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检索条件"主题词=data mining algorithms"

共 115 条记录，以下是81-90 订阅

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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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Combating Breast Cancer by an Intelligent Ensemble Classifier Approach

Combating Breast Cancer by an Intelligent Ensemble Classifie...

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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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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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Dynamic Forecasting of Solar Energy Microgrid Systems Using Feature Engineering

Dynamic Forecasting of Solar Energy Microgrid Systems Using ...

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IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES)

作者： Mohamed, Muamar Mahmood, Farhad E. E. Abd, Mehmmood A. A. Chandra, Ambrish Singh, Bhim Univ Quebec Ecole Technol Super Dept Elect Engn Montreal PQ H3C 1K3 Canada Univ Mosul Dept Elect Engn Mosul 00964 Iraq IBM Corp Austin TX 78758 USA Indian Inst Technol Delhi New Delhi 110016 India

The intermittent and stochastic nature of solar energy generation systems, climate change, and the inefficiency of modern power systems due to zero inertia have created many challenges for on-grid operators. Solar forecasting systems based on machine learning algorithms are an emerging and effective solution that uses Big data related to weather phenomena. However, the predictive ability of these algorithms is hampered by the sporadic nature of solar energy generation. In this article, a robust hybrid machine learning system that utilizes multiple linear regression (MLR) and a Pearson correlation coefficient (PCC) was tested on solar power plant sites of varying capacities in Germany (100-8500 kW). The volume of Big data features can be reduced by focusing on the features that significantly improve the reliability of the mid-term forecasting system. In this way, drastic fluctuations in the prediction of photovoltaic (PV) power generation can be avoided. The results of our approach are evaluated regarding real-world data using the extreme gradient boosting (XGBoost) with feature engineering, and principal component analysis (PCA), in order to forecast PV energy, rank, and track the importance of feature engineering for different PV capacities. Furthermore, we found that the need for selectivity and reduction of performance error was supported by ridge regression. In addition, the proposed novel XGBoost forecast system decreased the root-mean-square error (RMSE) and mean absolute error (MAE) by 30% and 18%, respectively, compared to the Autoencoder and long short-term memory (LSTM) for same datasets. Furthermore, the CoD determination coefficient (R-2) increased by 85% compared to the statistical model's autoregressive integrated moving average (ARIMA).

关键词： Artificial intelligence data mining algorithms forecasting techniques machine learning power system statistical analysis solar microgrid XGBoost algorithm

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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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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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Machine Learning for Improving Mobile User Satisfaction 19

Machine Learning for Improving Mobile User Satisfaction

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34th ACM/SIGAPP Annual International Symposium on Applied Computing (SAC)

作者： Draa, Ismat Chaib Bouquillon, Fabien Niar, Smail Grislin-Le Strugeon, Emmanuelle Univ Polytech Hauts De France CNRS UMR 8201 LAMIH Valenciennes France

ISBN: (纸本)9781450359337

Optimizing energy consumption in modern mobile handheld devices plays a crucial role as lowering power consumption impacts system's autonomy and system reliability. Recent mobile platforms have an increasing number of sensors and processing components. Added to the increasing popularity of power-hungry applications, battery life in mobile devices is an important issue. However, we think that the utilization of the large amount of data from the various sensors can be beneficial to detect the changing device context, the user needs or the running application requirements in terms of resources. When these information are used properly, an efficient control of power knobs can be implemented thus reducing the energy consumption. This paper presents URBOC, for User Request Based Optimization Component. This component is an extension of our previous framework [7] ENOrMOUS. This framework was able to identify and classify the user contexts in order to understand user habits, preferences and needs which allow to improve the operating system power scheme. In this paper, we extend the use of ENOrMOUS by allowing the user to send requests in order to extend the battery life up to a specific time. Machine Learning (ML) and data mining algorithms have been used to obtain an efficient trade-off between power consumption reduction opportunities and user requests satisfaction. The proposed solution increases battery life by up to seven hours depending on user requests vs. the out-of-the-box operating system power manager with a negligible overhead.

关键词： Mobile Power Consumption Mobile Systems Neural networks run-time analysis data mining algorithms

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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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Analysis of Maintenance Histories of Industrial Equipment with Frequent Maintenance Demand

Analysis of Maintenance Histories of Industrial Equipment wi...

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9th IEEE International Conference on Industrial Informatics (INDIN)

作者： Schwenke, Clemens Vasyutynskyy, Volodymyr Roeder, Andre Kabitzsch, Klaus Tech Univ Dresden Chair Tech Informat Syst D-01062 Dresden Germany

ISBN: (纸本)9781457704345

In order to guarantee high operational availability, the modular industrial equipment requires frequent maintenance, which oftentimes is carried out by the manufacturer. Reports about service technician's activities are stored in maintenance histories. Manufacturers of such equipment would benefit significantly from analysis of recorded maintenance and fault histories for planning of maintenance activities, offering scalable service contracts and finding reasons for product faults. This paper introduces a methodology that supports the interpretation of the maintenance histories to allow the manufacturers the analysis and optimization of maintenance operations. The methodology interprets the maintenance histories as sequences of events, containing meaningful patterns. Tailored data mining algorithms are applied, that provide causality details going beyond the results of standard techniques. The paper uses the example of maintenance reports of gas analytic equipment.

关键词： Analytical models Buildings data mining History Inspection Maintenance engineering Tagging data mining data mining algorithms fault diagnosis fault histories frequent maintenance demand gas analytic equipment history industrial equipments maintenance acti

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Impact of Social Attributes on Predictive Analytics in Telecommunication Industry

Impact of Social Attributes on Predictive Analytics in Telec...

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15th IEEE International Multitopic Conference (INMIC)

作者： Hashmi, Osama Z. Sheikh, Shahid SZABIST Islamabad Islamabad Pakistan

ISBN: (纸本)9781467322522;9781467322492

This paper will be presenting how the social attributes impact the Predictive Analytics results when applied on the telecommunication industry dataset. Predictive Analytics is the new emerging field in data mining, and is actively being applied to solve multiple business questions, such as Customer Churn, Product Up-Sell and Cross-Sell, etc. Predictive Analytical models exploit patterns that are found in historical and transactional data to identify risks, opportunities and future events. During comparative analysis it was noticed that with the Addition of Social Attributes, the Efficiency and Reliability of these Models is greatly enhanced.

关键词： Predictive Analytics Social Network Social Network Analysis Customer Churn Prediction data mining algorithms

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