Although the integration of virtual world in teaching and learning has been investigated, there is a scarcity of research considering its influence on assessing learners’ understanding and comparing it with traditional e-assessment tools such as that in Moodle quiz. In this research, a virtual reality (VR) game-based e-assessment application was proposed first. Subsequently, the study aims at (1) understanding whether the type of e-assessment method can affect students’ performance, and (2) investigating the difference in learners’ perceptions based on the type of e-assessment technology. A combination of quasi-pre-test and post-test experimental and survey research design methods were adopted. Overall, 32 undergraduate students were assigned to either control (N=17) or experimental (N=15) group. The key findings are (1) no statistical differences in students’ performance were found for both groups, and (2) significant differences between learners who completed the VR game-based e-assessment and those on the control group were found on perceived playfulness and ease of use. The conclusion drawn from the research outcomes is that a VR game-based e-assessment application is a successful approach to enhance learners’ engagement in evaluation sessions, although students may face a lack of experience in its use.

The crime rate increasing in developing countries cause of the unequal distribution of psychological, economic situation. This research aims to identify the crime mapping and investigate the hotspots and analyzing the spatial crime dataset and the predict of Spatio-temporal hotspot in Baltimore city for a period from 2012 to 2018. Analyzing crime data using data mining algorithms and The Geographic Information System (GIS) of Geographic dataset visualize and it possible for law enforcement to detect spatial crime patterns map easy and flexible and different analysis to identify the crime hotspot region efficiently. analysis crime hotspot using GIS is a useful way to the recognition for crime pattern and predicting hotspot over spatial correlation, analysis spatial data and revile crime pattern future detection. using spatial correlation, the G* statistic has been done with hotspot analysis the Getis-Ord Gi* to find the result of the spatial statistics pattern. analysis the crime to predict hotspot uses spatial variation and density crimes for clarifying the positions of statistically significant crime predict hotspots and cold spots and GIS interpolation method is used for more efficient visualization. This research using Grid network hotspots are applied to the crime data of Baltimore, Maryland state to recognize the hotspots for crime data like Shooting, Homicide and Assault by threat.

Spatially Data mining used efficiently to extract any potential patterns and associations to detect hidden information from multiple sources data. In this paper, data mining Density-based spatial clustering of applications with noise DBSCAN algorithm is emphasised. The importance in this work was using a prototype software to process the giving data into an understandable outcome throw clustering technique, it is a powerful method for criminal activities detection and pattern recognition to get useful information that can help police to reduce crimes. Spatial data mining is practical with geographical crimes data set and processing a large amount of crimes data. Police conventional way was manual and time-consuming using a pin on the wall. Therefore, it has to be developed and merged with advanced techniques. In this study, data mining clustering method was used to examine Baltimore, Maryland’s crimes information. The processed criminal data from the state of Maryland, Baltimore City was 340,924 cases and 16 attributes to reflect the cases between 2012-2018. DBSCAN algorithm is utilized to cluster crimes incidents focused on certain predefined events and the outcome of these clusters employed to find hotspots. The clustering findings are visualized by the GIS to make crimes distribution on the map at real-time for the law enforcement to understand and interact

GIS can manage remotely sensed images, users must have an appropriate digital map that represents lands each one has information according to its owner, status, and some other data. The classification of such lands is a great problem which take long time depending on human efforts. Many kinds of classifications had been used , one of them is the use of supervised multi-layer perceptron with backpropagation neural network classifier and using second order statistics Gray Level Co-occurrence Matrix (GLCM) to calculate eight textural features for each one of three visible bands (RGB) for each land sample. In this research we analyzed the GLCM feature extraction algorithm to detect the appropriate angle that can be chosen , relatively with the training of BP classifier had been used according to the number of hidden nodes inside the hidden layer of ANN. As a result the system produce high accuracy with the best angle choosing of GLCM , these results are achieved by comparing the classification results from system test trials with desired user predefined classification dataset.

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Bodyweight, blood pressure, and cholesterol are all risk variables that can aid people in making educated decisions regarding their health promotion activities. Food choices are among the most effective methods for preventing chronic illnesses, including heart disease, diabetes, stroke, and some malignancies. Because various meals give varying amounts of energy and minerals, good eating necessitates keeping track of the nutrients we ingest. Furthermore, there is a paucity of information on whether understanding food constituents might aid in more accurate nutrition calculations. Therefore, this research suggests processing food images on social media to anticipate the contents of each food and extracting nutrition information for each food image to serve as healthy implicit feedback to take advantage of the rapid accumulation of rich photos on social media. The proposed methodology is a framework based on a machine-learning model for predicting food ingredients. We also compute critical health metrics for each ingredient and combine them to obtain nutrition data for the food. The result revealed a promising way of extracting food components and nutrition information. Compared with other researchs, our proposed prediction and attribute extraction strategy achieves a remarkable accuracy of 85%.

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