Challenges for text processing in ancient document images are mainly due to the high degree of variations in foreground and background. Image binarization is an image segmentation technique used to separate the image into text and background components. Although several techniques for binarizing text documents have been proposed, the performance of these techniques varies and depends on the image characteristics. Therefore, selecting binarization techniques can be a key idea to achieve improved results. This paper proposes a framework for selecting binarizing techniques of palm leaf manuscripts using Support Vector Machines (SVMs). The overall process is divided into three steps: (i) feature extraction: feature patterns are extracted from grayscale images based on global intensity, local contrast, and intensity; (ii) treatment of imbalanced data: imbalanced dataset is balanced by using Synthetic Minority Oversampling Technique as to improve the performance of prediction; and (iii) selection: SVM is applied in order to select the appropriate binarization techniques. The proposed framework has been evaluated with palm leaf manuscript images and benchmarking dataset from DIBCO series and compared the performance of prediction between imbalanced and balanced datasets. Experimental results showed that the proposed framework can be used as an integral part of an automatic selection process.

Necrotizing fasciitis (NF) is a severe and life-threatening soft tissue infection that requires timely and accurate diagnosis to improve patient outcomes. The early diagnosis of NF remains challenging due to its similarity to other subcutaneous soft tissue infections like cellulitis. This study aims to employ machine learning techniques to differentiate NF from cellulitis and enhance the diagnostic accuracy of NF by developing a modified LRINEC (MLRINEC) score. These modifications aimed to improve the sensitivity and specificity of NF diagnosis. The study utilized three machine learning classifiers—Logistic Regression, decision tree, and Random Forest—to assess their effectiveness in distinguishing between NF and cellulitis cases. The MLRINEC score was developed by incorporating six key blood test parameters: creatinine, hemoglobin, platelet count, sodium, white blood cell count, and C-reactive protein using laboratory data from Maha Sarakham Hospital in Northeastern Thailand. Our findings indicate that the decision tree classifier demonstrated superior performance, achieving the highest recall, particularly in accurately identifying NF cases. A feature importance analysis revealed that hemoglobin levels and white blood cell counts were the most critical factors influencing the model’s predictions. The platelet count (PT), C-reactive protein (CRP), and creatinine (CT) also played important roles, while sodium levels (NA) were the least influential. The MLRINEC score demonstrates high accuracy in classifying NF and cellulitis patients, paving the way for improved diagnostic protocols in clinical settings.

Accurate and efficient detection of road damage is essential for maintaining road safety and supporting intelligent transportation systems. While recent approaches leverage deep learning-based object detection frameworks, they often struggle with high computational demands and suboptimal feature extraction in complex environments. To address these challenges, we propose an enhanced object detection network for road damage detection based on the YOLOv8 architecture. Specifically, we integrate the Spatial and Channel Reconstruction Convolution (SCConv) module into the backbone to reduce feature redundancy while improving spatial and channel representation through a separation-reconstruction strategy. To enhance multi-scale feature fusion, we incorporate the Efficient Multi-Scale Attention (EMA) module into the neck, enabling adaptive spatial-channel attention without introducing significant computational overhead. Extensive benchmark comparison identifies YOLOv8l as a strong baseline for road damage detection. Building upon this, we embed SCConv within the original convolutional modules to achieve a lightweight network and enhance feature representation. We further explore multiple EMA integration strategies to identify an effective model configuration. Experimental results demonstrate that our best-performing model achieves higher detection accuracy than the baseline model, while maintaining computational efficiency.

Space‐time video super‐resolution (STVSR) aims to construct high space‐time resolution video sequences from low frame rate and low‐resolution video sequences. While recent STVSR works combine temporal interpolation and spatial super‐resolution in a unified framework, they face challenges in computational complexity across both temporal and spatial dimensions, particularly in achieving accurate intermediate frame interpolation and efficient temporal information utilisation. To address these, we propose a deformable attention network for efficient STVSR. Specifically, we introduce a deformable interpolation block that employs hierarchical feature fusion to effectively handle complex inter‐frame motions at multiple scales, enabling more accurate intermediate frame generation. To fully utilise temporal information, we design a temporal feature shuffle block (TFSB) to efficiently exchange complementary information across multiple frames. Additionally, we develop a motion feature enhancement block incorporating channel attention mechanism to selectively emphasise motion‐related features, further boosting TFSB's effectiveness. Experimental results on benchmark datasets definitively demonstrate that our proposed method achieves competitive performance in STVSR tasks.