Currently, Chinese commercial banks are facing extremely tremendous pressure, including financial disintermediation, interest rate marketization, and internet finance. Meanwhile, increasing financial consumption demand of customers further intensifies the competition among commercial banks. Hence, it is very important to store, process, manage, and analyze the data to extract knowledge from the customer to predict their investment direction in future. Customer retention and fraud detection are the main information for the bank to predict customer behavior. It may involve the privacy data and sensitive data of the customer. Data security and data protection for the machine learning prediction is necessary before data collection. The research is focused on two parts: the first part is data security of machine learning and second part is machine learning prediction. The result is to prove the data security for the machine learning is important. Using different machining learning analysis tool to enhance the performance and reliability of machine learning applications, the customer behavior prediction accuracy can be enhanced.
Currently, Chinese commercial banks are facing extremely tremendous pressure, including financial disintermediation, interest rate marketization, and internet finance. Meanwhile, increasing financial consumption demand of customers further intensifies the competition among commercial banks. Hence, it is very important to store, process, manage, and analyze the data to extract knowledge from the customer to predict their investment direction in future. Customer retention and fraud detection are the main information for the bank to predict customer behavior. It may involve the privacy data and sensitive data of the customer. Data security and data protection for the machine learning prediction is necessary before data collection. The research is focused on two parts: the first part is data security of machine learning and second part is machine learning prediction. The result is to prove the data security for the machine learning is important. Using different machining learning analysis tool to enhance the performance and reliability of machine learning applications, the customer behavior prediction accuracy can be enhanced.
The acute locked knee is a common presentation of meniscal tears or other intra-articular injuries. However, a popliteus tendon tear, an uncommon cause of acute locked knee, is often overlooked as a possible diagnosis. Here, we present the case of a 29-year-old male who experienced an acute locked knee following a sports injury. An arthroscopic examination revealed an intrasubstance tear in the popliteus tendon and a complete anterior cruciate ligament tear, while the menisci remained intact. Due to the extension lag caused by the popliteus tendon tear, the anterior cruciate ligament reconstruction was postponed. The patient then underwent a course of physiotherapy before the anterior cruciate ligament reconstruction and eventually achieved full knee extension after six weeks. Further surgical intervention was then performed to address the ligament injury. Our case highlights the importance of considering a popliteus tendon tear as a possible cause of an acute locked knee. Proper diagnosis and management are crucial for achieving optimal outcomes for patients with an acute locked knee and concomitant ligamentous injury.
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