This paper proposes a hybrid machine translation (HMT) system that improves the quality of neural machine translation (NMT) by incorporating statistical machine translation (SMT). Therefore, two NMT systems and two SMT systems were built for the Slovenian–English language pair, each for translation in one direction. We used a multilingual language model to embed the source sentence and translations into the same vector space. From each vector, we extracted features based on the distances and similarities calculated between the source sentence and the NMT translation, and between the source sentence and the SMT translation. To select the best possible translation, we used several well-known classifiers to predict which translation system generated a better translation of the source sentence. The proposed method of combining SMT and NMT in the hybrid system is novel. Our framework is language-independent and can be applied to other languages supported by the multilingual language model. Our experiment involved empirical applications. We compared the performance of the classifiers, and the results demonstrate that our proposed HMT system achieved notable improvements in the BLEU score, with an increase of 1.5 points and 10.9 points for both translation directions, respectively.

Growing amounts of municipal waste and the need for efficient recycling demand automated and accurate classification systems. This paper investigates deep learning approaches for multi-class waste sorting based on image data, comparing three widely used convolutional neural network architectures (ResNet-50, EfficientNet-B0, and MobileNet V3) with a custom hybrid model (CustomNet). The dataset comprises 13,933 RGB images across 10 waste categories, combining publicly available samples from the Kaggle Garbage Classification dataset (61.1%) with images collected in house (38.9%). The three glass sub-categories (brown, green, and white glass) were merged into a single glass class to ensure consistent class representation across all dataset splits. Preprocessing steps include normalization, resizing, and extensive data augmentation to improve robustness and mitigate class imbalance. Transfer learning is applied to pretrained models, while CustomNet integrates feature representations from multiple backbones using projection layers and attention mechanisms. Performance is evaluated using accuracy, macro-F1, and ROC–AUC on a held-out test set. Statistical significance was assessed using paired t-tests and Wilcoxon signed-rank tests with Bonferroni correction across five-fold cross-validation runs. The results show that CustomNet achieves 97.79% accuracy, a macro-F1 score of 0.973, and a ROC–AUC of 0.992. CustomNet significantly outperforms EfficientNet-B0 and MobileNet V3 ($p<0.001$, Bonferroni corrected), and it achieves performance parity with ResNet-50 ($p=0.383$) at a substantially lower parameter count in the classification head (9.7 M vs. 25.6 M). These findings indicate that combining multiple feature extractors with attention mechanisms improves classification performance, supports qualitative model explainability via saliency visualization (Grad-CAM), and enables practical deployment on heterogeneous Edge AI platforms. Inference benchmarking on an NVIDIA Jetson Orin Nano demonstrated real-world deployment feasibility at 86.70 ms per image (11.5 FPS).

When it comes to game playing, evolutionary and tree-based approaches are the most popular approximate methods for decision making in the artificial intelligence field of game research. The evolutionary domain therefore draws its inspiration for the design of approximate methods from nature, while the tree-based domain builds an approximate representation of the world in a tree-like structure, and then a search is conducted to find the optimal path inside that tree. In this paper, we propose a novel metric for game feature validation in Real-Time Strategy (RTS) games. Firstly, the identification and grouping of Real-Time Strategy game features is carried out, and, secondly, groups are included into weighted classes with regard to their correlation and importance. A novel metric is based on the groups, weighted classes, and how many times the playtesting agent invalidated the game feature in a given game feature scenario. The metric is used in a series of experiments involving recent state-of-the-art evolutionary and tree-based playtesting agents. The experiments revealed that there was no major difference between evolutionary-based and tree-based playtesting agents.

Games can be more than just a form of entertainment. Game spaces can be used to test different research ideas quickly, simulate real-life environments, develop non-playable characters (game agents) that interact alongside human players and much more. Game agents are becoming increasingly sophisticated as the collaboration between game agents and humans only continues to grow, and there is an increasing need to better understand game players’ workings. Therefore, this work addresses the digital characterization (DC) of various game players based on the game feature values found in a game space, and based on the actions gathered from player interactions with the game space. High-confidence actions are extracted from rules created with association rule mining, utilizing advanced evolutionary algorithms (e.g., differential evolution) on the dataset of feature values. These high-confidence actions are used in the characterization process, resulting in the DC description of each player. The main research agenda of this study is to determine whether DCs manage to capture the essence of players’ action style behavior. Experiments reveal that characterizations do indeed capture behavior nuances, and consequently open up many research possibilities in the domains of player modeling, analyzing the behavior of different players and automatic policy creation, which can possibly be used for utilization in future simulations.

V prispevku predstavljamo razvoj simulacijskega okolja za preučevanje kibernetskih napadov in obrambnih strategij z uporabo inteligentnih agentov, podprtih z velikimi jezikovnimi modeli. Okolje omogoča realistično simulacijo napadov, kot je spletno ribarjenje, kjer napadalni agenti generirajo prepričljiva sporočila, obrambni agenti pa jih analizirajo in zaznavajo znake prevare. Takšna interakcija omogoča varno testiranje v nadzorovanem okolju ter prispeva k razumevanju učinkovitosti obrambnih mehanizmov, izboljšanju algoritmov za zaznavanje prevar in izobraževanju uporabnikov o sodobnih kibernetskih grožnjah.