Antarctic iceberg freeboard retrieval via deep learning from optical satellite imagery by Zitong Zhou, Outong Li, Songtao Ai, Xinde Chu, Shoukat Ali Shah

“…Antarctic icebergs are critical components of the Antarctic ice sheet-ice shelf-ocean system and play a key role in understanding the impacts of climate change, particularly in estimating iceberg volume through the freeboard measurements. This study presents an innovative deep learning-based approach (U-Net) for automatically measuring iceberg freeboard from optical remote sensing data in the Antarctic coastal region, following data acquisition. …”
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PROJECT-BASED LEARNING AND ITS EFFECTS ON DEVELOPING WRITING SKILLS IN BUSINESS ENGLISH STUDENTS by Cristina-Laura ABRUDAN

“…It is argued that project-based learning seems to be the solution to the problem. The present study wants to discuss the effect of Project-Based learning on developing writing skills for students at the University of Oradea, Faculty of Economics and their perception of Project-Based learning in Business English. …”
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Fault Detection in Induction Machines Using Learning Models and Fourier Spectrum Image Analysis by Kevin Barrera-Llanga, Jordi Burriel-Valencia, Angel Sapena-Bano, Javier Martinez-Roman

“…Induction motors are essential components in industry due to their efficiency and cost-effectiveness. This study presents an innovative methodology for automatic fault detection by analyzing images generated from the Fourier spectra of current signals using deep learning techniques. …”
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Optimal Markowitz portfolio using returns forecasted with time series and machine learning models by Damian Ślusarczyk, Robert Ślepaczuk

“…Abstract We aim to answer whether using forecasted stock returns based on machine learning and time series models in a mean-variance portfolio framework yields better results than relying on historical returns. …”
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Learning curve in robotic rectal cancer surgery: A national two-centre study by Anislav Ventsislavov Gabarski, Paulina T. Vladova, Martin P. Karamanliev, Nikolai Ramadanov, Aleksandar K. Zlatarov, Turgay T. Kalinov

“…Although robotic-assisted surgery benefits from technical advantages that shorten the learning curve, it nonetheless presents a steep and extended learning curve. …”
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Utilizing Machine Learning Techniques for Cancer Prediction and Classification based on Gene Expression Data by Mariwan Mahmood Hama Aziz, Sozan Abdullah Mahmood

“…Lately, several studies have delved into cancer classification by leveraging data mining techniques, machine learning algorithms, and statistical methods to thoroughly analyze high-dimensional datasets. …”
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Cardiovascular Disease Prevention During the COVID-19 Pandemic: Lessons Learned and Future Opportunities by Eamon Duffy, Michael Chilazi, Miguel Cainzos-Achirica, Erin D. Michos

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Meals, mealtimes and moments for learning: assessment of quality in early childhood education and care services by Bonnie Searle, Sandy Houen, Sally Staton, Karen Thorpe

“… Abstract Objective: Early education and care (ECEC) is part of the everyday life of most children in developed economies, presenting exceptional opportunity to support nutrition and ongoing food preferences. …”
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Contrastive Self-Supervised Learning for Sensor-Based Human Activity Recognition: A Review by Hui Chen, Charles Gouin-Vallerand, Kevin Bouchard, Sebastien Gaboury, Melanie Couture, Nathalie Bier, Sylvain Giroux

“…The empirical performance comparisons of different methods are presented on benchmark datasets in linear evaluation, semi-supervised learning, and transfer learning scenarios. …”
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A Review on Deep Learning for Quality of Life Assessment Through the Use of Wearable Data by Vasileios Skaramagkas, Ioannis Kyprakis, Georgia S. Karanasiou, Dimitris I. Fotiadis, Manolis Tsiknakis

“…Quality of Life (QoL) assessment has evolved over time, encompassing diverse aspects of human existence beyond just health. This paper presents a comprehensive review of the integration of Deep Learning (DL) techniques in QoL assessment, focusing on the analysis of wearable data. …”
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Prediction of Promotors in Agrobacterium and Klebsiella Using Novel Feature Engineering and Ensemble Learning Approach by Nagwan Abdel Samee, Rawan Talaat, Ali Raza, Hadil Shaiba, Souham Meshoul

