The associations of cardiovascular health and all-cause mortality among individuals with depression by Meili Li, Youwei Huang, Jie Zhou, Ruijie Xie, Xianzhou Lu, Yanyan Shen

“…Abstract The association between the recently updated cardiovascular health (CVH) assessment algorithm, the Life’s Essential 8 (LE8), and all-cause mortality among adults with depression remains unknown. …”
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ICT-Net: A Framework for Multi-Domain Cross-View Geo-Localization with Multi-Source Remote Sensing Fusion by Min Wu, Sirui Xu, Ziwei Wang, Jin Dong, Gong Cheng, Xinlong Yu, Yang Liu

“…To facilitate practical deployment, we propose a deep embedding clustering algorithm optimized for rapid parsing of geo-localization information. …”
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A review of machine learning and deep learning for Parkinson’s disease detection by Hajar Rabie, Moulay A. Akhloufi

“…We discuss the preprocessing methods applied, the state-of-the-art models utilized, and their performance. Our evaluation included different algorithms such as support vector machines (SVM), random forests (RF), convolutional neural networks (CNN). …”
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The Advanced Actuarial Data Science Based AI-Driven Solutions for Automated Loss Reserving Under IFRS 17 in Non-Life Insurance by Brighton Mahohoho, Charles Chimedza, Florance Matarise, Sheunesu Munyira

“…The model leverages advanced machine learning techniques to improve accuracy, efficiency, and adaptability in loss reserves, with a specific focus on inflation-adjusted frequency-severity modeling. …”
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Recognition of MI-EEG signals using extended-LSR-based inductive transfer learning by Zhibin Jiang, Zhibin Jiang, Keli Hu, Keli Hu, Jia Qu, Zekang Bian, Zekang Bian, Donghua Yu, Donghua Yu, Jie Zhou, Jie Zhou

“…Although transfer learning has been implemented in EEG signal recognition, many existing methods are designed specifically for certain intelligent models, limiting their application and generalization.MethodsTo broaden application and generalization, an extended-LSR-based inductive transfer learning method is proposed to facilitate transfer learning across various classical intelligent models, including neural networks, Takagi-SugenoKang (TSK) fuzzy systems, and kernel methods.Results and discussionThe proposed method not only promotes the transfer of valuable knowledge from the source domain to improve learning performance in the target domain when target domain training data are insufficient but also enhances application and generalization by incorporating multiple classic base models. …”
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Smart grid stability prediction using Adaptive Aquila Optimizer and ensemble stacked BiLSTM by Safwan Mahmood Al-Selwi, Mohd Fadzil Hassan, Said Jadid Abdulkadir, Mohammed Gamal Ragab, Alawi Alqushaibi, Ebrahim Hamid Sumiea

“…Results: Multiple rounds of empirical experiments using different sets of optimizers and configurations, supported by the visualization capabilities of TensorBoard, demonstrate significant improvements in the performance of the AAO-BiLSTM model. …”
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MANS-Net: Multiple Attention-Based Nuclei Segmentation in Multi Organ Digital Cancer Histopathology Images by Ibtihaj Ahmad, Zain Ul Islam, Saleem Riaz, Fuzhong Xue

“…MANS-Net efficiently learns color-based, spatial, rough, and granular features, improving nuclei segmentation. We report that MANS-Net significantly outperforms state-of-the-art segmentation algorithms. …”
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MW-UNet: Multi-Scale Weighted Connection UNet for Identification and Classification of Non-Meteorological Clutter over Big Radar Data by Mengmeng Cui, Chen Zeng, Xiaolong Xu, Muhammad Bilal, Xiaoyu Xia

“…The field of weather forecasting makes extensive use of radar big data to extract information about precipitation, storms, lightning, and other weather phenomena to aid in the prediction and monitoring of weather changes. To improve the quality of radar data, machine learning and fuzzy logic algorithms are often used to identify and classify non-meteorological clutter in weather data. …”
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Duck Egg Crack Detection Using an Adaptive CNN Ensemble with Multi-Light Channels and Image Processing by Vasutorn Chaowalittawin, Woranidtha Krungseanmuang, Posathip Sathaporn, Boonchana Purahong

