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Creating an Inclusive, Vibrant Learning Environment within a Large, software engineering program - Experiential Learning Experiences Created for Students, Faculty, and Senior Design Coaches & Sponsors

Creating an Inclusive, Vibrant Learning Environment within a...

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CoNECD 2021

作者： Bailey, Margaret B. Sharma, Naveen Verschage, Lana Conlon, Timothy T. Department of Mechanical Engineering Kate Gleason College of Engineering Rochester Institute of Technology Department of Software Engineering Golisano College of Computing and Information Sciences Rochester Institute of Technology Xerox Corporation Women in Computing Program Golisano College of Computing and Information Sciences Rochester Institute of Technology

Creating an Inclusive, Vibrant Learning Environment within a Large, software engineering program - Experiential Learning Experiences Created for Students, Faculty, and Senior Design Coaches & Sponsors This presentation describes several initiatives currently underway within a large software engineering (SE) department at a private university within the northeast US to positively affect levels of inclusiveness within the department's learning environment. Information gathered from faculty, staff, student interviews and focus group discussions, as well as a department goal to improve female freshmen retention in the SE major motivated the SE department chair, departmental academic advisors and faculty, and the college's women in computing director to launch this set of aligned activities in collaboration with the NSF ADVANCE funded program (NSF #1209115) at the university level. The resulting activities ideally enhance diversity and inclusion for students from all underrepresented groups in the program with a focus on groups based on gender. Four experiential learning experiences have been created and concurrently implemented within the SE department to promote an inclusive academic environment. These include: (1) faculty targeted discussions and summer readings, (2) an interactive workshop designed for all first year SE students, (3) a workshop created for the coaches and sponsors, most of who are not regular RIT employees, who directly mentor student teams for the two‐semester senior project course, and (4) the development of a resource flowchart which supports students, faculty, and staff in maintaining an inclusive learning environment within the department. The approach used in creating each is adaptive and the four resulting products are multi‐faceted in regards to target audience, modality of learning experience, and composition of creation team. Furthermore, we will discuss key metrics aligned with the department's goal to measure impacts resulting from these

关键词： Students

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RIT's BS program in software engineering: initial experiences

RIT's BS program in software engineering: initial experience...

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Frontiers in Education (FIE) Conference

作者： M.J. Lutz Software Engineering Program Rochester Institute of Technology Rochester NY USA

Summary form only given. In 1996, Rochester Institute of Technology launched the US's first Bachelor of Science program in software engineering. The program is innovative in many respects, including its interdisciplinary sponsorship by the Departments of Computer Science and Computer engineering, which are themselves housed in two distinct colleges of RIT. In addition, the curriculum was developed with input from across a broad range of industrial software development domains: commercial off-the-shelf (COTS), finance and banking, embedded real-time control systems, and telecommunications and networking. The result is a rich, broad-based curriculum, where the foundation laid by the core computer science and engineering courses provides the basis for deeper upper division study in software engineering. The curriculum is very much a work in progress. The paper focuses on experiences to date, including issues related to administering a multidisciplinary program, student response to the initial set of courses, and plans for the future.

关键词： software engineering Computer science Telecommunication computing Educational institutions Communication industry Computer industry Electrical equipment industry Industrial control programming Finance

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Association of maternal multi-metal exposure and dyslipidemia: a study of air pollution on pregnancy outcomes

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BMC Pregnancy and Childbirth 2025年第1期25卷 1-9页

作者： Go, Yoon-Young Hur, Young Min You, Young-Ah Park, Sunwha Lee, Gain Chae, Rin Kim, Soo-Min Kim, Young Ju Department of Obstetrics and Gynecology College of Medicine and Ewha Medical Institute Ewha Womans University 1071 Anyangcheon-Ro Yangcheon-Gu Seoul 07985 South Korea Graduate Program in System Health Science and Engineering Ewha Womans University Seoul 07985 South Korea Graduate Program in Division of Artificial Intelligence and Software/Artificial Intelligence Convergence Ewha Womans University Seoul 07985 South Korea

