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Year: 2023, Volume: 23, Number: 2
... mode epileptologist that observes the EEG continuously. decomposition algorithm, before creating a prediction model However, visual examination is time-consuming and can based on machine learning methods. ... been proposed to improve the performance segments to produce a representative dataset. SVM classification models and two CNN architectures are proposed of automatic prediction from EEG signals. This ...
Year: 2011, Volume: 11, Number: 3
... in Electrical and Computer Engineering Volume 11, Number 3, 2011 An Effective Distributed Model for Power System Transient Stability Analysis Balasingh Moses MUTHU1, Ramachandran VEILUMUTHU2, Lakshmi ... stated that new software technologies will be incorporated obtained using traditional client-server and Java RMI models. as they become widely accepted, such as the eXtensible Index Terms — ...
Year: 2017, Volume: 17, Number: 4
... Computer Engineering Volume 17, Number 4, 2017 Load Balancing of Large Distribution Network Model Calculations Lajos MARTINOVIC1,2, Darko CAPKO1, Aleksandar ERDELJAN1 1 Faculty of ... distribution companies on this investigation, recommendations regarding the choice of are managing large data models the scalability of the DMS load index are made. Short description of used ...
Year: 2024, Volume: 24, Number: 2
... against DoS and MitM attacks. vulnerabilities in the network infrastructure or flood The proposed model is then applied on widely used datasets communication channels with several fake data packets ... EVCS) researchers have proposed a variety of intrusion detection against DoS and MitM. models, integrating the latest advancements in machine learning. Although these models have shown ...
465. Generation of Visual Patterns from BoVW for Image Retrieval using modified Similarity Score Fusion
Year: 2020, Volume: 20, Number: 2
... efficient method to represent images for Content Based Some of the refinements done to BoVW model to Image Retrieval (CBIR). Despite their significant usage, the incorporate ... along with Flickr 1M dataset. The proposed method is compared with other BoVW based models called, Iterative Keypoint Selection (IKS) is performed that and proved that ...
Year: 2018, Volume: 18, Number: 4
... train features, or specific expert knowledge. The main features of and evaluate our model. To solve the overfitting problem caused by training the network on a limited ... performance than other existing visual recognition method based on video processing. A chromatically-based CNN models for the recognition of fire and smoke in static decision rule and diffusion-based dynamic ...
Year: 2010, Volume: 10, Issue: 4
... detuned NFO for shaft-sensor, which decreases the drives robustness and scheme. The dynamic model simulation results, around increases its price and production complexity. Above- different ... the detuned NFO shaft-sensorless applications. The attractive control solution for these operation. Both models are developed in MATLAB, applications is shaft-sensorless, providing it is robust to ...
Year: 2016, Volume: 16, Number: 1
... There are presented some useful qualitative and quantitative return paths. Nahman introduced a mathematical model and results obtained through a complex dedicated developed equivalent schemes for zero-sequence components for ... , soil resistivity etc. In this paper, we comparatively analyze, using different models, the ground fault evaluation of the significant parts of the ground fault current ...
Year: 2010, Volume: 10, Issue: 2
... the control system Mbps) assures a distributed control of the all components. The modeling and the control of different operating regimes are eZdsp 2808 permit the implementation of ... Electrical and Computer Engineering Volume 10, Number 2, 2010 Hybrid Electric Vehicle Experimental Model with CAN Network Real Time Control Gheorghe LIVINÅ¢1, Vasile HORGA2, Daniel STICEA3 ...
Year: 2022, Volume: 22, Number: 4
... qubit is zero, the other is also zero, SVM is a machine learning model developed for or if the first qubit is one, the other is also ... ) used in SVM are the hyper- directly affect the performance of machine learning models is parameters and affects the performance value of the method the correct selection of ...
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