Combining UAV Multispectral and Thermal Infrared Data for Maize Growth Parameter Estimation by Xingjiao Yu, Xuefei Huo, Long Qian, Yiying Du, Dukun Liu, Qi Cao, Wen’e Wang, Xiaotao Hu, Xiaofei Yang, Shaoshuai Fan

“…This study explores the integration of UAV-based multispectral (MS) and thermal infrared (TIR) data to improve the estimation of maize LAI and LCC across different growth stages, aiming to enhance nitrogen (N) management. …”
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A Novel Short‐Term Prediction Model for Regional Equatorial Plasma Bubble Irregularities in East and Southeast Asia by Xiukuan Zhao, Guozhu Li, Haiyong Xie, Lianhuan Hu, Wenjie Sun, Yi Li, Guofeng Dai, Jianfei Liu, Yu Li, Baiqi Ning, Michi Nishioka, Septi Perwitasari, Prasert Kenpankho

“…The model integrates the convolutional neural network and long short‐term memory (LSTM) network, together with attention mechanisms, to capture both spatial and temporal features of regional ionospheric irregularities. …”
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A Dual-Technology Approach: Handheld NIR Spectrometer and CNN for <i>Fritillaria</i> spp. Quality Control by Fengling Li, Wen Lei, Juan Li, Xiaoting Wang, Jingyu Su, Tangnuer Sahati, Xiahenazi Aierkenjiang, Ruyi Tian, Weihong Zhou, Jixiong Zhang, Jingjing Xia

“…<i>Fritillaria</i> spp. has an extremely high edible and medicinal value. Different parts of it exhibit significant variations in medicinal efficacy. …”
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AcuSim: A Synthetic Dataset for Cervicocranial Acupuncture Points Localisation by Qilei Sun, Jiatao Ma, Paul Craig, Linjun Dai, Eng Gee Lim

“…Abstract The locations of acupuncture points (acupoints) differ among human individuals due to variations in factors such as height, weight and fat proportions. …”
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WTDBNet: A Wavelet Transform-Based Dual-Stream Backbone Network for Fine-Grained Ship Detection by Wei Cao, Xinyu Zhao, Hongqi Wang, Yuxin Hu

“…The challenges of this task mainly lie in bird’s-eye viewpoints, scale variations, rotational changes, and environmental factors, which lead to minor inter-class differences and significant intra-class variations. …”
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Integrating Random Forest With Boundary Enhancement for Mapping Crop Planting Structure at the Parcel Level From Remote Sensing Images by Junyang Xie, Yan Li, Hao Wu, Ziwei Wu, Ruina Zhao, Anqi Lin, Marcos Adami, Guoqiang Li, Jian Zhang

“…Comparative experiments with different convolutional neural networks further highlighted FPENet&#x0027;s exceptional capability. …”
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Estimation of physico-chemical properties of soil using machine learning by Patience Chizoba Mba, Opegbemi Matthias Busoye, John Temitope Ajayi, Judith Nkechinyere Njoku, Cosmas Ifeanyi Nwakanma, Senorpe Asem-Hiablie, Rammohan Mallipeddi, Tusan Park, Daniel Dooyum Uyeh

“…The study integrated Machine Learning (ML) and Deep Learning (DL) techniques with soil image analysis, employing image feature extraction through Gray-Level Co-occurrence Matrix (GLCM) and Gabor filters to enhance the feature space across 1,388 samples (each consisting of 600 g of soil) and 16 properties. We evaluated different models, including Support Vector Regression (SVR), a Convolutional Neural Network (CNN), an optimized CNN using Grid Search, and a stacked ML-CNN approach. …”
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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

“…However, when more closely inspecting the predicted spatial patterns we find that the CNN predictions yield ecologically more sensible patterns that distinguish different habitat types, as opposed to the other approaches. …”
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Deep Learning Technology for Classification of Thyroid Nodules Using Multi-View Ultrasound Images: Potential Benefits and Challenges in Clinical Application by Jinyoung Kim, Min-Hee Kim, Dong-Jun Lim, Hankyeol Lee, Jae Jun Lee, Hyuk-Sang Kwon, Mee Kyoung Kim, Ki-Ho Song, Tae-Jung Kim, So Lyung Jung, Yong Oh Lee, Ki-Hyun Baek

“…Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. …”
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Electroencephalogram Based Emotion Recognition Using Hybrid Intelligent Method and Discrete Wavelet Transform by Duy Nguyen, Minh Tuan Nguyen, Kou Yamada

