The Study of Applying Incremental Classification for a New P2P e-learning System
نویسندگان
چکیده
During the learning process, sharing and participating is the important thing. Efficiently using the learning contents has been an active research topic in developing the educational material. It probes the environment of the learning process through Sharable Content Object Reference Model (SCORM). Peer to Peer (P2P) technology seems to be the most well suited mechanism to deal with the problems that the users have the difficult time to find the usable information and do not want to participate. In this paper, we explore an e-learning prototype based on P2P. It consists of 3-layer structure and uses P2P's concepts and methods. This e-learning system uses incremental kNN algorithm to analyze the data, combines of the SCORM's metadata information and the records of learners used particular contents. It improves the disadvantages of Napster and Gnutella, and features with content feature classification, file index distribution, overloading, and user's query.
منابع مشابه
A Hybrid Framework for Building an Efficient Incremental Intrusion Detection System
In this paper, a boosting-based incremental hybrid intrusion detection system is introduced. This system combines incremental misuse detection and incremental anomaly detection. We use boosting ensemble of weak classifiers to implement misuse intrusion detection system. It can identify new classes types of intrusions that do not exist in the training dataset for incremental misuse detection. As...
متن کاملClassification of encrypted traffic for applications based on statistical features
Traffic classification plays an important role in many aspects of network management such as identifying type of the transferred data, detection of malware applications, applying policies to restrict network accesses and so on. Basic methods in this field were using some obvious traffic features like port number and protocol type to classify the traffic type. However, recent changes in applicat...
متن کاملA Comparative Study of SVM and RF Methods for Classification of Alteration Zones Using Remotely Sensed Data
Identification and mapping of the significant alterations are the main objectives of the exploration geochemical surveys. The field study is time-consuming and costly to produce the classified maps. Therefore, the processing of remotely sensed data, which provide timely and multi-band (multi-layer) data, can be substituted for the field study. In this study, the ASTER imagery is used for altera...
متن کاملA New Method to Improve Automated Classification of Heart Sound Signals: Filter Bank Learning in Convolutional Neural Networks
Introduction: Recent studies have acknowledged the potential of convolutional neural networks (CNNs) in distinguishing healthy and morbid samples by using heart sound analyses. Unfortunately the performance of CNNs is highly dependent on the filtering procedure which is applied to signal in their convolutional layer. The present study aimed to address this problem by a...
متن کاملApplying Genetic Algorithm to EEG Signals for Feature Reduction in Mental Task Classification
Brain-Computer interface systems are a new mode of communication which provides a new path between brain and its surrounding by processing EEG signals measured in different mental states. Therefore, choosing suitable features is demanded for a good BCI communication. In this regard, one of the points to be considered is feature vector dimensionality. We present a method of feature reduction us...
متن کامل