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Bi-directional Recurrent End-to-End Neural Network Classifier for Spoken Arab Digit Recognition 2

Bi-directional Recurrent End-to-End Neural Network Classifie...

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2nd international conference on Natural Language and Speech processing (ICNLSP)

作者： Zerari, Naima Abdelhamid, Samir Bouzgou, Hassen Raymond, Christian Univ Batna 2 Fesdis Algeria INSA Rennes INRIA IRISA Rennes France

ISBN: (纸本)9781538645437

Automatic Speech Recognition can be considered as a transcription of spoken utterances into text which can be used to monitor/command a specific system. In this paper, we propose a general end-to-end approach to sequence learning that uses Long Short-Term Memory (LSTM) to deal with the non-uniform sequence length of the speech utterances. The neural architecture can recognize the Arabic spoken digit spelling of an isolated Arabic word using a classification methodology, with the aim to enable natural human-machine interaction. The proposed system consists to, first, extract the relevant features from the input speech signal using Mel Frequency Cepstral Coefficients (MFCC) and then these features are processed by a deep neural network able to deal with the non uniformity of the sequences length. A recurrent LSTM or GRU architecture is used to encode sequences of MFCC features as a fixed size vector that will feed a multilayer perceptron network to perform the classification. The whole neural network classifier is trained in an end-to-end manner. The proposed system outperforms by a large gap the previous published results on the same database.

关键词： Arabic digits Speech recognition Auto-encoder Mel Frequency Cepstral Coefficients Long Short-Term Memory Multilayer perceptron network

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Landslide Susceptibility Zonation (LSZ) Using machine learning Approach for DEM Derived Continuous Dataset 2nd

Landslide Susceptibility Zonation (LSZ) Using Machine Learni...

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2nd international conference on Recent Trends in Image processing and Pattern Recognition, RTIP2R 2018

作者： Jhunjhunwalla, Muskan Gupta, Sharad Kumar Shukla, Dericks P. National Institute of Technology Hamirpur HamirpurHimachal Pradesh177001 India School of Engineering Indian Institute of Technology Mandi KamandHimachal Pradesh175005 India

ISBN: (纸本)9789811391866

Landslide-prone areas can be shown by depicting occurrence of landslides;landslide susceptibility zonation map (LSZ), landslide hazard zonation map (LHZ) and landslide risk zonation map (LRZ). However, for the preparation of LRZ map, we need LHZ map and for LHZ map, we need LSZ map. In this work Logistic Regression (LR), Fisher Discriminant Analysis (FDA) associated with the weighted linear combination (WLC) and ANN are used for the preparation of LSZ maps. Seven causative factors that give continuous dataset are taken into consideration i.e. aspect, slope, digital elevation model (DEM), topographic wetness index (TWI), tangential curvature, profile curvature and plan curvature for the part of Mandakini river basin in Garhwal Himalayas. Geology, geomorphology, soil type, thrust/fault buffer, road buffer, drainage buffer etc., which are also important landslide governing factors, were not used as they give a discrete/classified dataset. The study area is spread in 275.60 km $$^2$$ area where total 122 landslides occurred between 2004 and 2017. The landslides occurred from 2004 to 2012 (46 landslides with 1203 pixels) have been used for training of the models and from 2013 to 2017 (76 landslides) have been used for testing of the models. The susceptibility maps were classified/categorized into five different zones (very low, low, moderate, high and very high) based on the natural break in data. The landslide locations with the index value greater than 0.55 have been considered for the validation of the maps. The assessment of accuracy is done based on the Heidke Skill Score (HSS). The HSS score for FDA, LR and ANN is obtained as 0.89, 0.98 and 0.96. Based on the HSS score, the LR method can be selected as the best method amongst the three. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Neural networks

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Non Invasive Cardiovascular Health Assessment in Post-adolescent Age Group Using Augmentation Index 2nd

Non Invasive Cardiovascular Health Assessment in Post-adoles...

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2nd international conference for Innovation in Biomedical Engineering and Life Sciences (ICIBEL)

作者： Karun, Sai Nath, Suhan S. Bharathi, Kunal Gaonkar, Manjunath Krupa, B. Niranjana PES Inst Technol Bengaluru 560085 India

ISBN: (纸本)9789811075544;9789811075537

This paper presents a non-invasive method of classifying a subject's health into either one of two classes depending on the condition of the subjects' cardiovascular health. Novelty of the work lies in segregating the subjects who had good cardiovascular health and the subjects who were at risk. In the proposed work, VPG signals and other general information such as weight, height, BP were collected from 38 individuals in the post-adolescent age group. Furthermore, a signal from a person who was known to have good cardio vascular health was collected. This signal became the reference that was used to compare with other signals. After the initial pre-processing, the PPG and APG were obtained from the VPG. Then a total of 7 features of the wave contour from the APG and PPG signals were extracted. Based on the Augmentation Index the signals were classified into two classes using SVM and ELM classifiers. Where one class represented healthy individuals and the other class represents the individuals at risk of CVD. The average values of the extracted features were used and the final accuracy obtained was also an average value. The accuracy obtained using ELM classifier with K-fold cross validation was 77%. Whereas the efficiency achieved using SVM was 94.59%. Hence the proposed method can be used to assess the vascular health analysing PPG signals in the post-adolescent age group.

