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An enhanced image restoration using deep learning and transformer based contextual optimization algorithm

sCIENTIFIC REPORTs 2025年第1期15卷 1-18页

作者： Anandhi, A. senthil Jaiganesh, M. Anna Univ Research Scholar ICE Chennai India New Horizon Coll Engn Dept CSE Bengaluru India Karpagam Coll Engn Dept IT Coimbatore India

Image processing and restoration are important in computer vision, particularly for images that are damaged by noise, blur, and other issues. Traditional methods often have a hard time with problems like periodic noise and do not effectively combine local and global data during the restoration process. To address these problems, we suggest an enhanced image restoration model that merges Lewin architecture with swinIR, using advanced deep learning methods. This approach combines these techniques for a better restoration process improved by 4.2%. The model's effectiveness is checked using PsNR and ssIM measurements, showing that it can lower noise while keeping key image details intact. When compared to traditional methods, our model shows better results, creating a new standard in image restoration for difficult situations. Test results show that this combined approach greatly enhances fixing performance across various image datasets, making it a strong solution for clearer images and noise reduction.

关键词： Image processing Image restoration Periodic noise Lewin architecture swinIR Deep learning PsNR ssIM

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Parameterization-based trajectory planning for an 8-DOF manipulator with multiple constraints

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Biomimetic Intelligence & Robotics 2025年第1期5卷 67-76页

作者： Ziwu Ren Zhongyuan Wang Xiaohan Liu Rui Lin Robotics and Microsystems Research Center Soochow UniversitySuzhou 215131China

A physically feasible,reliable,and safe motion is essential for robot operation.A parameterization-based trajectory planning approach is proposed for an 8-DOF manipulator with multiple *** inverse kinematic solution is obtained through an analytical method,and the trajectory is planned in joint *** such,the trajectory planning of the 8-DOF manipulator is transformed into a parameterization-based trajectory optimization problem within its physical,obstacle and task constraints,and the optimization variables are significantly *** teaching-learning-based optimization(TLBO)algorithm is employed to search for the redundant parameters to generate an optimal *** and physical experiment results demonstrate that this approach can effectively solve the trajectory planning problem of the ***,the planned trajectory has no theoretical end-effector deviation for the task *** approach can provide a reference for the motion planning of other redundant manipulators.

关键词： 8-DOF manipulator Trajectory planning Parameterization Obstacle constraints Teaching-learning-based optimization

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Improving learning material repositories using student profiles

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soft Computing 2025年第5期29卷 2417-2431页

作者： silva Bravo, Natalie Ferraz Martins, André Ferreira de souza Fonseca Rodrigues, Thales Brito Machado, Marcelo Bernardino, Heder soares Vieira, Alex Borges Barbosa, Hélio José Corrêa de souza, Jairo Francisco LApIC Research Group Federal University of Juiz de Fora Minas Gerais Juiz de Fora36036-900 Brazil Postgraduate Program in Computer Science Federal University of Juiz de Fora Minas Gerais Juiz de Fora36036-900 Brazil Postgraduate Program in Informatics Federal University of the State of Rio de Janeiro Rio de Janeiro22290-255 Brazil Department of Computer Science Federal University of Juiz de Fora Minas Gerais Juiz de Fora36036-900 Brazil

aaaa The adaptive learning community seeks to provide solutions to customize and enhance students’ learning experiences when accessing web-based learning systems. The adaptation usually occurs from the use of learning materials and user information data, which turns the adaptation process highly dependent on the quality of the repositories. Then, the best adaptation a system may offer might still not satisfy the users’ needs. In this work, we propose an approach to assist teachers and stakeholders in understanding repositories’ characteristics and their gaps according to students’ needs. Our approach, first, selects the best sequence of learning materials for each student, which is a well-known problem called Adaptive Curriculum sequencing. Then, based on the selected sequences, we use optimization approaches, such as GRAsP and simulated Annealing, to generate new learning materials possibilities that can improve ACs recommendations. This way, our new approach assists teachers in assembling their learning materials. We have evaluated our approach by comparing it to a traditional approach using a real dataset, and the results are promising. In fact, it is possible to design customized materials using a combination of GRAsP and brute force algorithms on the characteristics of the learning materials. © The Author(s), under exclusive licence to springer-Verlag GmbH Germany, part of springer Nature 2025.

