Search Results - (( course evaluation metric algorithm ) OR ( age classification issues algorithm ))

BONE AGE ANALYSIS FROM BONE X-RAY by ABD GHANI, NOOR SYAZANA

“…To achieve the objectives, the bone age classification method will involve image segmentation of the x-ray, feature extraction from the segmented images and lastly, classification of bone age level. …”
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Algorithm comparison for data mining classification: assessing bank customer credit scoring default risk by Elaf Adel Abbas, Nisreen Abbas Hussein

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Poverty risk prediction based on socioeconomic factors using machine learning approach by Mohd Zawari, Nur Farhana Adibah

“…The feature that was found to be the most influential predictor of poverty risk was age. These findings imply that Logistic Regression is the suitable and interpretable model that can be used with structured data in the classification of poverty. …”
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Data-driven state of charge estimation of lithium-ion batteries: Algorithms, implementation factors, limitations and future trends by Hossain Lipu M.S., Hannan M.A., Hussain A., Ayob A., Saad M.H.M., Karim T.F., How D.N.T.

The classification of wink-based eeg signals by means of transfer learning models by Jothi Letchumy, Mahendra Kumar

“…The implementation of pre-processing algorithms has been demonstrated to be able to mitigate the signal noises that arises from the winking signals without the need for the use signal filtering algorithms. …”
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Optimization Algorithms: A Comparison Study for Scheduling Problem at UIN Raden Fatah's Sharia and Law Faculty by Mustakim, ., Tri Basuki, Kurniawan, Misinem, ., Edi Surya, Negara, Izman, Herdiansyah

“…Our experimental evaluation compares the performance of GA and ACO across multiple metrics, including execution time, memory usage, fitness, and adaptability to dynamic conditions. …”
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Register Transfer Level Implementation Of Pooling - Based Feature Extraction For Finger Vein Identification by Mohamed Ali, Mohamed Hassan

“…The algorithm was mainly inspired by spatial pyramid pooling in generic image classification combined with PCA. …”
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An early warning system for students at risk using supervised machine learning by Yam, Zheng Hong, Mohd Norshahriel, Abd Rani, Nabilah Filzah, Mohd Radzuan, Lim, Huay Yen, Sarasvathi, Nagalingam

“…According to the research, 52% of students who sign up for a course would never read the course materials. Furthermore, throughout the course of five years, the dropout rate reached a stunning 96%. …”
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A comprehensive 3-phase framework for determining the customer’s product usage in a food supply chain by Mad Ali, Mohd Fahmi, Mohd Ariffin, Mohd Khairol Anuar, Delgoshaei, Aidin, Mustapha, Faizal, Supeni, Eris Elianddy

“…A fundamental issue in manufacturing systems is moving a local manufacturer into a supply chain network including wholesalers and retailers. …”
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Predicting Cheaters in PlayerUnknown’s Battlegrounds (PUBG) using Random Forest Algorithm by Nurin Alya, Haris

“…Patterns and relationships between input variables and cheating behaviours are analysed through the application of supervised learning techniques, specifically a classification model. The primary goal is to use the Random Forest algorithm, an effective machine learning technique, to predict instances of cheating based on the behavioural patterns of participants. …”
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Cornsense: leaf disease detection application / Iffah Fatinah Mohamad Nasir by Mohamad Nasir, Iffah Fatinah

“…The purpose of this project is to develop a mobile application for corn leaf disease detection leveraging the YOLOv8 (You Only Look Once version 8) object detection algorithm. Utilizing YOLOv8 allows for real-time and accurate classification of corn leaf images into various disease categories. …”
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Prediction of breast cancer diagnosis using machine learning in Malaysian women by Mokhtar, Tengku Muhammad Hanis Tengku

“…Five factors affecting mammographic density were age, number of children, body mass index, menopause status, and breast imaging-reporting and data system (BI-RADS) classification. …”
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Identifying the correct articulation point of a Quranic letters of the throat (al-halqu) makhraj by Othman, Ahmad Al Baqir, Ahmad, Salmiah, Badron, Khairayu, Altalmas, Tareq M. K.

“…Data was trained using an improved deep learning Convolutional Neural Network (CNN) classification model. Results shows that the algorithm was able to detect the letters produced at throat area, which are also known as Izhar Halqi letters. …”
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Effect of different modalities of facial images for diagnosis of ASD by deep neural network by Rashid, Muhammad Mahbubur, Alam, Mohammad Shafiul, Haque, M A, Ali, Mohammad Yeakub, Yvette, Susiapan

“…It delves into how deep learning algorithms perform with different facial image modalities, especially 2D and 3D, while addressing specific challenges associated with each. …”
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Artificial intelligence to predict pre-clinical dental student academic performance based on pre-university results: a preliminary study by Lestari, Widya, Abdullah, Adilah Syahirah, Ahmad Amin, Afifah Munirah, faridah, Nur, Sukotjo, Cortino, Ismail, Azlini, mohamad ibrahim, mohamad shafiq, Insani, Nashuha, Utomo, Chandra Prasetyo

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Recognizing complex human activities using hybrid feature selections based on an accelerometer sensor by Zainudin, Muhammad Noorazlan Shah, Sulaiman, Md Nasir, Mustapha, Norwati, Perumal, Thinagaran, Mohamed, Raihani

“…One of the limitations of the various classifications is to deal with the large dimension of the feature space. …”
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Modular deep neural network in reducing overfitting to enhance generalization / Mohd Razif Shamsuddin by Shamsuddin, Mohd Razif

“…Machine Learning (ML) and Artificial Intelligence (AI) are a hype in this new age. Some researcher may use it to do classification, recognitions, or even predictions. …”
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Predictive modelling of student academic performance using machine learning approaches : a case study in universiti islam pahang sultan ahmad shah by Nurul Habibah, Abdul Rahman

“…This study used Cramer’s V correlation and Spearman’s Rank Correlation Coefficient to determine the most correlated factor towards students’ performance level. Evaluation metrics encompass precision, recall, accuracy, F1-score, and area under the receiver operating characteristics curve. …”
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