Implementation of Moodle, an open-source solution for Team Based Learning by Reid Proctor, Renee Hayslett

“… Conventional Team-Based Learning (TBL) uses a paper-based system to deliver a team-quiz component of the instructional method. …”
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Implementation of Moodle, an open-source solution for Team Based Learning by Reid Proctor, Renee Hayslett

“… Conventional Team-Based Learning (TBL) uses a paper-based system to deliver a team-quiz component of the instructional method. …”
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Educational Connections: Museums, Universities, and Schools as Places of Supplementary Learning by Mᵃ Dolores Soto-González, Ricard Huerta, Ramona Rodríguez-López

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Computer Teaching System Based on Internet of Things and Machine Learning by Lei Chen, Li Zhang

“…In order to solve the problem that the traditional computer-aided teaching system is affected by communication technology, which leads to the inability to interact between teachers and students, the author proposes a research on a computer teaching system based on the Internet of Things and machine learning. The hardware structure is designed according to the functions of each module of the system, in which the student learning module is composed of a teaching coordination agent and a number of other agents, responsible for the presentation of specific teaching materials, problem solving, knowledge sharing through a collaborative mechanism, and providing personalized teaching basis for the system. …”
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The Impact of Tin Mining Activities and Its Integration in Science Learning by Tisrin Maulina Dewi, Margareta Rahayuningsih, Aditya Marianti, Fitria Meilina

“…Efforts need to be made to utilize these former tin mine pits properly so that they can function properly and they can benefit the community. …”
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On the minimum number of radiation field parameters to specify gas cooling and heating functions by David Robinson, Camille Avestruz, Nickolay Y. Gnedin

“…We use machine learning to analyze atomic gas cooling and heating functions in the presence of a generalized incident local radiation field computed by Cloudy. …”
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The potential functions of reading and writing activities within scientific inquiry in primary education by Miriam J. Rhodes, Martine A. R. Gijsel, Hanno van Keulen, Adrie J. Visscher

“…Specific functions belonging to each category are identified and illustrated with learning activities as described in the interventions. …”
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Selective transfer learning with adversarial training for stock movement prediction by Yang Li, Hong-Ning Dai, Zibin Zheng

“…All three tasks are jointly trained with a loss function. As a result, the pre-trained shared base model can be fine-tuned with the stock data in target domain. …”
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Toward global rooftop PV detection with Deep Active Learning by Matthias Zech, Hendrik-Pieter Tetens, Joseph Ranalli

“…However, locations of PV are often unknown, which is why a large number of studies have proposed variants of Deep Learning to detect PV panels in remote sensing data using supervised Deep Learning. …”
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The analysis of landscape design and plant selection under deep learning by Lian Li, JiYon Lee

“…Additionally, a domain-adaptive transfer learning strategy and region-weighted loss function are designed, further enhancing the model’s accuracy and robustness in plant classification tasks. …”
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Machine learning in environmental sustainability factor analysis in the agricultural sector by Kukartsev Vladislav, Kozlova Anastasia, Kukarceva Svetlana

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Robust reinforcement learning algorithm based on pigeon-inspired optimization by Mingying ZHANG, Bing HUA, Yuguang ZHANG, Haidong LI, Mohong ZHENG

