not readily available, as a clear majority of links in roads network are not equipped with traffic sensors. In this paper, data of travel time is collected for a link using Google Maps Application Programming Interface (API). Then, travel times are predicted for short horizons of up to one hour on the link by applying machine learning algorithms. The Mean Absolute Error (MAE) of predictions are compared. The study indicates that a shallow Artificial Neural Network (ANN) can provide more accurate prediction than the other algorithms.

关键词： Roads Google Artificial neural networks Predictive models machine learning algorithms Data models

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Detecting File Types Using machine learning algorithms

Detecting File Types Using Machine Learning Algorithms

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Innovations in Intelligent Systems and Applications Conference

作者： Serdar Korhan Konaray Aykut Toprak Gamze Mat Pek Hüseyin Akçekoce Deniz Kılınç Deytek Bilişim İZMİR Computer Engineering Department İzmir University of Bakırçay İZMİR

ISBN: (纸本)9781728128696

In this study, experiments have been carried out to analyze the file content and classify the type of file using machine learning algorithms. First a new benchmark dataset was created including popular file types used as malicious software. Then the proposed system was trained using different machine learning algorithms. The accuracy and runtime performance comparison of models were evaluated. As a result, the highest accuracy rate was obtained as 97.83% by XGBoost algorithm.

关键词： Prediction algorithms machine learning algorithms machine learning Training Classification algorithms Static VAr compensators Libraries

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Analysis Of machine learning algorithms For Wi-Fi-based Indoor Positioning System

Analysis Of Machine Learning Algorithms For Wi-Fi-based Indo...

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TEQIP III Sponsored International Conference on Microwave Integrated Circuits, Photonics and Wireless Networks (IMICPW)

作者： R Abishek K R Abishek N Hariharan M Rakesh Vaideeswaran C Sundara Paripooranan Electronics and Communication Engineering NIT Trichy India Electrical and Electronics Engineering NIT Trichy India Department of Production Engineering NIT Trichy India

Today, there is an increasing demand for low cost and accurate Indoor Positioning Systems (IPS) to cater to the needs of a growing customer base and provide better services. The main reason for the rise of IPS is the futility of Global Positioning Systems (GPS) indoors as the signals are highly attenuated by a number of physical objects. There are several methods, that compensate for the loss of GPS indoors, that achieve indoor positioning, a few of them being Wi-FI, LiFi, Bluetooth and RFID. Apart from using radio frequency and short-range radio frequency waves for indoor positioning, there are other methods to achieve the same using sensors such as inertial measurement unit (IMU), magnetometer, etc. This paper aims to explore the use of Wi-Fi for IPS as it is cost-effective, widely accessible and easy to set up. Among several Wi-Fi-based indoor positioning methods in existence, this paper introduces a novel approach towards improving the accuracy of low-cost Wi-Fi-based indoor positioning systems. This is achieved by selecting specific Wi-Fi channels that are non-overlapping and are subjected to least interference. The obtained received signal strength intensity (RSSI) is analyzed using machine learning algorithms such as K-Nearest Neighbours, Support Vector machine, Artificial Neural Networks, and the best-suited algorithm is identified. This model can be used for a variety of applications such as asset tracking, industrial resource management, etc. The effectiveness of these applications using IPS depends on the area of the indoor locality together with the accuracy of the machine learning models. The accuracy of the tested model is approximately 4 square metres.

关键词： Wireless fidelity IP networks Support vector machines machine learning algorithms Prototypes Bluetooth Neural networks

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Comparison of Statistical Signal Processing and machine learning algorithms for Spectrum Sensing

Comparison of Statistical Signal Processing and Machine Lear...

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IEEE Global Conference on Communications (GLOBECOM) / Workshop on Wireless Networking and Control for UAV

作者： Tiwari, Ayush Chenji, Harsha Devabhaktuni, Vijay Univ Toledo Dept Elect Engn & Comp Sci 2801 W Bancroft St Toledo OH 43606 USA Ohio Univ Sch Elect Engn & Comp Sci Athens OH 45701 USA

ISBN: (纸本)9781538647271

In a cognitive radio network, what is the trade off between statistical signal processing and machine learning algorithms that perform the same task? When should a system use the former and when should it use the latter? In this paper we present an empirical comparison study of different techniques for two tasks: detecting multiple transmitters in the same time-frequency domain, and automatic modulation classification. We develop and improve upon an unsupervised learning technique for the former, based on the log-Rayleigh distribution. Results are based on data generated from GNU Radio Companion, and implementations of these algorithms in software. They show that there is a tradeoff between accuracy and computation/implementation complexity signal processing has a several orders of magnitude advantage over machine learning, but slightly lower accuracy. Thus, there is a need for an overarching framework that can meld machine learning and statistical signal processing.

