Acceptance of any new approach by the organizations depends upon the users’ needs. Currently, reducing the cost and time invested in maintenance is a major challenge for the practitioners. Moreover, symmetrical and optimal maintenance is the need of the hour and it can be achieved by increasing the durability of software. Many attributes of the quality may affect the durability of the software and identification of durability attributes is a crucial task at the early stage of software development. Thus, it is a problem that contains multi-criteria within it. With the help of quantitative estimation, software durability may be assessed symmetrically and increased. In this row, the authors of this article have attempted to posit an effective technique to assess the durability of software. Based on empirical data through research, the presenters of this article suggest that fuzzy-based symmetrical method of Analytic Network Process (ANP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) would be an accurate methodology for assessing the durability of software. For determining the efficacy of this assessment, the researchers took six alternative software of a University. The results, as presented in this paper, would serve as guidelines for the practitioners who aim at achieving the preferred software durability.

The relevance of Industry 5.0 confirms the collaborative relationship between humans and machines through an inclusive automation process. The healthcare industry at present is facilitated by the use of these emerging technologies, which promise a more personalized, patient-centric approach, enabling more prompt, cost-effective, and efficacious medical care to the affected. However, managing enormous data volumes, lack of standards, risks to data security, and regulatory obstacles, such as regulatory compliance, are critical issues that must be addressed to ensure that Industry 5.0 can be effectively integrated into the healthcare industry. This research assumes significance in the stated context as it seeks to reveal the gaps between security risks and threats assessments for personalized healthcare services based on Industry 5.0. The study’s investigations cite that the identification of security risks and various threats is an imperative need and must be prioritized so as to ensure optimal security for the healthcare system. Furthermore, the study peruses various security threats and security risk assessments for enhancing and safeguarding the healthcare industry. Moreover, the study also proposes a framework for security risk assessment based on Industry 5.0 (SRVF^HI5.0) for the healthcare security system. A step-wise procedure is applied to validate the proposed framework and provide support for designing feasible security evaluation criteria and tools for future research. Statistical analysis was performed to evaluate the measure of the applicability of multiple criteria, the tool’s reliability, and factor analysis. This offers an adequate basis for accepting the suggested risk assessment methodology based on Healthcare Industry 5.0 for implementation as well as further research and analysis.

Introduction: The imperative need for ensuring optimal security of healthcare web applications cannot be overstated. Security practitioners are consistently working at improvising on techniques to maximise security along with the longevity of healthcare web applications. In this league, it has been observed that assessment of security risks through soft computing techniques during the development of web application can enhance the security of healthcare web applications to a great extent. Methods: This study proposes the identification of security risks and their assessment during the development of the web application through adaptive neuro-fuzzy inference system (ANFIS). In this article, firstly, the security risk factors involved during healthcare web application development have been identified. Thereafter, these security risks have been evaluated by using the ANFIS technique. This research also proposes a fuzzy regression model. Results: The results have been compared with those of ANFIS, and the ANFIS model is found to be more acceptable for the estimation of security risks during the healthcare web application development. Conclusion: The proposed approach can be applied by the healthcare web application developers and experts to avoid the security risk factors during healthcare web application development for enhancing the healthcare data security.

At a time when deepfake technology is extensively employed, this study explores the serious problems produced by deepfake misinformation and recommends a comprehensive cyber security architecture to lessen its effects. Deepfakes are a serious threat to the authenticity of digital information because they use sophisticated artificial intelligence algorithms that have outlived their original novelty. This study examines the psychological effects of manipulated media, sheds light on the intricacies of creating deepfakes, and examines actual incidents that highlight the pervasive effects of deepfake misinformation. Our suggested cyber security plans include cutting-edge detection algorithms, blockchain technologies and extensive outreach programmes. Through promoting a shared dedication to openness and truth, symbolised by the figurative ‘code of silence’, this study seeks to strengthen the digital environment against the ubiquitous impact of misleading content, thereby adding to the larger conversation about preserving authenticity and trust in an increasingly digital age.