Jimson Mathew

Abstract Class imbalance and high-dimensional flow features remain important challenges in learning-based intrusion detection. This study presents ADA-GNetIDS, a reproducible pipeline that combines training-only ADASYN oversampling, mutual-information feature selection, a cosine-similarity anchor graph, and a dual-branch graph neural network. Each network-flow record is represented as a node.

Abstract The identification and management of grocery items in retail environments have traditionally relied on barcode-based systems, which require significant human intervention and underutilize existing surveillance infrastructure. Computer vision–based approaches offer a promising alternative for automated product recognition. However, many existing grocery datasets remain relatively homogeneous or limited in scale, geographic diversity, or real-world variability.

Direct current biased optical orthogonal frequency division multiplexing (DCO-OFDM) is widely used in blue light-emitting diode (LED)–based underwater optical wireless communication (UOWC), but the limited LED bandwidth leads to nonlinear distortions at higher frequencies.