인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 저자정보
피인용 0
초록·키워드
A novel RPA technique for financial data analysis based on quantum neural network model is innovatively developed. Compared to the conventional model, the Hybrid Quantum Convolutional Neural Network model (HQCNN) reduces the loss value by 0.0056% and improves the training and testing accuracy by 0.93% and 0.36%, respectively. In addition, the hybrid quantum convolutional neural network model of this RPA exhibits less fluctuation in loss value after 60 iterations and remains stable after 90 iterations. Meanwhile, we combine the particle swarm optimization algorithm with HQCNN to transform classical data into quantum state data for joint optimization. The results of the recognition and validation accuracy plot show that PSOHQCNN has the best recognition effect, with a recognition accuracy of 96.85%, followed by HQCNN with a recognition accuracy of 96.20%. And CNN has the lowest recognition accuracy of 95.65%. However, PSO-HQCNN model increases the time cost while improving the accuracy. Therefore, in the process of applying RPA to financial systems, trade-offs should be made according to the actual situation, and the HQCNN model or PSO-HQCNN model should be selected. In conclusion, through the implementation process of RPA robot visualization application established by using the quantum neural network model, this study provides a new direction of exploration and a feasible implementation path for the automated process processing of enterprise financial data analysis.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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