Search Results - (( sequence optimization svm algorithm ) OR ( sequence optimization sensor algorithm ))
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An optimal mesh algorithm for remote protein homology detection
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An energy efficient reinforcement learning based cooperative channel sensing for cognitive radio sensor networks
Published 2017“…Simulation results show convergence and adaptability of the algorithm to dynamic environment in achieving optimal solutions. …”
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A reinforcement learning-based energy-efficient spectrum-aware clustering algorithm for cognitive radio wireless sensor network
Published 2016“…Simulation results show convergence, learning and adaptability of the RL based algorithms to dynamic environment toward achieving the optimal solutions. …”
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Development Of Vehicle Tracking And Counting System From Traffic Surveillance Video
Published 2015“…Visual Background Extractor (ViBE) is used to extract the vehicles (foreground) from the traffic surveillance video sequences. Simple tracking and counting algorithm is used to track and count the detected vehicle. …”
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Development of self-learning algorithm for autonomous system utilizing reinforcement learning and unsupervised weightless neural network / Yusman Yusof
Published 2019“…In the simulation the robot is equipped with thirteen distance sensing sensors. From the simulation result, by using these sensors information the AUTOWiSARD algorithm can successfully differentiate and classify states without supervision, while the Q-learning algorithm is able to produce and optimized states-actions policy. …”
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Time series data intelligent clustering algorithm for landslide displacement prediction
Published 2018“…After embedding dimension processing, the time series of landslide displacement is predicted by SVM data mining model. Dynamic time warping calculation is based on the correlation of time series sequence and the components. …”
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A study on component-based technology for development of complex bioinformatics software
Published 2004“…SOM and K-Means are integrated as a clustering algorithm to produce a granular input, while SVM is then used as a classifier. …”
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Supervised deep learning algorithms for process fault detection and diagnosis under different temporal subsequence length of process data
Published 2025“…Current FDD technologies mostly rely on data-driven solutions by making full use of abundant process data collected by the state-of-the-art distributed process instruments and sensors. Deep learning algorithms were widely used among all the data-driven algorithms. …”
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Simulated Kalman Filter with modified measurement, substitution mutation and hamming distance calculation for solving traveling salesman problem
Published 2022“…There were also attempts to hybridize SKF with other famous algorithms such as Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and Sine Cosine Algorithm (SCA) to improve its performance. …”
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Global Algorithms for Nonlinear Discrete Optimization and Discrete-Valued Optimal Control Problems
Published 2009“…We then consider the task of determining near globally optimal solutions of discrete-valued optimal control problems. …”
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Ensemble learning using multi-objective optimisation for arabic handwritten words
Published 2021“…The features were tested with Support Vector Machine (SVM) and Extreme Learning Machine (ELM). This work improved due to the SI feature. …”
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An Evolutionary Stream Clustering Technique for Outlier Detection
Published 2020“…Later, this algorithm will be extended to optimize the model in detecting outlier on data streams. …”
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Direct Adaptive Predictive Control For Wastewater Treatment Plant
Published 2012“…This N4SID plays the role of the software sensor for on-line estimation of prediction matrices and control matrices of the bioprocess, joint together with model predictive control (MPC) in order to obtain the optimal control sequence. …”
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Clarity-optimized wavelet with autoencoder-ReliefF ranking for enhanced UHF PD signal feature extraction
Published 2025“…This article investigates advanced signal processing methodologies, with a focus on wavelet-based techniques, for the analysis of time-domain partial discharge (PD) signals captured using ultra-high frequency (UHF) sensors. The raw signals are systematically processed through a sequence of operations including bandpass filtering, wavelet-based denoising, DC offset removal, and pulse extraction. …”
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