关键词： Federated learning

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Application of Machine learning for Aboveground Biomass Modeling in Tropical and Temperate Forests from Airborne Hyperspectral Imagery

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FOREsTs 2025年第3期16卷 477-477页

作者： Darko, Patrick Osei Metari, samy Arroyo-Mora, J. Pablo Fagan, Matthew E. Kalacska, Margaret McGill Univ Dept Geog Appl Remote Sensing Lab Montreal PQ H3A 0B9 Canada Natl Res Council Canada Automot & Surface Transportat Ottawa ON K1A 0R6 Canada Natl Res Council Canada Flight Res Lab Ottawa ON K1A 0R6 Canada Univ Maryland Baltimore Cty Dept Geog & Environm Syst Baltimore MD 21250 USA

Accurate operational methods used to measure, verify, and report changes in biomass at large spatial scales are required to support conservation initiatives. In this study, we demonstrate that machine learning can be used to model aboveground biomass (AGB) in both tropical and temperate forest ecosystems when provided with a sufficiently large training dataset. Using wavelet-transformed airborne hyperspectral imagery, we trained a shallow neural network (sNN) to model AGB. An existing global AGB map developed as part of the European space Agency's DUE GlobBiomass project served as the training data for all study sites. At the temperate site, we also trained the model on airborne-LiDAR-derived AGB. In comparison, for all study sites, we also trained a separate deep convolutional neural network (3D-CNN) with the hyperspectral imagery. Our results show that extracting both spatial and spectral features with the 3D-CNN produced the lowest RMsE across all study sites. For example, at the tropical forest site the Tortuguero conservation area, with the 3D-CNN, an RMsE of 21.12 Mg/ha (R-2 of 0.94) was reached in comparison to the sNN model, which had an RMsE of 43.47 Mg/ha (R-2 0.72), accounting for a similar to 50% reduction in prediction uncertainty. The 3D-CNN models developed for the other tropical and temperate sites produced similar results, with a range in RMsE of 13.5 Mg/ha-31.18 Mg/ha. In the future, as sufficiently large field-based datasets become available (e.g., the national forest inventory), a 3D-CNN approach could help to reduce the uncertainty between hyperspectral reflectance and forest biomass estimates across tropical and temperate bioclimatic domains.

关键词： convolutional neural network REDD wavelet scattering continuous wavelet transform deep learning spectra-spatial feature extraction

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Enhancing economic cycle forecasting based on interpretable machine learning and news narrative sentiment

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TECHNOLOGICAL FORECAsTING AND sOCIAL CHANGE 2025年 215卷

作者： sun, Weixin Wang, Yong Zhang, Li Chen, Xihui Haviour Hoang, Yen Hai Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China Keele Univ Keele Business Sch Keele England Univ Econ HCM City Sch Banking Ho Chi Minh City Vietnam Azerbaijan State Univ Econ Women Researchers Council Baku Azerbaijan

The growing prevalence of uncertainty in global events poses significant challenges to economic cycle forecasting, emphasizing the need for more robust predictive models. This study addresses this gap by developing a novel forecasting framework that integrates multiple uncertainty indices to improve accuracy, stability, and interpretability, particularly during uncertainty shocks. To achieve this, several methodological innovations were implemented. First, news sentiment-based uncertainty indices were incorporated as candidate variables to capture uncertainty dynamics. second, Bayesian least absolute shrinkage and selection operator (Bayesian LAssO) was employed for efficient variable selection, mitigating the curse of dimensionality in small samples. Third, the multi-objective Lichtenberg algorithm (MOLA) was applied to optimize the prediction window size, ensuring model robustness. Additionally, a MOLA-based extreme gradient boosting (MOLA-XGBoost) model was developed to fine-tune hyperparameters across dimensions of prediction accuracy, stability, and directional consistency. Finally, sHapley Additive exPlanations (sHAP) theory was used to enhance model interpretability. This study forecasts China's economic cycle using multiple indicators, demonstrating that the proposed approach consistently delivers accurate and robust predictions even under uncertainty shocks. The findings highlight the crucial role of uncertainty indices in improving economic forecasts, offering new insights and methodologies for predictive modeling in volatile environments.

