Search Results - (( modul active learning algorithm ) OR ( java application optimisation algorithm ))

Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

“…The system is essentially a three-part development, utilising Android, Java Servlets, and Arduino platforms to create an optimised and automated urban-gardening system. …”
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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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Pair-associate learning with modulated spike-time dependent plasticity by Yusoff, Nooraini, Grüning, André, Notley, Scott

“…We propose an associative learning model using reward modulated spike-time dependent plasticity in reinforcement learning paradigm. …”
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Spatio-temporal event association using reward-modulated spike-time-dependent plasticity by Yusoff, Nooraini, Ibrahim, Mohammed Fadhil

“…The results demonstrate that the algorithm can also learn temporal sequence detection.Learning has also been tested in face-voice association using real biometric data.The loose dependency between the model's anatomical properties and functionalities could offer a wide range of applications, especially in complex learning environments.…”
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Investigation of An Early Prediction System of Cardiac Arrest Using Machine Learning Techniques by Muhammad Afnan, Mohammad Nasir

“…An effective machine learning approach created from a separate examination of many machine learning algorithms in WEKA should be applied for the correct identification of heart disease. …”
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Stimulus-stimulus association via reinforcement learning in spiking neural network by Yusoff, Nooraini, Kabir Ahmad, Farzana

“…In this paper, we propose an algorithm that performs stimulus-stimulus association via reinforcement learning.In particular, we develop a recurrent network with dynamic properties of Izhikevich spiking neuron model and train the network to associate a stimulus pair using reward modulated spike-time dependent plasticity.The learning algorithm associates a prime stimulus, known as the predictor, with a second stimulus, known as the choice, comes after an inter-stimulus interval.The influence of the prime stimulus on the neural response after the onset of the later stimulus is then observed.A series of probe trials resemble the retrospective and prospective activities in human response processing…”
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Web-based expert system for material selection of natural fiber- reinforced polymer composites by Ahmed Ali, Basheer Ahmed

“…Finally, the developed expert system was deployed over the internet with central interactive interface from the server as a web-based application. As Java is platform independent and easy to be deployed in web based application and accessible through the World Wide Web (www), this expert system can be one stop application for materials selection.…”
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An ELM-based single input rule module and its application in power generation by Yaw C.T., Wong S.Y., Yap K.S.

“…Extreme Learning Machine (ELM) is widely known as an effective learning algorithm than the conventional learning methods from the point of learning speed as well as generalization. …”

Unified neural network controller of series active power filter for power quality problems mitigation by Ghazanfarpour, Behzad

“…First, Widrow-Hoff algorithm is examined and its constant learning rate is modified by adding an adaptive learning rule to change the learning rate value. …”
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A resource-aware content adaptation approach for e-learning environment / Mohd Faisal Ibrahim by Ibrahim, Mohd Faisal

“…It consists of a device database and two processing components: (1) device identification module and (2) device capabilities detection module. …”
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Towards Autonomous Farming -A Novel Scheme based on Learning to Prediction and Optimization for Smart Greenhouse Environment Control by Ullah, I., Fayaz, M., Aman, M., Kim, D.

“…Comparative analysis of the results shows that the proposed model maintains the desired indoor environment for maximizing plant production with reduced energy consumption i.e., it achieves 61.97 reduced energy consumption than baseline scheme, 11.73 better than optimization scheme without learning modules. Furthermore, proposed model achieves 67.96 and 12.56 reduction in cost when compared to the baseline scheme and optimization scheme without learning modules, respectively. …”
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Towards Autonomous Farming -A Novel Scheme based on Learning to Prediction and Optimization for Smart Greenhouse Environment Control by Ullah, I., Fayaz, M., Aman, M., Kim, D.

“…Comparative analysis of the results shows that the proposed model maintains the desired indoor environment for maximizing plant production with reduced energy consumption i.e., it achieves 61.97 reduced energy consumption than baseline scheme, 11.73 better than optimization scheme without learning modules. Furthermore, proposed model achieves 67.96 and 12.56 reduction in cost when compared to the baseline scheme and optimization scheme without learning modules, respectively. …”
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Toward Autonomous Farming - A Novel Scheme Based on Learning to Prediction and Optimization for Smart Greenhouse Environment Control by Ullah, I., Fayaz, M., Aman, M., Kim, D.

“…Comparative analysis of the results shows that the proposed model maintains the desired indoor environment for maximizing plant production with reduced energy consumption, i.e., it achieves 61.97 reduced energy consumption than the baseline scheme, 11.73 better than the optimization scheme without learning modules. Furthermore, the proposed model achieves 67.96 and 12.56 reduction in cost when compared to the baseline scheme and optimization scheme without learning modules, respectively. …”
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Revolutionizing Perimeter Intrusion Detection: A Machine Learning-Driven Approach with Curated Dataset Generation for Enhanced Security by Pitafi, S., Anwar, T., Dewa Made Widia, I., Yimwadsana, B.

“…After collecting the data from above mentioned sensors we applied machine learning algorithms DBSCAN to cluster the data points and K-NN classification to classify those clusters in one-dimensional data, but the results were not much satisfying. …”
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Output prediction of grid-connected photovoltaic system using artificial neural network / Nurul Khairaini Nor Adzman by Nor Adzman, Nurul Khairaini

“…The performance of ANN model was tested using different algorithm and activation function. The number of neuron has been varied from 1-20 while the momentum rate and the learning rate varies from 0.05 until 1. …”
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Output prediction of grid-connected photovoltaic system using artificial neural network: article / Nurul Khairaini Nor Adzman by Nor Adzman, Nurul Khairaini

“…The performance of ANN model was tested using different algorithm and activation function. The number of neuron has been varied from 1-20 while the momentum rate and the learning rate varies from 0.05 until 1. …”
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Cooperative spectrum sensing based on machine learning in cognitive radio vehicular network / Mohammad Asif Hossain by Mohammad Asif , Hossain

“…The selection would be made based on the hybrid machine learning (ML) algorithm. A fuzzy-based Naive Bayes algorithm has been used in this case. …”
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Query disambiguation approach using triple-filter by Agaie, Aliyu Isah

“…The proposed triple-filter disambiguation approach inculcates a learning mechanism that continues to automatically learn from user queries and continuously improves its performance capability. …”
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Modeling Teacher's Integrity Using Data Mining by Mohd Latifi, Abdul Ghani

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Incremental learning for large-scale stream data and its application to cybersecurity by Ali, Siti Hajar Aminah

“…To process large-scale data sequences, it is important to choose a suitable learning algorithm that is capable to learn in real time. …”
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