Side-channel attacks (SCAs) are powerful techniques used to recover keys from electronic devices by exploiting various physical leakages, such as power, timing, and heat. Although heat is one of the less frequently analyzed channels due to the high noise associated with thermal traces, it poses a significant and growing threat to the security of very large-scale integrated (VLSI) microsystems, particularly system in package (SiP) technologies. Thermal side-channel attacks (TSCAs) exploit temperature variations, risking not only hardware damage from excessive heat dissipation but also enabling the extraction of sensitive data, like cryptographic keys, by observing thermal patterns. This dual threat underscores the need for a synergistic approach to thermal management and security in designing integrated microsystems. In response, this paper presents a novel approach that improves the early detection of abnormal thermal fluctuations in SiP designs, preventing cybercriminals from exploiting such anomalies to extract sensitive information for malicious purposes. Our approach employs a new concept called Thermal Digital Twin (TDT), which integrates two previously separate methods and techniques, resulting in successful outcomes. It combines the gradient direction sensor scan (GDSSCAN) to capture thermal data from the physical field programmable gate array (FPGA), which guarantees rapid thermal scan with a measurement period that could be close to $10 \mathsf{\mu }\mathrm{s},$ a resolution of ${0.5 }^{\circ }$C, and a temperature range from −${40 }^{\circ }$C to ${140 }^{\circ }$C; once the data are transmitted in real time to a Digital Twin created in COMSOL Multiphysics^® 6.0 for simulation using the Finite Element Method (FEM), the real time required by the CPU to perform all the necessary calculations can extend to several seconds or minutes. This integration allows for a detailed analysis of thermal transfer within the SiP model of our FPGA. Implementation and simulations demonstrate that the Thermal Digital Twin (TDT) approach could reduce the risks associated with TSCA by a significant percentage, thereby enhancing the security of FPGA systems against thermal threats.

Lung cancer, the leading cause of cancer-related deaths globally, presents significant challenges in early detection and diagnosis. The effective analysis of pulmonary medical imaging, particularly computed tomography (CT) scans, is critical in this endeavor. Traditional diagnostic methods, which are manual and time-intensive, underscore the need for innovative, efficient, and accurate detection approaches. To address this need, we introduce the Adaptive Range Slice Grouping Network (ARSGNet), a novel deep learning framework that enhances early lung cancer diagnosis through advanced segmentation and classification techniques in CT imaging. ARSGNet synergistically integrates the strengths of EfficientNet and Transformer architectures, leveraging their superior feature extraction and contextual processing capabilities. This hybrid model proficiently handles the complexities of 3D CT images, ensuring precise and reliable lung nodule detection. The algorithm processes CT scans using short slice grouping (SSG) and long slice grouping (LSG) techniques to extract critical features from each slice, culminating in the generation of nodule probabilities and the identification of potential nodular regions. Incorporating shapley additive explanations (SHAP) analysis further enhances model interpretability by highlighting the contributory features. Our extensive experimentation demonstrated a significant improvement in diagnostic accuracy, with training accuracy increasing from 0.9126 to 0.9817. This advancement not only reflects the model’s efficient learning curve but also its high proficiency in accurately classifying a majority of training samples. Given its high accuracy, interpretability, and consistent reduction in training loss, ARSGNet holds substantial potential as a groundbreaking tool for early lung cancer detection and diagnosis.