Background: Exposure to air pollutants, including heavy metals, is a major environmental concern of public health and these environmental toxicants have been associated with pregnancy complications. Objectives: An air pollution on pregnancy outcome (APPO) study was performed to investigate the adverse effects of fine particulate matter (PM_2.5) exposure on pregnancy outcomes. This study examined the association between maternal urinary metal mixtures and pregnancy complications, including dyslipidemia and preterm birth (PTB). Methods: The concentrations of 16 metals were measured in 60 urine samples collected during the second trimester pregnancy. Logistic regression and Bayesian kernel machine regression (BKMR) models were used to analyze the single and overall effects of metal exposure on pregnancy complications, respectively. Results: Logistic regression analysis showed a significant difference in urinary Ni and Zn concentrations between those exposed to high and low concentrations of fine particulate matter with an aerodynamic diameter of less than 2.5 µm (PM_2.5) and those not exposed. Four metals (Ni, Sc, Mo, and Cs) were positively associated with total cholesterol (TC) levels, but not with triglyceride (TG) levels and PTB. The BKMR model showed that the overall mixture of 16 metals was positively correlated with high TC and TG levels during the third trimester of pregnancy, and the individual effects of Mo and Pb were the most significant. However, we were only able to identify a trend between maternal exposure to metal mixtures and PTB. Conclusions: BKMR analyses showed a positive association between exposure to multi-metal mixtures and higher maternal TC and TG levels, a factor that contributes to PTB. Therefore, this also suggests that multi-metal exposure during pregnancy may be a potential risk factor for PTB. © The Author(s) 2025.

关键词： Heavy metals Overall effect Pregnancy outcomes Total cholesterol Triglyceride

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Towards Trustworthy Aircraft Safety: Explainable AI for Accurate Incident and Accident Predictions 20th

Towards Trustworthy Aircraft Safety: Explainable AI for Accu...

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20th International Conference on the Economics of Grids, Clouds, Systems, and Services, GECON 2024

作者： Amin, Maryam Noor, Umara Fatima, Manahil Rashid, Zahid Altmann, Jörn Department of Software Engineering Faculty of Computing and Information Technology International Islamic University Islamabad44000 Pakistan Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul Korea Republic of College of Engineering Seoul National University Seoul Korea Republic of Integrated Major in Smart City Global Convergence Seoul National University Seoul Korea Republic of

ISBN: (纸本)9783031812255

Despite technological advancements, ensuring aircraft safety remains a challenge, however, Machine learning (ML)-based approaches for predicting future incidents play a crucial role in addressing flight safety. As ML models increase in complexity, their decision-making process becomes less transparent, posing significant challenges to trustworthiness. While simpler models demonstrate lower accuracy, more intricate models such as deep neural networks achieve higher accuracy but sacrifice interpretability. In this study, we enhance trustworthiness in aircraft safety prediction by leveraging a dataset of past accidents and incidents to prevent similar accidents from occurring in the future. To achieve this, we apply Random Forest and Extreme Gradient Boosting models to classify different categories of aircraft incidents. Additionally, we apply two powerful explainable artificial intelligence (XAI) techniques: Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP) to provide insights into both local and global predictions made by the models. Notably, our results reveal high accuracy in these predictions while maintaining trustworthiness. This research contributes to the advancement of XAI and offers valuable insights for safety-critical applications and decision support systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Aircraft accidents

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Machine Learning Stroke Prediction in Smart Healthcare:Integrating Fuzzy K-Nearest Neighbor and Artificial Neural Networks with Feature Selection Techniques

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Computers, Materials & Continua 2025年第3期82卷 5115-5134页

作者： Abdul Ahad Ira Puspitasari Jiangbin Zheng Shamsher Ullah Farhan Ullah Sheikh Tahir Bakhsh Ivan Miguel Pires Department Information System Study Program Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Software Northwestern Polytechnical UniversityXi’an710072China Research Center for Quantum Engineering Design Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Computer Science and Software Engineering Shenzhen UniversityShenzhen518061China Cybersecurity Center Prince Mohammad Bin Fahd University617Al JawharahKhobarDhahran34754Saudi Arabia Cardiff School of Technologies Cardiff Metropolitan UniversityWestern AvenueCardiffCF52YBUK Instituto de Telecomunicacoes Escola Superior de Tecnologia e Gestao de AguedaUniversidade de Aveiroágueda3750-127Portugal

This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both *** BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke *** findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.

