Drug pill detection is one of the most important tasks in medication safety. The correct identification of drug based on the visual appearance is a key step towards the improvement of medication safety. Previous studies have aimed to recognise a drug based on the front or back view of the drug under a fixed viewing angle. In cases with multiple drugs and randomly placed drugs, the previous methods have difficulties in detecting and recognising different drugs in practical applications. A convolution neural network‐based detector is proposed in this work to overcome the difficulties and to assist patients in drug identification. The proposed system includes a localisation stage and a classification stage. The enhanced feature pyramid network (EFPN), is proposed for drug localisation, and Inception‐ResNet v2 is used in drug classification. The proposed Drug Pills Image Database contains a collection of 612 categories of drug datasets for deep learning research in the pharmaceutical field. The proposed EFPN achieves over 96% accuracy in the localisation experiment. In the complete system evaluation, the proposed system has obtained the Top‐1, Top‐3, and Top‐5 accuracies of 82.1, 92.4, and 94.7%, respectively.
In this paper, an integrated emotion regulation system (IERS) is proposed based on the regulation process model for happiness improvement. Including extracting the valuable information from user's contents on social network, the IERS analyzes users' emotion variation and semanteme reflecting to the regulation process model and aim to appropriately feedback to users. The feedback sentences are chosen from regulation corpus which is positive and motivated. The proposed IERS works at the word level and the emotional topics is classified by SVM through the corpus collected from Facebook wall, whereas feedback strategy sentences is chosen through Point-Wise Mutual Information (PMI) features. The accuracy result of seven-type emotion recognition can achieve higher than 50%. The pre-and post-experiment results are evaluated by 20 participants in one week of observation, of which the result implies the proposed system can practically improve the happiness.
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