关键词： Signal processing algorithms machine learning algorithms Sensors Mixture models Transmitters Clustering algorithms Signal processing

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Predictive Models with Resampling: A Comparative Study of machine learning algorithms and their Performances on Handling Imbalanced Datasets

Predictive Models with Resampling: A Comparative Study of Ma...

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International Conference on machine learning and Applications (ICMLA)

作者： Adithi D. Chakravarthy Sindhura Bonthu Zhengxin Chen Qiuming Zhu College of IS&T University of Nebraska at Omaha Omaha NE USA

Class imbalance is a problem of crucial challenge in many real-world machine learning applications. Traditional machine learning algorithms are likely to produce good accuracy scores on such datasets due to an obvious bias towards the majority class. Thus, accuracy as a measure of performance for algorithms working on imbalanced data is not very clearly defined since the classifier has poor predictive accuracy over the minority class. While previous work has used several resampling techniques to aid in improving the predictive accuracy of the minority class, in this study, we explore and compare the effectiveness of the Synthetic Minority Oversampling and Random Oversampling techniques over multiple learning algorithms and resampling ratios for eight different performance measures against two datasets from diverse domains such as medicine and engineering. The results of this study show that the effectiveness of these resampling techniques is a multivariate function relative to both the learning algorithms and the resampling ratios, as well as the coherent characteristics of datasets. The choice of performance measures to evaluate models built using these resampling techniques also vary, thus giving us more relevant information useful for future research and applications.

关键词： Classification algorithms Predictive models machine learning machine learning algorithms Forestry Testing Training

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A Comparison of machine learning algorithms in Keystroke Dynamics

A Comparison of Machine Learning Algorithms in Keystroke Dyn...

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Kwesi Elliot Jonathan Graham Yusef Yassin Trenton Ward John Caldwell Tawab Attie Department of Computer Science Norfolk State University Norfolk USA Department of Computer Science Polytechnic University of Puerto Rico San Juan Puerto Rico Department of Computer Science North Carolina State Universtiy North Carolina USA College of Engineering Prairie View A&M University Texas USA

ISBN: (数字)9781728155845

ISBN: (纸本)9781728155852

Usernames and passwords have become weaker as the methods used to bypass this system have advanced. Today, secondary biometric authentication systems can be used to mitigate these vulnerabilities. Biometrics are unique traits that distinguish every individual from each other. One biometric technique is keystroke dynamics which is an authentication method based on a user's typing rhythm on a keyboard. These rhythm patterns are based on digraphs or the timing between two successive key presses. Our goal was to record the keystroke patterns of each user to determine if the users can be authenticated accurately by this method using machine learning. We recorded the latencies between keystrokes and taught machine learning algorithms the datasets to determine if the pattern can be learnable by the machine. We tested four different machine learning algorithms: Decision Trees, Random Forest, Support Vector machines, and Neural Networks, to determine which is most effective on accuracy. We also tested two text sizes to compare each algorithm's prediction rate based on input size.

关键词： Authentication Biometrics (access control) Password Rhythm machine learning algorithms Heuristic algorithms Timing

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House Prices Prediction with machine learning algorithms 2018

House Prices Prediction with Machine Learning Algorithms

引用

10th International Conference on machine learning and Computing (ICMLC)

作者： Fan, Chenchen Cui, Zechen Zhong, Xiaofeng Tsinghua Univ Elect Engn Dept ShuangQing Rd 30 Beijing Peoples R China Beijing Dongzhimen Middle Sch ShuangQing Rd 30 Beijing Peoples R China

ISBN: (纸本)9781450363532

Based on the data set compiled by D. D. Cock and the competition run by ***, we propose a house prices prediction algorithm in Ames, lowa by deliberating on data processing, feature engineering and combination forecasting. Our prediction ranks the 35th of the total 2221 results on the public leaderboard of *** and the RMSE of predicted results after taking logarithm from all the test data is 0.12019, which shows good performance and small of over-fitting.

