Search Results - probable distribution ((center algorithm) OR (sensor algorithm))
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Energy Efficient LEACH (EE-LEACH) Routing Algorithm for Wireless Sensor Networks
Published 2019“…Therefore, this research work proposes an energy-efficient LEACH (EE-LEACH) algorithm to elect CHs based on residual energy, RSSI, and random probability to distribute the load evenly among the CHs. …”
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Energy efficient cluster head distribution in wireless sensor networks
Published 2013“…For network clustering, the distribution of CH selection directly influences the networks lifetime. …”
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Class binarization with self-adaptive algorithm to improve human activity recognition
Published 2018“…In comparison, WISDM utilizes an accelerometer sensor embedded in Android smartphone. Meanwhile, PAMAP2 utilizes an accelerometer sensor equipped with three Inertial Measurement Unit (IMU) devices attached to three different placements. …”
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Enhanced replication strategy with balanced quorum technique and data center selection method in cloud environment
Published 2022“…In order to validate the competence of these proposed algorithms, results were compared with another similar research work known as DPRS algorithm. …”
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EFFICIENT DYNAMIC ADDRESSING BASED ROUTING FOR UNDERWATER WIRELESS SENSOR NETWORKS
Published 2011“…Besides long propagation delays and high error probability, continuous node movement also makes it difficult to manage the routing information during the process of data forwarding. …”
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Robust Kernel Density Function Estimation
Published 2010“…The research also develops two new approaches for outlier and potential outlier detection in unimodal and multimodal distributions. The distance of observations from the center of data set is incorporated in the formulation of the first outlier detection method in unimodal distribution. …”
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Prediction of rice biomass using machine learning algorithms
Published 2022“…The Q-TESI, C-TESI, and L-TESI overcame the LN-TESI in retaining the features’ original probability distribution, minimising the augmentation loss, reducing the VIF, increasing the rs, and decreasing the DNN under- and overfitting. …”
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Population genetic structure of Malayan Tapir (Tapirus indicus Desmarest) in Peninsular Malaysia
Published 2019“…Using K-means clustering algorithm, five clusters were inferred among the wild samples (N = 57), which showed a complex population structure probably comprising multiple continuous populations that also experiencing considerably restricted gene flow due to isolation by geographical barriers especially mountain ranges. …”
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