Classification of logical vulnerability based on group attacking method

New advancement in the field of e-commerce software technology has also brought many benefits, at the same time developing process always face different sort of problems from design phase to implement phase. Software faults and defects increases the issues of reliability and security, that’s reason why a solution of this problem is required to fortify these issues. The paper addresses the problem associated with lack of clear component-based web application related classification of logical vulnerabilities through identifying Attack Group Method by categorizing two different types of vulnerabilities in component-based web applications. A new classification scheme of logical group attack method is proposed and developed by using a Posteriori Empirically methodology

Proposing a secure component-based-application logic and system’s integration testing approach

Software engineering moved from traditional methods of software enterprise applications to com-ponent based development for distributed system’s applications. This new era has grown up forlast few years, with component-based methods, for design and rapid development of systems, butfact is that , deployment of all secure software features of technology into practical e-commercedistributed systems are higher rated target for intruders. Although most of research has been con-ducted on web application services that use a large share of the present software, but on the otherside Component Based Software in the middle tier ,which rapidly develops application logic, alsoopen security breaching opportunities .This research paper focus on a burning issue for researchersand scientists ,a weakest link in component based distributed system, logical attacks, that cannotbe detected with any intrusion detection system within the middle tier e-commerce distributed ap-plications. We proposed An Approach of Secure Designing application logic for distributed system,while dealing with logically vulnerability issue

Sufi Method of Treatment & Physical Illness Healing in Hindu Pak Sufis

Every aspect of human experience, including health and illness, has a spiritual component. Spirituality is now recognized as one of the key factors influencing health, and it is no longer just the domain of mysticism and religion. Spirituality has become a focus of neuroscience study in recent years, and it appears to have great promise for improving therapeutic therapies as well as our understanding of psychiatric morbidity. Sufism has been a well-known spiritual movement in Islam, drawing inspiration from major world faiths like Christianity and Hinduism and making a significant contribution to the spiritual health of many people both inside and outside the Muslim world.Sufism began in the early days of Islam and had many notable Sufis, but it wasn’t until the mediaeval era that it rose to its greatest height, culminating in a number of Sufi groups and its leading proponents. The Sufism promotes God as the sole source of genuine existence as well as the cause of all existence, and it seeks communication with God through spiritual realization, with the soul serving as the medium for this communion. It might offer a crucial connection for comprehending the origin of religious experience and how it affects mental health. In this connection author has attempted to address the Sufi of 18 century to 19 century, well-known Sufi Sain baba RA was benefited by haji Ali shah Buskhari

On Discrimination Discovery and Removal in Ranked Data using Causal Graph

Predictive models learned from historical data are widely used to help companies and organizations make decisions. However, they may digitally unfairly treat unwanted groups, raising concerns about fairness and discrimination. In this paper, we study the fairness-aware ranking problem which aims to discover discrimination in ranked datasets and reconstruct the fair ranking. Existing methods in fairness-aware ranking are mainly based on statistical parity that cannot measure the true discriminatory effect since discrimination is causal. On the other hand, existing methods in causal-based anti-discrimination learning focus on classification problems and cannot be directly applied to handle the ranked data. To address these limitations, we propose to map the rank position to a continuous score variable that represents the qualification of the candidates. Then, we build a causal graph that consists of both the discrete profile attributes and the continuous score. The path-specific effect technique is extended to the mixed-variable causal graph to identify both direct and indirect discrimination. The relationship between the path-specific effects for the ranked data and those for the binary decision is theoretically analyzed. Finally, algorithms for discovering and removing discrimination from a ranked dataset are developed. Experiments using the real dataset show the effectiveness of our approaches.Comment: 9 page

Radiomics approach to the detection of prostate cancer using multiparametric MRI:a validation study using prostate-cancer-tissue-mimicking phantoms

