Automated mosquito species identification is critical for combating vector-borne diseases. We introduce Q-MoFusion, a novel hybrid quantum-classical framework that fuses deep features from pre-trained Audio Spectrogram Transformer (AST) and Whisper models using a Variational Quantum Circuit (VQC). Our approach significantly outperforms individual backbones and prior state-of-the-art benchmarks, demonstrating superior accuracy and robustness, particularly on imbalanced classes. Q-MoFusion demonstrates the potential of hybrid quantum computing to enhance bioacoustic surveillance for addressing critical public health challenges.

Government verification systems are increasingly relying on internet-based platforms, where users authenticate their identities by uploading images captured with ordinary mobile devices. However, the rapid advancements in generative algorithms have enabled the creation of highly realistic forged ID cards that can easily bypass such verification pipelines. These forgeries are not restricted to a single modality; they may target facial imagery, textual content, or both, posing significant challenges to existing detection approaches. We present a framework that analyzes visual features for ID forgery detection by integrating feature fusion with attention mechanisms, leveraging both convolutional neural network (CNN) architectures, such as ResNet-50 and EfficientNet, and transformer-based models, including ViT-16 and Swin Transformer. This study emphasises the significance of feature fusion and attention-driven representation learning in developing robust and trustworthy ID forgery detection systems for real-world deployment.

Gestures serve as a powerful medium of nonverbal communication, facilitating the transmission of a wide range of feelings. Hand gestures, in particular, represent a type of nonverbal communication utilized in various areas, including communication among deaf individuals, home automation, and medical applications. This makes gesture recognition an essential focus of research and development in computer vision, robotics, and human-computer interaction (HCI). The primary limitations of the existing research are the limited exploration of Indian ethnicity and the gesture captured in nighttime uncontrolled settings. In response, we have curated a large-scale hand gesture recognition dataset comprising signs frequently used in emergency scenarios. A benchmark study using the proposed dataset has been performed using state-of-the-art hand detection and gesture classification networks through in-the-wild evaluation settings.