A car price prediction has been a high-interest research area, as it requires noticeable effort and knowledge of the field expert. Considerable number of distinct attributes are examined for the reliable and accurate prediction. To build a model for predicting the price of used cars in Bosnia and Herzegovina, we applied three machine learning techniques (Artificial Neural Network, Support Vector Machine and Random Forest). However, the mentioned techniques were applied to work as an ensemble. The data used for the prediction was collected from the web portal autopijaca.ba using web scraper that was written in PHP programming language. Respective performances of different algorithms were then compared to find one that best suits the available data set. The final prediction model was integrated into Java application. Furthermore, the model was evaluated using test data and the accuracy of 87.38% was obtained.

– Today, when looking at the quality of an online item, the feedback itself plays a very important role. Based on the feedback we can decide whether the desired item is good or not, get a picture of the seller and so on. Many companies that have online shops display the most positive feedback while hiding bad ones or display only a few of them. In this research, we will help people by automating the process of deciding whether a feedback is positive or negative, which will give them time for other jobs and save money for hiring people who will work on the feedback. Since feedback on online articles is very important today, the process of determining positive and negative feedback should be made as quick and easy as possible. In this research, we will show a very simple and fast way to classify feedback as positive or negative, which means that the main question of this research is how to facilitate and speed up the process of determining the polarity of the feedback. We will use NLP using Python’s library called TextBlob. The used algorithm is called Naïve Bayes, it gave the accuracy of around 80%.

Credit card default payment prediction studies are very important for any nancial institution dealing with credit cards. The purpose of this work is to evaluate the performance of machine learning methods on credit card default payment prediction using logistic regression, C4.5 decision tree, support vector machines (SVM), naive Bayes, k-nearest neighbors algorithms (k-NN) and ensemble learning methods voting, bagging and boosting. The performance of the algorithms is evaluated through following performance metrics: accuracy, sensitivity and specicity. The best result among all algorithms for overall accuracy rate was achieved by logistic regression model with a rate of 0.820. The best performing model for default credit card customer detection, with success of 71,3% was naive Bayes model. This approach could improve and ease the process of credit card default, and therefore help the banking system in decision making.

Cloud computing is a trending technology, as it reduces the cost of running a business. However, many companies are skeptic moving about towards cloud due to the security concerns. Based on the Cloud Security Alliance report, Denial of Service (DoS) attacks are among top 12 attacks in the cloud computing. Therefore, it is important to develop a mechanism for detection and prevention of these attacks. The aim of this paper is to evaluate Support Vector Machine (SVM) algorithm in creating the model for classification of DoS attacks and normal network behaviors. The study was performed in several phases: a) attack simulation, b) data collection, c)feature selection, and d) classification. The proposedmodel achieved 100% classification accuracy with true positive rate (TPR) of 100%. SVM showed outstanding performance in DoS attack detection and proves that it serves as a valuable asset in the network security area.

Social media is very important factor in analyzing modern society as a whole, their values, norms, and behaviors, as being a part of our everyday life. This study is oriented towards analyzing social media in order to allow users to create their own preferences to follow (analyze) a specific social media source. The web application has been developed to allow a user to follow specific Facebook accounts and categorize the Facebook posts on those accounts based on the user defined taxonomies. Results of this study are various reports generated from the Facebook posts and their statistics that are clustered based on the user defined taxonomies. The benefit of this project is that any user can track in real time when people are talking about some topic, and it enables anyone to have better insight about society as a whole, their values, norms, what they find interesting, and many other things. This tool is also useful for different companies to track the user feedback on social networks for their products.

E-mail still proves to be very popular and an efficient communication tool. Due to its misuse, however, managing e-mails problem for organizations and individuals. Spam, known as unwanted message, is an example of misuse. Specifically, spam is defined as the arrival of unwelcomed bulk email not being requested for by recipients. This paper compares different Machine Learning Techniques classification of spam e-mails. Random Forest (RF), C4.5 and Artificial Neural Network (ANN) were tested to determine which method provides the best results in spam e-mail classification. Our results show that RF is the best technique applied on dataset Labs, indicating that ensemble methods may have an edge in spam detection effective susceptible to is spam, also is defined messages not istaken with or religious he most email by a . Furthermore, by spam. around (which makes (Grant, 2003; Every e-mail user in America received an average of 2200 pieces of spam e-mails in 2002. In 2007 it reached 3600 pieces of spam e-mails due to increase rate of 2% per month conducted a survey revealing that a Chinese spam e-mails weekly. Due to spam e enterprises lose up to 9 billion yearly reveal that spam e-mails take about 60% of the incomin in a corporate network. With inappropriate or no countermeasures, the situation will worsen and, in the end, spam e-mails may destruct the usage of e countries are slowly starting to use anti (Gaikwad & Halkarnikar, 2014). The main argument supporting spam increase is the fact that spammers do not have any costs for it: “Because email technology allows spammers to shift the costs almost entirely to third parties, there is no incentive for the spammers to reduce the volume” (Hann, Hui, Lai, Lee, & Png, 2006) issue for spam is the annoying content they carry significant amount of spam contains some offensive materials (Maria & Ng, 2009). In China, some specialists suggest spam email measure as early as possible. However, because of 1210 Sarajevo,

In this paper we present parallel implementation of genetic algorithm using map/reduce programming paradigm. Hadoop implementation of map/reduce library is used for this purpose. We compare our implementation with implementation presented in [1]. These two implementations are compared in solving One Max (Bit counting) problem. The comparison criteria between implementations are fitness convergence, quality of final solution, algorithm scalability, and cloud resource utilization. Our model for parallelization of genetic algorithm shows better performances and fitness convergence than model presented in [1], but our model has lower quality of solution because of species problem.