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SPARK-A Big Data processing Platform for machine learning

SPARK-A Big Data Processing Platform for Machine Learning

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2nd international conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII)

作者： Fu, Jian Sun, Junwei Wang, Kaiyuan Wuhan Univ Technol Sch Automat Wuhan 430070 Hubei Peoples R China

ISBN: (纸本)9781509035755

Apache Spark is a distributed memory-based computing framework which is natural suitable for machine learning. Compared to Hadoop, Spark has a better ability of computing. In this paper, we analyze Spark's primary framework, core technologies, and run a machine learning instance on it. Finally, we will analyze the results and introduce our hardware equipment.

关键词： Apache Spark machine learning MLlib Memory computing Iteration

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Modification of Optimum-Path Forest Using Markov Cluster Process Algorithm 2

Modification of Optimum-Path Forest Using Markov Cluster Pro...

引用

2nd international conference of signal processing and Intelligent Systems (ICSPIS)

作者： Bostani, Hamid Sheikhan, Mansour Islamic Azad Univ Dept Comp Engn South Tehran Branch Tehran Iran Islamic Azad Univ Dept Commun Engn South Tehran Branch Tehran Iran

ISBN: (纸本)9781509058204

Optimum-path forest (OPF) is a novel supervised graph-based classifier which reduces the classification problem into partitioning of vertices in a graph derived from the data samples. One of the main processes in OPF is identifying the optimum set of key samples named prototypes. This process is based on creating a minimum spanning tree on a complete weighted graph which is derived from the training samples;hence, it is much time-consuming for large-scale problems. In this study, for overcoming this limitation, the process of finding the prototypes in traditional OPF is modified by using Markov cluster (MCL) algorithm. The graph partitioning in MCL is based on finding key samples named attractors, which attract other related samples;so the obtained attractors can be selected as prototypes for generating optimum-path trees. Experiments on public benchmark datasets show that the speed of proposed modified OPF is improved considerably as compared to the traditional OPF.

关键词： Optimum-path forest Markov cluster process supervised machine learning

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A New machine learning Approach to Select Adaptive IMFs of EMD 2

A New Machine Learning Approach to Select Adaptive IMFs of E...

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2nd international conference on Electrical, Computer & Telecommunication Engineering (ICECTE)

作者： Uddin, Md. Burhan Uddin, Jia Sultana, Razia Islam, Safat BRAC Univ Dept Comp Sci & Engn Dhaka Bangladesh

ISBN: (纸本)9781509057856

An adaptive algorithm for selection of Intrinsic Mode Functions ( IMF) of Empirical Mode Decomposition (EMD) is a time demand in the field of signal processing. This paper presents a new model of an effective algorithm for the adaptive selection of IMFs for the EMD. Our proposed model suggests the decomposition of an input signal using EMD, and the resultant IMFs are classified into two categories the relevant noise free IMFs and the irrelevant noise dominant IMFs using a trained Support Vector machine (SVM). The Pearson Correlation Coefficient (PCC) is used for the supervised training of SVM. Noise dominant IMFs are then de-noised using the Savitzky-Golay filter. The signal is reconstructed using both noise free and de-noised IMFs. Our proposed model makes the selection process of IMFs adaptive and it achieves high signal to Noise Ratio (SNR) while the Percentage of RMS Difference (PRD) and Max Error values are low. Experimental result attained up to 41.79% SNR value, PRD and Max Error value reduced to 0.814% and 0.081%, respectively compared to other models.

关键词： intrinsic mode functions empirical mode decomposition support vector machine Pearson's correlation coefficient

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An unsupervised learning based method for content-based image retrieval using hopfield neural network 2

An unsupervised learning based method for content-based imag...

引用

2nd international conference of signal processing and Intelligent Systems, ICSPIS 2016

作者： Sabahi, F. Omair Ahmad, M. Swamy, M.N.S. Department of Electrical and Computer Engineering Concordia University Montreal Canada

ISBN: (纸本)9781509058204

Presently, corporations and individuals have large image databases due to the explosion of multimedia and storage devices available. Furthermore, the accessibility to high speed internet has escalated the level of multimedia exchanged by users across cyberspace every second. Accordingly, it has increased the demand for searching among large databases of images. Conventionally, text-based image retrieval is used. The major problems in text-based image retrieval are related to annotation that is often impossible due to human perception of images being subjective, and also due to the size of the information that needs indexing. To overcome such limitations, content-based image retrieval systems have been proposed. However, there is a key hindrance, namely, the need to match the human visual system to overcome the semantic gap between human perception and low-level features. In this paper, we propose a new unsupervised method based on Hopfield neural networks that seeks to model human visual memory to increase the efficacy of retrieval and reduce the semantic gap. A comparative study with other neural-network based methods, such as the feed forward backpropagation and Boltzmann deep learning, shows the effectiveness of our method. © 2016 IEEE.

