• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.13-16
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• 2022

This paper proposes using micro-learning at Saudi universities. It commences with an account of the concept of micro-learning and the difference between micro-learning and electronic learning. Then it touches on the significance, principles, and examples of micro-learning, followed by some micro-learning applications and pitfalls. The paper closes with a proposal for using this learning mode at Saudi universities.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.335-343
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• 2021

The study aimed to elucidate how to design a learning environment on the premise of micro-learning (ML) and investigate its impact on website designing skills and achievement motivation among secondary school students. Adopting the experimental approach, data were collected through an achievement test, a product evaluation form, and a test to gauge motivation for achievement. The sample was divided into two experimental groups. Results revealed statistically significant differences at 0.05≥α between the mean scores of the two groups that experienced ML, irrespective of the two modes of presenting the video in the pre-test and post-test, as for the test of websites design skills, product evaluation form, and achievement motivation test. Besides, there have been statistically significant differences at 0.05≥α between the mean scores of the first experimental group that had exposure to ML using the split-video presentation style and the scores of the second experimental group that underwent ML using continuous video presentation style in the post cognitive test of website design and management skills in favor of the group that had segmented-video-presentation ML. Another salient finding is the nonexistence of significant differences at 0.05≥α between the mean scores of the first experimental group that underwent segmented-video-presentation ML and the grades of the second experimental group that received ML with continuous video presentation style in the post-application of the product scorecard of websites designing skills and the motivation test. In light of these salient findings, the study recommended using ML in teaching computer courses at different educational stages in Saudi Arabia, training computer and information technology teachers to harness ML in their teaching and using ML in designing courses at all levels of education.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.327-329
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• 2022

Education is affected by technical and scientific developments. Progress in one of these areas leads give way to new educational methods and strategies. One of these advanced learning modes is what has been conventionally termed as Micro-learning (ML). It has emerged in educational technology as a result of advances in information technology as well as advances in research in memory, brain, and social-cognitive processes.In this paper, the researcher discusses micro-learning in terms of its concepts, tools, and associated concepts, advantages and disadvantages.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.443-451
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• 2023

Machine Learning has introduced many solutions in data science, but its application in IoT faces significant challenges, due to the limitations in memory size and processing capability of constrained devices. In this paper we design an automatic gamma radiation detection and identification embedded system that exploits the power of TinyML in a SiPM micro radiation sensor leveraging the Edge Impulse platform. The model is trained using real gamma source data enhanced by software augmentation algorithms. Tests show high accuracy in real time processing. This design has promising applications in general-purpose radiation detection and identification, nuclear safety, medical diagnosis and it is also amenable for deployment in small satellites.

• Computers and Concrete
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• v.34 no.2
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• pp.247-265
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• 2024

Measuring the fracture toughness of concrete in laboratory settings is challenging due to various factors, such as complex sample preparation procedures, the requirement for precise instruments, potential sample failure, and the brittleness of the samples. Therefore, there is an urgent need to develop innovative and more effective tools to overcome these limitations. Supervised learning methods offer promising solutions. This study introduces seven machine learning algorithms for predicting concrete's effective fracture toughness (K-eff). The models were trained using 560 datasets obtained from the central straight notched Brazilian disc (CSNBD) test. The concrete samples used in the experiments contained micro silica and powdered stone, which are commonly used additives in the construction industry. The study considered six input parameters that affect concrete's K-eff, including concrete type, sample diameter, sample thickness, crack length, force, and angle of initial crack. All the algorithms demonstrated high accuracy on both the training and testing datasets, with R² values ranging from 0.9456 to 0.9999 and root mean squared error (RMSE) values ranging from 0.000004 to 0.009287. After evaluating their performance, the gated recurrent unit (GRU) algorithm showed the highest predictive accuracy. The ranking of the applied models, from highest to lowest performance in predicting the K-eff of concrete, was as follows: GRU, LSTM, RNN, SFL, ELM, LSSVM, and GEP. In conclusion, it is recommended to use supervised learning models, specifically GRU, for precise estimation of concrete's K-eff. This approach allows engineers to save significant time and costs associated with the CSNBD test. This research contributes to the field by introducing a reliable tool for accurately predicting the K-eff of concrete, enabling efficient decision-making in various engineering applications.