International Journal of System Assurance Engineering and Management最新文献

In the contemporary era, with the emergence of distributed computing and storage facilities, there has been an increase in the creation of textual data. The invention of the Internet of Things (IoT) and its use cases also led to the creation of big data in textual corpora. At the same time, there are emerging Artificial Intelligence (AI) techniques for processing data in unstructured format. In this context, an important research question is how Natural Language Processing (NLP) and text mining cope with emerging AI techniques. This paper investigates the hypothesis that “NLP and text mining play an increased role in emerging AI techniques.” The investigation uses a dual approach: a literature review and an empirical study. Different aspects of the study, including data science approaches covering AI techniques, are investigated. NLP and text mining are indispensable for meaningful AI outcomes in solving different real-world problems. This paper sheds light on the investigations made and paves the way for exciting future research into utilizing AI along with NLP and text mining. It has covered the research reflecting the dynamics of natural language processing and text mining under emerging artificial intelligence techniques.

Blockchain has gained the attention of scholars and industry practitioners due to its immutability, transparency, and operational features, which can improve overall supply chain efficiency. However, a holistic review of blockchain-based supply chains through the lens of digital trust has remained elusive, making participants reluctant to share information due to growing attacks on digital systems and fraud. Therefore, this study examines digital trust in using blockchain technology for supply chain operations by following a five-stage review process consisting of a systematic literature review protocol. The study performs a bibliometric and morphological analysis of 123 articles published between 2012 and 2023 to explore the current state and provide future research directions to develop safe, secure, reliable, and transparent blockchain-based supply chains. Further, our analysis reveals five characteristics of digital trust: transparency, cybersecurity, data protection, accountability, reliability and provenance, and regulatory compliance, which are essential to ensuring digital trust for supply chain sustainability, four research pathways, and keyword combinations for future research with other industry 4.0 technologies. Although blockchain applications for secure and trusted environments have been recognized, very little attention has been given to the detailed discussion on digitally trusted blockchain-based supply chains. The present study contributes to the literature by synthesizing the available literature on blockchain-based supply chains from the perspective of digital trust, thereby analyzing the current state and providing future opportunities for researchers and practitioners working in industry sectors and developing blockchain-based supply chains.

Simplified swarm optimization (SSO) and particle swarm optimization (PSO) are two types of modern swarm intelligence techniques that are often used for optimization. In order to identify the most effective system RRAP with a cold-standby strategic plan while aiming to exploit the reliability of the organization, the article discusses a PSSO procedure that combines UM of PSO and Simplified swarm optimization, PSSO is especially impressive in comparison with other recently incorporated algorithms into four popular applications, namely a sequences scheme, a complex organization, a series–parallel system, and an airspeed indicator defense system for a turbine, with extensive experiments conducted on the pretty standard and well-known four benchmarks of reliability-redundancy allocation problems. Finally, the experiment findings show that the particle-based simplified swarm optimization can successfully solution to address the reliability-redundancy allocation (RRAP) issues using the cold-standby method and performs well in terms of organization reliability, even though the best platform consistency is not attained in all four benchmarks and experiment is done using python and Google colab.

Twitter has improved in such a way people acquire knowledge or information by making them share their thoughts and opinions on everyday tweets. However, spammers have discovered Twitter to be desirable for spreading spam as a result of its enormous popularity. Twitter spam, in contrast to other types of spam, has recently become a big concern. The enormous number of users and volume of content or information published on Twitter contribute considerably to the rise of spam. To protect users, Twitter and the research team have developed several spam detection systems that employ various machine-learning techniques. According to a new study, existing machine learning-based detection algorithms are unable to detect spam correctly since the features of spam tweets vary over time. The issue is referred to as “Twitter Spam Drift.” In this paper, a semi-supervised learning approach (SSLA) using the YATSI algorithm has been suggested. YATSI is categorized into two steps. An initial prediction model is the first phase. The genuine predictions for unlabeled cases are identified in the second phase by using ML algorithms. To deal with the drift, the study utilizes a live Twitter stream of data acquired using Twitter API. This proposed method uses pre-processed labelled data to learn the structure of unlabeled data that is live-downloaded to distinguish between genuine and fake users. Experiments were conducted on live twitter data using KNN, SVM and NB machine learning classifiers. Among those classifiers SVM is showing the better results, in-terms of accuracy.

With a notable rise in the number of students opting for overseas education, study abroad has developed into a competitive industry over the past few decades. This study aims to identify the essential factors that affect students’ decisions to study abroad at Internalisation. A 32-item questionnaire spanning a range of topics was developed to address students’ goals and motivations for studying abroad. It was scored on a five-point Likert scale. Next, Structural Equation Modelling (SEM) with AMOS is used to test the factors’ validity and reliability. The study’s conclusions will aid in the understanding of the factors influencing students’ decisions and incentives to study abroad by policymakers and educational advisors.

Rotor-to-stator rubbing in rotating machinery, resulting from tight clearances, introduces complex dynamics that can potentially lead to high vibrations and machine failure. Historically, the rubbing models were addressed using cylinder-to-cylinder contacts; however, recent attention has shifted towards examining blade-tip contact in turbines, which affects the systems dynamics and efficiency. This study investigates the impact of the variations in blade number on bladed rotor systems, emphasizing on the types of motion that occur as function of the operational speed in the sub-critical range. A simplified bladed rotor model has been developed, using a Jeffcott rotor with blades represented as damped elastic pendulums. The equations of motion are derived and numerical simulations are performed to explore the system’s behaviour with varying blade numbers (3, 5, 7, and 10) in order to analyse displacements, contact forces and bifurcation diagrams as function of the rotating speed. Results reveal distinct regions: periodic motion (I and III) and chaotic motion (II and IV) appear alternatively in the bifurcation diagram, with the chaotic regions occurring at specific fractions of the natural frequency and the number of blades. The study concludes that chaotic motions are associated with larger displacements and higher contact forces, and the vibrational behaviour becomes less hazardous as the number of blades increases. In addition, the appearance of periodic and chaotic motions occur in the same regions by scaling the rotating speed with the number of blades and natural frequency of the system. From an operational perspective, this dynamic investigation offers valuable insights into the severity of blade rubbing in industrial systems. It can guide the implementation of mitigation solutions to prevent worst-case failure scenarios and help to perform adjustments to either operational or design parameters.