Anuj Kumar

A provocative piece of cinema that bravely tackles uncomfortable modern gender dynamics, even if it stumbles under the weight of its own ideological ambitions and casting choices.

Many dynamical systems, ranging from genetic circuits to the human brain to human social systems, are often characterized as computational. Althoug...

Director Ahmed Khan misses the forest for the trees in this meta-comedy that demands absolute intellectual surrender.

Background Otoscopic image analysis plays a critical role in diagnosing ear pathologies; however, existing artificial intelligence approaches often face challenges related to interpretability, robustness under acquisition variability, and reliable feature integration. Objective This study proposes a reliability-aware multi-feature fusion framework for ear disease classification by integrating complementary visual representations texture, color, and shape derived from a single otoscopic imaging modality. Methods Texture features are extracted using a Vision Transformer (ViT) with confidence-weighted patch enhancement, while color information is captured using CIE Lab histograms and statistical descriptors, and shape features are derived through clinically guided contour analysis following tympanic membrane segmentation. These feature representations are adaptively integrated using a Mamdani fuzzy inference system based on feature branch-specific reliability scores. Performance is evaluated using accuracy, macro-F1 score, AUROC, and negative log-likelihood (NLL), along with class-wise sensitivity and specificity. Robustness is assessed under variations in illumination, color, and blur. Results The proposed framework achieves 97.5% accuracy, 0.96 macro-F1 score, 0.98 AUROC, and 0.12 NLL, outperforming individual feature-based baselines (Texture: 92.0%, Color: 94.2%, Shape: 95.0%) and conventional late averaging (96.0%, 0.94 F1, NLL 0.14). Consistent performance is observed across all disease classes, with sensitivity and specificity values exceeding 0.95. Robustness analysis demonstrates improved performance under illumination (90.8% → 95.0%), hue/saturation shifts (91.2% → 95.5%), and blur (89.0% → 94.0%). Interpretability is enhanced through confidence-weighted patch maps, fused attention overlays, and feature branch attribution. Conclusion The proposed framework demonstrates that reliability-aware multi-feature fusion within a single imaging modality can achieve robust, interpretable, and clinically meaningful ear disease classification, offering a practical alternative to complex multimodal systems.

Together with Bobby Deol and Sharvari, the actor infuses every punch and kick with a wicked sense of personality, but the script runs out of serum.

Director Siddharth Malhotra puts together a puzzle where not all the pieces come together, but it whets the guilty pleasure.

Indra Kumar's gratuitous installment falters because even within its own hyper-exaggerated comic universe, most of the jokes simply flatline.

There is a particular kind of irony in a 130 year old French monogram going to war with a four year old Chinese milk tea chain over a flower. But that is exactly what happened this month, and the fallout has turned into one of the more revealing fashion law stories of the year, not because of what the court decided, but because of how China reacted to it.

Last updated: July 2026 A fashion historian researches, analyzes, and documents the evolution of dress, textiles, and style across different time periods and cultures.

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