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

A novel method for life estimation of power transformers using fuzzy logic systems: An intelligent predictive maintenance approach

Power transformers are a fundamental component of the modern power distribution network. The fault-free operation of step-up and step-down transformers is of prime importance to the continuous supply of electrical energy to the consumers. To ensure such efficient operation, power distribution companies carry out routine maintenance of distribution transformers through preplanned schedules. The efficacy of such maintenance depends on a proper understanding of the transformer and its components and efficient prediction of faults in these components. There are several components whose condition can be studied to predict transformer failures and therefore the overall health of a transformer. These include transformer windings, insulations, transformer oil, core insulations, and ferromagnetic cores. This work develops a new, simplified fuzzy logic-based method to predict the health of a transformer by taking into account the state of several individual components. Case studies are used to demonstrate the efficacy of the developed method

Evolving trends in the management of acute appendicitis during COVID-19 waves. The ACIE appy II study

Background: In 2020, ACIE Appy study showed that COVID-19 pandemic heavily affected the management of patients with acute appendicitis (AA) worldwide, with an increased rate of non-operative management (NOM) strategies and a trend toward open surgery due to concern of virus transmission by laparoscopy and controversial recommendations on this issue. The aim of this study was to survey again the same group of surgeons to assess if any difference in management attitudes of AA had occurred in the later stages of the outbreak. Methods: From August 15 to September 30, 2021, an online questionnaire was sent to all 709 participants of the ACIE Appy study. The questionnaire included questions on personal protective equipment (PPE), local policies and screening for SARS-CoV-2 infection, NOM, surgical approach and disease presentations in 2021. The results were compared with the results from the previous study. Results: A total of 476 answers were collected (response rate 67.1%). Screening policies were significatively improved with most patients screened regardless of symptoms (89.5% vs. 37.4%) with PCR and antigenic test as the preferred test (74.1% vs. 26.3%). More patients tested positive before surgery and commercial systems were the preferred ones to filter smoke plumes during laparoscopy. Laparoscopic appendicectomy was the first option in the treatment of AA, with a declined use of NOM. Conclusion: Management of AA has improved in the last waves of pandemic. Increased evidence regarding SARS-COV-2 infection along with a timely healthcare systems response has been translated into tailored attitudes and a better care for patients with AA worldwide

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified

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

A New Hybrid Approach to Forecast Wind Power for Large Scale Wind Turbine Data Using Deep Learning with TensorFlow Framework and Principal Component Analysis

Wind power forecasting plays a vital role in renewable energy production. Accurately forecasting wind energy is a significant challenge due to the uncertain and complex behavior of wind signals. For this purpose, accurate prediction methods are required. This paper presents a new hybrid approach of principal component analysis (PCA) and deep learning to uncover the hidden patterns from wind data and to forecast accurate wind power. PCA is applied to wind data to extract the hidden features from wind data and to identify meaningful information. It is also used to remove high correlation among the values. Further, an optimized deep learning algorithm with a TensorFlow framework is used to accurately forecast wind power from significant features. Finally, the deep learning algorithm is fine-tuned with learning error rate, optimizer function, dropout layer, activation and loss function. The algorithm uses a neural network and intelligent algorithm to predict the wind signals. The proposed idea is applied to three different datasets (hourly, monthly, yearly) gathered from the National Renewable Energy Laboratory (NREL) transforming energy database. The forecasting results show that the proposed research can accurately predict wind power using a span ranging from hours to years. A comparison is made with popular state of the art algorithms and it is demonstrated that the proposed research yields better predictions results

Dengue Presented with Pancytopenia- Call for Physicians to think in different perspective