Background: To propose an automatic liver and hepatic vessel segmentation solution based on a stacking model and decision fusion. This model combines the decisions of multiple models to achieve increased accuracy. It exhibits improved robustness due to the reduction of individual errors. Flexibility is also a key asset, with combination methods such as majority voting or weighted averaging. The model enables managing the uncertainty associated with individual decisions to obtain a more reliable final decision. The combination of decisions improves the overall accuracy of the system. Methods: This research introduces a new deep learning-based architecture for automatically segmenting hepatic vessels and tumors from CT scans, utilizing stacking, decision fusion, and deep transfer learning to achieve high-accuracy and rapid segmentation. This study employed two distinct datasets: the external “Medical Segmentation Decathlon (MSD) task 08” dataset and an internal dataset procured from Ibn Sina University Hospital encompassing a cohort of 112 patients with chronic liver disease who underwent contrast-enhanced abdominal CT scans. Results: The proposed segmentation model reached a DSC of 83.21 and an IoU of 72.76 for hepatic vasculature and tumor segmentation, thereby exceeding the performance benchmarks established by the majority of antecedent studies. Conclusions: This study introduces an automated method for liver vessels and liver tumor segmentation, combining precision and stability to bridge the clinical gap. Furthermore, decision fusion-based stacking models have a significant impact on clinical applications by enhancing diagnostic accuracy, enabling personalized care through the integration of genetic, environmental, and clinical data, optimizing clinical trials, and facilitating the development of personalized medicines and therapies.

Background/Objectives: In resource-limited settings, where access to advanced imaging modalities such as magnetic resonance imaging (MRI) and histopathological confirmation may be limited, differentiating between hepatic cysts and metastatic lesions based solely on computed tomography (CT) remains challenging. This limitation may affect diagnostic confidence and increase the risk of misclassification, potentially impacting clinical decision making and patient management. In this study, we aimed to explore a more direct and automated approach for classifying hepatic lesions from CT images. Methods: We developed a deep learning-based framework combining transfer learning, decision fusion, and a stacking strategy by integrating five CNN architectures. The study included 100 patients, equally divided between metastatic liver tumors and pathological hepatic cysts. The dataset was built from both public data (LiTS) and internal clinical cases, and then split into training and testing sets. Results: The proposed stacking model provided the most consistent results, reaching an accuracy of 0.98, with high precision and sensitivity. The improvements in individual models, although moderate, were observed across all evaluation metrics. Conclusions: Overall, this approach offers a practical and reliable way to classify hepatic lesions with minimal manual intervention. It may help improve consistency in diagnosis and could be integrated into clinical workflows to support decision making in low-resource areas.

Background: Metastatic liver tumors (MLT) and parasitic liver cysts (PLC) are common liver conditions that often exhibit similar imaging characteristics, making accurate diagnosis challenging using imaging alone. This overlap can result in diagnostic errors and delayed treatment, particularly in resource-limited settings or when invasive procedures such as biopsies are not feasible due to risk or unavailability. This study aimed to develop a reliable and transparent machine learning approach to distinguish MLT from PLC using radiomic features derived from computed tomography (CT). Methods: We propose an explainable radiomics-based machine learning framework for the non-invasive, accurate, and interpretable discrimination of MLT and PLC, designed to assist radiologists in reducing diagnostic ambiguity and expediting patient management. This retrospective study included 30 adult patients, comprising 15 with liver metastases and 15 with pathologic hepatic cysts. Radiomic features were extracted from pre-treatment CT scans using PyRadiomics. Feature selection was performed using three complementary methods: Mutual Information, Lasso regression, and LightGBM importance ranking. HistGradientBoosting classifiers were then trained on each selected feature set. Results: Model performance was evaluated using 5-fold cross-validation and assessed with ROC AUC, accuracy, precision, recall, and F1-score. SHAP analysis was applied to interpret the models and identify key radiomic biomarkers. Statistical comparisons were performed using DeLong’s test for AUCs, McNemar’s test for classification agreement, and paired t-tests for metrics such as accuracy and F1-score. The Mutual Information-based model achieved the highest mean AUC (0.9717 ± 0.0267), significantly outperforming the other models (p < 0.035). Key features contributing to classification included texture entropy, interquartile range, and gray level non-uniformity. Conclusion: We developed a robust and interpretable machine learning framework for differentiating metastatic liver tumors from parasitic liver cysts using CT-derived radiomic features. The integration of Mutual Information feature selection, ensemble learning, and SHAP explainability ensured high diagnostic accuracy, strong calibration, and transparency. The proposed framework demonstrates substantial clinical relevance and holds promise for real-world implementation.