关键词： Fuzzy K-nearest neighbor artificial neural network accuracy precision recall F-measure Chi-Square best search first heart stroke

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Unlocking the potential of RNN and CNN models for accurate rehabilitation exercise classification on multi-datasets

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Multimedia Tools and Applications 2025年第3期84卷 1261-1301页

作者： Zaher, Moamen Ghoneim, Amr S. Abdelhamid, Laila Atia, Ayman Software Engineering Program Faculty of Computers & Artificial Intelligence—Helwan University Cairo Ain Helwan11795 Egypt Computer Science Department Faculty of Computers & Artificial Intelligence—Helwan University Cairo Ain Helwan11795 Egypt Information System Department Faculty of Computers & Artificial Intelligence—Helwan University Cairo Ain Helwan11795 Egypt Cairo Ain Helwan11795 Egypt Giza October12451 Egypt

Physical rehabilitation is crucial in healthcare, facilitating recovery from injuries or illnesses and improving overall health. However, a notable global challenge stems from the shortage of professional physiotherapists, particularly acute in some developing countries, where the ratio can be as low as one physiotherapist per 100,000 individuals. To address these challenges and elevate patient care, the field of physical rehabilitation is progressively integrating Computer Vision and Human Activity Recognition (HAR) techniques. Numerous research efforts aim to explore methodologies that assist in rehabilitation exercises and evaluate patient movements, which is crucial as incorrect exercises can potentially worsen conditions. This study investigates applying various deep-learning models for classifying exercises using the benchmark KIMORE and UI-PRMD datasets. Employing Bi-LSTM, LSTM, CNN, and CNN-LSTM, alongside a Random Search for architectural design and Hyper-parameter tuning, our investigation reveals the (CNN) model as the top performer. After applying cross-validation, the technique achieves remarkable mean testing accuracy rates of 93.08% on the KIMORE dataset and 99.7% on the UI-PRMD dataset. This marks a slight improvement of 0.75% and 0.1%, respectively, compared to previous techniques. In addition, expanding beyond exercise classification, this study explores the KIMORE dataset’s utility for disease identification, where the (CNN) model consistently demonstrates an outstanding accuracy of 89.87%, indicating its promising role in both exercises and disease identification within the context of physical rehabilitation. © The Author(s) 2024.

关键词： Classification (of information)

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Generalize or Specialize? The Effect of Coil Combining in Deep Learning Based MR Image Reconstruction

Generalize or Specialize? The Effect of Coil Combining in De...

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IEEE International Symposium on Biomedical Imaging

作者： Natalia Dubljevic Richard Frayne Roberto Souza Biomedical Engineering Graduate Program Hotchkiss Brain Institute University of Calgary Calgary AB Canada Seaman Family MR Research Centre Foothills Medical Centre Calgary AB Canada Biomedical Engineering Graduate Program Radiology Clinical Neurosciences Hotchkiss Brain Institute University of Calgary Calgary AB Canada Biomedical Engineering Graduate Program Electrical and Software Engineering Hotchkiss Brain Institute University of Calgary Calgary AB Canada

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

With the advent of deep-learning based MR image reconstruction, new questions are raised regarding best design choices. In particular, when using recent reconstruction models that have been developed for processing multi-channel MR data. A fundamental question is whether to first combine channels to improve model generalizability (i.e., a “coil-combined” approach) or to keep channel processing separate to fully utilize multi-channel information (i.e., an “all-coil” approach). In this work, we compare three popular architectures using coil-combined and all-coil styles on brain MR images. All-coil styles improved in-distribution performance, such as when reconstructing only presumed healthy individu-als. Coil-combined designs better generalized to unseen data from patients with pathology.

关键词： Deep learning Coils Pathology Codes Magnetic resonance Brain modeling Data models Image reconstruction Biomedical imaging

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Enhancing Video Action Recognition with Spatial–Temporal Convolutional Attention Networks 15th

Enhancing Video Action Recognition with Spatial–Temporal Co...

