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International Conference on Bioinformatics and Systems Biology (BSB)

作者： Shaikh, Tawseef Ayoub Ali, Rashid Aligarh Muslim Univ Dept Comp Engn Aligarh Uttar Pradesh India

ISBN: (纸本)9781538664346

Breast cancer is the most vulnerable type of cancers and has posed a serious threat and challenge to human existence. Every domain of human innovation is trying in tackling this threat and the present IT age has also contributed for this noble cause through the innovative innovation of machine learning concepts. Here in this work, we present an intelligent ensemble classifier mix up using the all-time WEKA data mining toolkit on BCDR-F03 benchmark cancer dataset. Five individual classifiers Naive Bayes, SVM, Simple Logistics, Random Forest and iBK (Instance Based Learning) from different groups in machine learning are evaluated on selected dataset and their corresponding individual accuracies came out to be 76%, 80%, 80%, 81% and 79. 89% respectively. The final results revealed that three ensembles picked up from voting i.e. Voting Ensemble (SMO, RF, iBK), Voting Ensemble (SL, RF, iBK) and Voting Ensemble (SGD, SL, iBK) got highest accuracies of 83.0163%, 83.1522% and 82.7446% respectively. Similarly in stacking, Stacking Ensemble (SMO, RF, iBK, NB), Stacking Ensemble (SMO, RF, iBK) and Stacking Ensemble (SMO, RF) got highest accuracies of 82.7446%, 83.0163% and 81.9293%, when the combining algorithm used is Simple Logistics, respectively. The results clearly are far better than the individual performance of ML algorithms on our dataset.

关键词： machine learning ensemble methods BCDR (Breast Cancer Digital Repository) data mining algorithms classification performance

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A comparative study of the various clustering algorithms in e-learning systems using weka tools

A comparative study of the various clustering algorithms in ...

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JCCO Joint International Conference on ICT in Education and Training (TICET) / International Conference on Computing in Arabic (ICCA), and International Conference on Geocomputing (GECO)

作者： Khalfallah, Jihen Slama, Jaleleddine Ben Hadj Sousse Univ Natl Sch Engineers Sousse ENISo LATIS Erriadh 4023 Tunisia

ISBN: (纸本)9781538668801

Clustering also called cluster analysis is the task of data objects grouping where group objects are similar. Simultaneously, they should be different from other groups objects. Such groups are termed as Clusters. We are study in this paper several clustering algorithms applied in E-learning systems. Our objective lies in performing a deep analysis of clustering data mining techniques both theoretically and experimentally and to do a comparative study in order to distinguish which technique is more suitable to identify e-learners profile and also to evaluate better student performance in engineering education. A good clustering method is able to yield high quality clusters. The algorithms under investigation are: Canopy algorithm, Cobweb algorithm, EM algorithm, FarthestFirst algorithm, FilteredClusterer algorithm, MakeDensityBasedClusterer algorithm. The performance of clustering the six algorithms is compared through the use of clustering tool WEKA (version 3.7.12) as an open source tool.

关键词： clustering data mining algorithms E-learning systems WEKA

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Proposed Open Source Framework for Interactive IoT Smart Museums 18

Proposed Open Source Framework for Interactive IoT Smart Mus...

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22nd Pan-Hellenic Conference on Informatics (PCI)

作者： Dossis, Michael Kazanidis, Ioannis Valsamidis, Stavros Kokkonis, George Kontogiannis, Sotirios Tech Edu Inst Western Macedonia Dept Comp Engn Kastoria Greece Eastern Macedonia & Thrace Inst Technol Dept Accounting & Finance Kavala Greece Tech Edu Inst Western Macedonia Dept Business Adm Grevena Greece Univ Ioannina Dept Math Ioannina Greece

ISBN: (纸本)9781450366106

Nowadays, the behavior and needs of museum visitors are focused and demanding. New available technologies, innovative applications, museum exhibits digitization and exhibits information 3D content outlays, excite museum visitors who desire even more specific information through human-machine interactions. Based on the above, authors propose an open source IoT framework called IRME (Interactive, Responsive Museum Experience). The IRME framework have provisions of real-time, is responsive and focused on navigation and integrates indoor positioning technology, IoT sensors, actuators, haptic devices and cloud services. The contemporary museums equipped with the proposed framework should be able to offer normalized information classified in thematic sections. The visitors of an IRME capable museum will be able to explore specific sections of interest, with the use of a mobile phone application, obtain section navigation guidelines and interrogate smart exhibit signs-actuators in order to attain information of visitor interest. The IRME framework smart cognitive agents, integrated with cognitive services and algorithms, and equipped with data-mining and appropriate artificial intelligence logic, should enforce the functionality of an IRME capable system to learn from visitor preferences and respond more accurately on future content requests, thus providing to the visitors enhanced experiences.

