Background: Medical image segmentation is more complicated and demanding than ordinary image segmentation due to the density of medical pictures. A brain tumour is the most common cause of high mortality. Objectives: Extraction of tumorous cells is particularly difficult due to the differences between tumorous and non-tumorous cells. In ordinary convolutional neural networks, local background information is restricted. As a result, previous deep learning algorithms in medical imaging have struggled to detect anomalies in diverse cells. Methods: As a solution to this challenge, a deep convolutional generative adversarial network for tumour segmentation from brain Magnetic resonance Imaging (MRI) images is proposed. A generator and a discriminator are the two networks that make up the proposed model. This network focuses on tumour localisation, noise-related issues, and social class disparities. Results: Dice Score Coefficient (DSC), Peak Signal to Noise Ratio (PSNR), and Structural Index Similarity (SSIM) are all generally 0.894, 62.084 dB, and 0.88912, respectively. The model’s accuracy has improved to 97 percent, and its loss has reduced to 0.012. Conclusions: Experiments reveal that the proposed approach may successfully segment tumorous and benign tissues. As a result, a novel brain tumour segmentation approach has been created.

Purpose: ChatGPT, a cutting-edge language model, stands as an unparalleled, unmatched conversational ally, showcasing novel versatility and intelligence in its responses. This research delves into the incorporation of ChatGPT, a powerful generative AI tool, into professional communication. This study utilizes the information system success model (ISSM) to examine the role of ChatGPTs in strengthening information quality (IQ), system quality (SQ) and service quality (SEQ) for improving customer usage intention (UI) and satisfaction (SAT). The study also investigates the moderating impact of perceived innovativeness between these relationships.Design/methodology/approach: The research collected data from a sample of 400 customers through an online survey and validated the hypothesized relationships using structural equation modelling (SEM). Process Macros 4.1 in SPSS 22.0 is used to test the moderating role of perceived innovation between IQ, SQ and SEQ and UI and SAT.Findings: The results of SEM analysis indicate that IQ, SQ and SEQ all positively support UI to use ChatGPT for professional communication with SAT. The result also establishes that perceived innovativeness positively moderates the relationship between IQ, SQ and SEQ and UI and SAT.Originality/value: This research study offers novel contributions to the literature and body of knowledge by establishing the moderating role of perceived innovativeness in strengthening the relationship between IQ, SQ and SEQ and UI and SAT. Further, this study also proposes a 2*2 matrix to segment the UI and SAT of ChatGPT users in professional communication with varying degrees of perceived innovativeness.

This review explores the transformative potential of gesture and poses analysis in the realm of sports, aiming toenhance performance and reduce injuries among sportsperson. The integration of advanced technologies, suchas motion capture systems and artificial intelligence, enables a meticulous examination of athletes’ movements,surpassing the limitations of traditional methods. By breaking down complex motions into discrete elements,biomechanical assessments can identify subtle irregularities that may predispose athletes to injuries, offeringa data-driven and objective dimension to injury prevention.Gesture and poses analysis extends its impact to rehabilitation, personalizing programs based on an athlete’sunique movements for faster recovery and reduced re-injury risks. Motion capture technology, featuring highspeedcameras and inertial sensors, provides three-dimensional reconstructions, while artificial intelligenceprocesses vast datasets to develop predictive models for assessing injury risks.While applicable across various sports, ethical considerations surrounding athlete privacy and consent underscorethe importance of responsible technology use. As sports science enters a new era, the review delves into specificapplications, success stories, and potential challenges associated with the integration of gesture and posesanalysis in the pursuit of athletic excellence.

According to the Google I/O 2018 key notes, in future artificial intelligence, which also includes machine learning and deep learning, will mostly evolve in healthcare domain. As there are lots of subdomains which come under the category of healthcare domain, the proposed paper concentrates on one such domain, that is breast cancer and pneumonia. Today, just classifying the diseases is not enough. The system should also be able to classify a particular patient’s disease. Thus, this paper shines the light on the importance of multi spectral classification which means the collection of several monochrome images of the same scene. It can be proved to be an important process in the healthcare areas to know if a patient is suffering from a specific disease or not. The convolutional layer followed by the pooling layer is used for the feature extraction process and for the classification process; fully connected layers followed by the regression layer are used.