Improving parallel self-organizing map using heterogeneous uniform memory access / Muhammad Firdaus Mustapha

Self-organizing Map (SOM) is a very popular algorithm that has been used as clustering algorithm and data exploration. SOM consists of complex calculations where the calculation of complexity depending on the circumstances. Many researchers have managed to improve online SOM processing speed using Heterogeneous Computing (HC). HC is a combination of Central Processing Unit (CPU) and Graphic Processing Unit (GPU) that work closely together. Standard HC can be represented by CPU and GPU accessing separate memory blocks. In spite of excellent performance using standard HC, there is a situation that causes computer hardware underutilized when executing online SOM variant. In details, the situation occurs when number of cores is larger than the number of neurons on map. Moreover, the complexities of SOM steps also increase the usage of high memory capacity which leads to high rate memory transfer. This situation is caused by the standard HC implements "deep copies" in storing processing objects which lead to communication latency. Recently, combination CPU and GPU that integrated together on a single chip are rapidly attractive the design paradigm for recent platform because of their remarkable parallel processing abilities. This kind of microprocessor is based on Heterogeneous Unified Memory Access (HUMA) model. This model allows both CPU and GPU to access and store into the same memory location which avoids redundant copies of objects by "deep copies" method. Therefore, the main goal of this research is to reduce computation time of SOM training through implementing on HUMA platform and improve GPU cores utilization. This research has three main objectives to be achieved. Firstly, this research attempts to study the processing natures of original SOM algorithm on standard HC platform. Secondly is to model an enhanced parallel SOM on HUMA-GPU platform and adapting multiple stimuli approach in order to improve the processing speed. Lastly is to evaluate the enhanced parallel SOM in terms of performance accuracy, efficiency, and scalability. This research attempts to improve the processing of SOM algorithm through three stages. The research works start with conducting a preliminary study on sequential SOM algorithm. The research continues to design a parallel SOM architecture based on literature study and implements on two types of architecture; standard HC and HUMA model. Finally, this research designs and implements an enhanced parallel SOM architecture through combining two parallel methods which are network and data partitioning. The combination of the two methods are realized via adapting multiple stimuli approach. This research employs datasets that are acquired from UCI repository. As a result, the enhanced parallel SOM that executed on HUMA platform is able to score up to 1.27 of speed up overall for large map size compared to standard parallel SOM. The proposed work also scores better for smaller map size with scored up to 1.03 of speed up overall compared to standard SOM on the identical platform. Accordingly, the proposed work is able to offer a better solution for small to medium sized of data analysis software. Overall, the solution is enhanced through utilizing recent hardware technology and improved method

Improving parallel Self-organizing Map using heterogeneous uniform memory access / Muhammad Firdaus Mustapha

Self-organizing Map (SOM) is a very popular algorithm that has been used as clustering algorithm and data exploration. SOM consists of complex calculations where the calculation of complexity depending on the circumstances. Many researchers have managed to improve online SOM processing speed using Heterogeneous Computing (HC). HC is a combination of Central Processing Unit (CPU) and Graphic Processing Unit (GPU) that work closely together. Standard HC can be represented by CPU and GPU accessing separate memory blocks. In spite of excellent performance using standard HC, there is a situation that causes computer hardware underutilized when executing online SOM variant. In details, the situation occurs when number of cores is larger than the number of neurons on map. Moreover, the complexities of SOM steps also increase the usage of high memory capacity which leads to high rate memory transfer. This situation is caused by the standard HC implements "deep copies" in storing processing objects which lead to communication latency. Recently, combination CPU and GPU that integrated together on a single chip are rapidly attractive the design paradigm for recent platform because of their remarkable parallel processing abilities. This kind of microprocessor is based on Heterogeneous Unified Memory Access (HUMA) model. This model allows both CPU and GPU to access and store into the same memory location which avoids redundant copies of objects by "deep copies" method. Therefore, the main goal of this research is to reduce computation time of SOM training through implementing on HUMA platform and improve GPU cores utilization

University key performance indicator management model (e-KPIus) / Mohd Azry Abdul Malik ...[et al.]

Coordinating numerous projects conducted by various parties within the university is a daunting task as it involves many individuals and groups. To make it easier for Strategic Planning Unit (UPS) of UiTM Cawangan Kelantan to monitor all projects being worked on, an application named the Key Performance Indicator Management Model (e-KPIus) was developed. The „e-KPIus‟ is a systematic approach to data collection, and supervision of the project progress by the top management and serves as an information sharing platform. eKPIus gives significant and valuable contributions to UPS as it ensures the quality control and improvement in the context of UPS data management. Although the idea of the application developed is undoubtedly convincing, the effectiveness of the application is yet to proof. Thus, a study was undertaken aiming at assessing the effectiveness of e-KPIus. A total of 40 respondents consist of project executors, and performance indicators (PI) keepers at UiTM Cawangan Kelantan participated in this study. Findings revealed that e-KPIus has received a favorable response from users. This project suggested that the e-KPIus can be used to enhance the quality of data management process in UPS and has a great potential to be extended to other departments to ensure better quality of performance managemen

