This paper introduces a non-invasive method for monitoring the respiratory patterns of the patients and the specifications of apnea monitor hardware. The microcontroller based apnea monitor consists of a sensor system interfaced with a microcontroller to detect the apnea from the heat changes in the oro-nazal air flow and simultaneously measure the brain tissue oxygen level by Near Infrared spectroscopy(NIRS).Near-infrared spectroscopy (NIRS) has the potential to noninvasively monitor brain tissue oxygen saturation (SO2), and changes in concentration of oxyhemoglobin [O2Hb], deoxyhemoglobin [HHb] and total haemoglobin [tHb] with real-time resolution. We hypothesized that brain tissue oxygenation would be worse during sleep in OSA relative to controls and sought to determine the practical use of NIRS in the sleep laboratory.

Brain tumor detection in the initial stage is becoming an intricate task for clinicians worldwide. The diagnosis of brain tumor patients is rigorous in the later stages, which is a serious concern. Although there are related pragmatic clinical tools and multiple models based on machine learning (ML) for the effective diagnosis of patients, these models still provide less accuracy and take immense time for patient screening during the diagnosis process. Hence, there is still a need to develop a more precise model for more accurate screening of patients to detect brain tumors in the beginning stages and aid clinicians in diagnosis, making the brain tumor assessment more reliable. In this research, a performance analysis of the impact of different generative adversarial networks (GAN) on the early detection of brain tumors is presented. Based on it, a novel hybrid enhanced predictive convolution neural network (CNN) model using a hybrid GAN ensemble is proposed. Brain tumor image data is augmented using a GAN ensemble, which is fed for classification using a hybrid modulated CNN technique. The outcome is generated through a soft voting approach where the final prediction is based on the GAN, which computes the highest value for different performance metrics. This analysis demonstrated that evaluation with a progressive-growing generative adversarial network (PGGAN) architecture produced the best result. In the analysis, PGGAN outperformed others, computing the accuracy, precision, recall, F1-score, and negative predictive value (NPV) to be 98.85, 98.45%, 97.2%, 98.11%, and 98.09%, respectively. Additionally, a very low latency of 3.4 s is determined with PGGAN. The PGGAN model enhanced the overall performance of the identification of brain cell tissues in real time. Therefore, it may be inferred to suggest that brain tumor detection in patients using PGGAN augmentation with the proposed modulated CNN technique generates the optimum performance using the soft voting approach.

One of the most important and challenging goal of current and future communication network is transmission of high quality images from sender to receiver side quickly with least error where limitation of bandwidth is a prime problem. Here we will discuss a new approach towards compressing and decompressing with perfect accuracy for its suitable transmission and reception. This technology is also helpful in Server and Client models used in industries where a large number of clients work over a single Server. Hence to minimize the load during transmission of a volumetric image/video this process can be implemented.