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Effective k-Means Clustering in Greedy Prepruned Tree-based Classification for Obstructive Sleep Apnea by Sim, Doreen Ying Ying, Ahmad I., Ismail, Chee Siong, Teh

“…Incorporation of prepruned decision trees to kmeans clustering through one to three types of tree-depth controllers and cluster partitioning was done to develop a combined algorithm named as Greedy Pre-pruned Treebased Clustering (GPrTC) algorithm. …”
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Mid-infrared spectroscopy for early detection of basal stem rot disease in oil palm by Liaghat, Shohreh, Mansor, Shattri, Ehsani, Reza, Mohd Shafri, Helmi Zulhaidi, Meon, Sariah, Sankaran, Sindhuja

“…Leaf samples of healthy, mild, moderately, and severely-infected trees were measured using FT-IR spectrometers to obtain absorbance data from the range of 2.55–25.05 μm s (3921–399 cm−1). …”
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Early detection of basal stem rot disease (Ganoderma) in oil palms based on hyperspectral reflectance data using pattern recognition algorithms by Liaghat, Shohreh, Ehsani, Reza, Mansor, Shattri, Mohd Shafri, Helmi Zulhaidi, Meon, Sariah, Sankaran, Sindhuja, Azam, Siti H. M. N.

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Machine-learning approach using thermal and synthetic aperture radar data for classification of oil palm trees with basal stem rot disease by Che Hashim, Izrahayu

“…The sample size was comprised of 55 non-infected trees and 37 infected trees. During the field experiments, oil palm tree samples of non-infected (T0), mild infected (T1), moderate infected (T2), and severe infected (T3) were measured using the FLIR T620 IR infrared thermal imaging camera to obtain the temperature of the oil palm trees. …”
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The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

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An intra-severity classification and adaptation technique to improve dysarthric speech recognition accuracy / Bassam Ali Qasem Al-Qatab by Bassam Ali Qasem, Al-Qatab

“…The algorithms include Linear Discriminant Analysis (LDA), Artificial Neural Network (ANN), Support Vector Machine (SVM), Naive Bayes (NB), Classification And Regression Tree (CART), Random Forest (RF). …”
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Machine learning models for predicting the compressive strength of concrete with shredded pet bottles and m sand as fine aggregate by Nadimalla, Altamashuddinkhan, Masjuki, Siti Aliyyah, Gubbi, Abdullah, Khan, Anjum, Mokashi, Imran

“…Machine Learning (ML) and Artificial Intelligence (AI) are closely intertwined and represent the latest cutting-edge technologies that facilitate the development of intelligent prototypes. Machine learning is a critical subset of AI that deliberates the development of self-trained algorithms that use previous databases and analysis for result predictions. …”
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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush

Fuzzy Evaluation and Benchmarking Framework for Robust Machine Learning Model in Real-Time Autism Triage Applications by Shayea G.G., Zabil M.H.M., Albahri A.S., Joudar S.S., Hamid R.A., Albahri O.S., Alamoodi A.H., Zahid I.A., Sharaf I.M.

Ethnicity and Dietary Practices as Colorectal Cancer Risk Predictors: A Retrospective Case-control Study in Sabah, Malaysia by Wen-Li Tee, Fredie Robinson, Richard Avoi, Prabakaran Dhanaraj, Nirmal Kaur, Nur Hasanah Sanudin

“…Prediction model was computed using Logistic Regression (LR) and C5 Decision Tree algorithms and compared. Results: Age 60-69 (aOR = 7.44, 95% CI = 3.69-15.00); male (aOR = 4.49, 95% CI = 2.67-7.54), Chinese (aOR = 32.32, 95% CI = 7.20-145.13); moderate physical activity (aOR = 3.67, 95% CI = 2.03-6.63), pickled mango (aOR = 5.66, 95% CI = 1.62-19.81), pork (aOR = 2.29, 95% CI = 1.09-4.79) increased the odds of developing CRC. …”
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Assessment of near-infrared and mid-infrared spectroscopy for early detection of basal stem rot disease in oil palm plantation by Liaghat, Shohreh

“…Reflectance spectroscopy analysis ranging from visible to nearinfrared region (325-1075 nm) and mid-infrared region (2.55-25.05 μm/3921-399 cm-1) was used to analyze oil palm leaf and trunk samples of healthy (G0), mildly-infected (G1), moderately-infected (G2) and heavilyinfected (G3) trees in order to detect and quantify Ganoderma disease at different infection levels. …”
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