Automated Shopping Trolley Using Raspberry Pi B+ Model

Shopping center is where individuals get their everyday necessities. There has been a rising interest for speedy and simple installment of bills in shopping centers. Acquiring and shopping at uber shopping centers is ending up day by day movement in different metro-urban communities. There is enormous surge at these shopping centers on siestas and ends of the week. The surge is considerably more when there are exceptional offers and limits arrived. Individuals buy distinctive things and place them in trolley. In the wake of gathering every one of the items in trolley, one needs to go to charging counter for charging, tallying and further installment process for obtaining those items. To beat these issues, we have planned a shrewd trolley. As indicated by the writing study, existing shopping framework causes an immense surge at super shopping centers. In shopping center for obtaining assortment of things it requires trolley. Every time customer has to pull the trolley from rack to rack for collecting items and at the same time customer has to do calculation of those items and manually have to compare the total price with their budget. This process becomes hectic to the one who is in rush[4]. After this, customer has to wait in queue for billing at counter processed by the staff member present at that counter where firstly, the scanning of all the products in trolley needs to be done then final billing and payment has been occurred. Here, if the customer wants to remove any item from final bill then manually removal of that item has been done and then again final billing done by the staff member present at that counter. The information displayed on LCD includes the list of selected product, their prizes, and total prize. The keypad is introduced to confirmation of selected list as well as removal of products in such cases. If customer wants to remove any product from the list of scanned items, then by selecting that particular item customer can easily remove them. After the confirmation of selected items the final bill is being sent to the server of the mall as well as on customer’s mobile number. Then the payment process is done at cashiers’ counter. Thus, automated shopping trolley help to save time required for scanning and counting process and avoids rush at counter

Cloud Services for Patient Cohort Identification Using the Informatics for Integrating Biology and the Bedside Platform

Despite the widespread use of the “Informatics for Integrating Biology and the Bedside” (i2b2) platform, there are substantial challenges for loading electronic health records (EHR) into i2b2 and for querying i2b2. We have previously presented a simplified framework for semantic abstraction of EHR records into i2b2. Building on our previous work, we have created a proof-of-concept implementation of cloud services on an i2b2 data store for cohort identification. Specifically, we have implemented a graphical user interface (GUI) that declares the key components for data import, transformation, and query of EHR data. The GUI integrates with Azure cloud services to create data pipelines for importing EHR data into i2b2, creation of derived facts, and querying for generating Sankey-like flow diagrams that characterize the patient cohorts. We have evaluated the implementation using the real-world MIMIC-III dataset. We discuss the key features of this implementation and direction for future work, which will advance the efforts of the research community for patient cohort identification