Big data, modeling, simulation, computational platform and holistic approaches for the fourth industrial revolution

Naturally, the mathematical process starts from proving the existence and uniqueness of the solution by the using the theorem, corollary, lemma, proposition, dealing with the simple and non-complex model. Proving the existence and uniqueness solution are guaranteed by governing the infinite amount of solutions and limited to the implementation of a small-scale simulation on a single desktop CPU. Accuracy, consistency and stability were easily controlled by a small data scale. However, the fourth industrial can be described the mathematical process as the advent of cyber-physical systems involving entirely new capabilities for researcher and machines (Xing, 2017). In numerical perspective, the fourth industrial revolution (4iR) required the transition from a uncomplex model and small scale simulation to complex model and big data for visualizing the real-world application in digital dialectical and exciting opportunity. Thus, a big data analytics and its classification are a problem solving for these limitations. Some applications of 4iR will highlight the extension version in terms of models, derivative and discretization, dimension of space and time, behavior of initial and boundary conditions, grid generation, data extraction, numerical method and image processing with high resolution feature in numerical perspective. In statistics, a big data depends on data growth however, from numerical perspective, a few classification strategies will be investigated deals with the specific classifier tool. This paper will investigate the conceptual framework for a big data classification, governing the mathematical modeling, selecting the superior numerical method, handling the large sparse simulation and investigating the parallel computing on high performance computing (HPC) platform. The conceptual framework will benefit to the big data provider, algorithm provider and system analyzer to classify and recommend the specific strategy for generating, handling and analyzing the big data. All the perspectives take a holistic view of technology. Current research, the particular conceptual framework will be described in holistic terms. 4iR has ability to take a holistic approach to explain an important of big data, complex modeling, large sparse simulation and high performance computing platform. Numerical analysis and parallel performance evaluation are the indicators for performance investigation of the classification strategy. This research will benefit to obtain an accurate decision, predictions and trending practice on how to obtain the approximation solution for science and engineering applications. As a conclusion, classification strategies for generating a fine granular mesh, identifying the root causes of failures and issues in real time solution. Furthermore, the big data-driven and data transfer evolution towards high speed of technology transfer to boost the economic and social development for the 4iR (Xing, 2017; Marwala et al., 2017)

A cloud based framework for e-government implementation in developing countries

Cloud Computing technology is achieving a significant cost saving, business agility, and high scalability. However, it is a relatively new technology and its successful implementation in the governmental organizations needs careful consideration due to data sensitivity. Successful adoption of cloud-based solutions is the key for realizing the expected benefits of cloud computing technologies in the public agencies. The aim of this research is to develop a strategic framework to adopt cloud-based solutions in the public sector to improve e-government processes in developing countries. The purpose of the developed framework is to reduce the time and cost of the processes that contain interaction among governmental agencies and citizens through adopting cloud-based solutions. The framework was formulated based on the collected data analysis and the conclusions from experts' interviews. This study provides detailed guidelines to a successful launch and implementation of cloud-based solutions for e-government initiatives in the public sector

Strategies to manage electronic waste approaches: an overview in Malaysia

The issue of e-waste is becoming an increasingly threatening problem as it contains many toxic materials that can severely harm both human beings and the environment. This problem is expected to worsen if not serious efforts are taken to manage this e-waste. The current paper introduced quantity of e-waste generated and their negative impact on both environment and human beings in some countries including Malaysia. It also presents the managerial efforts taken in this regard to deal with the e-waste. The current study is an effort to decrease the danger and solve e-waste problems. For that, it utilized different tools such as LCA, MFA, MCA and EPR. Over and above all of these, no matter how well the policies are introduced and implemented benefits will only arise provided end users are prepared to accept introduced policies and adhere to them

A modeling of animal diseases through using artificial neural network

This paper studied the implementation of Artificial Neural Network (ANN) where it well-known recently in veterinary disease research field in Malaysia. The parameter identification under consideration is types of animal disease, types of species and locations of disease based on the Geographical Information System (GIS) data set. There are many types of animal diseases that affect farm animals in Malaysia. In this research, the method of multilayer perceptron neural network is used as main model since it is an effective solving method in predicting the future of veterinary disease. ANN has ability to visual animal diseases involving the computational model. The model is to present the rela-tionship between causes of the species and location and consequence of animal disease without emphasizing the process, considering the initial and boundary condition and considering the nature of the relations. The data collection of animal disease is considered as a large sparse data set. Therefore method of ANN is well suited for optimizing of the data, to train the data operational and to predict the parameter identification of animal disease. The output layers of ANN are plotted in SPSS software for statistical solution and MATLAB programming for sequential ANN implemented. The ANN will be compare to genetic algorithm for the performance and effectiveness of the method. The numerical simulation of ANN helps in future prediction of animal disease based on the species and location parameters

Strategies to manage electronic waste approaches: an overview in east Asia

The issue of e-waste is becoming an increasingly threatening problem as it contains many toxic materials that can severely harm both human beings and the environment. This problem is expected to worsen if not serious efforts are taken to manage this e-waste. The current research introduced the best strategies and techniques with managerial efforts taken in this regard to deal with the e-waste in East Asia countries. Countries have been using a variety of techniques to deal with this problem namely: Life Cycle Assessment (LCA), Multi Criteria Analysis (MCA), Material Flow Analysis (MFA), and Extended Producer Responsibility (EPR). Therefore, these strategies are prosed to work together to insure the best results in dealing with this problem. Moreover, this research involves a systematic and organized review of 308 research articles regarding electronic waste from 2000 to 2017. An analysis of studies dedicated to manage e-waste in East Asia countries was carried out on the basis of certain dimensions, namely, year of publication, journal, country, and subject area. Based on the obtained findings, the most of studies are from environmental science, chemistry, engineering, medicine and an energy area in the rate of (33.8%), (12.3%), (8.8%), (8.4%) and (5.8%) respectively. Furthermore, the findings have shown an increasing trend over recent years from 2010-2017