关键词： Cardiovascular diseases Photoplethysmogram (PPG) Velocity photoplethysmogram (VPG) Acceleration photoplethysmogram (APG) Extreme learning machine (ELM) Support vector machines (SVM)

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EMG based Gesture Recognition using machine learning 2

EMG based Gesture Recognition using Machine Learning

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2nd international conference on Intelligent Computing and Control Systems (ICICCS)

作者： Anil, Nikitha Sreeletha, S. H. LBS Inst Technol Women Dept Comp Sci & Engn Trivandrum Kerala India

ISBN: (纸本)9781538628423

Gesture recognition basically involves the usage of hardware equipments and software development tools where human movements are captured and Human Computer Interaction are improved. Gesture recogntion can be employed in various applications like gaming technology, virtual reality, in the domain of medicine, sign language interpretation, home automation etc. This work basically focuses on EMG based gesture recogntion taken in real time using Myoband. The Myoband is worn on the forearm and electrical impulses are measured for five gestures namely Rest, Fist, Wavein, Waveout and Fingerspread. These signals undergo a signal processing technique called Wavelet decompostion where the signals are fragmented into wavelet coefficients that are localised in time domain as well as in frequency domain. These coefficients make up the dataset and are classified using Support Vector machines. When a user poses for a particular gesture, the model would recognises it and labels accordingly.

关键词： Gesture recognition Electromyography signal processing Discrete Wavelet Transform Wavelet Coefficients Classification Support Vector machines

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3rd international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2018

3rd International Conference on Information and Communicatio...

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3rd international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2018

ISBN: (纸本)9789811317415

The proceedings contain 147 papers. The special focus in this conference is on Information and Communication Technology for Intelligent Systems. The topics include: Review of EEG signal classification;How to reverse engineer ICS protocols using pair-HMM;classification of plants using invariant features and a neural network;CSTeller: Client or server is a question;geographical zone-based cluster head for routing in urban vehicular network;Congestion management by Static Var Compensator (SVC) using power world simulator;combining user-based and item-based collaborative filtering using machine learning;Predictive monitoring system using K-NN, QDC classifiers of physiological data;Recommender system based on OSN data analytics;automation of process evaluation of saccharification of wheat starch followed by fermentation of glucose to prepare bioethanol using digital image processing;Early detection of ransomware by indicator analysis and WinAPI call sequence pattern;a novel integrated framework based on modular optimization for efficient analytics on twitter big data;study of controller techniques for precision positioning;an effective content-based strategy analysis for large-scale deduplication using a multi-level pattern-matching algorithm;tamil sign language translator—An assistive system for hearing- and speech-impaired people;aspect-based sentiment analysis of students’ feedback to improve teaching–learning process;fair mechanisms for combinatorial reverse auction-based cloud market;timbre-vibrato model for singer identification;a range-based adaptive and collaborative localization for wireless sensor networks;Image edge detection techniques using sobel, T1FLS, and IT2FLS;An optimal cryptographic approach for addressing security breaches to build resilient WSN;detection of mimicry attacks on speaker verification system for cartoon characters’ dataset;smart helmet for safety driving.

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3rd international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2018

3rd International Conference on Information and Communicatio...

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3rd international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2018

ISBN: (纸本)9789811317460

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An LSTM-based Indoor Positioning Method Using Wi-Fi signals 2018

An LSTM-based Indoor Positioning Method Using Wi-Fi Signals

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2nd international conference on Vision, Image and signal processing (ICVISP)

作者： Sahar, Ayesha Han, Dongsoo Korea Adv Inst Sci & Technol Dept Comp Sci 291 Daehak Ro Daejeon 34141 South Korea

ISBN: (纸本)9781450365291

Recently, Wi-Fi fingerprints are often used for constructing indoor positioning systems. Wi-Fi fingerprint is a vector of Received signal Strength (RSS) values at a particular location. Radio map is the collection of Wi-Fi fingerprints and their collected location at an area or a building. Positioning systems, mounted on top of the radio map, estimate locations using the information in the radio map. Many Wi-Fi fmgerprint-based positioning algorithms have been developed. K-Nearest Neighbor(KNN), probabilistic method, fuzzy logic, neural network, multilayer perceptron are the examples. However, this field has not yet fully benefited from the potential of deep learning approaches. The sequence of Wi-Fi fingerprints implies that the deep recurrent network approaches, especially designed to handle sequential data, can play a vital role to enhance the performance of fingerprint-based positioning systems. In this paper, deep and recurrent approaches are studied rigorously for the improvement of the accuracy of positioning systems. We focus mainly on Long Short-Term Memory (LSTM) networks. An LSTM-based approach was compared with other state of the art approaches. A complete explanation to select the best hyper parameters is presented so that they can be referenced by the researchers in this field. A simple vanilla LSTM architecture is also compared with a stacked LSTM architecture on a Wi-Fi fingerprint dataset.

