Informatica

Credit card fraud (CCF) poses a growing threat to the global economy, with payment card fraud losses reaching approximately USD 33.8 billion in 2023. While deep learning approaches such as CNNs, autoencoders, and Transformer-based models have improved detection accuracy, their “black box” nature restricts adoption in high-stakes financial domains where transparent and auditable reasoning is essential for trust and regulatory compliance. To address this challenge, we propose MTLFraudDetect, a multi-task framework that combines fraud detection with complementary model-analysis techniques. The model combines fraud classification with two auxiliary tasks—data reconstruction and amount prediction—while employing curriculum learning and PCGrad to stabilize multi-task optimization. For model analysis, the framework combines reconstruction-based anomaly-sensitivity signals with SHAP-based decision attribution. Reconstruction errors indicate transaction-level and feature-wise deviations from patterns learned by the reconstruction branch, whereas SHAP examines how the classifier’s shared latent representation and transaction amount contribute to its final prediction. Under an explicitly separated development-test protocol with training-fold-only preprocessing and out-of-fold hyperparameter selection, MTLFraudDetect achieves competitive F1-score and AUC-PR performance while providing complementary evidence at the levels of global anomaly detection, feature-wise error localization, and post-hoc decision attribution. These results do not by themselves establish formal explanatory faithfulness or complete interpretability.

In this paper the gaze tracking system based on the adaptively changing threshold value of the gray level, which automatically detects the pupil position in two dimensional data is proposed. The system detects closed eye using normalized accumulative luminosity function. The detection of closed eye allows the confirmation of voluntary selected command in a more natural way. The presented technique allows recalibration without intervention or help of other assistive persons. Natural head motion is evaluated, employing signals, generated by orientation sensor. Within two experiments, the proposed system is tested at different behavior modes in respect of speed and accuracy of the system.

In this paper, the main measure, an amount of information, of the information theory is analyzed and corrected. The three conceptions of the theory on the microstate, dissipation pathways, and self‐organization levels with a tight connection to the statistical physics are discussed. The concepts of restricted information were introduced as well as the proof of uniqueness of the entropy function, when the probabilities are rational numbers, is presented.

The development of Lithuanian HMM/ANN speech recognition system, which combines artificial neural networks (ANNs) and hidden Markov models (HMMs), is described in this paper. A hybrid HMM/ANN architecture was applied in the system. In this architecture, a fully connected three‐layer neural network (a multi‐layer perceptron) is trained by conventional stochastic back‐propagation algorithm to estimate the probability of 115 context‐independent phonetic categories and during recognition it is used as a state output probability estimator. The hybrid HMM/ANN speech recognition system based on Mel Frequency Cepstral Coefficients (MFCC) was developed using CSLU Toolkit. The system was tested on the VDU isolated‐word Lithuanian speech corpus and evaluated on a speaker‐independent ∼750 distinct isolated‐word recognition task. The word recognition accuracy obtained was about 86.7%.

An analytical review of recent publications in the area of digital speech signal processing is presented. The aim of the given paper is the analysis of these publications, where Artificial Neural Networks (ANNs) were successfully employed. Numerous methods of ANNs employment are discussed due to identify when and why they are reliable alternative to the conventional adaptive signal processing techniques.