China is the largest producer and consumer of cigarettes in the world. However, counterfeit and substandard cigarettes frequently appear in the market, which not only causes financial losses to the country but also disrupts normal market order and poses serious threats to public health. Traditional methods for authenticity identification of cigarettes are plagued by low efficiency, a high reliance on manual labor, and time-consuming procedures. These limitations render them increasingly ineffective against complex and diverse counterfeiting techniques. In this paper, we propose a method for authenticity identification of cigarettes by integrating YOLOv5 and ResNet34, aiming to improve automation and accuracy. The method operates in two stages. In the first stage, a pre-trained YOLOv5 network rapidly detects and crops cigarette pack images, removing distracting backgrounds to produce cleaner data. In the second stage, a ResNet34 network extracts finegrained features from these processed images to classify their authenticity. We construct a dataset comprising images of genuine and counterfeit cigarettes from 12 mainstream brands to train a ResNet34 model. Experiments demonstrate that our method achieves a classification accuracy of 94.464 % on the test set. This result validates the effectiveness and adaptability of our method in different environments. Furthermore, the method significantly reduces the need for human intervention, providing reliable technical support for both regulatory agencies and consumers.

A novel coronavirus disease (COVID-19) is a pandemic disease has caused 4 million deaths and more than 200 million infections worldwide (as of August 4, 2021). Rapid and accurate diagnosis of COVID-19 infection is critical to controlling the spread of the epidemic. In order to quickly and efficiently detect COVID-19 and reduce the threat of COVID-19 to human survival, we have firstly proposed a detection framework based on reinforcement learning for COVID-19 diagnosis, which constructs a mixed loss function that can integrate the advantages of multiple loss functions. This paper uses the accuracy of the validation set as the reward value, and obtains the initial model for the next epoch by searching the model corresponding to the maximum reward value in each epoch. We also have proposed a prediction framework that integrates multiple detection frameworks using parameter sharing to predict the progression of patients' disease without additional training. This paper also constructed a higher-quality version of the CT image dataset containing 247 cases screened by professional physicians, and obtained more excellent results on this dataset. Meanwhile, we used the other two COVID-19 datasets as external verifications, and still achieved a high accuracy rate without additional training. Finally, the experimental results show that our classification accuracy can reach 98.31%, and the precision, sensitivity, specificity, and AUC (Area Under Curve) are 98.82%, 97.99%, 98.67%, and 0.989, respectively. The accuracy of external verification can reach 93.34% and 91.05%. What's more, the accuracy of our prediction framework is 91.54%. A large number of experiments demonstrate that our proposed method is effective and robust for COVID-19 detection and prediction.