Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal IAES International Journal of Robotics and Automation (IJRA) International Journal of Public Health Science (IJPHS)
Hazizi Abu Saad
Universiti Putra Malaysia
Automotive Engineering Electrical & Electronics Engineering Health Professions

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Smoking habits, knowledge and smoking attitudes among primary healthcare workers in Perak, Malaysia Low Pei Kit; Hazizi Abu Saad; Rosita Jamaluddin; Chee Huei Phing
International Journal of Public Health Science (IJPHS) Vol 12, No 1: March 2023
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijphs.v12i1.21965

Abstract

A cross-sectional study was carried out to assess the smoking habits of primary healthcare workers, their knowledge about the harmful effects and health risks of smoking, as well as their attitudes towards not smoking. A validated self-administered questionnaire was used to collect the data. There were 261 primary healthcare workers in Perak, Malaysia recruited in this study. The results showed that there were only 4.6% (n=12) ever smokers and 2.7% (n=7) current smokers in this study. More than 75% of primary healthcare workers reported having friends and family members who smoked. The majority of the primary healthcare workers had good knowledge regarding the health risks and harmful effects of smoking. They also possessed positive attitudes towards not smoking. The females, those in the high-income group, the health service providers and the non-smokers had significantly higher scores in both their knowledge about smoking and positive attitudes towards not smoking (p<0.05). The primary healthcare workers with tertiary educational levels were significantly associated with positive attitudes towards not smoking (p<0.05). Gender and occupational status were the strongest predictors for knowledge about the health risks of smoking (p≤0.001), knowledge about the harmful effects of smoking (p≤0.001) and attitudes towards not smoking (p≤0.001). Health service providers, and females had the highest awareness of smoking. The high percentage of health service providers reported having friends and family members who smoked in this study should be given more attention. Implementation of more free-smoke areas could be use as strategy to reduce exposure to second-hand tobacco smoke.
An improved black-winged kite algorithm optimized back-propagation neural network for biceps curl classification Chunqing Liu; Kim Geok Soh; Hazizi Abu Saad; Haohao Ma
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 Chee Huei Phing Chunqing Liu Haohao Ma Kim Geok Soh Low Pei Kit Rosita Jamaluddin