Search Results - (( developing week effect algorithm ) OR ( java implication tree algorithm ))

An algorithm for the ambulatory monitoring of fetal heart rate by Ibrahimy, Muhammad Ibn, Mohd Ali, Mohd Alauddin, Zahedi, Edmond

“…A microcontroller based system has been developed and the proposed algorithm implemented in real-time. …”
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The effectiveness of energy management system on energy efficiency in the building by Abdullah, Haim Hilman, Abubakar, Ahmed, Kaliappen, Narentheren

“…Higher Education sector of any country plays a pivotal role in national development.If such a key sector is weak, there is every likelihood that research, development and innovation will be week.The purpose of this study was to examine the effects of quality culture on university performance.The hypothesis was developed based on the extant literature. …”
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Predicting factors of library traffic for UiTMCTKKT Cendekiawan Library using predictive analytics / Azzatul Husna Abdul Aziz by Abdul Aziz, Azzatul Husna

“…Key findings highlight the role of attributes, such as study year, week 13, week 10, week 14, age, week 12, open access, and Theory Of Reasoned Action, like student attitudes, intention, and subjective norms in influencing library usage and satisfaction. …”
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Computerised Heuristic Algorithm for Multi-location Lecture Timetabling by Kuan, Huiggy

“…This research focuses on multi-location coursework timetabling problem for Master of Science in Human Resource Development (MSc HRD) at the Faculty of Cognitive Sciences and Human Development (FCSHD), Universiti Malaysia Sarawak (UNIMAS). …”
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Using satellite-measured relative humidity for prediction of Metisa plana’s population in oil palm plantations: a comparative assessment of regression and artificial neural network... by Ruslan, Siti Aisyah, Muharam, Farrah Melissa, Zulkafli, Zed, Omar, Dzolkhifli, Zambri, Muhammad Pilus

“…Relative humidity values derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images were apportioned to 6 time lags; 1 week (T1), 2 weeks (T2), 3 week (T3), 4 weeks (T4), 5 week (T5) and 6 weeks (T6) and paired with the respective census data. …”
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NEXT-HOUR ELECTRICITY PRICE FORECASTING USING LEAST SQUARES SUPPORT VECTOR MACHINE AND GENETIC ALGORITHM by Razak I.A.W.A., Abidin I.Z., Siah Y.K., Sulaima M.F.

“…Thus, a hybrid model comprising least squares support vector machine (LSSVM) and genetic algorithm (GA) was developed in this work to predict electricity prices with higher accuracy. …”

Context-aware diabetic patient remote monitoring using wearable and mobile App / Mohd Izzat Ismail Hashim by Ismail Hashim, Mohd Izzat

“…Once the app has been developed, testing is done to ensure each functionality works as expected. …”
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Ganoderma boninense classification based on near-infrared spectral data using machine learning techniques by Mas Ira Syafila, Mohd Hilmi Tan, Mohd Faizal, Jamlos, Ahmad Fairuz, Omar, Kamarulzaman, Kamarudin, Mohd Aminudin, Jamlos

“…A PLS regression is used on NIR spectra to implement the prediction of ergosterol concentration which shows good corelation of R = 0.861 between the ergosterol concentration and oil palm NIR spectra. Four different ML algorithms are tested for prediction of G. boninense infection: K-Nearest Neighbour (kNN), Naïve Bayes (NB), Support Vector Machine (SVM) and Decision Tree (DT) are tested which depicted DT algorithm achieves a satisfactory overall performance with high accuracy up to 93.1% and F1-score of 92.6% compared to other algorithms. …”
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Machine learning-based risk prediction model for medication administration errors in neonatal intensive care units: a prospective direct observational study by Henry Basil, Josephine, Lim, Wern Han, Syed Ahmad, Sharifah M., Menon Premakumar, Chandini, Mohd Tahir, Nurul Ain, Mhd Ali, Adliah, Seman, Zamtira, Ishak, Shareena, Mohamed Shah, Noraida

“…Feature importance was determined using the permutation-feature importance for robust comparison across ML algorithms. Results: A total of 1093 doses were administered to 170 neonates, with mean age and birth weight of 33.43 (SD ± 5.13) weeks and 1.94 (SD ± 0.95) kg, respectively. …”
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Machine learning model to predict the contact of angle using mineralogy, TOC and process parameters in shale by Ameenuddin Irfan, S., Fadhli, M.Z., Padmanabhan, E.

“…The application aims to develop a machine learning program using the algorithm of Support Vector Machine or Gaussian Process Regression to successfully predict the contact angle. …”
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Fine-scale predictive modeling of Aedes mosquito abundance and dengue risk indicators using machine learning algorithms with microclimatic variables by Che Dom, Nazri, Mohd Hardy Abdullah, Nur Athen, Dapari, Rahmat, Salleh, Siti Aekbal

“…However, current models often rely on coarse regional data and fail to account for microclimatic variations, limiting their predictive accuracy in dengue hotspots. This study developed fine-scale predictive models using machine learning algorithms; Artificial Neural Networks (ANN), Random Forest (RF), and Support Vector Machines (SVM) to estimate mosquito abundance and dengue risk at the species level based on daily microclimatic data (temperature, relative humidity, and rainfall) collected over 26 weeks in Kuala Selangor, Malaysia. …”
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Preemies birth monitoring using color detection method by Muhammad Naufal, Mansor, Sazali, Yaacob, Prof. Dr., Hariharan, Muthusamy, Dr., Shafriza Nisha, Basah, Dr., Syahryull Hi-Fi Syam, Ahmad Jamil, Mohd Lutfi, Mohd Khidir, Muhammad Nazri, Rejab, Ku Mohd Yusri, Ku Ibrahim, Addzrull Hi-Fi Syam, Ahmad Jamil, Ahmad Kadri, Junoh

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Solving single and bi-objective surgery scheduling problems using local search heuristic by Ab Rashid, Nur Shafiqah

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Computerised Faculty Course Timetabling with Student Sectioning by Bong, Chia Lih

“…The computational result demonstrates that proposed heul;stic algorithm outperfoJ111 current practices in all datasets. …”
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Vehicular traffic noise prediction and propagation modelling using artificial neural network by Ahmed, Ahmed Abdulkareem

“…The measurement was carried out four times a day (morning, afternoon, evening, and midnight) all through two-days of the week (Sunday and Monday). The optimal radial basis function NN model was used which comprised of 17 hidden layers with a back-propagation algorithm. …”
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Machine learning application in predicting anterior cruciate ligament injury among basketball players by Longfei, Guo

“…Four machine learning algorithms—Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), and Logistic Regression (LR)—were developed to predict ACL injury. …”
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Government policy as a key moderator to contractors’ risk attitudes among Malaysian construction companies by Taofeeq, D. M., Adeleke, A. Q., Lee, Chia Kuang

“…Partial least square-SEM is an appropriate analysis that was used to assess the results in the current research because its algorithm permits the unrestricted computation of cause-effect relationship models that use both reflective and formative measurement models. …”
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