As a basic approach, a system with shared interests is used. The situation is considered when a passenger chooses between two types of transport: personal and public. Quality parameters of the route network and road infrastructure are fixed. A simulation model based on reinforcement learning. For the first time, a mechanism for finding the best option in the unchanged conditions of the route network, taking into account information about neighbors, was demonstrated. Our goal is to clarify the mechanism of choice based on satisfaction, which depends on comparing the quality of services in the past with that of the neighbors.

Handwritten character and numeral recognition have gained interest in the research community as part of the big picture of Machine Learning. Writer independent recognition systems are still in the working and the research is geared towards an optimized technique that can achieve this. In this paper, we propose a numeral recognition system that forms fuzzy sets of the features extracted using modified structural features for English, Arabic, Persian, and Devanagari Numerals. The structural features extract the geometrical primitives that distinguish each image. After the feature extraction phase, the results are input into a classifier, we test two different classifiers namely Neural Network and Nave Base. To further enhance the recognition process with low overhead the erroneously recognized numerals (confusion matrix) are processed through the fuzzy set-based decision mechanism to enhance the numeral recognition process. Results indicate that recognition is enhanced by applying the fuzzy set-based decision mechanism for both classifer.

In order for government organizations to be geared towards digital government they need to have the basic systems in terms of hardware, software, systems and data in order to support integration. Therefore, the exchange of government data is considered to be the major factor in developing digital government. This article aims to design the service-oriented architecture (SOA) for data exchange within the Ministry of Education in Thailand which has a varied internal structure, by using REST as the "Architectural style" and JSON as the standard for exchanging data according to the information standard that is prescribed by the Ministry of Education.

High-Performance Computing or HPC is a computer system that has high computing power. The HPC supports various computational domains. A huge amount of jobs from a large group of users prefer to complete their jobs in this kind of system. Therefore, managing the jobs or job scheduling is very important since it involves the overall system efficiency. The analysis of an HPC-workload log file is a solution to improve system efficiency. Because some information may appear in the log file, this information can help the system scheduler to make an appropriate decision for job scheduling in the HPC system. This research proposed predictive models for predicting the job status at the finishing state in the HPC system. The model can be used as a tool for monitoring the jobs in the HPC system. We develop and build the three models including HPC-CNN, HPC-AlexNet, and HPC-VGG16 based on the two different learning techniques, which comprise Initial and Transfer Learning of Convolutional Neural Network based on the HPC-work load dataset. Moreover, the three state-of-the-art Machine Learning methods: Classification and Regression Tree (CART), Artificial Neural Network (ANN), and Support Vector Machine (SVM) are used as the baseline models for performance comparison. The results show that the model that performs the best predictive performance is the proposed HPC-CNN model. It archives 76.48% accuracy of the prediction followed with the CART model (75.60%), while the SVM model performs lowest the accuracy at 66.80%.

policies have important strategic significance for China to build an innovative country. Scientific and objective evaluation of the effects of science and technology innovation policies is the main problem to be solved at present. In this context, this paper takes Beijing as an example to evaluate the effects of scientific and technological innovation policies by using lasso and BP neural network algorithms. The empirical results show that supply-driven policies have obvious effects, and the effects show an upward trend year by year, while the environmental policies have a significant decline, and the demand-driven policies still have much room for improvement. Finally, the next research direction is proposed.

Fall detection became a major concern especially for elderly who lives alone at home. Unexpected situations might happen that influence their health, security and well-being. The development of an intelligent surveillance system is required to alleviate the negative effects of unforeseen circumstances and assisting the elderly in independent living. Currently, convolutional neural network has been successfully used for solving various computer vision tasks, such as object detection and recognition. In this paper, we propose a new vision system for elderly fall detection based on new two stream convolutional neural networks. First, human silhouette is extracted based on background subtraction and person recognition. Second, history of binary motion image HBMI is fed into the first stream characterizing the human shape variations. The second stream is based on amplitude and orientation of optical flow defining the velocity and the direction of the human motion. The system classifies fall events using score fusion schema. Transfer learning is performed to deal with the small amount of fall datasets. Our final network outperforms state-of-the-art results on standard fall datasets.

The Convolutional Neural Network (CNN) is a class of deep artificial neural network and has recently gained special attention after demonstrating breakthrough accuracies in various classification tasks. CNNs have shown remarkable performance in machine vision tasks such as image classification, natural language processing and speech recognition. There is evidence that the depth of a CNN plays an important role in performance of CNNs. However, we investigated the feasibility of improving the performance of shallow networks via fusion of the features computed by a homogenous and heterogeneous set of pre-trained networks. We also explored a recently developed framework called the Generative Adversarial Network (GAN), in which we simultaneously train two models, a Generator and a Discriminator. The Generator attempts to produce data that mirrors the probability distribution of the "true" dataset. The Discriminator is trained to distinguish between the true dataset and the counterfeit data produced by the Generator. Our work involves the application of a GAN for generation and fine tuning of synthetic data to be used to train a deep CNN. Specifically, we investigate the use of a synthetic data generator along with a GAN to create an unlimited quantity of labeled training data, without the need for hand-labeling images. We apply this technique to the detection and localization of various vehicles. We attempt to distinguish between military trucks and other types of vehicles. A successful outcome could lead to improvements in addressing security threats rapidly, and cost-effectively. We also investigate an alternative method for generating synthetic data, the Variational Auto-Encoder (VAE). Variational auto-encoders are trained to encode then decode input vectors and can also be useful for generating new training data. VAEs are capable of dimensionality reduction and synthesizing data. Finally, we evaluate our multiplicative fusion method compared to the fusion methods that we investigated previously.

This research aims to study the intelligent information management with Digitization workflow, to synthesize the intelligent information management with Digitization workflow and evaluate the intelligent information management with Digitization workflow.By conducting research from related works and comparing and analyzing the intelligent information management model with Digitization workflow, the pattern model which has been synthesized was found by the researcher.The intelligent information management model with Digitization workflow is consists of 6 elements, 1) Create 2) Capture 3) Management 4) Deliver 5) Preserve and 6) Analytics.Each elements also divided into sub-module as follows; Create divided into 3 sub-modules which are creation and writing content sub-module, sync file and share sub-module, and social and collaboration sub-module; Capture divided into 2 sub-modules which are multi-channel capture sub-module and data recognition, standards and extract sub-module; Management divided into 4 sub-module which are metadata management sub-module, document version control sub-module, data-collection management and electronic portfolio sub-module, and digital asset management sub-module; Deliver divided into 4 sub-modules which are enterprise search sub-module, transformation technology sub-module, security technology sub-module, and content distribution sub-module; Preserve divided into 3 sub-modules which are records management and distribution sub-module, compliance-government and industry sub-module, and legal and electronic transaction sub-module; and finally Analytic divided into 3 sub-module which are semantics and text analytical sub-modules, system auditing and monitoring sub-module, and content and process analytics sub-module.To evaluation results of the design of intelligent information management with Digitization workflow, the result found that the appropriate of workflow design is at very high levels IndexTerms-Intelligent information management, digitization workflow. I. INTRODUCTIONTechnology has developed rapidly and came to be important role both in daily life and work for recently years.With technology that is changing rapidly and constantly changing, all agencies, both government and private sectors have more need to use technology in the operation of the organization to reform accurate, fast and the most modern to