Resilience, Confidence-Building, and Performance: by Audrey Ricke

“…Such an analysis, which considers mediating factors not previously analyzed together in adaptive digital learning studies, such as individual rather than aggregate performance, digital learning platform design differences, resiliency factors, and in-class activities, is an important step in clarifying some of the previously mixed results. …”
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Machine Learning-Based Software for Predicting <i>Pseudomonas</i> spp. Growth Dynamics in Culture Media by Fatih Tarlak

“…In predictive microbiology, both primary and secondary models are widely used to estimate microbial growth, often applied through two-step or one-step modelling approaches. This study focused on developing a tool to predict the growth of <i>Pseudomonas</i> spp., a prominent bacterial genus in food spoilage, by applying machine learning regression models, including Support Vector Regression (SVR), Random Forest Regression (RFR) and Gaussian Process Regression (GPR). …”
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A Diagnostic Model of Breast Cancer Based on Digital Mammogram Images Using Machine Learning Techniques by Farouk A. K. Al-Fahaidy, Belal Al-Fuhaidi, Ishaq AL-Darouby, Faheem AL-Abady, Mohammed AL-Qadry, Abdurhman AL-Gamal

“…This paper firstly aimed to propose an efficient method for diagnosing tumors based on mammogram images of breasts using a machine learning approach. Secondly, this paper aimed to the development of a CAD software program for breast cancer diagnosing based on the proposed method in the first step. …”
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Decomposing the effect of normal aging and Alzheimer’s disease in brain morphological changes via learned aging templates by Jingru Fu, Daniel Ferreira, Örjan Smedby, Rodrigo Moreno

“…In the second step, we apply deep learning-based diffeomorphic registration to align the given image of a subject with a reference imaging template. …”
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Three-Dimensional Extended Target Tracking and Shape Learning Based on Double Fourier Series and Expectation Maximization by Hongge Mao, Xiaojun Yang

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Needs Analysis for Designing Hybrid Learning with Problem-Based Scaffolding to Enhance Mathematical Problem-Solving Skills by Yani Setiani, Yayat Ruhiat, Aan Hendrayana

“…Additionally, 83% found step-by-step scaffolding helpful, and 88% emphasized the importance of timely feedback. …”
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Supporting the learning process of LSP learners with a coaching-oriented approach: examples in the context of an international project by Alexandra Szenich, Reka Asztalos, Agnes Pal

“…The main findings of the study underline that the coaching-oriented approach can be applied in teaching and learning LSP but also emphasise the need for a supportive environment and step-by-step implementation strategies for effective pedagogical transformation. …”
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Nursing Students' Learning Experiences About the Nursing Process in Laboratory and Clinical Practice: A Qualitative Study by Semiha Aslı Bozkurt, Fatma Dursun Ergezen, Emine Kol

“…The transcription and analysis of the data were done by the researchers and no analysis program was used.Results: Three themes were emerged: 1) Nursing process as clinical and professional guide 2) Step by step permanent learning 3) Mentoring need. …”
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Automatic location and recognition of horse freezing brand using rotational YOLOv5 deep learning network by Zhixin Hua, Yitao Jiao, Tianyu Zhang, Zheng Wang, Yuying Shang, Huaibo Song

“…In this research, a deep learning method for oriented horse brand location and recognition was proposed. …”
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Learning Gaussian Bayesian Network from Censored Data Subject to Limit of Detection by the Structural EM Algorithm by Ping-Feng Xu, Shanyi Lin, Qian-Zhen Zheng, Man-Lai Tang

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Narrative Sandplay Interviews: Methodology, Implications and Lessons Learned From a COVID-19 Border Study by Tarryn Phillips, Poh Lin Lee, Catherine Trundle

“…We outline the historical background and epistemological underpinnings of narrative sandplay, offer a practical step-by-step guide to the interviewing method, and report on lessons learned from its use in a project about COVID-related border closure in Australia. …”
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Modern possibilities for optimizing the calculation of intraocular lens optical power using deep machine learning capabilities by A.R. Vinogradov, B.G. Dzhashi, S.V. Balalin

“…To develop optimized machine learning technology to improve the accuracy of intraocular lens (IOL) optical power calculation. …”
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Electric load forecasting based on kernel extreme learning machine optimized by improved sparrow search algorithm by Diming Zhang, Yuchen Xu, Yuanjiang Li

“…In Dataset 2, which considers various additional factors influencing electric load, the model’s performance is assessed for three-step-ahead forecasting. It achieves average R2 metric improvements of 5.6%, 7.6%, and 10.9% for the three-step-ahead forecasts, respectively, compared to benchmark models. …”
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A Novel Reactive Power Sharing Control Strategy for Shipboard Microgrids Based on Deep Reinforcement Learning by Wangyang Li, Hong Zhao, Jingwei Zhu, Tiankai Yang

“…During the training of the DQN agent, the process is optimized step-by-step by observing the state and rewards of the system, thereby effectively improving the performance of the microgrids. …”
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Multi-View Data Augmentation to Improve Wound Segmentation on 3D Surface Model by Deep Learning by R. Niri, E. Gutierrez, H. Douzi, Y. Lucas, S. Treuillet, B. Castaneda, I. Hernandez

“…Finally, this segmentation is backprojected in each view to generate a complete set of well annotated real images to reinforce the learning step of the neural network. In our experiments, we compare several strategies to select the best view in the image sequence. …”
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Forecasting Foreign Direct Investment Inflow to Egypt and Determinates: Using Machine Learning Algorithms and ARIMA Model by Mohamed F. Abd El-Aal, Ali Algarni, Aisha Fayomi, RAahayu Abdul Rahman, Khudir Alrashidi

“…This study aims to determine the primary determination of FDI inflow to Egypt using machine learning algorithms and the ARIMA model and get an accurate prediction of FDI inflow to Egypt during the current decade (2020–2030) and approved that the gradient boosting model is the most accurate algorithms. …”
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A multicenter study of neurofibromatosis type 1 utilizing deep learning for whole body tumor identification by Cheng-Jiang Wei, Yan Tang, Yang-Bai Sun, Tie-Long Yang, Cheng Yan, Hui Liu, Jun Liu, Jing-Ning Huang, Ming-Han Wang, Zhen-Wei Yao, Ji-Long Yang, Zhi-Chao Wang, Qing-Feng Li

“…Abstract Deep-learning models have shown promise in differentiating between benign and malignant lesions. …”
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Connected and automated vehicle control at unsignalized intersection based on deep reinforcement learning in vehicle-to-infrastructure environment by Juan Chen, Vijayan Sugumaran, Peiyan Qu

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A safe-enhanced fully closed-loop artificial pancreas controller based on deep reinforcement learning. by Yan Feng Zhao, Jun Kit Chaw, Mei Choo Ang, Yiqi Tew, Xiao Yang Shi, Lin Liu, Xiang Cheng

“…These encouraging results indicate that the DRL-based fully closed-loop AP controller has taken an essential step toward clinical implementation.…”
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An Approach for Modeling and Simulation of Virtual Sensors in Automatic Control Systems Using Game Engines and Machine Learning by João Rosas, Luís Brito Palma, Rui Azevedo Antunes

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