Over the last few years, a number of studies have quantified the role of radiomics, dynamic contrast enhancement and standard MRI (T2WI + DWI) in detecting prostate cancer; however, the aim of this paper was to assess the advantage of combining radiomics with other multiparametric magnetic resonance imaging (mpMRI) (T2-DWI-DCE) in improving the detection of prostate cancer. This study used 10 prostate-cancer-tissue-mimicking phantoms to obtain preclinical data. We then focused on 46 patients who underwent mpMRI and Transrectal Ultrasound (TRUS) guided biopsy between September 2016 and December 2017. The texture analysis parameters combined with the mpMRI and compared with the histopathology of TRUS biopsy have been assessed statistically by principal component analysis (PCA) and discriminant component analysis (DCA). The prediction model and goodness-of-fit were examined with the Akaike information criterion (AIC) and McFadden pseudo-R-squared. In the PCA, there was a higher separation between cancerous and noncancerous tissue in the preclinical compared with the clinical data. Both AIC and R2 showed an improvement in the model in cancer prediction by adding the radiomics to mpMRI. The discriminant analysis showed an accuracy of cancer prediction of 81% compared with 100% in the pre-clinical phantom data. Combining radiomics with mpMRI showed an improvement in prostate cancer prediction. The ex vivo experiments validated the findings of this study

Concepts of Safety Critical Systems Unification Approach & Security Assurance Process

The security assurance of computer-based systems that rely on safety and security assurance, such as consistency, durability, efficiency and accessibility, require or need resources. This targets the System-of-Systems (SoS) problems with the exception of difficulties and concerns that apply similarly to subsystem interactions on a single system and system-as-component interactions on a large information system. This research addresses security and information assurance for safety-critical systems, where security and safety are addressed before going to actual implementation/development phase for component-based systems. For this purpose, require a conceptual idea or strategy that deals with the application logic security assurance issues. This may explore the vulnerability in single component or a reuse of specification in existing logic in component-based system. Keeping in view this situation, we have defined seven concepts of security assurance and security assurance design strategy for safety-critical systems

A CREDENCE Trial Substudy

Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.OBJECTIVES: The study compared the performance for detection and grading of coronary stenoses using artificial intelligence-enabled quantitative coronary computed tomography angiography (AI-QCT) analyses to core lab-interpreted coronary computed tomography angiography (CTA), core lab quantitative coronary angiography (QCA), and invasive fractional flow reserve (FFR). BACKGROUND: Clinical reads of coronary CTA, especially by less experienced readers, may result in overestimation of coronary artery disease stenosis severity compared with expert interpretation. AI-based solutions applied to coronary CTA may overcome these limitations. METHODS: Coronary CTA, FFR, and QCA data from 303 stable patients (64 ± 10 years of age, 71% male) from the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic DEtermiNants of Myocardial IsChEmia) trial were retrospectively analyzed using an Food and Drug Administration-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination. RESULTS: Disease prevalence was high, with 32.0%, 35.0%, 21.0%, and 13.0% demonstrating ≥50% stenosis in 0, 1, 2, and 3 coronary vessel territories, respectively. Average AI-QCT analysis time was 10.3 ± 2.7 minutes. AI-QCT evaluation demonstrated per-patient sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 94%, 68%, 81%, 90%, and 84%, respectively, for ≥50% stenosis, and of 94%, 82%, 69%, 97%, and 86%, respectively, for detection of ≥70% stenosis. There was high correlation between stenosis detected on AI-QCT evaluation vs QCA on a per-vessel and per-patient basis (intraclass correlation coefficient = 0.73 and 0.73, respectively; P < 0.001 for both). False positive AI-QCT findings were noted in in 62 of 848 (7.3%) vessels (stenosis of ≥70% by AI-QCT and QCA of <70%); however, 41 (66.1%) of these had an FFR of <0.8. CONCLUSIONS: A novel AI-based evaluation of coronary CTA enables rapid and accurate identification and exclusion of high-grade stenosis and with close agreement to blinded, core lab-interpreted quantitative coronary angiography. (Computed TomogRaphic Evaluation of Atherosclerotic DEtermiNants of Myocardial IsChEmia [CREDENCE]; NCT02173275).proofepub_ahead_of_prin