Titlebook: Biologically Inspired Techniques in Many-Criteria Decision Making; International Confer Satchidananda Dehuri,Bhabani Shankar Prasad Mishra

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書目名稱Biologically Inspired Techniques in Many-Criteria Decision Making影響因子(影響力)




書目名稱Biologically Inspired Techniques in Many-Criteria Decision Making影響因子(影響力)學科排名




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書目名稱Biologically Inspired Techniques in Many-Criteria Decision Making網(wǎng)絡(luò)公開度學科排名




書目名稱Biologically Inspired Techniques in Many-Criteria Decision Making被引頻次




書目名稱Biologically Inspired Techniques in Many-Criteria Decision Making被引頻次學科排名




書目名稱Biologically Inspired Techniques in Many-Criteria Decision Making年度引用




書目名稱Biologically Inspired Techniques in Many-Criteria Decision Making年度引用學科排名




書目名稱Biologically Inspired Techniques in Many-Criteria Decision Making讀者反饋




書目名稱Biologically Inspired Techniques in Many-Criteria Decision Making讀者反饋學科排名

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Biologically Inspired Techniques in Many-Criteria Decision MakingInternational Confer

混合

Conference proceedings 2020book introduces readers to state-of-the-art developments in biologically inspired techniques and their applications, with a major emphasis on the MCDM process. To do so, it presents a wide range of contributions on e.g. BIAs, MCDM, nature-inspired algorithms, multi-criteria optimization, machine lea

anthesis

THROB

Epidemiology of Breast Cancer (BC) and Its Early Identification via Evolving Machine Learning Classised for Machine Learning may increase our understanding about breast cancer prediction and progression. It is important to consider these approaches in daily clinical practice. Neural networks are now a day’s very key and popular field in computational biology, chiefly in the area of radiology, onco

curettage

Ensemble Classification Approach for Cancer Prognosis and Predictionbor (KNN), Multi-Layer Perceptron (MLP) and Decision Tree (DT). Training of classifier is implemented based on k-fold cross validation techniques. The predicted accuracy of the proposed model has been compared with recent fusion methods such as Majority Voting, Distribution Summation and Dempster–Sh

CRUE

https://doi.org/10.1007/978-3-663-14606-3for analysis. Earlier researches are made on the same concept but the present goal of the study is to develop such a model that is scalable, fault-tolerant and has a lower latency. The model rests on a distributed computing architecture called the Lambda Architecture which helps in attaining the goa

habitat

INTER

https://doi.org/10.1007/978-1-4612-1822-7bor (KNN), Multi-Layer Perceptron (MLP) and Decision Tree (DT). Training of classifier is implemented based on k-fold cross validation techniques. The predicted accuracy of the proposed model has been compared with recent fusion methods such as Majority Voting, Distribution Summation and Dempster–Sh

平項山

Satchidananda Dehuri,Bhabani Shankar Prasad MishraAddresses recent challenges in optimization methods and techniques associated with the exponential growth in data production.Gathers the Proceedings of the International Conference on Biologically Ins