关键词： Economic cycle Interpretable machine learning News narrative sentiment Uncertainty indices Prediction

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Evolutionary optimization of Yagi–Uda antenna design using grey wolf optimizer

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Neural Computing and Applications 2025年第6期37卷 4155-4183页

作者： Braik, Malik sheta, Alaa Aljahdali, sultan El-Hefnawi, Fatma Al-Hiary, Heba Elashmawi, Walaa H. Computer Science Department Al-Balqa Applied University Salt Jordan Department of Computer Science Southern Connecticut State University New HavenCT United States Computer Science Department Taif University Taif Saudi Arabia Microwave Department Electronics Research Institute Giza Egypt Department of Computer Information System Al-Balqa Applied University Salt Jordan Department of Computer Science Faculty of Computers & Informatics Suez Canal University Ismailia Egypt Department of Computer Science Faculty of Computer Science Misr International University Cairo Egypt

Meta-heuristic optimization algorithms have become widely used due to their outstanding features, such as gradient-free mechanisms, high flexibility, and great potential for avoiding local optimal solutions. This research explored the grey wolf optimizer (GWO) to find the ideal configuration for a six-element Yagi–Uda antenna. The GWO algorithm adjusted the lengths of the antenna wires and the spacings between them. The goal was to maximize the antenna’s ability to transmit signals (throughput gain). Optimal antenna selection relies on various parameters, including gain, bandwidth, impedance matching, frequency, side-lobe levels, etc. The optimization of a six-element Yagi–Uda antenna presents a challenging engineering design problem due to its multimodal and nonlinear nature. Achieving optimal performance hinges on the intricate interplay between the lengths of the constituent elements and the spacing configurations. To this end, a multiobjective function was adopted to design this antenna. The performance of several meta-heuristic algorithms, including genetic algorithms, biogeography-based optimization, simulated annealing, and grey wolf optimizer, was compared. The GWO-based approach has performed better than its competitors. This optimized antenna design based on GWO reported a gain of 14.21 decibel. Therefore, the GWO-based method optimizes antennas that can be further investigated for other antenna design problems. © The Author(s), under exclusive licence to springer-Verlag London Ltd., part of springer Nature 2024.

关键词： simulated annealing

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Federated learning and Parallel Prompt scheduling strategies for Large Language Models 24th

Federated Learning and Parallel Prompt Scheduling Strategie...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Lv, Guangtong Gu, Bruce Jia, Xiaocong Gao, Longxiang Qu, Youyang Cui, Lei Jinan China Shandong Provincial Key Laboratory of Computer Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9789819615278

Large language models have garnered significant attention and are widely utilized across different fields due to their impressive performance. However, centralized training of these models can pose privacy risks like data leakage, hindering their application and advancement. As a decentralized learning paradigm, Federated learning(FL) can effectively address data security in large language models and has specific application scenarios. In addition, the fine-tuning process of large language models also incurs significant communication expenses and leads to the loss of computational resources. The utilization of prompt tuning in federated learning is anticipated to decrease training costs, accelerate the training process, and enhance model performance. In this paper, we propose ‘FedLLM-PPs’ to explore a novel approach for large language models within federated learning. This method address data security concerns associated with large language models while enhancing performance and efficiency. Experiments based on several datasets for various Natural Language Processing(NLP) tasks demonstrate superior performance and efficiency compared to baseline methods. © The Author(s), under exclusive license to springer Nature singapore Pte Ltd. 2025.

