Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis by Mahreen Kiran, Ying Xie, Nasreen Anjum, Graham Ball, Barbara Pierscionek, Duncan Russell

“…Ensemble methods (e.g., Random Forest, Gradient Boosting) and deep learning models (e.g., Convolutional Neural Networks) dominate recent advancements. …”
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NEURAL NETWORKS INTEGRATION INTO LEGAL RESOURCES FOR ANTI-СORRUPTION MEASURES IN INTERNATIONAL ECONOMIC CO-OPERATION by Oleksii Makarenkov

“…This could be a LipNet neural network, which is trained for audio-visual recognition of human speech, or another recurrent neural network, as well as convolutional neural networks, deep contrastive neural networks, residual neural networks, and others. …”
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Ensemble Streamflow Simulations in a Qinghai–Tibet Plateau Basin Using a Deep Learning Method with Remote Sensing Precipitation Data as Input by Jinqiang Wang, Zhanjie Li, Ling Zhou, Chi Ma, Wenchao Sun

“…By employing a 1D Convolutional Neural Networks (1D CNN), streamflow simulations from multiple models are integrated and a Shapley Additive exPlanations (SHAP) interpretability analysis was conducted to examine the contributions of individual models on ensemble streamflow simulation. …”
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Interpretation of Bayesian-optimized deep learning models for enhancing soil erosion susceptibility prediction and management: a case study of Eastern India by Meshel Alkahtani, Javed Mallick, Saeed Alqadhi, Md Nawaj Sarif, Mohamed Fatahalla Mohamed Ahmed, Hazem Ghassan Abdo

“…To predict soil erosion probability, we employed Bayesian optimization to fine-tune Deep Neural Network (DNN), Convolutional Neural Network (CNN), Fully Connected Neural Network (FCNN), and DNN-CNN hybrid models. …”
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Machine learning approaches for EGFR mutation status prediction in NSCLC: an updated systematic review by Liu Haixian, Liu Haixian, Pang Shu, Pang Shu, Li Zhao, Li Zhao, Lu Chunfeng, Lu Chunfeng, Li Lun

“…BackgroundWith the rapid advances in artificial intelligence—particularly convolutional neural networks—researchers now exploit CT, PET/CT and other imaging modalities to predict epidermal growth factor receptor (EGFR) mutation status in non-small-cell lung cancer (NSCLC) non-invasively, rapidly and repeatably. …”
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Deep learning-based automated measurement of hip key angles and auxiliary diagnosis of developmental dysplasia of the hip by Ruixin Li, Xiao Wang, Tianran Li, Beibei Zhang, Xiaoming Liu, Wenhua Li, Qirui Sui

“…Abstract Objectives Anteroposterior pelvic radiographs remains the most widely employed method for diagnosing developmental dysplasia of the hip. …”
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A Neural Network for the Prediction of the Visual Acuity Gained from Vitrectomy and Peeling for Epiretinal Membrane by Rupert Kamnig, MD, Noah Robatsch, Anna Hillenmayer, MD, Denise Vogt, MD, Susanna F. König, MD, Efstathios Vounotrypidis, MD, Armin Wolf, MD, Christian M. Wertheimer, MD

“…The images were processed using a convolutional network. The output of both networks was concatenated and presented to a second multilayer perceptron. …”
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Large-Scale Apple Orchard Identification from Multi-Temporal Sentinel-2 Imagery by Chunxiao Wu, Yundan Liu, Jianyu Yang, Anjin Dai, Han Zhou, Kaixuan Tang, Yuxuan Zhang, Ruxin Wang, Binchuan Wei, Yifan Wang

“…Accurately extracting large-scale apple orchards from remote sensing imagery is of importance for orchard management. Most studies lack large-scale, high-resolution apple orchard maps due to sparse orchard distribution and similar crops, making mapping difficult. …”
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VdaBSC: A Novel Vulnerability Detection Approach for Blockchain Smart Contract by Dynamic Analysis by Rexford Nii Ayitey Sosu, Jinfu Chen, Edward Kwadwo Boahen, Zikang Zhang

“…We then combined bidirectional long short-term memory (BiLSTM), convolutional neural network, and the attention mechanism for vulnerability detection and classification. …”
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Multimodal fusion based few-shot network intrusion detection system by Congyuan Xu, Yong Zhan, Zhiqiang Wang, Jun Yang

“…The G-Model employs convolutional neural networks to capture spatial connections in traffic feature graphs, while the S-Model uses the Transformer architecture to process and fuse network feature sets with long-range dependencies. …”
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Multi-stage framework using transformer models, feature fusion and ensemble learning for enhancing eye disease classification by Abdulaziz AlMohimeed

