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Dimensionality reduction of feature vector size plays a vital role in enhancing the text processing capabilities; it aims in reducing the size of the feature vector used in the mining tasks (classification, clustering… etc.). This paper proposes an efficient approach to be used in reducing the size of the feature vector for web text document classification process. This approach is based on using...
Nowadays, research and development for optimal setting control (OSC) of complex industrial processes is becoming a hot topic. However, there is no configurable software for OSC design. In this paper, a novel software platform is proposed. The major superiority is that it not only integrates intelligent and data-driven mainstream algorithms within a unified framework, but also provides a powerful controller...
Understanding the shopping and purchasing behaviours of customers is an essential task for business and retail organizations. While customers look for useful information from retailers as they shop, businesses seek to collect increasing amounts of data in order to deliver added value to their customers. This requires an intensive analysis of sales data. Extracting shopping patterns across the many...
In many organizations huge amount of data is generated. Organizations use this data for their own benefit. Data mining extracts useful knowledge from huge data. Association rule mining is a powerful technique to find hidden patterns in large database. The limitation of mining association rules is that some sensitive patterns are revealed from sensitive rules. It is necessary to hide sensitive rules...
The calculation of higher order roots of Bessel function is computationally intensive, time consuming and not easily available in literature. In real life the solution of many problems comes in the form of Bessel function. Design of fast wave devices, like Gyrotron, requires zeros of the first derivatives of Bessel function of first kind. The Gyrotron is a high frequency, high power microwave device...
Crime pattern theory emphasize how crime appear from Spatio-temporal method. To get knowledge on crimes, it has to be studied in terms of space and time. Crime patterns and crime related activities research those spatio (location) distribution of crimes, omitting the temporal (time) dimensions. Exactly separation of crimes by space and time, example hour of a specific day, day of a specific week,...
This paper was primarily conducted to develop an Online Booking System where users can search for different varieties of hotels and are able to sort them according to their price, number of stars or their location distance to any particular place in the city. Once the hotel is selected, Users are able to guess the fastest routes to all the tourist attraction places and restaurants nearby each hotel...
The recent years have witnessed the remarkable expansion of publicly available biological data in the related research fields. Many researches in these fields often require massive data to be analyzed by utilizing high-throughput sequencing technologies. However, it is very challenging to interpret the data efficiently due to it high complexity. This paper introduces two new graph algorithms which...
Skyline queries are useful in many applications such as multicriteria decision-making, data mining, and user-preference queries. However, the probability that a point dominates another one reduces significantly as the number of dimensions increases, which results in the number of skyline points becoming too numerous to offer any interesting insights. The concept of the k-dominant skyline was previously...
The smartphone applications have taken place of the web browser and became the user's primary internet entrance. One application's popularity can be measured by its downloading times, and it is valuable for commercial advertising. Identifying app installation packages from network traffic is one of the most feasible approaches to collect these data. But asymmetric routing, incomplete capture and so...
Differential Privacy (DP) has received increasing attention as a rigorous privacy framework. Many existing studies employ traditional DP mechanisms (e.g., the Laplace mechanism) as primitives, which assume that the data are independent, or that adversaries do not have knowledge of the data correlations. However, continuous generated data in the real world tend to be temporally correlated, and such...
We present a novel approach for detecting malicious user activity in databases. Specifically, we propose a new machine learning algorithm for detecting attacks such as a stolen user account or illegal use by a user. Our algorithm relies on two main components that examine the consistency of a user's activity and compare it with activity patterns learned from past access. The first component tests...
Presentation attacks (or spoofing) on finger-vein biometric capture devices are gaining increased attention because of their wider deployment in multiple secure applications. In this work. we propose a novel method for fingervein Presentation Attack Detection (PAD) by exploring the transfer learning ability of Deep Convolutional Neural Network (CNN). To this extent, we have considered the pre-trained...
According to the World Health Organization, Morocco is classified in the third category of the number of deaths related to cardiovascular diseases. To reduce the number, it is necessary to early detect and identify cardiac problems by monitoring patient's heart activity, which is directly related to the location of the QRS complex. Hence, accurate electrocardiogram feature extraction is a primordial...
This paper explores the causality and responsibility problem (CRP) for the non-answers to probabilistic reverse skyline queries (PRSQ). Towards this, we propose an efficient algorithm called CP to compute the causality and responsibility for the non-answers to PRSQ. CP first finds candidate causes, and then, it performs verification to obtain actual causes with their responsibilities, during which...
For a large sum of data collected and stored continually, it is more and more necessary to mine association rules from database, and the Apriori algorithm of association rules mining is the most classical algorithm of database mining and is widely used. However, Apriori algorithm has some disadvantages such as low efficiency of candidate item sets and scanning data frequently. Support and confidence...
Feature-based modeling is very popular in generating the shape and history of a product in a three-dimensional (3D) CAD system. In the previous studies, features are the basic unit of the models. It often needs to traverse most of the features or even all of the features to extract the information of the models. In this paper, topological entities are divided into 6 levels according to the topological...
According to the demand of large data sorting algorithm, a new efficient algorithm for large data sorting is proposed. The algorithm by constructing the equivalent sort keys, optimized quicksort algorithm; through the technology of task driven, AIO and double buffer mechanism to make full use of system resources; in algorithm implementation, using multi thread processing tasks, by controlling the...
With the advent of the big data era, data mining technology has gradually become mature, association rules analysis is also applied in many fields. Web log mining is an important way to do some personalized services and achieve Web personalize. Apriori algorithm is a classical algorithm of association rules, but it has a lot of shortcomings. In recent years, the improvement about Apriori algorithm...
This paper proposes an efficient ear recognition algorithm using band limited phase only correlation (BLPOC) based local block matching. Phase information over the frequency field of an image has been acquired by discrete wavelet transform (DWT). The DWT may decompose an ear image into a set of basis function. The BLPOC is strong to resilient noise and rescaling variables may efficiently upgrade the...
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