Precision forecasting for hybrid energy systems using five deep learning algorithms for meteorological parameter prediction by Ceren Ceylan, Zehra Yumurtacı

“…This study presents a comprehensive comparative analysis of five deep learning algorithms, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and CNN-LSTM hybrid, for forecasting critical meteorological parameters (wind speed, ambient temperature, and solar radiation) that determine energy output in a wind and solar-based hybrid energy system (HES). …”
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A deep learning approach for heart disease detection using a modified multiclass attention mechanism with BiLSTM by Umesh Kumar Lilhore, Sarita Simaiya, Monish Khan, Roobaea Alroobaea, Abdullah M. Baqasah, Majed Alsafyani, Afnan Alhazmi

“…It surpassed methods such as the Classic Deep BiLSTM (SD-BiLSTM), and the standard approaches of Naive Bayes (NB), DNN-Taylos (DNNT), Multilayer perceptron (MLP-NN) and convolutional neural network (CNN). This work provides a solution to significant limitations of current methods and improves the accuracy of classification, indicating substantial progress in accurate diagnosis of heart diseases.…”
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Eeg-based detection of epileptic seizures in patients with disabilities using a novel attention-driven deep learning framework with SHAP interpretability by Tawfeeq Shawly, Ahmed A. Alsheikhy

“…In this research, we introduce a cutting-edge deep learning methodology for the automated prediction of epilepsy, incorporating a Novel Attention Module (NAM) into a new Convolutional Neural Network (CNN) to improve the extraction of features from EEG signals. …”
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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

“…Therefore, this paper presents duck egg crack detection using an adaptive convolutional neural network (CNN) model ensemble with multi-light channels. …”
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Do more with less: Exploring semi-supervised learning for geological image classification by Hisham I. Mamode, Gary J. Hampson, Cédric M. John

“…To evaluate this potential for subsurface data, we compare a high-performance semi-supervised learning (SSL) algorithm (SimCLRv2) with supervised transfer learning on a Convolutional Neural Network (CNN) in geological image classification.We tested the two approaches on a classification task of sediment disturbance from cores of International Ocean Drilling Program (IODP) Expeditions 383 and 385. …”
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Prediction of Sea Surface Chlorophyll-a Concentrations by Remote Sensing and Deep Learning by Qingfeng Ruan, Delu Pan, Difeng Wang, Xianqiang He, Fang Gong, Qingjiu Tian

“…This study aims to enhance the prediction of marine Chl-a concentrations by introducing the chlorophyll-a concentration prediction model (ChlaPM), which was developed on the basis of a convolutional long short-term memory (ConvLSTM) network. …”
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A CNN deep learning approach to scour depth estimation around complex bridge piers in steady flow environments by Ngoc Thi Huynh, Anh Thu Thi Phan, Tan Tai Trieu, Ho-Hong-Duy Nguyen, Thanh Nhan Nguyen

“…This study presents a one-dimensional convolutional neural network (1D CNN) and long short-term memory (LSTM) deep learning models for predicting the maximum scour depth around CBP under steady current conditions in a clear-water environment. …”
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Plant Leaf Disease Detection Using Deep Learning: A Multi-Dataset Approach by Manjunatha Shettigere Krishna, Pedro Machado, Richard I. Otuka, Salisu W. Yahaya, Filipe Neves dos Santos, Isibor Kennedy Ihianle

“…A combined dataset was utilised, incorporating the PlantDoc dataset with web-sourced images of plants from online platforms. State-of-the-art convolutional neural network (CNN) architectures, including EfficientNet-B0, EfficientNet-B3, ResNet50, and DenseNet201, were employed and fine-tuned for plant leaf disease classification. …”
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A Novel Open Circuit Fault Diagnosis for a Modular Multilevel Converter with Modal Time-Frequency Diagram and FFT-CNN-BIGRU Attention by Ziyuan Zhai, Ning Wang, Siran Lu, Bo Zhou, Lei Guo

“…In the model construction stage, the frequency–domain features are first extracted using the fast Fourier transform (FFT), and the local patterns are captured through a combination with a convolutional neural network; subsequently, the timing correlations are analyzed using bidirectional gated loop cells, and the Attention Mechanism is introduced to strengthen the key features. …”
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Application of artificial intelligence sensor and visual image technology in the analysis of hydrophilic space landscape characteristics by Dayan Li

