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5th IEEE-Region-10 Humanitarian Technology Conference (R10-HTC)

作者： Rahim, Md Shamsur Chowdhury, A. Z. M. Ehtesham Islam, Md. Asiful Islam, Mir Riyanul Amer Int Univ Bangladesh Dept Comp Sci Dhaka Bangladesh

ISBN: (纸本)9781538621752

YouTube is the most popular video contents sharing platform around the world. As a consequence, YouTube has become one of the most preferred choices to the movie producers and studios for connecting/communicating with their potential viewers through sharing trailers and teasers. data regarding the trailers of a movie from YouTube can provide useful insights for predicting the gross income of movies. In this paper, we have prepared a dataset of 7988 movie trailers from YouTube. The dataset contains different attributes like opening income, number of views, number of likes, number of dislikes, number of comments. We prepared two prediction models and applied four regression techniques to find out the most suitable technique for predicting the gross income of a movie. The comparative analysis has depicted that linear regression is the most suitable method regarding the prediction of movies gross income using these attributes. Furthermore, we have provided future research issues from where our work has ended.

关键词： predictive analytics trailer gross income gross income prediction movie data mining algorithm YouTube

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Design and Implementation of Network Security Audit and Monitoring System

Design and Implementation of Network Security Audit and Moni...

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2018 3rd International Symposium on Electronics,Electrical Engineering,Manufacturing and Systems(EEEMS 2018)

作者： Chen Xin Wuhan Polytechnic

This paper introduces an example system of network monitoring and auditing based on the technical requirements of network monitoring and security auditing. By sampling and analyzing the network data, the system monitors the behavior of network users, records and alarms the security behavior of the system through log auditing of the host and proxy server, facilitates the publication of network information, and provides the analysis results and statistical data to ADMI. The administrators have greatly improved the level of network security management and achieved satisfactory results. Firstly, this paper analyses the network security audit log system. According to the need of the design and construction of the network security audit log monitoring system, under the guidance of the design and implementation of the prototype of B/S C/S hybrid system, the architecture of the system is given. Current audit methods generally adopt some methods of analysis and comparison. On this basis, this paper proposes that the system adopts a security audit engine with learning ability, and gives the framework of the security audit engine. Through an example analysis, it proves that the prototype system can achieve real-time monitoring and centralized management. Realize intelligent audit. The implementation of this system can provide strong support for the security audit of network system.

关键词： Network security security auditing and monitoring network log data mining algorithm log fusion

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Adaptive nonparametric control chart for time-varying and multimodal processes

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JOURNAL OF PROCESS CONTROL 2016年 37卷 34-45页

作者： Kang, Ji Hoon Yu, Jaehong Kim, Seoung Bum Samsung SDS Algorithm Res Ctr Ind Technol Lab Seoul 138240 South Korea Korea Univ Dept Ind Management Engn Seoul 136713 South Korea Rutgers State Univ Ctr Discrete Math & Theoret Comp Sci Piscataway NJ 08854 USA

Multivariate statistical process control techniques have been widely used to improve processes by reducing variation and preventing defects. In modern manufacturing, because of the complexity and variability of processes, traditional multivariate control charts such as Hotelling's T-2 cannot efficiently handle situations in which the patterns of process observations are nonlinear, multimodal, and time varying. In the present study, we propose a nonparametric control chart, which is capable of adaptively monitoring time-varying and multimodal processes. Experiments with simulated and real process data from a thin film transistor-liquid crystal display (TFT-LCD) demonstrate the effectiveness and accuracy of the proposed method. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Clustering data mining algorithm Multivariate control chart Multimodality Time-varying process False alarms

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The Human Body Characteristic Parameters Extraction and Disease Tendency Prediction based on Multi-Sensing Fusion algorithms

The Human Body Characteristic Parameters Extraction and Dise...