Numerical performance of healthy processing for HMF content in honey

The objective of this study is to develop a kinetic model correlating the effect of heating temperature and the duration of thermal treatment on HMF formation for different types of honey from different geographical locations. In this study, the experimental data from previous re-search papers for European and Asian honey was collected from year 1999 to 2012. The data was analysed and performed visually in graphical representation to draw the relationship between the factors. Then, a descriptive mathematical model was developed by using Math Work to correlate the parameters and the model was validated based on the data from Malaysian and European honey samples. The study showed that both heating temperature and duration could accelerate the production of HMF content in honey. The formation of HMF con-tent is proportionally increased with the increase of heating temperature and duration

Investigating the Effect of Block Length on the Performance of Fractal Coding Using Audio Files

The goal of compression techniques is to reducing the size of data and decreasing the communication cost while transferring data. Fractal based coding technique is widely used to compress images files which provides high compression ratio and good image quality. However, like a compression technique, it is still limited because of the difference of the human perceptions between audio and image files, the long time for searching the best possible domain blocks and many comparisons in the encoding process. For those reasons, Fractal Coding had not broadly studied on audio data. Few years ago, Fractal Coding has been extended to apply on the audio data. In this paper, the application of the Fractal Coding on different types of audio files is investigated. Moreover, the effect of block length on the audio quality and compression performance are highlighted since block length is considered the main factor in the Fractal Coding algorithm. A GTZAN dataset is adopted in the evaluation and the experimental results show that there is an inverse relationship between block length and audio quality and proportional relationship between block length and compression ratio and factor. Furthermore, it can be noticed that the Fractal Coding can be compressed any speech and music audio signal directly with acceptable quality, PSNR 39 dB on average with a high compression ratio around 90 % with compression factor around 10 when the block length is 20 samples

Embedded system on high performance data for wearable augmented reality of eye blinks, muscle stress detection movement and observation

Eyes blinking and its movement can portray many reasons of the body and health state. Eyes can blink intentionally and sometimes randomly even in sleeping mode. Thus, the aim of this paper is to discover and observe the relationship between the frequency of eye blink and the level of eye muscle stress. The eye track data is fed directly into the electroencephalogram (EEG) record for parameter classification and identification. The EEG signal might have an artifact that has been analyzed and converted the observation into the mathematical library and repository software (HPC). The artificial neural network (ANN) is integrated with EEG digital data by the derivation of the mathematical modelling. The function of ANN is to train a large sparse digital data for future prediction of eye condition associated with the stress level. In order to validate the model and simulation, the numerical analysis and performance evaluation are compared to the real data set of eye therapy industry, IC Herbz Sdn Bhd. A library and repository software of mathematical model using EEG record data is developed to integrate with wearable augmented reality (WAR) based on EEG sensor device for predicting and monitoring the real time eye blinks, movement and muscle stress

Factors affecting students' learning strategies at school

English language is spoken, written, read, and understood widely in most parts of the world. The significance of English language today underlines the significance of its vocabulary learning strategies. The aim of this study is, therefore, to examine factors affecting vocabulary learning strategies (VLS) among Saudi students studying Saudi School in Malaysia (SSM). To achieve the objective of the paper, two different tools of data collection have been adopted by the researcher. The study employs both semi-structured interview, where a total of seven students participated, as well as class observation which complement the findings of the interview. The study generated three themes from the interviews as the factors affecting vocabulary learning of students: language learning environment, attitude and beliefs, and Motivation. The findings of the study show that vocabulary-learning strategies (VLS) in SSM is affected by three major factors: language learning environment, attitude and beliefs of students, and motivation

From student’s experience: does e-learning course structure influenced by learner’s prior experience, background knowledge, autonomy, and dialogue

Background: E-learning is increasingly becoming a preference in higher education institutions worldwide; this is intended to assist educational institutions in achieving objectives to meet the proportion of individuals with their educational opportunities. Nevertheless, instructors and students frequently have concerns with their capacity to succeed in E-learning environments. Objectives: This study aimed to presents common e-Learning challenges in regard to e-learning courses structure and its relations to various factors, for instance; students’ autonomy, prior knowledge and experience, students-students dialogue, and students-instructor dialogue, and proposes solutions to these challenges based on the transactional distance theory. Moreover, this study presents evidence from Malaysian higher institutions based on theoretical models for e-learning course structure and its relations to the factors mentioned above. Methods: Data have been collected from 680 university learners all over Malaysia. Data were then examined using exploratory factor analysis, confirmatory factor analysis, and structural equation modelling employing Smart PLS 3.0 software. Results and conclusion: Research findings indicated that e-learning course structure was affected by all dimensions of overall path analysis findings: student autonomy, students background, student-instructor dialogue, and student-student dialogue. However, the e-learning course structure showed insignificant with students’ prior experience. Implications: Implications for universities are discussed accordingly. Such findings provide vital support to the integrative association among collaborative control (CC) and transactional distance theory (TDT) regarding e-learning environments experience, which might support universities administrators in the higher education industry to implement, plan and evaluate online learning platforms applications in their institutions