Rajasthan College of Engineering for Women

Bidirectional Long short-term memory (LSTM) units have recently emerged as a boon in the analysis of time-series data. LSTM are a type of Recurrent neural network. In this study, a new DeepBiLSTMnet architecture is proposed that facilitates detection of cardiac arrhythmia. Using this detected arrhythmia beats an inference engine is designed which predicts the severity of illness. The methodology begins with collection of ECG data from MIT-BIH database. The ECG data are then pre-processed. Then, the proposed DeepBiLSTMnet architecture is designed using a wavelet sequence layer and Bi-LSTM layer followed by classification layer with SoftMax activation. Bi-LSTM layer is designed by sequentially connecting 200 hidden Bi-LSTM units. The model is then trained with different network training parameter configurations. Investigating the obtained results by the process of training, the model with system's best training accuracy is selected and tested with test data. To update the weights and offsets, our model is tested with different optimizers. To develop a prototype NVIDIA Jetson Nano Developer Kit is used. Our proposed model gave a high recognition performance with a model accuracy of 99.59

This study explores the interplay between the glass transition temperature (Tg) and nonlinear optical properties of a novel Polyurethane–Azobenzene–Carbon black (PAC) composite through Spatial Self-Phase Modulation (SSPM). By examining the temperature-dependent formation of SSPM diffraction rings, a strong correlation is established between the material’s thermo-mechanical transition and its optical nonlinearity. Below Tg, the composite exhibits suppressed SSPM activity due to restricted polymer chain mobility in the glassy state. Above Tg, enhanced segmental motion enables local refractive index modulation, leading to pronounced SSPM patterns. This transition is confirmed through both visual ring analysis and piecewise linear modeling, enabling accurate estimation of Tg. Complementary structural and optical characterizations, including XRD, Raman, and UV-Vis absorption spectroscopy, support the amorphous matrix and photoresponsive behavior of the PAC film. Notably, increased UV absorption at 20 °C indicates potential pre-transition softening, contributing to nonlinear effects even below bulk Tg. These results present SSPM as a powerful, non-contact optical tool for probing thermo-mechanical transitions in functional polymer composites and suggest the utility of PAC-based films in optically active sensor platforms.

Diabetic retinopathy is the leading cause of blindness worldwide; it is a consequence of diabetes that affects the retina’s blood vessels. Consequently, correct segmentation of the retinal arteries is essential for accurate diagnosis of such changes in disease progression, which is vital for adequate therapy. A novel approach to segmenting retinal blood vessels is introduced in this research. Starting with the unprocessed retinal picture Utilizing the wavelet transform, a method that incorporates many layers of the threshold approach, to improve samples. New way of brightening the selected vessels is associated with the Wavelet transform, which is effective in representing the multi-scale objects efficiently, and more accurate multilayered thresholding is used to segment the vessels. This approach is specifically aimed at enhancing segmentation of vessels which is exceptionally indispensable for the finding of Diabetic Retinopathy at its beginning phase. To test the proficiency of the proposed technique, different investigations on the openly accessible DRIVE and Gaze information bases. Concerning awareness, particularity, and exactness, that’s what the outcomes show ours outperforms other existing methods. In particular, the proposed technique has been tested and obtained the accuracy of 96

All-optical half subtractor (AHS) is the very essential circuit to perform very-high speed operations in the present electronic world. The paper focuses on implementation of AHS by using photonic crystal (PhC) T-shaped waveguides. This structure works on beam interference pattern, and the output results are simulated using finite-difference time-domain method (FDTD). The suggested structure is of compact size of 27.72 μm2 and good contrast ratio (CR) of 15.79 for difference (Diff) and 11.22 for borrow (Borr) with the bit rate of 90.9 Tbps that can bring out in optical signal processing.

Urban heat islands raise surface temperatures, which has an effect on city dwellers’ health and welfare. Urbanisation-related changes to the land surface, which are especially notable right after sunset, have an impact on radiative forcing. Recently, there has been a surge in the usage of unmanned aerial vehicle (UAV) technology, particularly for precision agriculture and plant phenotyping. This is due to the declining cost and increased accessibility of both UAVs and thermal imaging sensors. This interruption affects subsequent operations like target item categorisation and water quality parameter inversion. The goal of this study was to evaluate the potential for combining machine learning (ML) with unmanned aerial vehicle (UAV)–based imaging technology. In this research, the novel technique in UAV image–based temperature pattern detection of various regions of earth and their sea level rise analysis using image classification by machine learning model. Here, the input is collected as UAV images which have been processed for noise removal and normalisation. These image features have been extracted using convolutional active contour–based kernel component analysis and classified using region-based fuzzy stacked lasso elastic-net model. The classified output shows temperature analysis with sea level rise based on image analysis. The experimental analysis has been carried out for various UAV image dataset in terms of detection accuracy, RMSE, F-measure, mean average precision (MAP), and recall. Proposed technique F-measure of 89