There have been several researches of applying Deep Belief Networks (DBNs) to predict time series data. Most of these works pointed out that DBNs can bring out better prediction accuracy than traditional Artificial Neural Networks. However, one of the main shortcomings of using DBNs in time series prediction concerns with the proper selection of their parameters. In this paper, we investigate the use of Harmony Search algorithm for determining the parameters of DBN in forecasting time series. Experimental results on several synthetic and real world time series datasets revealed that the DBN with parameters selected by Harmony Search performs better than the DBN with parameters selected by Particle Swarm Optimization (PSO) or random method in most of the tested datasets.

Activity Analysis Systems or Activity Recognition Systems for the elderly is recently a part of the smart home systems design. This assisted system normally helps the senior people to live alone in a house, safely and improve a quality of life. Therefore, learning to recognize which activities are safe is necessary for classifying the activities of the elderly. This information will give the researchers in the assistive technology some insights to understand the basic daily lives of the elderly. Moreover, it is also help the caregivers to monitor activities of the senior people while they live alone in the house. In this paper, the novel method for detecting and recognizing the activities using Backpropagation Neural Networks has been proposed. The proposed model was tested on a set of basic daily activities (lie, stand, sit, walk and dine). The proposed model was trained to construct the Backpropagation Neural Networks model and used the trained model to classify basic daily activities of the elderly. The proposed model gives the results of 0.78, 0.72 and 0.74 of precision, recall and F1 score, respectively. The discussion and future extension are also given in this paper.

Credit risk evaluation (CRE) is a very challenging and important management science problem in the domain of financial analysis. Many popular methods have been applied to tackle this problem in recent years. However, feature extraction and imbalanced data problem have not been paid enough attention in the current research, which play significant function in field of CRE. In this paper, we employed a deep learning approach to extract effective features and under-sampling technique to balance dataset. Our model combine under-sampling technique, Deep Boltzmann Machine (DBM) and Discriminative Restricted Boltzmann Machine (DRBM) method. To examine the performance, real world credit data of Lending Club is applied in the proposed model. The stable and better performance results show that the Hybrid classifier we propose is more effective and powerful.

With the rapid development of machine learning, it is possible to use neural networks to build models to predict performance of Ceramic Matrix Composites (CMCs) with raw materials and environments. In the traditional material science engineering, it always took a long time to develop a new CMC. Furthermore, there is still no theoretical basis providing references to design experiments to develop CMCs with ideal performances. This work proposed a model to predict the bending strength of CMCs with a Convolution Neural Network (CNN) using 8 factors considered to affect the bending strength of CMCs mainly. For the data were all collected from papers published on journals and conferences, and there is no standard to describe an experiment, the incompleteness of data influences the performance of our model seriously. Then we tried several methods to fill the data, finally the regression imputation with a dual-hidden-layer neural network performed a significant improvement of the CNN bending strength prediction model.

In practice, automated manufacturing systems usually have multiple, incommensurate, and conflicting objectives to achieve. To deal with them, this paper proposes an extend Petri nets for the multiobjective scheduling of AMSs. In addition, a multiobjective heuristic A* search within reachability graphs of extended Petri nets is also proposed to schedule these nets. The method can obtain all Pareto-optimal schedules for the underlying systems if admissible heuristic functions are used. Finally, the effectiveness of the method is illustrated by some experimental systems.

Thailand and many countries in the Southeast Asia have long been suffered from the regional smoke-haze incidences. Smoke-haze is a kind of air pollution event frequently occurred from forest fires that had been intentionally set for vegetation purpose. The smoke-haze can cause serious health problem from high concentrations of small particulate matters that can retain in the lung or even spread through the whole body to cause obstruction in major organs. In the northern part of Thailand, smoke-haze normally occurs during the dry season from late January to early April with the peak polluted air around March. Controlling burning activity is an obvious solution but impractical when burning areas are in remotely high mountains that are hard to reach by ground officers. Monitoring incidences as well as estimating pollution level are more or less efficient and practical ways to handle the smoke-haze situation. We thus propose the application of machine learning technology to learn smoke-haze patterns from historical events. The specific approach used in our work is the cluster analysis with the k-means and Kohonen self-organizing map algorithms. The cluster with serious pollution effect is then further analysed to induce the predictive regression model using the meteorological factors. The built model can serve as a predictive pattern useful for invoking an early warning sign for air pollution awareness.

The aim of this paper is to design a prototype that integrates a big data framework with a mobile application on an iOS platform to support smart tourism; a case study in Lampang Province, Thailand. The research approach used Software Development Life Cycle (SDLC) methodologies to analyze, design, implement and test user satisfaction of the system. The results found that the design of the big data model used Apache Hadoop to co-ordinate with Apache Spark as the big data processing framework. The mobile application system was designed and developed on an iOS platform that works with user interaction and receives various information from the data base that stores information about tourism such as tourist attractions, accommodation, spa products, health, food, tourism routes, and emergency contacts. etc. The mobile application system was used and evaluated for effectiveness and satisfaction of 400 users with an in-depth interview of 10 users. The results found that the overall mobile application evaluation in six categories found the highest score was the benefit of the mobile application system (x =4.46, S.D. =0.67), second the mobile application system design ( x =4.43, S.D. =0.66), third the mobile application content design (x =4.32, S.D. =0.65) and finally the mobile application system ease of use (x =4.32, S.D. =0.68), respectively. Moreover, the qualitative results found that users were satisfied with the design, functionality, content, and the system overall.

Skin disease is one of disease that is often found in tropical countries, such as Indonesia. People who suffered from skin disease in Indonesia were still relatively high, the prevalence could range between 20% -80%. Therefore, the help of computer technology was expected to detect the disease earlier that attacked the skin in the human's body and it could reduce the possibility of the occurrence for other dangerous diseases. This study proposed the making of an application of identification image for skin disease by using one of the machines learning method, called Support Vector Machine (SVM) which was done by processing the image and machine learning processes that could perform early detection of skin diseases. This study aimed to determine the classification of skin diseases in humans into four classes, such as the class Benign Keratosis, Melanoma, Nevus, and Vascular. The segmentation method used was K-Means Clustering, while the feature extraction method that used was feature extraction of the Discrete Wavelet Transform (DWT) and Color Moments. Based on the results of the test that had conducted, the sensitivity was 95%, the specificity was 97.9% and the accuracy was 97.1% by using SVM parameters, that was kernel Radial Basis Function (RBF), Box Constraint = 1.5, RBF_Sigma () = 1, and iterations = 1000.

Convolutional neural networks (CNNs) are widely used in modern Artificial Intelligence (AI) systems.Compared with other classical methods, CNNs have superior performance in image classification, speech recognition and object detection.However the computational load of CNNs is very heavy and a large amount of data movement are expected.An efficient way of data movement is critical for both performance and power efficiency for an accelerator design.In this paper we propose a novel CNN accelerator architecture with unique parallel loading scheme and smart memory addressing solution.Our solution is 30% faster than others on Alexnet.Our proposal can achieve high efficiency for FC layer without using image batching.This will make our solution very suitable for edge applications.