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Evolution of the“ancient Kedah”:A study on urban forms at Sungai Batu Archaeological Complex(SBAC),Bujang Valley,Kedah,Malaysia

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Frontiers of Architectural Research 2024年第1期13卷 127-143页

作者： mohd Hasfarisham Abd Halim naizatul akma mohd mokhtar Nor Khairunnisa Talib Shyeh Sahibul Karamah Masnan Ahmad Fadly Jusoh mokhtar Saidin Kedah Malaysia Universiti Utara Malaysia KedahMalaysia Universiti Sains Malaysia PenangMalaysia

Archaeological research at the Sungai Batu Archaeological Complex(SBAC),which has been carried out until 2023,aims to complete the chronological data and urbanization process of the ancient *** study will especially prove that the ongoing urbanization process is aimed at developing the iron smelting industry and trade in ancient Kedah,especially in *** is because archaeological research at SBAC has proven that with the existence of the ancient Kedah iron smelting workshop at SBAC,river jetties,port management,rituals,and Buddhist monuments began to be erected,all of which can be determined by chronometric *** results of the chronometric dating have proven that the ancient Kedah Kingdom existed since the 6th century BC and was oriented towards the trade of iron resources,which allowed the use of the area to grow *** process of urbanization and land use clearly conforms to Lynch’s principle,which emphasizes the principles of 1)paths,2)edges,3)districts,4)nodes,and 5)landmarks in the development process of land use and urbanization in the complex.

关键词： Chronometric dating Urbanization Land use Monument Iron smelting site

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Cardiovascular Disease Prediction Among the Malaysian Cohort Participants Using Electrocardiogram

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Computers, Materials & Continua 2022年第4期71卷 1111-1132页

作者： mohd Zubir Suboh Nazrul Anuar Nayan Noraidatulakma Abdullah Nurul Ain Mhd Yusof Mariatul akma Hamid Azwa Shawani Kamalul Arinfin Syakila mohd Abd Daud mohd Arman Kamaruddin Rosmina Jaafar Rahman Jamal Faculty of Engineering and Built Environment Universiti Kebangsaan MalaysiaBangi43600Malaysia Medical Engineering Technology Section Universiti Kuala Lumpur British Malaysian InstituteGombak53100Malaysia Institut Islam Hadhari Universiti Kebangsaan MalaysiaBangi43600Malaysia UKM Medical Molecular Biology Institute(UMBI) Kuala Lumpur56000Malaysia Faculty of Health Sciences Universiti Kebangsaan MalaysiaBangi43600Malaysia

A comprehensive study was conducted to differentiate cardiovascular disease (CVD) subjects from non-CVD subjects using short recording electrocardiogram (ECG) of 244 Malaysian adults in The MalaysianCohort project. An automated peak detection algorithm to detect nine fiducialpoints of electrocardiogram (ECG) was developed. Forty-eight features wereextracted in both time and frequency domains, including statistical featuresobtained from heart rate variability and Poincare plot analysis. These includefive new features derived from spectrum counts of five different frequencyranges. Feature selection was then made based on p-value and correlationmatrix. Selected features were used as input for five classifiers of artificialneural network (ANN), k-nearest neighbors (kNN), support vector machine(SVM), discriminant analysis (DA), and decision tree (DT). Results showedthat six features related to T wave were statistically significant in distinguishingCVD and non-CVD groups. ANN had performed the best with 94.44% specificity and 86.3% accuracy, followed by kNN with 80.56% specificity, 86.49%sensitivity and 83.56% accuracy. The novelties of this study were in providingalternative solutions to detect P-onset, P-offset, T-offset as well as QRS-onsetpoints using discrete wavelet transform method. Additionally, two out of thefive newly proposed spectral features were significant in differentiating bothgroups, at frequency ranges of 1–10 Hz and 5–10 Hz. The prediction outcomeswere also comparable to previous related studies and significantly importantin using ECG to predict cardiac-related events among CVD and non-CVDsubjects in the Malaysian population.

关键词： Cardiovascular disease ECG fiducial point detection electrocardiogram feature extraction machine learning

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