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Due to the rise and rapid growth of E-Commerce, use of credit cards for online purchases has dramatically increased and it caused an explosion in the credit card fraud. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. In real life, fraudulent transactions are scattered with genuine transactions and...
To extract implicit knowledge and data relationships from the audio and audio similarity measure, this paper uses the audio mining techniques. A model for audio clustering and classification technique is proposed. Neural networks are used for classifying the data. The working prototype of the Music classification system has been developed and tested in MATLAB 6.5 using the signal Processing Toolbox...
Traditionally e-learning systems are emphasized on the online content generation and most of them fail in considering the requirements and learning styles of end user, while representing it. Therefore, appears the need for adaptation to the user's learning behavior. Adaptive e-learning refers to an educational system that understands the learning content and the user interface according to pedagogical...
Classification, or supervised learning, is one of the major data mining processes. Protein classification focuses on predicting the function or the structure of new proteins. This can be done by classifying a new protein to a given family with previously known characteristics. There are many approaches available for classification tasks, such as statistical techniques, decision trees and the neural...
The problem of spam detection is a crucial task in the web information retrieval systems. The dynamic nature of information resources as well as the continuous changes in the information demands of the users makes the task of web spam detection a challenging topic. So far many different methods from researchers with different backgrounds have been proposed to tackle with spam web pages problem. In...
This paper presents an improved fuzzy neural network (IFNN) for pattern recognition. The IFNN consists of several sub-networks, which represent different patterns. Each sub-network distinguishes a particular pattern from others, and each pattern corresponds to the certain inputs. In IFNN, an empirical formula tested many times is used to calculate the number of nodes in the hidden layer, and the learning...
Creating an applicable and precise failure prediction system is highly desirable for decision makers and regulators in the finance industry. This study develops a new Failure Prediction (FP) approach which effectively integrates a fuzzy logic-based adaptive inference system with the learning ability of a neural network to generate knowledge in the form of a fuzzy rule base. This FP approach uses a...
Mining data has attracted many researchers because of its usefulness of extracting valuable information from the huge volume of continuously increasing databases. In general using labeled data has been more difficult and time consuming than using unlabeled samples. There are several methods that could be used to build a classifier using unlabeled samples. However these may suffer from poor classification...
A tool for discovery of gait anomalies of elderly from motion sensor data is proposed. The gait of the user is captured with the motion capture system, which consists of tags attached to the body and sensors situated in the apartment. Position of the tags is acquired by the sensors and the resulting time series of position coordinates are analyzed with dynamic time warping and machine learning algorithms...
It is estimated that over 8 million cell phones are lost or stolen each year [7]; often the loss of a cell phone means the loss of personal data, time and enormous aggravation. In this paper we present machine-learning based algorithms by which a cell phone can discern that it may be lost, and take steps to enhance its chances of being successfully recovered. We use data collected from the Reality...
Network traffic classification can be employed for providing enhanced Quality of Service (QoS), network security, traffic management, etc. Classifying network traffic with statistical characteristics of traffic flows has the advantages of fast processing speed, fairly high accuracy, ability of handling encrypted traffic, etc. However, Nagle's algorithm coalesces small TCP packets, and sometimes there...
The problem of construction the neuronetworking systems for non-stationary information adaptive processing at various practical applications is formulated. The developed methods and algorithms of neural network training subset formation allow to take into account the conditions of information transfer, variation of statistical parameters and dynamic properties of data. The controlling algorithms which...
Feature selection continues to grow in importance in many areas of science and engineering, as large datasets become increasingly common. In particular, bioscience and medical datasets routinely contain several thousands of features. For effective data mining in such datasets, tools are required that can reliably distinguish the most relevant features. The latter is a useful goal in itself (e.g. such...
Feature selection is an important research problem in machine learning and data mining applications. This paper proposes a hybrid wrapper and filter feature selection algorithm by introducing the filter's feature ranking score in the wrapper stage to speed up the search process for wrapper and thereby finding a more compact feature subset. The approach hybridizes a Mutual Information (MI) based Maximum...
Learning in a non-stationary environment and in the presence of class imbalance has been receiving more recognition from the computational intelligence community, but little work has been done to create an algorithm or a framework that can handle both issues simultaneously. We have recently introduced a new member to the Learn++ family of algorithms, Learn++.NSE, which is designed to track non-stationary...
This paper presents a neural-network-based active learning procedure for computer network intrusion detection. Applying data mining and machine learning techniques to network intrusion detection often faces the problem of very large training dataset size. For example, the training dataset commonly used for the DARPA KDD-1999 offline intrusion detection project contained approximately five hundred...
Uncontrolled project investment attracts more and more public attention. The inaccuracy of cost estimation is one of main reasons that make the government investment out of control. Cost estimation is affected by many uncertain factors, and the relationship between these factors are nonlinear, and the traditional model is hard to solve. This paper brings forward a model based on rough set and neural...
Feature selection is viewed as an important preprocessing step for pattern recognition, machine learning and data mining. It is used to find an optimal subset to reduce computational cost, increase the classification accuracy and improve result comprehensibility. In this paper, a weighted distance learning approach is introduced to minimize Leaving-One-Out classification error using a gradient descent...
The K Nearest Neighbors (KNN) is strongly dependent on the quality of the distance metric used. For supervised classification problems, the aim of metric learning is to learn a distance metric for the input data space from a given collection of pair of similar/dissimilar points. A crucial point is the distance metric used to measure the closeness of instances. In the industrial context of this paper...
With the rapid advancement of information technology, flood of digital data collected by business, government, and scientific applications need analyzing, digesting, and understanding. Scalability has become a necessity for data mining algorithms to process large data more effectively and extract insightful information from large data. In this paper a scaling up neural network learning algorithm is...
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