Handwritten W-Net for High-Frequency Guided Single-Image Super-Resolution by Xiang Yang, Chen Fang, Xiaolan Xie, Minggang Dong

“…We experimentally implemented a biased strategy called parameter transfer, which is suitable for hybrid parameter models. This approach facilitates the blending of weight parameters between similar but different models, significantly improving the peak signal-to-noise ratio and structural similarity index measure metrics without sacrificing the learned perceptual image patch similarity. …”
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Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking by Ziqi Li, Dongyao Jia, Zihao He, Nengkai Wu

“…Thirdly, a Dynamic Motion Model (DMM) is developed, enabling the robust prediction of non-linear motion based on an improved Kalman filter framework. …”
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An enhanced CNN-Bi-transformer based framework for detection of neurological illnesses through neurocardiac data fusion by Kavita Rawat, Trapti Sharma

“…The suggested method overcomes the shortcomings of earlier studies, which tended to concentrate on single-modality data, lacked thorough neurocardiac data fusion, and made use of less advanced machine learning algorithms. The comprehensive experimental findings, which provide an average improvement in accuracy of 2.72%, demonstrate that the suggested work performs better than other cutting-edge AI techniques and generalizes effectively across diverse datasets.…”
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Vehicle detection method based on multi-layer selective feature for UAV aerial images by Yinbao Ma, Yuyu Meng, Jiuyuan Huo

“…However, this task remains challenging due to variable high-altitude viewpoints, complex environmental interference, and limitations in algorithmic efficiency. To address these issues, a lightweight vehicle detection model is developed based on UAV aerial imagery. …”
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Analysis of space solar array arc images based on deep learning techniques by Afaf M. Abd El-Hameed, Ahmed S. Farahat, Khaled Y. Youssef, M. Elfarran, I. M. Selim

“…Furthermore, algorithms and image processing tools, such as Python and Maxim-DL, are employed to examine variations in intensities and spatial variation in the arced region. …”
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GCBRGCN: Integration of ceRNA and RGCN to Identify Gastric Cancer Biomarkers by Peng Zhi, Yue Liu, Chenghui Zhao, Kunlun He

“…However, current strategies for identifying GC biomarkers often focus on a single ribonucleic acid (RNA) class, neglecting the potential for multiple RNA types to collectively serve as biomarkers with improved predictive capabilities. To bridge this gap, our study introduces the GC biomarker relation graph convolution neural network (GCBRGCN) model which integrates the competing endogenous RNA (ceRNA) network with GC clinical informations and whole transcriptomics data, leveraging the relational graph convolutional network (RGCN) to predict GC biomarkers. …”
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Confidence-Based Fusion of AC-LSTM and Kalman Filter for Accurate Space Target Trajectory Prediction by Caiyun Wang, Jirui Zhang, Jianing Wang, Yida Wu

“…A confidence-weighted fusion mechanism adaptively integrates the predictions from both models, significantly improving overall prediction performance. …”
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MUF-Net: A Novel Self-Attention Based Dual-Task Learning Approach for Automatic Left Ventricle Segmentation in Echocardiography by Juan Lyu, Jinpeng Meng, Yu Zhang, Sai Ho Ling

“…Experimental results demonstrate that our model outperforms existing segmentation methods. …”
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Explainable machine learning prediction of internet addiction among Chinese primary and middle school children and adolescents: a longitudinal study based on positive youth develop... by Jiahe Liu, Lang Chen, Yuxin Chen, Jingsong Luo, Kexin Yu, Linlin Fan, Chan Yong, Huiyu He, Simei Liao, Zongyuan Ge, Lihua Jiang, Lihua Jiang

“…Feature selection and SHapley Additive exPlanations (SHAP) analysis were utilised for model improvement and interpretability, respectively.ResultsExtraRFC achieved the best performance (Test AUC = 0.854, Accuracy = 0.798, F1 = 0.659), outperforming all other models across most metrics and external validations. …”
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A dual scheduling framework for task and resource allocation in clouds using deep reinforcement learning by Jiahui Pan, Yi Wei, Lei Meng, Xiangxu Meng

“…Meanwhile, as service providers for end users, they are responsible for handling changing workloads consisting of user-submitted tasks with some QoS requirements. Under this business model, how to minimize the cost of leasing VM instances while guaranteeing the quality of service for SaaS applications is a challenging issue. …”
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Optimization of microwave components using machine learning and rapid sensitivity analysis by Slawomir Koziel, Anna Pietrenko-Dabrowska

