Browsing by Author "Mohd Izzuddin Mohd Tamrin"

The supply chain is very important to the economic activities of a country because it directly involve in the delivery of services and products to the consumers. The supply chains are made up of complex network of nodes in which it rely on specialized functionalities of the relevant nodes in the network to generate the required services. In this project, we focused on five main challenges of integration which deterred the supply chain group to make amendment to their shared processes in case of knowable problems were to impinged in the middle of unfinished processes. Knowable problem refer to the events that were known to cause deviation to the current processes however the supply chain group unable to determine when it will strike the processes. The challenges that we have identified are information mismatch between the virtual and physical state, limited visibilities across vicinities of the group, limited control over the entire systems, unclear relationships for the shared processes and unable to identify important elements in the processes at individual level. We had proposed the Information Management Architecture (IMA) which utilized the data from Radio Frequency Identification (RFID) network and transformed them into process related information as RFID services to provide better visibilities and continuous monitoring across the vicinities in the supply chain group. In addition to this, we adapted the content filtering technique to support authorized team members to make decision on process restructuring in case of deviation from the planned processes. The problem, case and information models are designed in the form of tree-like structure as XML documents in order to detect process deviation, find suitable solutions and confirm to process constraints respectively. The component system technique is employed to break functionalities of the existing systems into smaller independent units and advertised them as services to the IMA components. This is to allow more control and flexible configuration of shared processes between the systems in the same supply chain group. The prototype of the IMA had been created and run in simulated environments to test the performance of the supply chain group with and without the support from the IMA. The results showed that with the support from IMA the number of processes completed by the supply chain group improved compared to without the IMA support in the present of knowable problems. With these results, we concluded that the IMA is one of the feasible solutions to facilitate the supply chain group with tighter integration.

This doctoral research examines how intensive cognitive training influences sexual dimorphism in brain structure, focusing on Huffaz—individuals who have memorized the entire Quran. Huffaz undergo sustained cognitive training, which may stimulate neuroplasticity and induce structural changes in the brain. This study addresses a critical gap in understanding how such culturally specific training modulates sex differences in brain morphology. Using voxel-based morphometry (VBM), voxel of interest (VOI) analyses, and machine learning classification, this study explores the interaction between cognitive training and biological sex. The primary objectives were to: (1) analyse sex differences in brain structure and assess the effects of Huffaz training using VBM, (2) investigate sexually dimorphic brain regions through VOI analysis with volume estimation and fractal analysis, and (3) develop machine learning models to classify sex based on brain structure while evaluating the impact of Huffaz training. A retrospective dataset of T1-weighted MRI scans from 47 healthy young adults (19 males, 28 females; aged 20-25) was used, including 23 Huffaz and 24 non-Huffaz. Both VBM and VOI analyses were applied across multiple brain atlases. Machine learning models—logistic regression, support vector machines, deep learning, and random forest—were trained to classify sex based on brain structure. VBM analysis revealed significant sexual dimorphism, with males exhibiting larger grey matter volumes in the cerebellum, fusiform gyrus, and temporal gyrus. After accounting for Huffaz status, additional regions such as the hypothalamus and amygdala were identified. VOI analysis confirmed larger male brain volumes, with the medial frontal gyrus reaching significance after controlling for Huffaz training. Fractal dimension analyses highlighted structural complexity variations, with males showing higher FDs in regions like the fusiform gyrus and superior occipital gyrus, while females exhibited higher values in the parahippocampal gyrus and anterior cingulate. Machine learning models demonstrated high accuracy in sex classification, with deep learning and random forest models achieving up to 92.86% accuracy when incorporating Huffaz status. These findings underscore the potential of AI in analysing complex brain structures and integrating cultural factors into sex classification models. This study enhances our understanding of how intensive cognitive training influences brain structure and sexual imorphism. It provides insights into the interaction between sex and culture in shaping brain plasticity, with implications for the development of sex-specific cognitive training programmes and therapeutic interventions.