Limitation of super-resolution machine learning approach to precipitation downscaling by P. Jyoteeshkumar Reddy, Richard Matear, John Taylor, Marcus Thatcher

“…Abstract The present study explores the potential of super-resolution machine learning (ML) models for precipitation downscaling from 100 to 12.5 km at hourly timescale using the Conformal Cubic Atmospheric Model (CCAM) data over the Australian domain. …”
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Identification of Megaconstellations in Wide-field Astronomical Images with Machine Learning by Liu Liu, Rongyu Sun, He Zhao

“…Here an automatic identification pipeline based on machine learning model ShuffleNet V2 is developed, after trained with large amount of raw data, high efficiency is achieved. …”
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Beyond observation: Deep learning for animal behavior and ecological conservation by Lyes Saad Saoud, Atif Sultan, Mahmoud Elmezain, Mohamed Heshmat, Lakmal Seneviratne, Irfan Hussain

“…Recent advancements in deep learning have profoundly impacted the field of animal behavioral research, offering researchers powerful tools for understanding the complexities of animal movements and cognition. …”
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Machine-learning crystal size distribution for volcanic stratigraphy correlation by Martin Jutzeler, Rebecca J. Carey, Yasin Dagasan, Andrew McNeill, Ray A. F. Cas

“…Abstract Volcanic stratigraphy reconstruction is traditionally based on qualitative facies analysis complemented by geochemical analyses. Here we present a novel technique based on machine learning identification of crystal size distribution to quantitatively fingerprint lavas, shallow intrusions and coarse lava breccias. …”
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Detection of Spark Erosion on Insulated Rail Joints by Deep Learning by Utku Kaya

“…If left unaddressed, such defects may lead to severe operational failures and safety risks, making effective monitoring systems essential. This paper presents a deep learning-based approach for automatic detection of spark erosion in IRJs. …”
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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

“…In light of these considerations, this study presents an innovative methodology for forecasting EV demand. …”
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An Exploration of EFL Students' Perceptions of AI-Integration in the Learning Process by Putri Rizki Syafrayani, Riska Ayunda, Merry Luz Molina, Anna Riana Suryanti Tambunan, Sri Minda Murni

“…The study highlights the dual impact of AI enhancing personalization and engagement, while presenting ethical and pedagogical challenges. It recommends balanced integration of AI in EFL learning and further research into its long-term effects.…”
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Implementation of Intrusion detection and prevention with Deep Learning in Cloud Computing by Doddi Srilatha, N. Thillaiarasu

“…On CICIDS2017, a standard dataset for network intrusion, we apply Self-Taught Learning (STL), which is a deep learning approach. …”
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Content-Adaptive Inference for State-of-the-Art Learned Video Compression by Ahmet Bilican, M. Akin Yilmaz, A. Murat Tekalp

“…While the BD-rate performance of recent learned video codec models in both low-delay and random-access modes exceed that of respective modes of traditional codecs on average over common benchmarks, the performance improvements for individual videos with complex/large motions is much smaller compared to scenes with simple motion. …”
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Heterogeneity Challenges of Federated Learning for Future Wireless Communication Networks by Lorena Isabel Barona López, Thomás Borja Saltos

“…Given the growing research in this area, we present a summary of heterogeneity characteristics in Federated Learning to provide a broader perspective on this emerging technology. …”
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Reinforcement Learning-Based Control for Robotic Flexible Element Disassembly by Benjamín Tapia Sal Paz, Gorka Sorrosal, Aitziber Mancisidor, Carlos Calleja, Itziar Cabanes

“…Traditional control systems struggle to handle these tasks efficiently, requiring adaptable solutions that can operate in unstructured environments that provide online adaptation. This paper presents a reinforcement learning (RL)-based control strategy for the robotic disassembly of flexible elements. …”
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Deep learning-based object detection and robotic arm grasping by ZHANG Lei, ZHANG Senhui, YAN Song, YUAN Yuan

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Artificial Intelligence in Healthcare A Review of Machine Learning Applications by Karajgi Santosh, R Vijaya Prakash, Kumar K. Gagan, Selvi P Tamil, Shankar Bhukya, S Jeyanthi

“…Despite its potential, however, there are significant barriers to widespread adoption, such as data privacy issues, high computational costs, AI bias, lack of standardized evaluation, regulatory barriers, and integration with legacy healthcare systems. At present, the challenges explored highlight the need for federated learning as a new way to train AI without exposing sensitive patient data, bias-aware models which promote equitable and fair healthcare decisions for all patients, cloud and edge AI to ensure that processing is cost effective and appropriate, and Explainable AI (XAI) to promote trust and transparency to patients and communities. …”
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Deep Learning Innovations for Underwater Waste Detection: An In-Depth Analysis by Jaskaran Singh Walia, Kavietha Haridass, L. K. Pavithra

“…Underwater waste detection is a critical challenge for preserving aquatic ecosystems, particularly due to inherent underwater distortions such as light refraction, occlusion, and scattering. In this study, we present a novel deep learning framework for real-time underwater waste detection by evaluating state-of-the-art object detection algorithms on a manually annotated custom dataset comprising images across various water bodies to represent real-world turbidity, illumination, and occlusion. …”
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Harnessing Generative Deep Learning for Enhanced Ensemble Data Assimilation by Ehsan Foroumandi, Hamid Moradkhani

“…To address these challenges, we present a new hydrologic DA method inspired by the similarities in theoretical backgrounds of DA and generative deep learning. …”
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Reservoir direct feedback alignment: deep learning by physical dynamics by Mitsumasa Nakajima, Yongbo Zhang, Katsuma Inoue, Yasuo Kuniyoshi, Toshikazu Hashimoto, Kohei Nakajima

“…Abstract The rapid advancement of deep learning has motivated various analog computing devices for energy-efficient non-von Neuman computing. …”
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The Use of Games in Mathematics Learning: An Experience with Primary School Teachers by Andreia Hall, Nuno Bastos, Sónia Pais, Paola Morando, Maria Luisa Sonia Spreafico

“… This article presents a workshop aimed at teachers from the 1st to 6th grades on the use of games as a pedagogical strategy for teaching mathematics. …”
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Machine Learning Detection of Melting Layers From Radar Observations by Yan Xie, Fraser King, Claire Pettersen, Mark Flanner

“…Traditional detection algorithms based on fixed thresholds or a priori assumptions lack general robustness across diverse weather conditions, which can be addressed by leveraging machine learning techniques. This study presents a binary semantic segmentation U‐Net model for automatic detection of melting layers, using Ka‐band vertical profiling ground radar observations collected at the North Slope of Alaska between March 2015 and February 2016. …”
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Predicting metabolite response to dietary intervention using deep learning by Tong Wang, Hannah D. Holscher, Sergei Maslov, Frank B. Hu, Scott T. Weiss, Yang-Yu Liu

“…Existing prediction methods are typically limited to traditional machine learning models. Deep learning methods dedicated to such tasks are still lacking. …”
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Low‐Frequency Reconstruction for Full Waveform Inversion by Unsupervised Learning by Ningcheng Cui, Tao Lei, Wei Zhang

“…Nevertheless, this approach presents challenges in terms of training complexity and potential output stability. …”
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