Abstract The paper proposes a cloud based data integrity verification algorithm for accounting informationization systems, leveraging rough set based data mining techniques. With the integration of cloud computing into financial management, data security and integrity have become critical... The paper proposes a cloud based data integrity verification algorithm for accounting informationization systems, leveraging rough set based data mining techniques. With the integration of cloud computing into financial management, data security and integrity have become critical concerns due to risks like data breaches and accidental loss as illustrated by real world incidents involving Huawei and Tencent Cloud. Traditional verification methods, such as HMAC or RSA based Provable Data Possession (PDP), suffer from high computational and communication overheads, especially under dynamic data operations. To address these limitations, the author introduces a parallel verification algorithm that supports multi user batch validation, significantly reducing communication costs and verification time. By employing rough set theory, the system performs efficient feature selection and attribute reduction, enhancing computational efficiency without compromising data integrity. The verification process uses BLS signatures with homomorphic properties, allowing multiple files to be verified at the communication cost of a single file. Experimental results show that the proposed algorithm outperforms conventional single point verification methods, especially as user scale and data volume increase. The system also offers tiered audit levels that balance detection accuracy, verification frequency, and cost. Implemented in a Java/SQL Server environment, the approach demonstrates improved scalability and suitability for accounting cloud environments, offering a practical solution for secure, efficient financial data management in the era of digital transformation. Read More Read Less

Abstract Rural women's entrepreneurship has been rising rapidly. More prominent businesses can be seen. Women entrepreneurs in rural areas have taken up business activities on the basis of many driving forces. As Indian women are in no way... Rural women's entrepreneurship has been rising rapidly. More prominent businesses can be seen. Women entrepreneurs in rural areas have taken up business activities on the basis of many driving forces. As Indian women are in no way inferior to men in all walks of life and they are as good as men in entrepreneurial skills, it is imperative to exploit the potential of Indian women. Women's participation in trade, industry and commerce requires entrepreneurship. Studies have been done to understand the women entrepreneurs in general. Rural entrepreneurship is relatively under researched. The present study is an attempt to understand the triggers and barriers to women entrepreneurs in rural areas, in particular. A sample of 280 respondents from 10 villages in Hassan and Mysore Districts was collected using an interview schedule. Factor analysis and ANOVA were applied to test the hypothesis. The results show that there is no difference in respondent's opinions regarding triggers. In contrast, there is no common opinion, i.e. no common problems for all the types of businesses. Every kind of enterprise has its own problems. We suggest for government financial assistance to rural women and educational support. We also recommend enhancing women's outlooks by fostering a positive attitude. Read More Read Less

Abstract This paper proposes a novel approach to improving personalized recommendations in English resource libraries. Addressing challenges such as information overload, lack of personalization, cold start problems, and algorithmic complexity, the study leverages the K-means clustering algorithman unsupervised... This paper proposes a novel approach to improving personalized recommendations in English resource libraries. Addressing challenges such as information overload, lack of personalization, cold start problems, and algorithmic complexity, the study leverages the K-means clustering algorithman unsupervised machine learning technique to group users and resources based on similarity. By transforming multidimensional resource attributes (e.g., topic, proficiency level, target audience) into onedimensional data via dimensionality reduction, the system improves storage, search, and recommendation efficiency. The model integrates user English proficiency data, processes it via dynamic multimodal modeling and principal component analysis, and clusters users for tailored suggestions. Experimental results demonstrate that the K-means based system outperforms traditional collaborative filtering in recommendation accuracy, although recall rates vary with list size. User activity data reveal consistent personalized recommendation usage (~40% daily peak) and search spikes at 15:00 and 21:00, likely reflecting student and working user learning patterns. The study concludes that the proposed method effectively enhances resource management and user satisfaction, ofering a scalable and efficient solution for digital English libraries. Future work includes refining algorithms to improve accuracy and user experience further. Read More Read Less