Blockchain technology has evolved as an innovative strategy that substantially transformed industries by offering a variety of applications across multiple domains including manufacturing, construction, supply chain, food, health, energy, transport and retailing industry. By offering a decentralized ledger that enables the source-tracking of materials and goods, blockchain enables a quick, safe, and open information platform. Blockchain is emerging around the globe in industries but limited studies have comprehensively examined its practical implementation toward industrial sustainability and circularity. This paper aimed to provide a comprehensive and thoughtful discussion of blockchain demand, literature gaps in blockchain implementation, type of blockchain platforms in large-scale industries and their contribution toward sustainability by utilizing the systematic literature review methodology. The descriptive analysis of 185 selected articles by thematic analysis provides an overview of the emergence of blockchain over the years and its practical implications in large-scale industries. The novelty of the paper is the in-depth discussion of the major industries of the various sectors that are utilizing different blockchain frameworks, especially P2P, in order to achieve sustainability and circularity in economy. Furthermore, the challenges and barriers that hinder blockchain adoption have also been evaluated.

E-government has developed the intention of achieving smart governance, and adoption of E-government has been recommended to eradicate corruption because it is perceived to be transparent and accountable. The purpose of this study is to examine whether the implementation of E-government in emerging economies is beneficial in eradicating corruption. The findings of this study, which employed a quantitative approach, demonstrated the significant ramifications of e-government in combating corruption. In Bangladesh and Pakistan, the implementation of E-government attempts to enhance behavioral intention by encouraging transparency and accountability in the fight against corruption. The modified version of the TAM model from previous research is proposed in this study. A total of 680 responses were examined using frequency, reliability, correlation, and multiple regression analysis, and Sobel test was employed for mediation analysis. The study finds substantial evidence for the positive role of E-government in terms of corruption reduction, with transparency and accountability also being impacted positively as mediators between E-government and the behavioral intention of users, and behavioral intention mediating the relationship between E-government and corruption reduction. Furthermore, the findings demonstrate that E-government services, transparency, and accountability are significant predictors of corruption reduction. Finally, the paper illuminates E-government’s success in reducing corruption, which can pave the way for future research, and policy implications to government for corruption eradication are recommended.

Cloud computing has revolutionized how industries store, process, and access data. However, the increasing adoption of cloud technology has also raised concerns regarding data security. Machine learning (ML) is a promising technique to enhance cloud computing security. This paper focuses on utilizing ML techniques (Support Vector Machine, XGBoost, and Artificial Neural Networks) to progress cloud computing security in the industry. The selection of 11 important features for the ML study satisfies the study’s objectives. This study identifies gaps in utilizing ML techniques in cloud cyber security. Moreover, this study aims at developing a practical strategy for predicting the employment of machine learning in an Industrial Cloud environment regarding trust and privacy issues. The efficiency of the employed models is assessed by applying validation matrices of precision, accuracy, and recall values, as well as F1 scores, R.O.C. curves, and confusion matrices. The results demonstrated that the X.G.B. model outperformed, in terms of all the matrices, with an accuracy of 97.50%, a precision of 97.60%, a recall value of 97.60%, and an F1 score of 97.50%. This research highlights the potential of ML algorithms in enhancing cloud computing security for industries. It emphasizes the need for continued research and development to create more advanced and efficient security solutions for cloud computing.

Cloud computing has revolutionized how industries store, process, and access data. However, the increasing adoption of cloud technology has also raised concerns regarding data security. Machine learning (ML) is a promising technique to enhance cloud computing security. This paper focuses on utilizing ML techniques (Support Vector Machine, XGBoost, and Artificial Neural Networks) to progress cloud computing security in the industry. The selection of 11 important features for the ML study satisfies the study's objectives. This study focused on identifying gaps in utilizing ML techniques in cloud cyber security. Moreover. this study aims at developing a practical strategy for predicting the employment of machine learning in an Industrial Cloud environment regarding trust and privacy issues. The efficiency of the employed models is assessed by applying validation matrices of Precision, Accuracy, Recall values, F1 score, R.O.C. curves, and Confusion matrix. The results demonstrated that the X.G.B. model outperformed in terms of all the matrices with an Accuracy of 97.50 %, 97.60 % Precision, 97.60 % Recall values, and 97.50 % F1 score. This research highlights the potential of ML algorithms in enhancing cloud computing security for industries. It emphasizes the need for continued research and development to create more advanced and efficient security solutions for cloud computing.