Titlebook: Data Management, Analytics and Innovation; Proceedings of ICDMA Saptarsi Goswami,Inderjit Singh Barara,Alfred M. B Conference proceedings 2

invigorating

书目名称Data Management, Analytics and Innovation影响因子(影响力)




书目名称Data Management, Analytics and Innovation影响因子(影响力)学科排名




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书目名称Data Management, Analytics and Innovation网络公开度学科排名




书目名称Data Management, Analytics and Innovation被引频次




书目名称Data Management, Analytics and Innovation被引频次学科排名




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书目名称Data Management, Analytics and Innovation年度引用学科排名




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书目名称Data Management, Analytics and Innovation读者反馈学科排名

Commodious

Automated Structured Data Extraction from Scanned Document Imagestructured format. The solution is driven by a configuration file, which can help in fine-tuning different processes to improve extracted data. The solution generates an XML for the scanned document which can be used further for storing and processing the data present in paper-based documents by diff

制造

灌溉

Itinerant

Hypothesis Testing of Tweet Text Using NLPs. Tweets are labeled as “believer” or “denier” for each country, and a hypothesis is being proved based on the statement made by rich and poor countries. The statistical result also shows that there exists a positive correlation between the GDP growth rate and the number of deniers and believers in

ectropion

ectropion

Ontology-Driven Scientific Literature Classification Using Clustering and Self-supervised Learningaper, we propose an ontology-driven classification technique based on zero-shot learning in conjunction with agglomerative clustering to automatically label a scientific literature dataset related to CE and CS. We further study and compare the effectiveness of multiple text classifiers such as logis

ANN

Modeling and Forecasting Tuberculosis Cases Using Machine Learning and Deep Learning Approaches: A C CNN-LSTM Hybrid and MLP networks achieved the lowest forecasting errors compared to the other models and were chosen for forecasting pulmonary negative, positive, and TB incidence cases from 2020 to 2029. The forecasting results revealed that there would be 117.861557 new pulmonary negative inciden

Vsd168

变化

Support Vector Machines and Random Forest Classification Models for Identification of Stability in Eon modes. We also used the Synthetic Minority Oversampling Technique (SMOTE) to handle data imbalance. Our simulation results indicate that prediction of stability/instability classes for different process parameters can be achieved with high degree of confidence with robust machine learning models.