Pub Date : 2025-12-05DOI: 10.1007/s40747-025-02141-y
Angela Cortecchia, Giovanni Ciatto, Roberto Casadei, Danilo Pianini
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Pub Date : 2025-12-05DOI: 10.1007/s40747-025-02179-y
Ch. Srilakshmi, N. Ramakrishnaiah, E. Laxmi Lydia
The last few years have witnessed rapid increase in skin cancer caused mortality rate. Despite innovations and growth in vision-computing and artificial intelligence technologies, the complex shapes, sizes, textural patterns and ambiguous edges limits the reliability of existing approaches. Nevertheless, unlike traditional approaches the deep learning methods have performed superior; yet, the demands for the superior skin-lesion segmentation, ROI-specific feature extraction and learning can’t be ruled out. Moreover, it requires addressing class-imbalance problems as well to avoid skewed learning and prediction. Considering it as motivation, in this paper a novel and robust semantic segmentation assisted deep ensemble feature learning environment for skin-cancer detection and classification (SDENet) is proposed. The proposed SDENet model is targeted to perform multi-class skin-cancer classification. To achieve it, the SDENet at first performs standard pre-processing followed by synthetic minority over-sampling (SMOTE) to alleviate class-imbalance problem. Subsequently, it performs firefly heuristic algorithm based Fuzzy C-means clustering to segment skin-lesions (say, ROI), which is followed by ROI-specific deep spatio-textural ensemble feature extraction and fusion (DeS-TEFF). Specifically, SDENet makes use of the AlexNet deep network, DenseNet121 and Gray level co-occurrence matrix (GLCM) feature extraction methods. Here, AlexNet serves high-dimensional information rich features, while DenseNet121 yields layer-wise learning and feature reuse driven feature-set. Performing horizontal concatenation over the AlexNet, DenseNet121 and GLCM features, the principal component analysis (PCA) feature selection was performed, which helped to avoid local minima and convergence. The selected features were normalized by means of the z-score normalization so as to avoid over-fitting problems. Finally, the normalized features were trained and classified by using Heterogenous Ensemble Classifier, embodying SVM, DT, Random Forest, Extra Tree Classifier and XGBoost classifiers. The maximum voting ensemble-based classification over HAM10000 dataset exhibited the average accuracy of 98.97%, precision 99.38%, recall 98.94% and F-Measure 0.99, confirming its superiority over other existing approaches for real-time skin cancer diagnosis purposes.
最近几年，皮肤癌引起的死亡率迅速上升。尽管视觉计算和人工智能技术不断创新和发展，但复杂的形状、大小、纹理模式和模糊的边缘限制了现有方法的可靠性。然而，与传统方法不同，深度学习方法表现得更优越；然而，也不能排除对更好的皮肤病变分割、roi特征提取和学习的需求。此外，它还需要解决阶级失衡问题，以避免学习和预测的偏差。以语义分割为动机，提出了一种新的鲁棒语义分割辅助深度集成特征学习环境（SDENet）用于皮肤癌检测与分类。提出的SDENet模型旨在进行多类皮肤癌分类。为了实现这一目标，SDENet首先执行标准预处理，然后进行合成少数过采样（SMOTE）来缓解类不平衡问题。随后，采用基于萤火虫启发式算法的模糊c均值聚类对皮肤病变（如ROI）进行分割，然后对ROI进行深度空间纹理集成特征提取与融合（DeS-TEFF）。具体来说，SDENet使用了AlexNet深度网络、DenseNet121和灰度共生矩阵（GLCM）特征提取方法。在这里，AlexNet提供高维信息丰富的功能，而DenseNet121提供分层学习和功能重用驱动的功能集。在AlexNet、DenseNet121和GLCM特征上进行水平拼接，进行主成分分析（PCA）特征选择，有助于避免局部最小值和收敛。选取的特征通过z-score归一化进行归一化，避免出现过拟合问题。最后，使用异构集成分类器对归一化特征进行训练和分类，包括SVM、DT、Random Forest、Extra Tree Classifier和XGBoost分类器。在HAM10000数据集上，基于投票集合的最大分类平均准确率为98.97%，精密度为99.38%，召回率为98.94%，F-Measure为0.99，证实了其在实时皮肤癌诊断方面优于其他现有方法。
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Pub Date : 2025-12-05DOI: 10.1007/s40747-025-02188-x
Nguyen Hoang Vu, Tran Van Duc, Pham Quang Tien, Nguyen Thi Ngoc Anh, Nguyen Tien Dat
{"title":"A real-time mobile solution for shoe try-on using foot pose estimation and 3D processing techniques","authors":"Nguyen Hoang Vu, Tran Van Duc, Pham Quang Tien, Nguyen Thi Ngoc Anh, Nguyen Tien Dat","doi":"10.1007/s40747-025-02188-x","DOIUrl":"https://doi.org/10.1007/s40747-025-02188-x","url":null,"abstract":"","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"69 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145680382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-04DOI: 10.1007/s40747-025-02195-y
Md. Najmul Mowla, Davood Asadi, Ferdous Sohel