关键词： Federated learning

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Real-time multi-depth holographic display using complex-valued neural network

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Optics Express 2025年第4期33卷 7380-7395页

作者： Zhang, Yuanzhe Cheng, Dewen Wang, Yesheng Wang, Yongdong shan, Yuefan Yang, Tong Wang, Yongtian Beijing Engineering Research Center of Mixed Reality and Advanced Display School of Optics and Photonics Beijing Institute of Technology Beijing100081 China Beijing NED+AR Ltd. Beijing100081 China

computer-generated holography (CGH) has made significant advancements and is considered a leading approach for near-eye 3D displays. Recent learning-based CGH methods address the time-quality trade-off of traditional approaches but often face challenges related to efficiency and computational demands, especially with real-valued networks in multi-depth settings. To overcome these issues, this study proposes a residual block-based complex-valued convolutional neural network (ResC-CNN) structure, integrated into a symmetric dual-network framework driven by a diffraction model, for real-time generation of multi-depth holographic displays. This approach enhances the network’s ability to handle complex domain calculations in CGH, making the learning process more efficient. A layered depth image (LDI) dataset is also incorporated to improve scene information prediction accuracy. Numerical and optical experiment results indicate that our proposed framework significantly increases the real-time generation frame rate of holograms and enhances the fidelity of displayed details, offering a practical solution for high-quality, real-time multi-depth holographic displays in applications such as augmented reality. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Augmented reality

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stock trend prediction based on multi-factor screening machine 24

Stock trend prediction based on multi-factor screening machi...

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5th International Conference on computer science and Management Technology, ICCsMT 2024

作者： Hu, Ruijie Faculty of Science and Technology Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College Zhuhai China

ISBN: (纸本)9798400709999

With the rapid development of financial markets, stock price prediction, as a highly complex and challenging topic, has attracted more and more attention from academia and industry. This study aims to use multiple machine learning algorithms to predict stock market prices, especially exploring the advantages of deep learning models in capturing market nonlinear and time series characteristics. In this article, a machine learning model is used for contribution analysis, analyzing multiple features of stocks, performing a weighted combination analysis of features, and then using a convolutional neural network to perform feature extraction and analysis of stocks, and combining the attention mechanism for deep feature extraction, and finally use the long short-term memory network (LsTM) model to output the model to analyze and predict future stock trends. The experimental results show that combining deep learning analysis technology and factor screening machine can significantly improve the prediction accuracy of the model. At the same time, this article also compared the performance of different models in different market environments, and concluded that in markets with higher volatility, models based on deep learning are more stable. Ultimately, this research provides a new technical means for risk management and investment decisions in financial markets. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Convolutional neural networks

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Graph Neural Networks: Architectures, Applications, and Future Directions

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IEEE ACCEss 2025年 13卷 62870-62891页

作者： Ponzi, Valerio Napoli, Christian Sapienza Univ Rome Dept Comp Control & Management Engn I-00185 Rome Italy Italian Natl Res Council Inst Syst Anal & Comp Sci I-00185 Rome Italy Silesian Tech Univ Dept Math Applicat & Methods Artificial Intelligen PL-44100 Gliwice Poland

In recent years, deep learning has revolutionized fields such as computer vision, speech recognition, and natural language processing, primarily through techniques applied to data in Euclidean spaces. However, many real world applications involve data from non-Euclidean domains, where graphs naturally represent entities and their complex interdependencies. Traditional machine learning methods have often struggled to process such data in an effective manner. Graph Neural Networks represent a crucial advance in the use of deep learning to interpret and extract knowledge from graph-based data. They have opened up new possibilities for tasks such as node categorization, link inference, and comprehensive graph analysis. This paper provides a detailed analysis of Graph Neural Network (GNN) methodologies, emphasizing their architectural diversity and wide ranging applications. GNN models are systematically categorized into fundamental frameworks such as message passing paradigms, spectral and spatial methods, and advanced extensions such as hypergraph neural networks and multigraph approaches. This paper also explores domains such as social network analysis, molecular biology, traffic forecasting, and recommendation systems. In addition, it emphasizes some critical open challenges, including scalability, dynamic graph modeling, and robustness against noisy or incomplete data. The paper concludes with a proposal for future research directions to improve the scalability, interpretability, and adaptability of GNNs in this fast-evolving field.

关键词： Deep learning Graph neural networks social networking (online) scalability Data models Feature extraction Analytical models Vectors Roads Message passing Graph neural network deep learning message passing spectral methods spatial methods hypergraph neural networks multigraph neural networks scalability

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