“…In current practice, human inspectors use standard white light for crack detection, and many researchers have focused primarily on improving detection algorithms without addressing lighting limitations. …”
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Synthetic Data-Driven Approaches for Chinese Medical Abstract Sentence Classification: Computational Study by Jiajia Li, Zikai Wang, Longxuan Yu, Hui Liu, Haitao Song

“…Additionally, we seek to develop more accurate text classification algorithms to improve the precision of Chinese medical abstract classification. …”
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Words high-frequency drying processes simulation of wooden tangent towers in a vacuum chamber by A. N. Kachanov, D. A. Korenkov, A. A. Revkov, V. V. Maksimov, O. V. Vorkunov

“…This model is characterized by the possibility of using simple algorithms for analyzing differential equation systems based on the finite differe nce method and requiring less initial data on the drying material properties. …”
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Predicting fertilizer treating of maize using digital image processing and deep learning approaches by Eshete Derb Emiru, Kassie Bishaw

“…The goal of this study is to develop a model for the recognition and classification of fertilizer treatment for maize based on maize leaf images, using deep learning algorithms to facilitate and improve the recognition and early control of fertilizer treatment for maize. …”
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Heart Disease Diagnostics Using Meta-Learning-Based Hybrid Feature Selection by Kaushalya Dissanayake, Md Gapar Md Johar

“…The urgent need for precise diagnostic techniques is crucial for improving patient outcomes, as early and accurate diagnosis can significantly influence the effectiveness of treatment and management strategies. …”
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Chronic liver disease detection using ranking and projection-based feature optimization with deep learning by Sumaiya Noor, Salman A. AlQahtani, Salman Khan

“…Our findings show that the proposed model can potentially improve diagnostic accuracy and support timely medical intervention, ultimately enhancing patient outcomes.…”
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3D‐Printed Titanium Trabecular Scaffolds with Sustained Release of Hypoxia‐Induced Exosomes for Dual‐Mimetic Bone Regeneration by Lincong Luo, Weihan Zheng, Jiaying Li, Tingting Chen, Wanting Xue, Tao Lin, Mingrui Liu, Zi Yan, Jiaxin Yang, Jiamin Li, Jiahao Pu, Yaobin Wu, Konghe Hu, Shiyu Li, Wenhua Huang

“…In vivo, BTPS&pDA@PGHExo improved bone volume, density, and neovascularization in a rabbit model. …”
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BoneCLIP-XGBoost: A Multimodal Approach for Bone Fracture Diagnosis by Zhihao Su, Yuhuai Zhou, Jianqun Zhou, Hanhua Cao, Huanping Zhang

“…The integration of visual and textual data in medical diagnostics holds significant potential for improving the accuracy and reliability of clinical decision-making. …”
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Overbounding of Near Real-Time Estimated Ionospheric Gradient Slope in Low-Latitude Regions by Maria Caamano, Jose Miguel Juan, Jaume Sanz, Sam Pullen

“…This paper proposes to expand this approach by including the derivation of an uncertainty model for the estimated gradient slope, allowing the threat model to be substituted with the near real-time estimated and overbounded gradient slope in current GBAS algorithms. …”
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Enhancing plant morphological trait identification in herbarium collections through deep learning–based segmentation by Hanane Ariouat, Youcef Sklab, Edi Prifti, Jean‐Daniel Zucker, Eric Chenin

“…We hypothesize that removing such backgrounds can improve the performance of these algorithms. Methods We propose a novel method based on deep learning to segment and generate plant masks from herbarium scans and subsequently remove the non‐plant backgrounds. …”
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Leveraging Advanced Technologies for Real-Time Plant Disease Monitoring by Mishra Archana, Afzal Md

“…The ensemble methods improve the accuracy and robustness of the diagnostic through using multiple ML models. …”
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Prediction of High-ozone Events Using GAM, SMOTE, and Tail Dependence Approaches in Texas (2005–2019) by Benjamin Brown-Steiner, Xiong Zhou, Matthew J. Alvarado, Brook T. Russell

“…Abstract We test three methods for ozone prediction in the El Paso (ELP) and Houston-Galveston-Brazoria (HGB) regions of Texas from 2005–2019: (1) a Generalized Additive Model (GAMs) approach; (2) a GAM approach with the addition of the Synthetic Minority Over-sampling TEchnique (SMOTE) and (3) a tail dependence modeling approach based in extreme value theory (EVT). …”
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