[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

Internet is flooded every day with a huge number of spam emails. This will lead the internet users to spend a lot of time and effort to manage their mailboxes to distinguish between legitimate and spam emails, which can considerably reduce their productivity. Therefore, in the last decade, many researchers and practitioners proposed different approaches in order to increase the effectiveness and safety of spam filtering models. In this paper, we propose a spam filtering approach consisted of two main stages; feature selection and emails classification. In the first step a Particle Swarm Optimization (PSO) based Wrapper Feature Selection is used to select the best representative set of features to reduce the large number of measured features. In the second stage, a Random Forest spam filtering model is developed using the selected features in the first stage. Experimental results on real-world spam data set show the better performance of the proposed method over other five traditional machine learning approaches from the literature. Furthermore, four cost functions are used to evaluate the proposed spam filtering method. The results reveal that the PSO based Wrapper with Random Forest can effectively be used for spam detection.

A Hybrid Approach Based on Particle Swarm Optimization and Random Forests for E-Mail Spam Filtering

The security of IoT networks is an important concern to researchers and business owners, which is taken into careful consideration due to its direct impact on the availability of the services offered by IoT devices and the privacy of the users connected with the network. An intrusion detection system ensures the security of the network and detects malicious activities attacking the network. In this study, a deep multi-layer classification approach for intrusion detection is proposed combining two stages of detection of the existence of an intrusion and the type of intrusion, along with an oversampling technique to ensure better quality of the classification results. Extensive experiments are made for different settings of the first stage and the second stage in addition to two different strategies for the oversampling technique. The experiments show that the best settings of the proposed approach include oversampling by the intrusion type identification label (ITI), 150 neurons for the Single-hidden Layer Feed-forward Neural Network (SLFN), and 2 layers and 150 neurons for LSTM. The results are compared to well-known classification techniques, which shows that the proposed technique outperforms the others in terms of the G-mean having the value of 78% compared to 75% for KNN and less than 50% for the other techniques.

https://www.mdpi.com/1424-8220/21/9/2987/pdf

A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning.

Ozone is one of the most important constituents of the Earth's atmosphere. Ozone is vital because it maintains the thermal structure of the atmosphere. However, exposure to high concentrations of Ozone can cause serious problems to human health, vegetation, and damage to surfaces. The complexity of the relationship between the main attributes that severely affect surface ozone levels have made the problem of predicting its concentration very challenging. Innovative mathematical modeling techniques are urgently needed to get a better understanding of the dynamics of these attributes. In this paper, prediction of the surface ozone layer problem is investigated. A comparison between two types of artificial neural networks (ANN) (multilayer perceptron trained with backpropagation and radial basis functions (RBF) networks) for short prediction of surface ozone is conclusively demonstrated. Two models that predict the expected values of the surface ozone based on three variables (i.e. nitrogen-di-oxide, temperature, and relative humidity) are developed and compared.

Artificial Neural Networks for Surface Ozone Prediction: Models and Analysis

When a new book is launched the publisher faces the problem of how many books should be printed for delivery to bookstores; printing too many is the main issue, since it implies a loss of investment due to inventory excess, but printing too few will also have a negative economic impact. In this paper, we are tackling the problem of predicting total sales in order to print the right amount of books and doing so even before the book has reached the stores. A real dataset including the complete sales data for books published in Spain across several years has been used. We have conducted an analysis in three stages: an initial exploratory analysis, by means of data visualisation techniques; a feature selection process, using different techniques to find out what are the variables that have more impact on sales; and a regression or prediction stage, in which a set of machine learning methods has been applied to create forecasting models for book sales. The obtained models are able to predict sales from pre-publication data with remarkable accuracy, and can be visualised as simple decision trees. Thus, these can be used as decision-aid tools for publishers, which can provide a reliable guidance on the decision process of publishing a book. This is also shown in the paper by addressing four example cases of representative publishers, regarding their number of sales and the number of different books they sell.

Applying computational intelligence methods for predicting the sales of newly published books in a real editorial business management environment

Extreme learning machine (ELM) is a novel and recent machine learning algorithm which was first proposed by Huang et al. (Proceedings of 2004 IEEE international joint conference on, pp 985–990, 2004). Over the last decade, ELM has gained a remarkable research interest with tremendous audiences from different domains in a short period of time due to its impressive characteristics over other single hidden-layer feedforward neural networks. Although ELM enjoys powerful advantages, it still has some potential weaknesses like performance sensitivity to the initial condition of the input weights, number of hidden neurons, and the selection of activation functions. In order to overcome the limitations of classical ELM, many metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, memetic and trajectory algorithms have been proposed for optimizing the different components of ELM by researchers aiming to improve the generalization performance of ELM networks for different types of complex problems and applications. Therefore our review paper intent to conduct a deep study of the important aspects of applying metaheuristic algorithms for optimizing ELM networks. Three main streams of research lines are identified: the optimization of input weights and hidden biases, selection of hidden neurons, and optimization of activation functions. Furthermore, this paper will discuss a wide spectrum of applications of metaheuristic-based ELM models. We will highlight the strengths of these models and the improvements that are suggested in the literature to overcome their weaknesses. We touch upon several interesting and challenging open issues in optimizing ELM using metaheuristics.

Hossam Faris

Papers

A Hybrid Approach Based on Particle Swarm Optimization and Random Forests for E-Mail Spam Filtering

A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning.

Artificial Neural Networks for Surface Ozone Prediction: Models and Analysis

Applying computational intelligence methods for predicting the sales of newly published books in a real editorial business management environment

Metaheuristic-based extreme learning machines: a review of design formulations and applications