Optimized hybrid deep learning for cross-linguistic sentiment analysis: a novel approach by Vipin Jain, Lokesh Malviya, Anjana .S

“…Abstract Sentiment analysis, the process of extracting and classifying opinions expressed in text, has gained significant traction in different fields, such as market research, customer feedback, and social media analysis. …”
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Human Action Recognition Method Based on Multi-channel Fusion by Zhiyong TAO, Xijun GUO, Xiaokui REN, Ying LIU, Zemin WANG

“…This network performs convolution operations on features at different instances, enabling the model to capture changes over time and identify long-term dependencies between action features. …”
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Advances in Online Grain Quality Assessment: Near-Infrared Spectroscopic Modeling and Transfer Strategies by CUI Chen-hao, FAN Chen

“…This review summarizes the application of NIR spectroscopy in online grain quality inspection, systematically outlining the development from traditional linear modeling (e.g., partial least squares regression), nonlinear modeling (e.g., support vector machines, artificial neural networks) to deep learning methods (e.g., convolutional neural networks). It focuses on the strategies, challenges, and latest advances of model transfer techniques in addressing issues such as instrument differences, environmental changes, and sample diversity, including calibration transfer with and without standards. …”

Occupational Therapy Practice Based on New-Generation Information Technology for Employee Emotion Analysis and Management by Yueyuan Cheng

“…There are also significant differences in emotion and work enthusiasm among employees with different educational backgrounds and positions. …”
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Dual-Domain deep prior guided sparse-view CT reconstruction with multi-scale fusion attention by Jia Wu, Jinzhao Lin, Xiaoming Jiang, Wei Zheng, Lisha Zhong, Yu Pang, Hongying Meng, Zhangyong Li

“…First, we establish a residual regularization strategy that applies constraints on the difference between the prior image and target image, effectively integrating deep learning-based priors with model-based optimization. …”
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LSTM and ResNet18 for optimized ambulance routing and traffic signal control in emergency situations by Madallah Alruwaili, Ali Ali, Mohammed Almutairi, Abdulaziz Alsahyan, Mahmood Mohamed

“…Abstract Traffic congestion, particularly in rapidly expanding urban centers, significantly impacts the timely delivery of emergency medical services (EMS), where every minute can mean the difference between life and death. Traditional traffic signal control systems often lack real-time adaptability to prioritize emergency vehicles, resulting in delays caused by congestion around ambulances. …”
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Machine learning models in evaluating the malignancy risk of ovarian tumors: a comparative study by Xin He, Xiang-Hui Bai, Hui Chen, Wei-Wei Feng

“…There was a statistically significant difference between the Vision Transformer and SA, and between the Vision Transformer and Swin Transformer models (AUC: 0.87 vs. 0.97, P = 0.01; AUC: 0.87 vs. 0.92, P = 0.04). …”
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An extensive experimental analysis for heart disease prediction using artificial intelligence techniques by D. Rohan, G. Pradeep Reddy, Y. V. Pavan Kumar, K. Purna Prakash, Ch. Pradeep Reddy

“…The feature selection techniques considered for the research are Information Gain, Chi-Square Test, Fisher Discriminant Analysis (FDA), Variance Threshold, Mean Absolute Difference (MAD), Dispersion Ratio, Relief, LASSO, Random Forest Importance, Linear Discriminant Analysis (LDA), and Principal Component Analysis (PCA). …”
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Identifying native grasslands and key phenological stages using time series Sentinel-2 data and deep learning models by Yihan Pu, Amy Nixon, Beatriz Prieto, Xulin Guo

“…This study aims to identify native grasslands in the Mixed Grasslands ecoregion of Saskatchewan using Sentinel-2 Normalized Difference Vegetation Index (NDVI) time series data through deep learning and multi-temporal approaches. …”
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A Novel Foundation Model-Based Framework for Multimodal Retinal Age Prediction by Christopher Nielsen, Matthias Wilms, Nils D. Forkert

