Our research analyses the emergence of real-time systems, their service conditions and possibilities. It compares IoT and enterprise solutions in terms of the feasibility of measurement and process control.

Real-time systems are not only emerging in industry, but also in education. The potential of stand-alone micromachines has been boosted, and web-based real-time systems have become a must (E-Lection).In this topic, we will explore how this old new world offers opportunities to support and assist education.

The group was formed in 2021 as part of a TÁMOP project, in which we are investigating the possibilities of creating real-time systems and applications.

Neuro-imaging data is necessary for timely and correct diagnosis of the disease Alzheimer’s (AD). Early clinical intervention is challenging due to the high-dimensional nature of the images, making it computationally difficult to work on representations, sparse annotated datasets and inter-subject variance. Deep convolutional VGG, ResNet, DenseNet, MobileNet and other neural networks (CNNs) have shown good performance. Diagnostic performance, but they are based on a high number of parameters and require highly trained personnel. Data can often times restrict resilience to data-skimy clinical settings. To address these challenges, the current paper presents a hybrid Quantum Machine Learning (QML) architecture for Alzheimer’s disease Detection and classification based on a combination of classical CNN-based feature extraction and Variational quantum classifier. In the suggested method, diseased, relevant, and compact features are extracted using pre-trained CNN backbones. Data from structural MRI scans is later translated into quantum states and computed. With parameterised variational quantum circuits optimised with a hybrid quantum-classical learning loop. The proposed method has been experimentally evaluated on benchmark datasets of MRI scans of Alzheimer’s patients. The hybrid CNNQML model has a mean classification accuracy of about 93% which is similar to deep CNN models, including ResNet and DenseNet (94%), and much more. Beating lightweight models like MobileNet and shallow CNNs by six percentage points. It is worth noting that the hybrid QML model performs better in conditions with limited training data. Performance degradation is observed in accuracy and AUC for all CNN variants, with up to 5% lower performance to deep CNNs. Cross-validation, confidence interval, paired significance test, and statistical validation. Effect size analysis proves the fact that the hybrid model produces statistically significant performance, as well as cases of clinically meaningful improvements over lightweight CNN baselines. Similar to the state-of-the-art deep networks with significantly reduced trainable parameters in the classification stage. These findings show that hybrid QML is a data-efficient and powerful substitute in the diagnosis of Alzheimer’s. On the whole, this is a work that sets a practical way to integrate quantum machine learning into clinical neuro-imaging pipelines and hybrid settings. QML has supplementary deep-learning enhancement to classics in next-generation Alzheimer’s Decision-support systems.

Optimization search algorithms plays a crucial role in resolving complex constrained engineering and Multi Layer Perceptron (MLP) classification problems, often requiring efficacious and robust search strategies. This study introduces the Chaotic Zebra Optimization Algorithm (CZOA), an enriched version of the classical Zebra Optimization Algorithm (ZOA), incorporating chaotic maps to enhance search efficiency and convergence speed. The CZOA variants (CZOA1, CZOA2, CZOA3, and CZOA4) are evaluated using 23 standard benchmark functions across multiple dimensions (10D, 30D, 50D, and 100D), the CEC 2022 benchmark test suite, and three constrained engineering optimal design problems (three-bar truss, pressure vessel, and multi-disk clutch optimization). Furthermore, CZOA’s effectiveness is evaluated on four MLP classification datasets (Balloon, Iris, Breast Cancer, and XOR dataset). Simulation results reveal that CZOA1 consistently outperforms other variants and classical ZOA, achieving the lowest fitness values, lowered standard deviations, and enriched convergence rates. Specifically, in F1 (10D), CZOA1 reduces computation time from 4559.9 s (ZOA) to just 0.1563 s, achieving faster speed up. In CEC 2022 functions test suit, CZOA1 delivers the best performance with an average fitness of 333.86 and a standard deviation of 0.69, surpassing state-of-the-art metaheuristics such as GWO, WOA, and TLBO. Engineering optimal design results confirm CZOA1’s superior fitness values, with 263.8965 in the three-bar truss problem and 6849.485 in pressure vessel optimization, outperforming classical methods like GWO, DE, and PSO. For MLP classification tasks, CZOA1 achieves a 99.00% classification rate in breast cancer detection, highlighting its superior ability in handling high-dimensional search spaces. Comparative evaluations demonstrate that chaotic maps significantly enhance exploration–exploitation balance, reduce computational search complexity, and improve solution accuracy. The findings establish CZOA as a promising optimization technique for real-world engineering and classification challenges.

The rise of 3-D data dependency across various fields, from medical imaging to virtual reality, requires robust security to prevent leakage or unauthorized access and to keep sensitive information secure from tampering. As 3-D models consist of floating-point numbers arranged in a specialized format, their protection necessitates a reliable encryption technique to safeguard sensitive data across various domains. In this paper, a symmetric encryption technique for 3-D models based on the quantum logistic map, Arnold cat map, improved logistic map, and DNA encoding is presented. Initially, the hash value derived from the SHA-256 function, combined with the plaintext data, is used as a symmetric key to establish the initial conditions for the chaotic system. Following this, the improved logistic map (ILM) is applied to introduce confusion among the coordinate values of plaintext data. Subsequently, the partially encrypted data is segmented into integer and fractional components. The integer matrix undergoes scrambling and diffusion through the complete framework of the quantum logistic map, Arnold cat map, DNA diffusion algorithm, and DNA complementary rule, enhancing the complexity of the encryption process. On the other hand, the fractional part is manipulated using the quantum logistic map. Finally, the transformed integer and fractional parts are systematically integrated to obtain the final encrypted data. The proposed algorithm undergoes rigorous testing on a variety of 3-D models, validating the effectiveness of the proposed encryption scheme by offering a high entropy value close to 8, 100% NPCR and 33.37% UACI values, near-zero correlation, strong resistance against differential attack, maintained computational efficiency, and high key sensitivity. Furthermore, encryption time varies linearly with the size of input 3-D model, demonstrating the applicability of the proposed algorithm for large-scale 3-D datasets.

AI enabled recruitment systems suffered with the challenges like bias, transparency, and fairness in student placement processes. Traditional manual hiring methods are always found slow and subjective, leaves doubt of unintentional discrimination. This study proposes a data-driven recruitment framework that combines Natural Language Processing (NLP), Machine Learning (ML), and Explainable AI (XAI) to enhance résumé screening, predict placement outcomes, and assess fairness. It investigate the four key questions here (1) the extent to which NLP models improve the résumé screening accuracy; (2) the effectiveness of fairness oriented ML methods in reducing demographic bias; (3) the contribution of XAI to transparency and interpretability; and (4) also the role of Aspect-Based Sentiment Analysis (ABSA) in deriving the actionable insights from recruiter student feedback.