[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

Maintaining a high quality of conversation between doctors and patients is essential in telehealth services, where efficient and competent communication is important to promote patient health. Assessing the quality of medical conversations is often handled based on a human auditory-perceptual evaluation. Typically, trained experts are needed for such tasks, as they follow systematic evaluation criteria. However, the daily rapid increase of consultations makes the evaluation process inefficient and impractical. This paper investigates the automation of the quality assessment process of patient–doctor voice-based conversations in a telehealth service using a deep-learning-based classification model. For this, the data consist of audio recordings obtained from Altibbi. Altibbi is a digital health platform that provides telemedicine and telehealth services in the Middle East and North Africa (MENA). The objective is to assist Altibbi’s operations team in the evaluation of the provided consultations in an automated manner. The proposed model is developed using three sets of features: features extracted from the signal level, the transcript level, and the signal and transcript levels. At the signal level, various statistical and spectral information is calculated to characterize the spectral envelope of the speech recordings. At the transcript level, a pre-trained embedding model is utilized to encompass the semantic and contextual features of the textual information. Additionally, the hybrid of the signal and transcript levels is explored and analyzed. The designed classification model relies on stacked layers of deep neural networks and convolutional neural networks. Evaluation results show that the model achieved a higher level of precision when compared with the manual evaluation approach followed by Altibbi’s operations team.

Toward an Automatic Quality Assessment of Voice-Based Telemedicine Consultations: A Deep Learning Approach.

Ponencia de la conferencia "26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018; Bruges; Belgium; 25 April 2018 through 27 April 2018"

Forecasting business failure in highly imbalanced distribution based on delay line reservoir

This paper addresses the parameter estimation problem for a manufacturing process based on the Auto-Regressive Moving Average (ARMA) model. The accurate estimation of the ARMA model’s parameter helps to reduce the production costs, provide better product quality, increase productivity and profit. Meta-heuristic algorithms are among these approximate techniques which have been successfully used to search for an optimal solution in complex search space. Meta-heuristic algorithms can converge to an optimal global solution despite traditional parameter estimation techniques which stuck by local optimal. A comparison between Meta-heuristic algorithms: Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and the Accelerated PSO, Cuckoo Search, Krill Herd and Firefly algorithm is provided to handle the parameter estimation problem for a Winding process in the industry. The developed ARMA-meta-heuristics models for a winding machine are evaluated based on different evaluation metrics. The results reveal that meta-heuristics can provide an outstanding modeling performance.

/pdf/estimating-arma-model-parameters-of-an-industrial-process-1o66zztyds.pdf

Estimating ARMA Model Parameters of an Industrial Process Using Meta-Heuristic Search Algorithms

Automatic monitoring, data collection, analysis and prediction of environmental changes is essential for all living things. Understanding future climate changes does not only helps in measuring the influence on people life, habits, agricultural and health but also helps in avoiding disasters. Giving the high emission of chemicals on air, scientist discovered the growing depletion in ozone layer. This causes a serious environmental problem. Modeling and observing changes in the Ozone layer have been studied in the past. Understanding the dynamics of the pollutants features that influence Ozone is ex-plored in this article. A short term prediction model for surface Ozone is offered using Multigene Symbolic Regression Genetic Programming (GP). The proposed model customs Nitrogen-di-Oxide, Temperature and Relative Humidity as the main features to predict the Ozone level. Moreover, a comparison between GP and Artificial Neural Network (ANN) in modeling Ozone is presented. The developed results show that GP outperform the ANN.

/pdf/influence-of-nitrogen-di-oxide-temperature-and-relative-3ju0d5lb41.pdf

Influence of Nitrogen-di-Oxide, Temperature and Relative Humidity on Surface Ozone Modeling Process Using Multigene Symbolic Regression Genetic Programming

Thermostable enzymes production depends on number of attributes such as temperature, pH, inoculum, time and agitation. Optimizing the relationship between these attributes has been a challenge in biochemical research field. Machine learning techniques such as Artificial Neural Networks (ANN), Fuzzy Logic (FL) and Genetic Algorithms (GAs) were used to solve the lipase activity modeling problem. In this paper, we explore the use of Multigene Symbolic Regression GeneticProgramming to solve the production problem of a solvent, detergent, and thermotolerantlipase using the Newly IsolatedAcinetobacter sp. in submerged and solid-state fermentation. Five attributes will be used to develop a mathematical model for the lipase activities. They are temperature, pH, inoculum, time and agitation. Genetic Programming shows promising results compared to reported results in the literature.

/pdf/optimizing-thermostable-enzymes-production-using-multigene-3b31abdp89.pdf

Hossam Faris

Papers

Toward an Automatic Quality Assessment of Voice-Based Telemedicine Consultations: A Deep Learning Approach.

Forecasting business failure in highly imbalanced distribution based on delay line reservoir

Estimating ARMA Model Parameters of an Industrial Process Using Meta-Heuristic Search Algorithms

Influence of Nitrogen-di-Oxide, Temperature and Relative Humidity on Surface Ozone Modeling Process Using Multigene Symbolic Regression Genetic Programming

Optimizing Thermostable Enzymes Production Using Multigene Symbolic Regression Genetic Programming