Search Results - (( course evaluation tree algorithm ) OR ( writer identification using algorithm ))

Improved random forest for feature selection in writer identification by Sukor, Nooraziera Akmal

“…Writer Identification (WI) is a process to determine the writer of a given handwriting sample. …”
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A New Swarm-Based Framework for Handwritten Authorship Identification in Forensic Document Analysis by Draman @ Muda, Azah Kamilah, Choo, Yun Huoy, Draman @ Muda, Noor Azilah

“…However in this chapter, feature selection is used to obtain the unique individual significant features which are proven very important in handwriting analysis of Writer Identification domain. …”
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A new swarm-based framework for handwritten authorship identification in forensic document analysis by Pratama, Satrya Fajri, Draman @ Muda, Azah Kamilah, Choo, Yun Huoy, Draman @ Muda, Noor Azilah

“…The use of feature selection as one of the important machine learning task is often disregarded in Writer Identification domain, with only a handful of studies implemented feature selection phase. …”
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Discretization of integrated moment invariants for writer identification by Draman @ Muda, Azah Kamilah, Shamsuddin, Siti Maryam, Darus, Maslina

“…Hence, in this study, an integrated scaling formulation of Aspect Scaling Invariant is presented in Writer Identification to hunt for the individuality perseverance. …”
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Tracking student performance in introductory programming by means of machine learning by Khan I., Al Sadiri A., Ahmad A.R., Jabeur N.

Invariants discretization for individuality representation in handwritten authorship by Draman @ Muda, Azah Kamilah, Shamsuddin, Siti Mariyam, Darus, Maslina

“…Writer identification is one of the areas in pattern recognition that have created a center of attention by many researchers to work in. …”
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An artificial intelligence approach to monitor student performance and devise preventive measures by Khan I., Ahmad A.R., Jabeur N., Mahdi M.N.

“…We developed a set of prediction models with distinct machine learning algorithms. Decision tree triumph over other models and thus is further transformed into easily explicable format. …”

A Systematic Approach to Transform Machine Learning Students� Performance Prediction Model into Preventive Procedures by Khan I., Ahmad A.R., Jabeur N., Mahdi M.N.

“…Educational Data Mining tools, specifically Machine learning classifiers, appear supportive to develop prediction models which forecast students� final outcome in a course. This research evaluates the effectiveness of machine learning classifiers to monitor students� academic progress and informs the instructor about the students at the risk of producing unsatisfactory final result in a course. …”

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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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

“…Particularly, the decision tree is identified as the most accurate predictive model, having a 0.60 accuracy value. …”
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DurianCare: optimising trunk disease detection and precision farming in a mobile application / Muhammad Haziq Azmi by Azmi, Muhammad Haziq

“…The project will implement automated detection and disease management recommendation algorithms while evaluating the functionality and usability of the designed application. …”
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Leveraging data lake architecture for predicting academic student performance by Abdul Rahim, Shameen Aina, Sidi, Fatimah, Affendey, Lilly Suriani, Ishak, Iskandar, Nurlankyzy, Appak Yessirkep

“…In addition to forecasting the student performance, appropriate machine learning algorithms such as Support Vector Classifier, Naive Bayes, and Decision Trees are used to build prediction models by using the data lake's scalability and parallel processing capabilities. …”
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