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Titlebook: Applied Informatics; Second International Hector Florez,Marcelo Leon,Simone Belli Conference proceedings 2019 Springer Nature Switzerland A

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51#
發(fā)表于 2025-3-30 11:19:39 | 只看該作者
52#
發(fā)表于 2025-3-30 14:52:31 | 只看該作者
Study of Crime Status in Colombia and Development of a Citizen Security Appuerrilla members have gone to the streets of the country to seek a new direction or occupation and unfortunately have fallen into crime, increasing delinquency levels of crimes such as robbery, extortion, rape, micro-trafficking and personal injury. Added to this, the increase of the migrant populat
53#
發(fā)表于 2025-3-30 16:32:16 | 只看該作者
University Quality Measurement Model Based on Balanced Scorecard almost real time. This article presents the design of a management and quality model of the processes in a university, through the integration of a Balance Scorecard (BSC) and the implementation of an information system. For which it was required: a review of existing tracing and monitoring systems
54#
發(fā)表于 2025-3-30 22:27:42 | 只看該作者
55#
發(fā)表于 2025-3-31 02:11:34 | 只看該作者
Continuous Variable Binning Algorithm to Maximize Information Value Using Genetic Algorithmare guidelines for good binning which can be treated as constraints. However, there are also statistics which should be optimized. Therefore, we view the binning problem as a constrained optimization problem. This paper presents a novel supervised binning algorithm for binary classification problems
56#
發(fā)表于 2025-3-31 07:19:22 | 只看該作者
CVRPTW Model for Cargo Collection with Heterogeneous Capacity-Fleetations with low time disponibility to attend any vehicle. The objective of the model is to reduce the routing time in a problem with mixed vehicle-fleet. The initial step is the creation of a distance matrix by using the Google Maps API, then cargo capacities for every vehicle and time-windows for e
57#
發(fā)表于 2025-3-31 11:00:43 | 只看該作者
58#
發(fā)表于 2025-3-31 15:59:45 | 只看該作者
Predicting Stock Prices Using Dynamic LSTM Models many factors, making the process of predicting them a challenging task. This paper describes a method to build models for predicting stock prices using long short-term memory network (LSTM). The LSTM-based model, which we call dynamic LSTM, is initially built and continuously retrained using newly
59#
發(fā)表于 2025-3-31 18:38:00 | 只看該作者
60#
發(fā)表于 2025-3-31 23:12:30 | 只看該作者
The Remaining Endopterygote Orderspplying find/replace functions. The results of this initial study, carried out using a focus group of volunteers, show that users were able clean dirty data-sets more accurately using ., and moreover, that this tool was generally preferred for this purpose.
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