HAJJRAH: an innovative application for pilgrims of Hajj and Umrah

This paper describes an application system named HAJJRAH which offer solutions to common problems faced by pilgrims in performing the obligatory and supplementary activities of Hajj and Umrah. Background of pilgrimage and problems faced by various agencies who manage pilgrims were also provided in this paper to give an understanding on the problem surrounding the need of HAJJRAH application. Analysis of market study among potential user and industrialists are presented in this paper to justify market needs and strength of the applications business idea. Results of the analysis shows that the features provided by HAJJRAH application accommodates and address common problems faced by most pilgrims and various agencies who involved in managing the pilgrims during Hajj and Umrah

Application of deep learning method in facilitating the detection of breast cancer

Breast cancer is a type of tumour that could be treated if the disease is identified at an earlier stage. Early diagnosis is crucial when it comes to reducing the mortality rate. In this study, deep neural network method is applied to facilitate the detection of breast cancer. The aim of this study is to implement deep neural network in breast cancer classification models that can produce high classification accuracy. Deep Neural Network (DNN) with multiple hidden layers was applied to learn deep features of the breast cancer data. Dataset used in this study was obtained from the UCI Machine Learning Repository which consists of Wisconsin Breast Cancer Dataset (WBCD) and used for the original and diagnostic dataset. The performance of the proposed DNN method was compared against previous machine learning classifier in terms of accuracy. From the results, the accuracy obtained for the original dataset was 97.14% and 97.66% for the diagnostic dataset, which is better than previous SVM method

Computational BIM for Building Envelope Sustainability Optimization

Building envelope plays an important role to protect a building from external climatic factors while providing a comfortable indoor environment. However, the choices of construction materials, opening sizes, and glazing types for optimized sustainability performance require discrete analyses and decision-making processes. Thereby this study explores the use of computational building information modelling (BIM) to automate the process of design decision-making for building envelope sustainability optimization. A BIM tool (Revit), a visual programming tool (Dynamo) and multi objective optimization algorithm were integrated to create a computational BIM-based optimization model for building envelope overall thermal transfer value (OTTV) and construction cost. The proposed model was validated through a test case; the results showed that the optimized design achieved 44.78% reduction in OTTV but 19.64% increment in construction cost compared to the original design. The newly developed computational BIM optimization model can improve the level of automation in design process for sustainability

Comparative analysis of deep learning algorithm for cancer classification using multi-omics feature selection

Advancement of high-throughput technologies in omics studies had produced large amount of information that enables integrated analysis of complex diseases. Complex diseases such as cancer are often caused by a series of interactions that involve multiple biological mechanisms. Integration of multi-omics data allows more advanced analysis using features from various aspects of biology. However, analysing cancer multi-omics data on a large scale could be challenging due to the high dimensionality of the data. The recent development of advanced computational algorithms, especially deep learning, had sparked numerous efforts in applying these algorithms in multi-omics studies. This study aims to investigate how deep learning algorithms, namely stacked denoising autoencoder (SDAE) and variational autoencoder (VAE) can be used in cancer classification using multi-omics data. Moreover, this study also investigates the impact of feature selection in multi-omics analysis through the implementation of an embedded feature selection. The multi-omics data used in this study includes genomics, methylomics, transcriptomics and clinical data for a case study of lung squamous cell carcinoma. The classification performance has been compared and discussed in terms of the effectiveness of different models and the impact of feature selection. Results showed that VAE outperforms SDAE with 91.86% accuracy, 22.73% specificity and 0.21% Matthews Correlation Coefficient (MCC)