关键词： House Prices Prediction machine learning algorithms Combination Forecasting Kaggle Competition

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Predicting Fitness and Performance of Diving using machine learning algorithms

Predicting Fitness and Performance of Diving using Machine L...

引用

IEEE Punecon

作者： Uma Mahajan Anup Krishnan Vineet Malhotra Deep Sharma Sharad Gore JJT University Rajasthan India Sports Sciences Faculty Army Sports Institute Pune India Exercise Physiology Armed Forces Medical College Pune India Dept of Sports Medicine Armed Forces Medical College Pune India Dept of Statistics JJT University Rajasthan India

ISBN: (数字)9781728119243

ISBN: (纸本)9781728119250

Various batteries of fitness tests are conducted to assess the fitness of divers. Testing and monitoring are conducted for the selection of divers based on their current fitness for future competitions. There is a paucity of data and knowledge of which fitness tests can be effective to predict fitness, performance, and selection of potential divers at national and international level competitions. Classical statistical methods could not identify the magnitude of the variable importance. Hence tree-based classifier Boruta algorithm was used to identify the most important features of the diving performance. Out of 19 variables, vertical jump, standing broad jump, sit & reach, shoulder flexibility, agility, pull-ups, maximum push-ups, maximum wall bar, handstand balance, age, height, and weight were more than 90% important for the diving performance. VO2max and static balance were 69% important while hyperextension and bridge flexibility were about 52% important. The ordinal forest and multi-ordinal logistic regression machine learning algorithms were used to develop models for predicting diving performance at national and international competitions. The diagnostic analysis was conducted to validate the model performances using unseen data. The predictive accuracy by multi-ordinal logistic regression with 6 variables was 52.1% (95% CI: 39.92%, 64.12%), which was poor as compared to the ordinal forest. Overall accuracy of the final ordinal forest, was 70.4% (95% confidence interval: 58.41%, 80.67%). The balanced accuracy was 68.5% and 82.8% for predicting the national and international performances respectively. Hence the ordinal forest is better for selecting high potential divers objectively at international competitions.

关键词： Talent selection Variable importance Ordinal forest Multi-ordinal logistic regression Physical fitness machine learning algorithms diving physical fitness health Fitness Testing Ordinal National Government Physical Education Static equilibrium diver Competition

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Streaming machine learning algorithms with Big Data Systems

Streaming Machine Learning Algorithms with Big Data Systems

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IEEE International Conference on Big Data

作者： Vibhatha Abeykoon Supun Kamburugamuve Kannan Govindrarajan Pulasthi Wickramasinghe Chathura Widanage Niranda Perera Ahmet Uyar Gurhan Gunduz Selahattin Akkas Gregor Von Laszewski School of Informatics Computing and Engineering IN USA

ISBN: (数字)9781728108582

ISBN: (纸本)9781728108599

Designing low latency applications that can process large volumes data with higher efficiency is a challenging problem. With the limited time to process data, usage of online algorithms are becoming important in the big-data applications. Stream processing is a well-known area that has been studied for a long time. In this research, our objective is to use state of the art big-data analytic engines to implement online algorithms and compare the strengths and weaknesses in each system. We use a streaming version of Support Vector machines (SVM) and KMeans to do the analysis. Apache Flink, Apache Storm and Twister2 streaming frameworks are used to implement these algorithms. Our study focuses on the efficiency of online training of these algorithms and the results show higher performance in Twister2 framework for these algorithms.

关键词： machine learning algorithms Microsoft Windows Task analysis Computational modeling Clustering algorithms Support vector machines Engines

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Predicting the Tensile Strength of Extrusion-blown High Density Polyethylene Film Using machine learning algorithms

Predicting the Tensile Strength of Extrusion-blown High Dens...

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEE IEEM)

作者： Alhindawi, F. Altarazi, S. German Jordanian Univ Ind Engn Dept Amman Jordan

ISBN: (纸本)9781538667866

This paper explores the utility of supervised machine learning algorithms in predicting the tensile strength of high density polyethylene film produced by extrusion-blown molding process. Three algorithms were used: Artificial Neural Networks, Decision Tree, and k-Nearest Neighbors. Eleven input parameters, five materials related and six process related;were modeled in the algorithms. The application of algorithms demonstrated their capability in predicting the intended property of the extrusion-blown process products.

关键词： machine learning algorithms extrusion-blown molding tensile strength HDPE film

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