Deep learning (DL) electrocardiogram (ECG) predictions are highly valuable for advancing early diagnosis, risk assessment, and treatment planning for cardiovascular diseases. For developing effective predictive models, ECG synthetic data generation is a promising solution for enhancing training datasets while ensuring patient privacy. This work investigates the use of Transformer-based Generative Adversarial Networks (GANs), specifically the TTS-CGAN model, to generate realistic synthetic ECG time-series data. The TTS-CGAN model architecture relies on Transformer encoders for both the generator and discriminator, incorporating state-of-art self-attention mechanisms to enhance time-series data modeling. The research contributions include (i) the analysis of TTS-CGAN for ECG synthesis, (ii) the assessment of synthetic data quality using both MIMICIV and MIT-BIH datasets, (iii) the key introduction of hybrid datasets combining real and synthetic ECG signals to assess model training performance, and (iv) the evaluation of generated data using qualitative and quantitative metrics. Experiments demonstrate that synthetic ECG signals maintain statistical properties similar to real data, with high Cosine similarity (up to 0.99) and low divergence (Jensen-Shannon distance 0.2). Hybrid dataset experiments reveal that models trained on a mix of real and synthetic data (up to 60 % synthetic) retain classification performance while improving dataset diversity.

This paper investigates synthetic data generation through Generative Adversarial Networks (GANs) and Transfer Learning (TL), focusing on Chronic Kidney Diseases (CKD). It analyzes whether GANs, particularly the medGAN and CorGAN architectures, can generate high-quality synthetic tabular data and how TL can enhance this process. The contributions include evaluating alternative medGAN and CorGAN setups, incorporating WGAN and WGAN-GP loss functions, and assessing how fine-tuning with TL impacts data realism and classifier performance. The models were pre-trained on a larger CKD dataset and fine-tuned on a smaller one, using consistent hyperparameters with reduced learning rates during fine-tuning. Experiments involved training Random Forest classifiers in various settings: using real data, synthetic data, and a combination of both, with and without TL. Metrics like dimension-wise probability and multiple training/testing scenarios are employed to assess the quality and utility of the generated data. The synthetic data, especially when generated using TL, improved classifier performance significantly in scenarios where training and testing datasets differed. Notably, F1-scores improved by up to 74.3 % when using TL-generated data. These findings support the use of GANs with TL as a powerful approach to overcome data limitations in healthcare research.

Computer games continue to present challenging experimental fields for developing Artificial Intelligence (AI) models. With Case-Based Reasoning and Clustering, this work proposes novel cases and clusters-based reuse criteria for implementing card-playing agents with diversified playing skills. Using the game of Truco, a common game in South America, we detail how game actions are reused from past cases selected as query answers for given game problems. In doing so, the majority rule, the probability-based lottery, the probability of victory, and the number of points won reuse policies are used to select a cluster of cases. Then these policies are also used to select game actions from the cases within the selected cluster. Investigating the combined exploration of reuse policies, experiments of different natures evaluate the performance of implemented Truco bots disputing matches against each other. Players in these tournaments are equipped with varied policies and use case bases constructed differently.

This work investigates the combination of cases and clusters in the reuse of game actions (e.g., cards played, bets made) recorded in the cases retrieved for a given query in Case-based Reasoning (CBR) card-playing agents. With the support of the K-MEANS clustering algorithm, clustering results detailing problem states/situations and game outcomes relationships recorded in cases from the case base guide the execution of augmented reuse policies. These policies consider the game actions recorded in the retrieved cases in the selection of the clusters to be used. Then, the cases that belong to the selected clusters are used in the determination of which game action is reused as a solution to the current game problem situation. With this two-step reuse process, the proposed policies rely on the majority with clusters, the probability with clusters, the number of points won with clusters and the chance of victory with clusters. To evaluate these proposals, card-playing agents implemented with different reuse policies competed against each other in duplicated game matches where all of them played using the same set of cards.

This paper proposes a method to predict word grammatical classes using automatically generated discrete-time Markov chains to model typical sentences.Such method advantage relies on the availability of input resources needed to build an efficient and effective solution to virtually any language, dialect, or domain lingo.One of the main advantages of the proposed method is its simplicity when compared to other sophisticated approaches based on Hidden Markov Models or even more complex formalisms.The proposed method is instantiated to an example and we show that the achieved efficiency and effectiveness bring advantages to traditional similar solutions.

Broadly used and applied in many domains, Hidden Markov Models are a well established formalism, both in computer science and statistics.Among other reasons, they owe their popularity to a fast fitting method, i.e., the Baum-Welch algorithm, allowing to adjust models to a variety of input data.Using expectation and maximization phases, BW assures an increase to the model likelihood at every iteration.Yet, to initialize the sequence of expectationmaximization (EM) steps, it is a standard procedure to start the BW algorithm from randomly generated values.We propose a, simple and fast, deterministic pre-fitting approach which derives the BW's initial values directly from the input data.

We propose a simple, fast, deterministic pre-fitting approach which derives the Baum–Welch algorithm initial values directly from the input data. Such pre-fitting has the purpose of improving the fitting time for a given Hidden Markov Model (HMM) while maintaining the original Baum–Welch algorithm as the fitting one. The fitting time is improved by avoiding the Baum–Welch algorithm sensitiveness through the generation of parameters closer to the global maximum likelihood. Furthermore, by keeping the original Baum–Welch algorithm as the fitting one, we guarantee that all related methods will continue to work properly. On the other hand, the pre-fitting generates the HMM parameters directly derived from time-series data, without any data transformation, using an [Formula: see text] operation.

Evaluate team's performance on long-duration projects can be a challenge.It relies on human expertise to perform estimations, which can generate uncertainty and dependency of experienced specialists.Statistical techniques, such as modeling and simulations, are suitable options as tools to support projects estimations.Our goal in this paper is a formal mapping of the main Waterfall model characteristics to stochastic model to predict performance indices of teams such as the real working time.

The use of stochastic formalisms, such as Stochastic Automata Networks (SAN), can be very useful for statistical prediction and behavior analysis.Once well fitted, such formalisms can generate probabilities about a target reality.These probabilities can be seen as a statistical approach of knowledge discovery.However, the building process of models for real world problems is time consuming even for experienced modelers.Furthermore, it is often necessary to be a domain specialist to create a model.This work illustrates a new method to automatically learn simple SAN models directly from a data source.This method is encapsulated in a tool called SAN GEnerator (SANGE).This new model fitting method is powerful and relatively easy to use; therefore this can grant access to a much broader community to such powerful modeling formalisms.