The Multiple Faces Recognition (MFR) system for video surveillance (VS) application or RTFaceDeep attempts to accurately detect the presence of several target individuals over a distributed network of cameras based on Ensemble Deep Learning Approach to improve the accuracy
The RTFaceDeep Prototype
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The RTFaceDeep Prototype II
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Gold Awards (ITEX2019 Malaysia, PECIPTA2019 Malaysia, IENA2019 Germany)
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Cyber Security Malaysia and Advanced Machine Intelligence Research Group
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The proposed Multiple Adaptive Diversified Boosting (MADBOOST) approach to Infrared face recognition system is suggested to be developed based on multiple feature extraction and selection methods and ensemble classifiers.
MadBoost Prototype
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GOLD AWARD (ITEX2018), SILVER AWARD (IENA2018 GERMANY)
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GOLD AWARD (ICAN2018, CANADA)
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Detecting and Tracking Person Having Flu
To screen passengers for corona virus flu and other contagious diseases, some airports use thermal imaging cameras to see whether travelers have fevers, without having to stick thermometers in their mouths. The devices are just like regular cameras, except that instead of recording light that objects reflect, these cameras are sensitive to heat. They can even work in the dark.
Recordings from these cameras show up on video screens with hotter objects looking brighter. The systems are very sensitive, measuring temperatures down to a fraction of a degree Fahrenheit. An algorithm can be designed and implemented to detect and track individuals having flu. Person re-identification systems based on deep learning algorithms can be put in placed to alert authorities for any occurrence of persons having flu.
Recordings from these cameras show up on video screens with hotter objects looking brighter. The systems are very sensitive, measuring temperatures down to a fraction of a degree Fahrenheit. An algorithm can be designed and implemented to detect and track individuals having flu. Person re-identification systems based on deep learning algorithms can be put in placed to alert authorities for any occurrence of persons having flu.
Thermal reading can be blocked using plastic material
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Thermal imaging analytics to see whether travelers have fevers
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Infrared Analytics
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Thermal Analytics
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Research on the Use of Fresh Agrofood In Malaysia
The Survey on the Use of Fresh Agriculture (KPASM) was conducted in 2018. This study aims to study the rate of consumption of fresh agriculture in Malaysia based on five main categories of fresh agriculture namely i) rice, ii) livestock, iii) vegetables, v) fishery products and vi) fruits. Primary data collection via the questionnaire involved three (3) major groups of fresh agribusiness users namely households (IRs), hospitality service organizations (PPHs) and factories (PKs). This data collection was conducted throughout Malaysia covering thirteen (13) states and two (2) Federal Territories.
A total of 5,819 IR respondents were actively involved in this study representing all states and Federal Territories in Malaysia. The active respondents for PK and PPH were 375 and 630 respectively. The analysis for the three groups of respondents was divided into three sections namely i) background distribution of respondents, ii) profile of fresh agricultural use and iii) fresh agricultural use trend.
A total of 264 fresh agricultural items were monitored for use in this study. This item was then subdivided into several sub-categories namely main category (containing 5 items), sub category 1 (containing 26 items), sub category 2 (containing 168 items) and also selected sub categories (containing 39 items). However, per capita consumption (PCC) estimates include only 70 items from sub-category 2 due to sample size constraints for other items in this sub-category. The PCC forecast for 2019 to 2025 involves a number of freshly selected agrarian items. In addition, a usage model for a number of selected fresh agendas has also been developed. This model of use has taken into account several important economic factors at the micro and macro level.
A total of 5,819 IR respondents were actively involved in this study representing all states and Federal Territories in Malaysia. The active respondents for PK and PPH were 375 and 630 respectively. The analysis for the three groups of respondents was divided into three sections namely i) background distribution of respondents, ii) profile of fresh agricultural use and iii) fresh agricultural use trend.
A total of 264 fresh agricultural items were monitored for use in this study. This item was then subdivided into several sub-categories namely main category (containing 5 items), sub category 1 (containing 26 items), sub category 2 (containing 168 items) and also selected sub categories (containing 39 items). However, per capita consumption (PCC) estimates include only 70 items from sub-category 2 due to sample size constraints for other items in this sub-category. The PCC forecast for 2019 to 2025 involves a number of freshly selected agrarian items. In addition, a usage model for a number of selected fresh agendas has also been developed. This model of use has taken into account several important economic factors at the micro and macro level.
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