Egyptian Informatics Journal最新文献

Graph-based temporal anomaly detection with self-supervised contrastive learning and dynamic adaptive thresholding for acoustic howling suppression 基于自监督对比学习和动态自适应阈值的基于图的时间异常检测用于啸叫抑制

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Egyptian Informatics Journal

Pub Date : 2026-01-29 DOI: 10.1016/j.eij.2026.100892

Xiaoqian Fan , Francisco Hernando-Gallego , Diego Martín , Mohammad Khishe

Acoustic howling due to feedback loops in audio systems is a major challenge in such fields as hearing aids or public address systems. Traditional approaches such as notch filters and adaptive feedback cancellation often have limitations such as lack of adaptability in dynamic environments, and a need for a large amount of labelled data. To overcome these shortcomings, a new deep learning approach, Dynamic Adaptive Thresholding and Self-Supervised Contrastive Learning for Graph-based Temporal Anomaly Recognition (GTAD-CL), is proposed in this paper. By representing audio signals as graphs, GTAD-CL uses graph neural networks to represent complex spatial–temporal patterns to detect howling with high precision as an anomaly. Self-supervised contrastive learning removes the requirement of having labeled datasets which improves the scalability and generalization of the AI models. A dynamic adaptive thresholding mechanism guarantees robust performance under different acoustic conditions, e.g. low signal to noise ratio environments. Integrated with neural filtering in real time, GTAD-CL makes howling suppression easy. Experimental results on a 100-hour custom dataset and six public benchmarks indicate that GTAD-CL has a precision of 0.92 (compared to 0.88, the best baseline, HybridAHS, showing a gain of 4.5%), recall of 0.90 (compared to 0.85, a gain of 5%) and F1-score of 0.91 (compared to 0.865, a gain of 4.5%). In suppression quality GTAD-CL achieves a PESQ score of 3.02 (compared to 2.68 for HybridAHS, i.e. ∼12.7% better), and a STOI of 0.90 (compared to 0.86, i.e. ∼4.7% better). Moreover, GTAD-Cl runs with a real-time factor of 0.36× which is better than HybridAHS’s 0.42× (approx. 14% faster). These results give validation to GTAD-CL as a powerful, scalable, and low-latency solution and high-fidelity solution that is superior to state-of-the-art results for varying acoustic scenarios.

音频系统中由反馈回路引起的声啸是助听器或公共广播系统等领域的主要挑战。传统的方法，如陷波滤波器和自适应反馈抵消往往有局限性，如缺乏对动态环境的适应性，需要大量的标记数据。为了克服这些缺点，本文提出了一种新的深度学习方法——动态自适应阈值和自监督对比学习的基于图的时间异常识别（GTAD-CL）。通过将音频信号表示为图形，GTAD-CL使用图形神经网络来表示复杂的时空模式，以高精度检测嚎叫作为一种异常。自监督对比学习消除了对标记数据集的要求，从而提高了人工智能模型的可扩展性和泛化性。动态自适应阈值机制保证了不同声学条件下的鲁棒性能，例如低信噪比环境。集成了实时神经滤波，GTAD-CL使嚎叫抑制容易。在100小时自定义数据集和6个公共基准上的实验结果表明，GTAD-CL的精度为0.92（与最佳基线HybridAHS的0.88相比，提高了4.5%），召回率为0.90（与0.85相比，提高了5%），f1得分为0.91（与0.865相比，提高了4.5%）。在抑制质量方面，GTAD-CL的PESQ得分为3.02（相比之下，HybridAHS为2.68，即提高了约12.7%），STOI为0.90（相比之下，0.86，即提高了约4.7%）。此外，GTAD-Cl运行时的实时因子为0.36×，优于HybridAHS的0.42×（约为0.42×）。快14%)。这些结果验证了GTAD-CL是一种功能强大、可扩展、低延迟和高保真的解决方案，在不同的声学场景中优于最先进的结果。

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引用次数: 0

Fuzzy Inference System-Based Prognostics for Remaining Useful Life Estimation 基于模糊推理系统的剩余使用寿命预测

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Egyptian Informatics Journal

