Pooja Sharma

Background and Objective: Transcutaneous electrical nerve stimulation (TENS) for musculoskeletal pain is almost always deliveredopen-loop, with fixed pulse trains that ignore the patient’s evolving physiological state—a primary source of variability inTENS outcomes. Adapting stimulation to state is fundamentally computational: it requires a patient-specific dynamical model ofpain, a way to estimate the latent state from noisy wearable signals, and a model-based rule mapping the estimate to a therapydecision. We develop and evaluate a computational framework addressing these jointly.Methods: The framework comprises (i) a four-state, two-timescale patient-specific digital twin of pain in discrete-time statespace, identified individually for each patient from measured pain, electrodermal, and cortical responses; (ii) a multimodal observationmodel coupling electroencephalographic (EEG) alpha power and galvanic skin response (GSR) to the latent state; (iii) arecursive Kalman filter fusing the cortical and autonomic channels into a drift-corrected pain-state estimate; and (iv) a model-baseddecision layer mapping the estimate to a stimulation regime with a posterior-derived confidence and an early-warning flag. It wasidentified for a prospectively recorded cohort of N = 71 participants and evaluated in silico under four modes (Fixed, EEG-only,GSR-only, Fusion).Results: The twins reproduced the measured Pre/During/Post observables within 8% and the per-patient endpoint at r = 0.93(95% CI 0.90–0.96; p < 0.001; mean absolute error 0.042). All three estimators reached comparably high regime concordance(94–95%, no significant difference), but only fusion remained dependable under channel loss, never failing catastrophically (RMSE0.038–0.053 across degradation scenarios); its gain over GSR-only was significant (p < 0.001) whereas the ideal-condition gainover EEG-only was not, locating fusion’s value in fault tolerance. Adaptive control significantly raised relaxed-regime occupancy(70%→78%; p < 0.001), and an early-warning rule flagged eventual slow responders within 90 s (AUC 0.81, 95% CI 0.70–0.91).The identified responsiveness explained residual pain (r = −0.87). Over a simulated 30-day horizon it sustained relief and trackedtolerance where fixed therapy lost efficacy.Conclusions: Per-patient twin identification, recursive multimodal state estimation, and model-based decision support form areproducible computational foundation for adaptive neuromodulation, validated in silico against real cohort physiology and generalto other latent-state-guided therapies.

Background: Over recent years, the security of the publicly available audio, video and text data has become a big challenge, i.e., social media communication; hence the author presents an exceedingly secure communication system to hide the data from an attentive adversary. Objective: The study aims to design and analyze a novel algorithm to be implemented for message transmission using multi-image LSB steganography and crucial variable-length cryptography. Encoder, decoder and social media as a channel are the main parts of the study. Methods: Various computer techniques like cryptography and steganography with video handling are used to perform the proposed work. The main objective of this work is to implement the raw video as the cover on the QR code image. This image is to be transmitted as information by enshrouding it in the raw footage. Additionally, encryption of QR code image using VLMKG (Variable Length Mixed key Generation) cryptography enhances the reliability of work. The variablelength Key is generated separately to implement the cryptography. Multi-image LSB steganography is imposed with one-bit replacement in the spatial domain. Wavelet Transform (Daubechies family) is applied for denoising, which enhances the accuracy of the received data. Results: The proposed work provides a secure way to transmit any information in the form of QR code image successfully. It is verified with the help of five QR Code images and three raw video groupings. Conclusion: Implementation of LSB steganography, variable-length key cryptography and MATLAB usage had superbly extracted the image features; calculations and the consequences were observed to be palatable.

Background: Magnetic-Resonance guided Focused Ultrasound (FUS) thalamotomy is a new and less invasive surgical technique for treating Parkinson’s disease (PD). During therapy, the required part of the cerebral (as STN, Internal Globus Pallidus, and Ventral Intermediate Nucleus) is ablated with less possibility of infection and brain hemorrhage as it normally happens in invasive procedures. Introduction: New advancement in the technique enable it for transcranial transportation of US. Now a days, US coupling with MRI confirms the accurate energy transferring and monitoring. So, MRI guided FUS lesioning is discovered for various psychiatrics and brain disorders. Methods: A technical overview of non-invasive MRI-FUS thalamotomy to treat various tremors is described here. Research, review articles, and book chapters are extracted from online resources using related search strings from year 1994-2020. Results: MRgFUS is concluded a non-invasive, satisfactory, and safe technique to reduce the tremor. Conclusion: MRgFUS is comparatively a new method that is being explored as a non-invasive cerebral ablation to solve the problems of movement disorder.

An outbreak of new coronavirus (COVID-19) originated by SARS-CoV has reached 212 countries throughout the world. India is the second-highest populated country, so it is critical to forecasting the confirmed cases and deaths due to pandemic. To fulfil the purpose, three machine learning models Linear Regression, Multilayer Perceptron, and Sequential Minimal Optimization Regression are used. The predictive data of three geographic regions (India, Maharashtra, and Tamil Nadu) are compared with the data considered to be adequate in practice. The analysis concluded that Sequential Minimal Optimization Regression can be adopted for possible pandemic predictions such as COVID-19.