“…Reinforcement learning(RL) is an artificial intelligence algorithm with the advantages of clear calculation logic and easy expansion of the model.Through interacting with the environment and maximizing value functions on the premise of obtaining little or no prior information, RL can optimize the performance of strategies and effectively reduce the complexity caused by physical models .The RL algorithm based on strategy gradient has been successfully applied in many fields such as intelligent image recognition, robot control and path planning for automatic driving.However, the highly sampling-dependent characteristics of RL determine that the training process needs a large number of samples to converge, and the accuracy of decision making is easily affected by slight interference that does not match with the simulation environment.Especially when RL is applied to the control field, it is difficult to prove the stability of the algorithm because the convergence of the algorithm cannot be guaranteed.Considering that swarm intelligence algorithm can solve complex problems through group cooperation and has the characteristics of self-organization and strong stability, it is an effective way to be used for improving the stability of RL model.The pigeon-inspired optimization algorithm in swarm intelligence was combined to improve RL based on strategy gradient.A RL algorithm based on pigeon-inspired optimization was proposed to solve the strategy gradient in order to maximize long-term future rewards.Adaptive function of pigeon-inspired optimization algorithm and RL were combined to estimate the advantages and disadvantages of strategies, avoid solving into an infinite loop, and improve the stability of the algorithm.A nonlinear two-wheel inverted pendulum robot control system was selected for simulation verification.The simulation results show that the RL algorithm based on pigeon-inspired optimization can improve the robustness of the system, reduce the computational cost, and reduce the algorithm’s dependence on the sample database.…”
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Robust asphaltene onset pressure prediction using ensemble learning by Jafar Khalighi, Alexey Cheremisin

“…This paper adopts a robust approach to training three machine learning models—Multi-Layer Perceptron (MLP), CatBoost, and Random Forest (RF)—to predict AOP as a function of oil composition, SARA fractions, saturation pressure, and temperature. …”
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A Lightweight Barcode Detection Algorithm Based on Deep Learning by Jingchao Chen, Ning Dai, Xudong Hu, Yanhong Yuan

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Students learning about evolution through a comic book by Johanna Aringer, Lars Wallner, Ammie Berglund

“…Results To explore what function the material has for students’ meaning making we analyze what students describe to have learned working with the comic Cats on the Run, and how aspects of the comic book are reflected in the students’ self-reported learning. …”
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On the Choice of Training Data for Machine Learning of Geostrophic Mesoscale Turbulence by F. E. Yan, J. Mak, Y. Wang

“…Here we consider the problem of eddy‐mean interaction in rotating stratified turbulence in the presence of lateral boundaries, where it is known that rotational components of the eddy flux plays no direct role in the sub‐grid forcing onto the mean state variables, and its presence is expected to affect the performance of the trained machine learning models. While an often utilized choice in the literature is to train a model from the divergence of the eddy fluxes, here we provide theoretical arguments and numerical evidence that learning from the eddy fluxes with the rotational component appropriately filtered out, achieved in this work by means of an object called the eddy force function, results in models with comparable or better skill, but substantially reduced sensitivity to the presence of small‐scale features. …”
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Modeling nitric oxide diffusion and plasticity modulation in cerebellar learning by Alessandra Maria Trapani, Carlo Andrea Sartori, Benedetta Gambosi, Alessandra Pedrocchi, Alberto Antonietti

“…By bridging the gap between molecular processes and network-level learning, this work underscores the critical role of NO in cerebellar function and offers a robust framework for exploring NO-dependent plasticity in computational neuroscience.…”
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Learning to rank quantum circuits for hardware-optimized performance enhancement by Gavin S. Hartnett, Aaron Barbosa, Pranav S. Mundada, Michael Hush, Michael J. Biercuk, Yuval Baum

“…We introduce and experimentally test a machine-learning-based method for ranking logically equivalent quantum circuits based on expected performance estimates derived from a training procedure conducted on real hardware. …”
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Heterogeneity-aware device selection for efficient federated edge learning by Yiran Shi, Jieyan Nie, Xingwei Li, Hui Li

“…Federated learning (FL) combined with mobile edge computing (FEEL) provides an end-to-edge synergetic learning approach to allow end devices to participate in machine learning model training parallelly while ensuring user privacy is maintained. …”
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Deep Learning Forecasting Model for Market Demand of Electric Vehicles by Ahmed Ihsan Simsek, Erdinç Koç, Beste Desticioglu Tasdemir, Ahmet Aksöz, Muammer Turkoglu, Abdulkadir Sengur

“…This model, called EVs-PredNet, is developed using deep learning methods such as LSTM (Long Short-Term Memory) and CNNs (Convolutional Neural Networks). …”
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