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All Journal IAES International Journal of Robotics and Automation (IJRA) Jurnal Gizi dan Pangan
Abu Saad, Hazizi
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Automotive Engineering Electrical & Electronics Engineering Health Professions

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Risk Factors Associated with Underweight Children Under the Age of Five in Putrajaya, Malaysia: A Case-Control Study Salleh, Ruhaya; Ahmad, Mohamad Hasnan; Man, Cheong Siew; Wong, Norazizah Ibrahim; Sallehuddin, Syafinaz Mohd; Palaniveloo, Lalitha; Che Abdul Rahim, Norsyamlina; Baharudin, Azli; Abu Saad, Hazizi; Omar, Mohd Azahadi; Ahmad, Noor Ani
Jurnal Gizi dan Pangan Vol. 18 No. 2 (2023)
Publisher : The Food and Nutrition Society of Indonesia in collaboration with the Department of Community Nutrition, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25182/jgp.2023.18.2.89-98

Abstract

The study aimed to determine the associated factors for underweight among children under five years old in Putrajaya, Malaysia. This was a case-control study with a one-to-one ratio matched by sex as well as by three age categories (6‒11 months, 12‒35 months, dan 36‒59 months) between underweight and normal-weight children. There were 364 underweight children and 364 children with normal weight recruited from four government clinics and 118 preschools in Putrajaya. Both groups were assessed via face-to-face interviews; anthropometric measurements; haemoglobin level through finger prick blood sample; and a self-administered 3-day food diary. Underweight is defined as a weight-for-age z score less than -2SD based on World Health Organization (WHO) 2006 Growth Chart. The logistic regression’s final model revealed that various factors were significantly associated with underweight among children under five in Putrajaya. These factors included father being employed as a non-government servant [aOR:1.45 (95% CI:1.04‒2.02) compared to government servant], children from B40 group with a monthly household income less than <RM 7,380 (USD 1727.33) [aOR:2.17 (95% CI:1.01‒4.66) compared to T20], monthly expenditure for childcare less than RM 1,000 (USD 234.06), [aOR:1.77 (95% CI:1.01‒3.10) compared to ≥RM 2,000], underweight mother during prepregnancy [aOR:1.89 (95% CI:1.10‒3.26)] compared to normal weight, anemic children [aOR:1.57 (95% CI:1.15‒2.16)] compared to normal children, children using pacifiers [aOR:1.75 (95% CI:1.21‒2.73)] compared to not using pacifiers and children staying with unregistered babysitters [aOR:2.33 (85% CI:1.52‒3.59)] compared to those attending kindergarten. The above findings suggest several factors are significantly associated with underweight among children under five years old. Therefore, it highlights on the importance of improving household socioeconomic status, maternal nutritional status, and infant and young child feeding practices to prevent underweight issues in this population.
An improved black-winged kite algorithm optimized back-propagation neural network for biceps curl classification Liu, Chunqing; Geok Soh, Kim; Abu Saad, Hazizi; Ma, Haohao
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i1.pp247-256

Abstract

Accurately identifying and classifying biceps curl types is of vital importance for sports training and upper limb joint rehabilitation training. It can improve the effect and reduce the risk of injury caused by incorrect training. In this study, a dataset of biceps curl training was obtained by measuring wearable sensors. After data preprocessing, 340 samples of 35-dimensional feature data were obtained. The classification labels of the dataset were marked as 1-5 according to the five types of biceps curl. This study proposed a black-winged kite algorithm (IBKA) that uses the good point set (GPS) method and the adaptive spiral search rule, a multi-strategy. IBKA optimized the initial weights, biases, and hidden layer numbers and provided them to the back-propagation neural network (BPNN) to establish the IBKA-BPNN model. The constructed IBKA-BPNN model improved the classification accuracy of the training set from 79.83% to 94.54%, and the accuracy of the test set from 69.61% to 88.33%. The IBKA-BPNN model proposed in this study provides a reliable decision-making basis for real-time coaching, athlete performance analysis, and upper limb rehabilitation. Future work will expand the dataset, integrate more bio signals, and explore lightweight deployment on wearable hardware.
Co-Authors Ahmad, Mohamad Hasnan Ahmad, Noor Ani Baharudin, Azli Che Abdul Rahim, Norsyamlina Geok Soh, Kim Liu, Chunqing Ma, Haohao Man, Cheong Siew Omar, Mohd Azahadi Palaniveloo, Lalitha Salleh, Ruhaya Sallehuddin, Syafinaz Mohd Wong, Norazizah Ibrahim