Assessing the effect of ensemble learning algorithms and validation approach on estimating forest aboveground biomass: a case study of natural secondary forest in Northeast China by Hungil Jin, Yinghui Zhao, Unil Pak, Zhen Zhen, Kumryong So

“…This study highlights the potential challenges of applying a leave-one-out cross-validation approach and provides a scientific foundation for the feasibility of different ensemble learning algorithms and cross-validation approaches for accurate AGB estimation.…”
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Automatic Identification of Amharic Text Idiomatic Expressions Using a Deep Learning Approach by Habtamu Hunegnaw Limenih, Abebe Belay Adege, Abrham Yaregal Alene, Habtamu Tariku Demasu, Habtamu Molla Belachew

“…Developing a model to identify Amharic idiomatic terms is helpful for different NLP applications like machine translation, sentiment analysis, spam classification, intent recognition, and so on. …”
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MHRA-MS-3D-ResNet-BiLSTM: A Multi-Head-Residual Attention-Based Multi-Stream Deep Learning Model for Soybean Yield Prediction in the U.S. Using Multi-Source Remote Sensing Data by Mahdiyeh Fathi, Reza Shah-Hosseini, Armin Moghimi, Hossein Arefi

“…An attention mechanism further refines the model’s focus by dynamically weighting the significance of different input features for efficient yield prediction. …”
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Enhancing Cross-Domain Remote Sensing Scene Classification by Multi-Source Subdomain Distribution Alignment Network by Yong Wang, Zhehao Shu, Yinzhi Feng, Rui Liu, Qiusheng Cao, Danping Li, Lei Wang

“…First, it includes a pre-trained convolutional neural network (CNN) as a common feature extractor to fully exploit the shared invariant features across one target and multiple source domains. …”
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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 spatial and temporal distributions of EPB irregularities, including their latitudinal variation and delayed onset after sunset, and the occurrence across different days in East and Southeast Asia, were well predicted by the STEP. …”
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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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Onset and progression of postmortem histological changes in the kidneys of RccHanTM:WIST rats by Ricardo de Miguel, Raquel Vallejo, Kristel Kegler, Robert Kreutzer, Francisco José Mayoral, Yoshimasa Okazaki, Paula Ortega, Laura Polledo, Tanja Razinger, Olivia Kristina Richard, Raúl Sanchez, Nils Warfving, Anna Domènech, Klaus Weber

“…Necropsies were conducted at different time points postmortem (i.e., 0.5, 1, 4, 8, 12, 24, 36, 48 h for carcasses stored at room temperature, and 7 and 14 days for carcasses stored under refrigeration). …”
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Biological heart and brain ageing in subjects with cardiovascular diseases by Elizabeth Mcavoy, Elizabeth Mcavoy, Elizabeth Mcavoy, Matthias Wilms, Matthias Wilms, Matthias Wilms, Matthias Wilms, Matthias Wilms, Nils D. Forkert, Nils D. Forkert, Nils D. Forkert, Nils D. Forkert

“…However, it remains unclear to what extent different cardiovascular diseases affect brain health quantitatively across subjects and if that is associated with the extent the heart is affected by a disease. …”
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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

“…However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.…”
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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

“…Univariate and multiple linear regression analyses explored the associations between perfusion and risk factors.ResultsThe inner ring retinal vessel density and CVI in all areas were significantly different between groups (P &lt; 0.05); CPV was not significantly changed except for the inferotemporal area among the groups. …”
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A dual-branch deep learning model based on fNIRS for assessing 3D visual fatigue by Yan Wu, Yan Wu, Yan Wu, TianQi Mu, SongNan Qu, XiuJun Li, XiuJun Li, XiuJun Li, Qi Li, Qi Li, Qi Li

“…Given the time-series nature of fNIRS data and the variability of fatigue responses across different brain regions, a dual-branch convolutional network was constructed to separately extract temporal and spatial features. …”
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Digital Biomarkers for Parkinson Disease: Bibliometric Analysis and a Scoping Review of Deep Learning for Freezing of Gait by Wenhao Qi, Shiying Shen, Chaoqun dong, Mengjiao Zhao, Shuaiqi Zang, Xiaohong Zhu, Jiaqi Li, Bin Wang, Yankai Shi, Yongze Dong, Huajuan Shen, Junling Kang, Xiaodong Lu, Guowei Jiang, Jingsong Du, Eryi Shu, Qingbo Zhou, Jinghua Wang, Shihua Cao

“…In addition, 31 (78%) studies indicated that the best models were primarily convolutional neural networks or convolutional neural networks–based architectures. …”
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A novel method for soil organic carbon prediction using integrated ‘ground-air-space’ multimodal remote sensing data by Yilin Bao, Xiangtian Meng, Huanjun Liu, Mengyuan Xu, Mingchang Wang

“…To address this challenge, we propose an innovative multimodal remote sensing data integration framework, ResoCroS-Net, which integrates ground (soil samples), air (unmanned aerial vehicle (UAV) images), and space (satellite images) data, realized effective integration of multimodal data at different resolutions, with particular innovations in hierarchical design and data-processing logic. …”
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Advancing the application of the analytical renal pathology system in allograft IgA nephropathy patients by Xumeng Liu, Huiwen Fang, Dongmei Liang, Qunjuan Lei, Jiaping Wang, Feng Xu, Shaoshan Liang, Dandan Liang, Fan Yang, Heng Li, Jianghua Chen, Yuan Ni, Guotong Xie, Caihong Zeng

“…Background The analytical renal pathology system (ARPS) based on convolutional neural networks has been used successfully in native IgA nephropathy (IgAN) patients. …”
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Myoelectric signal and machine learning computing in gait pattern recognition for flat fall prediction by Shuo Zhang, Biao Chen, Chaoyang Chen, Maximillian Hovorka, Jin Qi, Jie Hu, Gui Yin, Marie Acosta, Ruby Bautista, Hussein F. Darwiche, Bryan E. Little, Carlos Palacio, John Hovorka

“…Four basic ML algorithms including support vector machine (SVM), K-nearest neighbor (kNN), decision tree (DT), and naive Bayes (NB), and five deep learning models including convolutional neural network (CNN), long-short term memory (LSTM), bidirectional long short-term memory (BiLSTM), and CNN-BiLSTM were used to process the EMG signals recorded under different gaits. …”
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