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This article presents the technology of data mining civil infrastructure. The application of technology for solving the problem of estimating the state of the linear sections of pipeline networks. The technology combines the mechanisms of fuzzy modeling and GIS-tools. The technology has been tested at a number of companies that operate water and heat networks.
Many computer vision applications adopting consumer depth cameras have recently received much attention due to the availability at low prices and the potential benefits to provide more useful information, which can result in a higher accuracy (e.g., for object recognition). In this work, to address the problem of drinking activity recognition in vision-based Ambient Assisted Living by using depth...
Even though wine-drinkers generally agree that wines may be ranked by quality, wine-tasting is famously subjective. There have been many attempts to construct a more methodical approach to the assessment of wines. We propose a method of assessing wine quality using a decision tree, and test it against the wine-quality dataset from the UC Irvine Machine Learning Repository. Results are 60% in agreement...
In this paper, we discuss a method to define semi-automatically fuzzy partition rules to provide a powerful and accurate insight into cardiac arrhythmia. As suggested by our evaluation, this provide a robust, scalable, and accurate system, which can successfully tackle the challenges posed by the utilization of big data in the medical sector.
In this paper, we discuss the evaluation of the probabilistic extraction as introduced in [1], by considering three different datasets introduced in [1] -- [3]. the results show the potential of the approach, as well as its reliability and efficiency when analyzing datasets with different properties and structures. This is part of ongoing research aiming to provide a tool to extract, assess and visualize...
Recent years have witnessed a series of occupy protest events all over the world. Detecting and monitoring these events is an important and challenging task in social science research and also can provide reference for government's emergency management. Existing methods mainly solve this problem by document clustering techniques. This paper proposes a novel graph-based occupy protest event detection...
Data clustering is usually time-consuming since it by default needs to iteratively aggregate and process large volume of data. Approximate aggregation based on sample provides fast and quality ensured results. In this paper, we propose to leverage approximation techniques to data clustering to obtain the trade-off between clustering efficiency and result quality, along with online accuracy estimation...
In this paper, we propose a hybrid method for intrusion detection which is based on k-means, naive-bayes and back propagation neural network (KBB). Initially we apply k-means which is partition-based, unsupervised cluster analysis method. In the form of clusters, we attain the gathered data which can be easily processed and learned by any machine learning algorithm. These outcomes are provided to...
Social media is becoming a critical avenue for businesses today to target new customers and create brand loyalty. In order to target users effectively, companies need to know basic information about their users. However, in many cases, user profiles are either incomplete or completely wrong, and one of the most critical pieces of private information is gender. In this paper we examine the case of...
By virtue of recent developments in machine learning techniques, higher-level information can now to be extracted from big data. To analyze big data, efficient and smart representations of data achieved by using sufficiently fast algorithms, as well as highly accurate results, are important. In this paper, we focus on extracting multiple semantic relations using light-weight processing through the...
The number of security incidents is increasing and many of them are derived from malware activities. However, recent malware have become so sophisticated that commercial anti-virus software is not capable of detecting 100% of them. NTT Global Threat Intelligence Report shows that more than half of malware are not detected by commercial antivirus software [1]. Nowadays, post-infection countermeasure...
Sentiment analysis deals with identifying polarity orientation embedded in users' comments and reviews. It aims at discriminating positive reviews from negative ones. Sentiment is related to culture and language morphology. In this paper, we investigate the effects of language morphology on sentiment analysis in reviews written in the Arabic language. In particular, we investigate, in details, how...
We present in this paper the analysis results of prominent educational characteristics differentiating people from the two regions in the world: advanced economies versus east Asia and the pacific countries. The automatic multivariate analysis of classification trends has been demonstrated through the visual data mining tool called KNIME. We found from the empirical studies that from the years 1950...
Stereo-footprint is a vital trace evidence of criminal detection. Thus, it is important to make a high accuracy feature extraction of the stereo-footprint. In this paper, a system of stereo-footprint data acquisition-recognition is designed. In the system, feature extraction is completed through CP35MHT80 which offered by Wenglor, image extraction is gotten through C920 which is offered by Logitech...
Rainfall prediction is an important part of weather prediction. Compared to conventional methods predicting rainfall rate, the approach applying historical records and data mining technology shows obviously advantage in computing cost. Many excellent works have been done attempting to build predicting model with data mining methods, however, most of them just test the predicting accuracy on data set...
For the purpose of discovering White Dwarf + Main Sequence (WDMS) from massive spectra, in this paper, an unsupervised learning algorithm for Nonlinear Dimensionality Reduction (NLDR) named Isometric Feature Mapping (Isomap) is discussed. The applicability of Isomap to Sloan Digital Sky Survey Data Release 10 (SDSS-DR10) is confirmed. Furthermore, Particle Swarm Optimization (PSO) is implemented to...
As the number of cyber attacks have increased, detecting the intrusion in networks become a very tough job. For network intrusion detection system (NIDS), many data mining and machine learning techniques are used. However, for evaluation, most of the researchers used KDD Cup 99 data set, which has widely criticized for not showing current network situation. In this paper we used a new labelled network...
The traffic identification is a preliminary and essential step for stable network service provision and efficient network resource management. While a number of identification methods have been introduced in literature, the payload signature-based identification method shows the highest performance in terms of accuracy, completeness, and practicality. However, the payload signature-based method's...
In data mining, a well known problem of “Curse of Dimensionality” occurs due to presence of large number of dimensions in a dataset. This problem leads to reduced accuracy of machine learning classifiers because of presence of many insignificant and irrelevant dimensions or features in the dataset. Data mining applications such as bioinformatics, risk management, forensics etc., generally involves...
The idea of opposition-based learning was introduced 10 years ago. Since then a noteworthy group of researchers has used some notions of oppositeness to improve existing optimization and learning algorithms. Among others, evolutionary algorithms, reinforcement agents, and neural networks have been reportedly extended into their “opposition-based” version to become faster and/or more accurate. However,...
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