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Call for Paper 3rd 2019 EIConCIT

Call for Paper 3rd 2019 EIConCIT

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East Indonesia conference on Computer and Information Technology (EIConCIT)

The 3rd 2019 East international conference on Computer and Information Technology is hosted by Universitas YAPIS Papua and is jointly organized with Universitas Muslim Indonesia, Makassar and Universitas Mulawarman, Universitas Negeri Malang, Universitas Hasanuddin, Institut Teknologi Kalimantan, Universitas UDAYANA, Universitas Negeri Gorontalo, Universitas Cokroaminoto Palopo, Universitas Andi Djemma, Politeknik Negeri Samarinda, Politeknik Negeri Bali, STMIK Bumigora and Politeknik Sains dan Teknologi Wiratama Maluku Utara. The conference, taking the following theme: "Smart technology for the world", is aimed at keeping abreast of the current development as well as providing an engaging forum for participants to share knowledge and expertise in related issues. The scope topics include, but are not limited to Cloud & Grid Computing, Artificial Intelligence, Mobile Computing, Decision Support System, learning Management System, IT for Education, Software Engineering, Semantic Web, E-Government, E-learning, E-Business, Human Computer Interaction, Big Data processing, Data Mining, machine learning, IT for Industry, IT for Society, Natural Language processing, Modelling and Simulation, Soft Computing, Network & Data Communication, and Cryptography.

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Reliable Classification of Ventricular Premature and Tachycardia Beats with novel feature extraction method and Classifier Ensembles 2

Reliable Classification of Ventricular Premature and Tachyca...

引用

2nd IEEE international conference on signal and Image processing (ICSIP)

作者： Wang, Tao Shi, Ruo Xi Xu, Xiao Ying Harbin Inst Technol Harbin Heilongjiang Peoples R China Beijing Sport Univ Sport Coaching Coll Beijing Peoples R China

ISBN: (纸本)9781538609699

In this paper, we present an effective method of recognizing ventricular premature and tachycardia beats. We extract characteristics in dynamic electrocardiogram and classify beats by using confirmed cases as training set. In order to achieve smart recognition, the character values of ventricular premature beats are obtained by novel methods. For example, we developed the 'variable-size windows' algorithm based on gray prediction for P-wave detection, the "triple insurance plan" to guarantee detection accuracy, Slope threshold method for S-wave detection and Curvature threshold method for T-wave detection(We can see P,R,Q,S,T wave in Figure 1). Aiming at the ventricular tachycardia detection, the character values are obtained by using fast Fourier Transform. Then we analyzed the characteristics by utilizing ensemble support vector machines. Genetic algorithm is integrated into the support vector machine system to select support vector machines with good classification performance. This idea can reduce classifier redundancy effectively. Extensive experimental results based on real-world data sets show that the proposed approach recognizes these two diseases with higher accuracy compared to existing approaches.

关键词： ventricular premature beat ventricular tachycardia beat support vector machine ensemble learning feature extraction

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Eye-2-I: Eye-Tracking for Just-in-Time Implicit User Profiling 2

Eye-2-I: Eye-Tracking for Just-in-Time Implicit User Profili...

引用

2nd IEEE international conference on signal and Image processing (ICSIP)

作者： Ma, Keng-Teck Xu, Qianli Lim, Rosary Li, Liyuan Sim, Terence Kankanhalli, Mohan ASTAR Inst Infocomm Res 1 Fusionopolis Way Singapore 138632 Singapore Natl Univ Singapore 13 Comp Dr Singapore 117417 Singapore

ISBN: (纸本)9781538609699

For many applications, such as targeted advertising and content recommendation, knowing users' traits and interests is a prerequisite. User profiling is a helpful approach for this purpose. However, current methods, i.e. self-reporting, web-activity monitoring and social media mining are either intrusive or require data over long periods of time. Recently, there is growing evidence in cognitive science that a variety of users' profile is significantly correlated with eye-tracking data. A novel just-in-time implicit profiling method, Eye-2-I, which learns the user's demographic and personality traits from the eye-tracking data while the user is watching videos is proposed. Although seemingly conspicuous by closely monitoring the user's eye behaviors, the proposed method is unobtrusive and privacy-preserving owing to its unique combination of speed and implicitness. As a proof-of-concept, the proposed method is evaluated in a user study with 51 subjects.

关键词： eye-gaze user profiling classification machine learning

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Effect of negative and positive emotions on the fundamental frequency and formants 17

Effect of negative and positive emotions on the fundamental ...

