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Background: Out-of-hospital cardiac arrest (OHCA) is a major cause of mortality in developing countries such as India. Most cardiac arrests happen outside the hospital and are associated with poor survival rates due to delay in recognition and in performing early cardiopulmonary resuscitation (CPR). Community CPR training and telephone CPR (T-CPR) in the dispatch centers have been shown to increase bystander CPR rates and survival. Objectives: The aim of this study is to identify the significance of T-CPR in OHCA and to discuss its implementation in the health system to improve OHCA outcomes in India. Materials and Methods: A descriptive research study methodology was adopted following a literature search. Results: The search criterion \"Cardiovascular diseases\" resulted in 162, \"Out-side hospital cardiac arrest\" in 50; For a comprehensive overview, these publications were evaluated looking for data on T-CPR incidence, criteria for detecting OHCA by emergency medical dispatchers, sensitivity and specificity, and BCPR. Conclusion: This current research stresses the scale and seriousness of the implementation of T-CPR in OHCA in India.

Rapid improvements in ultrasound imaging technology have made it much more useful for screening and diagnosing breast problems. Local-speckle-noise destruction in ultrasound breast images may impair image quality and impact observation and diagnosis. It is crucial to remove localized noise from images. In the article, we have used the hybrid deep learning technique to remove local speckle noise from breast ultrasound images. The contrast of ultrasound breast images was first improved using logarithmic and exponential transforms, and then guided filter algorithms were used to enhance the details of the glandular ultrasound breast images. In order to finish the pre-processing of ultrasound breast images and enhance image clarity, spatial high-pass filtering algorithms were used to remove the extreme sharpening. In order to remove local speckle noise without sacrificing the image edges, edge-sensitive terms were eventually added to the Logical-Pool Recurrent Neural Network (LPRNN). The mean square error and false recognition rate both fell below 1.1% at the hundredth training iteration, showing that the LPRNN had been properly trained. Ultrasound images that have had local speckle noise destroyed had signal-to-noise ratios (SNRs) greater than 65 dB, peak SNR ratios larger than 70 dB, edge preservation index values greater than the experimental threshold of 0.48, and quick destruction times. The time required to destroy local speckle noise is low, edge information is preserved, and image features are brought into sharp focus.

To improve the accuracy of tumor identification, it is necessary to develop a reliable automated diagnostic method. In order to precisely categorize brain tumors, researchers developed a variety of segmentation algorithms. Segmentation of brain images is generally recognized as one of the most challenging tasks in medical image processing. In this article, a novel automated detection and classification method was proposed. The proposed approach consisted of many phases, including pre-processing MRI images, segmenting images, extracting features, and classifying images. During the pre-processing portion of an MRI scan, an adaptive filter was utilized to eliminate background noise. For feature extraction, the local-binary grey level co-occurrence matrix (LBGLCM) was used, and for image segmentation, enhanced fuzzy c-means clustering (EFCMC) was used. After extracting the scan features, we used a deep learning model to classify MRI images into two groups: glioma and normal. The classifications were created using a convolutional recurrent neural network (CRNN). The proposed technique improved brain image classification from a defined input dataset. MRI scans from the REMBRANDT dataset, which consisted of 620 testing and 2480 training sets, were used for the research. The data demonstrate that the newly proposed method outperformed its predecessors. The proposed CRNN strategy was compared against BP, U-Net, and ResNet, which are three of the most prevalent classification approaches currently being used. For brain tumor classification, the proposed system outcomes were 98.17% accuracy, 91.34% specificity, and 98.79% sensitivity.

Aim: This paper employed Social Network Analysis approach to visualize and calculate different social network metrics. It identifies key communicators who play pivotal roles in information flow. Methodology: Research was conducted at Jammulapalem and Perali villages of Bapatla district, Andhra Pradesh, India, during 2022-23 using an exploratory research design. A total of 120 farmers Eigen vector were selected using simple random technique, and data was collected using a well-developed interview schedule. Network metrics such as Degree centrality, betweenness centrality, and eigen vector centrality were computed to evaluate the network structure using R software (version 4.3.1). R packages, namely igraph, statnet and network D3, were used for network creation, analysis and visualization to identify the influential nodes. Results: The study revealed a complex web of relationships among various stakeholders within the agricultural network through a network graph, identifying key communicators with the highest Degree centrality. Interpretation: The focal points identified through Social Network Analysis represent a specific demographic and socio-economic group, typically aged between 35 and 55, primarily medium-scale farmers,with landholdings spanning 10 to 25 acres and high annual income, with educational backgrounds ranging from high school to pre-university college, and wield significant influence within their local communities. They require sensitization, training, practical demonstrations, and personalized support to effectively disseminate agricultural information. Key words: Agricultural Information System Network, Centrality measures, Information Sources, Key Communicators, Network visualization, Social Network Analysis

Adders are a crucial component of microprocessors' data channel logic; therefore, their design has been at the forefront of VLSI research for quite some time. While EDA flow helps designers get closer to an optimal adder architecture, it isn't always enough. The design space is huge, which is why this is the case. A machine learning-based strategy was offered in earlier studies as a means to investigate the design space. Weak feature representations and an inefficient two-stage learning loop cause prefix adder structures to underperform. A multi-branch framework that combines a variational graph autoencoder and a neural process (NP) is first demonstrated; this is the graph neural process.