A Unified Deep Learning Framework for Short-Duration Speaker Verification in Adverse Environments by Youngmoon Jung, Yeunju Choi, Hyungjun Lim, Hoirin Kim

“…To the best of our knowledge, this is the first work combining these three models in a deep learning framework. …”
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Estimating chlorophyll content in tea leaves using spectral reflectance and deep learning methods by Yuta Tsuchiya, Yuhei Hirono, Rei Sonobe

“…The key innovation of this study is the introduction of a self-supervised learning framework specifically adapted for spectral data, in which an autoencoder is first trained on unlabeled spectra to learn compact and noise-tolerant representations. …”
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The utility of combining deep learning with metabarcoding to model biodiversity dynamics at a national scale by Adrian Baggström, Robert Goodsell, Laura van Dijk, Ela Iwaszkiewicz-Eggebrecht, Andreia Miraldo, Ayco J.M. Tack, Tobias Andermann

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Performance Comparison of Large Language Models on Brazil’s Medical Revalidation Exam for Foreign-Trained Graduates by Renato Freitas Bessa, Adonias Caetano de Oliveira, Rafael Freitas Bessa, Daniel Lima Sousa, Rafaela Alves, Amanda Barbosa, Alinne Carneiro, Carla Soares, Ariel Soares Teles

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From underwater to drone: A novel multi-scale knowledge distillation approach for coral reef monitoring by Matteo Contini, Victor Illien, Julien Barde, Sylvain Poulain, Serge Bernard, Alexis Joly, Sylvain Bonhommeau

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A Hierarchical Attention-Guided Data–Knowledge Fusion Network for Few-Shot Gearboxes’ Fault Diagnosis by Xin Feng, Tianci Zhang

“…Second, a deep convolutional neural network is designed to hierarchically capture abstract features from monitoring data. …”
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Integrating Traditional Ecological Knowledge and Frost Management Strategies for Sustainable Tree Planting in Legambo District, Ethiopia by Mesfin Boja, Bethelhem Solomon

“…These practices reflect a deep understanding of local ecological conditions and highlight the need to combine traditional knowledge with modern forestry techniques. …”
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Enhancing Hierarchical Sales Forecasting with Promotional Data: A Comparative Study Using ARIMA and Deep Neural Networks by Mariana Teixeira, José Manuel Oliveira, Patrícia Ramos

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A Novel Policy Distillation With WPA-Based Knowledge Filtering Algorithm for Efficient Industrial Robot Control by Gilljong Shin, Seongjin Yun, Won-Tae Kim

“…Policy distillation is a kind of model compression schemes of deep reinforcement learning by means of teacher-student model which make a pre-trained teacher model transfer its knowledge to structurally simplified student models in order to enhance the computing efficiency. …”
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A lightweight knowledge graph-driven question answering system for field-based mineral resource survey by Mingguo Wang, Chengbin Wang, Jianguo Chen, Bo Wang, Wei Wang, Xiaogang Ma, Jiangtao Ren, Zichen Li, Yicai Ye, Jiakai Zhang, Yue Wang

“…The rapid increase in the volume of geoscience data makes it challenging to acquire knowledge quickly. In this study, we proposed and built a workflow that employs knowledge graph techniques, deep learning, question templates, and matching algorithms to provide a lightweight question-answering service for field-based geologists involved in mineral resource surveys. …”
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Real-Time Forest Fire Detection with Lightweight CNN Using Hierarchical Multi-Task Knowledge Distillation by Ismail El-Madafri, Marta Peña, Noelia Olmedo-Torre

“…The method integrates multi-task knowledge distillation, transferring knowledge from a high-performance DenseNet201 teacher model that was trained on a hierarchically structured wildfire dataset. …”
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Explicitly Constrained Black-Box Optimization With Disconnected Feasible Domains Using Deep Generative Models by Naoki Sakamoto, Rei Sato, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto

“…However, the design of a reasonable decoder requires deep prior knowledge of the optimization problem to be solved and, hence, human effort. …”
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Enhancing efficient deep learning models with multimodal, multi-teacher insights for medical image segmentation by Khondker Fariha Hossain, Sharif Amit Kamran, Joshua Ong, Alireza Tavakkoli

“…Additionally, introducing a novel training strategy optimizes knowledge transfer, ensuring the student model captures the intricate mapping of features essential for high-fidelity segmentation. …”
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Access and benefit sharing biological materials for machines: Artificial intelligence, machine learning and deep learning by Charles Lawson, Elizabeth Englezos, Michelle Rourke

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A review of deep learning-based few sample fault diagnosis method for rotating machinery by Ke WU, Jun WU, Qiming SHU, Weiming SHEN, Wenbin SONG

“…ObjectivesDeep learning has shown great potential in the field of rotating machinery fault diagnosis. …”
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Real-Time Common Rust Maize Leaf Disease Severity Identification and Pesticide Dose Recommendation Using Deep Neural Network by Zemzem Mohammed Megersa, Abebe Belay Adege, Faizur Rashid

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GM-VGG-Net: A Gray Matter-Based Deep Learning Network for Autism Classification by Ebenezer Daniel, Anjalie Gulati, Shraya Saxena, Deniz Akay Urgun, Biraj Bista

“…We trained our deep network with 132 MRI T1 images from normal controls and 140 MRI T1 images from ASD patients sourced from the Autism Brain Imaging Data Exchange (ABIDE) dataset. …”
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Structured hashing with deep learning for modality, organ, and disease content sensitive medical image retrieval by Asim Manna, Dipayan Dewan, Debdoot Sheet

“…The network of MODHash is trained by minimizing characteristic-specific classification loss and Cauchy cross-entropy loss across training samples. …”
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Action research on implementing the BOPPPS model in teaching mechanical prophylaxis techniques for deep vein thrombosis by Mingyan Shen, Pengxia Wan, Zhixian Feng

“…Abstract Objective To explore the application effects of the BOPPPS model in teaching mechanical prophylaxis techniques for deep vein thrombosis (DVT). Methods Following the “Plan-Act-Observe-Reflect” four-step process of action research, continuous improvements were made to the teaching process of DVT mechanical prophylaxis techniques based on the BOPPPS model. …”
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Pothole detection and segmentation in the Bushveld Complex using physics-based data augmentation and deep learning by Glen T. Nwaila, Musa S.D. Manzi, Emmanuel John M. Carranza, Raymond J. Durrheim, Hartwig E. Frimmel

“…Both components of the framework are general enough to permit further development, for example, as deep-learning architectures evolve or as the knowledge of potholes improve. …”
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