引用

2nd international conference on Computing and Wireless Communication Systems (ICCWCS)

作者： Agrima, A. Elmazouzi, L. Mounir, I. Farchi, A. Univ Hassan First Fac Sci & Tech IMII Lab Settat Morocco Univ Cadi Ayyad Grad Sch Technol LAPSSII Lab Safi Morocco

ISBN: (纸本)9781450353069

The speech signal provides rich information about the speaker's emotional state. Therefore, this article provides an experimental study and examines the detection of negative emotion (Sadness) and positive emotion (Joy) with regard to the neutral emotional state. The data set is collected from speeches recorded in the Moroccan Arabic dialect. Our aim is first to study the effects of emotion on the selected acoustic characteristics, namely the first four formants F1, F2, F3, F4, the fundamental frequency FO, and then compare our results to previous works. We also study the influence of speaker gender on the relevance of these characteristics in the detection of emotion. The main tool is classification algorithms using the WEKA software. We found that FO presents the best rates of recognition regardless speaker gender.

关键词： Emotion Classification learning machine algorithms Speech processing Fundamental frequency Formants

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Recognition of Arrhythmic Electrocardiogram using Wavelet based Feature Extraction 2

Recognition of Arrhythmic Electrocardiogram using Wavelet ba...

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2nd international conference for Convergence in Technology (I2CT)

作者： Singh, Atrija Bhowmick, Debanshu Biswas, Subhadeep Acad Technol Dept Elect & Commun Engn Kolkata India Acad Technol Dept Appl Elect & Instrumentat Engn Kolkata India

ISBN: (纸本)9781509043071

Arrhythmia is one of the most common cardiac diseases. Efficient methods of detecting arrhythmia have been proposed in literatures. Our study proposes a unique feature extraction approach with entropy and Hjorth descriptor to classify a set of ECG signals into normal and arrhythmic with a considerable amount of accuracy. The conventional approach involving wavelet decomposition as the primary feature extraction method yields classification accuracy of 81.8% The method proposed in the study using entropy and Hjorth descriptor provides higher classification rate at 82.9% Our study is validated by a reliable dataset.

关键词： Eleetrocardiogrant arrhythmia machine learning wavelet transform biomedical signal processing

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Smart Meter based on Time Series Modify and Extreme learning machine 2

Smart Meter based on Time Series Modify and Extreme Learning...

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2nd international conference on Automation, Cognitive Science, Optics, Micro Electro-Mechanical System, and Information Technology (ICACOMIT)

作者： Arrachman, Samudra R. Adiatmoko, M. F. Soeprijanto, Adi Syai'in, Mat Sidik, M. S. A. Rohiem, N. H. Inst Teknol Sepuluh Nopember ITS Dept Elect Engn Surabaya Indonesia Shipbldg Inst Polytech Surabaya SHIPS PPNS Study Program Automat Engn Surabaya Indonesia

ISBN: (纸本)9781538605103

The world's economic instability makes people very sensitive to the costs incurred to consume electrical energy. In this paper proposed smart meter that can record the consumption of electrical energy of any electrical equipment. The proposed method is employing Non-Intrusive Load Monitoring (NILM) concept which is combined with time series modify data processing. The advantages of the proposed method are the efficiency of the current signal reader and the least amount of data taken in the training process of artificial neural network Extreme learning machine (ELM). The proposed method was using transient signals and steady state signals as sign to identify the condition of equipment ON or OFF. The time series modify method is helpful for data retrieval when many electrical devices are operated. From the experiment results, smart-meter are expected to be utilized to make an electric bill with details of the load usage of any electrical equipment.

关键词： Smart Meter Non-Intrusive Load Monitoring Artificial Neural Network Time Series Modify signal Microprocessor

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Image processing Techniques for Identification of Fish Disease 2

Image Processing Techniques for Identification of Fish Disea...