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15th International Conference on Intelligent Computing and Networking, IC-ICN 2024

作者： Kumar, Pramod Quraishi, Aadam Rusho, Maher Ali Raparthi, Mohan Degadwala, Sheshang Computer Science and Technology Ganga Institute of Technology and Management Haryana Jhajjar Kablana124104 India Intervention Treatment Institute HoustonTX United States Degree Program University of Colorado Boulder CO United States Software Engineer Alphabet Life Science DallasTX75063 United States Department of Computer Engineering Sigma University Gujarat Vadodara India

ISBN: (纸本)9789819786305

Effectively harnessing feature correlations is crucial for optimal performance in video action recognition tasks, whether in spatial or temporal dimensions. Convolutional operations excel at capturing local features through correlations among nearby points, while self-attention mechanisms capture global information by enabling interactions among all feature points. However, the limitations of a single convolutional layer in comprehending holistic feature correlations and the tendency of self-attention layers to overlook local positional characteristics necessitate an innovative approach. We introduce the Spatial–Temporal Convolutional Attention Network, comprising the Spatial Convolutional Attention Network and the Temporal Convolutional Attention Network. This approach strategically combines the strengths of self-attention for global context and convolution for local features, mitigating the limitations of individual layers. Key innovations include [explicitly state innovations]. To reinforce the uniqueness of our approach, we provide specific comparisons with existing methods, highlighting its superiority. In rigorous experiments, our proposed network demonstrates enhanced recognition performance, quantified through metrics such as accuracy and F1 score improvements over baseline models. By effectively capturing feature correlations, our approach elevates the neural network's capacity for spatiotemporal modeling, marking a significant advancement in video action recognition. Elevating the neural network's capacity for spatiotemporal modeling. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Quantifying White Matter Hyperintensities: Predicting Periventricular Fazekas Scores with Uncertainty Estimation

Quantifying White Matter Hyperintensities: Predicting Perive...

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IEEE International Symposium on Biomedical Imaging

作者： Susanne Schmid Amirmohammed Shamaei Roberto Souza Richard Frayne Biomedical Engineering Graduate Program University of Calgary Hotchkiss Brain Institute University of Calgary Seaman Family MR Research Centre Foothills Medical Centre Calgary AB Canada Electrical and Software Engineering University of Calgary Radiology University of Calgary Clinical Neurosciences University of Calgary

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

White matter hyperintensities (WMH) are crucial markers in brain magnetic resonance (MR) images, often quantified using the Fazekas score. This study presents a deep learning model to predict Fazekas scores in the periventricular region from T1-weighted and FLAIR images. Our model achieved a Matthew correlation coefficient of 0.68 and F1 scores of 0.69, 0.88, 0.86, and 1.0 for Fazekas scores from 0 to 3, respectively, surpassing previous methods. We introduce a novel integration of t-distributed stochastic neighbor embedding (t-SNE) with uncertainty analysis using Monte Carlo dropout, offering insights into the model's decision-making. Results show effective class distinction, with increased uncertainty at class transitions indicating ambiguity, and confident misclassified cases suggesting overlapping features or label noise. These findings highlight that simple, well-tuned models, coupled with interpretability techniques, can provide robust predictions of WMH severity.

关键词： Deep learning Uncertainty Monte Carlo methods Noise Stochastic processes Magnetic resonance Estimation Predictive models Brain modeling White matter

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Erratum to “Investigation of the effect of colorless distributed combustion method on the flame of methane/hydrogen fuel mixture using image processing method” [Int. J. Hydrogen Energy 116 4 (2025) 312-322]

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International Journal of Hydrogen Energy 2025年 128卷 359-366页

作者： Bugrahan Alabas Osman Kumuk Ahmet Ozcan Department of Aeronautical Engineering Faculty of Aeronautics and Astronautics Erciyes University Kayseri Turkiye Unmanned Aerial Vehicle Technology and Operatorship Program Iskenderun Vocational School of Higher Education Iskenderun Technical University Hatay Turkiye Deparment of Software Development School of Applied Sciences Cappadocia University Nevsehir Turkiye

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