A New Custom Deep Learning Model Coupled with a Flood Index for Multi-Step-Ahead Flood Forecasting by Jianming Shen, Moyuan Yang, Juan Zhang, Nan Chen, Binghua Li

“…We propose a new custom deep learning model, IF-CNN-GRU, for multi-step-ahead flood forecasting that incorporates the flood index (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>I</mi></mrow><mrow><mi>F</mi></mrow></msub></mrow></semantics></math></inline-formula>) to improve the prediction accuracy. …”
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A KWS System for Edge-Computing Applications with Analog-Based Feature Extraction and Learned Step Size Quantized Classifier by Yukai Shen, Binyi Wu, Dietmar Straeussnigg, Eric Gutierrez

“…To enable efficient deployment, the GRU classifier is trained using a Learned Step Size (LSQ) and Look-Up Table (LUT)-aware quantization method. …”
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With eyes of a machine: A three-step guide for applying machine learning to visual content analysis in social research by Anna Helene Kvist Møller, Massimo Airoldi

“…Our custom approach combines inductive, deductive, and abductive logics of scientific inquiry and consists of three complementary steps: (a) Pattern exploration—employing unsupervised learning to explore visual patterns within image datasets; (b) Theory-driven image classification—utilizing supervised learning with convolutional neural networks to systematically label visual content; and (c) Context-sensitive interpretation—to provide critical and creative engagement with the patterns identified in the previous steps. …”
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A novel fixed-point based two-step inertial algorithm for convex minimization in deep learning data classification by Kobkoon Janngam, Suthep Suantai, Rattanakorn Wattanataweekul

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Machine Learning-Enabled Prediction and Optimization of Sulfur Recovery Units: A Step towards Industry 4.0 Integration by Imran Khan, Husnain Saghir, Muhammad Ahsan

“…This study employs a machine learning algorithm to predict sulfur recovery efficiency under uncertain conditions. …”
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Solar Cycle Prediction Using a Temporal Convolutional Network Deep-learning Model with a One-step Pattern by Cui Zhao, Kun Liu, Shangbin Yang, Jinchao Xia, Jingxia Chen, Jie Ren, Shiyuan Liu, Fangyuan He

“…The results showed that a one-step pattern fits solar cycles 20 to 25 well. The average fitting errors are mean absolute error = 1.74 and RMSE = 2.34. …”
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Predicting pathological response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer with two step feature selection and ensemble learning by Changshun Qian, Shuxin Yang, Yijing Chen, Ran Ge, Fangmin Shi, Chengnan Liu, Hui Wang, You Guo

“…This study aims to identify effective predictive biomarkers and develop an ensemble learning-based prediction model to assess the response of LARC patients to nCRT. …”
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Two-Step CNN Framework for Text Line Recognition in Camera-Captured Images by Yulia S. Chernyshova, Alexander V. Sheshkus, Vladimir V. Arlazarov

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Tracking teachers’ experience of adopting my English step to infuse differentiated instruction by Ilza Mayuni, Yinghui Chen, Ni Made Ratminingsih, Tara Mustikaning Palupi, Nurhasanah Halim

“…As a post-developmental study, this qualitative research is intended to evaluate teachers’ continued experience in adopting My English Step (MES) digital learning material and promoting inclusion in Differentiated Instruction (DI) at junior high school (JHS). …”
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Multi-Step Object Extraction Planning From Clutter Based on Support Relations by Tomohiro Motoda, Damien Petit, Takao Nishi, Kazuyuki Nagata, Weiwei Wan, Kensuke Harada

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Understanding the Basics of Linear Endoscopic Ultrasound: A Step-by-Step Guide to Anatomy and Orientation by Radhika Chavan, Dario Quintini, Chaiti Gandhi, Suprabhat Giri, Zaheer Nabi, Sundeep Lakhtakia, Sanjay Rajput

“…This article provides a systematic, step-by-step approach to understanding the basics of linear EUS, focusing on spatial anatomy and procedural technique. …”
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A Lightweight Two-Step Detection Method for Real-Time Small UAV Detection by Sungkyu Jung, Jaeyeon Jang, Chang Ouk Kim

“…However, several major challenges remain in this domain, including the difficulty of detecting small UAVs and the increased computational cost associated with achieving performance improvements for deep learning models. To address the frequent misclassification of small UAVs in one-stage object detectors, this study proposes a two-step detection method, as illustrated in the graphical abstract, which summarizes the proposed approach. …”
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Ensemble Strategy With Multi-Step Hard Sample Mining for Improved UXO Localisation and Classification by Marian Craioveanu, Grigore Stamatescu, Dan Popescu

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An explainable Bayesian gated recurrent unit model for multi-step streamflow forecasting by Lizhi Tao, Yueming Nan, Zhichao Cui, Lei Wang, Dong Yang

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Temporal Attention-Enhanced Stacking Networks: Revolutionizing Multi-Step Bitcoin Forecasting by Phumudzo Lloyd Seabe, Edson Pindza, Claude Rodrigue Bambe Moutsinga, Maggie Aphane

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Groundwater Arsenic and Cancer Risk Assessment Prediction model via Machine Learning: A Step Towards Modernizing Academic Research by Sobia Iftikhar, Sania Bhatti, Zulfiqar Ali Bhatti, Mohsin Ali Memon, Faisal Memon

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A novel deep learning model for predicting marine pollution for sustainable ocean management by Michael Onyema Edeh, Surjeet Dalal, Musaed Alhussein, Khursheed Aurangzeb, Bijeta Seth, Kuldeep Kumar

“…The classification was done using two types of machine learning algorithms: two-step clustering and a fully convolutional network (FCN). …”
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Multi-Step Parking Demand Prediction Model Based on Multi-Graph Convolutional Transformer by Yixiong Zhou, Xiaofei Ye, Xingchen Yan, Tao Wang, Jun Chen

Subjects: “…multi-step prediction of parking demand…”
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Secure Coding in Five Steps by Mini Zeng, Feng Zhu

“…To achieve these two goals, we present five learning steps that we have been revising over the last four years. …”
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Adam Algorithm with Step Adaptation by Vladimir Krutikov, Elena Tovbis, Lev Kazakovtsev

“…As shown by computational experiments, with an increase in the degree of conditionality of the problem and in the presence of interference, Adam is prone to looping, which is associated with difficulties in step adjusting. In this paper, an algorithm for step adaptation for the Adam method is proposed. …”
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