“…Domain confinement reduces the cost of surrogate model establishment and improves its predictive power. …”
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Enhancing environmental sustainability and operational efficiency in a case study of limestone quarry in an arid climate by Hussein A. Saleem, Abebe Temesgen Ayalew

“…Automated environmental monitoring systems, incorporating IoT sensors and machine-learning algorithms, provide real-time data on air quality, dust levels, and noise pollution. …”
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Enhanced CLIP-GPT Framework for Cross-Lingual Remote Sensing Image Captioning by Rui Song, Beigeng Zhao, Lizhi Yu

“…Remote Sensing Image Captioning (RSIC) aims to generate precise and informative descriptive text for remote sensing images using computational algorithms. Traditional “encoder-decoder” approaches face limitations due to their high training costs and heavy reliance on large-scale annotated datasets, hindering their practical applications. …”
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Digital Image Copyright Protection and Management Approach—Based on Artificial Intelligence and Blockchain Technology by Jikuan Xu, Jiamin Zhang, Junhan Wang

“…It introduces an originality detection model based on deep learning technology after conducting both off-chain and on-chain detection of unidentified images, providing triple protection for digital image copyright infringement detection and enabling efficient active defense and passive evidence storage. …”
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The Lattice Boltzmann Method with Deformable Boundary for Colonic Flow Due to Segmental Circular Contractions by Irina Ginzburg

“…The population “refill” of “fresh” fluid nodes, including sharp corners, is reformulated with the improved reconstruction algorithms by combining bulk and advanced boundary LBM steps with a local sub-iterative collision update. …”
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Detecting Unbalanced Network Traffic Intrusions With Deep Learning by S. Pavithra, K. Venkata Vikas

“…It enables the system to prioritize and focus on these important features during model training, thereby enhancing detection accuracy while reducing computational complexity. …”
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The Neural Frontier of Future Medical Imaging: A Review of Deep Learning for Brain Tumor Detection by Tarek Berghout

“…Some models integrate with Internet of Things (IoT) frameworks or federated learning for real-time diagnostics and privacy, often paired with optimization algorithms. …”
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Large-scale S-box design and analysis of SPS structure by Lan ZHANG, Liangsheng HE, Bin YU

“…A class of optimal linear transformation P over a finite field<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow> <msup> <mrow> <mrow><mo>(</mo> <mrow> <msubsup> <mi>F</mi> <mn>2</mn> <mi>m</mi> </msubsup> </mrow> <mo>)</mo></mrow></mrow> <mn>4</mn> </msup> </mrow></math></inline-formula> was constructed based on cyclic shift and XOR operation.Using the idea of inverse proof of input-output relation of linear transformation for reference, a proof method was put forward that transformed the objective problem of optimal linear transformation into several theorems of progressive relation, which not only solved the proof of that kind of optimal linear transformation, but also was suitable for the proof of any linear transformation.By means of small-scale S-box and optimal cyclic shift-XOR linear transformation P, a large-scale S-box model with 2-round SPS structure was established, and a series of lightweight large-scale S-boxes with good cryptographic properties were designed.Only three kind of basic operations such as look-up table, cyclic shift and XOR were used in the proposed design scheme, which improved the linearity and difference uniformity of large-scale S-boxes.Theoretical proof and case analysis show that, compared with the existing large-scale S-box construction methods, the proposed large-scale S-box design scheme has lower computational cost and better cryptographic properties such as difference and linearity, which is suitable for the design of nonlinear permutation coding of lightweight cryptographic algorithms.…”
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From Misinformation to Insight: Machine Learning Strategies for Fake News Detection by Despoina Mouratidis, Andreas Kanavos, Katia Kermanidis

“…Through extensive experimentation across multiple datasets, our results demonstrate that BERT-based models consistently achieve superior performance, significantly improving detection accuracy in complex misinformation scenarios. …”
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Review on Key Technologies for Autonomous Navigation in Field Agricultural Machinery by Hongxuan Wu, Xinzhong Wang, Xuegeng Chen, Yafei Zhang, Yaowen Zhang

“…Future research is expected to focus on enhancing multi-modal perception under occlusion and variable lighting conditions, developing terrain-aware path planning algorithms that adapt to irregular field boundaries and elevation changes and designing robust control strategies that integrate model-based and learning-based approaches to manage disturbances and non-linearity. …”
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