The primary goal of this research study, in the field of information technology (IT), is to improve the security and durability of software. A quantum computing-based security algorithm springs quite a lot of symmetrical approaches and procedures to ensure optimum software retreat. The accurate assessment of software’s durability and security is a dynamic aspect in assessing, administrating, and controlling security for strengthening the features of security. This paper essentially emphasises the demarcation and depiction of quantum computing from a software security perspective. At present, different symmetrical-based cryptography approaches or algorithms are being used to protect different government and non-government sectors, such as banks, healthcare sectors, defense, transport, automobiles, navigators, weather forecasting, etc., to ensure software durability and security. However, many crypto schemes are likely to collapse when a large qubit-based quantum computer is developed. In such a scenario, it is necessary to pay attention to the security alternatives based on quantum computing. Presently, the different factors of software durability are usability, dependability, trustworthiness, and human trust. In this study, we have also classified the durability level in the second stage. The intention of the evaluation of the impact on security over quantum duration is to estimate and assess the security durability of software. In this research investigation, we have followed the symmetrical hybrid technique of fuzzy analytic hierarchy process (FAHP) and fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS). The obtained results, and the method used in this estimation, would make a significant contribution to future research for organising software security and durability (SSD) in the presence of a quantum computer.

Sultan H. Almotiri, Mohd Nadeem, Mohammed A. Al Ghamdi, and Raees Ahmad Khan. International Journal of Fuzzy Logic and Intelligent Systems 2023;23:336-52. https://doi.org/10.5391/IJFIS.2023.23.3.336

The healthcare industry has been put to test the need to manage enormous amounts of data provided by various sources, which are renowned for providing enormous quantities of heterogeneous information. The data are collected and analyzed with different Data Analytic (DA) and machine learning algorithm approaches. Researchers, scientists, and industrialists must manage or select the best approach associated with DA in healthcare. This scientific study is based on decision analysis between the DA factors and alternatives. The information affects the whole system in a rational manner. This information is very important in healthcare sector for appropriate prediction and analysis. The evaluation discusses its benefits and presents an analytic framework. The Fuzzy Analytic Hierarchy Process (Fuzzy AHP) approach is used to address the weight of the factors. The Fuzzy Techniques for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) address the rank of the data analytic alternatives used in healthcare sector. The models used in the article briefly discuss the challenges of DA and approaches to address those challenges. The assorted factors of DA are capture, cleaning, storage, security, stewardship, reporting, visualization, updating, sharing, and querying. The DA alternatives include descriptive, diagnostic, predictive, prescriptive, discovery, regression, cohort and inferential analyses. The most influential factors of the DA and the most suitable approach for the DA are evaluated. The ‘cleaning’ factor has the highest weight, and ‘updating’ is achieved at least by the Fuzzy-AHP approach. The regression approach of data analysis had the highest rank, and the diagnostic analysis had the lowest rank. Decision analyses are necessary for data scientists and medical providers to predict diseases appropriately in the healthcare domain. This analysis also revealed the cost benefits to hospitals.

The exponential growth of Artificial Intelligence (AI) has led to the emergence of cutting edge methods and a plethora of new tools for media editing. The use of these tools has also facilitated the spread of false information, propaganda, and harassment through the creation of sophisticated fake video and audio content, commonly referred to as deepfakes. While existing efforts exist for identifying deepfakes videos, they have received less attention when it comes to social media videos. This paper presents 59-Layer Fake Dense Inception Network (FDINet59), designed to detect deepfakes contents. The dataset generated by Multi Task Cascaded Convolutional Networks (MTCNN) crop for training the system and evaluated its ability to spot deepfakes on datasets. The results show that FDINet59 provides impressive performance in identifying deepfakes material, achieving a maximum accuracy of 70.02% with a loss of 0.688 log units while using the training dataset. The ability of FDINet59 to detect deepfakes content generated by auto encoders and Generative Adversarial Network (GAN), commonly used to create deepfake videos. The results show that FDINet59 is 94.95% accurate with a log loss of 0.205. The proposed model can play an important role in preventing the spread of deceptive deepfake videos on social media. The development of more sophisticated deepfake detection algorithms is crucial to counter the negative impacts of this technology on society.