The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper explores the potential of Machine Learning (ML) and Artificial Intelligence (AI) to lever Internet of Things (IoT) and Big Data in the development of personalised services in Smart Cities. We do this by studying the performance of four well-known ML classification algorithms (Bayes Network (BN), Naïve Bayesian (NB), J48, and Nearest Neighbour (NN)) in correlating the effects of weather...
In the world today, the security of the computer system is of great importance, And in the last few years, there have seen an affected growth in the amount of intrusions that intrusion detection has become the dominant of current information security. Firewalls cannot provide complete protection. Applying on a firewall system alone is not enough to prevent a corporate network from all types of network...
This paper presents a new hybrid HPSO-DE classification algorithm that combines the advantages of particle swarm optimization algorithm and differential evolution algorithm. Major improvements achieved by this combination are 1) flight improvement — flight behaviors are more and better diversified because each of the top 3 particles gets put into 3 different groups of the rest and then each group...
The loyalty and retention of students in educational institutions has become one of the greatest challenges for the management area of these institutions. A promising solution to achieve this goal is the use of educational data mining to identify patterns that aid in decision making. This paper presents a proposal for the creation of temporal attributes with the purpose of helping to predict the avoidance...
Multiplayer Online Battle Arena (MOBA) games are very popular in the current eSport scenario, being highlighted in several competitions around the world. However, the domain of knowledge contained in these games is large, which makes it difficult to discover and predict the course of a match. The present work proposes the application of classification algorithms to determine the team with more chances...
This paper presents a method for identification of fuzzy classifiers by means of data analysis. The method is based on the assumption that the data of the same classes form compact regions (clusters) in the input space. The algorithms for structure generation and parameter optimization of the fuzzy classifiers are proposed.
Traditional methods for hyperspectral image classification typically use raw spectral signatures without considering spatial characteristics. In this work, a classification algorithm based on Gabor features and decision fusion is proposed. First, the adjacent and high correlated spectral bands are intelligently grouped by coefficient correlation matrix. Following that, Gabor features in each group...
Knowledge Discovery in Databases (KDD) is a major innovation in knowledge extraction. This knowledge can be extracted to recognize patterns or behaviors. Board games playing patterns are a concise experiment on testing data mining methods in order to find such patterns and behaviors. In this work a Connect-4 game is simulated with several distinct players with different characteristics. Most of these...
The analysis of the human migration process has been studied in various fields of science. This work, focuses on migration indicators proposed by the International Migration Policy, with the aim of identifying the most important indicator from the point of view of data mining. This study identifies migrant stock as the most important factor related to the values obtained by the F1Score and the ROC...
Online customers can profit from online customers' reviews (OCR) to make a better decision of purchase. Reviews consist of advantages and disadvantages of products which provide an important resource for individuals and companies to evaluate their customers' real needs as well as precious resource for product and service improvement. In this research, we proposed a novel algorithm for mining online...
The second largest cause of death in Palestine is Cancer at a rate 12.4% of all deaths. Predicting the survivability of a disease is one of the most interesting purposes of developing a medical data mining applications. This paper applies two classification models (Rule Induction and Random Forest) on the Gaza Strip 2011 cancer patient's dataset, to predict the survivability of cancer patients. The...
Data mining techniques is rapidly increasing in the research of educational domains. Educational data mining aims to discover hidden knowledge and patterns about student performance. This paper proposes a student performance prediction model by applying two classification algorithms: KNN and Naïve Bayes on educational data set of secondary schools, collected from the ministry of education in Gaza...
Data mining is the process of extracting a meaningful information from raw data. Classical data mining algorithms can be used to extract an offline static model for classification problems which has a collected dataset. Unfortunately, Offline algorithms cannot give a solution for nowadays' technologies with streaming data. Streaming algorithms are proposed to deal with data-streams for online learning...
Opinion mining is an interested area of research, which epitomize the customer reviews of a product or service and express whether the opinions are positive or negative. Various methods have been proposed as classifiers for opinion mining such as Naïve Bayesian, and Support vector machine, these methods classify opinion without giving us the reasons about why the instance opinion is classified to...
Algorithms used in data mining techniques are of great importance in the field of health care, especially in the case of getting patterns or models that are undiscovered in databases. In the area of health care, leukemia affects the blood status and can be discovered by using the Blood Cell Counter (CBC). This study aims to predict the leukemia existence by determining the relationships of blood properties...
We present the key steps in the dynamogram classification algorithm development. These are data processing, procedures of generation and selection of features, constructing of a neural network classifier and estimation of its work quality. To estimate the possibility to single out complex defects (subclasses), we analyzed the structure of the input pattern sample with the aid of clusterization algorithms...
Anomaly detection is the process of finding outlying records from a given data set. The aim of this paper is to study a well-known anomaly detection technique on the “Short Message Service Centre” server, used in the telecommunications field to handle and store messages. This server was studied in details, a script was written to gather all the required data that went through a cleaning phase and...
This paper will give basic knowledge understanding how to discriminate between two datasets with Emerging Patterns (Eps) upon famous weather dataset. This paper didn't use previous data mining techniques such as border-based algorithm or so on, but only to give the systematic basic knowledge understanding to discriminate between two datasets by finding score of support, growthrate and confidence....
Data analysis plays an indispensable role in the knowledge discovery process of extracting of interesting patterns or knowledge for understanding various phenomena or wide applications. Visual Data Mining is further presenting implicit but useful knowledge from large data sets using visualization techniques, to create visual images which aid in the understanding of complex, often massive representations...
The key technology to analyzing electricity data is cluster methods, of which the traditional way has already lost its agility and quality due to the increasing data volume. To this end, this paper presented an electricity data mining structure: first the higher dimensional data should be reduced to lower ones, second the reduced-dimensional results should be classified into typical usage behavior...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.