Business Computer

Rice is vital to people all around the world. The demand for an efficient method in rice seed variety classification is one of the most essential tasks for quality inspection. Currently, this task is done by technicians based on experience by investigating the similarity of colour, shape and texture of rice. Therefore, we propose to find an appropriate process to develop an automation system for rice recognition. In this paper, several hand-crafted descriptors and Convolutional Neural Networks (CNN) methods are evaluated and compared. The experiment is simulated on the VNRICE dataset on which our method shows a significant result. The highest accuracy obtained is 99.04% by using DenNet21 framework.

In a visual driver assistance system, traffic sign detection and recognition are important functions. This paper presents automatic traffic sign detection and recognition systems based on neural networks and particle swarm optimization. Our system is able to detect and recognize all types of traffic signs used in Thailand, namely, prohibitory signs (red or blue), general warning signs (yellow) and construction area warning signs (amber). Traffic signs provide drivers with important information that help them to drive more safely and easily by guiding and warning them. The systems consist of four main stages: 1) color filtering according to color of RGB pixels; 2) color segmentation and traffic sign detection by black-white color transformation; 3) feature extraction; 4) traffic sign recognition based on classification techniques. Experiments show that system has high accuracy of traffic sign detection and recognition for the traffic signs used in Thailand.

In this paper a comprehensive approach to traffic sign detection and recognition is proposed. An RGB roadside image is acquired. Color filtering and segmentation is used to detect the boundary of traffic sign in binary mode. At the feature extraction stage, the RGB traffic sign region is cropped. The image is resized to 100x100 pixels. Finally, particle swarm optimization is used to identify the traffic sign. Experimental results show that our system can give a high recognition rate for all types of traffic signs used in Thailand: namely, prohibitory signs (red or blue), general warning signs (yellow) and construction area warning signs (amber).

The objectives of this research were to study the current use of e-commerce in business and to find the guidelines for enhancing the competitiveness of e-commerce for businesses. This research employed quantitative and qualitative research methodologies. The research samples were 400 customers in e-commerce in Bangkok, Thailand. These samples were collected by simple random sampling method. The research tool for data collection was a questionnaire. Data were analyzed in terms of percentage, mean and standard deviation. The researcher also conducted interviews with 5 executives and computer staffs of e-commerce organizations in Bangkok, Thailand. They were selected by purposive sampling. The research result revealed that enhancing the competitiveness of e-commerce business were as follows: 1) e-commerce organizations must set up clear policies for e-commerce business, 2) appropriate marketing and promotions of e-commerce must be reviewed and improved, 3) good system security must be ...

This paper compares classification performances of two techniques for traffic sign recognition, namely, neural networks and particle swarm optimization. Neural networks and particle swarm optimization are applied to the problem of identifying all types of traffic signs used in Thailand, namely, prohibitory signs (red or blue), general warning signs (yellow) and construction area warning signs (amber). The comparison indicates that the neural network technique has higher correct recognition rates than particle swarm optimization for traffic sign recognition. Moreover, neural networks require less computer processing time than particle swarm optimization in the traffic sign recognition system. Key-Words: Classification techniques, traffic sign recognition, neural networks, particle swarm optimization

This qualitative research aims to study on Information Technology (IT) use for Supply Chain Management (SCM) and logistics in agricultural product industry and IT guidelines for development of sustainable SCM and logistics in agricultural product industry. The research samples compose of two groups. The first group was 400 samples who are entrepreneurs, famers, processing plants, wholesalers and exporters in logistics and supply chain commodity in Nakorn Pathom province. The samples are selected using simple random sampling. The tool use to collect data for this group is questionnaire. The second group was corporate executives and technician representatives, total 10 samples, who are corporate executives and computer staff in agricultural product industries. The samples are selected by purposive sampling. The tool use to collect data for this group is in-depth interview. Then research data was analyzed by descriptive analysis. The result found out problems in IT use for SCM and logi...

Most of vehicle have the similar structures and designs. It is extremely complicated and difficult to identify and classify vehicle brands based on their structure and shape. As we require a quick and reliable response, so vehicle logos are an alternative method of determining the type of a vehicle. In this paper, we propose a method for vehicle logo recognition based on feature selection method in a hybrid way. Vehicle logo images are first characterized by Histograms of Oriented Gradient descriptors and the final features vector are then applied feature selection method to reduce the irrelevant information. Moreover, we release a new benchmark dataset for vehicle logo recognition and retrieval task namely, VLR-40. The experimental results are evaluated on this database which show the efficiency of the proposed approach.

Image classification is an essential task in computer vision with various applications such as bio-medicine, industrial inspection. In some specific cases, a huge training data is required to have a better model. However, it is true that full label data is costly to obtain. Many basic pre-processing methods are applied for generating new images by translation, rotation, flipping, cropping, and adding noise. This could lead to degrade the performance. In this paper, we propose a method for data augmentation based on color features information combining with feature selection. This combination allows improving the classification accuracy. The proposed approach is evaluated on several texture datasets by using local binary patterns features.

Face analysis is an essential topic in computer vision that dealing with human faces for recognition or prediction tasks. The face is one of the easiest ways to distinguish the identity people. Face recognition is a type of personal identification system that employs a person’s personal traits to determine their identity. Human face recognition scheme generally consists of four steps, namely face detection, alignment, representation, and verification. In this paper, we propose to extract information from human face for several tasks based on recent advanced deep learning framework. The proposed approach outperforms the results in the state-of-the-art.