关键词： Unsupervised learning

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Detecting High Risk Taxpayers Using Data Mining Techniques 2

Detecting High Risk Taxpayers Using Data Mining Techniques

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2nd international conference of signal processing and Intelligent Systems (ICSPIS)

作者： Rad, Mehdi Samee Shahbahrami, Asadollah Islamic Azad Univ Fac Engn Rasht Branch Rasht Iran Univ Guilan Fac Engn Dept Comp Engn Rasht Iran

ISBN: (纸本)9781509058204

Risk refers to a set of events that lead to loss but risk from the tax perspective refers to the taxpayers' behaviors that may lead to negligence from the public property by the taxpayers due to tax evasion. Such actions cause unusual volatilities in the amounts envisaged in the government budgeting. The fiscal and financial transactions outside the scope of the precautionary bound and failure to achieve the expected revenues of the country. One of the most important types of tax risks is concealing the information on buying, selling and contracts that in case of being uncovered in the financial sector it leads to the issuance of amendments to taxpayers. But if it is uncovered in the due course, it leads to the non-fulfillment of tax collection and thus negative financial waves at the national level and ultimately leads to detrimental financial impact to the financial framework of states and countries. The main purpose of this paper is to analyze, design and implement a system to extract high risk taxpayers and provide a model to forecast the amount of tax assessment notification of the taxpayers for the coming years so that it would play the role of the assistance system for the tax experts to issue the assessment notifications with realistic amounts during the assessment and tax audit to prevent major errors in the tax assessment. To extract high risk taxpayers using the variance and the mean standard deviation the suspicious financial behavior is detected and then the previously supervised data that exist in the tax base as amendment forms are used to classify the taxpayers and also the job coefficient field is used and high risk occupations are identified and classified. One of the strongest and best practices in this field is the use of statistical and financial calculations in time domain. The main feature is the amount of taxable income based on which the purchasing, sales, revenue and profit can be calculated. By studying the volatilities and noise detection

关键词： Data mining task risk assessment tax fraud detection machine learning artificial neural networks big data

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proceedings - 2016 2nd international conference of signal processing and Intelligent Systems, ICSPIS 2016

Proceedings - 2016 2nd International Conference of Signal Pr...

引用

2nd international conference of signal processing and Intelligent Systems, ICSPIS 2016

ISBN: (纸本)9781509058204

The proceedings contain 39 papers. The topics discussed include: segmentation of retinal blood vessels by means of 2D Gabor wavelet and fuzzy mathematical morphology;inner-knuckle-print for human authentication by using ring and middle fingers;cost sensitive modeling of credit card fraud using neural network strategy;an unsupervised learning based method for content-based image retrieval using Hopfield neural network;writer identification using a probabilistic model of handwritten digits and approximate Bayesian computation;a probabilistic framework for dense image registration using relaxation labelling;resource allocation using task similarity distance;enhance evoked potentials detection using RBF neural networks: application to brain-computer interface;fast FPGA-based method for Matsuoka parameters tuning;an annotated corpus for extracting the phenotypic plasticity and the association of SNP-phenotypes from the text;design of a low power multiple object tracking vision sensor for use in robot localization and surveillance tasks;FPGA-based convolutional neural network accelerator design using high level synthesize;modification of optimum-path forest using Markov cluster process algorithm;and improved shuffled frog leaping algorithm by using orthogonal experimental design.

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Usage of Text Sentiment Analysis Models and Methods in the Sociological Studies

Usage of Text Sentiment Analysis Models and Methods in the S...

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2017 2nd international conference on Software, Multimedia and Communication Engineering（SMCE 2017)

作者： Evgeny KOTELNIKOV Ekaterina MITIAGINA Vyatka State University Institute of Mathematics and Information Systems Vyatka State University Institute of Humanities and Social Sciences

Natural language processing has recently become very popular in the sociological studies due to a wide expansion of social media such as social networks,blogs,forums,etc.,as well as online *** important direction of this area is a text sentiment analysis,used to find out people's opinions on various actual *** paper deals with two methods of sentiment analysis:known support vector machine(SVM) as supervised learning and proposed lexicon-based classifier as unsupervised *** proposed classifier is domain-independent,does not require training data,and uses ready-made sentiment *** lexicon-based classifier is shown to exceed the SVM for small text *** article provides analysis of errors and offers the ways to increase classifier's quality.

关键词： Text sentiment analysis Sentiment lexicons Sociological studies Supervised learning Unsupervised learning Vector space model

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A Multi-Disciplinary Framework for Continuous Biomedical Monitoring Using Low-Power Passive RFID-based Wireless Wearable Sensors 2

A Multi-Disciplinary Framework for Continuous Biomedical Mon...

