• Smart Structures and Systems
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• v.32 no.1
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• pp.37-47
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• 2023

Indirect determination of pan evaporation (PE) has been highly regarded, due to the advantages of intelligent models employed for this objective. This work pursues improving the reliability of a popular intelligent model, namely multi-layer perceptron (MLP) through surmounting its computational knots. Available climatic data of Fresno weather station (California, USA) is used for this study. In the first step, testing several most common trainers of the MLP revealed the superiority of the Levenberg-Marquardt (LM) algorithm. It, therefore, is considered as the classical training approach. Next, the optimum configurations of two metaheuristic algorithms, namely cuttlefish optimization algorithm (CFOA) and teaching-learning-based optimization (TLBO) are incorporated to optimally train the MLP. In these two models, the LM is replaced with metaheuristic strategies. Overall, the results demonstrated the high competency of the MLP (correlations above 0.997) in the presence of all three strategies. It was also observed that the TLBO enhances the learning and prediction accuracy of the classical MLP (by nearly 7.7% and 9.2%, respectively), while the CFOA performed weaker than LM. Moreover, a comparison between the efficiency of the used metaheuristic optimizers showed that the TLBO is a more time-effective technique for predicting the PE. Hence, it can serve as a promising approach for indirect PE analysis.

• International Journal of Advanced Culture Technology
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• v.8 no.1
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• pp.199-206
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• 2020

I suggested the design learning environment based on affordance concept approach for supporting and improving learners' behavior and outcome for convergent design education in this study. The design learning space should be applied teaching and learning activity, especially learners' behavior, physical space condition to support the design thinking process. The design learning space needs openness, individuality and connectivity to support the learners' behavioral to immerse, participate, cooperate, understand, think and fulfill the design thinking process. The composition principles of the learning environment for convergent design education supports communication and collaboration among members for independence and interaction. The spaces for design research and teaching needs high privacy while facilitating visual communications through special materials and wall structure design. Also, for connectivity to improve the learners' physical and visual contact, the environment of the classrooms requires flexibility and mobility by providing an open space integrating unit cells for realizing learning purpose. These are provided by formed of an open structure for inducing visual communication and physical contact to involve the design activities and the mutual interchange.

• International Journal of Internet, Broadcasting and Communication
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• v.4 no.2
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• pp.12-16
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• 2012

This paper presents a learning framework that fits the digital age - an e-Friendly PLE. The learning framework is based on the theory of connectivism which asserts that knowledge and the learning of knowledge is distributive and is not located in any given place but rather consists of the network of connections formed from experiences and interactions with a knowing community, thus, the newly empowered learner is thinking and interacting in new ways. The framework's approach to learning is based on conversation and interaction, on sharing, creation and participation, on learning not as a separate activity, but rather as embedded in meaningful activities such as games or workflows. It sees learning as an active, personal inquiry, interpretation, and construction of meaning from prior knowledge and experience with one's actual environment.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.169-180
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• 2021

Since machine learning was invented, there have been many different machine learning-based algorithms, from shallow learning to deep learning models, that provide solutions to the classification tasks. But then it poses a problem in choosing a suitable classification algorithm that can improve the classification/detection efficiency for a certain network context. With that comes whether an algorithm provides good performance, why it works in some problems and not in others. In this paper, we present a data-centric analysis to provide a way for selecting a suitable classification algorithm. This data-centric approach is a new viewpoint in exploring relationships between classification performance and facts and figures of data sets.

• Advances in nano research
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• v.15 no.6
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• pp.513-520
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• 2023

The exponential proliferation of cutting-edge computing technologies has spurred organizations to outsource their data and computational needs. In the realm of cloud-based computing environments, ensuring robust security, encompassing principles such as confidentiality, availability, and integrity, stands as an overarching imperative. Elevating security measures beyond conventional strategies hinges on a profound comprehension of malware's multifaceted behavioral landscape. This paper presents an innovative paradigm aimed at empowering cloud service providers to adeptly model user behaviors. Our approach harnesses the power of a Particle Swarm Optimization-based Probabilistic Neural Network (PSO-PNN) for detection and recognition processes. Within the initial recognition module, user behaviors are translated into a comprehensible format, and the identification of malicious nano-structures behaviors is orchestrated through a multi-layer neural network. Leveraging the UNSW-NB15 dataset, we meticulously validate our approach, effectively characterizing diverse manifestations of malicious nano-structures behaviors exhibited by users. The experimental results unequivocally underscore the promise of our method in fortifying security monitoring and the discernment of malicious nano-structures behaviors.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.205-211
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• 2024

