Search Results - (( pattern classification parallel algorithm ) OR ( basic optimization sensor algorithm ))
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EMG motion pattern classification through design and optimization of neural network
Published 2012“…A back-propagation neural network with Levenberg-Marquardt training algorithm has been utilized for the classification of EMG signals. …”
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Proceeding Paper -
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EMG motion pattern classification through design and optimization of Neural Network
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Working Paper -
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Probabilistic ensemble fuzzy ARTMAP optimization using hierarchical parallel genetic algorithms
Published 2015“…To further augment the ARTMAP's pattern classification ability, multiple ARTMAPs were optimized via genetic algorithm and assembled into a classifier ensemble. …”
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On Clustering Algorithm Of Coverage Area Problems In Wireless Sensor Networks
Published 2024“…For the proliferation of wireless sensor network, in different environments, an escalation in the lifetime of wireless sensors is mandatory, because among the basic issues concerning WSN is a successful effort to document the coverage of the number of target fields, while maximizing the lifetime of this network. …”
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Development an accurate and stable range-free localization scheme for anisotropic wireless sensor networks
Published 2022“…This study developed an optimized variation of the DV-Hop localization algorithm for anisotropic wireless sensor networks. …”
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Robust tweets classification using arithmetic optimization with deep learning for sustainable urban living
Published 2024“…In this view, this research develops an arithmetic optimization algorithm with deep learning based tweets classification (AOADL-TC) approach for sustainable living. …”
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Efficient transmission based on genetic evolutionary algorithm
Published 2022“…In this paper, an energy-saving mechanism based on genetic algorithm in wireless sensor network (WSN) is proposed. …”
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Proceedings -
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Even-odd scheduling based energy efficient routing for wireless sensor network (WSN) / Muhammad Zafar Iqbal Khan
Published 2022“…Wireless Sensor Network (WSN) is basically composed of battery powered devices which have an obvious limitation of energy on sensors nodes, so it is the foremost motivation to develop a method to save energy of wireless sensor networks where networks are kept alive for a long time. …”
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Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining.
Published 2005“…The accuracy for rules and classification resulted from the TIP method are compared with other methods such as Standard Integer Programming (SIP) and Decision Related Integer Programming (DRIP) from Rough Set, Genetic Algorithm (GA), Johnson reducer, HoltelR method, Multiple Regression (MR), Neural Network (NN), Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5); all other classifiers that are mostly used in the classification tasks. …”
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A Survey Of Supervised Machine Learning In Wireless Sensor Network: A Power Management Perspective
Published 2013“…This survey paper is focused on the discussion of best optimal path routing algorithms in wireless sensor networks by using supervised machine learning approaches. …”
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Conference or Workshop Item -
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Fpga-Based Accelerator For The Identification Of Finger Vein Pattern Via K-Nearest Centroid Neighbors
Published 2016“…In recent years, finger vein recognition has emerged as a promising biometric technology due to the fact that each person in this world has unique finger vein pattern. Over the past few years, various finger vein recognition algorithms and techniques have been proposed by researchers and scholars. …”
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Modeling and control of a Pico-satellite attitude using Fuzzy Logic Controller
Published 2010“…The performances obtained show that the optimized APFLC is better than the non-optimize APFLC in terms of RMSE and the settling time.…”
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Electromygraphy (EMG) signal based hand gesture recognition using Artificial Neural Network (ANN)
Published 2011“…ANNs are particularly useful for complex pattern recognition and classification tasks. The capability of learning from examples, the ability to reproduce arbitrary non-linear functions of input, and the highly parallel and regular structure of ANNs make them especially suitable for pattern recognition tasks. …”
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Proceeding Paper -
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A hybrid prediction model for pipeline corrosion using Artificial Neural Network with Particle Swarm Optimization
Published 2018“…This project aims to develop a hybrid prediction Model which can target specific corrosion damage mechanisms. The basic ANN Model will be improved by integrating the Particle Swarm Optimization (PSO) algorithm to achieve a better and optimal performance. …”
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A hybrid prediction model for pipeline corrosion using Artificial Neural Network with Particle Swarm Optimization
Published 2018“…This project aims to develop a hybrid prediction Model which can target specific corrosion damage mechanisms. The basic ANN Model will be improved by integrating the Particle Swarm Optimization (PSO) algorithm to achieve a better and optimal performance. …”
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Hybrid harmony search-artificial intelligence models in credit scoring
Published 2019“…This is believed to be due to the different approaches of both classifiers in capturing data pattern for classification. In terms of computational time, compared to GS-tuned models and the respective HS hybrids, the proposed hybrid MHS-SVM and MHS-RF have reported time improvement of more than 50%, while the parallel computation have saved up approximately 80% of the computational time. …”
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A review of navigation systems (integration and algorithms)
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Performance study of the coexistence of Wireless Sensor Networks (WSN) and Wireless Local Area Networks (WLAN) / Muhammad Adib Haron … [et al.]
Published 2008“…To implement WSN integrated with cognitive radio techno 10gy, a new algorithm that provides access mechanism is needed to interactively working with sensor nodes hardware. …”
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Research Reports -
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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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