Ahana Chanda — accepted AI-conference papers

2026

Q-MoFusion: A Quantum Classifier for Masquito Species Classification (Student Abstract)

Vishesh Kumar, Ahana Chanda, Poulomi Bhattacharya, Akshay Agarwal

AAAI 2026technical

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). O

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2026

WingBeats and Snapshots: Fusing Sound and Vision for Mosquito Monitoring (Student Abstract)

Ahana Chanda, Akshay Agarwal

AAAI 2026technical

Accurate identification of mosquito species is crucial for controlling vector-borne diseases, yet visual or acoustic methods alone are often insufficient. We propose a multimodal deep-learning framework that combines high-resolution images with wingbeat audio using a SwinV2 vision transformer and an

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