Artificial intelligence (AI) and machine learning (ML) have introduced new approaches to medical rehabilitation. These technological advances facilitate the development of large-scale adaptive rehabilitation platforms that can be tailored to individual patients. This review focuses on key technologies, including AI-driven rehabilitation planning, IoT-based patient monitoring, and Large Language Model (LLM)-powered virtual assistants for patient support. This review analyzes existing systems and examines how technologies can be combined to create comprehensive rehabilitation platforms that provide personalized care. For this purpose, a targeted literature search was conducted across leading scientific databases, including Scopus, Google Scholar, and IEEE Xplore. This process resulted in the selection of key peer-reviewed articles published between 2018 and 2025 for a detailed analysis. These studies highlight the latest trends and developments in medical rehabilitation, showcasing how digital technologies can transform rehabilitation processes and support patients. This review illustrates that AI, the IoT, and LLM-based virtual assistants hold significant promise for addressing current healthcare challenges through their ability to enhance, personalize, and streamline patient care.

The article presents materials, methods, and results obtained from the modernization and improvement of the Abay/Saina transport interchange in the metropolis of Almaty to the level of an IoT (Internet of Things) transport object with AI (Artificial Intelligence). This approach is an important practical and relevant task within the framework of the Smart City Almaty concept. The developed hardware and software complex (HSC) allows transforming the existing hybrid analog-digital system for monitoring and telemetric control of traffic lights on Abay Avenue into a modern digital intelligent control system. The presented software and circuit design solutions allow creating a neuro-physical digital twin of the Abay/Saina interchange. The semi-industrial prototype of the mobile PC (Personal Computer) server and traffic light objects with streaming neural video vision have significant practical importance for the widespread implementation of electric taxis with autopilots into the transport infrastructure of the Republic of Kazakhstan.

This article discusses the integration of smart sensors and the Internet of Things (IoT) in digital twins for Industry 4.0, representing a transformative approach to industrial operations. We explore the applications, benefits, and challenges associated with using smart sensors and IoT technologies in the context of digital twins. Digital twins, virtual replicas of physical assets or processes, rely on real- time data provided by smart sensors and IoT devices for continuous monitoring, analysis, and optimization of industrial operations. The focus is on the role of smart sensors and $I o T$ in creating and maintaining accurate and efficient digital twins, addressing challenges such as data security issues, compatibility concerns, and the complexity of managing heterogeneous sensor networks in large industrial environments.

Sustainable energy systems such as anaerobic digestion (AD) bioreactors exhibit complex nonlinear dynamics that complicate the monitoring of key stability indicators using conventional laboratory-based methods. As a preliminary investigation, this pilot study explores the feasibility of using machine learning-based soft sensing to estimate Total Volatile Fatty Acids (TVFA(M)) from routinely measured physicochemical parameters. Using a short-term laboratory dataset obtained from controlled CO₂ biomethanisation experiments, several regression models were benchmarked, including an attention-based deep learning architecture (TabNet), multi-architecture artificial neural networks (ANNs), gradient-boosting ensembles (CatBoost, XGBoost, LightGBM), and classical kernel-based approaches. Model performance was evaluated under a cross-validated framework to assess predictive capability and consistency across folds within the limited experimental scope. Among the tested models, TabNet achieved highly competitive performance, yielding an R² of 0.8551, an RMSE of 0.0090, and an MAE of 0.0067. To support model transparency and interpretability, Explainable Artificial Intelligence (XAI) techniques based on SHapley Additive exPlanations (SHAP) were applied, identifying pCO₂ as the dominant contributor to TVFA(M) predictions within the studied operational range. The results demonstrate the potential of explainable machine learning models as soft sensors for TVFA(M) estimation under controlled laboratory conditions. Although restricted to controlled laboratory conditions and a short observation period, this pilot study demonstrates the potential of explainable machine learning models for TVFA(M) estimation and provides a methodological benchmark for future validation using larger and more diverse datasets.

Background: Coronary artery disease (CAD) remains a leading cause of morbidity and mortality. Early diagnosis reduces adverse outcomes and alleviates the burden on healthcare, yet conventional approaches are often invasive, costly, and not always available. In this context, machine learning offers promising solutions. Objective: The objective of this study is to evaluate the feasibility of reliably predicting both the presence and the severity of CAD. The analysis is based on a harmonized, multi-center UCI dataset that includes cohorts from Cleveland, Hungary, Switzerland, and Long Beach. The work aims to assess the accuracy and practical utility of models built exclusively on routine tabular clinical and demographic data, without relying on imaging. These models are designed to improve risk stratification and guide patient routing. Methods and Results: The study is based on a uniform and standardized data processing pipeline. This pipeline includes handling missing values, feature encoding, scaling, an 80/20 train–test split and applying the SMOTE method exclusively to the training set to prevent information leakage. Within this pipeline, a standardized comparison of a wide range of models (including gradient boosting, tree-based ensembles, support vector methods, etc.) was conducted with hyperparameter tuning via GridSearchCV. The best results were demonstrated by the CatBoost model: accuracy—0.8278, recall—0.8407, and F1-score—0.8436. Conclusions: A key distinction of this work is the comprehensive evaluation of the models’ practical suitability. Beyond standard metrics, the analysis of calibration curves confirmed the reliability of the probabilistic predictions. Patient-level interpretability using SHAP showed that the model relies on clinically significant predictors, including ST-segment depression. Calibrated and explainable models based on readily available data are positioned as a practical tool for scalable risk stratification and decision support, especially in resource-constrained settings.

Unlike reactive systems, mechanical ventilation controlled by CO₂ concentration operates at a target efficiency that dynamically increases whenever the target CO₂ level is exceeded. This approach eliminates the typical ‘dead-time’ and prevents air quality degradation by ensuring the system adjusts its performance immediately in response to concentration changes. In this work, the study focuses on the development and evaluation of data-driven predictive models for near-term indoor CO₂ forecasting that can be integrated into pre-occupancy ventilation strategies, rather than designing a complete control scheme. Experimental data were collected over four months in a 48 m² smart laboratory configured as an open-plan office, where a heterogeneous IoT sensing architecture logged synchronized time-series measurements of CO₂ and microclimate variables (temperature, relative humidity, PM_2.5, TVOCs), together with acoustic noise levels and appliance-level energy consumption used as indirect occupancy-related signals. Raw telemetry was transformed into a 22-feature state vector using a structured feature engineering method incorporating z-score standardization, cyclic time encodings, multi-horizon CO₂ lags, rolling statistics, momentum features, and non-linear interactions to represent temporal autocorrelation and daily periodicity. The study benchmarks multiple regression paradigms, including simple baselines and ensemble methods, and found that an automated multi-level stacked ensemble achieved the highest predictive fidelity for short-term forecasting, with an Mean Absolute Error (MAE) of 32.97 ppm across an observed CO₂ range of 403–2305 ppm, representing improvements of approximately 24% and 43% over Linear Regression and K-Nearest Neighbors (KNN), respectively. Temporal diagnostics showed strong phase alignment with observed CO₂ rises during occupancy transitions and statistically reliable prediction intervals. Five-fold walk-forward cross-validation confirmed the temporal stability of these results, with top models achieving consistent R² values of 0.93–0.95 across Folds 2–5. These results demonstrate that, within a single-room university laboratory setting, historical sensor data from low-cost IoT devices can support accurate short-term CO₂ forecasting, providing a predictive layer that could support future proactive ventilation scheduling aimed at reducing CO₂ lag at the start of occupancy while avoiding unnecessary ventilation runtime. Generalization to other building types and occupancy profiles requires further validation.