Pub Date : 2026-01-29 DOI: 10.1016/j.eij.2026.100897

Mahmut Sami Şaşmaztürk , Ferhat Yuna

Prognostics and health management (PHM) plays a critical role in ensuring the reliability and safety of complex engineering systems such as aircraft engines. In this field, estimating the Remaining Life (RUL) of systems is vital for optimizing maintenance strategies and preventing unexpected failures. This study proposes a Fuzzy Inference System (FIS)-based approach for RUL estimation. The proposed model uses expert-defined fuzzy rules and membership functions to effectively address uncertainties and nonlinear degradation patterns in sensor data. The industry-standard NASA C-MAPSS (Commercial Modular Aero-Propulsion System Simulation) dataset was used for model development and validation. Multiple features extracted from the dataset were input to the developed Fuzzy Inference System, and the system’s performance was comprehensively evaluated under different operating conditions. Experimental results demonstrate that the FIS model performs competitively compared to traditional machine learning methods and produces interpretable and robust RUL estimates. This study demonstrates the potential of fuzzy logic in data-driven prognostics and makes a significant contribution to the literature by laying a solid groundwork for future hybrid approaches that integrate expert knowledge and learning algorithms.

预测和健康管理（PHM）在确保飞机发动机等复杂工程系统的可靠性和安全性方面起着至关重要的作用。在该领域中，系统剩余寿命（RUL）的估算对于优化维护策略和防止意外故障至关重要。本研究提出一种基于模糊推理系统（FIS）的规则估计方法。该模型采用专家定义的模糊规则和隶属函数来有效地处理传感器数据中的不确定性和非线性退化模式。工业标准NASA C-MAPSS（商业模块化航空推进系统仿真）数据集用于模型开发和验证。将从数据集中提取的多个特征输入到所开发的模糊推理系统中，并在不同的运行条件下对系统的性能进行综合评价。实验结果表明，与传统的机器学习方法相比，FIS模型具有竞争力，并产生可解释和鲁棒的RUL估计。本研究展示了模糊逻辑在数据驱动预测中的潜力，并为未来整合专家知识和学习算法的混合方法奠定了坚实的基础，从而对文献做出了重大贡献。

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引用次数: 0

Enhanced machine learning algorithm for predicting energy consumption in smart buildings 智能建筑能耗预测的增强机器学习算法

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Egyptian Informatics Journal

Pub Date : 2026-01-29 DOI: 10.1016/j.eij.2026.100903

Ahmed A. Ewees , Shaymaa E. Souror , Abdullah M. Alharthi , Mohammed M. Alshahrani

Energy consumption in buildings represents a substantial share of global energy use and underscores the need for intelligent solutions to improve efficiency. Accurate prediction of heating and cooling loads is essential for optimizing energy usage in smart buildings and supporting broader sustainability goals. SRSm is a new variant of the Special Relativity Search (SRS) algorithm that incorporates a mutation phase to increase population diversity, enhance exploration, and reduce the risk of local optima. This variant is used to optimize a neural network for the accurate prediction of heating and cooling loads. The proposed model is evaluated using several statistical performance metrics and compared with conventional and advanced optimization techniques. The results show that SRSm consistently achieves competitive predictive accuracy. The integration of the mutation mechanism improves predictive performance and leads to high accuracy in both heating and cooling load estimation during the testing phase.

建筑能耗占全球能源消耗的很大一部分，因此需要智能解决方案来提高效率。准确预测供热和制冷负荷对于优化智能建筑的能源使用和支持更广泛的可持续发展目标至关重要。SRSm是狭义相对论搜索（SRS）算法的一种新变体，该算法引入了突变阶段，以增加种群多样性，增强探索能力，降低局部最优风险。该变体用于优化神经网络，以准确预测加热和冷却负荷。使用几种统计性能指标评估了所提出的模型，并与传统和先进的优化技术进行了比较。结果表明，SRSm的预测精度具有一定的竞争力。突变机制的集成提高了预测性能，并在测试阶段提高了热负荷和冷负荷估计的准确性。

引用次数: 0

Reconstruction of project quality assessment through a data-driven machine learning model 通过数据驱动的机器学习模型重建项目质量评估

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Egyptian Informatics Journal

