Search Results - (( course evaluating factors algorithm ) OR ( panel classification modeling algorithm ))
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Identifying Damage Types in Solar Panels Through Surface Image Analysis with Naive Bayes
Published 2024“…Statistical criteria, including recall, specificity, and area under the curve (AUC), are employed to assess model performance. The findings indicate that the GNB algorithm surpasses BNB, with a mean average precision (mAP) of 39.83% with an 85:15 training-test ratio, whereas BNB reaches a maximum mAP of 29.25% at a 90:10 ratio. …”
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Using algorithmic taxonomy to evaluate lecturer workload
Published 2006“…Teaching workload is influenced by various factors such as level of taught courses, number of student, credit and contact hour and off campus or on campus course design. …”
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Using algorithmic taxonomy to evaluate lecture workload: a case study of services application prototype in the UPM KM Portal
Published 2006“…Teaching workload is influence by various factors such as level taught courses, number of student, credit and contact hour and off campus or on campus course design. …”
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Using algorithmic taxonomy to evaluate lecture workload: A case study of services application prototype in the UPM KM portal
Published 2006“…Lecturer workload at universities includes three major categories: teaching, research and services.Teaching workload is influence by various factors such as level taught courses, number of student, credit and contact hour and off campus or on campus course design.The UPM has a KM Portal that contains sets of metadata on lecturer profile and knowledge assets.The Lecturer profile contains information lecturer teaching, research, publication and many more.We constructed an algorithmic taxonomy based at the lecturer profile data to measure lecturer teaching workload.This method measures the lecturer teaching workload.The taxonomy is a dynamic hierarchy that extracts validated parameters from the dataset.Results of the study highlight the contributions of this algorithmic method in better evaluation of teaching workload for lecture.…”
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The classification of skateboarding trick manoeuvres: A K-nearest neighbour approach
“…It was shown from the present preliminary investigation, that the k-NN model which employs k = 1 with an equal weight applied to the Euclidean distance metric yielded a classification accuracy of 85%. …”
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Fuzzy expert system to evaluate programming question / Norfarhana Syamiza Amir Sham
Published 2017“…In the university education system, examination result is one of the factors that contribute to passing and fail in sentence course. …”
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Evaluation of genetic algorithm based solar tracking system for photovoltaic panels
Published 2008“…It depends on environmental factors such as the solar irradiation and the temperature of these panels. …”
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An evolutionary based features construction methods for data summarization approach
Published 2015“…In the process of summarizing relational data, a genetic algorithm is also applied and several feature scoring measures are evaluated in order to find the best set of relevant constructed features. …”
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An early warning system for students at risk using supervised machine learning
Published 2024“…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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Cryptanalysis on the modulus N=p2q and design of rabin-like cryptosystem without decryption failure
Published 2015“…Moreover, for the purpose of empirical evidences, some parameters are chosen in the course of the process to validate the efficiency in terms of algorithmic running time and memory consumptions. …”
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Predictive modelling of student academic performance using machine learning approaches : a case study in universiti islam pahang sultan ahmad shah
Published 2024“…Drawing from a dataset spanning students enrolled in the Business Statistics course at Universiti Islam Pahang Sultan Ahmad Shah from 2013 to 2022, this study identifies students’ carry marks as the most correlated factor in determining performance levels. …”
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