Mapping of soil sampling sites using terrain and hydrological attributes by Tan-Hanh Pham, Kristopher Osterloh, Kim-Doang Nguyen

“…Traditional site selection methods are labor-intensive and fail to capture soil variability comprehensively. This study introduces a deep learning-based tool that automates soil sampling site selection using spectral images. …”
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Hybrid deep learning for IoT-based health monitoring with physiological event extraction by Sivanagaraju Vallabhuni, Kumar Debasis

“…Methods This paper presents a novel hybrid machine-learning model by amalgamating Convolutional Neural Networks (CNNs) with Long Short-Term Memory models (LSTMs) to boost prediction accuracy. …”
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Handwritten Text Recognition for Documentary Medieval Manuscripts by Sergio Torres Aguilar, Vincent Jolivet

“…The architecture of the models is based on a Convolutional Recurrent Neural Network (CRNN) coupled with a Connectionist Temporal Classification (CTC) loss. …”
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A systematic literature review on the role of artificial intelligence in citizen science by Germain Abdul-Rahman, Andrej Zwitter, Noman Haleem

“…However, challenges such as data quality variability, algorithmic opacity, and scalability constraints persist. …”
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Detecting Lameness in Dairy Cows Based on Gait Feature Mapping and Attention Mechanisms by Xi Kang, Junjie Liang, Qian Li, Gang Liu

“…The proposed system comprises (1) a Cow Lameness Feature Map (CLFM) model extracting holistic gait kinematics (hoof trajectories and dorsal contour) from walking sequences, and (2) a DenseNet-Integrated Convolutional Attention Module (DCAM) that mitigates inter-individual variability through multi-feature fusion. …”
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Computer Vision Meets Generative Models in Agriculture: Technological Advances, Challenges and Opportunities by Xirun Min, Yuwen Ye, Shuming Xiong, Xiao Chen

“…However, challenges persist, including environmental variability, edge deployment limitations, and the need for interpretable systems. …”
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Automated Risser Grade Assessment of Pelvic Bones Using Deep Learning by Jeoung Kun Kim, Donghwi Park, Min Cheol Chang

“…(1) Background: This study aimed to develop a deep learning model using a convolutional neural network (CNN) to automate Risser grade assessment from pelvic radiographs. (2) Methods: We used 1619 pelvic radiographs from patients aged 12–18 years with scoliosis to train two CNN models—one for the right pelvis and one for the left. …”
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Liver Semantic Segmentation Method Based on Multi-Channel Feature Extraction and Cross Fusion by Chenghao Zhang, Lingfei Wang, Chunyu Zhang, Yu Zhang, Peng Wang, Jin Li

“…Firstly, a multi-scale input strategy is employed to account for the variability in liver features at different scales. A multi-scale convolutional attention (MSCA) mechanism is integrated into the encoder to aggregate multi-scale information and improve feature representation. …”
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Progressive Cluster-Guided Knowledge Distillation for Remote Sensing Image Scene Classification by Zhaopeng Deng, Zheng Zhou, Haoran Zhao, Xiaolin Chen, Danfeng Hong, Xin Sun

“…Knowledge distillation (KD) has recently demonstrated remarkable potential in developing lightweight convolutional neural networks for remote sensing image (RSI) scene classification tasks. …”
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Toward long-range ENSO prediction with an explainable deep learning model by Qi Chen, Yinghao Cui, Guobin Hong, Karumuri Ashok, Yuchun Pu, Xiaogu Zheng, Xuanze Zhang, Wei Zhong, Peng Zhan, Zhonglei Wang

“…Abstract El Niño-Southern Oscillation (ENSO) is a prominent mode of interannual climate variability with far-reaching global impacts. Its evolution is governed by intricate air-sea interactions, posing significant challenges for long-term prediction. …”
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Flood Classification and Improved Loss Function by Combining Deep Learning Models to Improve Water Level Prediction in a Small Mountain Watershed by Rukai Wang, Ximin Yuan, Fuchang Tian, Minghui Liu, Xiujie Wang, Xiaobin Li, Minrui Wu

“…Flash floods are highly nonlinear and exhibit rapid spatiotemporal variability. Existing methods struggle to capture these features, leading to suboptimal long‐term and peak flood prediction accuracy. …”
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Implications of artificial intelligence in periodontal treatment maintenance: a scoping review by Raafat Musief Sarakbi, Sudhir Rama Varma, Sudhir Rama Varma, Lovely Muthiah Annamma, Vinay Sivaswamy, Vinay Sivaswamy

“…Deep learning algorithms such as convolutional neural networks (CNNs) and segmentation techniques were analyzed for their diagnostic accuracy. …”
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Deep Learning for Video Fluoroscopic Swallowing Study Analysis: A Survey on Classification, Detection, and Segmentation Techniques by Ahmed Fakhry, Sarah Mary Antony, Eunhee Park, Jong Taek Lee

“…Classification methods utilizing convolutional neural networks achieve high accuracy, ranging from 91.7% to 95.98%, and Area Under the ROC Curve scores between 0.71 and 0.97, thus enhancing the consistency and reliability of swallowing phase identification. …”
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Deep learning analysis for rheumatologic imaging: current trends, future directions, and the role of human by Jucheol Moon, Pratik Jadhav, Sangtae Choi

“…Convolutional neural networks, a DL model type, have shown great potential in medical image classification, segmentation, and anomaly detection, often surpassing human performance in tasks like tumor identification and disease severity grading. …”
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Bridging technology and ecology: enhancing applicability of deep learning and UAV-based flower recognition by Marie Schnalke, Jonas Funk, Andreas Wagner, Andreas Wagner

“…Challenges remain, such as detecting flowers in dense vegetation and accounting for environmental variability.…”
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Computer-Aided Diagnosis Techniques for Brain Tumor Segmentation and Classification Using MRI by Gadicha A. B., Kale Prachi V., Dalvi G. D., Mohod M. M., Pakhale S. C., Khan S. M.

“…Additionally, the paper highlights the challenges associated with model generalization, dataset limitations, preprocessing variability, and computational resource constraints. …”
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Influence of cognitive networks and task performance on fMRI-based state classification using DNN models by Murat Kucukosmanoglu, Javier O. Garcia, Justin Brooks, Kanika Bansal

“…This study highlights the application of interpretable DNNs in revealing cognitive mechanisms associated with task performance and individual variability.…”
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Enhanced Heart Disease Classification Using Dual Attention Mechanisms and 3D-Echo Fusion Algorithm in Echocardiogram Videos by S Deepika, N. Jaisankar

“…In this paper, we present a novel hybrid deep learning framework that integrates convolutional neural networks (CNNs) with recurrent neural networks (RNNs) alongside a 3D-Echo Fusion approach and a Dual Attention Model for heart valve disease classification using echocardiogram videos. …”
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Preliminary Electroencephalography-Based Assessment of Anxiety Using Machine Learning: A Pilot Study by Katarzyna Mróz, Kamil Jonak

“…However, challenges such as data variability, noise, and model interpretability remain significant. …”
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A Novel Multimodal Deep Learning Approach With Loss Function for Detection of Sleep Apnea Events by Alireza Fakhim Babaei, Jafar Tanha, Mohammad Ali Balafar, Seyedehsan Roshan

“…Detecting sleep apnea accurately and efficiently presents several challenges, including variability in physiological signals among individuals and class imbalance for apnea events. …”
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