关键词： Indoor Localization Sequence learning Recurrent Networks

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2nd international conference on Vision, Image and signal processing (ICVISP)

作者： Li, Wei Meleis, Waleed Northeastern Univ 360 Huntington Ave Boston MA 02115 USA

ISBN: (纸本)9781450365291

A major challenge in reinforcement learning (RL) is use of a tabular representation to represent learned policies with a large number of states or state-action pairs. Function approximation is a promising tool to overcome this deficiency. This approach uses parameterized functions instead of a table to represent learned knowledge and enables generalization. However, existing schemes cannot solve realistic RL problems, with their rapidly increasing demands for approximating accuracy and efficiency. In this paper, we extend the architecture of Sparse Distributed Memories (SDMs) and propose a novel on-line methodology, similarity-aware Kanerva coding (SAK), that closely represents the learned knowledge for very large-scale problems with significantly fewer parameterized components. SAK directly measures the state variables' real distances in all dimensions and reformulates a new state similarity metric with an improved definition of state closeness. As a result, our scheme accurately distributes and generalizes knowledge among related states. We further enhance SAK's efficiency by allowing a limited number of prototype states that have certain similarities to be activated for value approximation so that the risk of over-generalization is hindered. In addition, SAK eliminates size tuning and prototype reallocation for the prototype set, resulting in not only broadened scalability but also significant savings in the amount of necessary prototypes and computational overhead needed for RL. Our extensive experimental results show that SAK achieves more than 48% improvements over existing schemes in learning quality, and reveal that SAK is able to consistently learn good policies for RL with small overhead and short training times, even given roughly tuned scheme parameters.

关键词： Reinforcement learning function approximation Kanerva coding

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2nd international conference on Computer, Communication, and signal processing: Special Focus on Technology and Innovation for Smart Environment, ICCCSP 2018

2nd International Conference on Computer, Communication, and...

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2nd international conference on Computer, Communication, and signal processing, ICCCSP 2018

ISBN: (纸本)9781538611418

The proceedings contain 41 papers. The topics discussed include: design and development of flexible on-board diagnostics and mobile communication for internet of vehicles;analysis of brain wave due to stimulus using EEG;design analysis of multi band FSS antenna in C and KU band;robust dwt-SVD based blind watermarking for DIBR 3D video;analysis of supervised and unsupervised learning in content based multimedia retrieval;word sign recognition of invariant images based on surf with Laplacian eigenmaps;a survey on IOT technologies, evolution and architecture;smart bus rerouting;an approach to detect replay attack in automatic speaker verification system;SIW flower shaped fractal antenna backed with cavity for 60GHz frequency applications;bilingual speech recognition system for isolated words using deep neural network;analysis of single and dual band microstrip FSS annular ring antenna for KU band;automatic code evaluation system;multi-level smart parking system;and a feature based comparison study of big data scheduling algorithms.

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CNN-Based CAD for Breast Cancer Classification in Digital Breast Tomosynthesis 18

CNN-Based CAD for Breast Cancer Classification in Digital Br...

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2nd international conference on Graphics and signal Proceesing (ICGSP)

作者： Yeh, Jinn-Yi Chan, Siwa Natl Chiayi Univ Dept Management Informat Syst 580 Sinmin Rd Chiayi Taiwan Taichung Tzu Chi Hosp Dept Med Imaging 66Sec 1Fengxing Rd Taichung Taiwan

ISBN: (纸本)9781450363860

Digital breast tomosynthesis (DBT) is a promising new technique for breast cancer diagnosis. DBT has the potential to overcome the tissue superimposition problems that occur on traditional mammograms for tumor detection. However, DBT generates numerous images, thereby creating a heavy workload for radiologists. Therefore, constructing an automatic computer-aided diagnosis (CAD) system for DBT image analysis is necessary. This study compared feature-based CAD and convolutional neural network (CNN)-based CAD for breast cancer classification from DBT images. The research methods included image preprocessing, candidate tumor identification, three-dimensional feature generation, classification, image cropping, augmentation, CNN model design, and deep learning. The accuracy rates (standard deviation) of the CNN-and feature-based CAD for breast cancer classification were 74.85% (0.122) and 87.12% (0.035), respectively. The T value was -6.229, and the P value was 0.00 < 0.05, which indicated that the CNN-based CAD significantly outperformed feature-based CAD. The results can be applied to clinical medicine and assist radiologists in breast cancer identification.

关键词： Digital breast tomosynthesis computer-aided diagnosis deep learning breast cancer classification

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