Incisor malocclusion using cut-out method and convolutional neural network

Malocclusion is a condition of misaligned teeth or irregular occlusion of the upper and lower jaws. This condition leads to poor performance of vital functions such as chewing. A common procedure in orthodontic treatment for malocclusion is a conventional diagnostic procedure where a dental health professional takes dental x-rays to examine the teeth to diagnose malocclusion. However, the manual orthodontic diagnostic procedure by dental experts to identify malocclusion is time-consuming and vulnerable to expert bias that results in delayed treatment completion time. Recently, artificial intelligence technology in image processing has gained attention in orthodontics treatment, accelerating the diagnosis and treatment process. However, several issues concerning the dental images as input of the classification model may affect the accuracy of the classification. In addition, unstructured images with varying sizes and the problem of a machine learning algorithm that does not focus on the region of interest (ROI) for incisor features bring challenges in delivering the treatment. This study has developed a malocclusion classification model using the cut-out method and Convolutional Neural Network (CNN). The cut-out method restructures the input images by standardising the sizes and highlighting the incisor sections of the images which assisted the CNN in accurately classifying the malocclusion. From the results, the implementation of the cut-out method generates higher accuracy across all classes of malocclusion compared to the non-implementation of the cut-out method

Classification of attention deficit hyperactivity disorder using variational autoencoder

Attention Deficit Hyperactivity Disorder (ADHD) categorize as one of the typical neurodevelopmental and mental disorders. Over the years, researchers have identified ADHD as a complicated disorder since it is not directly tested with a standard medical test such as a blood or urine test on the early-stage diagnosis. Apart from the physical symptoms of ADHD, clinical data of ADHD patients show that most of them have learning problems. Therefore, functional Magnetic Resonance Imaging (fMRI) is considered the most suitable method to determine functional activity in the brain region to understand brain disorders of ADHD. One of the ways to diagnose ADHD is by using deep learning techniques, which can increase the accuracy of predicting ADHD using the fMRI dataset. Past attempts of classifying ADHD based on functional connectivity coefficient using the Deep Neural Network (DNN) result in 95% accuracy. As Variational Autoencoder (VAE) is the most popular in extracting high-level data, this model is applied in this study. This study aims to enhance the performance of VAE to increase the accuracy in classifying ADHD using fMRI data based on functional connectivity analysis. The preprocessed fMRI dataset is used for decomposition to find the region of interest (ROI), followed by Independent Component Analysis (ICA) that calculates the correlation between brain regions and creates functional connectivity matrices for each subject. As a result, the VAE model achieved an accuracy of 75% on classifying ADHD

Improving operational effectiveness at vehicle inspection centres

In this research, operational problems at vehicle inspection centres (VIC) are explored and investigated. Simulation is used in this new application area to improve operational effectiveness at the VIC. In particular, simulation is used to gain insights, model the interactions of operational activities and experiment with specific operational policies. Critical input random variables corresponding to service times and arrival patterns are modelled based upon empirical data collected at the VIC. Modelling the arrival process involves a thorough analysis of separate patterns of arrival for scheduled and non- scheduled vehicles at a typical VIC. Developing the most appropriate of several ideas, we generate methods for simulating typical streams of arrivals at the VIC. For non- scheduled arrivals, we adopt distinct beta distributions for morning and afternoon activities. For scheduled arrivals, this research demonstrates that customer punctuality varies according to planned appointment times, which leads to an adaptive model for customer punctuality that takes account of these appointment times. Specifically, we construct generalised beta distributions with parameters that evolve over time. In this research, we integrate the design of experiments within our simulation model. In particular, we propose a fractional factorial design matrix to replace traditional ways of planning simulation experiments. Further work involves incorporating blocking into our design matrix, which reduces the experimental size and provides a useful backup support feature for our VIC simulation. The proposed design matrix is then used in the output analysis along with other statistical methods, to determine factors that contribute significantly to specific performance measures. In this study, general linear models that link the control factors and the performance measures are developed. A major contribution of this research is its impact on simulation modelling, which helps us to characterize more realistic arrival patterns for customers compared with previous research techniques. Additionally, aspects of our research that include experimental design enrich the literature on simulation and modelling particularly in the application of simulation to service shop problems.EThOS - Electronic Theses Online ServiceGBUnited Kingdo