Titlebook: Big Data Analytics; 10th International C Partha Pratim Roy,Arvind Agarwal,R. Uday Kiran Conference proceedings 2022 The Editor(s) (if appli

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书目名称Big Data Analytics影响因子(影响力)




书目名称Big Data Analytics影响因子(影响力)学科排名




书目名称Big Data Analytics网络公开度




书目名称Big Data Analytics网络公开度学科排名




书目名称Big Data Analytics被引频次




书目名称Big Data Analytics被引频次学科排名




书目名称Big Data Analytics年度引用




书目名称Big Data Analytics年度引用学科排名




书目名称Big Data Analytics读者反馈




书目名称Big Data Analytics读者反馈学科排名

Foreknowledge

cipher

串通

A Deep Learning Based Approach to Automate Clinical Coding of Electronic Health Recordsmer encoder model, and a BERT (Bidirectional Encoder Representations from Transformers) model for the automated clinical coding. The presented models are evaluated using a publicly available Medical Information Mart for Intensive Care III (MIMIC-III) dataset. The used dataset consists of various pat

Simulate

AFFIX

注视

Drugomics: Knowledge Graph & AI to Construct Physicians’ Brain Digital Twin to Prevent Drug Side-Effhe right disease for the right person at the right time at any Point-of-Care. This functions like a physicians’ brain digital twin to reduce clinical errors, reduce medication errors, and increase general health equity at a reduced cost. This will eliminate the patient harm caused by drug interactio

外面

Extremely Randomized Tree Based Sentiment Polarity Classification on Online Product Reviewshas better outcomes with the accuracy of 98% for positive and 85% for negative cases, with an overall accuracy of 96.8%. The error rate of all the three ensemble classifiers is also under 0.5% which uncovers that ensemble classifiers performs better compared to individual classifiers.

climax

Discovering Top-k Periodic-Frequent Patterns in Very Large Temporal Databasesn input was presented to find all desired patterns in a database. Experimental results on synthetic and real-world databases demonstrate that our algorithm is memory and runtime efficient and highly scalable.

斜谷

https://doi.org/10.1007/978-3-662-28581-7e is to highlight our responsibility to create algorithms and automated systems that do not harm and are equitable and just. I also hope to create awareness that leads to businesses and software laboratories that focus on testing software and data that alleviates our fear - modeling the work of the