Being endemic in over one hundred twenty-two countries, at least four billion people are at risk of being infected by the dengue virus (DENV). Dengue fever is caused by the bite of infected arthropods: mosquitoes and ticks [1]. It has four variants (DENV 1-4), and the infection is often presented with acute symptoms like fever, myalgia, arthralgia, eye pain, headache, rash, thrombocytopenia, and leukopenia. In literature, it has been mentioned that only five percentages of cases manifested with severe illness that was characterized by plasma leakage that leads to effusion, respiratory distress, shock, and haemorrhage [2]. Infection with DENV may also leads to a sequel of uncontrolled inflammatory responses and resulted in the development of haemophagocytic lymphohistiocytosis (HLH). Assertion with dengue was first documented in 10-month-old patient in Puerto Rico in 2010 [2]. Hyperinflammatory conditions cause HLH due to over activated immune response. Primary haemophagocytic lymphohistiocytosis (HLH) is a familial disorder while secondary HLH is commonly associated with numerous infections, malignancy, and autoimmune diseases [3]. The diagnosis of HLH is mostly based on clinical and laboratory findings. The clinical features of HLH are prolonged fever and organomegaly; laboratory investigations shows high level of ferritin, triglycerides, low level of fibrinogen and bone marrow shows haemophagocytes [3]. Jha VK ET all presented a case of DENV with symptoms of persistent fever, pancytopenia, and multi-organ failure secondary to HLH, and early treatment with corticosteroid had shown promising results [4]. Complicated DENV has an atypical presentation with pancytopenia associated HLH which is challenging for a physician to look into this rare presentation. If prompt treatment is started such cases of complicated DENV can be saved. It is high time for physicians to think of different approaches when they are dealing with patients with unusual presentation of dengue with anti-dengue antibodies or non-structural protein 1(NS1) antigen positive. An immediate approach to such cases can save patients’ life. Along with treatment of such cases we also should focus on vector control that will be beneficial to break the cycle of DENV and related complication [5]

Image Steganography Based on Chaos Function and Randomize Function

The exchange of data is not limited to personal text information or information about institutions and governments, but includes digital media transferred via the Internet including everything, whether texts, images or videos and audio, or animation. These media need high-security protection and high speed during its transmission from one site to another. In this study, a new method is suggested for hiding a gray-level image within a larger color image based on the proposed steganography map that merged chaotic function and randomize function. The size of the chaos and randomize functions is 16 bytes. Experimental results obtained a successful method based on mean squared error, signal-to-noise ratio, peak signal noise rate, embedding capacity, entropy, and histogram. This method can rapidly hide and extract ciphertext in and from the gray image. The original image and the stego image are difficult to distinguish because the correlation between them is very close to 1, indicating that attackers cannot easily differentiate these images with the naked eye. This condition can successfully hide information on the Internet

The Steganography Based On Chaotic System for Random LSB Positions

The objective of hiding text in an image is hiding text without raising suspicions that the image contains a hidden message or text, which leads to protecting and maintaining text confidentiality. The previous hiding methods have problems in capacity, randomization, and imperceptibility. This paper will be solved some of these problems; we suggested a new method for hiding text in an image. Firstly, encrypting the text by the AES-192 bit algorithm for obtaining a secret message. When the initial key of the AES-192 (bit) algorithm is generated by a chaotic system for randomness purposes, secondly, hiding the secret message is into a gray image for obtaining a stego-image. The hiding step is based on a proposed map that chooses from the last round of key expansion in the AES-192 algorithm. This map represented random positions of LSB in each byte of the gray image. The experimental result of this method proved a successful method based on metric criteria. Also, this method is the very speed for hiding ciphertext in the gray image as well as extracting ciphertext from the gray image. Also, it is very safe because it is difficult for attackers to distinguish between the original image and the stego image therefore the correlation between the original image and the stego- image is very close to 1

Understanding the Experiences Lived by Nurses Caring for Patients with COVID-19: A Hermeneutic Approach

Background: Nursing is highlighted among professions that value caring and is perceived as the profession’s heart and soul because of its critical role in providing and delivering high-quality patient care, especially during this coronavirus disease 2019 (COVID-19) pandemic. However, little is understood about the experiences of the frontline workers in caring for persons diagnosed with COVID-19. This study aimed to explore the experiences of nurses in caring for persons diagnosed with COVID-19 inspired by the four lived worlds of van Manen. Methods: The hermeneutic phenomenology was used in nine nurses working in hospitals of Hail region. This study employed a one-to-one interview approach using the Zoom platform, conducted between June and July 2020. Results: Nine nurses articulated their experiences in caring for patients with COVID-19. Six themes emerged within the four lifeworld such as the feeling of vulnerability to COVID-19, time of uncertainties, price of being a hero, social stigma, holistic care, and sense of belongingness. Conclusions: The feeling of vulnerability to COVID-19 infection, time of uncertainties, price of being a hero, social stigma, and sense of belongingness have been understood in the context of lifeworld existential of van Manen. Issues are articulated directly from those who experienced them. Still, revisiting the existing intervention strategies of the government and institution, including regulating negative emotions, reducing related issues, and improving quality of life, is important