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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Deep Fusion of Skeleton Spatial–Temporal and Dynamic Information for Action Recognition by Song Gao, Dingzhuo Zhang, Zhaoming Tang, Hongyan Wang

“…Furthermore, physical structure constraints of the human body were considered to enhance class differences. Additionally, the speed information for each joint was estimated and encoded as a color texture map to achieve the skeleton motion feature descriptor. …”
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A feature enhancement FCOS algorithm for dynamic traffic object detection by Tuqiang Zhou, Wei Liu, Haoran Li

“…First, the dynamic convolution module was designed in the backbone network to identify different object features to the maximum extent. …”
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Research on Unsupervised Domain Adaptive Bearing Fault Diagnosis Method Based on Migration Learning Using MSACNN-IJMMD-DANN by Xiaoxu Li, Jiahao Wang, Jianqiang Wang, Jixuan Wang, Qinghua Li, Xuelian Yu, Jiaming Chen

“…To address the problems of feature extraction, cost of obtaining labeled samples, and large differences in domain distribution in bearing fault diagnosis on variable operating conditions, an unsupervised domain-adaptive bearing fault diagnosis method based on migration learning using MSACNN-IJMMD-DANN (multi-scale and attention-based convolutional neural network, MSACNN, improved joint maximum mean discrepancy, IJMMD, domain adversarial neural network, DANN) is proposed. …”
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Action Recognition with 3D Residual Attention and Cross Entropy by Yuhao Ouyang, Xiangqian Li

“…Simultaneously, we used the cross-entropy loss function to describe the difference between the predicted value and GT to guide the model’s backpropagation. …”
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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

“…The analysis has been conducted for a coastal region in China, where derived features, such as normalized difference vegetation index (NDVI), salinity indices, and SAR-based soil moisture proxies, have been used as inputs to the CNN 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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Comparative analysis of data transformation methods for detecting non-technical losses in electricity grids by Maria Gabriel Chuwa, Daniel Ngondya, Rukia Mwifunyi

“…Six encoding techniques for time series data were evaluated: Markov transition fields (MTF), Gramian angular summation field (GASF), Gramian angular difference field (GADF), Recurrence plots (RP), and time–frequency analysis methods, including short-time Fourier transform (STFT) and continuous wavelet transform (CWT). …”
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DSNET: A Lightweight Segmentation Model for Segmentation of Skin Cancer Lesion Regions by Yucong Chen, Guang Yang, Xiaohua Dong, Junying Zeng, Chuanbo Qin

“…To reduce the model size and guarantee model segmentation performance, we proposed a detail-enhanced separable difference convolution as a base module in the model. …”
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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

“…The temperature measurement range and the emissivity of the IR camcorder are set at 300-2000°C and 0.95, respectively, having an average temperature of about 40°C difference/uncertainty as compared to that measured by the thermocouples. …”
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YOLO-EFM: Efficient traffic flow monitoring algorithm with enhanced multi-level information fusion by Shizhou Xu, Kaidi Cui

“…The study establishes a generalized efficient layer aggregation network incorporating Sobel convolution and develops a novel feature focus module that effectively aggregates information from different feature map levels. …”
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A hybrid deep learning-based approach for optimal genotype by environment selection by Zahra Khalilzadeh, Motahareh Kashanian, Saeed Khaki, Lizhi Wang

“…The ability to accurately predict the yields of different crop genotypes in response to weather variability is crucial for developing climate resilient crop cultivars. …”
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Vision-based detection algorithm for monitoring dynamic change of fire progression by Yongyoon Suh

“…This study aims to define vision-based patterns of fire events to identify multiple objects that contribute to different types of fire accidents. To achieve this, a convolutional neural network (CNN) based on deep learning is applied to detect fire events through vision-based patterns. …”
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A Closed-loop Detection Algorithm Based on Dynamic Time Warping by ZHU Tian, PENG Zhichuan, ZHANG Zhiteng, ZHU Zemin, XIE Yongbo, WANG Wenming

“…Considering the influence of matching sequence length on the experimental results, the quasi-call curves and ROC curves obtained under different sequence lengths are compared in detail to determine the most suitable matching sequence length. …”
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Abnormal traffic detection method based on LSTM and improved residual neural network optimization by Wengang MA, Yadong ZHANG, Jin GUO

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Research on Hybrid Architecture Neural Networks for Time Series Prediction by Fujin Zhuang, Xiao Chen, Punyaphol Horata, Khamron Sunat

“…Even with medium-level noise (<inline-formula> <tex-math notation="LaTeX">$\sigma \approx 0.05$ </tex-math></inline-formula>), the model maintains 77% of its R2 value, and when applied to strawberry price prediction using the dataset published by the United States Department of Agriculture Economic Research Service (USDA ERS)&#x2014;a product with significantly different market characteristics&#x2014;it achieves an R2 value of 0.7499 without any retraining, demonstrating strong adaptability to different data distributions. …”
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TCN-GRU Based on Attention Mechanism for Solar Irradiance Prediction by Zhi Rao, Zaimin Yang, Xiongping Yang, Jiaming Li, Wenchuan Meng, Zhichu Wei

“…Considering the time series nature of the GHI and monitoring sites dispersed over different latitudes, longitudes, and altitudes, this study proposes a model combining deep neural networks and deep convolutional neural networks for the multi-step prediction of GHI. …”
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