بابک مجیدی

Through advanced analytical models such as machine learning (ML) and, conceptually, Large Language Models (LLMs), this study explores how Industrial Internet of Things (IIoT) applications can transform educational experiences in the context of smart steel production. To mitigate the shortage of authentic industrial datasets for research, we developed an industry-validated IIoT educational dataset drawn from three months of operational records at a steel plant and enriched with domain-specific annotations—most notably distinct operational phases. Building on this foundation, we propose an IIoT framework for intelligent steel manufacturing that merges ML-driven predictive analytics (employing Lasso regression to optimize energy use) with LLM-based contextualization of data streams within IIoT environments. At its core, this architecture delivers real-time process monitoring alongside adaptive learning modules, effectively simulating the dynamics of a smart factory. By promoting human–machine collaboration and mirroring quality-control workflows, the framework bridges the divide between theoretical instruction and hands-on industrial practice. A key feature is an interactive decision-support dashboard: this interface presents ML model outcomes and elucidates IIoT measurements—such as metallization levels and H 2 /CO ratios—through dynamic visualizations and scenario-based simulations that invite risk-free exploration of energy-optimization strategies. Such tools empower learners to grasp the intricate multivariate dependencies that govern steel manufacturing processes. Our implementation of the Lasso regression model resulted in a 9% reduction in energy consumption and stabilization of metallization levels. Overall, these findings underscore how embedding advanced analytics within IIoT education can cultivate a more engaging, practice-oriented learning environment that aligns closely with real-world industrial operations.

Early cancer detection is crucial for improving patient survival rates, as timely intervention greatly enhances treatment efficacy. One promising method for early detection is identifying cancerous cells through the detection of protein-level modifications, which serve as early indicators of malignancy. These protein modifications often result from complex biochemical processes that occurs before visible cellular abnormalities, making them critical targets for diagnostic technologies. In recent years, wireless biomedical sensors have advanced significantly, enabling precisely detecting these protein-level changes. These sensors have the potential to detect cancer at its earliest stages by monitoring the subtle alterations in protein structures and functions that distinguish healthy cells from cancerous ones. As the costs of genetic analysis continue to decrease, the development of Medical Internet of Things (MIoT) devices has become increasingly feasible. These devices are designed to perform real-time analyses of biological specimens—such as blood and urine—by detecting protein-level changes indicative of cancer. In this paper, a new machine learning method based on Extreme Randomized Trees (ERT) is developed to increase the speed of classification of cancerous cells based on single-cell Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq). The proposed method enhances the classification speed of the limited and noisy ATAC-seq data as it requires less computation to determine the best splits at each node of the decision trees. This method can significantly improve near real-time cancer risk assessment using samples collected by MIoT. Our proposed method achieves classification accuracy comparable to state of the art single-cell ATAC-seq data analysis techniques while reducing processing time by 259%, challenged by various low-data scenarios. This approach presents an efficient solution for rapid cancer monitoring within the MIoT framework.