Abstract
The methods used to produce news rankings by recommender systems are not public and it is unclear if they reflect the real importance assigned by readers. We address the task of trying to forecast the number of times a news item will be tweeted, as a proxy for the importance assigned by its readers. We focus on methods for accurately forecasting which news will have a high number of tweets as these are the key for accurate recommendations. This type of news is rare and this creates difficulties to standard prediction methods. Recent research has shown that most models will fail on tasks where the goal is accuracy on a small sub-set of rare values of the target variable. In order to overcome this, resampling approaches with several methods for handling imbalanced regression tasks were tested in our domain. This paper describes and discusses the results of these experimental comparisons.

Abstract
The automation of tasks is increasingly a current practice in the organizational environment, and this practice reduces the need for manpower and often reduces the errors associated with the human factor. In the present document a solution will be presented to automatically generate the source code of a mockup, having as input an image corresponding to the prototype. In the development of this project techniques of Deep Learning will be used, especially Convolutional Neural Networks for the detection and classification of objects in images. The developed solution provides the code base of a mockup in less than 60 seconds, with an average error rate 15.85%.

Abstract
The emergency department of a hospital plays an extremely important role in the healthcare of patients. To maintain a high quality service, clinical professionals need information on how patient flow will evolve in the immediate future. With accurate emergency department forecasts it is possible to better manage available human resources by allocating clinical staff before peak periods, thus preventing service congestion, or releasing clinical staff at less busy times. This paper describes a solution developed for the presentation of hourly, four-hour, eight-hour and daily number of admissions to a hospital's emergency department. A 10-year history (2009-2018) of the number of emergency admissions in a Portuguese hospital was used. To create the models several methods were tested, including exponential smoothing, SARIMA, autoregressive and recurrent neural network, XGBoost and ensemble learning. The models that generated the most accurate hourly time predictions were the recurrent neural network with one-layer (sMAPE = 23.26%) and with three layers (sMAPE = 23.12%) and XGBoost (sMAPE = 23.70%). In terms of efficiency, the XGBoost method has by far outperformed all others. The success of the recurrent neuronal network and XGBoost machine learning methods applied to the prediction of the number of emergency department admissions has been demonstrated here, with an accuracy that surpasses the models found in the literature.

Abstract

Abstract
This review discusses the integration of intelligent technologies into customer interactions in organizations and highlights the benefits of using artificial intelligence systems based on a multimodal approach. Multimodal learning in marketing is explored, focusing on understanding trends and preferences by analyzing behavior patterns expressed in different modalities. The study suggests that research in multimodality is scarce but reveals that it is as a promising field for overcoming decision-making complexity and developing innovative marketing strategies. The article introduces a methodology for accurately representing multimodal elements and discusses the theoretical foundations and practical impact of multimodal learning. It also examines the use of embeddings, fusion techniques, and explores model performance evaluation. The review acknowledges the limitations of current multimodal approaches in marketing and encourages more guidelines for future research. Overall, this work emphasizes the importance of integrating intelligent technology in marketing to personalize customer experiences and improve decision-making processes.

Abstract
Football is one of the most popular sports in the world, so the perception of the game and the prediction of results is of general interest to fans, coaches, media and gamblers. Although predicting football results is a very complex task, the football betting business has grown over time. The unpredictability of football results and the growing betting business justify the development of prediction models to support gamblers. In this article, we develop machine learning methods that take multiple statistics of previous matches and attributes of players from both teams as inputs to predict the outcome of football matches. Several prediction models were tested, with the experimental results showing encouraging performance in terms of the profit margin of football bets. © 2022 Elsevier B.V.. All rights reserved.