Abstract
In recent years growing volumes of data have made the task of applying various machine learning algorithms a challenge in a great number of cases. This challenge is posed in two main ways: training time and processing load. Normally, problems in these two categories may be attributed to irrelevant, redundant, or noisy features. So as to avoid this type of feature most pre-processing pipelines include a step dedicated so selecting the most relevant features or combining existing ones into a single better representation. These techniques are denominated dimensionality reduction techniques. In this work, we aim to present a short look at the current state of the art in this area. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
[No abstract available]

Abstract
This paper presents several case studies that show that it is possible to use clean energy to produce electricity, we have environmental benefits and benefits for the management of the electrical transmission network. In this case wind energy are used.For this work, software was developed in Matlab for the model we developed and the results of this were compared with the results obtained by the simulator Power World.To make the decision to replace generators fossil generators with wind generators, Local Marginal Prices (LMP) were used. Some case studies were created using a model system, with the objective of evaluating the benefits of this allocation based on the LMP.The test network presented in this paper is a 9 Bus network. However, the developed software was also tested on an IEEE 30 Bus network. © 2023 IEEE.

Abstract
Diabetic foot ulcers (DFU) are one of the most frequent and debilitating complications of diabetes. DFU wound healing is a highly complex process, resulting in significant medical, economic and social challenges. Therefore, early identification of patients with a high-risk profile would be important to adequate treatment and more successful health outcomes. This study explores risk assessment profiles for DFU healing and healing prognosis, using machine learning predictive approaches and decision tree algorithms. Patients were evaluated at baseline (T0; N = 158) and 2 months later (T1; N = 108) on sociodemographic, clinical, biochemical and psychological variables. The performance evaluation of the models comprised F1-score, accuracy, precision and recall. Only profiles with F1-score >0.7 were selected for analysis. According to the two profiles generated for DFU healing, the most important predictive factors were illness representations on T1 IPQ-B (IPQ-B <= 9.5 and < 10.5) and the DFU duration (<= 13 weeks). The two predictive models for DFU healing prognosis suggest that biochemical factors are the best predictors of a favorable healing prognosis, namely IL-6, microRNA-146a-5p and PECAM-1 at T0 and angiopoietin-2 at T1. Illness perception at T0 (IPQ-B <= 39.5) also emerged as a relevant predictor for healing prognosis. The results emphasize the importance of DFU duration, illness perception and biochemical markers as predictors of healing in chronic DFUs. Future research is needed to confirm and test the obtained predictive models.

Abstract
Digital Marketing sets a sequence of strategies responsible for maximizing the interaction between companies and their target audience. One of them, known as Customer Success, establishes long-term techniques capable of projecting the sustainable value of a given customer to a company, monitoring the indexers that translate its activities. Therefore, this paper intends to address the need to develop an innovative tool that allows the creation of a temporal knowledge base composed of the behavioral evolution of customers. The CRISP-DM model benefits the processing and modeling of data capable of generating knowledge through the application and combination of the results obtained by machine learning algorithms specialized in time series. Time Series K-Means allows the clustering and differentiation of consumers characterized by their similar habits. Through the formulation of profiles, it is possible to apply forecasting methods that predict the following trends. The proposed solution provides the understanding of time series that profile the flow of customer activity and the use of the evidenced dynamics for the future prediction of these behaviors. © MIR Labs, www.mirlabs.net/ijcisim/index.html

Abstract
Renewable energy sources have transformed the electricity market, allowing virtual power players or aggregators to participate and benefit from selling surplus energy. However, meeting demand and ensuring energy production stability can be challenging due to the intermittent nature of renewable sources. Accurate forecasting of energy consumption, generation, and electricity prices is critical to address these issues. Moreover, the selection of the best algorithm for forecasting is usually based on the predictions’ accuracy, neglecting other factors such as the impact of errors on the real context. This paper presents a study on the economic risk of price forecasting errors on the electricity market’s trading. For this, a simulation model is proposed to analyze the deviations between actual and predicted prices and how these deviations can affect trading in the electricity market, where the main purpose is to maximize profit, depending on whether the player is buying or selling electricity. The economic risk analysis and the predictions scores are used to improve the forecasts, using an approach based on reinforcement learning to evaluating and selecting which models demonstrated better performance in past transactions. The study involved simulating an aggregator’s transactions in the Iberian electricity market for two consecutive days in October 2021. Real data from the market operator between 2020 and 2021 and seven forecasting models were used. The findings showed that errors have a significant impact on profit. Including the economic impact analysis and score evaluation of forecasting methods to determine which method can offer better results has proven to be a viable approach. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.