The trend of personalized medicine and the increasing flexibility of drug dosage relevant goals of the 21st century represent the foundation for the current research. To obtain doses smaller than the smallest available, physicians frequently write prescriptions for children and adults, without preserving the integrity of the pill. Moreover, patients purchase large amounts of medication for cost-saving reasons. To support the correct administration of the remedies and the partial alignment to the personalized treatment trend, this paper proposes a flexible and user-friendly solution for determining the medication quantity given to patients, using augmented reality and optical character recognition algorithm capabilities. Via the MATLAB development environment and a Logitech HD Pro C920 webcam, the results were 80% correct in identifying the cutting position of the pill, by means of the Hough transform, and 30% correct in weight recognition exploitation using an optical character recognition (OCR) algorithm. In future work, a higher resolution camera and a more powerful computer can be used to increase the percentages mentioned above. In addition, a 3D scan of the pill fragmentation, combined with the weight recognition functionality, could increase the accuracy of the splitting procedure, conducted by the patient or the patient caretaker.
Statistical Techniques and Artificial Intelligence are becoming much more a necessity in a fastened world rather than just a theoretical use case. In order to satisfy this need, the optimization process starts with data collecting and cleaning. The aim of this paper is to provide a short overview of the outlier detection methods and to explain the need for data cleaning in the field of energy consumption by analyzing the energetic profile data from the Technical University of Cluj-Napoca's swimming complex. In the first and second parts of the article, a short overview of cleaning methods are presented. The third part compares the efficiency of the proposed methods. Finally, but not least the fourth part of the article is dedicated to conclusions and future work.
In the past years both companies and academic's communities pulled their efforts in generating input that consist in new abstractions, interfaces, approaches for scalability and crowdsourcing techniques. Quantitative and qualitative methods were created with the scope of error reduction and were covered in multiple surveys and overviews in order to cope with outlier detection. The aim of tis paper is to provide an outlier analysis over the consumption data of twelve public buildings from the Technical University of Cluj-Napoca, collected during an EU 2020 Horizon project.
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