Raspberry Pi controlled Traffic Density monitoring system. Raspberry Pi is a single board computer which can be effectively used for multi-functionalities. Here is the one of the ways of using this for multiple purposes. It is used for traffic surveillance purpose where the traffic is continuously monitored and viewed through live streaming. In addition to this, it is used for detecting the traffic density and gives the traffic report to the travelers. Here we are monitoring the traffic ,based upon the density of the vehicles on each side the time period for that side changes automatically, for eg : If the density is low on a particular side the time period for that side is normal, if the density is medium the time period for that side will automatically increases ,if the density is high the time period will automatically increase compared to normal density , after finishing the time period the signal will pass to other junction .Here time period means time given to green light to glow to that particular side The density of vehicles of each side can be identified through using IR sensors Traffic for each side can be monitored by live streaming for this we are using a usb camera interfaced to pi3 , by rotate camera 360 degrees , one step 90 degrees In raspberry pi3 are a great choice for traffic sensing because it is equipped with a variety of sensors such as wi-fi, L293D,IR sensors ,DC gear motor ,Camera and microphone. These sensors can be exploited to collect traffic data. This traffic report is updated periodically and displayed on the screens installed at the public places.

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Interoperable clinical decision support system (CDSS) rules are a pathway to achieving interoperability which is a well-recognized challenge in health information technology. Building an ontology facilitates the creation of interoperable CDSS rules, which can be achieved by identifying the keyphrases (KP) from the existing literature. However, KP identification for labeling the data requires human expertise, consensus, and contextual understanding. This paper aims to present a semi-supervised framework for the CDSS using minimal labeled data based on hierarchical attention over the documents fused with domain adaptation approaches. Then, evaluate the effectiveness of KP identification with this framework. In the view of semi-supervised learning, our methodology toward building this framework outperforms the prior neural architectures by learning with document-level context, no explicit hand-crafted features, knowledge transfer from pre-trained models (on unlabeled corpus), and post-fine-tuning with smaller gold standard-labeled data. To the best of our knowledge, this is the first functional framework for the CDSS sub-domain to identify the KP, which is trained on limited labeled data. It contributes to the general natural language processing (NLP) architectures in areas such as clinical NLP, where manual data labeling is challenging.

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