Search

This paper systematically discusses the application and development of federated learning in data privacy protection and data value sharing. With the rapid development of global information technology, especially the explosive growth of data from Internet of Things devices, data security and privacy protection are facing unprecedented challenges. This paper first analyzes the growth trend of global data volume and its importance to next generation technologies such as artificial intelligence technologies such as deep learning. Second, the paper provides an in-depth look at the impact of current data privacy regulations on data flows and value creation, particularly the EU's GDPR and China's Data Security and Personal Information Protection Law. Then, this paper introduces in detail federated learning, as a new distributed machine learning paradigm, which effectively solves the contradiction between existing data sharing and privacy protection by protecting individual data privacy and realizing global model collaborative construction. Finally, this paper discusses the combination of blockchain technology and federated learning, and proposes BeFL architecture as a new secure, decentralized and trusted federated learning system, which is expected to provide a comprehensive solution for large-scale data processing and value creation in multi-party scenarios. The research in this paper not only deepens the understanding of federation learning in theory, but also provides important reference and enlightenment for future research and application in related fields.

This study explores integrating big data and advanced deep learning techniques for enhancing personal credit risk assessment in commercial banks. Traditional methods must be improved in high-dimensional, sparse, and noisy big data environments. Key challenges include data source diversity, variable selection complexity, and methodological differences in modeling. By leveraging deep learning approaches like Stack Denoising Autoencoder Neural Networks (SDAE-NN) and addressing imbalanced data using Generative Adversarial Networks (GANs), this research aims to develop robust frameworks that improve the accuracy and efficiency of credit risk evaluation.

This article introduces the importance and challenges of detecting anomalies in high-frequency volatility data in financial markets. Traditional methods such as SV and GARCH models have been unable to cope with the rapidly changing and increasing complexity of the market environment, so new strategies must be developed to identify abnormal fluctuations quickly. This paper proposes a method based on local linear mapping (LLM), which aims to improve anomaly detection accuracy, monitor market fluctuations in real-time, and identify potential risk events, enhancing investment decisions and promoting financial market stability and sustainable development.

The present research is a comparative study of Jhumpa Lahiri's The Namesake and Chitra Banerjee Divakaruni's Queen of Dreams. This study highlights how cultural identity shapes, maintains, and evolves within diasporic communities. The struggle of the first-generation diaspora in preserving their cultural traditions and values on foreign soil and the dilemma of the second generation in balancing their cultural background with their upbringing forms the central theme of this research. While the first generation creates an 'imaginary homeland’ to cope with their longing for home, the second generation seeks to establish a new identity influenced by their surroundings. The goal of this study is to gain a better understanding of the challenges faced by members of diaspora communities while attempting to reconcile their cultural heritage with newly encountered cultures. A methodology involving in-depth analysis, close examination, and interpretation was used to achieve this goal.

The purpose of the present investigation was to develop a gastro-retentive cilnidipine loaded floating microspheres. The floating microspheres of cilnidipine were prepared by the solvent evaporation method for oral drug delivery using HPMCK4M and ethyl cellulose as polymers. The drug loaded microspheres were evaluated for particle size, drug content, entrapment efficiency and floating time. The in-vitro study was performed in pH 1.2 to determine the amount of drug released. Field emission scanning electron microscopy (SEM) was performed to check the surface morphology of microspheres. The mean particle size of the microspheres was in the range of 226.85 – 339.42µm. The drug content of the floating microspheres was more than 70% and drug entrapment efficiency of microspheres obtained between 64.64 – 83.0%. The in-vitro buoyancy was more than70%. Cumulative % of drug release obtained between 73.12 – 89.68%. Field emission scanning microscopy results showed that the prepared microspheres were smooth and spherical.The study reveals that more the particle size extended the floating time, this methods reveals that the drug content  of the microspheres enhanced as the amount of polymer increased or increased in ethyl cellulose concentration also leads to increase in entrapment efficiency and particle size. Cumulative % drug release decreased as ethyl cellulose concentration increased. Keywords: cilnidipine, microspheres, floating drug delivery system, entrapment efficiency, cumulative drug release, ethyl cellulose, Field emission scanning electron microscopy, polymer, concentration

Research evaluation plays a vital role to find out the strength and weakness of the given field. The current research was based on the Bibliometrics analysis of fourteen private universities of Himachal Pradesh between 2004 and 2019 using the Scopus database. A total of 3923 publications were examined in this study. In this research, we took basic parameters like year wise growth of publications, citation growth by University, year-wise citation growth, most prolific author, most favourite source and most global cited documents. The result showed that journal was the most preferred form of publication in this study. Further, the study reported that 2019 was the most productive year because there were 629 publications published in that year. In this study, we found that the most preferred journal for publication was “Journal of High Energy Physics” and the most prolific author was “Singh, G.” with 158. The highly cited paper found in this study was “Surface modification of inorganic nanoparticles for development of organic-inorganic nanocomposites - A review by Kango S., with 1102 citations”.