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Full Description
This book constitutes the refereed proceedings of the 9th Annual International Conference on Information Management and Big Data, SIMBig 2022, held in Lima, Peru, during November 16-18, 2022.
The 18 full papers and 1 short paper included in this book were carefully reviewed and selected from 50 submissions. The volume presented novel methods for the analysis and management of large data, in fields like Artificial Intelligence (AI), Data Science, Machine Learning, Natural Language Processing, Semantic Web, Data-driven Software Engineering, Health Informatics.
Contents
A Preliminary Analysis of Twitter's LGBTQ + Discussions.- User-agnostic Model for Prediction of Retweets Based on Social Neighborhood Information.- Segmentation and classification of pages for digitized documents of the Public Prosecutor's Oce.- A Semantic Query Engine for Knowledge Rich Legal Digital Libraries.- Getting Quechua Closer to Final Users through Knowledge Graphs.- Using features based on elongation to enhance sentiment analysis.- Image Generation from sketches and text-guided attribute edition.- ADRAS: Airborne Disease Risk Assessment System for Closed Environments.- Predictive Sentiment Analysis Model regarding the variation of the Dollar Exchange Rate.- Maximising Influence Spread in Complex Networks by Utilising Community-based Driver Nodes as Seeds.- Peak Anomaly Detection from Environmental Sensor-Generated Watershed Time Series Data.- Gas Sensors and Machine Learning for Quality Evaluation of Grape Spirits (Pisco).- Optimal layer selection on Deep Convolutional Neural Networks using backward freezing and binary Search.- Profiling Public Service Accessibility based on the Public Transport Infrastructure.- Multiple Scale Comparative Analysis of Classical, Dynamic and Intelligent Edge Detection Schemes.- Soil Organic Carbon Prediction using Digital Color Sensor in Peru.- Ideation of Computational Thinking Programs by Assembling Code Snippets from the Web.- Smart doorbell with Telegram notification for multifamily dwellings.- Performance analysis of machine learning for food fraud prediction.