Pub Date : 2026-01-27 DOI: 10.1016/j.eij.2026.100900

Ching-Lung Fan

Public construction quality in Taiwan is commonly assessed through committee-based inspections, yet the resulting scores are often subjective and heavily concentrated within narrow grading ranges. To address this limitation, this study proposes a data-driven framework that integrates Principal Component Analysis (PCA) with a Multilayer Neural Network (MNN) to reconstruct objective and discriminative ranges of project quality scores. Using 962 inspection records from the Public Construction Intelligence Cloud (PCIC), PCA is first applied to reduce 499 defect items into 13 representative serious defects, mitigating multicollinearity and retaining the most informative quality indicators. These defects, together with the project contract amount and construction progress, are then used as inputs to an optimized MNN classifier. A systematic hyperparameter search and stratified 10-fold cross-validation are employed to ensure robust model generalization. Based on the learned relationships, new grading thresholds are derived: A+ (86–100), A (83–86), A– (80–83), and B+ (<80). The proposed PCA–MNN framework achieves an overall accuracy of 95% and significantly alleviates the extreme class imbalance observed in the original scoring scheme. Results demonstrate that the reconstructed ranges provide a more balanced, interpretable, and objective representation of project quality, enabling fairer multi-class evaluation and supporting more reliable decision-making in public construction quality management.

台湾的公共建筑质量通常是通过委员会的检查来评估的，但结果的分数往往是主观的，而且严重集中在狭窄的评分范围内。为了解决这一限制，本研究提出了一个数据驱动的框架，该框架将主成分分析（PCA）与多层神经网络（MNN）相结合，以重建项目质量得分的客观和判别范围。利用公共建设智能云（Public Construction Intelligence Cloud， PCIC）中的962条检验记录，首先运用主成分分析法将499个缺陷项减少为13个具有代表性的严重缺陷，减轻了多重共线性，保留了信息量最大的质量指标。然后将这些缺陷与项目合同额和施工进度一起作为优化后的MNN分类器的输入。采用系统的超参数搜索和分层的10倍交叉验证来确保模型的鲁棒泛化。基于学习到的关系，导出了新的评分阈值：A+(86-100)、A（83-86）、A -(80 - 83)和B+ (<80)。本文提出的PCA-MNN框架总体准确率达到95%，显著缓解了原始评分方案中存在的极端类别不平衡问题。结果表明，重建的范围提供了更平衡、可解释和客观的工程质量表征，实现了更公平的多等级评价，为公共建筑质量管理提供了更可靠的决策支持。

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引用次数: 0

Binary classification for imbalanced datasets using a novel metric method 基于度量方法的不平衡数据集二值分类

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Egyptian Informatics Journal

Pub Date : 2026-01-27 DOI: 10.1016/j.eij.2026.100890

Jian Zheng , Shengye Wang , Huyong Yan , Haichao Sun

This work proposes a kernel amplification method with non-stationary characteristics for binary classification of non-noisy imbalanced datasets. Our methodology features two key innovations, including that a derived non-stationary kernel construction enables adaptive exploration of minority class regions, and a Riemannian metric–guided kernel amplification mechanism effectively induces minority class migration in feature space, tightening the spatial distance inner minority class instances. Experimental validation across ten UCI benchmark datasets with class imbalance demonstrate the superior performance of our proposed method. The method achieves statistically significant superiority over all six baseline approaches on five highly imbalanced datasets (with imbalance ratios (IR) > 10:1), notably achieving 0.883 F1-score on datasets with 40.22:1 imbalance ratio and 0.800 sensitivity to the minority class. Furthermore, our approach maintains competitive advantages on the remaining five moderately imbalanced datasets (IR < 10:1), outperforming a subset of the baseline methods across all evaluation metrics. Furthermore, the kernel amplification mechanism boosts the sensitivity to perception minority classes by a maximum 6.35-fold enhancement on highly imbalanced datasets, and by a maximum 2.17-fold enhancement on moderately imbalanced datasets. The derived amplification factor exhibits dimension-dependent characteristics, showing independence from both sample size and imbalanced ratio——a critical advantage for high-dimensional imbalanced classification.