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6th lEEE Annual International Conference on Cyber Technalogy in Automation, Control and Intelligent Systems (IEEE-CYBER)

作者： Shi, Guanyi Geng, Chao Liu, Hadiang Su, Hang Jin, Yufeng Sun, Shengli Peking Univ Shenzhen Grad Sch Shenzhen Peoples R China Sun Yat Sen Univ Res Inst Shenzhen Peoples R China

ISBN: (纸本)9781509027323

Disease prediction and the pursuit of health has always been the relish topic, especially as the aggravating population aging phenomenon. However, the existing medical knowledge used in disease prediction system is given by experts, which is vulnerable to restrictions of real-time and convenience. On the other hand, people don't want to use the high cost, complex medical monitoring system which could induce the inconvenience of everyday life. At the same time, the design and development of wearable bio-sensor systems for health monitoring has garnered lots of attention in the scientific community and the industry during the last years. In this article, we used an electronic health system based on multi-sensor fusion strategy. On the basis of not affecting the daily life, we got a variety of sensors with sensing, computational and data communication features, worn on the body and extracted the targeted health characteristic parameters of human. Therefore, we used the data mining algorithm to process the data in order to predict the disease tendency in long-term. Taking stroke prediction model as an example, after analyzing factors which are related to stroke, we used multi-sensor fusion system and collected 25 healthy attributes, coupled with a class attribute, built the training sample which contains a total of 125 cases of samples with 26 attributes. Based on the training sample, we used machine learning algorithms, where recognition rate could achieve 94.35%. On the basis of the classification weights, we got the most closely attributes associated with stroke and 9 healthy attributes combination with the best classification accuracy of 96.77%. Result shows that the system has achieved good classification accuracy and verified the feasibility of stroke prediction model proposed. Thus, electronic health system based on multi-sensor fusion has a high research value.

关键词： E-health multi-sensor fusion data mining algorithm disease tendency prediction

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IPARBC: An Improved Parallel Association Rule based on MapReduce Framework

IPARBC: An Improved Parallel Association Rule based on MapRe...

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International Conference on Networking and Network Applications (NaNA)

作者： Chang, Hong-Yi Hong, Zih-Huan Lin, Tu-Liang Chang, Wan-Kun Lin, Yi-Ying Natl Chiayi Univ Dept Management Informat Syst Chiayi Taiwan Ind Technol Res Inst Informat & Resource Technol Dept Taichung Taiwan

ISBN: (纸本)9781467398039

Recently, data mining in big data has become an important concern for researchers. data mining, which refers to mining the relationship between items in a dataset, has been applied by businesses to seek profitable outcomes. Association rule mining algorithms such as Apriori and the FP-growth are efficient methods for discovering relations between items in large databases. To enhance the performance, many researches tend to enhance the traditional method using the MapReduce framework. In this paper, we proposed an improved association rule algorithm (IPARBC) based on MapReduce framework. The concept of combinatorial mathematics is used as the theoretical basis of the algorithm, and in order to improve mining performance by MapReduce framework, we address the high volume problem of big data. Experimental results show that the proposed algorithm outperform other algorithms substantially in terms of runtime.

关键词： data mining algorithm Big data MapReduce Framework Cloud Computing

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Using Different Substitution Matrices in a String-Matching Technique for Identifying Viral Polymorphic Malware Variants

Using Different Substitution Matrices in a String-Matching T...

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IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Naidu, Vijay Narayanan, Ajit Auckland Univ Technol Sch Comp & Math Sci Auckland New Zealand

ISBN: (纸本)9781509006229

Antivirus systems have difficulty in detecting polymorphic variants of known viruses without explicit signatures for such variants. Initial work on investigating efficient and effective string-based approaches for the automatic generation of signatures for the identification of some or all new polymorphic variants, was initially encouraging. That initial work was restricted by a number of experimental aspects. The aim of the research reported here is to examine the effects of using different substitution matrices in a string-based methods for the automatic generation of signatures for the detection of some or all new polymorphic variants. We establish how our proposed syntactic-based method using the well-known string matching Smith-Waterman algorithm can successfully identify the known polymorphic variants of *** virus. Our string-matching technique may metamorphose our understanding of polymorphic variant generation and may lead to a new phase of syntactic-based anti-viral software.

关键词： string matching algorithm Smith-Waterman algorithm JS.Cassandra virus local pairwise sequence alignment substitution matrix multiple sequence alignment data mining algorithm automatic signature generation

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Evaluation and Optimization on Applicability of Big data Technologies in Smart Grid

Evaluation and Optimization on Applicability of Big Data Tec...

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作者： Chenjun Sun Jing Zhou State Grid Hebei Electric Power Corporation School of Control and Computer Engineering North China Electric Power University

As a newly emerging solution to electricity problems in China, smart grid is also the development tendency of electric power industry worldwide in the future. In smart grid, huge data of electricity production involve multi-type big data technologies. Selecting suitable ones from these technologies, which will be used to analyze, compute and manage big data, is a vital to find out the potential value, law and pattern of smart ***, this paper proposes evaluation criterion and approach based on data encapsulation analysis and characteristics of big data technologies in smart grid, and finally explores the applicability of various big data technologies in smart grid.

