Titlebook: Information Management and Big Data; 9th Annual Internati Juan Antonio Lossio-Ventura,Jorge Valverde-Rebaza, Conference proceedings 2023 Th

Uncultured

他日关税重重

画布

,A Preliminary Analysis of Twitter’s LGBTQ+ Discussions,ty levels are more prevalent in LGBTQ+ discussions? Do the emotions and toxicity levels in tweets change over time? What do we know about the users who have frequently been tweeting about LGBTQ+-related topics since 2006? Upon our analyses, we find that emotions such as joy and anger and toxicity su

Infant

疯狂

,A Semantic Query Engine for Knowledge Rich Legal Digital Libraries,gineering method to embed legal premise graphs, called the AND-OR graph, in the document to facilitate semantic match. A prototype implementation of Prism as a component of a document management system, called ., is also discussed.

semble

,ADRAS: Airborne Disease Risk Assessment System for Closed Environments,del based on MobileNetV2. The generated data from several devices is visible in a web platform to monitor multiple areas and locations. Finally, an OAK-D camera and a Jetson device are embedded in a end device meant to monitor a closed environment and send spread risk data continually to the web pla

palliative-care

Peak Anomaly Detection from Environmental Sensor-Generated Watershed Time Series Data, detection accuracy while computationally far more efficient than the deep learning approach. Results indicate the advantage of using the two approaches in combination, while a more general study involving other watersheds’ time series data would be needed.

septicemia

Mammal

Multiple Scale Comparative Analysis of Classical, Dynamic and Intelligent Edge Detection Schemes,ales, such as the 24-bit RGB scale, 8-bit gray scale and single bit binary scale. In this article, some typical edge detection techniques of Canny edge detection, Gabor edge detection and ACO edge detection are used to illustrate classical, dynamic and intelligent edge detection schemes, respectivel

CRAB

,Performance Analysis of Machine Learning for Food Fraud Prediction,e of fraud they are committing. Several techniques already implemented will be used to compare the results. Naive Bayes model, logistic regression, multilayer perceptron, decision tree, random forest and SVM. The best model was the multilayer perceptron.