Search Results - (( parallel distribution factor algorithm ) OR ( variable extractions sensor algorithm ))
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Parallel power load abnormalities detection using fast density peak clustering with a hybrid canopy-K-means algorithm
Published 2025“…Parallel power loads anomalies are processed by a fast-density peak clustering technique that capitalizes on the hybrid strengths of Canopy and K-means algorithms all within Apache Mahout's distributed machine-learning environment. …”
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Investigation on the dynamic of computation of semi autonomous evolutionary computation for syntactic optimization of a set of programming codes
Published 2007“…Genetic Algorithm as one of the Evolutionary Computation method improve the execution of parallel programming codes by optimizing the number of processors and the distribution of data. …”
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Research Report -
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Improved black-winged kite algorithm and finite element analysis for robot parallel gripper design
Published 2024“…The Good Point Set (GPS), nonlinear convergence factor, and adaptive t-distribution method improve BKA, which enhances exploration and exploitation performance, convergence speed, and solution quality. …”
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Design and analysis of management platform based on financial big data
Published 2023“…In addition, a financial data management platform based on distributed Hadoop architecture is designed, which combines MapReduce framework with the fuzzy clustering algorithm and the local outlier factor (LOF) algorithm, and uses MapReduce to operate in parallel with the two algorithms, thus improving the performance of the algorithm and the accuracy of the algorithm, and helping to improve the operational efficiency of enterprise financial data processing. …”
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Multi-sensor fusion and deep learning framework for automatic human activity detection and health monitoring using motion sensor data / Henry Friday Nweke
Published 2019“…This is further worsen by the use of single sensors modality and machine learning algorithms. …”
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Simultaneous measurement of multiple soil properties through proximal sensor data fusion: a case study
Published 2019“…In this field, it was not possible to predict extractable P and K using all tested sensor combinations or algorithms. …”
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Google the earth: what's next?
Published 2010“…Technologically, the challenge is to design sensors that exhibit high sensitivity to the parameters of interest while minimizing instrument noise and impacts of other natural variables. …”
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Inaugural Lecture -
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Wavelet based fault tolerant control of induction motor / Khalaf Salloum Gaeid
Published 2012“…The fault detection algorithm identifies the time and location of each fault. …”
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Conjunctions in biological neural architectures for visual pose estimation / Tom´As Maul
Published 2006“…A highly parallel approach based on simple inter-map conjunctions, or correspondence distributions, is chosen and tested on synthetic and real patterns. …”
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New global maximum power point tracking and modular voltage equalizer topology for partially shaded photovoltaic system / Immad Shams
Published 2022“…Despite the effective proposed GMPPT algorithm, the PSCs reduce the maximum power extraction capability of the PV system heavily due to the activation of bypass diodes. …”
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Oil palm female inflorescences anthesis stages identification using selected emissivities through thermal imaging and Machine Learning
Published 2022“…This research studies different Machine Learning (ML) classification and ensemble techniques for the assessment of the four pollination stages consist of pre-anthesis I, pre-anthesis II, pre-anthesis III, and anthesis using thermal imaging. Different ML algorithms such as Random Forest (RF), k Nearest Neighbor (kNN), Support Vector Machine (SVM), Artificial Neural Network (ANN) as well as an ensemble method are used on data extracted from thermal images collected during infield oil palms pollination stages monitoring. …”
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Evaluation of multiple In Situ and remote sensing system for early detection of Ganoderma boninense infected oil palm
Published 2018“…In the first phase, spectral features and structural features were extracted for feature extraction. In the spectral features part, the descriptors include red (R), green (G), blue (B), near-infrared (NIR) digital numbers and a vegetation index (VI) was considered. …”
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