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Abnormal gait detection by classifying inertial sensor data using transfer learning 18

Abnormal gait detection by classifying inertial sensor data ...

引用

18th IEEE international conference on machine learning and applications, icmla 2019

作者： Pandit, Tejul Nahane, Harshal Lade, Dhanshree Rao, Vaibhav Dept. of Electronics and Telecom. K. J. Somaiya College of Engineering Mumbai India

ISBN: (纸本)9781728145495

Gait analysis is a method used to assess the way we walk or run to highlight possible biomechanical abnormalities. In this paper, we aim at performing this analysis utilizing 4 wireless body adhering modules, each equipped with an accelerometer and gyroscope pair (MPU6050). the data transmitted from these modules are converted to images to facilitate further processing. the sensor data images are then used as an input for transfer learning to the Deep Convolutional Neural Network (CNN) model, Inception v3, wherein the last two layers of the model are trained. Information that flows through the network shapes the structure of the model utilizing the labeled training dataset. the network can differentiate/classify 4 walking styles, namely: 'Normal walk,' 'Hemiplegic gait,' 'Diplegic gait,' and 'Parkinsonian gait.' By identifying which biomechanical trait a person has, one can further assess the neurological disorder that he/she may be suffering from cost-effectively compared to the traditional rigid methods currently available. © 2019 IEEE.

关键词： Accelerometers

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Prediction-directed Compression of POMDPs

Prediction-directed Compression of POMDPs

引用

7th international conference on machine learning and applications

作者： Boularias, Abdeslam Izadi, Masoumeh Chaib-Draa, Brahim Univ Laval Dept Comp Sci Ste Foy PQ G1K 7P4 Canada McGill Univ Sch Comp Sci Montreal PQ H3A 2A7 Canada

ISBN: (纸本)9780769534954

High dimensionality of belief space in Partially Observable Markov Decision Processes (POMDPs) is one of the major causes that severely restricts the applicability of this model. Previous studies have demonstrated that the dimensionality of a POMDP can eventually be reduced by transforming it into an equivalent Predictive State Representation (PSR). In this paper we address the problem of finding an approximate and compact PSR model corresponding to a given POMDP model. We formulate this problem in an optimization framework. Our algorithm tries to minimize the potential error that missing some core tests may cause. We also present an empirical evaluation on benchmark problems, illustrating the performance of this approach.

关键词： Benchmarking

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Automatic Gender Classification System From Finger 2D:4D Ratio and Comparison of Successes with Using Different Algorithm 13

Automatic Gender Classification System From Finger 2D:4D Rat...

引用

13th international conference on machine learning and applications (icmla)

作者： Ceyhan, Eyup Burak Sagiroglu, Seref Cesur, Ramazan Oner, Kadriye Gazi Univ Fac Engn Dept Comp Engn Ankara Turkey Gazi Univ Technol & Innovat Ctr Ankara Turkey

ISBN: (纸本)9781479974153

High rate of correctness of the information in determining the identity of corpses in mass deaths in such disasters as aircraft, high-speed train or sea accidents and fires, where people are damaged to an extent that they cannot be identified, and the method followed are main elements. In this study, a dataset was formed by taking the index finger, ring finger, height and ages of 67 Turkish men and 56 Turkish women. the dataset was used for identifying the sex of the person and thus, information that could reveal the identity of the information was obtained. LAD Tree algorithm, Naive Bayes algorithm, KNN algorithm and C4.5 algorithm were tried on the datasets. C4.5 algorithm had the highest rate of success in determining sex with a rate of 93%. therefore, automatic sex classification was made using C4.5 algorithm. Studies revealed that 2D:4D ratio varies in different sexes and races. Moving from these studies, sex was determined from the ratio of index finger to ring finger. When compared with the existing studies in the literature on different races, it was proved that race also can be determined. It was found that left hand provided higher success in finger ratio use in identifying sex compared to the right hand.

关键词： 2D:4D Mass Deaths Automatic Gender Estimation Index and Ring Finger Ratio Classification Algorithms

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Discretization Techniques and Genetic Algorithm for learning the Classification Method PROAFTN

Discretization Techniques and Genetic Algorithm for Learning...

