DETERMINATION OF THE BEST OPTIMIZER FOR A NEURONETWORK IN THE DEVELOPMENT OF AUTOMATIC IMAGE TAGGING SYSTEMS by Andrian Kozynets

“…In particular, for neural networks based on convolutional neural networks (CNNs), the choice between popular optimization methods such as Adam (Adaptive Moment Estimation) and SGD (Stochastic Gradient Descent, SGD) can significantly affect their performance. …”
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Hybrid CNN-Transformer Architecture With Xception-Based Feature Enhancement for Accurate Breast Cancer Classification by Alireza Zeynali, Mohammad Ali Tinati, Behzad Mozaffari Tazehkand

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Towards Optimizing Neural Network-Based Quantification for NMR Metabolomics by Hayden Johnson, Aaryani Tipirneni-Sajja

“…<b>Results:</b> The transformer was the most effective network for NMR metabolite quantification, especially as the number of metabolites per spectra increased or target concentrations were low or had a large dynamic range. …”
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A hybrid deep learning model for predicting atmospheric corrosion in steel energy structures under maritime conditions based on time-series data by Mohamed El Amine Seghier Ben, Tam T. Truong, Christian Feiler, Daniel Höche

“…Atmospheric corrosion of maritime structures remains one of the most challenging issues facing offshore industry. …”
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Predicting Wealth Score from Remote Sensing Satellite Images and Household Survey Data Using Deep Learning by Shashank Shekhar, Pratibha Singh, Rashmi Mishra, Sunil Kumar

“… The most exigent call of the United Nations’ 17 sustainable goals is to end poverty everywhere by 2030. …”
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An enhanced deep learning model for accurate classification of ovarian cancer from histopathological images by Anik Kumar Saha, Muntezar Rabbani, Anika Saba Ibte Sum, M. F. Mridha, Md Mohsin Kabir

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Hybrid CNN-LSTM With Attention Mechanism for Robust Credit Card Fraud Detection by Iman Akour, Nour Mohamed, Said Salloum

“…This paper proposes a hybrid fraud detection model integrating Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and an attention mechanism to address these challenges. …”
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HTC-HAD: A Hybrid Transformer-CNN Approach for Hyperspectral Anomaly Detection by Minghua Zhao, Wen Zheng, Jing Hu

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Bringing Intelligence to SAR Missions: A Comprehensive Dataset and Evaluation of YOLO for Human Detection in TIR Images by Mostafa Rizk, Israa Bayad

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Regional distributed photovoltaic power forecasting considering spatiotemporal correlation and meteorological coupling by HUANG Xiaoyan, GUO Sasa, CHEN Chengyou, XU Tengchong, HAN Xiao, WANG Tao

“…First， based on an analysis of the output characteristics of distributed photovoltaic power stations， an adaptive graph convolutional neural network combined with a long short-term memory network （LSTM） is used to extract the spatiotemporal features of the photovoltaic output. …”
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An Advanced Spatio-Temporal Graph Neural Network Framework for the Concurrent Prediction of Transient and Voltage Stability by Chaoping Deng, Liyu Dai, Wujie Chao, Junwei Huang, Jinke Wang, Lanxin Lin, Wenyu Qin, Shengquan Lai, Xin Chen

“…In contrast, a temporal convolutional network captures the system’s dynamic behavior over time. …”
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Estimating canopy height in tropical forests: Integrating airborne LiDAR and multi-spectral optical data with machine learning by Brianna J. Pickstone, Hugh A. Graham, Andrew M. Cunliffe

“…The S2 data at 10 m spatial resolution combined with RF were most appropriate, yielding an R2 of 0.68, RMSE of 3.52 m, and MAE of 2.63 m. …”
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Using VGG Models with Intermediate Layer Feature Maps for Static Hand Gesture Recognition by Osamah Y. Fadhil, Bashar S. Mahdi, Ayad R. Abbas

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Spectral-spatial wave and frequency interactive transformer for hyperspectral image classification by Tahir Arshad, Bo peng, Ali Rahman, Rahim khan, Sajid Ullah khan, Sultan Alnazi, Nazik Alturki

“…Abstract Efficient extraction of spectral-spatial features is essential for accurate hyperspectral image (HSI) classification, where capturing both local texture and global semantic relationships is critical. While Convolutional Neural Networks (CNNs) and Transformers have shown strong capabilities in modeling local and global dependencies, most existing architectures operate directly on raw spectral-spatial inputs and lack explicit mechanisms for frequency-domain decomposition thereby overlooking potentially discriminative phase and frequency components. …”
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An efficient approach for diagnosing faults in photovoltaic array using 1D-CNN and feature selection Techniques by Yousif Mahmoud Ali, Lei Ding, Shiyao Qin

“…Next, a feature permutation technique-based method is proposed for selecting the most relevant features. A simple and accurate one-dimensional convolutional neural network (1D-CNN) model is developed to classify the faults based on the selected features. …”
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Daily insider threat detection with hybrid TCN transformer architecture by Xiaoyun Ye, Huangrongbin Cui, Faqin Luo, Jinlong Wang, Xiaoyun Xiong, Wencui Zhang, Jiawei Yu, Wenhao Zhao

“…This framework combines the strengths of Temporal Convolutional Networks (TCNs) and the Transformer architecture. …”
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Deep Learning for Glioblastoma Multiforme Detection from MRI: A Statistical Analysis for Demographic Bias by Kebin Contreras, Julio Gutierrez-Rengifo, Oscar Casanova-Carvajal, Angel Luis Alvarez, Patricia E. Vélez-Varela, Ana Lorena Urbano-Bojorge

“…Glioblastoma, IDH-wildtype (GBM), is the most aggressive and complex brain tumour classified by the World Health Organization (WHO), characterised by high mortality rates and diagnostic limitations inherent to invasive conventional procedures. …”
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Segmentation Techniques Applied to CNNs for Cervical Cancer Classification by Ana Ortiz-Gonzalez, Raquel Martinez-Espana, Juan Morales-Garcia, Baldomero Imbernon, Jose Martinez-Mas, Mauricio A. Alvarez, Oscar David Romero, Juan Pedro Martinez-Cendan, Andres Bueno-Crespo

“…Cervical cancer continues to be a significant global health issue, ranking as the fourth most prevalent cancer affecting women. Enhancing population screening programs by refining the examination of cervical samples conducted by skilled pathologists offers a compelling alternative for early detection of this disease. …”
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Compressive strength prediction of fly ash/slag-based geopolymer concrete using EBA-optimised chemistry-informed interpretable deep learning model by Yang Yu, Iman Munadhil Abbas Al-Damad, Stephen Foster, Ali Akbar Nezhad, Ailar Hajimohammadi

“…This study develops a deep learning (DL) model based on convolutional neural networks (CNN) to predict the CS of FA/GGBS-based GPC. …”
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Facial expression deep learning algorithms in the detection of neurological disorders: a systematic review and meta-analysis by Shania Yoonesi, Ramila Abedi Azar, Melika Arab Bafrani, Shayan Yaghmayee, Haniye Shahavand, Majid Mirmazloumi, Narges Moazeni Limoudehi, Mohammadreza Rahmani, Saina Hasany, Fatemeh Zahra Idjadi, Mohammad Amin Aalipour, Hossein Gharedaghi, Sadaf Salehi, Mahsa Asadi Anar, Mohammad Saeed Soleimani

“…Deep learning algorithms, especially convolutional neural networks (CNNs), have shown promise in detecting these facial expression changes, aiding in diagnosing and monitoring neurological conditions. …”
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