Tuberculosis (TB) is still one of the most serious health issues today with a high fatality rate. While attempts are being made to make primary diagnosis more reliable and accessible in places with high tuberculosis rates, Chest X-rays has become a popular source. However, specialist radiologists are required for the screening process, which could be a challenge in developing countries. For early diagnosis of tuberculosis utilizing CXR images, a complete automatic system of tuberculosis detection can decrease the need for trained staff. Various deep learning and machine learning technologies have been introduced in recent years for examining digital chest radiographs for TB-related variances with the goal of reducing inter-class reader variability and reproducibility, as well as providing radiologic services in areas where radiologists are not available. Tuberculosis is sometimes misclassified as other conditions with similar radiographic patterns as a result of CXR images, resulting in inefficient therapy. The current approach, however, is limited to Computer-Aided Detection (CAD), which has only been evaluated with non-deep learning models. Deep neural networks open potentially new avenues for tuberculosis treatment. There are no peer-reviewed studies comparing the effectiveness of various deep learning systems in detecting TB anomalies, and none compare multiple deep learning systems with human readers. In this paper, the aim of the proposed method is to develop an efficient tuberculosis detection system based on stochastic learning with artificial neural network (ANN) model by random variations using Chest X-ray images. This approach can able to incorporate random functions into the network, either by assigning stochastic transfer functions to the network or by assigning stochastic weights to the network. This proposed method is to learn features from CXR images and optimize the parameters of an ANN model by randomly mixing the training dataset before each iteration, resulting in varied ordering of model parameter updates. Furthermore, in a neural network, model weights are frequently initialized at a random beginning point. By focusing on randomness functions with optimization, the proposed technique achieved great accuracy. The motivation of the proposed method is to detect abnormalities in CXR with the different levels of complexity of TB by strong or weak evidence with different deep geometric contexts such as shape, size, cavitation, and density. ANN's primary benefit is extracting hidden linear and non-linear relationships of high-dimensional and complex data. The proposed method was thoroughly tested with the Shenzhen and Montgomery datasets using metrics such as sensitivity, specificity, and accuracy, and it was discovered that the proposed method attained better accuracy when compared to state-of-the-art methods. The proposed method shows an improved efficiency with sensitivity of 96.12%, specificity of 98.01%, accuracy 98.45% and F-Score 95.88% respectively.

The present generation of youth begins the day with the Facebook or other social website. Hundreds of millions of people all over the world make use of social websites, Internet portals, blogs, Wikis, etc. These sites such as MySpace, Facebook and YouTube have the essential features and equipped with the necessary computing facilities to keep gigantic online communities get going with secure manner. Due to rapid growth of networking, use of social networking sites in day to day life, data sharing, computer security has made a vital part of computer research & development. For maintaining the security in various applications like Ecommerce Online goods and services, Banking, Marketplace services Advertising, Auctions, Comparison shopping, Mobile commerce Payment, Ticketing, An electronic payment system (EPS),Online insurance policy management, we have to use high secured operating systems. In this regard a number of extremely secure operating systems i.e. Trusted Operating Systems like SELinux, Argus, Trusted Solaris, Virtual Vault have been developed by companies such as Argus-Systems Group, Hewlett-Packard, and Sun Microsystems to handle the increasing need of security. Normally, due to high security reason these operating systems are being used in defense. But still these secure operating systems have limited scope in commercial sector and are not popular in corporate due to lower performance; actually this security will come at a cost. In this paper we will propose SPF Model to maintain the balance between security and performance for these operating systems. This SPF model of TOS can be implement for various applications. For implementation of these SPF based trusted operating system we propose object oriented based Code generation i.e. forward engineering i.e. process of generating source code from one or more OO Rational Rose model for web application like social networking. In this research paper we will discuss the issues and UML-based software development solutions for SPF to manage the security, performance and modeling for Social networking sites.As the architecture in Figure 1.b shows, the additional security checks in the kernel will cause Trusted Operating Systems to be slower than traditional operating systems [5]. If we implement the same trusted operating system for web applications like social networking sites, e-commerce sites, job portals, then we get lower performance but more security User Applications Middleware Read(),Write(), Send(),Receive(), Share (), Post (), etc. Kernel Security Checks in ordinary Operating Systems Rest of the Kernel Hardware Part (a) User Applications Middleware Read(),Write(), Send(),Receive(), Share (), Post (), etc.

The adoption of sustainable electronic healthcare infrastructure has revolutionized healthcare services and ensured that E-health technology caters efficiently and promptly to the needs of the stakeholders associated with healthcare. Despite the phenomenal advancement in the present healthcare services, the major obstacle that mars the success of E-health is the issue of ensuring the confidentiality and privacy of the patients' data. A thorough scan of several research studies reveals that healthcare data continues to be the most sought after entity by cyber invaders. Various approaches and methods have been practiced by researchers to secure healthcare digital services. However, there are very few from the Machine learning (ML) domain even though the technique has the proactive ability to detect suspicious accesses against Electronic Health Records (EHRs). The main aim of this work is to conduct a systematic analysis of the existing research studies that address healthcare data confidentiality issues through ML approaches. B.A. Kitchenham guidelines have been practiced as a manual to conduct this work. Seven well-known digital libraries namely IEEE Xplore, Science Direct, Springer Link, ACM Digital Library, Willey Online Library, PubMed (Medical and Bio-Science), and MDPI have been included to perform an exhaustive search for the existing pertinent studies. Results of this study depict that machine learning provides a more robust security mechanism for sustainable management of the EHR systems in a proactive fashion, yet the specified area has not been fully explored by the researchers. K-nearest neighbor algorithm and KNIEM implementation tools are mostly used to conduct experiments on EHR systems' log data. Accuracy and performance measure of practiced techniques are not sufficiently outlined in the primary studies. This research endeavour depicts that there is a need to analyze the dynamic digital healthcare environment more comprehensively. Greater accuracy and effective implementation of ML-based models are the need of the day for ensuring the confidentiality of EHRs in a proactive fashion.

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In this paper we will propose model driven software development and Security Performance Framework (SPF) Model to maintain the balance between security and performance for web applications. We propose that all security in a Trusted Operating System is not necessary. Some non-essential security checks can be skipped to increase system performance. These non essential security checks can be identified in any web application. For implementation of this Security Performance framework based trusted operating system, we propose object oriented based Code generation through forward engineering. This involves generating source code of web application from one or more Object oriented Rational Rose model. The novel integration of security engineering with model-driven software expansion approach has varied advantages. To maintain security in various applications like Ecommerce, Banking, Marketplace services, Advertising, Auctions, Comparison shopping, Mobile commerce Payment, Ticketing, Online insurance policy management, we have to use high secured operating systems. In this regard a number of trusted operating systems like Argus, Trusted Solaris, and Virtual Vault have been developed by various companies to handle the increasing need of security. Due to high security reason these operating systems are being used in defense. But still these secure operating systems have limited scope in commercial sector due to lower performance; actually this security will come at a cost. This paper analyzes UML-based software development solutions for SPF to manage the security, performance and modeling for web applications.