Research on Supply Chain Financial Risk Prevention Based on Machine Learning.

Artificial intelligence (AI) proves decisive in today's rapidly developing society and is a motive force for the evolution of financial technology. As a subdivision of artificial intelligence research, machine learning (ML) algorithm is extensively used in all aspects of the daily operation and development of the supply chain. Using data mining, deductive reasoning, and other characteristics of machine learning algorithms can effectively help decision-makers of enterprises to make more scientific and reasonable decisions by using the existing financial index data. At present, globalization uncertainties such as COVID-19 are intensifying, and supply chain enterprises are facing bankruptcy risk. In the operation process, practical tools are needed to identify and opportunely respond to the threat in the supply chain operation promptly, predict the probability of business failure of enterprises, and take scientific and feasible measures to prevent a financial crisis in good season. Artificial intelligence decision-making technology can help traditional supply chains to transform into intelligent supply chains, realize smart management, and promote supply chain transformation and upgrading. By applying machine learning algorithms, the supply chain can not only identify potential risks in time and adopt scientific and feasible measures to deal with the crisis but also strengthen the connection and cooperation between different enterprises with the advantage of advanced technology to provide overall operation efficiency. On account of this, the paper puts forward an artificial intelligence-based corporate financial-risk-prevention (FRP) model, which includes four stages: data preprocessing, feature selection, feature classification, and parameter adjustment. Firstly, relevant financial index data are collected, and the quality of the selected data is raised through preprocessing; secondly, the chaotic grasshopper optimization algorithm (CGOA) is used to simulate the behavior of grasshoppers in nature to build a mathematical model, and the selected data sets are selected and optimized for features. Then, the support vector machine (SVM) performs classification processing on the quantitative data with reduced features. Empirical risk is calculated using the hinge loss function, and a regular operation is added to optimize the risk structure. Finally, slime mould algorithm (SMA) can optimize the process to improve the efficiency of SVM, making the algorithm more accurate and effective. In this study, Python is used to simulate the function of the corporate business finance risk prevention model. The experimental results show that the CGOA-SVM-SMA algorithm proposed in this paper achieves good results. After calculation, it is found that the prediction and decision-making capabilities are good and better than other comparative models, which can effectively help supply chain enterprises to prevent financial risks.

Semi-rigid thoracoscopy for undiagnosed exudative pleural effusions: a comparative study.

BACKGROUND: Thoracoscopy is highly sensitive and accurate for detecting pleural effusions. However, most respiratory physicians are not familiar with the use of the more common rigid thoracoscope or the flexible bronchoscope, which is difficult to manipulate within the pleural cavity. The semi-rigid thoracoscope combines the best features of the flexible and rigid instruments. Since the practice with this instrument is limited in China, the diagnostic utility of semi-rigid thoracoscopy (namely medical thoracoscopy) under local anesthesia for undiagnosed exudative pleural effusions was evaluated. METHODS: In 50 patients with undiagnosed pleural effusions who were studied retrospectively, 23 received routine examinations between July 2004 and June 2005 and the rest 27 patients underwent medical thoracoscopy during July 2005 and June 2006. Routine examinations of the pleural effusions involved biochemistry and cytology, sputum cytology, and thoracentesis. The difference in diagnostic sensitivity, costs related to pleural fluid examination and complications were compared directly between the two groups. RESULTS: Medical thoracoscopy revealed tuberculous pleurisy in 6 patients, adenocarcinoma in 7, squamous-cell carcinoma in 2, metastatic carcinoma in 3, mesothelioma in 2, non-Hodgkin's lymphoma in 1, and others in 4. Only 2 patients could not get definite diagnoses. Diagnostic efficiency of medical thoracoscopy was 93% (25/27). Only 21% patients were diagnosed after routine examinations, including parapneumonic effusion in 2 patients, lung cancer in 2 and undetermined metastatic malignancy in 1. Twelve patients with tuberculous pleurisy were suspected by routine examination. Costs related to pleural effusion testing showed no difference between the two groups (P=0.114). Twenty-three patients in the routine examination group underwent 97 times of thoracentesis. Two pleural infection patients and 2 pneumothorax patients were identified and received antibiotic treatment and drainage. Medical thoracoscopy could be well tolerated by all the patients. The semi-rigid thoracoscope could be easily controlled by chest physicians. The most common complication was transient chest pain (20 of 27 patients) from the indwelling chest tube, which would be managed with conventional analgesics. One case of subcutaneous emphysema and 2 cases of postoperative fever were self-limiting. No severe complications occurred. CONCLUSIONS: Medical thoracoscopy is a simple, safe, and cost-effective tool, with a high positive rate. Physicians should extend its access to proper patients if the facilities for medical thoracoscopy are available.