Brand perception has increasingly been recognized as a critical source of competitive intelligence in digital markets, yet existing studies have largely examined consumer sentiment, discussion topics, or product attributes independently, thereby limiting their ability to support strategic brand positioning. A competitive brand perception framework was proposed to address this limitation by integrating sentiment analysis, latent Dirichlet allocation-based topic modeling, and aspect-based sentiment analysis into a unified analytical framework capable of transforming consumer-generated social media data into competitive intelligence. The framework was validated using 12,603 posts collected from X (formerly Twitter) concerning four leading beauty and personal care brands—Dove, Nivea, Estée Lauder, and L’Oréal. Overall consumer attitudes were quantified through sentiment analysis, dominant perception themes were identified using latent Dirichlet allocation-based topic modeling, and evaluations of brand- and product-related attributes were examined through aspect-based sentiment analysis. Three complementary competitive positioning indicators—topic salience, aspect sentiment, and brand distinctiveness—were introduced. The findings demonstrate that overall sentiment alone is insufficient for explaining competitive positioning. Although Nivea generated the greatest volume of consumer discussion, the highest sentiment score and strongest topic distinctiveness were observed for Estée Lauder. Dove was primarily associated with cleansing and body care, whereas L’Oréal demonstrated a distinctive perceptual position centered on facial skincare and makeup-related discussions. These findings further indicate that competitive differentiation is shaped not only by the polarity of consumer evaluations but also by the thematic structure and attribute-specific associations embedded within online conversations. Consequently, brand perception should be conceptualized as a multidimensional and competitive construct rather than as a single sentiment-based metric. The proposed competitive brand perception framework extends the methodological foundation of social media analytics by integrating complementary analytical perspectives into a unified competitive intelligence framework and provides a practical decision-support tool for identifying perceptual advantages, uncovering differentiation opportunities, and prioritizing strategic brand positioning using large-scale consumer-generated data.

Large-scale Vision-Language Models (VLMs) like Contrastive Language-Image Pre-training (CLIP) have demonstrated their impressive zero-shot capabilities. However, adapting them to downstream tasks remains challenging, especially under domain shifts where visual features become unreliable. Existing training-free methods, such as Tip-Adapter, rely heavily on visual similarity, which often fails in out-of-distribution (OOD) scenarios. To address this, Decoupled Correction Adapter (DeCo-Adapter), a robust adaptation framework that integrates a Decoupled Knowledge Stream into the visual baseline, is proposed. Specifically, a novel Negative Semantic Suppression mechanism is introduced, leveraging Large Language Models (LLMs) to generate and penalize distractor descriptions. This mechanism effectively corrects visual ambiguities without requiring any training. Extensive experiments on ImageNet-Sketch, ImageNet-V2, and ImageNet-A demonstrate that DeCo-Adapter consistently outperforms state-of-the-art methods. Notably, it achieves a top-1 accuracy of 54.11% on ImageNet-Sketch, surpassing the strong Tip-Adapter baseline by leveraging negative knowledge for error correction.

An adaptive learning framework driven by artificial intelligence (AI) was proposed in which cognitive, emotional, and cultural dimensions of learner diversity were jointly modeled to address heterogeneous educational needs in a personalized and inclusive manner. Within the proposed system, learner adaptation was achieved through the coordinated deployment of multiple machine learning paradigms: models based on Decision Trees (DTs) were employed to dynamically align instructional content with learners’ cognitive profiles, Recurrent Neural Networks (RNNs) were utilized to capture temporal patterns in emotional engagement, and Collaborative Filtering (CF) techniques were applied to accommodate cultural preferences. The framework operates as a continuously adaptive system, enabling instructional content to be refined based on learner data derived from a dataset comprising 10,000 students. Experimental evaluation demonstrated that the proposed approach yielded statistically significant improvements in learning outcomes when compared with conventional instructional methods. Specifically, mean quiz and assignment scores were increased by 15.7% and 14.4%, respectively, while emotional engagement indicators exhibited an improvement of 35.8%. In addition, cultural satisfaction metrics were enhanced by 24.2%. These results suggest that the synergistic integration of cognitive, emotional, and cultural adaptation mechanisms contributes substantively to academic performance gains, heightened learner engagement, and improved educational equity. Beyond performance improvements, the proposed framework is designed with scalability and robustness, allowing for deployment across personalized educational contexts. As such, the framework offers a viable pathway for the development of next-generation personalized education systems capable of supporting diverse learners at scale while maintaining pedagogical effectiveness and inclusivity.

