An Exploration To Determine Essential Requirements For Smart Home Application

The revolution of Internet of Things (IoT) will be able to revive the way people use the technology for a greater benefit. As we are embarking towards the golden age of technology, smart home application is gaining popularity as it adds convenience, comfort and peace of mind. There are variety of smart home applications worldwide which has diverse functionality with different perspectives and embedded assumptions. These scenario leads to uncertainty among the developers and leads to unnecessary effort to elicit requirements every time new application wants to be developed. Therefore, this paper presents an exploration to determine essential requirements for smart home application based on end user needs. An empirical investigation based on survey technique was conducted to determine essential requirements for smart home application. A case study of residents in Satellite City of Muadzam Shah, Pahang was conducted. The analysis was done by using T-Test and One Way Analysis of Variance (ANOVA). The results show that the respondents agreed the essential requirements for smart home application are Time Needs, Simplicity Needs, Security and Safety Needs and Mobility Needs

Automated study plan generator using rule-based and knapsack problem

Undergraduate students are given the flexibility of arranging courses throughout their study duration especially when they are eligible for credit exemption for the courses taken during their diploma study. Issues arise when students arrange their studies manually. Improper course arrangement in the study plan may be resulting some of the selected courses do not correspond to the courses offered, and imbalance credit hours. Hence, this study aims to propose an algorithm to generate an automated and accurate study plan throughout the study duration. A combination of rule-based and knapsack problem were proposed to generate an automated study plan. A quantitative methodology through expert’s reviews and questionnaire survey was conducted to evaluate the accuracy of the proposed algorithm. The proposed algorithm shows high accuracy. In conclusion, the combination of rule-based and knapsack problem is appropriate to generate an automated and accurate study plan. The automated study plan generator can help students generate an effective study plan

IoT Based Temperature And Humidity Monitoring Framework

This study explored the use of Internet of Things (IoT) in monitoring the temperature and humidity of a data centre in real-time using a simple monitoring system to determine the relationship and difference between temperature and humidity with respect to the different locations of measurements. The development of temperature and humidity monitoring system was accomplished using the proposed framework and has been deployed at the data centre of Politeknik Muadzam Shah, where the readings were recorded and sent to an IoT platform of AT&T M2X to be stored. The data was then retrieved and analysed showing that there was a significant difference in temperature and humidity measured at different locations. X The monitoring system was also successful in detecting extreme changes in temperature and humidity and automatically send a notification to IT personnel via e-mail, short messaging service (SMS) and mobile push notification for further actio

Analysis Of Texture Features For Wood Defect Classification

Selecting important features in classifying wood defects remains a challenging issue to the automated visual inspection domain. This study aims to address the extraction and analysis of features based on statistical texture on images of wood defects. A series of procedures including feature extraction using the Grey Level Dependence Matrix (GLDM) and feature analysis were executed in order to investigate the appropriate displacement and quantisation parameters that could significantly classify wood defects. Samples were taken from the Kembang Semangkuk (KSK), Meranti and Merbau wood species. Findings from visual analysis and classification accuracy measures suggest that the feature set with the displacement parameter, d=2, and quantisation level, q=128, shows the highest classification accuracy. However, to achieve less computational cost, the feature set with quantisation level, q=32, shows acceptable performance in terms of classification accurac

Multivariate Time Series Forecasting Of Crude Palm Oil Price Using Machine Learning Techniques

The aim of this paper was to study the correlation between crude palm oil (CPO) price,selected vegetable oil prices (such as soybean oil,coconut oil,and olive oil, rapeseed oil and sunflower oil),crude oil and the monthly exchange rate.Comparative analysis was then performed on CPO price forecasting results using the machine learning techniques.Monthly CPO prices,selected vegetable oil prices,crude oil prices and monthly exchange rate data from January 1987 to February 2017 were utilized. Preliminary analysis showed a positive and high correlation between the CPO price and soy bean oil price and also between CPO price and crude oil price. Experiments were conducted using multi-layer perception, support vector regression and Holt Winter exponential smoothing techniques.The results were assessed by using criteria of root mean square error (RMSE),means absolute error (MAE),means absolute percentage error (MAPE) and Direction of accuracy (DA).Among these three techniques, support vector regression(SVR) with Sequential minimal optimization (SMO) algorithm showed relatively better results compared to multi-layer perceptron and Holt Winters exponential smoothing method

Classification of wood defect images using local binary pattern variants

This paper presents an analysis of the statistical texture representation of the Local Binary Pattern (LBP) variants in the classification of wood defect images. The basic and variants of the LBP feature set that was constructed from a stage of feature extraction processes with the Basic LBP, Rotation Invariant LBP, Uniform LBP, and Rotation Invariant Uniform LBP. For significantly discriminating, the wood defect classes were further evaluated with the use of different classifiers. By comparing the results of the classification performances that had been conducted across the multiple wood species, the Uniform LBP was found to have demonstrated the highest accuracy level in the classification of the wood defects

Systematic feature analysis on timber defect images

Feature extraction is unquestionably an important process in a pattern recognition system. A defined set of features makes the identification task more efficiently. This paper addresses the extraction and analysis of features based on statistical texture to characterize images of timber defects. A series of procedures including feature extraction and feature analysis was executed to construct an appropriate feature set that could significantly separate amongst defects and clear wood classes. The feature set aimed for later use in a timber defect detection system. For Accessing the discrimination capability of the features extracted, visual exploratory analysis and confirmatory statistical analysis were performed on defect and clear wood images of Meranti (Shorea spp.) timber species. Results from the analysis demonstrated that there was a significant distinction between defect classes and clear wood utilizing the proposed set of texture features

A Pipeline To Data Preprocessing For Lipreading And Audio-Visual Speech Recognition

Studies show that only about 30 to 45 percent of English language can be understood by lipreading alone. Even the most talented lip readers are unable to collect a complete message based on lipreading only, although they are often very good at interpreting facial features, body language, and context to find out. As you can imagine, this technique affects the brain in different ways and becomes exhausting over a period of time. If a person who is deaf, uses language and is able to read lips, hearing people may not understand the challenges they are facing just to have a simple one-on-one conversation. The hearing person may be annoyed that they are often asked to repeat themselves or to speak more slowly and clearly. They could lose patience and break off the conversation. In our modern world, where technology connects us in a way never thought possible, there are a variety of ways to communicate with another person. Deaf people come from all walks of life and with different backgrounds. In this study, a lipreading model is being developed that is able to record, analyze, translate the movement of lips and display them into subtitles. A model is trained with GRID Corpus, MIRACL-VC1 and pre-trained dataset and with the LipNet model to build a system which deaf people can decode text from the movement of a speaker’s mouth. This system will help the deaf people understand what others are actually saying and communicate more effectively. As a conclusion, this system helps deaf people to communicate effectively with others

Healthcare Practitioner Behaviours That Influence Unsafe Use Of Hospital Information Systems

This study aims to investigate healthcare practitioner behaviour in adopting Health Information Systems which could affect patients’ safety and quality of health. A qualitative study was conducted based on a semi-structured interview protocol on 31 medical doctors in three Malaysian government hospitals implementing the Total Hospital Information Systems. The period of study was between March and May 2015. A thematic qualitative analysis was performed on the resultant data to categorize them into relevant themes. Four themes emerged as healthcare practitioners’ behaviours that influence the unsafe use of Hospital Information Systems. The themes include (1) carelessness, (2) workarounds, (3) noncompliance to procedure, and (4) copy and paste habit. By addressing these behaviours, the hospital management could further improve patient safety and the quality of patient care