Authors - Manar Eloued , Narjes Benameur , Sonia Esseghaier, Salam Labidi Abstract - Magnetic Resonance Elastography (MRE) is a novel, non-invasive im-aging technique for assessing liver stiffness. However, the lack of standardization introduces variability in measurements. The Manual selection of the Region of Interest (ROI) remains subjective and operator-dependent, often including areas with blood vessels or poor wave propagation, which can compromise measurement accuracy. This study proposes a deep learning- based approach to automatically identify an optimal region for liver stiffness measurement (LSM). A total of 160 MRE ex-ams, comprising paired magnitude and wave attenuation images from both healthy individuals and patients with liver disease, were used. A 3D U-Net architecture was trained to segment the liver, blood vessels, gallbladder, and biliary ducts from magnitude images, as well as regions of good wave propagation from attenuation images. The final ROI was obtained by intersecting these segmented regions. The model performance was evaluated on a separate test set using the Dice Similarity Coefficient (DSC), paired Student’s t-test, and Bland-Altman analysis. The resulting LSM region achieved a DSC of 0.89. The t-test yielded p = 0.68, indicating no significant difference between the automated and manual ROIs (p > 0.05). This automated pipeline reduced analysis time from approximately 20 minutes manually to less than 10 seconds automatically, while ensuring reproducibility and reducing operator dependency in MRE by standardizing ROI selection while maintaining diagnostic accuracy. It offers a promising solution to improve the reliability of LSM, particularly for longitudinal follow-up.
Authors - Hector Rafael Morano Okuno Abstract - One of the applications of LLMs (Large Language Models) has been their use as assistants, enabling users to perform specific tasks via prompts, from solving mathematical problems to generating images or videos. This article aims to explore the capabilities of an LLM in generating scripts for the API (Application Programming Interface) of the CAD software Fusion 360, identify the types of geometries it can create, and determine whether it can reproduce images in CAD models. This work was developed during the Manufacturing Systems Automation course for Mechatronics Engineering students, with the intention of introducing them to the use of LLMs in their field. Among the find-ings, it was determined that the user must be familiar with the Fusion 360 API to correct potential errors in the scripts generated by the LLM. Furthermore, the user must be able to specify, via prompts, the characteristics of the parts to be modeled, ensuring that the specifications are compatible with the instructions Fusion 360 understands. Regarding students' experience with LLMs, they found them useful, as they saved time in designing components that require complex automation systems.
Authors - Zilola Mamatvaliyevna Aliyeva, Nigora Primova, Dildor Abduraxmanovna Shadibekova, Malika Akbarova, Azizbek Mahkamov, Gulchehra Raxmatjonovna Xusanova, Shoh-Jakhon Khamdаmov Abstract - The construction industry plays a strategic role in Uzbekistan’s economic development; however, it continues to face challenges related to cost overruns, project delays, and limited financial transparency. Digital transformation offers new opportunities to enhance economic efficiency and project management performance. This study examines the impact of digitalization on construction economics in Uzbekistan, focusing on the implementation of Building Information Modeling (BIM), digital cost estimation systems, electronic procurement platforms, and enterprise resource planning (ERP) solutions. The research develops a conceptual model linking digital adoption level, cost control effectiveness, project performance, and financial outcomes. A quantitative survey of construction companies operating in Uzbekistan was conducted, and Structural Equation Modeling (SEM) was applied to test the proposed relationships. The findings indicate that higher levels of digital integration significantly improve cost estimation accuracy, reduce budget deviations, and shorten project completion time. Digital procurement systems also enhance financial transparency and reduce operational inefficiencies. The study provides empirical evidence that digital transformation positively influences economic performance in Uzbekistan’s construction sector. The results contribute to construction economics literature and offer policy recommendations for accelerating digital adoption in emerging markets.
Authors - Jorge Duque, Antonio Godinho, Jose Moreira, Firmino Silva Abstract - Student dropout in higher education remains a persistent academic, institutional and social challenge, requiring evidence-informed responses. This paper develops and evaluates an explainable machine learning artefact for early dropout-risk identification and for translating predictions into tiered institutional interventions. The study follows six phases of Design Science Research and uses the public Predict Students' Dropout and Academic Success benchmark, with 4,424 students and 37 variables. The pipeline integrates one-hot encoding, derived features, stratified validation, SMOTE applied only inside training folds, Random Forest, XGBoost and SVM as base learners, stacking with a logistic meta-learner and SHAP explanations. The final ensemble achieved 96.4% accuracy, 0.965 weighted precision, 0.964 weighted recall, 0.964 weighted F1-score and 0.99 weighted AUC. The contribution lies in combining performance, interpretability, governance and responsible human intervention.
Authors - Hasna Noushad, Geetha KN Abstract - The brain tumor remains a serious health concern and diagnosis in the primary stage is mandatory for effective treatment. Medical image analysis plays a vital role in understanding the underlying disturbances, monitoring, treatment planning, and intervention strategies. Graph theory is one of the popular techniques used for medical image analysis. The study focuses on the introduction of a systematic approach by using the application of vertex addition of graph theory to synthetically construct a glioma brain network utilizing the normal and abnormal brain Magnetic Resonance Image (MRI). The paper presents the idea of the initial emergence and growth of tumor from a graphtheoretical perspective. Comparative analyses are carried out between normal and abnormal brain network due to the growth of glioma. Results demonstrate that the presence of strong structural deformations and alterations in brain network due to the introduction and growth of glioma. In addition, the article also examines the corresponding increase in the correlation values of the tumor with other normal regions of brain as the tumor grows. This work provides a robust foundation for future studies in epidemiological modeling, machine learning and deep learning methodologies where lack of required data is an issue.
Authors - A Aruna kumari, Sri Vishnu Prabhu Gudavalli, Tamminana Visweswari Abstract - Diabetic Retinopathy (DR) is one of the biggest contributors of visual impairment and blindness in diabetic patients who do not receive proper measures and treatment at the early stages. Due to the ever rising instances of diabetes by the day, more concern has been raised on the effectiveness of methods of effective, scalable and early diagnosis. The given paper is a proposal of a deep learning-based algorithm of DR diagnosis, specifically, the algorithm named Patch-Wise Segmentation and Classification using Convolutional Neural Networks (CNNs). The system in this case is in contrast to the traditional systems, which require the entire retina image to be processed simultaneously by the system, whereby high-resolution fundus photographs are broken into patches. The model addresses each patch individually in order to have the model closely observe minute-scale details and sensitive pathological changes such as hemorrhages and exudates. Patch-wise processing dramatically enhances the capacity of reporting early and mild cases of DR that are hard to recognize in the fullimage processing due to intricacy of an image and noise. The CNN architecture is additionally medical image particular and operates by use of layers and regularization methods so as to permit not only accuracy but also generalization over a wide range of datasets. As shown in the results of the experiments, the patchwise technique performs better in comparison with the traditional ones, i.e., sensitivity, specificity, and classification accuracy on each of the stages of the DR. In addition, the system is fully automated and stable that minimizes the use of human marking and offers the facility to operate worldwide