Skin burns represent a major clinical concern due to their association with pain, functional impairment, sensory damage, and even life-threatening complications. Early and accurate assessment is critical for first aid, clinical intervention, and the prevention of secondary complications. However, conventional burn diagnosis remains highly dependent on visual inspection and clinical expertise, which can introduce subjectivity and delay timely decision-making. To address these limitations, a hybrid automated skin burn detection framework was proposed, integrating transformer-based feature extraction with classical machine learning classification. In this framework, discriminative visual features were extracted using multiple Vision Transformer (ViT) architectures, including ViT-B/16, ViT-L/16, ViT-B/32, and DINOv2 (a self-supervised Vision Transformer model). The extracted features were subsequently fused. Given the resulting high-dimensional feature space, dimensionality reduction was performed using the Chi-square (Chi$^2$) algorithm, through which 500 features were retained, reducing computational complexity and mitigating the risk of model overfitting. The reduced feature set was then employed for burn classification using six classifiers. Model effectiveness was assessed using accuracy, precision, sensitivity, and F1-score metrics. Experimental results demonstrated that the Support Vector Machine (SVM) classifier achieved the highest classification performance, yielding an accuracy of 82.29%. Comparable yet slightly lower accuracies were observed for the Light Gradient Boosting Machine (LGBM) (80.51%) and Extreme Gradient Boosting (XGBoost) (80.17%) classifiers. Overall, the proposed hybrid model consistently outperformed baseline models, highlighting its superior discriminative capability. These findings indicate that the proposed framework holds strong potential for integration into clinical decision support systems, offering a reliable and objective tool for automated skin burn detection.

Facial expression recognition (FER) remains a challenging problem in computer vision owing to subtle inter-class visual differences, substantial intra-class variability, and severe class imbalance in commonly adopted benchmark datasets. In this study, a statistically rigorous comparative evaluation of two pretrained Convolutional Neural Network (CNN) architectures, MobileNetV2 and EfficientNet-B0, was conducted using the FER2013 dataset. To ensure methodological fairness and reproducibility, both architectures were fine-tuned and evaluated under strictly identical experimental conditions. Model performance was systematically assessed using overall classification accuracy and macro-averaged precision, recall, and F1-score to account for class imbalance, complemented by confusion matrix analysis and multi-class receiver operating characteristic area under the curve (ROC–AUC) evaluation. Beyond conventional performance reporting, the reliability and robustness of the observed differences were examined through McNemar’s test and paired bootstrap confidence intervals (CIs). The experimental results demonstrate that EfficientNet-B0 consistently outperforms MobileNetV2 across all evaluation criteria. Statistical analysis confirms that the observed performance gains are significant at the 5% significance level. These findings provide empirically grounded evidence for informed model selection in FER tasks and highlight the importance of integrating statistical validation into comparative deep learning studies. The results further suggest that EfficientNet-B0 offers a favorable balance between recognition accuracy and computational efficiency, making it a compelling candidate for real-world FER applications, including human–computer interaction, affect-aware systems, and assistive computing environments.

This study introduces a grammar-based, chaotic-oriented programming language, termed ChaosL, to address persistent numerical precision and reproducibility challenges in the computational analysis of chaotic systems. The language, along with its compiler and parser, is designed end-to-end with consideration of chaotic maps. Numerical accuracy is systematically managed through grammar-level precision specification and automated error monitoring mechanisms, enabling exact control over floating-point representations, including single precision, double precision, and arbitrary-precision BigDecimal arithmetic with configurable decimal resolution of up to 100 digits. The proposed grammar natively supports ten widely studied one-dimensional and two-dimensional discrete chaotic maps, which may be composed using newly defined hybrid composition paradigms, namely alternate, blend, cascade, and feedback-driven coupling. To ensure computational reliability, multiple error assessment strategies are integrated, including direct error estimation, shadow computation, and interval arithmetic. In addition, ensemble-based simulation capabilities are incorporated to evaluate trajectory separation and estimate predictability horizons. The automated computation of Lyapunov exponents is embedded at the language level, achieving an accuracy of up to 99.6% while simultaneously enabling code-size reductions of approximately 85–92%. The adaptable architecture of ChaosL establishes a reproducible computational framework for discrete chaos research and facilitates the systematic identification of emergent behaviors in hybrid dynamical systems. Moreover, the design provides a scalable foundation for future extensions toward continuous-time systems, interactive visualization environments, and cloud-based collaborative experimentation, thereby advancing precision-aware computational practices in nonlinear dynamics and chaos theory.