本文提出了一种具有非平稳特征的核放大方法，用于非噪声不平衡数据集的二值分类。我们的方法有两个关键的创新，包括派生的非平稳核结构可以自适应地探索少数类区域，以及黎曼度量引导的核放大机制有效地诱导少数类在特征空间中的迁移，从而缩小少数类实例内部的空间距离。在10个具有类不平衡的UCI基准数据集上的实验验证表明了我们所提出的方法的优越性能。该方法在5个高度失衡的数据集（失衡比（IR） > 10:1）上取得了优于6种基线方法的统计学显著优势，特别是在失衡比为40.22:1的数据集上取得了0.883 f1得分，对少数类的敏感性为0.800。此外，我们的方法在剩余的五个中度不平衡数据集（IR < 10:1）上保持了竞争优势，在所有评估指标上都优于基线方法的子集。此外，核放大机制提高了感知少数类的敏感性，在高度不平衡的数据集上最大提高了6.35倍，在中度不平衡的数据集上最大提高了2.17倍。衍生的放大因子表现出维度依赖特征，显示出与样本大小和不平衡比例无关——这是高维不平衡分类的关键优势。

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引用次数: 0

A formal approach towards IoT-enabled mechanism for digital polio system using Colured Petri Net 使用彩色Petri网实现数字脊髓灰质炎系统物联网启用机制的正式方法

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Egyptian Informatics Journal

Pub Date : 2026-01-27 DOI: 10.1016/j.eij.2026.100894

Shahbaz Ahmad , Adeel Ahmed , Nazik Alturki , Sajjad Hussain , Saima Abdullah

Polio remains an extreme health challenge in numerous weak and high-risk regions such as Pakistan, Afghanistan, Nigeria mainly due to ineffective monitoring and the lack of integrated digital systems for immunization organization and management. The current Polio Vaccination System (PVS) of Pakistan faces many problems and issues while eradicating poliovirus because the current system is completely paper based where data is added to sheets manually and there is a big chance of data loss. A system is efficient that make the vaccination campaign successful and remove flaws that we have faced in the current system. This research framework presents a formal approach to design and verification with integration of IoT-enabled digital polio system using Colored Petri Nets (CPNs). Its helps to leverage the IoT devices for real-time data collection, communication, and control of field-level immunization activities. With the help of modeling the system of CPNs, we ensured the formal verification of process correctness, consistency, and deadlock-free process. The model simulates interactions amongst the key mechanisms, including health workers, supervising and observing units to enable an accurate analysis of system behaviour. Formal validation through Colored Petri Nets and state space analysis confirms the reliability, correctness and scalability of the system. The approach proposes a verifiable, adaptable and flexible solution in support of the national polio eradication programs with the help of digitation, transparency, and intelligent management. The proposed system will prove efficient and flawless as compared to the current manual system because it will use a data entry device in place of a hand-held register, the security personnel with the community health workers will move Door-to-Door to vaccinate the children. Results validate the proposed model as robust a good, reliable and complete with IoT enabled smart system along Colured Petri Net and formal method.

在巴基斯坦、阿富汗、尼日利亚等许多脆弱和高风险地区，脊髓灰质炎仍然是一个极端的卫生挑战，主要原因是监测无效以及缺乏免疫组织和管理的综合数字系统。巴基斯坦目前的脊髓灰质炎疫苗接种系统（PVS）在根除脊髓灰质炎病毒时面临许多问题和问题，因为目前的系统完全是纸质的，数据是手动添加到表格上的，数据丢失的可能性很大。一个有效的系统可以使疫苗接种运动取得成功，并消除我们在当前系统中面临的缺陷。本研究框架提出了一种正式的方法来设计和验证使用彩色Petri网（cpn）集成物联网支持的数字脊髓灰质炎系统。它有助于利用物联网设备进行实时数据收集、通信和现场免疫活动控制。通过对cpn系统的建模，保证了流程正确性、一致性和无死锁的形式化验证。该模型模拟关键机制（包括卫生工作者、监督和观察单位）之间的相互作用，以便对系统行为进行准确分析。通过彩色Petri网和状态空间分析的形式化验证证实了系统的可靠性、正确性和可扩展性。该方法提出了一种可验证的、适应性强的、灵活的解决方案，在数字化、透明和智能管理的帮助下，为国家根除脊髓灰质炎规划提供支持。与目前的人工系统相比，拟议的系统将被证明是高效和完美的，因为它将使用数据输入设备代替手持登记册，安全人员和社区卫生工作者将挨家挨户地为儿童接种疫苗。结果验证了所提出的模型具有鲁棒性，具有良好，可靠和完整的物联网智能系统，以及彩色Petri网和形式化方法。

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引用次数: 0

Epileptic seizure detection using information Gain-Based hybrid Features: Deep Neural network and comparative Machine learning approaches 基于信息增益的混合特征的癫痫发作检测：深度神经网络和比较机器学习方法

