Developing a location viewer for Residential Property System (ReProS) using spatial database / Farashazillah Yahya

Over the years, the importance of spatial databases is growing rapidly, partly as a result of the recognition that the applications extend well beyond the traditional domain of GIS. Location is a powerful way of identifying and characterizing information because many data sets have footprints m space. Obviously, for a residential area, information is worthless if there is lacking knowledge about its location. At this time, there are no help on gaining location maps of residential area in Malaysia websites. It is a particular problem for house buyers in retrieving information. On the other hand, developers confh)nt troubles in describing their new residential area. An analysis of user requirement by verifying the existing user requirement had proposed a spatial database design to encounter the situation. The creation of spatial database for the location viewer has enabled a development of a model. Presently it had been demonstrated as capable of viewing a location map and other location information. Precisely adding up, the location viewer model had assisted the user in retrieving more information of a residential area in terms of viewing a location map and measuring distance between the residential area and required facilities

Machine learning in dam water research: an overview of applications and approaches

Dam plays a crucial role in water security. A sustainable dam intends to balance a range of resources involves within a dam operation. Among the factors to maintain sustainability is to maintain and manage the water assets in dams. Water asset management in dams includes a process to ensure the planned maintenance can be conducted and assets such as pipes, pumps and motors can be mended, substituted, or upgraded when needed within the allocated budgetary. Nowadays, most water asset management systems collect and process data for data analysis and decision-making. Machine learning (ML) is an emerging concept applied to fulfill the requirement in engineering applications such as dam water researches. ML can analyze vast volumes of data and through an ML model built from algorithms, ML can learn, recognize and produce accurate results and analysis. The result brings meaningful insights for water asset management specifically to strategize the optimal solution based on the forecast or prediction. For example, a preventive maintenance for replacing water assets according to the prediction from the ML model. We will discuss the approaches of machine learning in recent dam water research and review the emerging issues to manage water assets in dams in this paper

A preliminary study on identifying the level of student engagement in blended learning

Blended learning is one of the learning methods used in any educational institution worldwide. It involves a combination of face-to-face learning and teaching with students using computer technology. Therefore, student engagement is essential to ensure the efficiency of blended learning. However, blended learning is found challenging where active collaboration between lecturers and students are highly needed to integrate learning. To overcome the issue, Polytechnic provides a Learning Management System known as the Curriculum Information Document Online System or known as CIDOS. CIDOS is a fully automated document management platform that manages the uploading, downloading, updating, and sharing digital content through a single integrated component. This study aims to determine the level of student engagement in blended learning at Polytechnic, Kota Kinabalu. A preliminary study with 125 students found that students are medium in the learning management system, which is CIDOS

A review of hyperspectral imaging-based plastic waste detection state-of-the-arts

Plastic waste issues emerged from the build-up of plastics that negatively impacts the environment. As a result, plastic waste detection is proposed in many research studies to tackle the problems. Therefore, this paper aims to review hyperspectral imaging techniques and machine learning in plastic waste detection. Hyperspectral imaging techniques are found to be effective in detecting plastic waste and microplastics as they were able to capture plastic reflectance spectral by using the near-infrared sensor. However, the review also shows that hyperspectral imaging techniques were less efficient in capturing the electromagnetic spectrum of black plastics due to carbon-black absorption properties. Carbon-black strongly absorbs light in the ultraviolet and infrared spectral range of the electromagnetic spectrum, therefore not detected by the near-infrared sensor. This paper also reviews how machine learning can alternatively detect and sort all types of waste, including plastics. Multiple studies show that the machine learning model achieved good accuracy in detecting all types of plastics based on the waste dataset. Finally, it can be seen that the spectral information of plastic can be used as feature extraction for machine learning models for better plastic detection. It is hoped that this study will contribute to more systematic research on the same topic

Salt water intrusion in Kudat peninsula, Sabah using geophysical subsurface interpretation techniques

The presence of saltwater intrusion is important to be monitored to avoid potential contamination on fresh groundwater. Therefore, the measurement of saltwater intrusion is crucial in determining the extent to which land has been contaminated by seawater. The region within Kudat Peninsula has a potential for saltwater intrusion due to the area surrounded by coastal line. Kudat Peninsula was formed by the ophiolite basement, Chert- Spilite Formation, Kudat Formation and alluvium deposit. Geo-electrical resistivity imaging survey were carried out to characterize the subsurface by using Wenner configuration. Two different survey lines were carried out with total length of 200 m for each survey line which gives up to 39 m depth of subsurface information. The collected data were processed using RES2DINV software to produce geo-electric subsurface model. The preliminary results of the geo-electrical resistivity imaging indicates that saltwater intrusion detected at depth of 1.25 m to 36.90 m and intruded up to 213.00 m towards mainland. Further investigation should be carried out to evaluate the impact of saltwater intrusion towards groundwater in the study area