The increasing number of botnet attacks incorporating new evasion techniques making it infeasible to completely secure complex computer network system. The botnet infections are likely to be happen, the timely detection and response to these infections helps to stop attackers before any damage is done. The current practice in traditional IP networks require manual intervention to response to any detected malicious infection. This manual response process is more probable to delay and increase the risk of damage. To automate this manual process, this paper proposes to automatically select relevant countermeasures for detected botnet infection. The propose approach uses the concept of flow trace to detect botnet behavior patterns from current and historical network activity. The approach uses the multiclass machine learning based approach to detect and classify the botnet activity into IRC, HTTP, and P2P botnet. This classification helps to calculate the risk score of the detected botnet infection. The relevant countermeasures selected from available pool based on risk score of detected infection.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.76-78
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• 2014

Naive Bayes (NB, for shortly) learning is more popular, faster and effective supervised learning method to handle the labeled datasets especially in which have some noises, NB learning also has well performance. However, the conditional independent assumption of NB learning imposes some restriction on the property of handling data of real world. Some researchers proposed lots of methods to relax NB assumption, those methods also include attribute weighting, kernel density estimating. In this paper, we propose a novel approach called NB Based on Attribute Weighting in Kernel Density Estimation (NBAWKDE) to improve the NB learning classification ability via combining kernel density estimation and attribute weighting.

• Journal of Engineering Education Research
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• v.11 no.3
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• pp.12-23
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• 2008

The purpose of this study is to explore teaching and learning strategies through analyzing cases of foreign engineering education. With the analysis criteria composed of engineering education model, teaching and learning method, evaluation strategy, and technology supporting strategy, 10 foreign colleges of engineering in 5 countries were examined and analyzed. Teaching and learning strategies deduced from analysis state as follows. First of all, it need to develop engineering education models that reform should be made in systematic approach to teaching and learning, workplaces and laboratories, evaluation, technology support, etc. Secondly, the strategy for teaching and learning recommends supporting student directed learning, active learning participation, and collaboration learning by inductive learning strategies such as problem based learning, inquiry learning, project based learning, studio based learning, and blended learning. Thirdly, the evaluation strategy suggests that evaluation should be made to reflect students' learning and facilitate continuous learning based current learning results while it is necessary to build up a whole evaluation system. Finally, it is the educational technology approach for systematic engineering education that is required considering that many foreign colleges of engineering have reformed engineering education through technology supporting systems and are maximizing research and education in connection with other universities. This study is expected to contribute as preliminary data in developing further teaching and learning models and strategies for nurturing engineering students.

• Hwankyungkyoyuk
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• v.21 no.4
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• pp.94-110
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• 2008

Recently environmental problem becomes such a big issue all over the world that the necessity and importance of the environmental education in school has been simultaneously emphasized. While diverse methods for the environmental education have been researched, this paper, especially focused on a teaching-learning model called GBS (Goal-based scenario), aims to provide a new learner-centered approach for the environmental education. For this purpose, this paper first briefly presents two theoretical backgrounds of GBS (i.e., constructivism and Schank's dynamic memory theory), which is followed by specific and concrete strategies and methods of how to apply GBS in class for the teacher. GBS(Goal-Based Scenario) is a learner-centered model in which learners are presented with a reality-based scenario (or task or problem) and go through several stages of 'missions' to get to a final solution of the given scenario. GBS, while completely resonant with other constructivist learning models in terms of learner-centered approaches, is distinctive from others, when it supplies more specific, structured guides of learning, called 'missions', to the students throughout the whole learning process. In a words, GBS ought to be recognized as an unique learner-centered model compromising the contradictory concepts of 'learner control' and 'structure and specifics' in learning environments still without any damage of constructivist learning principles.

• Journal of the Korean School Mathematics Society
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• v.19 no.4
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• pp.417-439
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• 2016

The purpose of this study is to compare and analyze the effects of teaching based on learning-communities approach to mathematics for 7th grade students. Two classes of 7th graders at two different middle schools in same city were selected as the experimental group and the control group respectively. Students in the experimental group learned mathematics in a learning-community while their counterparts in the control group received the traditional whole-class instruction employed in most schools. The results are as follows. Firstly, there was no statistically significant difference in mathematics achievement between the experimental group and the control group. Secondly, between the two groups, there were statistically significant differences in students' attitudes toward mathematics. Participants enjoyed the learning process itself by asking difficult questions to one another, sharing their ideas freely and figuring problems out for themselves. During these active participations, they displayed more confidence and interest in learning mathematics.