Empirical Evaluation of Pre-Trained Deep Learning Networks for Pneumonia Detection

Pneumonia is a significant global health issue, characterized by a substantial mortality risk, impacting a vast number of individuals on a global scale. The quick and precise identification of pneumonia is crucial for the optimal treatment and management of this condition. This research work aims to answer the pressing need for precise diagnostic methods by using two advanced deep learning models, namely VGG19 and ResNet50, for the purpose of pneumonia detection in chest X-ray pictures. Furthermore, the present area of research is on the use of deep learning methodologies in the domain of medical image analysis, namely in the identification of pneumonia cases via the examination of chest X-ray images. The study challenge pertains to the pressing need for accurate and automated pneumonia diagnosis to assist healthcare professionals in making timely and educated judgements. The VGG19 and ResNet50 models were trained and assessed using the comprehensive RSNA Pneumonia dataset. In order to enhance their performance in the classification of chest X-ray pictures as either normal or pneumonia-affected, the models underwent rigorous training and meticulous fine-tuning. Based on the results obtained from our investigation, it was seen that the VGG19 model exhibited a notable accuracy rate of 93\%, surpassing the ResNet50 model's accuracy of 84\%. Furthermore, it is worth noting that both models demonstrated a notable level of precision, recall, and f1-scores in the identification of normal and pneumonia patients. This indicates their potential for accurately classifying such instances. Furthermore, our research findings indicate that deep learning models, namely VGG19, have a high level of efficacy in reliably detecting pneumonia via the analysis of chest X-ray pictures. These models has the capacity to function as helpful tools for expediting and ensuring the precise identification of pneumonia by healthcare practitioners

Virtual Machine’s Network Security

Network virtualization has become progressively unmistakable lately. It enables the creation of organizational frameworks that are expressly tailored to the requirements of distinctive organizational applications and facilitates the introduction of favorable circumstances for the occurrence and evaluation of new designs and conventions. Despite the extensive materiality of organizational virtualization, the widespread use of communication channels and steering devices raises a number of safety-related issues. To enable their use in real, large-scale settings, virtual organization foundations must be given security. In this paper, we see the details of industry's top practices for virtual organization security. We discuss some of the major risks, the main challenges associated with this type of climate, as well as the arrangements suggested in the text that aim to handle various security vantage points. Virtualization is a notable thought having applications in different fields of registering. This strategy takes into consideration the production of numerous virtual stages on a solitary actual framework, taking into consideration the execution of heterogeneous models on a similar equipment. It might likewise be used to streamline the use of actual assets, on the grounds that a manager can progressively make and erase virtual hubs to satisfy fluctuated degrees of need. Virtual Machine’s Network Security is an important topic in today’s world, due to the rapid increase in the use of virtual machines. Virtual machines provide a more efficient, cost effective and secure way of running applications and services. However, there are some security risks associated with virtual machines which must be tackled to ensure the safety and security of the network. This paper presents security principal known as Nonrepudiation which authenticates the delivery of messages and transaction using Digital Signature method. Furthermore, an overview of the security threats and solutions associated with virtual machines and their networks, including the different types of threats, solutions and best practices to protect against them. Additionally, the paper discusses the importance of monitoring and logging in virtual machines. Finally, the paper concludes with a few recommendations for countermeasure the security of virtual machines and their networks

Reactive oxygen species (ROS) in cancer pathogenesis and therapy: An update on the role of ROS in anticancer action of benzophenanthridine alkaloids

Reactive oxygen species play crucial role in biological homeostasis and pathogenesis of human diseases including cancer. In this line, now it has become evident that ROS level/concentration is a major factor in the growth, progression and stemness of cancer cells. Moreover, cancer cells maintain a delicate balance between ROS and antioxidants to promote pathogenesis and clinical challenges via targeting a battery of signaling pathways converging to cancer hallmarks. Recent findings also entail the therapeutic importance of ROS for the better clinical outcomes in cancer patients as they induce apoptosis and autophagy. Moreover, poor clinical outcomes associated with cancer therapies are the major challenge and use of natural products have been vital in attenuation of these challenges due to their multitargeting potential with less adverse effects. In fact, most available drugs are derived from natural resources, either directly or indirectly and available evidence show the clinical importance of natural products in the management of various diseases, including cancer. ROS play a critical role in the anticancer actions of natural products, particularly phytochemicals. Benzophenanthridine alkaloids of the benzyl isoquinoline family of alkaloids, such as sanguinarine, possess several pharmacological properties and are thus being studied for the treatment of different human diseases, including cancer. In this article, we review recent findings, on how benzophenanthridine alkaloid-induced ROS play a critical role in the attenuation of pathological changes and stemness features associated with human cancers. In addition, we highlight the role of ROS in benzophenanthridine alkaloid-mediated activation of the signaling pathway associated with cancer cell apoptosis and autophagy