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8th international conference on machine learning and applications

作者： Al-Obeidat, Feras Belacel, Nabil Mahanti, Prabhat Carretero, Juan A. UNB Dept Comp Sci POB 5050 St John NB E2L 4L5 Canada NRC Inst Informat Technol Moncton NB E1A7R1 Canada UNB Dept Comp Sci St John NB E2L 4L5 Canada UNB Dept Mech Engn Fredericton NB E3B 5A3 Canada

ISBN: (纸本)9780769539263

this paper introduces new techniques for learning the classification method PROAFTN from data. PROAFTN is a multi-criteria classification method and belongs to the class of supervised learning algorithms. To use PROAFTN for classification, some parameters must be obtained for this purpose. therefore, an automatic method to extract these parameters from data with minimum classification errors is required. Here, discretization techniques and genetic algorithms are proposed for establishing these parameters and then building the classification model. Based on the obtained results, the newly proposed approach outperforms widely used classification methods.

关键词： Genetic algorithms

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N-BEATS-RNN: Deep learning for time series forecasting 19

N-BEATS-RNN: Deep learning for time series forecasting

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19th IEEE international conference on machine learning and applications, icmla 2020

作者： Sbrana, Attilio Debiaso Rossi, Andre Luis Coelho Naldi, Murilo Federal University of São Carlos Department of Computer Science Sorocaba Brazil Campus of Itapeva Itapeva Brazil

ISBN: (纸本)9781728184708

this work presents N-BEATS-RNN, an extended version of an existing ensemble of deep learning networks for time series forecasting, N-BEATS. We apply a state-of-the-art Neural Architecture Search, based on a fast and efficient weight-sharing search, to solve for an ideal Recurrent Neural Network architecture to be added to N-BEATS. We evaluated the proposed N-BEATS-RNN architecture in the widely-known M4 competition dataset, which contains 100,000 time series from a variety of sources. N-BEATS-RNN achieves comparable results to N-BEATS and the M4 competition winner while employing solely 108 models, as compared to the original 2,160 models employed by N-BEATS, when composing its final ensemble of forecasts. thus, N-BEATS-RNN's biggest contribution is in its training time reduction, which is in the order of 9x compared with the original ensembles in N-BEATS. © 2020 IEEE.

关键词： Time series

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learning from uncertain image data using granular fuzzy sets and bio-mimetic applicability functions

Learning from uncertain image data using granular fuzzy sets...

引用

Joint 4th conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2005 and 11th French Days on Fuzzy Logic and applications, LFA 2005

作者： Rossiter, Jonathan Mukai, Toshiharu 2271-130 Anagahora Shimoshidami Moriyama-ku Nagoya 463-0003 Japan

ISBN: (纸本)8476538723

In this paper we present a new method for machine learning from images using uncertain information granules which has been inspired by bio-mimetic study of human perceptual processing. We present a method for generating labelled image data using the rapid application of rough labelled regions to the image under study. Over each region is defined an applicability function which acts as a centre of focus for the uncertain information contained within the region. We present a number of alternative applicability functions inspired by the human visual system and particularly by the centre-weighted effect of the fovea within the retina. We also show how this uncertain data can be used to directly train a granular fuzzy machine learner.

关键词： Image processing

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Comparison of Evaluation Metrics in Classification applications with Imbalanced Datasets

Comparison of Evaluation Metrics in Classification Applicati...

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7th international conference on machine learning and applications

作者： Fatourechi, Mehrdad Ward, Rabab K. Mason, Steven G. Huggins, Jane Schloegl, Alois Birch, Gary E. Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada Neil Squire Soc Burnaby BC V5M 3Z3 Canada Inst Computing Informat & Cognit Vancouver BC V6T 1Z4 Canada Univ Michigan Dept Phys Med & Rehabilitat Dept Biomed Engn Ann Arbor MI USA Graz Univ Technol Inst Human Comp Interfaces A-8010 Graz Austria

ISBN: (纸本)9780769534954

A new framework is proposed for comparing evaluation metrics in classification applications with imbalanced datasets (i.e., the probability of one class vastly exceeds others). For model selection as well as testing the performance of a classifier this framework finds the most suitable evaluation metric amongst a number of metrics. We apply this framework to compare two metrics: overall accuracy and Kappa coefficient. Simulation results demonstrate that Kappa coefficient is more suitable.

关键词： Classification (of information)

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DeL-haTE: A Deep learning Tunable Ensemble for Hate Speech Detection 19

DeL-haTE: A Deep Learning Tunable Ensemble for Hate Speech D...

