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Virtual reality in algorithm programming course: practicality and implications for college students by Dewi, Ika Parma, Ambiyar, Mursyida, Lativa, Effendi, Hansi, Giatman, Muhammad, Efrizon, Hanafi, Hafizul Fahri, Ali, Siti Khadijah

“…Overall, the VR products developed have proven to be practical and effective in learning programming algorithms. …”
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Industry 5.0 and Education 5.0: Transforming Vocational Education through Intelligent Technology by Zhang, Hongli, Leong, Wai Yie

“…By analyzing the research gaps in personalized learning paths, emotion-driven learning, crossdisciplinary integration, and long-term learning behavior analysis, the paper proposes four improved algorithms: the adaptive learning path generation algorithm, the emotion-driven personalized learning algorithm, the cross-disciplinary knowledge graph algorithm, and the long-term learning behavior prediction algorithm. …”
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Computational Thinking (Algorithms) Through Unplugged Programming Activities: Exploring Upper Primary Students’ Learning Experiences by Bih Loong, Lim, Chwen Jen, Chen

“…A total of 31 students from a rural primary school were exposed to the learning about the algorithm concept (an aspect of CT skills) via UPA learning materials. …”
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A modified generalized RBF model with EM-based learning algorithm for medical applications by Ma, Li Ya, Abdul Rahman, Abdul Wahab, Quek, Chai

“…This will make the learning process simpler and more convenient in practice. …”
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Machine learning model for performance prediction in mobile network management / Muhammad Hazim Wahid by Wahid, Muhammad Hazim

“…One of the major challenges when applying machine learning is to identify the best algorithm from a variety of algorithms to solve a problem. …”
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Automated model selection for corporation credit risk assessment using machine learning / Zulkifli Halim by Halim, Zulkifli

“…The models are based on the four machine learning algorithms: logistic regression, support vector machine, decision tree, and neural network; two ensemble techniques: adaptive boost and bootstrap aggregation; three deep learning algorithms: recurrent neural network, long short-term memory(LSTM), and gated recurrent unit (GRU). …”
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Graph-Based Algorithm With Self-Weighted And Adaptive Neighbours Learning For Multi-View Clustering by He, Yanfang

“…To address this issue, this study incorporated joint graph learning from the gmc algorithm into swmcan, creating a new algorithm called swmcan-jg. …”
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Machine Learning Regression Approach for Estimating Energy Consumption of Appliances in Smart Home by Husin N.S.I.M., Mostafa S.A., Jaber M.M., Gunasekaran S.S., Al-Shakarchi A.H., Abdulsattar N.F.

“…This paper attempts to use machine learning algorithms to estimate the energy consumption of appliances in a smart home environment. …”

Advancements and challenges in mobile robot navigation: a comprehensive review of algorithms and potential for self-learning approaches by Al Mahmud, Suaib, Kamarulariffin, Abdurrahman, Mohd Ibrahim, Azhar, Haja Mohideen, Ahmad Jazlan

“…In this review paper, a comprehensive review of mobile robot navigation algorithms has been conducted. The findings suggest that, even though the self-learning algorithms require huge amounts of training data and have the possibility of learning erroneous behavior, they possess huge potential to overcome challenges rarely addressed by the other traditional algorithms. …”
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Comparative study of machine learning algorithms in data classification by Tan, Kai Jun

“…This research conducts a comparative study of various machine learning algorithms for dataset classification to identify the most accurate and reliable classifier. …”
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Active force control with iterative learning control algorithm for a vehicle suspension by Rosmazi, Rosli

“…The new control scheme named active force control with iterative learning control algorithm (AFCIL) is complemented by the classic proportionalintegral-derivative (PID) control incorporated and designed as the outermost control loop. …”
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Interpretation of machine learning model using medical record visual analytics by Mohd Khalid, Nur Hidayah, Ismail, Amelia Ritahani, Abdul Aziz, Normaziah

“…This paper studied on the effectiveness of visual analytics techniques to identify the appropriate technique to be instantiated to the machine learning algorithm to further elaborate the results obtained by demonstrating transparency, interpreta- bility and explainability of the machine learning algorithm. …”
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Computational Thinking Through Unplugged Programming Activities : Exploring Students’ Learning Experiences by Lim, Bih Loong

“…The result shows that there is no significant effect of the learning material in increasing the participants’ algorithm skill. …”
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Evaluation of rehearsal effects of multimedia content based on EEG using machine learning algorithms by Mazher, M., Aziz, A.A., Malik, A.S.

“…Three frequency based features are used to discriminate the three learning states mentioned as L1, L2 and L3 using machine learning algorithms. …”
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An improved teaching-learning-based optimization for extreme learning machine in floating photovoltaic power forecasting by Mohd Redzuan, Ahmad, Nor Farizan, Zakaria, Mohd Shawal, Jadin, Mohd Herwan, Sulaiman

“…This study presents an improved teaching-learning-based optimization algorithm with extreme learning machine for floating photovoltaic power forecasting. …”
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Federated deep learning for automated detection of diabetic retinopathy by Zainal Abidin, Nadzurah, Ismail, Amelia Ritahani

“…Federated learning allows deep learning algorithms to learn from a diverse set of data stored in multiple databases. …”
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An Educational Tool Aimed at Learning Metaheuristics by Kader, Md. Abdul, Jamaluddin, Jamal A., Kamal Z., Zamli

“…This tool can be an educational assistant for beginners to learn metaheuristic in theoretical lectures as well as practical sessions. …”
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The influence of machine learning on the predictive performance of cross-project defect prediction: empirical analysis by Bala, Yahaya Zakariyau, Samat, Pathiah Abdul, Sharif, Khaironi Yatim, Manshor, Noridayu

“…This empirical investigation delves into the influence of machine learning (ML) algorithms in the realm of cross-project defect prediction, employing the AEEEEM dataset as a foundation. …”
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Interpretation of machine learning model using medical record visual analytics by Mohd Khalid, Nur Hidayah, Ismail, Amelia Ritahani, Abdul Aziz, Normaziah

“…This paper studied on the effectiveness of visual analytics techniques to identify the appropriate technique to be instantiated to the machine learning algorithm to further elaborate the results obtained by demonstrating transparency, interpretability and explainability of the machine learning algorithm. …”
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Estimating Depressive Tendencies Of Twitter User Via Social Media Data by Loh, Hooi Teng

“…The twitter dataset can be used to test the level of depressive tendencies with three different machine learning algorithms. These three different machine learning algorithms which are Support Vector Machine, XGBoost, and Random Forest. …”
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