Mining Students' Data with Holland Model Using Neural Network and Logistic Regression

Education domain provides many interesting and challenging in data mining applications that potentially identified as a tool to help both educators and students, and improve the quality of education system. Nowadays, the impact of Minister of Education (MOE) regarding surplus graduates particularly from public universities somehow had an impact on Universiti Utara Malaysia's (UUM) undergraduate intake. As a result, students who applied to undertake a program at Faculty of Information Technology and Faculty of Management Technology come from various background. Hence this study aims to get some insight into first year students undertaking undergraduate program such as Bachelor of Information Technology (BIT), Bachelor of Multimedia (BMM) and Bachelor in Management of Technology (BMoT) at Universiti Utara Malaysia. The Holland Personality Model was used to indicate the students' personality traits. The study concluded that BIT students are not from the Social type since none of the Social personality type is significant. Most of BIT students have Arts background, expect a few who have sat for Perkom (Perkomputeran) subject during the STPM examination. As for the Holland Model, It also appears that BIT students are more Artistic since 50% of the questions that measure the personality type is significant. In addition, the BIT students are Realistic (33.33%) and Investigative (33.33%) type. The results also reveal that the BIT students concluded as Artistic, Investigative and Realistic (AIR) in personality types that ar ein accordance to Holland personality theory, this finding were also supported by Hansen and Campbell (1985) that suggested that Investigative, Realistic and Artistic (IRA) should be the code for computer professionals

Exploring hidden relationships within students' data using neural network and logistic regression

Considerable attention has been given to the development of sophisticated techniques for exploring data sets.One of the most commonly used techniques is neural networks that have the abilities to detect nonlinear effects and/or interactions.Due to the reduced interpretability of the output model of neural networks the some data set has been analyzed using logistic regression.In this study both techniques have been applied to education data set.The study aims to provide some insight into fist year students undertaking undergraduate programs namely Bachelor of Information Technology (BIT),Bachelor of Multimedia (BMM) and Bachelor in Management of Technology (BMoT) at University Utara Malaysia. The Holland Personality Model was used to indicate the students personality traits in conjunction with students academic achievement of accuracies in both methods the methods were used in this exploratory study in a complementary manner

遺伝子変異株の代謝流束を推定するためのデータベース構築と Genetic Modification Fluxソフトウェアの開発

In understanding the complexity of a metabolic network structure, flux distribution is the key information to observe as it holds direct representation of cellular phenotype. To examine this, the study on genetically perturbed conditions (e.g. gene deletion/knockout) is one of the useful methods, which significantly contributes to metabolic engineering and biotechnology applications. Currently, metabolic flux analysis (MFA) is proven to be suitable mechanism for specific gene knockout studies, yet the method involves exhaustive computational effort since the calculation are derived by a stoichiometric model of major intracellular reactions and applying mass balances to the intracellular metabolites. Metabolic Flux Analysis (MFA) is widely used to investigate the metabolic fluxes of a variety of cells. MFA is based on the stoichiometric matrix of metabolic reactions and their thermodynamic constraints. The matrix is derived from a metabolic network map, where the rows and columns represent metabolites, chemical/transport reactions, respectively. MFA is very effective in understanding the mechanism of how metabolic networks generate a variety of cellular functions and in rationally planning a gene deletion/amplification strategy for strain improvements. Flux Balance Analysis (FBA) is used to predict the steady-state flux distribution of genetically modified cells under different culture conditions. Minimization of Metabolic Adjustment (MOMA) was developed to predict the flux distributions of gene deletion mutants. FBA and MOMA often lead to incorrect predictions in situations where the constraints associated with regulation of gene expression or activity of the gene products are dominant, because they apply the Boolean logics or its related simple logics to gene regulations and enzyme activities. On the other hand, network-based pathway analyses, elementary modes (EMs) and extreme pathways emerge as alternative ways for constructing a mathematical model of metabolic networks with gene regulations. EM analysis was suggested to be convenient for integrating an enzyme activity profile into the flux distribution. Enzyme Control Fluxes (ECFs) uses the relative enzyme activity profile of a mutant to wild type to predict its flux distribution. In facilitating the analysis of metabolic flux distributions, the support of computational approaches is significantly essential. In addition, the availability of real sample data particularly for further observation, a large number of knockout mutant data provides assistance in enhancing the process. We had presented Genetic Modification Flux (GMF) that predicts the flux distribution of a broad range of genetically modified mutants. The feasibility of GMF to predict the flux distribution of genetic modification mutants is validated on various metabolic network models. The prediction using GMF shows higher prediction accuracy as compared to FBA and MOMA. To enhance the feasibility and usability of GMF, we developed two versions of simulator application with metabolic network database to predict flux distribution of genetically modified mutants. 112 data sets of Escherichia coli (E.coli), Corynebacterium glutamicum (C.glutamicum), Saccharomyces cerevisiae (S.cerevisiae), and Chinese Hamster Ovary (CHO) were registered as standard models.九州工業大学博士学位論文 学位記番号：情工博甲第313号 学位授与年月日：平成28年6月30日1: INTRODUCTION AND BACKGROUND|2: MATERIALS AND METHODS|3: RESULT AND DISCUSSION|4: CONCLUSION九州工業大学平成28年

