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

“…However, MCC detection remains challenging due to their features, such as small size, texture, shape, and impalpability. Convolutional neural networks (CNNs) offer a solution for MCC detection. …”
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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

“…Myelin and myelinated axons were automatically segmented using deep convolutional neural networks (CNNs), assisted by transfer learning and dropout regularization techniques. …”
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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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State-of-Health Estimation for Lithium-Ion Batteries Based on Lightweight DimConv-GFNet by Kehao Huang, Jianqiang Kang, Jing V. Wang, Qian Wang, Oukai Wu

“…Particularly, the DimConv-GFNet comprises the dimension-wise convolutions (DimConv), which collect the multi-scale local features from different sensor signals, and lightweight global filter networks (GFNet) to capture long-range dependencies in the Fourier frequency domain. …”
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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

“…This study investigates the effectiveness of combining handcrafted feature-based methods with convolutional neural networks for the determination of cancer histological type, emphasizing the role of feature selection in enhancing classification accuracy. …”
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Deep Fusion of Skeleton Spatial–Temporal and Dynamic Information for Action Recognition by Song Gao, Dingzhuo Zhang, Zhaoming Tang, Hongyan Wang

“…Focusing on the issue of the low recognition rates achieved by traditional deep-information-based action recognition algorithms, an action recognition approach was developed based on skeleton spatial–temporal and dynamic features combined with a two-stream convolutional neural network (TS-CNN). Firstly, the skeleton’s three-dimensional coordinate system was transformed to obtain coordinate information related to relative joint positions. …”
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Assessment of Scientific Creative-Potential by Near-Infrared Spectroscopy Using Brain-Network-Based Deep-Fuzzy Classifier by Sayantani Ghosh, Amit Konar, Atulya K. Nagar

“…Hence, the proposed approach may prove effective for recruiting individuals with varying degrees of scientific creativity across different research sectors.…”
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Evaluation of Peroxide Value and Free Fatty Acid Content in Coconut Oil and Palm Oil under Different Heating Temperature Treatments using Reflectance–Fluorescence-based computer vi... by Luluk Oktaviana, Wahyunanto Agung Nugroho, Dimas Firmanda Al Riza Al Riza

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Monitoring Moso bamboo (Phyllostachys pubescens) forests damage caused by Pantana phyllostachysae Chao considering phenological differences between on-year and off-year using UAV h... by Anqi He, Zhanghua Xu, Yifan Li, Bin Li, Xuying Huang, Huafeng Zhang, Xiaoyu Guo, Zenglu Li

“…Analyzing the impact of the phenological differences between on-year and off-year Moso bamboo on pest identification accuracy revealed that when four machine learning models accounted for these phenological characteristics, their accuracy in identifying pests was significantly higher than that of a model which did not take into account the bamboo phenology. …”
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Research and Application of Complex Lithology Identification Method Based on CNN-GRU by ZHANG Xiaofeng, PANG Chunyang, HU Rui, ZHU Yunfeng, LI Hongxing

“…This study integrates convolutional neural networks with gated recurrent units (CNN-GRU) and selects six logging parameters, including sonic time difference, natural potential, natural gamma, density, and shallow and deep lateral resistivity, to train sample wells in the Hailar basin. …”
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Mapping Coastal Soil Salinity and Vegetation Dynamics Using Sentinel-1 and Sentinel-2 Data Fusion With Machine Learning Techniques by Wen Liu, Tiezhu Shi, Zhinian Zhao, Chao Yang

“…This study introduces a multisensor data fusion approach, integrating Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery with advanced machine learning techniques, specifically a convolutional neural network (CNN) based classification model. …”
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Prediction of State-of-Health and Remaining-Useful-Life of Battery Based on Hybrid Neural Network Model by Le Thi Minh Lien, Vu Quoc Anh, Nguyen Duc Tuyen, Goro Fujita

“…Firstly, capacity and different health indicators with high correlation extracted from the battery’s charging and discharging characteristics are considered inputs. …”
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Deep Learning-Based Surface Temperature Prediction for a Porous Radiant Burner Using Thermocouple-Calibrated Thermal Infrared Images by Hao-Yu Hsieh, Shenqyang Shy, Wei-Wun Wang, Yung-Chien Chou

“…This paper presents an artificial intelligence (AI)-based method for surface temperature prediction, applying a hybrid convolutional neural network and LASSO regression (CNN-LASSO) to thermocouple-calibrated thermal infrared (IR) images of a large cylindrical top-dome hollow porous radiant burner for the first time. …”
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