Microbiological analysis of drinking water from water vending machines

Commercial water vending machines are gaining popularity nowadays among the general public, probably due to the ease of obtaining clean drinking water. However, improper maintenance of the machines can lead to bacterial contamination. Hence, this study aimed to investigate and determine the microbiological characteristics of drinking water from Water Vending Machines (WVM) by isolating and characterizing culturable bacteria in the water and nozzle swab samples. The samples were obtained from WVM at eight different locations around Johor Bahru, Johor, Malaysia. Several unique bacterial isolates were found, from both Gram-positive and Gram-negative groups. Polymerase chain reaction amplification and 16S rRNA sequence analysis suggested that these isolates are from Pseudomonas, Bacillus, and Stenotrophomonas genera. In situ water quality tests which include pH, conductivity, and total dissolved solids were also conducted. Two samples from the inlet source have pH and conductivity values slightly above the reference values stipulated in drinking water regulations. The findings presented here suggest the importance of regular service maintenance of the WVM to ensure that the water samples meet the standard stipulated by the authority

Enhanced parallel SOM based on heterogeneous system platform

In this paper, we propose an enhanced parallel Self organizing Map (SOM) framework based on heterogeneous system platform, specifically Central Processing Unit (CPU) and Graphic Processing Unit (GPU) soldered together on a single chip.The framework is to improve speed of parallel SOM using GPU since processing parallel SOM on GPU burden by communication latency due to isolate device architecture with CPU.The parallel SOM has been extended to heterogeneous system platform and double kernel for calculation distance and find Best Matching Unit (BMU) are introduced.The results are tested using benchmark data on two different platforms: GPU and heterogeneous system. The proposed framework shows improvement compared to standard parallel SOM on GPU and heterogeneous system

Student project monitoring system (SPMS) using text recognition / Muhammad Firdaus Mustapha ...[et al.]

Recently, most of the courses at the university require students to complete a specific project within a given timeline. Some courses contain several projects that have to be completed in several phases. Therefore, managing and monitoring students project can be nontrivial. One of the solutions to simplify project monitoring is by developing a system that can monitor progress of student project. Thus, we design and develop a student project monitoring system known as SPMS. The uniqueness of the proposed system is it integrates text recognition algorithm into student project monitoring system. At the same time, the proposed system has advantage to simplify student project monitoring process rather than manual process especially during report submission for each project phases. It also can be accessed online through any web browser. As a result, the proposed work is capable to monitor progress of student project effectively for students and lecturers. The proposed work can be implemented in relevant industry such as in monitoring the worker’s tas

A survey on video face recognition using deep learning

The research on facial recognition consists of Still-Image Face Recognition (SIFR) and Video Face Recognition (VFR), is a common subject being debated among researchers since it does not require any touch like other biometric identification, such as fingerprints and palm prints. Various methods have been proposed and developed to solve the problems of face recognition. Convolutional Neural Network (CNN) is one of the deep learning techniques that is suggested for both SIFR and VFR. However, several issues related to VFR have still not been solved. Hence, the objective of this paper is to review VFR using deep learning that specifically focuses on several steps of VFR. The VFR steps consists of six main stages; input video of the face, face anti-spoofing module, face and landmark detection, preprocessing, facial feature extraction and face output that include identification or verification result. A summary of implementation of deep learning within VFR steps is discussed. Finally, some directions for future research are also discussed

Identification of strain diversity and phylogenetic analysis based on two major essential proteins of Orf viruses isolated from several clinical cases reported in Malaysia

There is a little information on the characterization of Orf virus strains that are endemic in Malaysia. The relationship between the severity of disease and the molecular genetic profile of Orf virus strains has not been fully elucidated. This study documented the first confirmed report of contagious ecthyma causing by Orf virus in goats from a selected state of eastern peninsular Malaysia. The disease causes significant debilitation due to the inability of affected animals to suckle which brings a great economic loss to the farmers. A total of 504 animals were examined individually to recognize the affected animals with Orf lesion. Skin scrapping was used to collect the scab material from the infected animals. The presence of Orf virus was confirmed by combination of methods including virus isolation on vero cells, identification by Transmission Electron Microscopy (TEM) and molecular technique using PCR and Sanger sequencing. The results showed the successful isolation of four Orf virus strains with a typical cytopathic effects on the cultured vero cells line. The morphology was confirmed to be Orf virus with a distinctive ovoid and criss cross structure. The phylogenetic analysis revealed that these isolated strains were closely related to each other and to other previously isolated Malaysian orf viruses. In addition these Orf virus strains were closely related to Orf viruses from China and India. This study provides more valuable insight in terms of genotype of Orf virus circulating in Malaysia

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries