A roadmap to implementing machine learning in healthcare: from concept to practice by Adam Paul Yan, Adam Paul Yan, Lin Lawrence Guo, Jiro Inoue, Santiago Eduardo Arciniegas, Emily Vettese, Agata Wolochacz, Nicole Crellin-Parsons, Brandon Purves, Steven Wallace, Azaz Patel, Medhat Roshdi, Karim Jessa, Karim Jessa, Bren Cardiff, Lillian Sung, Lillian Sung, Lillian Sung

“…Its goal was to develop, deploy, evaluate and maintain clinical ML models to improve pediatric patient outcomes using electronic health records data.ObjectiveTo provide examples from the PREDICT experience illustrating how common challenges with clinical ML deployment were addressed.Materials and methodsWe present common challenges in developing and deploying models in healthcare related to the following: identify clinical scenarios, establish data infrastructure and utilization, create machine learning operations and integrate into clinical workflows.ResultsWe show examples of how these challenges were overcome and provide suggestions for pragmatic solutions while maintaining best practices.DiscussionThese approaches will require refinement over time as the number of deployments and experience increase.…”
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An ultrasonography of thyroid nodules dataset with pathological diagnosis annotation for deep learning by Xiaowen Hou, Menglei Hua, Wei Zhang, Jianxin Ji, Xuan Zhang, Huiru Jiang, Mengyun Li, Xiaoxiao Wu, Wenwen Zhao, Shuxin Sun, Lei Cao, Liuying Wang

“…Here, we present a large US dataset with pathological diagnosis annotation for each case, designed for developing deep learning algorithms to directly infer histological status from thyroid ultrasound images. …”
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Deep FS: A Deep Learning Approach for Surface Solar Radiation by Fatih Kihtir, Kasim Oztoprak

“…The amount of solar radiation reaching Earth’s surface (Global Horizontal Irradiance, GHI) varies with atmospheric conditions, geographical location, and temporal factors. This paper presents a novel methodology for estimating surface sun exposure using advanced deep learning techniques. …”
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Deep Learning Joint Inversion of Electrical Data for Ahead-Prospecting in Tunneling by Peng Jiang, Benchao Liu, Chuanwu Wang, Lei Chen, Yuting Tang

“…This work presents a novel deep learning-based electrical approach that combines resistivity and polarizability to estimate water-bearing location and morphology. …”
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Identification of Writing Strategies in Educational Assessments with an Unsupervised Learning Measurement Framework by Cheng Tang, Jiawei Xiong, George Engelhard

“…An empirical example is presented to demonstrate the performance of the framework. …”
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Leveraging satellite observations and machine learning for underwater sound speed estimation by Madusanka Madiligama, Zheguang Zou, Likun Zhang

“…However, limited data and computational constraints hinder real-time, high-resolution mapping of three-dimensional sound speed fields. We present an integrated approach that combines remote sensing, machine learning, and underwater acoustics to estimate sound speed across vast ocean regions. …”
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Group project -based learning at a university: a social effect by Natalya Mikhailovna Suetina, Sabina Tofikovna Shefrukova

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Federated Learning for a Dynamic Edge: A Modular and Resilient Approach by Leonardo Almeida, Rafael Teixeira, Gabriele Baldoni, Mário Antunes, Rui L. Aguiar

“…The increasing demand for distributed machine learning like Federated Learning (FL) in dynamic, resource-constrained edge environments, 5G/6G networks, and the proliferation of mobile and edge devices, presents significant challenges related to fault tolerance, elasticity, and communication efficiency. …”
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Combination of dynamic TOPMODEL and machine learning techniques to improve runoff prediction by Pin‐Chun Huang

“…To encompass spatial discretization, diffusion‐wave characteristics, depth‐dependent flow velocity, and flux estimation in the unsaturated zone, a generalized dynamic TOPMODEL is developed by introducing a greater number of physical parameters. The present study aims to evaluate the optimal combination of these parameters within the dynamic TOPMODEL framework using machine learning techniques to improve the accuracy of runoff predictions and bolster the model's reliability. …”
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Reinforcement learning-enhanced adaptive sliding mode control for nonlinear systems by Omnia Youssef, Mohamed Wafa, Raafat Shalaby

“…Abstract This paper presents a novel Adaptive Relaxed Sliding Mode Control (ARSMC) framework for nonlinear systems, enhanced by an actor-critic reinforcement learning algorithm. …”
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Deep dive into deep learning methods for cervical cancer detection and classification by Pooja Patre, Dipti Verma

“…This review presents a thorough analysis of deep learning methods utilized for cervical cancer diagnosis, with an emphasis on critical approaches, evaluation metrics, and the ongoing challenges faced in the field. …”
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Online english learning: main problems and some ways to solve them by Amalia L. Kuregyan, Ekaterina A. Pertsevaya

“…Any foreign language requires the mastering of a large number of teaching materials, this number increasing due to the language skills heterogeneity in each group and this requires the development of specific skills in each individual group of students. Hence, learning priorities must be correctly prioritized and the specific area of knowledge must be presented in details. …”
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Istituzionalizzazione del Service learning e costruzione di comunità educanti by Chiara Giunti, Patrizia Lotti

“… Il presente articolo vuole indagare il ruolo dell’approccio pedagogico del Service Learning nella collaborazione tra scuola e territorio. …”
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Using Adaptive Content Recommendations to Improve Logic and Programming Teaching and Learning by Aluizio Haendchen Filho, Adson Marques da Silva Esteves, Hércules Antonio do Prado, Edilson Ferneda, André Luis Alice Raabe

“…This study presents a content recommendation system that uses learning paths to group students and provide personalized recommendations based on peers' progress. …”
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PRAGMATIC TRANSFER OF ALGERIAN LEARNERS IN LEARNING ENGLISH AS A THIRD LANGUAGE by Belfarhi Allia Khadija

“…The recent developments in cognitive linguistics turn EFL research interest to investigate the role of pragmatic aspects in the learn ability of English instead of grammar-governed learning. …”
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The Effect of Image-Schema-Based Instruction on Learning/ Acquiring Polysemous Prepositions by Hasna Lamis BOUCHENEK

“…This paper aims at presenting the characteristics of the semantics of polysemous prepositions and reports on an experimental study which examines the efficacy of image-schema-based instruction on learning the semantics of the English prepositions above, across, in, on, out, over and through. …”
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Improved phase prediction of high-entropy alloys assisted by imbalance learning by Libin Zhang, Chang-Seok Oh, Yoon Suk Choi

“…Predicting phase formation is crucial in novel high-entropy alloys (HEAs) design. Herein, machine learning and imbalance learning algorithms were combined together to improve the phase prediction of HEAs. …”
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Accelerating Subglacial Hydrology for Ice Sheet Models With Deep Learning Methods by Vincent Verjans, Alexander Robel

“…However, current numerical subglacial hydrology models are computationally expensive, and, consequently, evolving subglacial hydrology is neglected in large‐scale ice sheet simulations. We present a deep learning emulator of a state‐of‐the‐art subglacial hydrology model, trained at multiple Greenland glaciers. …”
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Detection and location of EEG events using deep learning visual inspection. by Mohammad Amin Fraiwan

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Experimental economics for machine learning-a methodological contribution on lie detection. by Dmitri Bershadskyy, Laslo Dinges, Marc-André Fiedler, Ayoub Al-Hamadi, Nina Ostermaier, Joachim Weimann

“…We argue that with machine learning (ML), a new technology is at hand, where for the first time experimental economics can contribute to enabling substantial improvement of technology. …”
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