2026· International Journal of Advanced Computer Science and Applications· Vol 17· 0 citations· 34 references
TL;DR
The results support direct multi-label CNN recognition for the selected protocol, and show stable performance around a decision threshold of 0.5, strong cyclic and systematic recognition, and lower performance for short shift cases.
Abstract
Control charts do more than raise an alarm: their shapes can give an early indication of what has changed in a process. This study considers the case in which one chart window contains more than one abnormal behavior. The observed sequence is then a mixture rather than a pure pattern. We formulate this problem directly as multi-label classification. A one-dimensional CNN receives a raw-scale window of 32 observations and predicts the active elementary labels. The controlled protocol contains twelve scenarios: normal behavior, six single abnormal patterns, and five selected concurrent patterns. Raw-scale input is retained because shift patterns depend partly on level information that may be weakened by window-wise normalization. The retained training setup gives additional exposure to difficult shift and trend cases, while validation and testing remain balanced. Across five repeated trainings, the model achieved 96.11% exact match accuracy, 96.41% precision, 96.46% recall, 96.44% F1-score, and 1.04% Hamming loss. The 95% confidence interval for exact match was 96.05–96.17%. Additional analyses show stable performance around a decision threshold of 0.5, strong cyclic and systematic recognition, and lower performance for short shift cases. The results support direct multi-label CNN recognition for the selected protocol. Broader shift-containing mixtures, more complex combinations, varying noise conditions, and real industrial validation remain outside the scope of the present controlled study.
This work explores CXAI use cases in multi-label classification by training two DNNs, VGG16 and ResNet50, on the 20 most annotated labels in the MS-COCO dataset and demonstrates the potential of CXAI to enhance the understanding of model generalizability and to diagnose bias instigated by the dataset.
Skin cancer remains a global health threat with increasing incidence rates. Accurate and early classification of skin lesions into benign or malignant types is essential for timely treatment and prevention of severe outcomes. In this paper, we present a comprehensive deep learning-based framework that leverages three benchmark datasets—PH2, ISIC (Benign vs Malignant), and HAM10000—using transfer learning and ensemble techniques. Pre-trained models including VGG16, ResNet50, and EfficientNetB4 were fine-tuned on each dataset, and majority voting was employed to combine predictions. The Gradient-weighted Class Activation Mapping (Grad-CAM) was also used to improve visual explainability. The findings demonstrate a notable increase in classification accuracy, surpassing current techniques and reaching over 98% accuracy on certain datasets. This study highlights the impact of hybrid architectures and explainable AI in advancing the state of skin cancer diagnosis systems.
A. D. Hayder, J. Saeed· passer of basic and applied...· 0 citations
Medical coding is the task of assigning a set of diagnosis and procedure codes for a hospitalization using recorded notes. It requires aggregating information from different parts of the text and focus to different sections for each individual code, making it a very difficult problem even for professional human coders. We model the task as a multi-label text classification problem. To overcome the mentioned difficulties, we propose a deep neural model consisting of a multi-layer temporal convolution network (TCN) followed by label-wise attention. While multi-layer TCN helps extract a global document representation with the ability to learn relations over very long sequences, label-specific attention mechanism allows the model to focus on different aspects of the same document for each individual label. Our method achieves significantly better F-1 scores (9% increase) compared to the previous state-of-the-art model, with a remarkable increase in recall score (28% increase), which we believe is the more important metric for a clinical decision support setting.
Muhammed Yavuz Nuzumlali, Alexander R. Fabbri, Irene Li et al.· 0 citations
Modern deep networks often rely on attention modules, which are still at a modest level due to using either one type of channel-wise pattern or an expensive combination of two types of them. In the case of using all of those, the obtained weights can be less discriminative due to the disjointed excitations, while the model complexity would double. To deal with these limitations, an efficient attention is proposed by addressing two novel components of residual attentive information as follows: 1) top- $n$ channel-residual attentive patterns with a unitary excitation perceptron, and 2) multiple spatial-residual attentive features. A simple fusion of these complementary components forms a robust volume of selective residual attentive patterns (named SRAP). Experiments on benchmark datasets for image classification have proved the prominent performance of SRAP versus other attention modules. Particularly, SRAP boosted the performance of lightweight CNN-based networks by up to ~7% on ImageNet-100 without increasing the computational complexity. The implementation code of SRAP is available at https://github.com/nttbdrk25/SRAP.
Thanh Tuan Nguyen, Hoang Anh Pham, Thinh Le Vinh et al.· IEEE Access· 0 citations
Convolutional neural networks (CNNs) are widely used in medical image classification, yet their robustness to localized perturbations remains limited. This study evaluates one-pixel attacks on VGG16, MobileNetV2, and EfficientNetV2-B0 using brain tumor MRI images resized to 96 × 96 pixels. Each pixel was systematically perturbed across grayscale intensities, and model responses were analyzed in terms of vulnerability, recoverability, and pixel-level sensitivity. The relationship between prediction confidence and influential pixel locations was also examined. Results show that all models remain vulnerable to one-pixel perturbations despite high accuracy. Misclassified samples exhibit more successful attack locations, while correctly classified samples are more robust. Higher-intensity perturbations more often restore correct predictions in misclassified cases. A monotonic relationship is observed between prediction confidence and pixel sensitivity, where lower confidence corresponds to more influential pixels. Recovery points show spatially concentrated patterns. Overall, pixel-level sensitivity is more strongly associated with prediction correctness and local perturbations than with confidence. These findings are consistent across architectures and suggest that one-pixel analysis is useful for assessing CNN robustness in medical imaging.
Wiktoria Tajak, Adam Piórkowski, Karolina Nurzyńska· Applied Sciences· 0 citations
This work proposes a novel hybrid approach that combines the strengths of the extreme learning machine (ELM) and the twin support vector machine (TSVM) to address the challenges of robustness and scalability in multi-label classification, particularly in settings where deep learning is not practical due to limited training instances.
Amisha Bharti, Vasudha Bhatnagar, Vikas Kumar· International Journal of Dat...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