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Egyptian Informatics Journal

Pub Date : 2026-01-24 DOI: 10.1016/j.eij.2026.100889

Nuri Ikizler, Gunes Ekim

Automatic detection of epileptic seizures is crucial in clinical diagnosis to enable early intervention and ensure patient safety. However, systematic comparisons across multi-class combinations and quantitative evaluation of discriminative features remain limited in the literature. This study aims to identify the most effective features for seizure detection and to develop a high-accuracy classification model. Statistical, spectral, and wavelet-based features from time, frequency, and time–frequency domains were selected using the Information Gain method, and four models were integrated into a hybrid framework. The approach was evaluated on 26 class combinations using Random Forest, Support Vector Machines, k-Nearest Neighbors, Gradient Boosting, and a Deep Neural Network. The proposed method achieved an average accuracy of 99%, with the Deep Neural Network reaching 99.69% in combinations including class E, demonstrating strong generalizability in multi-class scenarios. The main novelty of this work lies in combining Information Gain-based hybrid feature selection with a systematic multi-class analysis, a gap not fully addressed in previous studies. This approach enhances accuracy, interpretability, and generalizability, thereby contributing to improved clinical decision-making in epilepsy diagnosis.

癫痫发作的自动检测在临床诊断中至关重要，可以实现早期干预并确保患者安全。然而，在文献中，跨多类组合的系统比较和判别特征的定量评估仍然有限。本研究旨在找出最有效的特征，以检测癫痫发作，并开发一个高精度的分类模型。利用信息增益方法从时间、频率和时频域选择基于统计、频谱和小波的特征，并将四个模型集成到一个混合框架中。该方法使用随机森林、支持向量机、k近邻、梯度增强和深度神经网络对26个类组合进行了评估。该方法的平均准确率达到99%，其中Deep Neural Network在包括E类在内的组合中达到99.69%，在多类场景中表现出较强的泛化能力。这项工作的主要新颖之处在于将基于信息增益的混合特征选择与系统的多类分析相结合，这是以往研究中没有完全解决的空白。这种方法提高了准确性、可解释性和通用性，从而有助于改善癫痫诊断的临床决策。

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引用次数: 0

Adaptive sampling enhanced deep learning framework for accurate interpretable stroke risk prediction 自适应采样增强深度学习框架，用于准确的可解释中风风险预测

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Egyptian Informatics Journal

Pub Date : 2026-01-23 DOI: 10.1016/j.eij.2026.100887

Rasha M. Abd El-Aziz, Alanazi Rayan

Stroke is a leading cause of global mortality and long-term disability, emphasizing the urgent need for predictive models that are accurate, interpretable, and equitable to support precision medicine. Conventional risk assessment methods often rely on a limited set of clinical indicators and ignore subgroup-specific patterns, which reduces predictive performance and can bias outcomes against underrepresented populations. To address these challenges, this study proposes ASTab-Stroke (Adaptive Stratified TabNet for Stroke Prediction), a deep learning framework integrating Adaptive Stratified Sampling (ASS) with TabNet’s sequential attention mechanism. ASS dynamically reweights patient strata based on their contribution to prediction errors, ensuring fair representation of minority and high-risk subgroups without introducing synthetic data. TabNet’s sequential attention provides step-wise feature attribution, enabling clinicians to interpret the influence of predictors such as age, hypertension, heart disease, glucose level, BMI, and lifestyle factors on stroke risk. The framework was implemented in Python 3.10 and evaluated using the Stroke Prediction Dataset, which includes diverse demographic, clinical, and lifestyle variables. ASTab-Stroke achieved 98% accuracy, 0.998 AUC, 0.97 F1-score, 0.99 recall, and 0.98 precision, outperforming existing baselines by approximately 3% in accuracy while demonstrating improved sensitivity and fairness across clinically significant subgroups. The age and comorbidity features proved to be critical in ablation studies and work on cross-validation showed strong generalization. This framework is a clinically interpretable, scalable, and ethically rationalized method of stroke risk prediction, which gives dependable information to support clinical decision-making with data. The flexibility of it implies that it has a wide potential to be used in other fields of precision medicine, where interpretability and subgroup fairness are crucial in promoting equitable and informed patient care.

