Objective:The study was aimed to assess the incidence and characteristics of drug-related problems (DRPs).Methods:A prospective, observational study was conducted among 133 patients with stroke disease who were aged 18 years or older and admitted to the general medicine ward. During the 6 months study period, the incidence of DRPs was identified using the Pharmaceutical Care Network Europe Foundation classification system, version 6.2.Findings:A total of 133 patients were screened for DRPs. Among them, 120 patients have at least one DRP. A total of 254 DRPs were identified (on average, 2.015 DRPs per each patient case).Conclusion:Increasing the evidence of the incidence of medication-related problems in tertiary care hospitals indicates the need for the establishment of a clinical pharmacist in hospital settings.

With the advancement of technologies intelligent and automated environments are rapidly evolved with deep learning and transfer learning techniques. However, the existing technique exhibits different difficulties due to increases in processing data complexity. This research developed an Artificial Intelligence (AI) framework for e-commerce product classification. The data for analysis is collected from different website sources and images are classified. The proposed AI framework is stated as Autoregressive Co-Variance Matrix and Gabor Filter Ensemble Convolutional Neural Network (ARCM-GF-E-CNN). The ARCM-GF-E-CNN incorporates an auto-regressive Co-variance matrix for the classification of online product images. The collected database is categorized into class based on features of the image. The simulation results expressed that the proposed ARCM-GF-E-CNN exhibits higher accuracy for the validation and testing dataset. Further, the analysis of ARCM-GF-E-CNN with existing technique expressed that the proposed classifier increases accuracy, precision, recall, and F1-score.

Quick Response (QR) code, a trademark for a two-dimensional code, has gained significant popularity in various sectors due to its innovative automatic identification and data detection capabilities in images. This research aims to enhance QR code identification rates by employing an effective pre-processing and detection method to mitigate noise levels in images with complicated backgrounds or uneven illumination. High-speed transformations on image blocks are utilized to improve recognition in these challenging conditions. A Convolutional Neural Network (CNN), a specialized network architecture for deep learning algorithms, is employed for QR image recognition and other pixel-based processing tasks. CNNs simplify the visuals without sacrificing essential information required for accurate predictions. In this paper, we propose an efficient Noise Removal in Quick Response Code Images using Hough Transformation (NRQRCI-HT) combined with CNN for noise reduction and accurate data identification. This method is benchmarked against traditional techniques, demonstrating superior performance levels in both noise removal and data identification accuracy.