Short-term solar irradiance forecasting using deep learning models by Syed Saad Ahmed, Chang Wei Bin, Nisar Humaira, Riaz Hannan Naseem, Yeap Kim Ho, Zaber Nursaida Mohamad

“…The data for Penang, Malaysia is used in this research. A Dense Neural Network (DNN) with 32 units achieved a validation MAE of 21.33 and MSE of 1343.68 in the 6th fold. …”
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qArI: A Hybrid CTC/Attention-Based Model for Quran Recitation Recognition Using Bidirectional LSTMP in an End-to-End Architecture by Sumayya Alfadhli, Hajar Alharbi, Asma Cherif

“…The model combines a connectionist temporal classification (CTC)/attention loss function with a Bidirectional Long Short-Term Memory with projections (BLSTMP) architecture and a token-based recurrent neural network language model (RNNLM) using CQDV1 dataset. …”
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Physically Meaningful Surrogate Data for COPD by Harry J. Davies, Ghena Hammour, Hongjian Xiao, Patrik Bachtiger, Alexander Larionov, Philip L. Molyneaux, Nicholas S. Peters, Danilo P. Mandic

“…As a final stage of verification, a simple convolutional neural network is trained on surrogate data alone, and is used to accurately detect COPD in real-world patients. …”
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Assessing machine learning for fair prediction of ADHD in school pupils using a retrospective cohort study of linked education and healthcare data by Johnny Downs, Robert Stewart, Alice Wickersham, Sumithra Velupillai, Lucile Ter-Minassian, Natalia Viani, Lauren Cross

“…Using a unique linked health and education data resource, we examined how machine learning (ML) approaches can predict risk of ADHD.Design Retrospective population cohort study.Setting South London (2007–2013).Participants n=56 258 pupils with linked education and health data.Primary outcome measures Using area under the curve (AUC), we compared the predictive accuracy of four ML models and one neural network for ADHD diagnosis. Ethnic group and language biases were weighted using a fair pre-processing algorithm.Results Random forest and logistic regression prediction models provided the highest predictive accuracy for ADHD in population samples (AUC 0.86 and 0.86, respectively) and clinical samples (AUC 0.72 and 0.70). …”
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Reconstruction of the Radiation Belts for Solar Cycles 17–24 (1933–2017) by A. A. Saikin, Y. Y. Shprits, A. Y. Drozdov, D. A. Landis, I. S. Zhelavskaya, S. Cervantes

“…A nonlinear auto regressive network with exogenous inputs (NARX) neural network was trained off GOES 15 measurements (January 2011–March 2014) and used to supply the upper boundary condition (L* = 6.6) over the course of solar cycles 17–24 (i.e., 1933–2017). …”
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RMHA-Net: Robust Optic Disc and Optic Cup Segmentation Based on Residual Multiscale Feature Extraction With Hybrid Attention Networks by Mohammad J. M. Zedan, Siti Raihanah Abdani, Jaesung Lee, Mohd Asyraf Zulkifley

“…This network’s encoder is designed based on advanced convolutional neural network (CNN) blocks that combine dilated convolution, which allows field-of-view expansion by capturing larger-scale features. …”
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Analysis of ADR reports of cetuximab based on the FDA adverse event reporting system database by Shuai Zhao, Yan Wang, Xiaoli Deng, Xi Chen, Zhaoyi Lu

“…Disproportionality analyses, including the reporting odds ratio (ROR), the proportional reporting ratio (PRR), the Bayesian confidence propagation neural network (BCPNN), and the empirical Bayesian geometric mean (EBGM) algorithms, were employed to quantify the signals of cetuximab-associated AEs. …”
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Resnet-1DCNN-REA bearing fault diagnosis method based on multi-source and multi-modal information fusion by Xu Chen, Wenbing Chang, Yongxiang Li, Zhao He, Xiang Ma, Shenghan Zhou

“…At the same time, the time-frequency statistical features of the fused 1D signal were extracted from the integrated perspective of time and frequency domains and inputted into the improved 1D convolutional neural network model based on the residual block and attention mechanism (1DCNN-REA) model to realize fault diagnosis. …”
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Liquid-based cytological diagnosis of pancreatic neuroendocrine tumors using hyperspectral imaging and deep learning by Taojing Ran, Wei Huang, Xianzheng Qin, Xingran Xie, Yingjiao Deng, Yundi Pan, Yao Zhang, Ling Zhang, Lili Gao, Minmin Zhang, Dong Wang, Yan Wang, Qingli Li, Chunhua Zhou, Duowu Zou

“…This study developed a method that combines hyperspectral imaging (HSI) technology and a convolutional neural network (CNN) to conduct a cytological diagnosis of PanNETs. …”
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Structural and functional connectivity coupling as an imaging marker for bone metastasis pain in lung cancer patients by Jiahui Zheng, Chengfang Wang, Xiaoyu Zhou, Yu Tang, Lin Tang, Yong Tan, Jing Zhang, Hong Yu, Jiuquan Zhang, Daihong Liu

“…In addition, the convolutional neural network (CNN) model was selected to analyze and classify three groups based on individualized networks. …”
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A Huge Innovation in Diagnosis of Obstructive Sleep Apnea Syndrome: With an Artificial Intelligence-Based Algorithm, Obstructive Sleep Apnea Syndrome Can Now Be Diagnosed With Pulm... by Seval Bulut Eris, Mehmet Recep Bozkurt, Omer Eris, Cahit Bilgin

