Search Results - machine ((loading program) OR (((loading problem) OR (learning problems))))

Mixed integer goal programming model for flexible job shop scheduling problem (FJSSP) with load balancing / Shirley Sinatra Gran by Gran, Shirley Sinatra

“…FJSSP allows an operation to be processed by any machine out of a set of alternative machines. Thus, the objectives of this study are to analyze the production schedules and operations of the machines in FJSSP, to construct a load balancing constraint function, to formulate a Mixed Integer Goal Programming (MIGP) model to solve FJSSP with load balancing; and to propose an optimal production job shop scheduling strategies based on the solution model. …”
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Daily maximum load forecasting of consecutive national holidays using OSELM-based multi-agents system with weighted average strategy by Yap K.S., Yap H.J.

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Comparison of Electricity Load Prediction Errors Between Long Short-Term Memory Architecture and Artificial Neural Network on Smart Meter Consumer by Salleh N.S.M., Suliman A., J�rgensen B.N.

“…Brain; Errors; Forecasting; Learning algorithms; Mean square error; Memory architecture; Network architecture; Smart meters; Time series; Demand-side; Electricity load; Error values; Load predictions; Machine learning algorithms; Mean absolute error; Mean squared error; Prediction errors; Regression problem; Times series; Long short-term memory…”

Multi-step time series prediction using recurrent kernel online sequential extreme learning machine / Liu Zongying by Liu , Zongying

“…Besides, concept drift problem in on-line learning model is solved by Drift Detection Machine (DDM). …”
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Sediment load prediction in Johor river: deep learning versus machine learning models by Latif S.D., Chong K.L., Ahmed A.N., Huang Y.F., Sherif M., El-Shafie A.

“…The statistical results showed that, despite their ability (deep learning and machine learning) to provide sediment predictions based on historical input datasets, machine learning, such as ANN, might be more prone to overfitting or being trapped in a local optimum than deep learning, evidenced by the worse in all metrics score. …”

Static and dynamic impacts of emergency generator in distribution systems by Nurashikin, Mamat

“…By placing an emergency generator, it has no problem in order to distribute energy to the load either in normal condition or abnormal condition.…”
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Predicting uniaxial compressive strength using Support Vector Machine algorithm by Zakaria, Hafedz, Abdullah, Rini Asnida, Ismail, Amelia Ritahani, Amin, Mohd For

“…It is worth mentioning, that the program module that has been set up could be used repeatedly for other correlation problems.…”
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Network bandwidth utilization based on collaborative web caching using machine learning algorithms in peer-to-peer systems for media web objects by Mohammed, Waheed Yasin

“…In this work, intelligent collaborative web caching approaches based on C4.5 decision tree and Naïve Bayes (NB) supervised machine learning algorithms are presented. The proposed approaches take the advantage of structured peer-to-peer systems where peers' caches contents are shared in order to enhance the performance of the web caching policy. …”
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Distributed learning based energy-efficient operations in small cell networks by Mughees, Amna

“…The thesis proposed a solution that employs unique characteristics of machine learning and game-theoretic framework to enable a model-free and energy-efficient small cell network. …”
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Application of artificial neural network for voltage stability monitoring / Valerian Shem by Shem, Valerian

“…This project is about monitoring the voltage stability of a system bus. Voltage stability problem has been one of the major concerns for electric utilities as a result of system heavy loading and needs to be solved. …”
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A comparison of machine learning models for suspended sediment load classification by AlDahoul N., Ahmed A.N., Allawi M.F., Sherif M., Sefelnasr A., Chau K.-W., El-Shafie A.

“…To this end, reliable and applicable models are required to compute and classify the SSL in rivers. The application of machine learning models has become common to solve complex problems such as SSL modeling. …”

A New Approach of Adaptive Network-based Fuzzy Inference System Modeling in Laser Processing-A Graphical User Interface Based by Sivarao, Subramonian

“…Problem statement: The power of Artificial Intelligent (AI) becomes more authoritative when the system is programmed to cater the need of complex applications. …”
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Application of Extreme Learning Machine in Predicting Short- Term Wind Speed by Chen C.P., Tiong S.K., Koh S.P., Nasser A.A., Abbas D., Chooi F.Y.

Mixed pixel classification on hyperspectral image using imbalanced learning and hyperparameter tuning methods by Purwadi, Abu, Nur Azman, Mohd, Othman, Kusuma, Bagus Adhi

“…Hyperspectral has a huge phenomenon that makes computations heavy compared to other types of images because this image is 3D. The problem faced in hyperspectral image classification is the high computational load, especially if the spatial resolution of the image also has mixed pixel problems. …”
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Green machine learning approach for QoS improvement in cellular communications by Saeed, Mamoon M., Saeed, Rashid A., Azim, Mohammad Abdul, Ali, Elmustafa Sayed, Mokhtar, Rania A., Khalifa, Othman Omran

“…Artificial intelligent algorithms such as machine learning (ML) enable to detection of the dynamics in cellular networks by analyzing the complex cellular network processes and evaluating the spectrum and links qualities. …”
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Inspection - Hole shape optimization of a lower wing panel based on geomatric boundary shapes / Wahyu Kuntjoro and Zon S. Hernedhi by Kuntjoro, Wahyu, Hernedhi, Zon S.

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Development of capability-based virtual cellular manufacturing systems in dual-resource constrained settings over semi-distributed layouts by Hamedi, Maryam

“…In addition, by considering the developed system in DRC settings, in all test problems, the dissimilarity of parts assigned to a cell and load unbalances among cells decreased in average 12.41% and 41.48% respectively compared to the same system without DRC settings.…”
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Bayesian model averaging of load demand forecasts from neural network models by Hassan, S., Khosravi, A., Jaafar, J.

“…Neural network ensembles are designed to provide solutions to particular problems. Many researchers and academicians have adopted this NN ensemble technique, especially in machine learning, and has been applied in various fields of engineering, medicine and information technology. …”
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Bayesian model averaging of load demand forecasts from neural network models by Hassan, S., Khosravi, A., Jaafar, J.

“…Neural network ensembles are designed to provide solutions to particular problems. Many researchers and academicians have adopted this NN ensemble technique, especially in machine learning, and has been applied in various fields of engineering, medicine and information technology. …”
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Bayesian model averaging of load demand forecasts from neural network models by Hassan, S., Khosravi, A., Jaafar, J.

“…Neural network ensembles are designed to provide solutions to particular problems. Many researchers and academicians have adopted this NN ensemble technique, especially in machine learning, and has been applied in various fields of engineering, medicine and information technology. …”
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