This paper appraises convolutional neural network (CNN) models’ capabilities in emotion detection from facial expressions, seeking to aid the diagnosis of psychosomatic illnesses, typically made in clinical setups. Using the FER-2013 dataset, two CNN models were designed to detect six emotions with 64% accuracy—although not evenly distributed; they demonstrated higher effectiveness in identifying “happy” and “surprise.” The assessment was performed through several performance metrics—accuracy, precision, recall, and F1-scores—besides further validation with an additional simulated clinical environment for practicality checks. Despite showing promising levels for future use, this investigation highlights the need for extensive validation studies in clinical settings. This research underscores AI’s potential value as an adjunct to traditional diagnostic approaches while focusing on wider scope (broader datasets) plus focus (multimodal integration) areas to be considered among recommendations in forthcoming studies. This study underscores the importance of CNN models in developing psychosomatic diagnostics and promoting future development based on ethics and patient care.

This study introduces an AI-based framework for stroke diagnosis that merges clinical data and curated imaging data. The system utilizes traditional machine learning and advanced deep learning techniques to tackle dataset imbalances and variability in stroke presentations. Our approach involves rigorous data preprocessing, feature engineering, and ensemble techniques to optimize the predictive performance. Comprehensive evaluations demonstrate that gradient-boosted models outperform in accuracy, while CNNs enhance stroke detection rates. Calibration and threshold optimization are utilized to align predictions with clinical requirements, ensuring diagnostic reliability. This multi-modal framework highlights the capacity of AI to accelerate stroke diagnosis and aid clinical decision making, ultimately enhancing patient outcomes in critical care.

Simulation allows to comprehensively examine the various processes and systems, and to evaluate the expected results before decision. To create reliable transportation simulation model, it should be carefully and in depth validated. The purpose of this paper is to describe the basis for the transportation microscopic simulation model validation. The paper contains the basic steps for the transportation simulation model development and validation. Various definitions for the simulation model verification, validation and calibration are considered. Validation and calibration parameters selection depends on the complexity of analysable task, initial amount and quality of input data. A summary of the transportation simulation model validation methods and parameters is presented.

In today’s dynamically changing environment, we need to be able to respond in a timely manner to changes in supply chain processes. Software agents are successfully used in supply chain management tasks for a variety of purposes. The behaviour of agents is determined by the purpose of their development, and the effectiveness of the use of agents is considered in accordance with the purpose of their development. The paper presents research on the development of a multi-agent system for supply chain management, focusing on the steps of developing a multi-agent system. The choice of each algorithm for agents is analysed and argued. The application of the developed multi-agent system for supply chain management is also described in the paper. The efficiency of application of the developed multi-agent system is presented as well.

This article proposes an innovative methodology for data-driven modeling and simulation of transportation management through cross-sectoral collaboration in small businesses. The present research is multidisciplinary and interdisciplinary in nature. We investigate the improvements in logistics management that can be achieved through cross-sector collaboration in agriculture and forestry. A data-driven method, such as symbolic regression, is used to identify the relationships between factors in a modeled system using mathematical expressions. These expressions are directly integrated into the simulation models. Simulation spreads the modeling of transportation processes over a period of time. The system dynamics model is designed to analyze and assess the performance of a system based on its past behavior and is, therefore, deterministic. The discrete-event model enables the simulation of future scenarios and outcomes over time, given random input variables. As new data become available, relationships within the symbolic regression method are discovered more accurately, and simulations are updated accordingly. The tools offered for implementation are supplemented by a multi-user web simulation. The proposed case study is based on a real-life example. The obtained results allow small agricultural companies to use transportation and labor resources more efficiently when organizing the transportation of their agricultural and forestry products. Integrating data-driven models into simulations enables a better interpretation of data across the entire data value chain.

We present a neuro-fuzzy digital twin for cardiac disease recognition on the PTB-XL dataset that keeps the accuracy of a strong convolutional model while exposing its reasoning as readable fuzzy rules. The key design choice is to separate the two jobs instead of forcing one network to do both. A convolutional backbone (xresnet1d101 with cross-lead attention) is trained first and acts as the predictor. A second phase then freezes the backbone and trains a five-expert mixture of interval Type-2 Adaptive Neuro-Fuzzy Inference Systems as an interpretation layer, attached through a learnable trust gate. Four experts read complementary signal streams (the raw 12-lead waveform, a Fourier–Bessel Series Expansion, a Tunable-Q Wavelet Transform, and a morphogram); a fifth reads 22 clinical and demographic descriptors. A concept bottleneck maps the backbone embedding to eight named clinical concepts (ST elevation, T inversion, QT prolongation, Sokolov–Lyon and Cornell voltages, RVH, axis deviation, QRS widening), so the downstream rules read in clinical language. Two genetic-algorithm stages keep the model compact: NSGA-II selects the clinical features, and a multi-objective rule-pruning search trades macro-F1 and hypertrophy recall against the number of active rules. On the official test fold the twin reaches a macro-F1 of 0.751 (bootstrap 95% CI 0.740 to 0.767), a macro-AUC of 0.928, a Matthews correlation coefficient of 0.672, and a macro expected calibration error of 0.021, which is above the Strodthoff xresnet1d101 baseline of 0.74 and well below the calibration error of our earlier coupled design. Because the fuzzy layer is attached as a trust-gated residual and the best epoch is chosen on threshold-optimized validation F1, the interpretable twin matches the backbone within about 0.004 (twin 0.751 versus backbone 0.754) rather than paying the usual “interpretability tax”. The fuzzy mixture on its own still reaches a macro-F1 of 0.69, so the rules carry real diagnostic signal. Subgroup macro-F1 stays within about 0.045 across sex, age, and body-mass groups. A concept-level intervention simulator estimates how cardioactive drug classes would shift a patient’s risk by perturbing the named concepts and re-running the same twin.