“…The feature subsets are estimated by differently intelligent models and wise-subject 5-fold cross validation procedure on the validation set. …”
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Deep learning-based sex estimation of 3D hyoid bone models in a Croatian population using adapted PointNet++ network by Ivan Jerković, Željana Bašić, Ivana Kružić

“…The model, optimized for small datasets with 1D convolutional layers and global size features, was first applied in an unsupervised framework. …”
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Intelligent evaluation method for design education and comparison research between visualizing heat-maps of class activation and eye-movement by Jia Jiayi, Zhao Tianjiao, Yang Junyu, Wang Qian

“…An automatic design evaluation model based on convolutional neural networks has been established, which enables intelligent evaluation of student design works. …”
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A Multi-Scale Deep Learning Framework Combining MobileViT-ECA and LSTM for Accurate ECG Analysis by Abduljabbar S. Ba Mahel, Mehdhar S. A. M. Al-Gaashani, Reem Ibrahim Alkanhel, Dina S. M. Hassan, Mohammed Saleh Ali Muthanna, Ammar Muthanna, Ahmed Aziz

“…We analyzed ECG signals of three different lengths (1, 2, and 3 seconds) to investigate how segment length affects model performance. …”
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Early detection of retinal and choroidal microvascular impairments in diabetic patients with myopia by Yufei Wu, Yufei Wu, Jiahui Jiang, Xiaoyu Deng, Xixi Zhang, Jinger Lu, Zian Xu, Yitian Zhao, Zai-Long Chi, Zai-Long Chi, Qinkang Lu, Qinkang Lu

“…Automatic segmentation of retinal and choroidal layers was performed using a deep learning-based U-Net architecture. A ResNet-50 convolutional neural network was further applied to analyze vascular density patterns and assist in DR grading. …”
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Knowledge Distillation‐Based Zero‐Shot Learning for Process Fault Diagnosis by Yi Liu, Jiajun Huang, Mingwei Jia

“…Process data and image data are equivalent in their spatiotemporal dimensions, and convolutional neural networks are selected as the teacher model, pretrained on image data. …”
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A deep learning approach for automatic 3D segmentation of hip cartilage and labrum from direct hip MR arthrography by Malin Kristin Meier, Ramon Andreas Helfenstein, Adam Boschung, Andreas Nanavati, Adrian Ruckli, Till D. Lerch, Nicolas Gerber, Bernd Jung, Onur Afacan, Moritz Tannast, Klaus A. Siebenrock, Simon D Steppacher, Florian Schmaranzer

“…Abstract The objective was to use convolutional neural networks (CNNs) for automatic segmentation of hip cartilage and labrum based on 3D MRI. …”
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Crop Type Classification by DESIS Hyperspectral Imagery and Machine Learning Algorithms by Nizom Farmonov, Khilola Amankulova, Jozsef Szatmari, Alireza Sharifi, Dariush Abbasi-Moghadam, Seyed Mahdi Mirhoseini Nejad, Laszlo Mucsi

“…A Wavelet-attention convolutional neural network (WA-CNN), random forest and support vector machine (SVM) algorithms were utilized to automatically map the crops over the agricultural lands. …”
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Near real-time LOD prediction using ConvLSTM model through integrating IGS rapid LOD and effective angular momentum by Kehao Yu, Xiaoya Wang, Zhao Li, Jian Wang, Lihua Li, Weiping Jiang, Kai Yang, Jingwen Long, Yunzhao Li

“…To address the aforementioned issues, we propose an algorithm for predicting LOD that adopts a convolutional long-short-term memory (ConvLSTM) method with different base sequence lengths based on the LOD series from the IERS EOP C04, effective angular momentum (EAM) datasets and GNSS near-real-time (NRT) LOD data from the International GNSS Services (IGS) Rapid Products. …”
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State-of-Health Estimation for Lithium-Ion Batteries via Incremental Energy Analysis and Hybrid Deep Learning Model by Yan Zhang, Anxiang Wang, Chaolong Zhang, Peng He, Kui Shao, Kaixin Cheng, Yujie Zhou

“…This paper proposes a novel SOH estimation method for lithium-ion batteries, utilizing incremental energy features and a hybrid deep learning model that combines Convolutional Neural Network (CNN), Kolmogorov–Arnold Network (KAN), and Bidirectional Long Short-Term Memory (BiLSTM) (CNN-KAN-BiLSTM). …”
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Super-Resolution of Landsat-8 Land Surface Temperature Using Kolmogorov–Arnold Networks with PlanetScope Imagery and UAV Thermal Data by Mahdiyeh Fathi, Hossein Arefi, Reza Shah-Hosseini, Armin Moghimi

“…Additionally, many deep learning (DL)-based super-resolution approaches, such as Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), require significant computational resources. …”
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