中风是全球死亡和长期残疾的主要原因，因此迫切需要准确、可解释和公平的预测模型来支持精准医学。传统的风险评估方法往往依赖于一组有限的临床指标，忽略了亚组特定模式，这降低了预测效果，并可能使结果对代表性不足的人群产生偏差。为了解决这些挑战，本研究提出了ASTab-Stroke (Adaptive Stratified TabNet for Stroke Prediction)，这是一个将自适应分层采样（ASS）与TabNet的顺序注意机制相结合的深度学习框架。ASS根据患者阶层对预测误差的贡献动态地重新加权，确保在不引入合成数据的情况下公平地代表少数群体和高风险亚群。TabNet的顺序关注提供了阶梯式特征归因，使临床医生能够解释诸如年龄、高血压、心脏病、血糖水平、BMI和生活方式等预测因素对中风风险的影响。该框架在Python 3.10中实现，并使用中风预测数据集进行评估，该数据集包括各种人口统计、临床和生活方式变量。ASTab-Stroke的准确度为98%，AUC为0.998，f1评分为0.97，召回率为0.99，精密度为0.98，准确度比现有基线提高了约3%，同时在临床显著亚组中表现出更高的敏感性和公平性。年龄和合并症特征在消融研究中被证明是至关重要的，交叉验证的工作显示出很强的通用性。该框架是一种临床可解释、可扩展、伦理合理的脑卒中风险预测方法，为临床决策提供可靠的数据支持。它的灵活性意味着它在其他精准医学领域具有广泛的应用潜力，在这些领域，可解释性和亚组公平性对于促进公平和知情的患者护理至关重要。

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引用次数: 0

SF-YOLOv9: PGI based hybrid backbone with dual-path attention for small object detection in aerial imagery SF-YOLOv9：基于PGI的双路径关注混合主干航拍图像小目标检测

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Egyptian Informatics Journal

Pub Date : 2026-01-22 DOI: 10.1016/j.eij.2026.100888

Shahzad Hussain , Iqra Mumtaz , Chong Wang , Pei Lv

Small object detection in aerial imagery is a challenging task due to the minimal pixel information in dense clutter, scale variation, and complex backgrounds. YOLOv9 has demonstrated the effectiveness of Programmable Gradient Information (PGI) in mitigating feature degradation. However, its fully convolutional architecture lacks the capability for global context modeling, which is critical for resolving ambiguities in small targets. To address these limitations, we propose SF-YOLOv9, a hybrid architecture that enhances YOLOv9c by improving the backbone through the integration of a novel PGI-Aware Swin Fusion Block (Transformer-GELAN) at its final stage. This module effectively preserves high-resolution local features while injecting long-range global context through Swin Transformer-based fusion. It results in richer and more discriminative semantic representations. We introduce a Dual-Path Spatial and Channel Attention Module (DSCAM) into the main detection head and the reversible auxiliary branches of PGI. By refining attention across all supervisory signals, DSCAM significantly improves gradient flow and feature fidelity during PGI training, reducing missed detections and false positives. We evaluate SF-YOLOv9 on VisDrone and NWPU-VHR-10 datasets to demonstrate the effectiveness of SF-YOLOv9. It outperformed the baseline models, achieving 49.1% [email protected] on VisDrone and 98.3% [email protected] on NWPU VHR-10 in small-object detection.

由于在密集杂波、尺度变化和复杂背景下像素信息最少，航空图像中的小目标检测是一项具有挑战性的任务。YOLOv9已经证明了可编程梯度信息（PGI）在减轻特征退化方面的有效性。然而，它的全卷积架构缺乏全局上下文建模的能力，这对于解决小目标的模糊性至关重要。为了解决这些限制，我们提出了SF-YOLOv9，这是一种混合架构，通过在其最后阶段集成新颖的gi感知Swin融合块（Transformer-GELAN）来改进骨干，从而增强了YOLOv9c。该模块有效地保留了高分辨率的局部特征，同时通过基于Swin transformer的融合注入了远程全局上下文。它产生了更丰富、更有区别的语义表示。我们在PGI的主检测头和可逆辅助分支中引入了一个双路空间和通道注意模块（DSCAM）。通过精炼所有监控信号的注意力，DSCAM显著改善了PGI训练期间的梯度流和特征保真度，减少了漏检和误报。我们在VisDrone和NWPU-VHR-10数据集上对SF-YOLOv9进行了评估，以证明SF-YOLOv9的有效性。它优于基准模型，在VisDrone上达到49.1% [email protected]，在NWPU VHR-10上达到98.3% [email protected]。

