[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Today, Android stands out amongst the most well-known and far reaching smartphones’ operating systems. It has millions of applications that are distributed at either accredited or informal stores. Botnet applications are classified as malwares that can be distributed by utilizing these stores and downloaded by the unfortunate users on their smartphones. This work investigates Android botnets using static analysis to extract possible features from the applications source code after being reverse engineered. The features are then used to develop effective machine learning models to detect such malicious applications. Additionally, the study proposes a new set of features related to accessing resources on the target mobile. The features are extracted from 1928 Android botnet applications (ISCX dataset) and 2224 of Android benign applications (downloaded and scanned by special tools developed as part of this work). The extracted features are categorized into six groups of features in addition to a group that contains all the extracted features. Each group of features undergoes training and testing processes using four popular ML classifiers (i.e. Random Forest, Multi-Layer Perceptron neural networks, Decision trees, and Naive Bayes). After comparing the results and performing features importance analysis, it can be noted that the URL set of features play the key role in the Android botnet detection problem and the Random Forest classifier obtains the best results based on all sets of features.

Android botnet detection using machine learning models based on a comprehensive static analysis approach

An evolutionary optimized artificial intelligence model for modeling scouring depth of submerged weir

This book provides an in-depth analysis of the current evolutionary machine learning techniques. Discussing the most highly regarded methods for classification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning machines, evolutionary feature selection, artificial neural networks including feed-forward neural networks, multi-layer perceptron, probabilistic neural networks, self-optimizing neural networks, radial basis function networks, recurrent neural networks, spiking neural networks, neuro-fuzzy networks, modular neural networks, physical neural networks, and deep neural networks. 
The book provides essential definitions, literature reviews, and the training algorithms for machine learning using classical and modern nature-inspired techniques. It also investigates the pros and cons of classical training algorithms. It features a range of proven and recent nature-inspired algorithms used to train different types of artificial neural networks, including genetic algorithm, ant colony optimization, particle swarm optimization, grey wolf optimizer, whale optimization algorithm, ant lion optimizer, moth flame algorithm, dragonfly algorithm, salp swarm algorithm, multi-verse optimizer, and sine cosine algorithm. The book also covers applications of the improved artificial neural networks to solve classification, clustering, prediction and regression problems in diverse fields.

Evolutionary Machine Learning Techniques

A churn consumer can be defined as a customer who transfers from one service provider to another service provider. Recently, business operators have investigated many techniques that identify the customer churn since churn rates leads to serious business loss. In this paper, a hybrid technique has been used which combines K-means clustering with Genetic Programming to predict churners in telecommunication companies. First, K-means clustering is used to filter the training dataset from outliers and non representative customer behaviors then Genetic Programming is applied in order to build classification trees that are able to classify customers into churners and non churners. The proposed approach is evaluated and compared with other common classification approaches. Experimental results show that K-means clustering with Genetic Programming has promising

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Customer churn prediction using a hybrid genetic programming approach

The problem of spam e-mail has gained a tremendous amount of attention. Although entities tend to use e-mail spam filter applications to filter out received spam e-mails, marketing companies still tend to send unsolicited e-mails in bulk and users still receive a reasonable amount of spam e-mail despite those filtering applications. This work proposes a new method for classifying e-mails into spam and non-spam. First, several e-mail content features are extracted and then those features are used for classifying each e-mail individually. The classification results of three different classifiers (i.e. Decision Trees, Random Forests and k-Nearest Neighbor) are combined in various voting schemes (i.e. majority vote, average probability, product of probabilities, minimum probability and maximum probability) for making the final decision. To validate our method, two different spam e-mail collections were used.

Hossam Faris

Papers

Android botnet detection using machine learning models based on a comprehensive static analysis approach

An evolutionary optimized artificial intelligence model for modeling scouring depth of submerged weir

Evolutionary Machine Learning Techniques

Customer churn prediction using a hybrid genetic programming approach

Voting-based Classification for E-mail Spam Detection