Emotion Recognition Model of EEG Signals Based on Double Attention Mechanism by Yahong Ma, Zhentao Huang, Yuyao Yang, Shanwen Zhang, Qi Dong, Rongrong Wang, Liangliang Hu

“…DACB extracts features in both temporal and spatial dimensions, incorporating not only convolutional neural networks but also SE attention mechanism modules for learning the importance of different channel features, thereby enhancing the network’s performance. …”
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A non-sub-sampled shearlet transform-based deep learning sub band enhancement and fusion method for multi-modal images by Sudhakar Sengan, Praveen Gugulothu, Roobaea Alroobaea, Julian L. Webber, Abolfazl Mehbodniya, Amr Yousef

“…Abstract Multi-Modal Medical Image Fusion (MMMIF) has become increasingly important in clinical applications, as it enables the integration of complementary information from different imaging modalities to support more accurate diagnosis and treatment planning. …”
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Advancements in Landmine Detection: Deep Learning-Based Analysis With Thermal Drones by Daniel Heuschmid, Oliver Wacker, Yannick Zimmermann, Pascal Penava, Ricardo Buettner

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3-D–2-D Hybrid Lightweight CNN Model: Enhancing Canopy Feature Retrieval in Hyperspectral Imaging for Accurate Plant Species Classification by Chinsu Lin, Hung-Yi Chien, Keng-Hao Liu

“…Deep learning (DL), particularly convolutional neural networks (CNNs), has been widely used to identify images of plant organs and canopies from various sensor-derived images. …”
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Deep learning driven methodology for the prediction of mushroom moisture content using a novel LED-based portable hyperspectral imaging system by Kai Yang, Ming Zhao, Dimitrios Argyropoulos

“…For comparison purposes, state-of-the-art machine learning algorithms, i.e., support vector machine regression (SVMR) and partial least squares regression (PLSR) were also investigated for the model development based on five spectra pre-processed methods using two different lighting systems i.e., enhanced light-emitting diode (LED) and tungsten halogen (TH). …”
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Deep Learning Innovations: ResNet Applied to SAR and Sentinel-2 Imagery by Giuliana Bilotta, Luigi Bibbò, Giuseppe M. Meduri, Emanuela Genovese, Vincenzo Barrile

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PDCNet: A Polarimetric Data-Enhanced Contrastive Learning Network for PolSAR Land Cover Classification by Bo Ren, Chaoyue Hua, Biao Hou, Jian Lv, Chen Yang, Licheng Jiao, Jocelyn Chanussot

“…Specifically, the encoder of PDCNet is designed as an extraction module for a real-convolutional composite complex convolutional network. …”
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Comparative Analysis of Deep Learning Methods for Classification of Ablated Regions in Hyperspectral Images of Atrial Tissue by Yeva Gabrielyan, Arpi Hunanyan, Sona Bezirganyan, Lusine Davtyan, Ani Avetisyan, Narine Sarvazyan, Aram Butavyan, Varduhi Yeghiazaryan

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Dataset Dependency in CNN-Based Copy-Move Forgery Detection: A Multi-Dataset Comparative Analysis by Potito Valle Dell’Olmo, Oleksandr Kuznetsov, Emanuele Frontoni, Marco Arnesano, Christian Napoli, Cristian Randieri

“…Convolutional neural networks (CNNs) have established themselves over time as a fundamental tool in the field of copy-move forgery detection due to their ability to effectively identify and analyze manipulated images. …”
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Temporal Segment Method in Sign Word Recognition Using a Pretrained CNN-LSTM Network by Seungju Lee, Irina Polyakova

“…Experiments included a comparative analysis of different pretrained ResNet models (ResNet18, ResNet34, ResNet50, ResNet101, ResNet152), resulting in the identification of the optimal configuration. …”
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Reconfigurable and Scalable Artificial Intelligence Acceleration Hardware Architecture With RISC-V CNN Coprocessor for Real-Time Seizure Detection by Shuenn-Yuh Lee, Ming-Yueh Ku, Sing-Yu Pan, Chou-Ching Lin

“…Thus, the accelerator can execute different deep-learning models to fit various wearable applications for biomedical acquisition systems.…”
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Cross-Domain Transfer Learning Architecture for Microcalcification Cluster Detection Using the MEXBreast Multiresolution Mammography Dataset by Ricardo Salvador Luna Lozoya, Humberto de Jesús Ochoa Domínguez, Juan Humberto Sossa Azuela, Vianey Guadalupe Cruz Sánchez, Osslan Osiris Vergara Villegas, Karina Núñez Barragán

