Gaze estimation model for eye drawing

This paper describes a model that can be employed in eye drawing software applications. Unlike most of the existing interfaces for eye typing, eye drawing focuses on small target selection and moves the cursor to a precise location. This is made possible by a proposed Gaze Estimation Model which interprets users’ interest when they want to draw new objects in a particular position

Developing Reading Skills Using Sight Word Reading Strategy through Interactive Mobile Game-Based Learning for Dyslexic Children

Reading skill is crucial in our daily activities. On the other hand, there are some people who experience difficulties in reading. This is due to the way they see things are different than normal people. This learning disability is known as dyslexia. The symptoms started from young. Children with dyslexia use all of their senses to interact with their surroundings. They are easily attracted to pictures rather than words and are highly imaginative. In this project, a mobile application to help children with dyslexia to develop reading skills is proposed. This paper will discuss about the development of the mobile application. The methodology used in this project will be discussed along with the implementation and testing. The results have shown that respondents gave positive feedbacks prove that the application is effective in developing reading and spelling among dyslexic children. Lastly, limitations, future works and conclusion of the overall project will be discussed. The mobile application is named as “Mr Read”

Imputation of Rainfall Data Using the Sine Cosine Function Fitting Neural Network

Missing rainfall data have reduced the quality of hydrological data analysis because they are the essential input for hydrological modeling. Much research has focused on rainfall data imputation. However, the compatibility of precipitation (rainfall) and non-precipitation (meteorology) as input data has received less attention. First, we propose a novel pre-processing mechanism for non-precipitation data by using principal component analysis (PCA). Before the imputation, PCA is used to extract the most relevant features from the meteorological data. The final output of the PCA is combined with the rainfall data from the nearest neighbor gauging stations and then used as the input to the neural network for missing data imputation. Second, a sine cosine algorithm is presented to optimize neural network for infilling the missing rainfall data. The proposed sine cosine function fitting neural network (SC-FITNET) was compared with the sine cosine feedforward neural network (SCFFNN), feedforward neural network (FFNN) and long short-term memory (LSTM) approaches. The results showed that the proposed SC-FITNET outperformed LSTM, SC-FFNN and FFNN imputation in terms of mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (R), with an average accuracy of 90.9%. This study revealed that as the percentage of missingness increased, the precision of the four imputation methods reduced. In addition, this study also revealed that PCA has potential in pre-processing meteorological data into an understandable format for the missing data imputation

Crowd Evacuation Behaviour Modeling and Simulation in 3D Platform

Crowd simulation is an active research domain and is crucial for simulating crowd behaviour in certain condition such as normal or panic situation. The simulation is to show the interaction between the individual in a crowd. Nowadays, there are many kinds of scenarios as well as simulation softwares that can be adapted to simulate a crowd simulation such as during emergency situation e.g. building evacuation. Crowd simulation in three-dimensional platform is fairly important in order to have a more realistic looks and movement of the crowd in one particular environment. The evacuation simulation is useful for the crowd in one confinement to seek for a safe exit path in shortest time possible and thus increase the occupant’s safety. The evacuation time is said to be in safe condition if all the evacuees successfully can get through the exit in minimal time. To aid in minimal exit time, the concept of faster-is-slower (bottleneck) must be solved as it can lead to more waiting time or delay during evacuation process. In this paper, it will discuss about the crowd simulation behavior, crowd simulation based on agent-based model, existing crowd simulation tools and the result of simulating the three-dimensional (3D) crowd evacuation time based on a number of exits variation in panic situation. The tools used to carry out the experiment is Anylogic software whereby the results show that it adheres to shorter evacuation time when the number of exit increases. The 3D layout design was following the original layout the faculty’s lower ground floor where the classrooms are mostly resided. The simulation is useful in order to estimate of evacuation time with different total number of exits to alleviate the faster-is-slower effect in case of any emergency situation happens at the faculty buildin

Taipei's Use of a Multi-Channel Mass Risk Communication Program to Rapidly Reverse an Epidemic of Highly Communicable Disease

BACKGROUND: In September 2007, an outbreak of acute hemorrhagic conjunctivitis (AHC) occurred in Keelung City and spread to Taipei City. In response to the epidemic, a new crisis management program was implemented and tested in Taipei. METHODOLOGY AND PRINCIPAL FINDINGS: Having noticed that transmission surged on weekends during the Keelung epidemic, Taipei City launched a multi-channel mass risk communications program that included short message service (SMS) messages sent directly to approximately 2.2 million Taipei residents on Friday, October 12th, 2007. The public was told to keep symptomatic students from schools and was provided guidelines for preventing the spread of the disease at home. Epidemiological characteristics of Taipei's outbreak were analyzed from 461 sampled AHC cases. Median time from exposure to onset of the disease was 1 day. This was significantly shorter for cases occurring in family clusters than in class clusters (mean+/-SD: 2.6+/-3.2 vs. 4.39+/-4.82 days, p = 0.03), as well as for cases occurring in larger family clusters as opposed to smaller ones (1.2+/-1.7 days vs. 3.9+/-4.0 days, p<0.01). Taipei's program had a significant impact on patient compliance. Home confinement of symptomatic children increased from 10% to 60% (p<0.05) and helped curb the spread of AHC. Taipei experienced a rapid decrease in AHC cases between the Friday of the SMS announcement and the following Monday, October 15, (0.70% vs. 0.36%). By October 26, AHC cases reduced to 0.01%. The success of this risk communication program in Taipei (as compared to Keelung) is further reflected through rapid improvements in three epidemic indicators: (1) significantly lower crude attack rates (1.95% vs. 14.92%, p<0.001), (2) a short epidemic period of AHC (13 vs. 34 days), and (3) a quick drop in risk level (1 approximately 2 weeks) in Taipei districts that border Keelung (the original domestic epicenter). CONCLUSIONS AND SIGNIFICANCE: The timely launch of this systematic, communication-based intervention proved effective at preventing a dangerous spike in AHC and was able to bring this high-risk disease under control. We recommend that public health officials incorporate similar methods into existing guidelines for preventing pandemic influenza and other emerging infectious diseases

