Search Results - (( java implication based algorithm ) OR ( programming learning goal algorithm ))

HELM based Reinforcement Learning for Goal Localization by AlDahoul, Nouar, Htike@Muhammad Yusof, Zaw Zaw

“…The objective of goal localizationHELM based reinforcement learning for goal localization is to find the location of goals in noisy environments. …”
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A review on object detection algorithms based deep learning methods / Wan Xing ... [et al.] by Wan Xing, Sultan Mohd, Mohd Rizman, Johari, Juliana, Ahmat Ruslan, Fazlina

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Dam haji game using A* search algorithm / Siti Farah Najwa Mukhlis by Mukhlis, Siti Farah Najwa

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The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

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Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam by Toghroli, Ali, Suhatril, Meldi, Ibrahim, Zainah, Safa, Maryam, Shariati, Mahdi, Shamshirband, Shahaboddin

“…In comparison to the other widely used conventional learning algorithms, the ELM has a much faster learning ability.…”
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DESIGN OF IMPROVED GRID FOR TURTLE ROBOT by Adnan, Muhammad Zulhilmi

“…It is recommended to improve the algorithm of this turtle robot so that in can follow grid with less mistakes and to achieve its ultimate goal which is for learning purposes.…”
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My little learner : E-learning wonderland by Teoh, Wei En

“…The app will also use adaptive learning algorithms to tailor the instructional material to each child's unique learning style and speed. …”
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Students’ attitude towards video-based learning: machine learning analysis with rapid software / Abdullah Sani Abd Rahman ... [et al.] by Abd Rahman, Abdullah Sani, Meutia, Rita, Hamid, Yusnaliza, Abdul Rahman, Rahayu

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Towards a better feature subset selection approach by Shiba, Omar A. A.

“…The selection of the optimal features subset and the classification has become an important issue in the data mining field.We propose a feature selection scheme based on slicing technique which was originally proposed for programming languages.The proposed approach called Case Slicing Technique (CST).Slicing means that we are interested in automatically obtaining that portion 'features' of the case responsible for specific parts of the solution of the case at hand.We show that our goal should be to eliminate the number of features by removing irrelevant once.Choosing a subset of the features may increase accuracy and reduce complexity of the acquired knowledge.Our experimental results indicate that the performance of CST as a method of feature subset selection is better than the performance of the other approaches which are RELIEF with Base Learning Algorithm (C4.5), RELIEF with K-Nearest Neighbour (K-NN), RELIEF with Induction of Decision Tree Algorithm (ID3) and RELIEF with Naïve Bayes (NB), which are mostly used in the feature selection task.…”
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My little learner: E-learning wonderland by Teoh, Wei En

“…The app will also use adaptive learning algorithms to tailor the instructional material to each child's unique learning style and speed. …”
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Automatic extraction of digital terrain model and Building Footprint from airborne LiDAR data using rule-based learning techniques by Jifroudi, Hamidreza Maskani

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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush

AI chatbot system for educational institutions by Tan, Hui Hui

“…This project presents the design and development of an advanced chatbot system powered by deep learning algorithms for intent classification. The chatbot's primary goal is to facilitate effective communication and support for users, particularly students inquiring about admission processes. …”
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Nutritious digital menu system for Malaysian religious primary school children: improving good memories / Azila M. Sudin, Suliadi Firdaus Sufahani and Mohd Asrul Affendi Abdullah by M. Sudin, Azila, Sufahani, Suliadi Firdaus, Abdullah, Mohd Asrul Affendi

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Chili crop segregation system design and development strategies by Wan Daud, Wan Mohd Bukhari, Abdul Aziz, Mohd Fareezuan, Ahmad Izzuddin, Tarmizi, Norasikin, Mohd Adili, Abdul Rasid, Ahmad Fuad, Wakhi Anuar, Nur Farah Bazilah, M. N. Sukhaimie

“…The image data taken from chili samples can be trained by using Learning Algorithm in the MATLAB program. The performance of the trained network then can be evaluated by using the Confusion Matrix technique. …”
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Multistage quality control in manufacturing process using blockchain with machine learning technique by Gu, J., Zhao, L., Yue, X., Arshad, N.I., Mohamad, U.H.

“…The demand for predictive machine reliability and quality drives the development of intelligent manufacturing technologies. To this goal, a variety of machine learning algorithms are being studied. …”
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Demystifying Artificial Intelligence (AI) for Early Childhood and Elementary Education: A Case Study of Perceptions of AI of State of Missouri Educators by Arnone, K., Hutson, J., Woodruff, K.

“…Yet with all the potential benefits that AI and machine learning (ML) may provide students, there remains a general reticence to adopt this technology because of misconceptions and perceptions that elementary educators will need expensive equipment, robust support from IT, or to retool and learn programming or coding. …”
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Double Deep RL-based strategy for UAV-assisted energy harvesting optimization in disaster-resilient IoT networks by Elmadina, Nahla Nur, Saeed, Rashid A, Saeid, Elsadig, Ali, Elmustafa Sayed, Nafea, Ibtehal, Ahmed, Mayada A, Mokhtar, Rania A, Khalifa, Othman Omran

“…Due to the problem's complexity, we propose a lightweight DDRL solution capable of efficiently learning system dynamics. Extensive simulations and comparisons with Deep RL and DDPG algorithms demonstrate the superior performance of DDRL in enhancing EH, covering strategic locations effectively, and achieving high satisfaction and accuracy rates.…”
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FPGA Implementation of Emergency Door Car Entry System by Zaini Sulaiman

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Enhancing teaching and learning through data-driven optimization of servicing code demand and lecturer allocation using WEKA analysis by Rochin Demong, Nur Atiqah, Mohamed Razali, Murni Zarina, Kamaruddin, Juliana Noor, Shamsuddin, Sazwan, Awang, Nor Ain, Kamarudin, Norjuliatie, Wan Othman, Noor Faradilla

“…The dataset of 297 instances comprised attributes such as Course Code, Course Name, Course Type, Faculty, Program, Campus, Total Number of Students Enrolled and Approval Status. …”
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