Search Results - (( loading classification system algorithm ) OR ( using optimization _ algorithm ))

An optimized variant of machine learning algorithm for datadriven electrical energy efficiency management (D2EEM) by Shamim, Akhtar

“…This study recommends a selection trade-off as the function of prediction efficiency and efficacy of the algorithm. Particularly, the proposed optimized Bagged Trees are the most effective algorithm for energy demand prediction applications, and the proposed optimized Medium Trees are the most efficient algorithm for real-time systems. …”
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An application of a novel technique for assessing the operating performance of existing cooling systems on a university campus by Abdalla, E.A.H., Nallagownden, P., Nor, N.B.M., Romlie, M.F., Hassan, S.M.

“…Two contributions in this work are: (1) the prediction of a model by using Adaptive Neuro-Fuzzy Inference System (ANFIS)-based Fuzzy Clustering Subtractive (FCS), and (2) the classification and optimization of the predicted models by using an Accelerated Particle Swarm Optimization (APSO) algorithm. …”
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Fault classification in smart distribution network using support vector machine by Chuan O.W., Ab Aziz N.F., Yasin Z.M., Salim N.A., Wahab N.A.

“…In this paper, a machine-learning algorithm known as Support Vector Machine (SVM) for fault type classification in distribution system has been developed. …”

Service based load balance mechanism using software-defined networks / Ahmed Abdelaziz Abdelltif Osman by Ahmed Abdelaziz, Abdelltif Osman

“…The SDN controller is leveraged to provide online flow classification. The proposed mechanism is evaluated using benchmarking experiments and validated using a statisticalmodel. …”
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Electricity Anomaly Point Detection using Unsupervised Technique Based on Electricity Load Prediction Derived from Long Short-Term Memory by Salleh N.S.M., Saripuddin M., Suliman A., Jorgensen B.N.

Development of electromyography-controlled 3D printed robot hand and supervised machine learning for signal classification by Abdul Wahit, Mohamad Aizat

“…The current study of the hand posture classification requires a higher number of EMG sensor used to achieve an accurate classification performance that leads the system to be complicated. …”
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Intuitive content management system via manipulation and duplication with if-else rules classification by Chan, Chung Hoong

“…As a result, ICMS can transform dynamic websites into static websites with faster load speed using manipulation method mixed with data mining classification prediction and Boyer-Moore Horspool algorithm which can be classified, edited, adjustable and searched more precisely. …”
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Review of Plug-Based Load Energy Management Systems (PLEMS) for energy and comfort management of buildings by Amin, F.B.M., Mahmood, A.K.

“…This paper reviews the goals of PLEMS, along with related definitions, classification of PLEMS equipments and PLEMS control systems. …”
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Dynamic load balancing policy in grid computing with multi-agent system integration by Yahaya, Bakri, Latip, Rohaya, Othman, Mohamed, Abdullah, Azizol

“…The unique capabilities offered by multi-agent systems can be integrated or combined with the structure of dynamic load balancing to produce a better strategy to produce a better dynamic load balancing algorithm with multi-agent systems.…”
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Power system security assessment using artificial neural network: article / Mohd Fathi Zakaria by Zakaria, Mohd Fathi

“…Extensive testing and training of the proposed ANN based apprnach indicates its viability for power system steady state stability classification assessment.…”
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Development of Phasor Measurement Unit Based Fault Detection and Faulty Line Classification in Electrical Power System by Muhammad Qasim, Khan

“…This study first focuses on modifying the PMU model for standard test systems in a MATLAB Simulink environment. This model implements Non-Recursive Phasor Estimation (NRPE) algorithm. …”
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Dynamic load balancing policy with communication and computation elements in grid computing with multi-agent system integration by Yahaya, Bakri, Latip, Rohaya, Othman, Mohamed, Abdullah, Azizol

“…The unique capabilities offered by multi-agent systems can be integrated or combined with the structure of dynamic load balancing to produce a better strategy and better load balancing algorithm. …”
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Assessment of cognitive load using multimedia learning and resting states with deep learning perspective by Qayyum, A., Faye, I., Malik, A.S., Mazher, M.

“…The results shows that alpha brain wave produced consistent behavior using for all cognition tasks based on pre-trained CNN models for classification and cognitive load assessment. © 2018 IEEE…”
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An optimal under frequency load shedding scheme for islanded distribution network / Amalina Izzati Md Isa by Md Isa, Amalina Izzati

“…Two new algorithms i.e., Load Classification based Fuzzy Logic (LCFL) and Binary Evolutionary Programming (BEP) are introduced in the module. …”
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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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Load balancing and server consolidation in cloud computing environments: a meta-study by Fadhil, Mohammed Alaa, Othman, Mohamed

“…This meta-study, reviews the literature on load balancing and server consolidation and presents a ready reference taxonomy on the most efficient algorithms that achieve load balancing and server consolidation. …”
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Teleworking monitoring system using NILM and K-NN algorithms: a strategy for sustainable smart cities by Yang, Chuan Choong, Noh, Adriana, Ibrahim, Siti Noorjannah, Asnawi, Ani Liza, Mohamed Azmin, Nor Fadhillah

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Development of vehicle detection and counting system for traffic analysis using computer vision by Saw, Kenny Wei Wen

“…Initially, the image and video dataset of road traffic is gathered from various online sources and undergoes categorization before loaded into the system for model training. Then two model training process begins, one is for vehicle model detection to learn the feature of a vehicle and the image background, while another training is for binary weather classification for rainy and sunny conditions. …”
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Spectral Estimation And Supervised Classification Technique For Real Time Electromyography Pattern Recognition by Burhan, Nuradebah

“…Electromyography (EMG) signal is a biomedical signal which measures physical activity of human muscle.It has been acknowledged to be widely used in rehabilitation or recovery application system assisting physiotherapist to monitor a patient’s physical strength,function,motion and overall well-being by addressing the underlying physical issues.In application system associated with rehabilitation,a signal processing and classification techniques are implemented to classify EMG signal obtained.For real time application in the rehabilitation, the classification is crucial issue.The success of the signal classification depends on the selection of the features that represent a raw EMG signal in the signal processing.Therefore,a robust and resilient denoising method and spectral estimation technique have been acknowledged as necessary to distinguish and detect the EMG pattern.The present study was undertaken to determine the characteristic of EMG features using denoising method and spectral estimation technique for assessing the EMG pattern based on a supervised classification algorithm.In the study,the combination of time-frequency domain (TFD) and time domain (TD) were identified as the preferred denoising method and spectral estimation techniques.In the first part of study, the recorded EMG signal filtered the contaminated noise by using wavelet transform (WT) approach which implemented discrete wavelet transform (DWT) method of the wavelet-denoising signal. …”
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Ganoderma boninense classification based on near-infrared spectral data using machine learning techniques by Mas Ira Syafila, Mohd Hilmi Tan, Mohd Faizal, Jamlos, Ahmad Fairuz, Omar, Kamarulzaman, Kamarudin, Mohd Aminudin, Jamlos

“…The result represents a significant step in the development of a nondestructive and in-situ detection system which validated by both chemometric and machine learning (ML) classification techniques…”
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