引用

19th IEEE international conference on machine learning and applications, icmla 2020

作者： Melton, Joshua Bagavathi, Arunkumar Krishnan, Siddharth University of North Carolina at Charlotte Department of Computer Science United States Oklahoma State University Department of Computer Science United States

ISBN: (纸本)9781728184708

Online hate speech on social media has become a fast-growing problem in recent times. Nefarious groups have developed large content delivery networks across several mainstream (Twitter and Facebook) and fringe outlets (Gab, 4chan, 8chan, etc.) to deliver cascades of hate messages directed both at individuals and communities. thus addressing these issues has become a top priority for large-scale social media outlets. three key challenges in automated detection and classification of hateful content are the lack of clearly labeled data, evolving vocabulary and lexicon - hashtags, emojis, etc - and the lack of baseline models for fringe outlets such as Gab. In this work, we propose a novel framework with three major contributions. (a) We engineer an ensemble of deep learning models that combines the strengths of state-of-the-art approaches, (b) we incorporate a tuning factor into this framework that leverages transfer learning to conduct automated hate speech classification on unlabeled datasets, like Gab, and (c) we develop a weak supervised learning methodology that allows our framework to train on unlabeled data. Our ensemble models achieve an 83% hate recall on the HON dataset, surpassing the performance of the state of the art deep models. We demonstrate that weak supervised training in combination with classifier tuning significantly increases model performance on unlabeled data from Gab, achieving a hate recall of 67%. © 2020 IEEE.

关键词： Deep learning

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A machine learning approach to maximizing broadband capacity via dynamic DOCSIS 3.1 profile management 18

A machine learning approach to maximizing broadband capacity...

引用

18th IEEE international conference on machine learning and applications, icmla 2019

作者： Gaydashenko, Anastasia Ramakrishnan, Sangeeta Yandex Technologies Distinguished Engineer Cisco Systems United States

ISBN: (纸本)9781728145495

A majority of American households with broadband today subscribe to a cable broadband service. the technology underpinning this cable broadband service is DOCSIS (Data-Over-Cable Service Interface Specifications). the latest version of this technology, DOCSIS 3.1, enables higher modem speeds compared to the previous DOCSIS 3.0 technology. DOCSIS 3.1 provides faster speeds by increasing the modulation order, and by using Orthogonal Frequency-Division Multiplexing (OFDM) to enable the use of such higher modulation orders. DOCSIS 3.1 allows different modems to use different modulations on different parts of the spectrum. Due to the complexity of selecting and configuring these varying modulation levels across the spectrum, most operators use the same modulation level across the entire spectrum. they configure multiple such profiles on a channel, so that different modems can use the most appropriate profile for them. this enable modems on a cleaner part of the plant to run higher modulations. However, the problem with such an approach is that noise in one part of the spectrum might reduce the entire channel's modulation to limit bit errors. A more sophisticated approach is to use profiles that dynamically vary the modulation on different sub-carriers to best match the noise conditions experienced in the plant. We leverage a machine learning based dynamic profile management approach and tested it on data from a top 10 US Cable Company and we observe more than 4% broadband bandwidth gains on average, with some consumers in noisy plant conditions seeing over 30% increase in their bandwidth speeds. And the gains are far more dramatic when compared with using lower modulation profiles (similar to DOCSIS 3.0), with average gain itself being over 37%. © 2019 IEEE.

关键词： Modems

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Design of a cost-effective deep convolutional neural network-based scheme for diagnosing faults in smart grids 18

Design of a cost-effective deep convolutional neural network...

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18th IEEE international conference on machine learning and applications, icmla 2019

作者： Hassani, Hossein Farajzadeh-Zanjani, Maryam Razavi-Far, Roozbeh Saif, Mehrdad Palade, Vasile Department of Electrical and Computer Engineering University of Windsor 401 Sunset Avenue WindsorN9B 3P4 Canada Faculty of Engineering and Computing Coventry University Priory Street Coventry United Kingdom

ISBN: (纸本)9781728145495

there has been a growing interest in using smart grids due to their capability in delivering automated and distributed energy level to the consumption units. However, in order to guarantee the safe and reliable delivery of the high-quality power from the generation units to the consumers, smart grids need to be equipped with diagnostic systems. this paper presents an efficient data-driven scheme for diagnosing faults in smart grids. In order to reduce the computational burden and monitor the state of the system with a lower number of smart meters, a method based on the affinity propagation clustering algorithm is suggested for the placement of meters, that makes use of the graph-based representation of the system. the collected voltage data measurements from the installed meters are then decomposed by matching pursuit decomposition in order to generate informative features. Extracted features are then used to train a convolutional neural network, and the constructed deep learning model is then tested using unseen samples of normal and faulty conditions. Simulation results based on the IEEE 39-Bus System demonstrate the effectiveness of the proposed data-driven fault diagnostic system. © 2019 IEEE.

关键词： Smart power grids

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