Information Management and Big Data 9th Annual International Conference, SIMBig 2022, Lima, Peru, November 16-18, 2022, Proceedings için kapak resmi
Başlık:
Information Management and Big Data 9th Annual International Conference, SIMBig 2022, Lima, Peru, November 16-18, 2022, Proceedings
Yazar:
Lossio-Ventura, Juan Antonio. editor. (orcid)
ISBN:
9783031354458
Basım Bilgisi:
1st ed. 2023.
Fiziksel Tanımlama:
XVI, 272 p. 121 illus., 88 illus. in color. online resource.
Seri:
Communications in Computer and Information Science, 1837
İçerik:
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.
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