关键词： IoT IoT protocols BLE technology Haptic protocols indoor positioning iBeacons smart agents cognitive services chat-bots classification algorithms data mining algorithms mobile phone applications

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An Efficient Firewall For Web Applications (EFWA) 2

An Efficient Firewall For Web Applications (EFWA)

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2017 International Conference on Computer Science and Engineering (UBMK)

作者： Sahin, Metin Sogukpinar, Ibrahim Gebze Tech Univ Comp Engn Dept Kocaeli Turkey

ISBN: (纸本)9781538609309

Today, with the development of technology, communication tools like computers, phones, tablets and etc. use the http as default protocol. The fact that large data stream transactions are over the http protocol has caused this protocol to become a target for attackers. It is important to analyse HTTP traffic for attack prevention systems and to detect attack attempts. Signature based, anomaly based and mixed methods are used for the intrusion detection system. The most common attack methods applied by attackers using http traffic are SQL injection, cross-site scripting (XSS), cross-site request forgery (CRLF injection), malicious file execution, unsafe direct object reference (OSS) etc. For these attacks, we will talk about anomalous attack prevention. The study used the CSIC 2010 HTTP dataset, which contains popular types of attacks and is openly accessible. Decision Tree (C4.5), K Nearest Neighborhood (KNN) algorithms were used for the emerging classes. As a result, a significant result of 96.26% has been achieved.

关键词： Web Attacks Detection Web Applications Security Walls Machine Learning data mining algorithms

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Predicting large scale fine grain energy consumption 8

Predicting large scale fine grain energy consumption

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8th International Conference on Sustainability in Energy and Buildings (SEB)

作者： Cerquitelli, Tania Politecn Torino Control & Comp Engn Dept Cso Duca Abruzzi 24 I-10129 Turin Italy

Today a large volume of energy-related data have been continuously collected. Extracting actionable knowledge from such data is a multi-step process that opens up a variety of interesting and novel research issues across two domains: energy and computer science. The computer science aim is to provide energy scientists with cutting-edge and scalable engines to effectively support them in their daily research activities. This paper presents SPEC, a scalable and distributed predictor of fine grain energy consumption in buildings. SPEC exploits a data stream methodology analysis over a sliding time window to train a prediction model tailored to each building. The building model is then exploited to predict the upcoming energy consumption at a time instant in the near future. SPEC currently integrates the artificial neural networks technique and the random forest regression algorithm. The SPEC methodology exploits the computational advantages of distributed computing frameworks as the current implementation runs on Spark. As a case study, real data of thermal energy consumption collected in a major city have been exploited to preliminarily assess the SPEC accuracy. The initial results are promising and represent a first step towards predicting fine grain energy consumption over a sliding time window. (C) 2017 The Authors. Published by Elsevier Ltd.

关键词： energy data big data frameworks data mining algorithms data stream analysis

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Predicting Large Scale Fine Grain Energy Consumption

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Energy Procedia 2017年 111卷 1079-1088页

作者： Tania Cerquitelli Control and Computer Engineering Department Politecnico di Torino C.so Duca degli Abruzzi 24 10129 Torino ITALY

关键词： energy data big data frameworks data mining algorithms data stream analysis

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RGCA: A Reliable GPU Cluster Architecture for Large-Scale Internet of Things Computing Based on Effective Performance-Energy Optimization

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SENSORS 2017年第8期17卷 1799-1799页

作者： Fang, Yuling Chen, Qingkui Xiong, Neal N. Zhao, Deyu Wang, Jingjuan Univ Shanghai Sci & Technol Shanghai 200093 Peoples R China Northeastern State Univ Dept Math & Comp Sci Tahlequah OK 74464 USA

This paper aims to develop a low-cost, high-performance and high-reliability computing system to process large-scale data using common data mining algorithms in the Internet of Things (IoT) computing environment. Considering the characteristics of IoT data processing, similar to mainstream high performance computing, we use a GPU (Graphics Processing Unit) cluster to achieve better IoT services. Firstly, we present an energy consumption calculation method (ECCM) based on WSNs. Then, using the CUDA (Compute Unified Device Architecture) Programming model, we propose a Two-level Parallel Optimization Model (TLPOM) which exploits reasonable resource planning and common compiler optimization techniques to obtain the best blocks and threads configuration considering the resource constraints of each node. The key to this part is dynamic coupling Thread-Level Parallelism (TLP) and Instruction-Level Parallelism (ILP) to improve the performance of the algorithms without additional energy consumption. Finally, combining the ECCM and the TLPOM, we use the Reliable GPU Cluster Architecture (RGCA) to obtain a high-reliability computing system considering the nodes' diversity, algorithm characteristics, etc. The results show that the performance of the algorithms significantly increased by 34.1%, 33.96% and 24.07% for Fermi, Kepler and Maxwell on average with TLPOM and the RGCA ensures that our IoT computing system provides low-cost and high-reliability services.