Imputation of rainfall data using the sine cosine function fitting neural network

Missing rainfall data have reduced the quality of hydrological data analysis because they are the essential input for hydrological modeling. Much research has focused on rainfall data imputation. However, the compatibility of precipitation (rainfall) and non-precipitation (meteorology) as input data has received less attention. First, we propose a novel pre-processing mechanism for non-precipitation data by using principal component analysis (PCA). Before the imputation, PCA is used to extract the most relevant features from the meteorological data. The final output of the PCA is combined with the rainfall data from the nearest neighbor gauging stations and then used as the input to the neural network for missing data imputation. Second, a sine cosine algorithm is presented to optimize neural network for infilling the missing rainfall data. The proposed sine cosine function fitting neural network (SC-FITNET) was compared with the sine cosine feedforward neural network (SC-FFNN), feedforward neural network (FFNN) and long short-term memory (LSTM) approaches. The results showed that the proposed SC-FITNET outperformed LSTM, SC-FFNN and FFNN imputation in terms of mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (R), with an average accuracy of 90.9%. This study revealed that as the percentage of missingness increased, the precision of the four imputation methods reduced. In addition, this study also revealed that PCA has potential in pre-processing meteorological data into an understandable format for the missing data imputation

MODELLING THE EFFECTS OF SOCIO-ECONOMIC DEMOGRAPHICS ON URBAN WATER USAGE IN KOTA SAMARAHAN, SARAWAK : A NEW EDUCATION HUB IN BORNEO ISLAND

This study was carried out to investigate the influence of socio-economic status on household water usage patterns in Kota Samarahan, which is an education hub in Sarawak, Malaysia. This study commenced with a random sampling of 200 respondents, categorised into low-, medium- and high-income households. The medium-income household category was found to have the highest amount of water usage. The results showed that an increase in income leads to an increase in socio-economic status, dwelling size, and household occupancy. It was also observed that the “numbers of children” influences the increase in water usage within a family. In addition, the data set was further analysed using multiple linear regression modelling (STEPWISE). It was found that an increase in socio-economic demographic factors, including education level, number of female adults, number of clothing washed daily, number of wage earners, and number of dishes washed daily, increased the water usage per household. The findings of this study are crucial to ensuring a sustainable urban water supply in Kota Samarahan

Imbalanced Classification Methods for Student Grade Prediction : A Systematic Literature Review

Student success is essential for improving the higher education system student outcome. One way to measure student success is by predicting students’ performance based on their prior academic grades. Concerning the significance of this area, various predictive models are widely developed and applied to help the institution identify students at risk of failure. However, building a high-accuracy predictive model is challenging due to the dataset’s imbalanced nature, which caused biased results. Therefore, this study aims to review the existing research article by providing a state-of-the-art approach for handling imbalanced classification in higher education, including the best practices of dataset characteristics, methods, and comparative analysis of the proposed algorithms, focusing on student grade prediction context problems. The study also presents the most common balancing methods published from 2015 to 2021 and highlights their impact on resolving imbalanced classification in three approaches: data-level, algorithm-level, and hybrid-level. The survey results reveal that the data-level approach using SMOTE oversampling is broadly applied in determining imbalanced problems for student grade prediction. However, the application of hybrid and feature selection methods supporting the generalization of the predictive model to boost student grade prediction performance is generally lacking. Other than that, some of the strengths and weaknesses of the proposed methods are discussed and summarized for the direction of future research. The outcomes of this review will guide the professionals, practitioners, and academic researchers in dealing with imbalanced classification, mainly in the higher education field

Application of Building Information Modelling (BIM) Technology in Drainage System Using Autodesk InfraWorks 360 Software

The increased number of physical drainage drawings at Samarahan district, Sarawak for new development areas is difficult to manage and handle by relevant authorities. Hence, this research is conducted to determine the feasibility of Building Information Technology (BIM) to create a proper drainage inventory system to accurately list and record current drainage information using Autodesk Infraworks 360 software. This inventory system will be employed to examine and validate corresponding drainage parameters based on the recorded information. Taman UniCentral, a residential neighbourhood in Kota Samarahan, has been chosen for this case study. Drainage data, such as drainage size, length, invert level, are entered into GIS-integrated Model Builder in Autodesk InfraWorks 360. Autodesk InfraWorks 360 will conduct a preliminary analysis, including watershed analysis, to delineate the catchment area and drainage performance inspections at rainfall intensities of 2, 5, 10, 20, and 50 years (ARI). Thereafter, the InfraWorks model will be exported into Autodesk Civil3D to conduct a more extensive hydraulic analysis. The results show that full integration of these two Autodesk software packages had created a proper inventory system of existing drainage information and simulated its sufficiency in catering surcharge runoff from the new development area at the upper catchment