Engineering Innovations Vol. 14

Abstract: Cloud computing eliminates the need for expensive hardware and software expenditures by revolutionising access to computing resources through internet-based utility services. However, Data Integrity (DI) in this paradigm faces a variety of challenging issues, including complexity, security, privacy, control limits, fallibility of human beings, and financial limitations. The shortcomings of current DI solutions in terms of guaranteeing data verification, preventing replay attacks, and controlling computational overhead have led to an increasing need for access to cloud infrastructures by third-party verifiers. The suggested Cryptographic Accumulator Provable Data Possession with Merkle Hash Tree (CAPDP-MHT) scheme demonstrates significantly improved performance over Provable Data Possession (PDP) and Rivest Shamir Adleman (RSA) algorithms in various domains, as demonstrated by thorough simulation and MATLAB-based evaluation. In particular, CAPDP-MHT outperforms PDP and RSA with an average data verification success rate of 25%, compared to their respective rates of 10% and 5%. Moreover, it identifies replay attacks in about 30 seconds, compared to 45 and 70 seconds for PDP and RSA, respectively. Furthermore, the computational overhead of CAPDP-MHT is about 27 seconds, while that of PDP and RSA is 45 and 60 seconds, respectively. Therefore, as compared to PDP and RSA-based systems, CAPDP-MHT not only exhibits exceptional computing efficiency but also outperforms in reliability.

Abstract: The aim of this research paper focused on using PRISMA to reveal most artificial intelligence techniques that were used for fingerprint classification. Biometric technology such as fingerprints plays a key role in authenticating and identifying people’s identities. Therefore, with the increasing number of population and the usage of biometrics for authentication, fingerprint classification systems are becoming important and indispensable for recognizing and authenticating individuals. Therefore, Artificial Super-Intelligence (ASI) techniques such as bioinspired algorithm, deep learning and machine learning were used to improve fingerprint classification accuracy. The proposed method aimed to assess fingerprint classification models based on ASI. The researchers employed PRISMA approach, which is based on systematic analysis and is used to select, evaluate and analyze journals. Although IEEEXplore and Web of Science were utilized to extract journal articles from 2019 to 2023. As a result, 1350 articles were found in both databases. Furthermore, a total of 35 publications were assessed to determine their eligibility and 19 articles were eliminated with reasons and 16 matched the requirements for a meta-analysis. Our findings demonstrate and highlight the need for developing a new approach to improve fingerprint classification accuracy.

Abstract: It is possible to address learning challenges in a way that is unique to the needs and preferences of students through adaptive learning environments. Using these platforms, learning content can be personalized to reflect a user's interest, past knowledge, present abilities, and strengths and limitations. Conversely, the efficiency of adaptive learning systems depends on the techniques adopted to classify and present the content according to students’ needs and preferences. Artificial Intelligence (AI) techniques have recently been applied in personalized adaptive education systems to address content delivery-related learning challenges. However, not much is known about content adaptation based on bioinspired optimization algorithms. This study offers a comprehensive evaluation of the literature on personalized adaptive learning Management systems based on bioinspired optimization algorithms. The study examined conference proceedings and journal papers published in Scopus, Web of Science, and IEEE databases between 2013 and 2023. Nevertheless, Web of Science yielded no papers that were connected to this investigation. Web of Science was thus left out of the research. 5442 were screened in total, 303 were evaluated, and 6 were deemed eligible for the systematic review. Our findings suggest that there have been a limited number of research or personalized adaptive learning systems based on bioinspired algorithms.