关键词： Big data data mining algorithm Daily electricity load

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Running Genome Wide data Analysis Using a Parallel Approach on a Cloud Platform 15th

Running Genome Wide Data Analysis Using a Parallel Approach ...

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15th Conference on Artificial Intelligence in Medicine (AIME)

作者： Demartini, Andrea Capozzi, Davide Malovini, Alberto Bellazzi, Riccardo Univ Pavia I-27100 Pavia Italy Biomeris Srl Pavia Italy IRCCS Fdn Salvatore Maugeri Pavia Italy Spindox Spa Milan Italy

ISBN: (纸本)9783319195513;9783319195506

Hierarchical Naive Bayes (HNB) is a multivariate classification algorithm that can be used to forecast the probability of a specific disease by analysing a set of Single Nucleotide Polymorphisms (SNPs). In this paper we present the implementation of HNB using a parallel approach based on the Map-Reduce paradigm built natively on the Hadoop framework, relying on the Amazon Cloud Infrastructure. We tested our approach on two GWAS datasets aimed at identifying the genetic bases of Type 1 (T1D) and Type 2 Diabetes (T2D). Both datasets include individual level data of 1,900 cases and 1,500 controls with similar to 420,000 SNPs. For T2D the best results were obtained using the complete set of SNPs, whereas for T1D the best performances were reached using few SNPs selected through standard univariate association tests. Our cloud-based implementation allows running genome wide simulations cutting down computational time and overall infrastructure costs.

关键词： Map reduce Cloud computing data mining algorithm Genome-wide association studies

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The impact of training data tailoring on demand forecasting models in retail

The impact of training data tailoring on demand forecasting ...

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37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

作者： Karan, M. Pintar, D. Skocir, Z. Vranic, M. Alajkovic, A. Milojevic, J. Plesa, M. Univ Zagreb Fac Elect Engn & Comp Zagreb 41000 Croatia Konzum DD Zagreb Croatia

ISBN: (纸本)9789532330816

Demand forecasting plays a very important role in retail business. Retail information systems commonly store large amounts of data which are subsequently used by sophisticated data mining tools for building forecasting models. Quality of these models is usually measured through their predictive accuracy as their most important property, followed by other measures which consider average underestimate and overestimate costs etc. Even though the choice of data mining algorithm is usually paramount, training set cleansing and preparation has a significant influence on final model performance. This article discusses and analyses the impact of training set preparation and tailoring on a final forecasting model performance used in a real world example from the retail industry.

关键词： data mining demand forecasting retail data processing building forecasting models data mining algorithm data mining tools demand forecasting models final forecasting model retail business retail information systems training data tailoring training set preparation Analytical models Biological system modeling Buildings data models Forecasting Predictive models Training Demand Prediction data mining business data processing Biological system modeling data models ANALYSIS MODELS Buildings Prediction models Functional training Tailoring mining Equipment

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Novel techniques and an efficient algorithm for closed pattern mining

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EXPERT SYSTEMS WITH APPLICATIONS 2014年第11期41卷 5105-5114页

作者： Kiraly, Andras Laiho, Asta Abonyi, Janos Gyenesei, Attila Univ Pannonia Dept Proc Engn H-8200 Veszpreem Hungary Univ Turku Turku Ctr Biotechnol Finnish Microarray & Sequencing Ctr FIN-20520 Turku Finland Abo Akad Univ FIN-20520 Turku Finland

In this paper we show that frequent closed itemset mining and biclustering, the two most prominent application fields in pattern discovery, can be reduced to the same problem when dealing with binary (0-1) data. FCPMiner, a new powerful pattern mining method, is then introduced to mine such data efficiently. The uniqueness of the proposed method is its extendibility to non-binary data. The mining method is coupled with a novel visualization technique and a pattern aggregation method to detect the most meaningful, non-overlapping patterns. The proposed methods are rigorously tested on both synthetic and real data sets. (c) 2014 Elsevier Ltd. All rights reserved.

关键词： Biclustering Closed frequent itemset mining Clustering visualization data mining algorithm Pattern detection

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