“…Using only five features extracted from the flow-volume curve (TLC/PIF, PIF/PEF, TLC/FIF50, TLC/FIF25, and FIF25/FEF25), OSAS was diagnosed with 97.1% accuracy using the Neural Network (NN) algorithm. The results showed that OSAS can be diagnosed quickly and reliably using PFT available at every hospital. …”
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Abnormality detection in nailfold capillary images using deep learning with EfficientNet and cascade transfer learning by Mona Ebadi Jalal, Omar S. Emam, Cristián Castillo-Olea, Begoña García-Zapirain, Adel Elmaghraby

“…Our proposed model achieved superior performance, with accuracy, precision, recall, F1 score, and ROC_AUC of 1.00, significantly outperforming both models of single transfer learning on the pre-trained EfficientNet-B0 and cascade transfer learning on a convolutional neural network, which each attained an accuracy, precision, recall, and F1 score of 0.67 and a ROC_AUC of 0.83. …”
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Predicting home delivery and identifying its determinants among women aged 15–49 years in sub-Saharan African countries using a Demographic and Health Surveys 2016–2023: a machine... by Adem Tsegaw Zegeye, Binyam Chaklu Tilahun, Makida Fekadie, Eliyas Addisu, Birhan Wassie, Berihun Alelign, Mequannet Sharew, Nebebe Demis Baykemagn, Abdulaziz Kebede, Tirualem Zeleke Yehuala

“…Machine learning models such as Random Forest, Decision Tree, K-Nearest Neighbor, Logistic Regression, Extreme Gradient Boosting, AdaBoost, Artificial Neural Network, and Naive Bayes were used. The predictive model was evaluated by area under the curve, accuracy, precision, recall, and F-measure. …”
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Portable optical spectroscopy and machine learning techniques for quantification of the biochemical content of raw food materials by Cosimo Ricci, Agata Gadaleta, Annamaria Gerardino, Angelo Didonna, Giuseppe Ferrara, Francesca Romana Bertani

“…Results Considering the specific samples, the obtained results of the classification models indicate a validation mean absolute error of 0.8% (percentage of total protein content in dry matter) for two species of wheat using Convolutional Neural Network following normalization procedures and 0.32% using Partial Least Square (PLS) analysis applied to Tritordeum samples; visible reflectance spectra have been used to discriminate the two cereal species. …”
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A performance-based generative design framework based on a design grammar for high-rise office towers during early design stage by Liwei Chen, Ye Zhang, Yue Zheng

“…Case study results demonstrate that, with the support of Artificial Neural Network, utilizing this system can not only globally explore the diversity of tower morphologies but also efficiently uncover greater energy-saving potential in complex architectural forms compared to simpler cubic forms, with an improvement of up to 7.76% during the early stages of design. …”
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Cache Aging with Learning (CAL): A Freshness-Based Data Caching Method for Information-Centric Networking on the Internet of Things (IoT) by Nemat Hazrati, Sajjad Pirahesh, Bahman Arasteh, Seyed Salar Sefati, Octavian Fratu, Simona Halunga

“…The proposed method uses an artificial neural network to make predictions. These predictions closely match the actual values, with a low error margin of 0.0121. …”
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Detecting anomalies in smart wearables for hypertension: a deep learning mechanism by C. Kishor Kumar Reddy, Vijaya Sindhoori Kaza, R. Madana Mohana, Mohammed Alhameed, Fathe Jeribi, Shadab Alam, Mohammed Shuaib

“…This research aims to address the limitations of current healthcare systems, particularly in remote areas, by leveraging deep learning techniques in Smart Health Monitoring (SHM).MethodsThis paper introduces a novel neural network architecture, ResNet-LSTM, to predict BP from physiological signals such as electrocardiogram (ECG) and photoplethysmogram (PPG). …”
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Delayered IC image analysis with template‐based Tanimoto Convolution and Morphological Decision by Deruo Cheng, Yiqiong Shi, Tong Lin, Bah‐Hwee Gwee, Kar‐Ann Toh

“…The proposed TCMD‐PL model utilises the output of TCMD model as the pseudo labels for training a deep convolutional neural network in supervised manner, and it is able to achieve further performance improvement of ∼4% in comparison to TCMD model without extra data labelling.…”
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Decentralized control system for unlimited street lighting poles with an intelligent, energy-saving off-grid maximum power point tracking battery charger by Hussain Attia, Ali Al-Ataby, Maen Takruri, Amjad Omar

“…A deep artificial neural network (ANN) algorithm is designed to have an effective response of maximum power point tracking (MPPT) in terms of accuracy and speed to obtain maximum electrical power from the incident light on a pair of photovoltaic panels fixed above an off-grid street light pole. …”
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Hydrological and hydrodynamic coupling simulation under composite underlying surfaces in urban polder areas by Cheng Chen, Binquan Li, Yang Xiao, Huihui Li, Taotao Zhang, Dong Xu, Huanghao Yu

“…The model was applied in flood forecasting and risk assessment. A BP neural network (BPNN) was employed for error correction to reduce model uncertainty in forecasting. …”
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