Geoelectrical subsurface characterization: A case study of saltwater intrusion in Kudat, Sabah

Saltwater intrusion is one of the primary sources of groundwater contamination, especially near the coastal line, and it is crucial to monitor saltwater intrusion to protect fresh groundwater. Due to the proximity of the coastal line, the area within Kudat Peninsula that was formed by the ophiolite basement, Kudat Formation Chert-Spilite Formation, and alluvium deposit is susceptible to saltwater intrusion. In this study, two survey lines of geoelectrical resistivity and induced polarization imaging survey with a total length of 200 m were carried out. The Wenner configuration was used to characterize the subsurface of the Kudat Peninsula. The interpolation between geoelectrical resistivity and chargeability profiles provided better interpretations of the subsurface environment. The saltwater intrusion zone displays a resistivity value between 0 – 5.0 Ωm a nd a chargeability value between 0 – 3.0 ms. The results showed that saltwater intrusion was detected at depths as shallow as 1.25 m to 36.90 m and intruded as far as 218.00 m into the mainland, from the coastline

Effective dashboards for urban water security monitoring and evaluation

This paper reviews the factors affecting effective dashboards for urban water security monitoring and evaluation. Urban water security is a constantly evolving field influenced by several factors, including changes in climate, ecosystems, socio-economic status, and human beings. Although urban water security has been discussed in some parts of the literature, there has been minimal literature review that focused on the factors of urban water security and the effective dashboards for monitoring and evaluation. Using systematic literature review (SLR) and preferred reporting items for systematic reviews and meta-analysis (PRISMA), this paper reviewed 143 articles. The result shows growth in the environmental informatics landscape since the last ten years when the first article on the urban water management dashboard was published. The visual design was the most frequently discussed factor for dashboards, followed by user customization. It also shows that this topic can go deeper to integrate both factors and design an effective environmental dashboard. The discussion identified three potential opportunities for future research in water security and informatics: i) exploring other dimensions of effective dashboards, ii) considering more research on the environmental dashboard, and iii) investigating the real-life application of dashboards in urban water security

Anomaly Detection for System Log Analysis using Machine Learning: Recent Approaches, Challenges and Opportunities in Network Forensics

Anomaly detection identifies unusual patterns or items in a dataset. The anomalies identified for system logs will signify critical points to help debug system failures and perform root cause analysis. Various system logs are crucial sources to uncover meaningful information on a system condition. Typically, system administrators do manual review using keyword search or rule matching. However, the size of the logs keeps increasing making it a difficult and time-consuming effort to be undertaken manually. Machine learning has been widely used for anomaly detections. In this paper, we reviewed several anomaly detections for system logs using machine learning and discuss emerging research challenges and the opportunities raised from the challenges for network forensics. This paper presents the current research landscape in the area of machine learning and network forensics. It may be beneficial for references to researchers exploring the stated topics

Mobile learning application: flipped classroom

This study attempts to illustrate the phases of designing a flipped learning mobile application. It is worth noting that changes in students’ learning behavior should be met by changes in the classroom – particularly on the way a course should be delivered. Studies have shown that students who learn using the flipped learning method are less likely to fail as opposed to their counterparts in the traditional classroom setting. The rising importance and popularity of flipped learning necessitates the development of a mobile application that assists both students to learn and allow instructors to manage their course via their mobile devices, almost anywhere and anytime. The software development life cycle (SDLC) is divided into four distinct phases: 1) Preliminary study, 2) content design, 3) System design and development, and 4) System evaluation. The effectiveness of the application is tested using electroencephalography (EEG). The findings suggest effectiveness of the mobile application falls within the acceptable range. Improvements for the flipped learning mobile application is also presented

Modelling the enterprise architecture implementation in the public sector using HOT-Fit framework

Enterprise architecture is very important to the public sector's IT systems that are developed, organized, scaled up, maintained and strategized. Despite an extensive literature, the research of enterprise architecture is still at the early stage in public the sector and the reason to explain the acceptance, as well as the understanding of the implementation level of EA services still remains unclear. Therefore, this study examines the implementation of EA by measuring the Malaysian public sector's influence factors of EA. Grounded by the Human-Organization-Technology (HOT-Fit) Model, this study proposes a conceptual framework by decomposing Human characteristics, Organizational characteristics and Technological characteristics as main categories in assessing the identified factors. A total of 92 respondents in the Malaysian public sector participated in this study. Structural Equation Modelling with Partial Least Square is the main statistical technique used in this study. The study has revealed that human characteristics such as knowledge and innovativeness to EA and technological characteristics such as relative advantage and complexity of EA influence its implementation by the Malaysian public sector. Based on the findings, the theoretical and practical implications of the study as well as limitations and future works are also discussed