“…However, current methods mostly use single-model architectures, including convolutional neural networks (CNNs), which might not adequately capture the long-range spatial correlations and local fine-grained features required for classification. …”
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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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Enhancing Radiologist Productivity with Artificial Intelligence in Magnetic Resonance Imaging (MRI): A Narrative Review by Arun Nair, Wilson Ong, Aric Lee, Naomi Wenxin Leow, Andrew Makmur, Yong Han Ting, You Jun Lee, Shao Jin Ong, Jonathan Jiong Hao Tan, Naresh Kumar, James Thomas Patrick Decourcy Hallinan

“…The included studies were categorized into five themes: reducing scan times, automating segmentation, optimizing workflow, decreasing reading times, and general time-saving or workload reduction. Convolutional neural networks (CNNs), especially architectures like ResNet and U-Net, were commonly used for tasks ranging from segmentation to automated reporting. …”
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Traditional Chinese medicine diagnostic prediction model for holistic syndrome differentiation based on deep learning by Zhe Chen, Dong Zhang, Chunxiang Liu, Hui Wang, Xinyao Jin, Fengwen Yang, Junhua Zhang

“…Based on the Bidirectional Encoder Representations from Transformers (BERT) and Convolutional Neural Networks (CNN) models, with the classification constraints from TCM holistic syndrome differentiation, the TCM-BERT-CNN model was constructed, which completes the end-to-end TCM holistic syndrome text classification task through symptom input and syndrome output. …”
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A hybrid deep learning framework for global irradiance prediction using fuzzy C-Means, CNN-WNN, and Informer models by Walid Mchara, Lazhar Manai, Mohamed Abdellatif Khalfa, Monia Raissi, Wissem Dimassi, Salah Hannachi

“…Artificial intelligence (AI) is revolutionizing solar energy forecasting, enabling precise irradiance prediction for electric solar vehicles (ESVs) to optimize energy efficiency and extend driving range.This study introduces a novel AI-powered hybrid deep learning framework that synergistically combines fuzzy C-means (FCM) clustering, convolutional neural networks (CNNs), wavelet neural networks (WNNs), and an Informer model to achieve superior accuracy. …”
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A Novel Bilateral Data Fusion Approach for EMG-Driven Deep Learning in Post-Stroke Paretic Gesture Recognition by Alexey Anastasiev, Hideki Kadone, Aiki Marushima, Hiroki Watanabe, Alexander Zaboronok, Shinya Watanabe, Akira Matsumura, Kenji Suzuki, Yuji Matsumaru, Hiroyuki Nishiyama, Eiichi Ishikawa

“…We introduce a hybrid deep learning model for recognizing hand gestures from electromyography (EMG) signals in subacute stroke patients: the one-dimensional convolutional long short-term memory neural network (CNN-LSTM). …”
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Rapid estimation of DON content in wheat flour using close‐range hyperspectral imaging and machine learning by Dinesh Kumar Saini, Anshul Rana, Jyotirmoy Halder, Mohammad Maruf Billah, Harsimardeep S. Gill, Jinfeng Zhang, Subash Thapa, Shaukat Ali, Brent Turnipseed, Karl Glover, Maitiniyazi Maimaitijiang, Sunish K. Sehgal

“…Abstract Fusarium head blight (FHB) is one of the most destructive fungal diseases affecting wheat (Triticum aestivum). …”
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Artificial Intelligence in Pediatric Orthopedics: A Comprehensive Review by Andrea Vescio, Gianluca Testa, Marco Sapienza, Filippo Familiari, Michele Mercurio, Giorgio Gasparini, Sergio de Salvatore, Fabrizio Donati, Federico Canavese, Vito Pavone

“…In spinal deformities, models such as support vector machines and convolutional neural networks achieved over 90% accuracy in classification and curve prediction. …”
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Leveraging Machine Learning and Remote Sensing for Water Quality Analysis in Lake Ranco, Southern Chile by Lien Rodríguez-López, Lisandra Bravo Alvarez, Iongel Duran-Llacer, David E. Ruíz-Guirola, Samuel Montejo-Sánchez, Rebeca Martínez-Retureta, Ernesto López-Morales, Luc Bourrel, Frédéric Frappart, Roberto Urrutia

“…Employing four advanced machine learning models (recurrent neural network (RNNs), long short-term memory (LSTM), recurrent gate unit (GRU), and temporal convolutional network (TCNs)), the research focuses on the estimation of chlorophyll-a concentrations at three sampling stations within Lake Ranco. …”
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Dynamic UAV data fusion and deep learning for improved maize phenological-stage tracking by Ziheng Feng, Jiliang Zhao, Liunan Suo, Heguang Sun, Huiling Long, Hao Yang, Xiaoyu Song, Haikuan Feng, Bo Xu, Guijun Yang, Chunjiang Zhao

“…Near real–time maize phenology monitoring is crucial for field management, cropping system adjustments, and yield estimation. Most phenological monitoring methods are post–seasonal and heavily rely on high–frequency time–series data. …”
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