(LTCOE or LT), established in 1992, is an engineering college in Navi Mumbai affiliated with the University of Mumbai. It is a part of the University of Mumbai and its degrees are issued by the University. The college is approved by AICTE, New Delhi and is recognized by the government of Maharashtra..
The rapid growth of multimedia technologies has led to massive amounts of video data, thus necessitating effective and fast Video Summarization (VS) methods. Although recent VS methods have shown significant progress, many existing approaches based on frame-wise analysis have performed limited semantic alignment, thus resulting in redundant keyframe selection. To address this limitation, this paper proposes a novel user-centric, query-based video summarization framework. Initially, input video and textual queries are collected and pre-processed. Then, deep visual features extracted from segmented video frames are fused with sentence-transformer-based textual features using Hadamard element-wise multiplication and Multimodal Compact Bilinear (MCB) pooling to compute relevance-aware keyframe rankings and eliminate redundancy. Pre-trained EfficientNet and Residual Network (ResNet18) models are employed to generate customized summaries. Experimental results on the MMSum dataset show that the proposed EfficientNet_b0_MiniLM model achieves 99.41
In this work, graphene nanoplatelets rather than graphite were used as the precursor in a modified Hummer’s method to create graphene oxide (GO) and reduced graphene oxide (rGO). The XRD, FESEM and EDX examination confirms the transformation of graphene into GO and rGO. A notable increase in the intensity ratio of ID/IG from 1.10 to 1.62 was observed, signifying the presences of flaws within graphitic domain. The restoration of graphitic C=C bonds was verified by the FTIR and XPS analysis. Optical characterization of GO and rGO demonstrates a redshift in the absorption spectra, accompanied by a variation in the optical band gap from 3.93 eV (GO) to 3.78 eV (rGO). It has been observed that optimized reaction conditions and proper low annealing temperature changed optical characteristics and removed the hydroxyl (-OH) group, as per the experimental observation. This promoted the densification of the oxide-based network and made it suitable for application in energy storage systems, electronics, optoelectronics and sensors.
Medical data, by its nature, has the wicked problem of class imbalance which affects both model performance and the quality of synthetic data. Traditional oversampling methods typically overlook the internal distribution of the minority class, leading to overrepresentation in dense regions and underrepresentation in sparse ones. This uneven intra-class distribution driven by feature similarity and the presence of both sparse and dense regions can cause models to misclassify minority instances, particularly those located in less populated subspaces. We propose KG-CTGAN, a novel hybrid oversampling framework that integrates K-Means clustering optimized via Glowworm Swarm Optimization with Conditional Tabular Generative Adversarial Network (CTGAN) to address both inter-class and intra-class imbalance in tabular datasets. By improving centroid initialization with GSO, the clustering operation better identifies the internal structure of the minority class, particularly in sparse and borderline areas. This focused clustering enables CTGAN to concentrate synthetic data generation on the regions where it is most required, enhancing class balance and model performance. Evaluated on real-world medical datasets using Random Forest and XGBoost, KG-CTGAN significantly outperforms baseline imbalance data and K-Means SMOTE, achieving up to especially in F1-score with +29
Accurate brain tumor classification from magnetic resonance imaging requires models capable of capturing both structural complexity and subtle radiomic heterogeneity within lesion regions. Handcrafted radiomic approaches offer interpretability but lack hierarchical spatial abstraction, whereas deep convolutional networks, despite strong predictive capability, may overlook complementary statistical descriptors. This study proposes an Adaptive Hybrid Feature Fusion framework that integrates convolutional embeddings and handcrafted radiomic features through an instance-aware dynamic weighting mechanism. Radiomic descriptors derived from Gray-Level Co-occurrence Matrix statistics and histogram-based measures are combined with deep convolutional representations via a learned adaptive coefficient that regulates feature dominance prior to classification. The framework is evaluated on a publicly available multi-class MRI dataset using a standardized validation protocol. Experimental results demonstrate that adaptive fusion achieves 91.42% classification accuracy, outperforming standalone convolutional models by 2.85%, with statistically significant improvement (p = 0.012) and reduced cross-validation variance. Interpretability is incorporated through Grad-CAM to verify spatial alignment between predictive attention and tumor regions. The proposed approach advances hybrid medical image analysis by introducing statistically validated, stability-oriented, and deployment-aware adaptive feature-level integration, providing a reproducible foundation for interpretable and robust neuro-diagnostic intelligence.
