Search Results - (( course evaluation tree algorithm ) OR ( label classification modified algorithm ))

Modified word representation vector based scalar weight for contextual text classification by Abbas Saliimi, Lokman

“…In addition, a contextual text classification experiment is conducted using benchmarked datasets to assess the performance of the modified word vectors in the targeted classification task. …”
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Content-based feature selection for music genre classification by Muda, Noor Azilah, Choo, Yun Huoy, Norashikin, Ahmad

“…We then proposed the Modified AIS-based classification algorithm to solve the music genre classification problem. …”
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A modified weighted support vector machine (WSVM) to reduce noise data in classification problem by Mohd Dzulkifli, Syarizul Amri

“…Classification refers to a predictive modeling problem where a class label is predicted for a given example of input data. …”
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A modified weighted support vector machine (WSVM) to reduce noise data in classification problem by Mohd Dzulkifli, Syarizul Amri

“…Classification refers to a predictive modeling problem where a class label is predicted for a given example of input data. …”
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Improvement anomaly intrusion detection using Fuzzy-ART based on K-means based on SNC Labeling by Zulaiha Ali Othman, Afaf Muftah Adabashi, Suhaila Zainudin, Saadat M. Al Hashmi

“…This paper presents our work to improve the performance of anomaly intrusion detection using Fuzzy-ART based on the K-means algorithm. The K-means is a modified version of the standard K-means by initializing the value K from the value obtained after data mining using Fuzzy-ART and SNC labeling technique. …”
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Boosting and bagging classification for computer science journal by Wibawa, Aji Prasetya, Putri, Nastiti Susetyo Fanany, Al Rasyid, Harits, Nafalski, Andrew, Hashim, Ummi Rabaah

“…In the DT algorithm, both variables are altered, whereas, in the GNB algorithm, just the estimator's value is modified. …”
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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.

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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