“…This paper presents an efficient machine learning-based model for the prediction of promoters in bacterial genomes, specifically targeting Agrobacterium tumefaciens, Klebsiella aerogenes, and Xanthomonas campestris. …”
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Deep Learning-Based Flap Detection System Using Thermographic Images in Plastic Surgery by Răzvan Danciu, Bogdan Andrei Danciu, Luiz-Sorin Vasiu, Adelaida Avino, Claudiu Ioan Filip, Cristian-Sorin Hariga, Laura Răducu, Radu-Cristian Jecan

“…This provides a narrow color spectrum image that is amenable to the development of an artificial neural network in the context of current technological progress. In the present study, we introduce a novel attention-enhanced recurrent residual U-Net (AER2U-Net) model that is able to accurately segment flaps on thermographic images. …”
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Deep Learning-Enabled Dynamic Model for Nutrient Status Detection of Aquaponically Grown Plants by Mohamed Farag Taha, Hanping Mao, Samar Mousa, Lei Zhou, Yafei Wang, Gamal Elmasry, Salim Al-Rejaie, Abdallah Elshawadfy Elwakeel, Yazhou Wei, Zhengjun Qiu

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From Voxels to Viruses: Using Deep Learning and Crowdsourcing to Understand a Virus Factory by Avery Pennington, Oliver N. F. King, Win Min Tun, Mark Boyce, Geoff Sutton, David I. Stuart, Mark Basham, Michele C. Darrow

“…Together, this allowed mask annotation to be generated for training a deep learning–based segmentation model. From this model, segmentations were produced that allowed for measurements such as counts of the viruses by virus class. …”
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A Multi-Stream Approach to Mixed-Traffic Accident Recognition Using Deep Learning by Swee Tee Fu, Lau Bee Theng, Brian Loh Chung Shiong, Chris McCarthy, Mark Tee Kit Tsun

“…Road traffic accidents are a leading cause of injuries and fatalities globally, prompting extensive research into deep learning-based accident recognition models for their superior performance in computer vision tasks. …”
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Enhancing Genomic Prediction Accuracy of Reproduction Traits in Rongchang Pigs Through Machine Learning by Junge Wang, Jie Chai, Li Chen, Tinghuan Zhang, Xi Long, Shuqi Diao, Dong Chen, Zongyi Guo, Guoqing Tang, Pingxian Wu

“…The increasing volume of genome sequencing data presents challenges for traditional genome-wide prediction methods in handling large datasets. …”
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LAST-PAIN: Learning Adaptive Spike Thresholds for Low Back Pain Biosignals Classification by Freek Hens, Mohammad Mahdi Dehshibi, Leila Bagheriye, Ana Tajadura-Jimenez, Mahyar Shahsavari

“…Spiking neural networks (SNNs) present the potential for ultra-low-power computation, especially when implemented on dedicated neuromorphic hardware. …”
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Automated classification of chest X-rays: a deep learning approach with attention mechanisms by Burcu Oltu, Selda Güney, Seniha Esen Yuksel, Berna Dengiz

“…However, the interpretation of CXRs is a challenging task. Methods This study presents an automated deep learning (DL) model that outperforms multiple state-of-the-art methods in diagnosing COVID-19, Lung Opacity, and Viral Pneumonia. …”
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Research on optimal selection of runoff prediction models based on coupled machine learning methods by Xing Wei, Mengen Chen, Yulin Zhou, Jianhua Zou, Libo Ran, Ruibo Shi

“…Abstract Runoff fluctuations under the influence of climate change and human activities present a significant challenge and valuable application in constructing high-accuracy runoff prediction models. …”
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Deep Learning Identification of the Governing Equation for Water Flow in Heterogeneous Soils From Data by Wenxiang Song, Liangsheng Shi, Leilei He, Yuanyuan Zha, Xiaolong Hu, Mehdi Rahmati, Harry Vereecken

“…However, due to the inherent complexity of soils, current data‐driven discovery approaches cannot deal with heterogeneous soil scenarios. In this study, we present a new group sparse regression theory and a deep learning framework to extend previous studies to be able to identify the governing equations for soil water flow in heterogeneous soils from observational data. …”
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