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In applications of personalized recommendation, user similarity of common clustering algorithms only considers user relationship without considering relationship between users and items, the similarity above reduces the accuracy of clustering, making it difficult to find similar users, and the same with item similarity. This paper improves the distance function of data clustering algorithm by Hamming...
Data fragment classification is an important problem in many fields, such as intrusion detection, reverse engineering, data recovery, digital forensics and so on. Most of the existing methods try to classify the fragment depending on file type. But the results are poor, because compound file types can contain many other file types, and some file types use the similar data encoding scheme. In this...
Feature selection plays an important role in text categorization, and contributes directly to the accuracy of the categorization. In the process of feature selection, due to the lack of consideration of the traditional expected cross entropy algorithm for document frequency, we first improve the expected cross entropy formula of the traditional, and then propose an improved text feature selection...
Network packet classification plays a pivotal role in packet-switched networks; it is at the heart of many functions including firewalling, QoS routing, and OpenFlow-based switching. However, packet classification is a hard problem, as packets must be classified within a short time frame. Existing classification techniques use sophisticated data structures which are traversed by generic search algorithms—that...
With the development of digital cable interactive business and the diversification of the customers' demand, grouping TV programmes based on preferences of users effectively is vital for market segmentation and differentiation. The study summarizes the main principle and characteristic of clustering algorithm, and uses K-Means algorithm to show TV programmes preference grouping based on 52392 subscribers...
Genetic programming lacks convergence prematurely and operating efficiency. This paper is to study this problem that integrates the genetic programming theory with the framework of Map/Reduce. This is to improve the efficiency by parallel and distributed capability proved by Map/Reduce. Our experiments show that the improved parallel algorithm of genetic programming under the framework of Map/Reduce...
With the increase in the popularity of e-commerce, there is large amount of opinions available on the web. So there is a need to generate clustered summary of products based on features. Most of the opinions contain opinion words which has same polarity in all contexts. But there are some opinion words called context dependent words which have different polarity in different context. So there is a...
The QRS complex is the most striking waveform within the electrocardiogram (ECG). QRS provides the fundamentals for almost all automated ECG analysis algorithms. We proposed a novel method for classification of ECG beats using shared counters. We first developed a real-time QRS detection technique using two-phase hashing to extract precise QRS points. Then we proposed a classifier for profiling an...
ECG beats is the most significant waveform within the electrocardiogram (ECG). QRS provides the basis for all ECG classification methods. We proposed a novel method for classification of ECG beats using repetition based packet processing and ECG waveform profiling. We first developed a real-time QRS detection technique using two-phase hashing to find exact QRS points. Then we proposed a classifier...
Before the Auction the team shave the liberty to retain some of Its previously selected players and the rest of the players can be selected via auction. Initially, all owners of the team shave the same limited amount of funds to build their team. The more the players an owner retains, the less funds the owner would have to take to the auction. Hence, the decision of retaining players has to be perfect...
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...
In today's environment, in which digital data is continuously increasing, it is of prime importance for students to find data appropriate for their readability level. In this study, our aim is to classify educational data in Turkish based on their readability level. Three readability formulas and new syllable and word level features are used in this study. K12 level Turkish Language course textbooks...
As Internet continues to influence our day to day activities like eCommerce, eGoverence, eEducation etc. the threat from hackers has also increased. Due to which many researcher thinking intrusion detection systems as fundamental line of defense. However, many commercially available intrusion detection systems are predominantly signature-based that are designed to detect known attacks. These systems...
Classification refers to the computational techniques for classifying whether the sentiments of text are positive or negative. Sentiment Classification being a specialized domain of text mining is expected to benefit after preprocessing such as removing stopwords. Stopwords are frequently occurring words that hardly carry any information and orientation. In this paper the effect of stopwords removal...
As an important branch of the computers, machine vision technology has a great influence on intelligent surveillance, human-computer interaction, and virtual reality. This paper introduced an intelligent emulator which used machine vision technology to guide navigation. The intelligent emulator is controlled by the human posture. The hardware of intelligent emulator includes control unit, obstacle...
In many radio frequency identification (RFID) applications, a RFID reader repeatedly identifies the tags in its scope. An anti-collision algorithm with blocking mechanism lets the staying tags and arriving tags reply in different time periods and the collisions between the staying and arriving tags are prevented. However, in the existing blocking algorithms, the reader can only identify two staying...
A detection method for cheating behavior in examination room based on artificial bee colony algorithm is presented. The problem of moving objects detection is transformed into the difference function of color value between foreground and background. Artificial bee colony algorithm is applied for optimizing the objective function. The background component is separated from the sequence images by value...
The network traffic classification is one of the most fundamental work in the network measurement and management, and this problem is more and more impact as the network scale grows. Many methods are proposed by researchers, but methods based on flow statistics seem more popular than the others. In this paper, we proposed a novel method based on refined flow statistical features. The new statistics,...
In the proposed approach, an attempt was made to disambiguate Bengali ambiguous words using Naïve Bayes Classification algorithm. The whole task was divided into two modules. Each module executes a specific task. In the first module, the algorithm was applied on a regular text, collected from the Bengali text corpus developed in the TDIL project of the Govt. of India and the accuracy of disambiguation...
There is huge growth of online text documents in the Internet today. We can easily find documents written in languages from all over part of the just from a single click. Increasing number of online text document in Internet makes the increased availability of information on the Internet. In fact that none in the world can understand all languages of the digital documents. Hence, there is a significant...
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