Ground-Based Cloud-Type Recognition Using Manifold Kernel Sparse Coding and Dictionary Learning by Qixiang Luo, Zeming Zhou, Yong Meng, Qian Li, Miaoying Li

“…MKSCDL is composed of three steps: feature extraction, dictionary learning, and classification. …”
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A Hybrid Deep Learning Model for Link Dynamic Vehicle Count Forecasting with Bayesian Optimization by Chunguang He, Dianhai Wang, Yi Yu, Zhengyi Cai

“…The proposed hybrid deep learning method is tested on two roads of Hangzhou, China. …”
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Achieving equitable recruitment through inclusive protocol design: lessons learned from the ILANA study by Rosalie Hayes, Melanie Smuk, Bakita Kasadha, Nishat Halim, Vanessa Apea, Sara Paparini, Chloe M. Orkin, the ILANA study group

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Development of Kalibataku Fraction Board Media to Improve Student Learning Results and Interest in Fraction Material by Putri Puspitasari, Murtono Murtono, Mulyani Mulyani

“…Mathematics learning in basic education institutions is minimal in the use of learning media, and student's interest in mathematics is very low. …”
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Development of Picture Card Learning Media to Improve Sexual Understanding in Group B Kindergarten Children by Ni Made Putri Widiarsini, Putu Rahayu Ujianti, Mutiara Magta

“…This study aimed to use picture card learning media to increase sexual understanding in early childhood. …”
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Harnessing Deep Learning With AlexNet for Tomato Leaf Disease Detection in the Indian Himalayan Terrain by Ruchika Sharma, Sameena Naaz, Pankaj Vaidya

“…Our research leverages deep learning (DL), a dynamically growing technology proficient in handling large datasets. …”
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Analysis of student’s mathematical problem-solving ability with moderate learning readiness based on Polya’s theory by Nur Fauziyah, Zattira Nadia Rahma

“…This study suggests implementing problem-based learning and continuous assessment to enhance students' mathematical problem-solving abilities.…”
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Deep learning-based prediction of atrial fibrillation from polar transformed time-frequency electrocardiogram. by Daehyun Kwon, Hanbit Kang, Dongwoo Lee, Yoon-Chul Kim

“…The results demonstrated that deep learning-based predictions using polar transformed spectrograms were comparable to existing methods. …”
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A Long-Term Target Search Method for Unmanned Aerial Vehicles Based on Reinforcement Learning by Dexing Wei, Lun Zhang, Mei Yang, Hanqiang Deng, Jian Huang

“…Unmanned aerial vehicles (UAVs) are increasingly being employed in search operations. Deep reinforcement learning (DRL), owing to its robust self-learning and adaptive capabilities, has been extensively applied to drone search tasks. …”
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Improving students' problem-solving abilities by using geogebra learning media on three-dimensional material by Anastasya Ramadiana, Bertu Rianto Takaendengan, Nurwan Nurwan, Perry Zakaria, Kartin Usman, Lailany Yahya

“…The COVID-19 pandemic has had an impact on the learning process in schools. For this reason, researchers observed learning at SMA Negeri 1 Gorontalo, it was found that students were still not optimal in solving mathematical problems, especially in formulating steps to solve problems. …”
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Problem Based Learning (PBL) Model for Science Material on Prevention of Dengue Fever for Elementary School by Laila Fajrin Rauf, Sigit Prasetyo

“…Of the five syntaxes, there are three main steps, namely learning preconditions, core learning agenda and post-learning. …”
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What we can learn about mission from the conversion stories of unchurched seekers in Germany by Nils Dreiling

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Improving Unplugged Computational Thinking Skills Through Integrated Problem-Based and Differentiated Learning in Indonesia by Dewi Oktaviani, Susilo Satanti

“…This Classroom Action Research aims to improve students’ computational thinking (abstraction, data collection, data analysis and algorithms) in solving problems about probability through problem-based learning integrated with differentiation learning. …”
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Early Detection of Alzheimer’s Disease via Machine Learning-Based Microwave Sensing: An Experimental Validation by Leonardo Cardinali, Valeria Mariano, David O. Rodriguez-Duarte, Jorge A. Tobón Vasquez, Rosa Scapaticci, Lorenzo Crocco, Francesca Vipiana

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Machine learning-based voice assistant: optimizing the efficiency of speech conversion for people with speech disorders by M.H. Antor, N.V. Chudinovskikh, M.V. Bachurin, A.A. Shurpikov, N.A. Khlebnikov, B.A. Bredikhin

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‘What do you mean I failed?’ Using in year retrieval as a learning tool by Karen Fitzgibbon

“…This piece calls for a change in resit practices and outlines the steps taken to introduce ‘in year retrieval’ (IYR) within one university. …”
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Variation-Aware Bernstein-Based Upper Confidence Reinforcement Learning for Environment With Endogenous and Exogenous Uncertainty by Ruoqi Wen, Rongpeng Li

“…We successfully overcome the challenges due to both endogenous and exogenous uncertainty and establish a regret bound of saving at most <inline-formula> <tex-math notation="LaTeX">$\sqrt {S}$ </tex-math></inline-formula> or <inline-formula> <tex-math notation="LaTeX">$S^{\frac {1}{6}}T^{\frac {1}{12}}$ </tex-math></inline-formula> compared with the latest results in the literature, where S denotes the size of the state space of the MDP and T indicates the iteration index of learning time-steps. Finally, we show via simulation that our algorithm VB-UCRL significantly outperforms the existing algorithms in the literature.…”
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machine learning-driven Six Sigma framework for enhancing the quality improvement and productivity in the Aircraft Manufacturing by Dwi Adi Purnama, Alfiqra Alfiqra, Winda Nur Cahyo

“…The DMAIC (Define-Measure-Analyze-Improve-Control) stage is a reference in the implementation steps of the Six Sigma method of the Airbus A320. …”
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Unmanned Aerial Vehicle Path Planning in Complex Dynamic Environments Based on Deep Reinforcement Learning by Jiandong Liu, Wei Luo, Guoqing Zhang, Ruihao Li

“…Compared to DQN, dueling DQN, M-DQN, improved Q-learning, DDM-DQN, EPF (enhanced potential field), APF-DQN, and L1-MBRL, our algorithm achieves the highest success rate of 77.67%, while also having the lowest average number of moving steps. …”
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Applying deep learning to teleseismic phase detection and picking: PcP and PKiKP cases by Congcong Yuan, Jie Zhang

“…The availability of a tremendous amount of seismic data demands seismological researchers to analyze seismic phases efficiently. Recently, deep learning algorithms exhibit a powerful capability of detecting and picking on P- and S-wave phases. …”
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