“…Combined with the key nodes of 3D object AI feature recognition, multi-sensor collaborative Dempster Shafer evidence theory and 3D convolutional neural network waterfront space landscape feature recognition sub-model are constructed, and the waterfront space landscape recognition analysis model is tested and analyzed. …”
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Research on the Identification of Wheat Fusarium Head Blight Based on Multispectral Remote Sensing from UAVs by Ping Dong, Ming Wang, Kuo Li, Hongbo Qiao, Yuyang Zhao, Fernando Bacao, Lei Shi, Wei Guo, Haiping Si

“…Addressing the limitations of current deep learning models in capturing detailed features from UAV imagery, this study proposes an advanced identification model for FHB in wheat based on multispectral imagery from UAVs. …”
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AI approaches for phenotyping Alzheimer's disease and related dementias using electronic health records by Sara Knox, Stephanie Aghamoosa, Paul M. Heider, Maxwell Cutty, Andrew Wright, Dmitry Scherbakov, Gabriel Hood, Sara A. Nolin, Jihad S. Obeid

“…Several of the AI‐based models, including convolutional neural networks, also outperformed the CCW algorithm. …”
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Comparative Study of CNN Architectures for Brain Tumor Classification Using MRI: Exploring GradCAM for Visualizing CNN Focus by Areli Chinga, Wilden Bendezu, Antonio Angulo

“…This study aims to refine the diagnosis of brain tumors using convolutional neural network algorithms. Currently, diagnostic accuracy is limited, therefore, our approach uses five different CNN architectures to accurately identify and classify affected brain regions, specifically glioma, meningioma, or pituitary tumors. …”
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Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluids by Xin-Ru Wen, Jia-Wei Tang, Jie Chen, Hui-Min Chen, Muhammad Usman, Quan Yuan, Yu-Rong Tang, Yu-Dong Zhang, Hui-Jin Chen, Liang Wang

“…Multiple ML models were constructed and optimized, with the convolutional neural network (CNN) model achieving the highest prediction accuracy at 99%. …”
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Research on multi-branch residual connection spectrum image classification based on attention mechanism by Zhong Xiaohui, Dong Sheng, Zhang Yiyi, Lu Wei, Jiang Lincen

“…Abstract The acoustic spectrogram arranges the frequencies in the sound along the frequency spread, and translates the spectral changes into the intensity, wavelength and frequency of the electrical signals. Currently, the extensive use of convolutional neural networks for spectral image classification can extract signal features in the spectrogram, but the redundancy of noisy data generated by a large number of bands of the spectrum affects the feature information at different levels of the image. …”
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Passive Sensing for Mental Health Monitoring Using Machine Learning With Wearables and Smartphones: Scoping Review by ShiYing Shen, Wenhao Qi, Jianwen Zeng, Sixie Li, Xin Liu, Xiaohong Zhu, Chaoqun Dong, Bin Wang, Yankai Shi, Jiani Yao, Bingsheng Wang, Xiajing Lou, Simin Gu, Pan Li, Jinghua Wang, Guowei Jiang, Shihua Cao

“…Deep learning models (eg, convolutional neural networks and long short-term memory) showed high accuracy, while traditional ML (eg, random forest) remained prevalent due to better interpretability. …”
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A rapid, low-cost deep learning system to classify strawberry disease based on cloud service by Guo-feng YANG, Yong YANG, Zi-kang HE, Xin-yu ZHANG, Yong HE

“…Compared with popular Convolutional Neural Networks (CNN) and five other methods, our network achieves better disease classification effect. …”
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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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Wi-FiAG: Fine-Grained Abnormal Gait Recognition via CNN-BiGRU with Attention Mechanism from Wi-Fi CSI by Anming Dong, Jiahao Zhang, Wendong Xu, Jia Jia, Shanshan Yun, Jiguo Yu

“…For the gait classification module, we design a hybrid deep learning architecture that integrates convolutional neural networks (CNNs), bidirectional gated recurrent units (BiGRUs), and an attention mechanism to enhance performance. …”
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Deep Learning Based on Facial Expression Recognition from Images to Videos by Deng Rui

“…Special attention is given to advanced models based on Convolutional Neural Networks (CNNs), with a detailed comparison of their architectures and characteristics, analyzing their performance under various conditions. …”
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