Adaptive k-means clustering algorithm based on grid and domain centroid weight by Wei CAI, Guangyu SUN, Fei YANG, Bo YAO, Chuanxian LI

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AWI-BS: An adaptive weight incentive for blockchain sharding by Zuobin Ying, Laican Song, Deng Chen, Wusong Lan, Ximeng Liu

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Florivory Modulates the Seed Number-Seed Weight Relationship in Halenia elliptica (Gentianaceae) by Linlin Wang, Lihua Meng, Jian Luo

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Evaluation of technological and bakery properties of winter rye in dependence on predecessor in adaptive-landscape crop farming by L. M. Kozlova, A. V. Denisova, I. V. Lyskova, S. N. Zhuk

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Research on the A* Algorithm Based on Adaptive Weights and Heuristic Reward Values by Xizheng Wang, Gang Li, Zijian Bian

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A Decomposition-Based Multiobjective Evolutionary Algorithm with Adaptive Weight Adjustment by Cai Dai, Xiujuan Lei

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A Study of Racket Weight Adaptation in Advanced and Beginner Badminton Players by Zhengye Pan, Lushuai Liu, Xingman Li, Yunchao Ma

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The choice of the number of processing channels in the radiomonitoring equipment on the basis of adaptive multibeam antenna arrays by S. V. Kozlov

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TEXT CLASSIFICATION USING ADAPTIVE BOOSTING ALGORITHM WITH OPTIMIZATION OF PARAMETERS TUNING ON CABLE NEWS NETWORK (CNN) ARTICLES by Dewi Retno Sari Saputro, Krisna Sidiq, Harun Al Rasyid, Sutanto Sutanto

“…One of the algorithms for classification is adaptive boosting (AdaBoost). The AdaBoost algorithm performs classification by building several weighted decision trees (stumps), then the class determination is based on the number of stumps with the highest weight. …”
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Volume-weighted Bellman error method for adaptive meshing in approximate dynamic programming by Leopoldo Armesto, Antonio Sala

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An Adaptive Weight Physics-Informed Neural Network for Vortex-Induced Vibration Problems by Ping Zhu, Zhonglin Liu, Ziqing Xu, Junxue Lv

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Inheritance of the main elements of productivity and adaptability parameters in diallelic hybrids of spring soft wheat by L. V. Volkova, O. S. Amunova

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Adaptability parameters of spring barley according to the trait ‘1000-grain weight’ in the Rostov region by R. N. Bragin

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Adaptive Cooperative Quality Weight Spectrum Sensing for Mitigating Byzantine Attacks in Cognitive Radio by Kanabadee Srisomboon, Luepol Pipanmekaporn, Akara Prayote, Wilaiporn Lee

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Practical Concerns Associated with Single-Number Ratings in Measuring Sound Transmission Loss Properties of Partition Panels by Naveen GARG, Anil KUMAR, Sagar MAJI

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Ensemble Network Graph-Based Classification for Botnet Detection Using Adaptive Weighting and Feature Extraction by Muhammad Aidiel Rachman Putra, Tohari Ahmad, Dandy Pramana Hostiadi, Royyana Muslim Ijtihadie

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Greenhouse and open-field Tomato farming. A comparison through yield and growth parameters investigated in Dar es Salaam by James Chacha, Murugan Thirumalai, Obeid Idawa, Josephine Chilwea, Concheska Kilamba, Bertha Hussy, Haania Ishaq, Kalidoss Rajendran

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GEOGRAPHICALLY WEIGHTED GENERALIZED POISSON REGRESSION AND GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION MODELING ON PROPERTY CRIME CASES IN CENTRAL JAVA by Prizka Rismawati Arum, Rahmad Putra Gautama, M. Al Haris

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Black-Start Scheme Evaluation Based on Improved ELECTRE Method and Comprehensive Weight by Yinxing XIANG, Jikeng LIN

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Variability of the constituent elements of the productivity of maize hybrids of different ripeness groups under irrigation conditions by Т. Ю. Марченко, Р. А. Вожегова, Ю. О. Лавриненко, Т. М. Хоменко

“…A close correlation of the mass of grains per cob was observed with the following signs: grain productivity, length of the core, length of the cob with grains, diameter of the cob, weight of 1000 seeds, grain yield. Conclusions. Under irrigation conditions the genotypic variability of the constituent elements of the maize hybrids productivity was revealed, which allows predicting the conduct of effective screening on specific characteristics according to ripeness groups.The revealed correlation dependences between quantitative signs of the cob structure and grain yield will allow to make a preliminary assessment of potential yield by factorial charac­teristics adapted to the conditions of irrigation of corn hybrids with FAO 180–600.…”
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