Search Results - adaptive k different selection algorithm

Adaptive filtering of EEG/ERP through bounded range artificial Bee Colony (BR-ABC) algorithm by Ahirwal, M.K., Kumar, A., Singh, G.K.

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The non-uniform communication performance of adaptive routing for hierarchical interconnection network for 3D VLSI by Miura, Yasuyuki, Watanabe, Shigeyoshi, Rahman, M.M. Hafizur

“…Many adaptive routing algorithms for k-ary n-cube networks have already been proposed. …”
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Developing a hybrid model for accurate short-term water demand prediction under extreme weather conditions: a case study in Melbourne, Australia by Zubaidi S.L., Kumar P., Al-Bugharbee H., Ahmed A.N., Ridha H.M., Mo K.H., El-Shafie A.

Survey on Clustered Routing Protocols Adaptivity for Fire Incidents: Architecture Challenges, Data Losing, and Recommended Solutions by Abbas O.K., Abdullah F., Radzi N.A.M., Salman A.D., Abdulkadir S.J.

Improved K-means clustering and adaptive distance threshold for energy reduction in WSN-IoTs by Azamuddin, Ab Rahman, Hamim, Sakib Iqram

“…This study introduces an enhanced energy aware clustering approach that combines an improved K-Means algorithm with an adaptive distance threshold to optimize relay node selection and cluster formation. …”
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A survey on signal processing methods in fiber optic sensor for oxidized carbon steel by Jaafar, N.S.M., Aziz, I.A., Jaafar, J., Mahmood, A.K., Gilal, A.R.

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A survey on signal processing methods in fiber optic sensor for oxidized carbon steel by Jaafar, N.S.M., Aziz, I.A., Jaafar, J., Mahmood, A.K., Gilal, A.R.

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A guided hybrid k-means and genetic algorithm models for children handwriting legibility performance assessment / Norzehan Sakamat by Sakamat, Norzehan

“…K-Means algorithm a popular efficient clustering techniques and genetic algorithm a widely used evolutionary algorithm and known for its adaptive nature were combined to determine the level of handwriting legibility for each child. …”
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Wavelet-based compression of medical images: Filter-bank selection and evaluation by Saffor, A., Bin Ramli, A.R., Ng, K.H.

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Modeling flood occurences using soft computing technique in southern strip of Caspian Sea Watershed by Borujeni, Sattar Chavoshi

“…Three algorithms of Linear (PURELIN), hyperbolic tangent sigmoid (TANSIG) and logistic sigmoid (LOGSIG) activation functions were selected for output layer. …”
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Affect classification using genetic-optimized ensembles of fuzzy ARTMAPs by Liew, W.S., Seera, M., Loo, C.K., Lim, E.

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Adaptive Similarity Component Analysis in Nonparametric Dynamic Environment by Sojodishijani, Omid

“…Consequently, the proposed adaptive feature extraction technique and neighborhood-based classifier family are tightly integrated in an adaptive K-nearest neighbor classifier.…”
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Application of augmented bat algorithm with artificial neural network in forecasting river inflow in Malaysia by Wee W.J., Chong K.L., Ahmed A.N., Malek M.B.A., Huang Y.F., Sherif M., Elshafie A.

Research on the construction of an efficient and lightweight online detection method for tiny surface defects through model compression and knowledge distillation by Chen, Qipeng, Xiong, Qiaoqiao, Huang, Haisong, Tang, Saihong, Liu, Zhenghong

“…Firstly, data augmentation is employed in the preprocessing stage to increase the diversity of training samples, thereby improving the model’s robustness and generalization capability. The K-means++ clustering algorithm generates candidate bounding boxes, adapting to defects of different sizes and selecting finer features earlier. …”
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Development of an artificial neural network algorithm for predicting the cutting force in end milling of Inconel 718 alloy by Hossain, Ishtiaq, Amin, A. K. M. Nurul, Patwari, Muhammed Anayet Ullah

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Solubility enhancement of decitabine as anticancer drug via green chemistry solvent: Novel computational prediction and optimization by Nader Ibrahim Namazi, Sameer Alshehri, Rawan Bafail, Bader Huwaimel, Amal M. Alsubaiyel, Ali H. Alamri, Ahmed D. Alatawi, Hossam Kotb, Mohd Sani Sarjadi, Md. Lutfor Rahman, Mohammed A.S. Abourehab

“…One of the two input features is P (bar) and the other is T (k). ADA-DT (Adaboost Algorithm Decision Tree), ADA-LR (Adaboost Algorithm-Linear Regresion), and ADA-GRNN (Generative Regression Neural Network) models showed MAE of 6.54 ˣ 10ˉ⁵, 4.66 10 ˉ⁵, and 8.35 10 ˉ⁵, respectively. …”
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Investigating the influence of meteorological parameters on the accuracy of sea-level prediction models in Sabah, Malaysia by Muslim T.O., Ahmed A.N., Malek M.A., Afan H.A., Ibrahim R.K., El-Shafie A., Sapitang M., Sherif M., Sefelnasr A., El-Shafie A.