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引用次数: 0

Automatic action recognition technology combining pyramid convolution and attention mechanism in gymnastics training 结合金字塔卷积和注意机制的体操训练动作自动识别技术

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Egyptian Informatics Journal

Pub Date : 2026-01-16 DOI: 10.1016/j.eij.2025.100881

Li Shang

This study proposes an action recognition method that combines pyramid convolution and attention mechanism to address the problem of low efficiency and inability to effectively capture the detailed features of movements in traditional gymnastics training. The aim is to achieve the level of intelligence in gymnastics training and improve the precision of action recognition. This method improves the efficiency and accuracy of finite element by constructing a multi-scale feature detection model based on a pyramid convolutional kernel dual stream neural network. To further enhance the performance of action analysis, an improved selective kernel attention mechanism is introduced, and a method for automatic motion analysis that incorporates attention mechanism alongside multi-level features is proposed. The outcomes indicate that in comparison with conventional dual stream neural networks, the proposed method improves the accuracy of spatial flow and event flow by 5.00 % and 3.12 %, respectively. In comparison with the original attention mechanism, the recall rate of the proposed method increases by 9.73 %, accuracy increases by 5.79 %, and the average accuracy of motion analysis for spatial and temporal streams increases by 1.99 % and 0.83 %. The outcomes reveal that the proposed action recognition method can efficiently extract key features and has excellent accuracy in recognizing gymnastics training actions. This study introduces an innovative technological approach in the realm of sports science, which has the potential to enhance the intelligence quotient of athlete training and maximize training efficiency.

针对传统体操训练中效率低、无法有效捕捉动作细节特征的问题，本研究提出了一种结合金字塔卷积和注意机制的动作识别方法。目的是达到体操训练的智能化水平，提高动作识别的精度。该方法通过构建基于金字塔卷积核双流神经网络的多尺度特征检测模型，提高了有限元的效率和精度。为了进一步提高动作分析的性能，引入了一种改进的选择性核注意机制，提出了一种结合注意机制和多层次特征的自动动作分析方法。结果表明，与传统的双流神经网络相比，该方法对空间流和事件流的识别精度分别提高了5.00%和3.12%。与原注意机制相比，该方法的查全率提高了9.73%，准确率提高了5.79%，对时空流运动分析的平均准确率分别提高了1.99%和0.83%。实验结果表明，所提出的动作识别方法能够有效地提取关键特征，在体操训练动作识别中具有良好的准确率。本研究在运动科学领域引入了一种创新的技术方法，具有提高运动员训练智商和最大化训练效率的潜力。

{"title":"Automatic action recognition technology combining pyramid convolution and attention mechanism in gymnastics training","authors":"Li Shang","doi":"10.1016/j.eij.2025.100881","DOIUrl":"10.1016/j.eij.2025.100881","url":null,"abstract":"<div><div>This study proposes an action recognition method that combines pyramid convolution and attention mechanism to address the problem of low efficiency and inability to effectively capture the detailed features of movements in traditional gymnastics training. The aim is to achieve the level of intelligence in gymnastics training and improve the precision of action recognition. This method improves the efficiency and accuracy of finite element by constructing a multi-scale feature detection model based on a pyramid convolutional kernel dual stream neural network. To further enhance the performance of action analysis, an improved selective kernel attention mechanism is introduced, and a method for automatic motion analysis that incorporates attention mechanism alongside multi-level features is proposed. The outcomes indicate that in comparison with conventional dual stream neural networks, the proposed method improves the accuracy of spatial flow and event flow by 5.00 % and 3.12 %, respectively. In comparison with the original attention mechanism, the recall rate of the proposed method increases by 9.73 %, accuracy increases by 5.79 %, and the average accuracy of motion analysis for spatial and temporal streams increases by 1.99 % and 0.83 %. The outcomes reveal that the proposed action recognition method can efficiently extract key features and has excellent accuracy in recognizing gymnastics training actions. This study introduces an innovative technological approach in the realm of sports science, which has the potential to enhance the intelligence quotient of athlete training and maximize training efficiency.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"33 ","pages":"Article 100881"},"PeriodicalIF":4.3,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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