“…Nevertheless, CNNs are typically trained on single-resolution images, limiting their generalizability across different image resolutions. We propose a CNN trained on digital mammograms with three common resolutions: 50, 70, and 100 <inline-formula><math display="inline"><semantics><mi mathvariant="sans-serif">μ</mi></semantics></math></inline-formula>m. …”
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Novel deep learning for multi-class classification of Alzheimer’s in disability using MRI datasets by Sumaiya Binte Shahid, Maleeha Kaikaus, Md. Hasanul Kabir, Mohammad Abu Yousuf, A. K. M. Azad, A. S. Al-Moisheer, Naif Alotaibi, Salem A. Alyami, Touhid Bhuiyan, Mohammad Ali Moni, Mohammad Ali Moni, Mohammad Ali Moni

“…Next, by utilizing the modified ResNet152V2 as a feature extractor, a Convolutional Neural Network based model, namely, the ‘IncepRes’, is proposed by fusing the Inception and ResNet architectures for multiclass classification of AD categories. …”
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Quantifying axonal features of human superficial white matter from three-dimensional multibeam serial electron microscopy data assisted by deep learning by Qiyuan Tian, Chanon Ngamsombat, Hong-Hsi Lee, Daniel R. Berger, Yuelong Wu, Qiuyun Fan, Berkin Bilgic, Ziyu Li, Dmitry S. Novikov, Els Fieremans, Bruce R. Rosen, Jeff W. Lichtman, Susie Y. Huang

“…This work fills a gap in knowledge of axonal morphometry in the superficial white matter and provides a large 3D human EM dataset and accurate segmentation results for a variety of future studies in different fields.…”
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Deep Learning Approach Predicts Longitudinal Retinal Nerve Fiber Layer Thickness Changes by Jalil Jalili, Evan Walker, Christopher Bowd, Akram Belghith, Michael H. Goldbaum, Massimo A. Fazio, Christopher A. Girkin, Carlos Gustavo De Moraes, Jeffrey M. Liebmann, Robert N. Weinreb, Linda M. Zangwill, Mark Christopher

“…Our custom models used a novel approach that incorporated longitudinal OCT imaging to achieve consistent performance across different demographics and disease severities, offering potential clinical decision support for glaucoma diagnosis. …”
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Field-level Comparison and Robustness Analysis of Cosmological N-body Simulations by Adrian E. Bayer, Francisco Villaescusa-Navarro, Sammy Sharief, Romain Teyssier, Lehman H. Garrison, Laurence Perreault-Levasseur, Greg L. Bryan, Marco Gatti, Eli Visbal

“…We follow this with a statistical out-of-distribution (OOD) analysis to quantify distributional differences between simulations, revealing insights not captured by the traditional metrics. …”
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Development of approach to an automated acquisition of static street view images using transformer architecture for analysis of Building characteristics by Seunghyeon Wang

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Predicting Architectural Space Preferences Using EEG-Based Emotion Analysis: A CNN-LSTM Approach by Ju Eun Cho, Se Yeon Kang, Yi Yeon Hong, Han Jong Jun

“…Event-related potential (ERP) analysis focusing on N100, N200, P300, and late positive potential confirmed reliable differences in neural signals between preferred and non-preferred stimuli. …”
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Origin and Variety Identification of Dried Kelp Based on Fluorescence Fingerprinting and Machine Learning Approaches by Kana Suzuki, Rikuto Akiyama, Yvan Llave, Takashi Matsumoto

“…In addition, genetically close varieties have almost no differences in their base sequences; therefore, the accuracy of conventional identification methods using genetic analysis is limited. …”
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Optimizing Cervical Cancer Diagnosis with Feature Selection and Deep Learning by Łukasz Jeleń, Izabela Stankiewicz-Antosz, Maria Chosia, Michał Jeleń

“…These reduced feature sets were evaluated using several classifiers including support vector machines and compared with CNN-based approach, highlighting differences in accuracy and precision. The results demonstrate that optimized feature sets, paired with SVM classifiers, achieve classification performance comparable to those of CNNs while significantly reducing computational complexity. …”
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