This paper presents the electronic marketing application for users involved with the food industry in Thailand. The purpose of this research is to study and develop the supply chain management and the Thai food export to global market. Moreover, this project will improve the knowledge entrepreneurship in this industry to export the food products to the world market through the electronic marketing system. This research will be focused in the two provinces of Thailand that are Phrae and Phayao. The sample group of this research includes the food export manufacturers, the raw material suppliers, and the family business trainees. The research methodology exploits the system development process with the cycle of information system development. The process will develop all fundamentals of the system in every part, which are analysis, design, development, installation, performance testing, and user’s application training. According to the study, the sample users demand the chain management system to produce and export their products. The reason is that they need a tool to assist all sectors of stakeholders; upstream, midstream and downstream. As the results, the proposed system provides functionality that covers the needs of all stakeholders, which are the raw material suppliers, the customers (franchise and restaurant), and the managers (manufacturing, procurement, inventory, restaurant, and sales). From the system's efficiency evaluation by our sample group, the proposed system has been given a satisfied result of system performance from all users: the food export manufacturers (good: = 3.64), the raw material suppliers (good: = 3.63), and the family business trainees (good: = 3.63).

Data mining is the analysis of a large dataset to discover patterns and use those patterns to predict the likelihood of the future events. Data mining is becoming a very important field in educational sectors and it holds great potential for the schools and universities. There are many data mining classification techniques with different levels of accuracy. The objective of this paper is to analyze and evaluate the university students' performance by applying different data mining classification techniques by using WEKA tool. The highest accuracy of classifier algorithms depends on the size and nature of the data. Five classifiers are used NaiveBayes, Bayesian Network, ID3, J48 and Neural Network Different performance measures are used to compare the results between these classifiers. The results shows that Bayesian Network classifier has the highest accuracy among the other classifiers.

Resolving a wide domain of issues and offering a variety of benefits to software engineering, makes the Agile process models attractive for researchers. Scrum has been recognized as one of the most promising and successfully adopted agile process models at software industry. The reason behind vast recognition is its contribution towards increased productivity, improved collaboration, quick response to fluctuating market needs and faster delivery of quality product. Though Scrum performs better for small projects but there are certain challenges that practitioners encounter while implementing it. Experts have made some efforts to adapt the Scrum in a way that could remove those drawbacks and limitations, however, no single effort addresses all the issues. This paper is intended to present a tailored version of Scrum aimed at improving documentation, team’s performance, and visibility of work, testing, and maintenance. The proposed model involves adapting and innovating the traditional Scrum practices and roles to overcome the problems while preserving the integrity and simplicity of the model.

This paper highlights important issues of higher education system such as predicting student’s academic performance. This is trivial to study predominantly from the point of view of the institutional administration, management, different stakeholder, faculty, students as well as parents. For making analysis on the student data we selected algorithms like Decision Tree, Naive Bayes, Random Forest, PART and Bayes Network with three most important techniques such as 10-fold cross-validation, percentage split (74%) and training set. After performing analysis on different metrics (Time to build Classifier, Mean Absolute Error, Root Mean Squared Error, Relative Absolute Error, Root Relative Squared Error, Precision, Recall, F-Measure, ROC Area) by different data mining algorithm, we are able to find which algorithm is performing better than other on the student dataset in hand, so that we are able to make a guideline for future improvement in student performance in education. According to analysis of student dataset we found that Random Forest algorithm gave the best result as compared to another algorithm with Recall value approximately equal to one. The analysis of different data mini g algorithm gave an in-depth awareness about how these algorithms predict student the performance of different student and enhance their skill.

Over the decades, numerous kinds of knowledge discovering and sharing of the data techniques are playing a major role to reach the information quickly. Among these since last few years, social networks or media and own blogging are playing a major in sharing the personal information, updating the status, tagging the location and many more features. These data are considered to examine and the acceptance for emergency services to respond with the information gathered from the social network. Taking this into the consideration, proposed an algorithm to find out the location of the person based upon the information shared. This is implemented on a most popular social media twitter to identify the tweets.

Population census is an enormous and challenging national exercise with many stakeholders whose participation is required at all levels of government or public administration. The problem of high cost in conducting the traditional census process imposes extra and unaffordable cost in most of the developing countries which resulted into ten-year defaulting for census enumerations. This challenge compels nations to seek for assistance mostly from various donors nations in every census enumerations exercise. Virtual Census enumerations play a vital role in demographic data enumerations since it does not require physical involvement in Enumeration Area as in traditional enumerations approach. In this paper the main focus is on data integration from different heterogeneous sources, addressing cleansing challenge for data integrated from data sources with no common key for integrations, building virtual data integration framework for enhancing virtual censuses enumeration process. The developed framework and algorithms can be used to guide design of any other data integration system that need to address similar challenges in related aspects. The outcome of this work is suitable and cheaper technique of demographic data enumeration as compared to traditional technique which involves a lot of manual works and processes.

Particle Swarm Optimization (PSO) is one of most widely used metaheuristics which is based on collective movement of swarm like birds or fishes. The inertia weight (w) of PSO is normally used for maintaining balance between exploration and exploitation capability. Many strategies for updating the inertia weight during iteration were already proposed by several researchers. In this paper, a Modified Particle Swarm Optimization (MPSO) algorithm based on self-adaptive acceleration constants along with Linear Decreasing Inertia Weight (LDIW) technique is proposed. Here, in spite of using fixed values of acceleration constants, the values are updated themselves during iteration depending on local and global best fitness value respectively. Six different benchmark functions and three others inertia weight strategies were used for validation and comparison with this proposed model. It was observed that proposed MPSO algorithm performed better than others three strategies for most of functions in term of accuracy and convergence although its execution time was larger than others techniques.