Content co-creation for novice programmers

We ran an action research on content co-creation for 53 novice programming students at Master level from diverse backgrounds, aiming to increase motivation and understanding. An optional survey gathered positive feedback from all 26 respondents regarding their motivation and understanding. These findings highlight the positive impact of inclusive approaches in programming education, warranting further exploration in larger settings.</p

MSTAC: a multi-stage automated classification of COVID-19 chest X-ray images using stacked CNN models

This study introduces a Multi-Stage Automated Classification (MSTAC) system for COVID-19 chest X-ray (CXR) images, utilizing stacked Convolutional Neural Network (CNN) models. Suspected COVID-19 patients often undergo CXR imaging, making it valuable for disease classification. The study collected CXR images from public datasets and aimed to differentiate between COVID-19, non-COVID-19, and healthy cases. MSTAC employs two classification stages: the first distinguishes healthy from unhealthy cases, and the second further classifies COVID-19 and non-COVID-19 cases. Compared to a single CNN-Multiclass model, MSTAC demonstrated superior classification performance, achieving 97.30% accuracy and sensitivity. In contrast, the CNN-Multiclass model showed 94.76% accuracy and sensitivity. MSTAC’s effectiveness is highlighted in its promising results over the CNN-Multiclass model, suggesting its potential to assist healthcare professionals in efficiently diagnosing COVID-19 cases. The system outperformed similar techniques, emphasizing its accuracy and efficiency in COVID-19 diagnosis. This research underscores MSTAC as a valuable tool in medical image analysis for enhanced disease classification

Ensemble Deep Learning Architectures for Automated Diagnosis of Pulmonary Tuberculosis using Chest X-ray

Tuberculosis (TB) is still a serious public health concern across the world, causing 1.4 million deaths each year. However, there has been a scarcity of radiological interpretation skills in many TB-infected locations, which may cause poor diagnosis rates and poor patient outcomes. A cost-effective and efficient automated technique might help screening evaluations in underprivileged countries and provide early illness diagnosis. In this work, we proposed a deep ensemble learning framework that integrates multisource data of two deep learning-based techniques for the automated diagnosis of TB. The integrated model framework has been tested on two publicly available datasets and one private dataset. While both proposed deep learning-based automated detection systems have shown high accuracy and specificity compared to state-of-the-art, the en- semble method significantly improved prediction accuracy in detecting chest radiographs with active pulmonary TB from a multi-ethnic patient cohort. Extensive experiments were used to validate the methodology, and the results were superior to previous approaches, showing the method’s practicality for application in the real world. By integrating supervised prediction and unsupervised representation, the ensemble method accu- rately classified TB with the area under the receiver operating characteristic (AUROC) up to 0.98 using chest radiography outperforming the other tested classifiers and achieving state- of-the-art. The methodology and findings provide a viable route for more accurate and quicker TB detection, especially in low and middle-income nations. </p

Euro-Asian Collaboration for Enhancing STEM Education: EASTEM WP4: Industry engagement and competence integration into STEM educational programs. Deliverable D4

EASTEM is a capacity-building project funded by Erasmus+ (KA2) with the aim of improving employability of STEM graduates from partner universities by ensuring students acquire skills needed in the workplace. Over the course of 3 years (2019-2022) and with a budget of 999.000 EUR, the project brings together 3 universities from Europe and 10 from Asia, creating a platform for partner universities to exchange best practices on student-centered and competency-based STEM education. The EASTEM project is co-funded with support from the European Commission, project (number 598915-EPP-1-2018-1-SE-EPPKA2-CBHE-JP) under the Erasmus+ program. This document reflects only the views of the authors. The Commission is not responsible for any use that may be made of the information contained therein. This document and its annexes in their latest versions are available from the EASTEM website (www.eastemproject.eu). EASTEM Work Package 4 (WP4) is to facilitate industry engagement and competence integration into educational programs. Building on good case practises from all partner institutions, IMT Atlantique guided partners on how to integrate competence development for students into STEM education programmes and university strategies