关键词： Internet of Things data mining algorithms GPU cluster performance energy consumption reliability

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Apache Mahout's k-Means vs. Fuzzy k-Means Performance Evaluation 8

Apache Mahout's k-Means vs. Fuzzy k-Mea...

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8th International Conference on Intelligent Networking and Collaborative Systems (INCoS)

作者： Xhafa, Fatos Bogza, Adriana Caballe, Santi Barolli, Leonard Univ Politecn Cataluna Barcelona Spain Univ Oberta Catalunya Barcelona Spain Fukuoka Inst Technol Fukuoka Japan Univ Oberta Catalunya SmartLearn Grp Barcelona Spain

ISBN: (纸本)9781509041237

The emergence of the Big data as a disruptive technology for next generation of intelligent systems, has brought many issues of how to extract and make use of the knowledge obtained from the data within short times, limited budget and under high rates of data generation. The foremost challenge identified here is the data processing, and especially, mining and analysis for knowledge extraction. As the old data mining frameworks were designed without Big data requirements, a new generation of such frameworks is being developed fully implemented in Cloud platforms. One such frameworks is Apache Mahout aimed to leverage fast processing and analysis of Big data. The performance of such new data mining frameworks is yet to be evaluated and potential limitations are to be revealed. In this paper we analyse the performance of Apache Mahout using large real data sets from the Twitter stream. We exemplify the analysis for the case of two clustering algorithms, namely, k-Means and Fuzzy k-Means, using a Hadoop cluster infrastructure for the experimental study.

关键词： data mining algorithms Apache Mahout Big data k-Means Fuzzy k-Means Performance Hadoop Cluster

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Automated Flow-based Rule Generation for Network Intrusion Detection Systems 24

Automated Flow-based Rule Generation for Network Intrusion D...

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24th Iranian Conference on Electrical Engineering (ICEE)

作者： Fallahi, Naser Sami, Ashkan Tajbakhsh, Morteza Shiraz Univ Dept Comp Sci & Engn Shiraz *** Iran Shiraz Univ IT Shiraz *** Iran

ISBN: (纸本)9781467387897

Snort is a popular open-source Intrusion Detection System (IDS). Since rules are updated offline and network environment changes dynamically, Snort has a low detection rate especially for new types of attacks. Since attack signatures are not stored in the system, attackers could intrude without being detected. The aim of this research is to automate rule generation for system by use of logs of performed attacks. This approach has been implemented using two data mining algorithms called Ripper and C5.0. Automatic rule generation improves security of Snort and attacks are detected better. Five types of attacks, like Denial of service and Brute Force, have been investigated in this work and tested on newly released ISCX 2012 dataset which has 84.42 Gigabytes. By processing the dump, it can be used to generate general rules and eight new features from known features of streams. Detection rate of more than 99 percent was obtained for some attacks, which represent sensible impact of this approach on Snort software.

关键词： network intrusion detection systems signature-based detection automatic rule generation computer security data mining algorithms

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Discovering Causal Structures from Time Series data via Enhanced Granger Causality 28th

Discovering Causal Structures from Time Series Data via Enha...

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28th Australasian Joint Conference on Artificial Intelligence (AI)

作者： Luo, Ling Liu, Wei Koprinska, Irena Chen, Fang Univ Sydney Sch Informat Technol Sydney NSW 2006 Australia NICTA Australian Technol Pk Lab Sydney NSW Australia Univ Technol Sydney Fac Engn & Informat Technol Sydney NSW 2007 Australia

ISBN: (纸本)9783319263502;9783319263496

Granger causality has been applied to explore predictive causal relations among multiple time series in various fields. However, the existence of non-stationary distributional changes among the time series variables poses significant challenges. By analyzing a real dataset, we observe that factors such as noise, distribution changes and shifts increase the complexity of the modelling, and large errors often occur when the underlying distribution shifts with time. Motivated by this challenge, we propose a new regression model for causal structure discovery - a Linear Model with Weighted Distribution Shift (linear WDS), which improves the prediction accuracy of the Granger causality model by taking into account the weights of the distribution-shift samples and by optimizing a quadratic-mean based objective function. The linear WDS is integrated in the Granger causality model to improve the inference of the predictive causal structure. The performance of the enhanced Granger causality model is evaluated on synthetic datasets and real traffic datasets, and the proposed model is compared with three different regression-based Granger causality models (standard linear regression, robust regression and quadratic-mean-based regression). The results show that the enhanced Granger causality model outperforms the other models especially when there are distribution shifts in the data.

关键词： data mining algorithms Causal inference Time series regression

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