Titlebook: Machine Learning and Big Data Analytics(Proceedings of International Conference on Machine Learning ; Rajiv Misra,Rudrapatna K. Shyamasund

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書目名稱Machine Learning and Big Data Analytics(Proceedings of International Conference on Machine Learning 影響因子(影響力)




書目名稱Machine Learning and Big Data Analytics(Proceedings of International Conference on Machine Learning 影響因子(影響力)學(xué)科排名




書目名稱Machine Learning and Big Data Analytics(Proceedings of International Conference on Machine Learning 網(wǎng)絡(luò)公開度




書目名稱Machine Learning and Big Data Analytics(Proceedings of International Conference on Machine Learning 網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Machine Learning and Big Data Analytics(Proceedings of International Conference on Machine Learning 被引頻次




書目名稱Machine Learning and Big Data Analytics(Proceedings of International Conference on Machine Learning 被引頻次學(xué)科排名




書目名稱Machine Learning and Big Data Analytics(Proceedings of International Conference on Machine Learning 年度引用




書目名稱Machine Learning and Big Data Analytics(Proceedings of International Conference on Machine Learning 年度引用學(xué)科排名




書目名稱Machine Learning and Big Data Analytics(Proceedings of International Conference on Machine Learning 讀者反饋




書目名稱Machine Learning and Big Data Analytics(Proceedings of International Conference on Machine Learning 讀者反饋學(xué)科排名

替代品

親屬

https://doi.org/10.1007/978-3-030-82469-3Machine Learning; Big Data; Big Data Analytics; ICMLBDA; ICMLBDA 2021; Intelligent Systems

燒瓶

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Machine Learning and Big Data Analytics(Proceedings of International Conference on Machine Learning 978-3-030-82469-3Series ISSN 2367-3370 Series E-ISSN 2367-3389

Corporeal

2367-3370 Patna, India.Written by experts in the fieldThis edited volume on machine learning and big data analytics (Proceedings of ICMLBDA 2021) is intended to be used as a reference book for researchers and practitioners in the disciplines of computer science, electronics and telecommunication, information

混合,攙雜

Engagement Analysis of Students in Online Learning Environments,ship between eye-gaze and engagement intensity. OpenFace 2.0 toolbox abilities are leveraged for feature extraction. Experimental results on the test datasets give an accuracy of 55.64% on DAiSEE and an MSE of 0.0598 on Engagement in the Wild Dataset.

詳細(xì)目錄

Concurrent Vowel Identification Using the Deep Neural Network,n tested to predict the concurrent vowel scores for the other 5 F0 difference conditions. The proposed perceptron model was successful in qualitatively predicting the concurrent vowel scores across F0 differences, as observed in concurrent vowel data.

APO

Application of Artificial Intelligence to Predict the Degradation of Potential mRNA Vaccines Develoith the LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) architectures to predict the degradation of each sequence in the given data, which comprised of sequences of mRNA. The performance of the model was evaluated using the MCRMSE (Mean Columnwise Root Mean Squared Error) as the scoring metric.

抵消

Searching Pattern in DNA Sequence Using ECC-Diffie-Hellman Exchange Based Hash Function: An Efficiee is obtained and effective security with power consumption achieved. Experimentation results are carried out by using the publicly available dataset. The effectiveness is proved from the comparison results of the proposed and existing study.