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2nd IEEE international conference on signal and Image processing (ICSIP)

作者： Malik, Shaveta Kumar, Tapas Sahoo, A. K. Lingayas Univ Nachauli India Reverie Ecol & Fisheries Div Biodivers Lab Barakpur India

ISBN: (纸本)9781538609699

Fish disease diagnosis is a difficult process and needs high level of expertise. Any attempt of developing the system dealing with the fish disease diagnosis and to overcome various difficulties is in it has still not met any extra ordinary success. Identification of diseased fish at early stage is necessary step to prevent from spreading disease. This paper recognizes and identifies the EUS (Epizootic Ulcerative syndrome) disease which is caused by bn Aphanomyces invadans, a fungal pathogen. It is a red spot disease and looks like ulcer hence it is generally misidentified by the people. The paper is divided into two parts, in the first part segmentation has been applied to enhance the image, various edge detection techniques have applied to get the useful information and Morphological operations have been applied on the EUS diseased fish image. In second part features are extracted from the EUS infected fish image through different feature Descriptors i.e. HOG (Histogram of Gradient), FAST (Features from Accelerated Segment Test) and classify the EUS infected and Non-EUS infected fish image through machine learning Algorithms and find the classification accuracy through Classifier. If PCA applied after feature extraction then it increases the accuracy as it is a dimensional reduction. The proposed combination of techniques gives better accuracy as compared to the others. The Experimentation has been done on MATLAB environment on real images of EUS infected fish database.

关键词： HOG (histogram of gradient) FAST (features from accelerated segment test) PCA (principle component analysis) the EUS (epizootic ulcerative syndrome)

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A Study on Software Vulnerability Prediction Model 2

A Study on Software Vulnerability Prediction Model

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2nd IEEE international conference on Wireless Communications, signal processing and Networking (WiSPNET)

作者： Shamal, P. K. Rahamathulla, K. Akbar, Ali Govt Engn Coll Comp Sci & Engn Trichur Kerala India Govt Engn Coll Comp Sci & Engn Wayanad Kerala India

ISBN: (纸本)9781509044429

Developing a secure software is time consuming and a complex activity. The main source of insecurity is vulnerabilities in the software. So the prediction of software vulnerability plays important role in software engineering, especially in web application development. A software vulnerability prediction model forecasts whether a software component is vulnerable or not. This paper describes various software vulnerability prediction models. Mainly two types of software vulnerability models are used to predict the vulnerability component in software. In software metrics based prediction model, different software metrics are used as an indicator of software vulnerability. In text analysis based method, source code of the software is used as input to the prediction model. Source code is converted into tokens and frequencies. These are used to predict the vulnerability.

关键词： Web application software vulnerability prediction machine learning text mining software metrics

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Convolutional Neural Network Based Vehicle Turn signal Recognition 2

Convolutional Neural Network Based Vehicle Turn Signal Recog...

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2nd international conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)

作者： Yoneda, Keisuke Kuramoto, Akisue Suganuma, Naoki Kanazawa Univ Inst Frontier Sci Initiat Kanazawa Ishikawa Japan

ISBN: (纸本)9781509066643

This Automated driving is an emerging technology in which a car performs recognition, decision making, and control. Recognizing surrounding vehicles is a key technology in order to generate a trajectory of ego vehicle. This paper is focused on detecting a turn signal information as one of the driver's intention for surrounding vehicles. Such information helps to predict their behavior in advance especially about lane change and turn left-or-right on intersection. Using their intension, the automated vehicle is able to generate the safety trajectory before they begin to change their behavior. The proposed method recognizes the turn signal for target vehicle based on mono-camera. It detects lighting state using Convolutional Neural Network, and then calculates a flashing frequency using Fast Fourier Transform.

关键词： Automated Vehicle Environmental Recognition Image processing machine learning

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Classification of Low Resolution Astronomical Images using Convolutional Neural Networks 2

Classification of Low Resolution Astronomical Images using C...

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2nd IEEE international conference on Recent Trends in Electronics, Information and Communication Technology (RTEICT)

作者： Patil, Jyoti S. Pawase, Ravindra S. Dandawate, Y. H. VIIT Dept Elect & Telecommun Engn Pune Maharashtra India VIIT Dept Engn & Appl Sci Pune Maharashtra India

ISBN: (纸本)9781509037049

Currently deep machine learningtechniques are widely adopted by computer vision and signal processing communities. Deep learning, in particular, the Convolutional Neural Networks (CNN), are the most impressive classifiers widely used for image classification in recent years. CNN model allows the machine to learn automatically about the complex image features from its representation, minimizing the need of human experts in feature extraction. Such a hierarchical representation learning of the images makes CNN a more promising model for classification of different kinds of images as compared to the traditional machine learning models. In this paper, one such successful implementation of CNN is performed for classifying low resolution radio astronomical images containing objects like 'Radio Halos and Relics', and several other ` Point Radio Sources'. For such images, low resolution makes feature extraction a difficult task. Hence, a CNN based classification model proved more efficient in this casegiving a classification accuracy of 88%.

关键词： image classification machine learning convolutional neural networks deep learning

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