Robust fault detection and diagnosis (FDD) in multirotor unmanned aerial vehicles (UAVs) remains challenging due to limited actuator redundancy, nonlinear dynamics, and environmental disturbances. This work introduces two lightweight deep learning architectures: the Convolutional-LSTM Fault Detection Network (CLFDNet), which combines multi-scale one-dimensional convolutional neural networks (1D-CNN), long short-term memory (LSTM) units, and an adaptive attention mechanism for spatio-temporal fault feature extraction; and the Autoencoder LSTM Multi-loss Fusion Network (AELMFNet), a soft attention–enhanced LSTM autoencoder optimized via multi-loss fusion for fine-grained fault severity estimation. Both models are trained and evaluated on UAV-Fault Magnitude V1, a high-fidelity simulation dataset containing 114,230 labeled samples with motor degradation levels ranging from 5% to 40% in the take-off, hover, navigation, and descent phases, representing the most probable and recoverable fault scenarios in quadrotor UAVs. Including coupled faults enables models to learn correlated degradation patterns and actuator interactions while maintaining controllability under standard flight laws. CLFDNet achieves 96.81% precision in fault severity classification and 100% accuracy in motor fault localization with only 19.6K parameters, demonstrating suitability for real-time onboard applications. AELMFNet achieves the lowest reconstruction loss of 0.001 with Huber loss and an inference latency of 6 ms/step, underscoring its efficiency for embedded deployment. Comparative experiments against 15 baselines, including five classical machine learning models, five state-of-the-art fault detection methods, and five attention-based deep learning variants, validate the effectiveness of the proposed architectures. These findings confirm that lightweight deep models enable accurate and efficient diagnosis of UAV faults with minimal sensing.
{"title":"Real-time fault detection in multirotor UAVs using lightweight deep learning and high-fidelity simulation data with single and double fault magnitudes","authors":"Md. Najmul Mowla, Davood Asadi, Ferdous Sohel","doi":"10.1007/s40747-025-02195-y","DOIUrl":"https://doi.org/10.1007/s40747-025-02195-y","url":null,"abstract":"Robust fault detection and diagnosis (FDD) in multirotor unmanned aerial vehicles (UAVs) remains challenging due to limited actuator redundancy, nonlinear dynamics, and environmental disturbances. This work introduces two lightweight deep learning architectures: the Convolutional-LSTM Fault Detection Network (CLFDNet), which combines multi-scale one-dimensional convolutional neural networks (1D-CNN), long short-term memory (LSTM) units, and an adaptive attention mechanism for spatio-temporal fault feature extraction; and the Autoencoder LSTM Multi-loss Fusion Network (AELMFNet), a soft attention–enhanced LSTM autoencoder optimized via multi-loss fusion for fine-grained fault severity estimation. Both models are trained and evaluated on UAV-Fault Magnitude V1, a high-fidelity simulation dataset containing 114,230 labeled samples with motor degradation levels ranging from 5% to 40% in the take-off, hover, navigation, and descent phases, representing the most probable and recoverable fault scenarios in quadrotor UAVs. Including coupled faults enables models to learn correlated degradation patterns and actuator interactions while maintaining controllability under standard flight laws. CLFDNet achieves 96.81% precision in fault severity classification and 100% accuracy in motor fault localization with only 19.6K parameters, demonstrating suitability for real-time onboard applications. AELMFNet achieves the lowest reconstruction loss of 0.001 with Huber loss and an inference latency of 6 ms/step, underscoring its efficiency for embedded deployment. Comparative experiments against 15 baselines, including five classical machine learning models, five state-of-the-art fault detection methods, and five attention-based deep learning variants, validate the effectiveness of the proposed architectures. These findings confirm that lightweight deep models enable accurate and efficient diagnosis of UAV faults with minimal sensing.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"141 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145680385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-02DOI: 10.1007/s40747-025-02151-w
Edward B. Ssekulima, Amir H. Etemadi
{"title":"Stochastic optimization framework for capacity planning of hybrid solar PV–small hydropower systems using metaheuristic algorithms","authors":"Edward B. Ssekulima, Amir H. Etemadi","doi":"10.1007/s40747-025-02151-w","DOIUrl":"https://doi.org/10.1007/s40747-025-02151-w","url":null,"abstract":"","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"11 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145657751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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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