A mobile application of augmented reality for periodic table with speech recognition

A periodic table of elements is a group of various elements arranged according to their various chemical properties which can provides some valuable information. Augmented Reality can play a role in making the learning process more interesting by providing some visualization and interaction with the periodic table

A survey on current malicious javascript behavior of infected web content in detection of malicious web pages

In recent years, the advance growth of cybercrime has become an urgent issue to the security authorities. With the improvement of web technologies enable attackers to launch the web-based attacks and other malicious code easily without having prior expert knowledge. Recently, JavaScript has become the most common attack construction language as it is the primary browser scripting language which allow developer to develop sophisticated client-side interfaces for web application. This lead to the growth of malicious websites and as main platform for distributing malware or malicious script to the user's computer when the user access to these webpages. Initial act and detection on such threats early in a timely manner is vital in order to reduce the damages which have caused billions of dollars lost every year. A number of approaches have been proposed to detect malicious web pages. However, the efficient detection of malicious web pages previously has generated many false alarm by the use of sophisticated obfuscation techniques in benign JavaScript code in web pages. Therefore, in this paper, a thoroughly survey and detailed understanding of malicious JavaScript code features will be provided, which have been collected from the web content. We conduct a thorough analysis and studies on the usage of different JavaScript features and JavaScript detection technique systematically and present the most important features of malicious threats in web pages. Then the analysis will be presented along with different dimensions (features representation, detection techniques analysis, and sample of malicious script)

A Comparison of Particle Swarm optimization and Global African Buffalo Optimization

The performance of Particle Swarm Optimization (PSO) brings attention to the field of algorithms when deals with different optimization problems. Due to her simple implementation, small consumption, and very effective in finding a solution in many problems, (PSO) becomes well known to the field of algorithms. In addition, the late proposed algorithms mostly are compared to the well-known algorithm such as PSO. Thus, the Global African Buffalo Optimization (GABO) was proposed lately and yet not been compared to the old well-known algorithms in terms of accuracy and time consumption. However, in this paper, a comparison between Particle Swarm Optimization (PSO) and Global African Buffalo Optimization (GABO) algorithms was performed. Five different nonlinear equations with their upper and lower boundaries values were selected as the test optimization functions problem in addition to PSO was applied to real case study. The experimental results illustrated the differences in the performances of both algorithms toward the optimum solution. At the end of the experiments, the PSO algorithm quickly convergence towards the optimum solution using a few particles and iterations rather than GABO. However, the experimental result showed that PSO achieved good results in all the test cases within a short time. In many cases, PSO and GABO are promising optimization methods