[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

Online media has an increasing presence on the restaurants’ activities through social media websites, coinciding with an increase in customers’ reviews of these restaurants. These reviews become the main source of information for both customers and decision-makers in this field. Any customer who is seeking such places will check their reviews first, which usually affect their final choice. In addition, customers’ experiences can be enhanced by utilizing other customers’ suggestions. Consequently, customers’ reviews can influence the success of restaurant business since it is considered the final judgment of the overall quality of any restaurant. Thus, decision-makers need to analyze their customers’ underlying sentiments in order to meet their expectations and improve the restaurants’ services, in terms of food quality, ambiance, price range, and customer service. The number of reviews available for various products and services has dramatically increased these days and so has the need for automated methods to collect and analyze these reviews. Sentiment Analysis (SA) is a field of machine learning that helps analyze and predict the sentiments underlying these reviews. Usually, SA for customers’ reviews face imbalanced datasets challenge, as the majority of these sentiments fall into supporters or resistors of the product or service. This work proposes a hybrid approach by combining the Support Vector Machine (SVM) algorithm with Particle Swarm Optimization (PSO) and different oversampling techniques to handle the imbalanced data problem. SVM is applied as a machine learning classification technique to predict the sentiments of reviews by optimizing the dataset, which contains different reviews of several restaurants in Jordan. Data were collected from Jeeran, a well-known social network for Arabic reviews. A PSO technique is used to optimize the weights of the features, as well as four different oversampling techniques, namely, the Synthetic Minority Oversampling Technique (SMOTE), SVM-SMOTE, Adaptive Synthetic Sampling (ADASYN) and borderline-SMOTE were examined to produce an optimized dataset and solve the imbalanced problem of the dataset. This study shows that the proposed PSO-SVM approach produces the best results compared to different classification techniques in terms of accuracy, F-measure, G-mean and Area Under the Curve (AUC), for different versions of the datasets.

Sentiment Analysis of Customers’ Reviews Using a Hybrid Evolutionary SVM-Based Approach in an Imbalanced Data Distribution

Automated credit approval helps credit-granting institutions in reducing time and efforts in analyzing credit approval requests and to distinguish good customers from bad ones. Enhancing the automated process of credit approval by integrating it with a good business intelligence (BI) system puts financial institutions and banks in a better position compared to their competitors. In this paper, a novel hybrid approach based on neural network model called Cycle Reservoir with regular Jumps (CRJ) and Support Vector Machines (SVM) is proposed for classifying credit approval requests. In this approach, the readout learning of CRJ will be trained using SVM. Experiments results confirm that in comparison with other data mining techniques, CRJ with SVM readout gives superior classification results.

Credit Risk Evaluation Using Cycle Reservoir Neural Networks with Support Vector Machines Readout

In recent years, the utilization of natural language processing (NLP) and Machine Learning (ML) techniques in clinical decision support systems have shown their ability in improving and automating the diagnosis process, and reducing potential clinical errors. NLP in the Arabic language is more intricate due to several limitations, such as the lack of datasets and analytical resources compared to other languages like English. However, a clinical decision support system in the Arabic context is of significant importance. A fundamental process in NLP is extracting features from text-based data via text embedding. Word embedding is a representation of words in a numeric format that encodes the statistic, semantic, or context information. Building a neural word embedding model requires hundreds of thousands of data instances to find hidden patterns of relationships within sentences. Essentially, extracting relevant and informative features promotes the performance of the learning algorithms. The objective of this paper is to propose an Arabic neural-based word embedding model in the medical and healthcare context (called “AltibbiVec”). Around 1.5 million medical consultations and questions written in different dialects are obtained from Altibbi telemedicine company and used to train the embedding model. Three different embedding models are developed and compared, which are Word2Vec, fastText, and GloVe. The trained models were evaluated by different criteria, including the word clustering and the similarity of words. Besides, performing a specialty-based question classification. The results show that Word2Vec and fastText capture sufficiently the semantics of text more than GloVe. Hence, they are recommended for healthcare NLP-based applications.

/pdf/altibbivec-a-word-embedding-model-for-medical-and-health-3gc42dd19t.pdf

AltibbiVec: A Word Embedding Model for Medical and Health Applications in the Arabic Language

Extreme Learning Machine (ELM) is a learning algorithm proposed recently to train single hidden layer feed forward networks (SLFN). It has many attractive properties that include better generalization performance and very fast learning. ELM starts by assigning random values to the input weights and hidden biases and then in one step it determines the output weights using Moore-Penrose generalized inverse. Despite the aforementioned advantages, ELM performance might be affected by the random initialization of weights and biases or by the large generated network which might contain unnecessary number of neurons. In order to increase the generalization performance and to produce more compact networks, a hybrid model that combines ELM with competitive swarm optimizer (CSO) is proposed in this paper. The proposed model (CSONN-ELM) optimizes the weights and biases and dynamically determines the most appropriate number of neurons. To evaluate the effectiveness of the CSONN-ELM, it is experimented using 23 benchmark datasets, and compared to a set of static rules extracted from literature that are used to determine the number of neurons of SLFN. Moreover, it is compared to two dynamic methods that are used to enhance the performance of ELM, that are Optimally pruned ELM (OP-ELM) and metaheuristic based ELMs (Particle Swarm Optimization-ELM and Differential Evolution-ELM). The obtained results show that the proposed method enhances the generalization performance of ELM and overcomes the static and dynamic methods.

A competitive swarm optimizer with hybrid encoding for simultaneously optimizing the weights and structure of Extreme Learning Machines for classification problems

Breathing is a crucial human process that requires easy and accurate monitoring. There are several available breathing monitoring methods which are based on strapping subjects with special sensors for monitoring their breathing rate. The results of these methods have been acceptable but connecting people with such equipment is awkward, especially in the case of monitoring the breathing of infants who have tiny and sensitive bodies. Therefore, providing a noncontact method is a good alternative. Thermal imaging is one of the latest non-contact passive techniques that are used for monitoring and controlling different human processes, including breathing. In this paper, a thermal image feature extraction method with neural network based classification system is developed to be used as part of a breathing monitoring system. The classification is based on matching the entries of mixed breathing images, either breath-in or breath-out images, with their desired values. The extracted breathing image features are used as entries for the neural network classifier. The neural network based model has produced promising classification results. Additionally, it has been possible to calculate the breathing rate of each subject from the classification results.

/pdf/pattern-recognition-of-thermal-images-for-monitoring-of-3swn38rdtg.pdf

Hossam Faris

Papers

Sentiment Analysis of Customers’ Reviews Using a Hybrid Evolutionary SVM-Based Approach in an Imbalanced Data Distribution

Credit Risk Evaluation Using Cycle Reservoir Neural Networks with Support Vector Machines Readout

AltibbiVec: A Word Embedding Model for Medical and Health Applications in the Arabic Language

A competitive swarm optimizer with hybrid encoding for simultaneously optimizing the weights and structure of Extreme Learning Machines for classification problems

Pattern Recognition of Thermal Images for Monitoring of Breathing Function