“…The retinal age gap (RAG; the difference between the retina’s biological and chronological age) has recently gained increased attention as a potential image-based, non-invasive, and accessible biomarker for a broad spectrum of ocular and non-ocular diseases. …”
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A Practical Framework for Estimating Façade Opening Rates of Rural Buildings Using Real-Scene 3D Models Derived from Unmanned Aerial Vehicle Photogrammetry by Zhuangqun Niu, Ke Xi, Yifan Liao, Pengjie Tao, Tao Ke

“…Regarding the mean relative error (MRE), a critical evaluation metric which measures the relative difference between the estimated FOR and its ground truth, the proposed method outperforms the closest baseline by 5%. …”
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Deep Learning-Based Instance Segmentation of Galloping High-Speed Railway Overhead Contact System Conductors in Video Images by Xiaotong Yao, Huayu Yuan, Shanpeng Zhao, Wei Tian, Dongzhao Han, Xiaoping Li, Feng Wang, Sihua Wang

“…Consequently, segmentation outcomes from neighboring frames are utilized, and mask-difference analysis is performed to autonomously detect conductor galloping locations, emphasizing their contours for the clear depiction of galloping characteristics. …”
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Real-Time Corn Variety Recognition Using an Efficient DenXt Architecture with Lightweight Optimizations by Jin Zhao, Chengzhong Liu, Junying Han, Yuqian Zhou, Yongsheng Li, Linzhe Zhang

“…Corn varieties from different regions have significant differences inblade, staminate and root cap characteristics, and these differences provide a basis for variety classification. …”
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Deep-Learning Techniques Applied for State-Variables Estimation of Two-Mass System by Grzegorz Kaczmarczyk, Radoslaw Stanislawski, Marcin Kaminski

“…The design stages and the overall concept in this case are completely different than with the applications of classical observers (e.g., the Luenberger, the Kalman filter) often used for similar objects. …”
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HyperKon: A Self-Supervised Contrastive Network for Hyperspectral Image Analysis by Daniel La’ah Ayuba, Jean-Yves Guillemaut, Belen Marti-Cardona, Oscar Mendez

“…We also perform a thorough ablation study on different kinds of layers, showing their performance in understanding hyperspectral layers. …”
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Experimentally validated inverse design of FeNiCrCoCu MPEAs and unlocking key insights with explainable AI by Fangxi Wang, Allana G. Iwanicki, Abhishek T. Sose, Lucas A. Pressley, Tyrel M. McQueen, Sanket A. Deshmukh

“…This computational workflow, along with the fundamental insights gained, can be readily expanded and applied to the design of MPEAs with different elemental compositions, as well as to materials beyond MPEAs.…”
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Enhancing Remote Sensing Image Quality through Data Fusion and Synthetic Aperture Radar (SAR): A Comparative Analysis of CNN, Lightweight ConvNet, and VGG16 Models by Desynike Puspa Anggreyni, Indriatmoko, Aniati Murni Arymurthy, Andie Setiyoko

“…The method involves using Synthetic Aperture Radar (SAR) to combine adjacent satellite images from different viewpoints, thereby improving image coverage. …”
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Altitude aware trajectory prediction methods for non towered terminal airspace by Haipeng Zhu, Qiang Tong, Jinqing Hu, Xiulei Liu, Shoulu Hou

“…The model independently extracts altitude features using temporal convolutional networks(TCN), it then incorporates a channel attention fusion mechanism to dynamically fuse altitude features into the trajectory representation across different channels. …”
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Multiclass Incremental Learning for Fault Diagnosis in Induction Motors Using Fine-Tuning with a Memory of Exemplars and Nearest Centroid Classifier by Magdiel Jiménez-Guarneros, Jonas Grande-Barreto, Jose de Jesus Rangel-Magdaleno

“…Experimental results reveal the proposed framework as an effective solution to incorporate and detect new induction motor faults to already known, with a high accuracy performance across different incremental phases.…”
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Efficient dataset extension using generative networks for assessing degree of coating degradation around scribe by Dominik Stursa, Pavel Rozsival, Petr Dolezel

“…Moreover, the advantages and limitations of different GAN architectures for dataset expansion are explored, with specific attention to their ability to produce realistic and diverse samples. …”
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