Study on incentive mechanism of reward and punishment on work efficiency of PCB welder based on recurrence quantification analysis and electroencephalogram signals by Zhang Qian, Mingyue Guo, Fuwang Wang

“…This study not only confirms the feasibility of EEG in incentive evaluation but also addresses the insufficient sensitivity of traditional cognitive load monitoring by integrating RQA features and a dynamic classification framework, providing a quantifiable neuroscientific basis for optimizing enterprise incentive mechanisms.…”
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Mapping of soil sampling sites using terrain and hydrological attributes by Tan-Hanh Pham, Kristopher Osterloh, Kim-Doang Nguyen

“…This study introduces a deep learning-based tool that automates soil sampling site selection using spectral images. The proposed framework consists of two key components: an extractor and a predictor. …”
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AGU2-Net: Multi-Scale U²-Net Enhanced by Attention Gate Mechanism for Image Tampering Localization by Kefei Wu, Lin Li, Qingyan Li

“…Although deep learning algorithms based on convolutional neural networks (CNNs) have made notable progress in image forgery detection, they still face certain limitations in effectively detecting and localizing tampered areas due to the subtle nature of existing manipulation traces. …”
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Flexible integration of spatial and expression information for precise spot embedding via ZINB-based graph-enhanced autoencoder by Jiacheng Leng, Jiating Yu, Ling-Yun Wu, Hongyang Chen

“…To address these issues, we introduce Spot2vector, a computational framework that leverages a graph-enhanced autoencoder integrating zero-inflated negative binomial distribution modeling, combining both graph convolutional networks and graph attention networks to extract the latent embeddings of spots. …”
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Scalable 3D reconstruction for X-ray single particle imaging with online machine learning by Jay Shenoy, Axel Levy, Kartik Ayyer, Frédéric Poitevin, Gordon Wetzstein

“…Here, we introduce X-RAI (X-Ray single particle imaging with Amortized Inference), an online reconstruction framework that estimates the structure of 3D macromolecules from large X-ray single particle datasets. …”
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Towards Explainable Graph Embeddings for Gait Assessment Using Per-Cluster Dimensional Weighting by Chris Lochhead, Robert B. Fisher

“…The latent graph embeddings produced by this framework led to a novel semi-supervised weighting function which quantifies and ranks the most important joint features, which are used to provide a description for each pathology. …”
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Bridging the Gap Between Computational Efficiency and Segmentation Fidelity in Object-Based Image Analysis by Fernanda Pereira Leite Aguiar, Irenilza de Alencar Nääs, Marcelo Tsuguio Okano

“…This study presents a scalable and efficient framework designed to advance machine learning applications in complex image analysis tasks by incorporating methodologies for image quantization and automated segmentation.…”
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Neural Image Compression and Explanation by Xiang Li, Shihao Ji

“…In this article, we propose a novel end-to-end Neural Image Compression and Explanation (NICE) framework that learns to (1) explain the predictions of convolutional neural networks (CNNs), and (2) subsequently compress the input images for efficient storage or transmission. …”
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Physics-Informed Decoupled Calibration for Fourier Ptychographic Microscopy by Mingdi Liu, Junzhao Liang, Yanxiong Wu, Zicong Luo, Rui Xie, Jiaxiong Luo, Lisong Yan

“…To address this, we propose a physically decoupled correction framework integrating convolutional neural network (CNN), simulated annealing (SA) algorithms, and GPU parallel acceleration. …”
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Hybrid CNN–BiLSTM–DNN Approach for Detecting Cybersecurity Threats in IoT Networks by Bright Agbor Agbor, Bliss Utibe-Abasi Stephen, Philip Asuquo, Uduak Onofiok Luke, Victor Anaga

“…The model’s performance is evaluated using the IoT-23 and Edge-IIoTset datasets, which encompass over ten distinct attack types. The proposed framework achieves a remarkable 99% accuracy on both datasets, outperforming existing state-of-the-art IoT cybersecurity solutions. …”
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Data augmentation-assisted deep learning of hand-drawn partially colored sketches for visual search. by Jamil Ahmad, Khan Muhammad, Sung Wook Baik

“…To cope with these issues, we propose to fine-tune a deep convolutional neural network (CNN) using augmented dataset to extract features from partially colored hand-drawn sketches for query specification in a sketch-based image retrieval framework. …”
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Enhancing elderly care services through integrated sentiment analysis and knowledge reasoning: A deep learning approach by Yongguan Ai, Shiwei Chu, Juan Wang, Nianfang Xu

“…This study proposes a pioneering integrated care model for elderly care service robots that integrates sentiment analysis and knowledge reasoning through a deep learning framework. The primary objective of this research is to address the limitations of current elderly care robots in providing emotionally intelligent and personalized care. …”
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The Application of a Marine Weather Data Reconstruction Model Based on Deep Super-Resolution in Ship Route Optimization by Shangfu Li, Junfu Yuan, Zhizheng Wu

“…Firstly, the model uses a convolutional neural network to extract features from wind speed and wave height data. …”
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CyclicAugment: Optimized Medical Image Analysis via Adaptive Augmentation Intensity by Min-Jun Kim, Jung-Woo Chae, Hyun-Chong Cho

“…Our framework achieved a maximum accuracy improvement of 8.8%, reaching a peak performance of 0.927 and thereby demonstrating its effectiveness in enhancing medical image analysis.…”
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Detecting Lameness in Dairy Cows Based on Gait Feature Mapping and Attention Mechanisms by Xi Kang, Junjie Liang, Qian Li, Gang Liu

“…This study presents an integrated computer vision and deep-learning framework for dairy cattle lameness detection and severity classification. …”
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Deep Feature Fusion via Transfer Learning for Multi-Class Network Intrusion Detection by Sunghyuk Lee, Donghwan Roh, Jaehak Yu, Daesung Moon, Jonghyuk Lee, Ji-Hoon Bae

“…To address these limitations, this study proposes a deep learning-based intrusion detection framework that employs feature fusion through incremental transfer learning between source and target domains. …”
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Global-Frequency-Domain Network: A Semantic Segmentation Method for High-Resolution Remote Sensing Images Based on Fine-Grained Feature Extraction and Global Context Integration by Ye Zhou, Mingyue Zhang, Yechenzi Wang

“…Hence, we propose a novel global-frequency-domain network (GFDNet) designed for the semantic segmentation of high-resolution remote sensing images. The GFDNet framework incorporates a global-frequency-domain feature module that employs a learnable global filter to extract contextual information, leveraging the Fourier transform to capture both the spatial- and frequency-domain features. …”
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Deep learning-based strategies for evaluating and enhancing university teaching quality by Ying Gao

“…This study aims to address these issues by leveraging deep learning techniques, specifically Convolutional Neural Networks (CNNs), to accurately assess and enhance the quality of university teaching. …”
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DeepLASD countermeasure for logical access audio spoofing by Hamed Al-Tairi, Ali Javed, Tasawer Khan, Abdul Khader Jilani Saudagar

“…These findings affirm that the proposed end-to-end anti-spoofing framework enhances security and detection capabilities in voice authentication systems.…”
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Enhanced Face Detection Using Multi-Cascade Face Detection and Deep Ladder Neural Network by Pande Anshul, Voditel Priti

“…This hybrid system is a combination of Multi-task Cascaded Convolutional Neural Networks (MTCNN). This combined system use Deep Ladder Imputation Networks (DLIN). …”
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