University B.D.T College of Engineering

Sign Language Recognition (SLR) systems have gained significant popularity in recent years. Despite the existence of various Deep Learning (DL) models to classify sign languages, the deployment of these models on resource-constrained devices remains a challenge. Majority of the research on SLR focuses on popular sign languages, such as American Sign Language (ASL), German Sign Language (GSL) etc., while Kannada Sign Language (KSL) remains significantly underexplored. This study focuses on creating a word-level custom dataset of 33 classes consisting of 2319 videos and training three DL models, namely, LSTM, BiLSTM, and Encoder-only Transformer for dynamic KSL recognition. Trained models were then optimized with quantization techniques and converted to .tflite format, for efficient deployment on resource-constrained devices like smartphones. Dynamic Post Training Quantization (PTQ) model, for the BiLSTM model achieved the highest accuracy of 95.71%, followed by the LSTM model with 94.7%, and the Transformer-based architecture achieved 94.19%. Transformer model deployed on smartphone achieved the smallest inference time of 16.2 ms with a model size of 1097.77 KB, followed by LSTM recording 19.6 ms as inference time with a model size of 1254.49 KB and BiLSTM recording the highest inference time of 27.8 ms with a model size of 3862.01 KB.

Hyperspectral images (HSIs) frequently experience various types of noise due to atmospheric interference and sensor instability, which impairs the efficiency of subsequent operations. Consequently, HSI denoising has emerged as a crucial component of HSI preprocessing. Conventional approaches often target a single kind of noise and eliminate it repeatedly, which has disadvantages including inefficiency when handling heterogeneous noise. Lately, models based on deep neural networks have shown encouraging results in the general image denoising domain. This study, which aims to overcome shortcomings in previous techniques, provides a novel denoising methodology by leveraging the effectiveness of the SqueezeNet model. For a thorough assessment, the evaluation framework includes four main indicators: PSNR, SSIM, SAM, and ERGAS. The evaluation is based on real-world hyperspectral images from the [Harvard Hyperspectral Dataset], which cover a variety of scenarios and illumination circumstances. Fire blocks are used by the SqueezeNet-based denoising model to optimize feature extraction with fewer parameters.Benchmarks for comparison include deep learning technique QRNN3D and classical techniques like ITSReg and BM4D.In order to avoid convergence to suboptimal local minima and to speed up and stabilize the learning process, this work presents an incremental training policy. The suggested SqueezeNet-based HSI denoising model performs exceptionally well, attaining competitive results in terms of PSNR of 34.15, SSIM of 0.92, and SAM of 4.56 in addition to impressive ERGAS of 20.47. This study offers an effective denoising solution for hyperspectral images by addressing shortcomings in current techniques, showcasing improvements in efficiency and accuracy.

In this work, we investigate the photophysical and DNA‐binding characteristics of phenoxazine‐based organic dyads comprising donor–acceptor configuration, with the goal of boosting their potential for biointeractive applications. The interaction of these dyes with calf thymus DNA was analyzed using UV spectroscopy, confirming their intercalative binding to DNA base pairs. A straightforward UV spectroscopic approach was developed to elucidate the binding mechanisms between DNA and the dyes. Furthermore, density functional theory (DFT) and time‐dependent DFT calculations provided valuable insights into their electronic structures, optical parameters, and insights of frontier molecular orbitals. Molecular electrostatic potential simulations and mapping helped clarify the electron density distributions that are key for DNA interactions. Finally, molecular docking studies backed up these findings that the structural configuration of nucleic acids significantly influences the binding interactions of push–pull organic dyads, suggesting that these dyes could be promising for use in biological imaging and therapeutic applications.

See the retraction notice EPJ Web of Conferences 359 , 00002 (2026), https://doi.org/10.1051/epjconf/202635900002