ABSTRACT
In today's medical research, breast cancer is a severe problem, so it is imperative to develop a reliable and efficient approach for identifying cancerous breast cells. PCF, with its exceptional sense-making abilities, simplifies and distinguishes that procedure. The research presents a unique structural hybrid PCF for detecting breast cancer cells using sensors based on PCF that are specifically built for the terahertz-frequency range. The improvement in sensor sensitivity and specificity in identifying cancer cells at these frequencies is a notable progress compared to conventional approaches, which could potentially result in earlier and more precise diagnosis. In our analysis, we discovered the most common malignancies in breast cancer. We investigate the features of the cancerous cell detector using the COMSOL-Multiphysics 5.6 software. This PCF detector achieves a Confinement Loss of 4.75 × 10-12 and 3.42 × 10-13 dB/m for Type-1 and Type-2 cancer cells, respectively, at 1.2 THz, as well as about 99.946% and 99.969% relative sensitivity. This sensor ensures the highest level of sensitivity for the identification of cancerous breast cells. This sensor's physical architecture is quite straightforward, making it simple to build using current manufacturing techniques. Therefore, it seems that this sensor will pave a new path for identifying and treating cancerous cells.
ABSTRACT
The study aims to assess the detection performance of a rapid primary screening technique for COVID-19 that is purely based on the cough sound extracted from 2200 clinically validated samples using laboratory molecular testing (1100 COVID-19 negative and 1100 COVID-19 positive). Results and severity of samples based on quantitative RT-PCR (qRT-PCR), cycle threshold, and patient lymphocyte numbers were clinically labeled. Our suggested general methods consist of a tensor based on audio characteristics and deep-artificial neural network classification with deep cough convolutional layers, based on the dilated temporal convolution neural network (DTCN). DTCN has approximately 76% accuracy, 73.12% in TCN, and 72.11% in CNN-LSTM which have been trained at a learning rate of 0.2%, respectively. In our scenario, CNN-LSTM can no longer be employed for COVID-19 predictions, as they would generally offer questionable forecasts. In the previous stage, we discussed the exactness of the total cases of TCN, dilated TCN, and CNN-LSTM models which were truly predicted. Our proposed technique to identify COVID-19 can be considered as a robust and in-demand technique to rapidly detect the infection. We believe it can considerably hinder the COVID-19 pandemic worldwide.
ABSTRACT
A plasmonic material-coated circular-shaped photonic crystal fiber (C-PCF) sensor based on surface plasmon resonance (SPR) is proposed to explore the optical guiding performance of the refractive index (RI) sensing at 1.7-3.7 µm. A twin resonance coupling profile is observed by selectively infiltrating liquid using finite element method (FEM). A nano-ring gold layer with a magnesium fluoride (MgF2) coating and fused silica are used as plasmonic and base material, respectively, that help to achieve maximum sensing performance. RI analytes are highly sensitive to SPR and are injected into the outmost air holes of the cladding. The highest sensitivity of 27,958.49 nm/RIU, birefringence of 3.9 × 10-4, resolution of 3.70094 × 10-5 RIU, and transmittance dip of -34 dB are achieved. The proposed work is a purely numerical simulation with proper optimization. The value of optimization has been referred to with an experimental tolerance value, but at the same time it has been ensured that it is not fabricated and tested. In summary, the explored C-PCF can widely be eligible for RI-based sensing applications for its excellent performance, which makes it a solid candidate for next generation biosensing applications.