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2nd IEEE international conference on Smart Computing (SMARTCOMP)

作者： Mongan, William Anday, Endla Dion, Genevieve Fontecchio, Adam Joyce, Kelly Kurzweg, Tim Liu, Yuqiao Montgomery, Owen Rasheed, Ilhaan Sahin, Cem Vora, Shrenik Dandekar, Kapil Drexel Univ Comp Sci Philadelphia PA 19104 USA Drexel Univ Pediat Philadelphia PA 19104 USA Drexel Univ Fash Design Philadelphia PA 19104 USA Drexel Univ Elect & Comp Engn Philadelphia PA 19104 USA Drexel Univ Sociol Philadelphia PA 19104 USA Drexel Univ Obstet & Gynecol Philadelphia PA 19104 USA

ISBN: (纸本)9781509008988

We have applied passive Radio Frequency Identification (RFID), typically used for inventory management, to implement a novel knit fabric strain gauge assembly using conductive thread. As the fabric antenna is stretched, the strength of the received signal varies, yielding potential for wearable, wireless, powerless smart-garment devices based on small and inexpensive passive RFID technology. Knit fabric sensors and other RFID biosensors can enable comfortable, continuous monitoring of biofeedback, but requires an integrated framework consisting of antenna modeling and fabrication, signal processing and machine learning on the noisy wireless signal, secure HIPAA-compliant data storage, visualization and human factors, and integration with existing medical devices and electronic health records (EHR) systems. We present a multidisciplinary, end-to-end framework to study, model, develop, and deploy RFID-based biosensors.

关键词： learning systems

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2nd international conference on Young Computer Scientists, Engineers and Educators, ICYCSEE 2016

2nd International Conference on Young Computer Scientists, E...

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2nd international conference on Young Computer Scientists, Engineers and Educators, ICYCSEE 2016

ISBN: (纸本)9789811020520

The proceedings contain 63 papers. The special focus in this conference is on Social Computing. The topics include: A context-aware model using distributed representations for Chinese zero pronoun resolution;a hierarchical learning framework for steganalysis of jpeg images;a multi-agent organization approach for developing social-technical software of autonomous robots;a novel approach for the identification of morphological features from low quality images;a novel filtering method for infrared image;a personalized recommendation algorithm with user trust in social network;a preprocessing method for gait recognition;a real-time fraud detection algorithm based on usage amount forecast;a self-determined evaluation method for science popularization based on IOWA operator and particle swarm optimization;a strategy for small files processing in HDFS;a SVM-based feature extraction for face recognition;a transductive support vector machine algorithm based on ant colony optimization;an optimized buffer replacement algorithm for flash storage devices;an approach for automatically generating r2rml-based direct mapping from relational databases;an improved asymmetric bagging relevance feedback strategy for medical image retrieval;an incremental graph pattern matching based dynamic cold-start recommendation method;an optimized load balancing algorithm of dynamic feedback based on stimulated annealing;application progress of signal clustering algorithm;automated artery-vein classification in fundus color images;clarity corresponding to contrast in visual cryptography;a community-oriented group recommendation framework and improvement for leach algorithm in wireless sensor network.

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2nd international conference on Computer and Communication Technologies, IC3T 2015

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2nd international conference on Computer and Communication Technologies, IC3T 2015

ISBN: (纸本)9788132225225

The proceedings contain 80 papers. The special focus in this conference is on Computer and Communication Technologies. The topics include: Human gait recognition using gait flow image and extension neural network;improved topology preserving maps for wireless sensor networks through d-VCS;real-time processing and analysis for activity classification to enhance wearable wireless ECG;adaptive video quality throttling based on network bandwidth for virtual classroom systems;an enhanced security pattern for wireless sensor network;computational intelligence-based parametrization on force-field modeling for silicon cluster using ASBO;natural language-based self-learning feedback analysis system;adaptive filter design for extraction of fetus ECG signal;linear and non-linear buckling testing on aluminium structures;a performance analysis of openstack open-source solution for IAAS cloud computing;the application of sub-pattern approach in 2d shape recognition and retrieval;moderator intuitionistic fuzzy sets and application in medical diagnosis;an empirical comparative study of novel clustering algorithms for class imbalance learning;analysis of student feedback by ranking the polarities;seizure onset detection by analyzing long-duration eeg signals;prototype of a coconut harvesting robot with visual feedback;wireless personal area network and PSO-based home security system;a framework for ranking reviews using ranked voting method;dynamic multiuser scheduling with interference mitigation in sc-FDMA-based communication systems;adaptive MAC for bursty traffic in wireless sensor networks and a minimal subset of features using correlation feature selection model for intrusion detection system.

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