An Internet of Things (IoT) enabled intelligent sensor node has been designed and developed for smart city applications. The fabricated sensor nodes count the number of pedestrians, their direction of travel along with some ambient parameters. The Field of View (FoV) of Fresnel lens of commercially available passive infrared (PIR) sensors has been specially tuned to monitor the movements of only humans and no other domestic animals such as dogs, cats etc. The ambient parameters include temperature, humidity, pressure, Carbon di Oxide (CO2) and total volatile organic component (TVOC). The monitored data are uploaded to the Internet server through the Long Range Wide Area Network (LoRaWAN) communication system. An intelligent algorithm has been developed to achieve an accuracy of 95% for the pedestrian count. There are a total of 74 sensor nodes that have been installed around Macquarie University and continued working for the last six months.

Business Processes, i.e., a set of coordinated tasks and activities to achieve a business goal, and their continuous improvements are key to the operation of any organization. In banking, business processes are increasingly dynamic as various technologies have made dynamic processes more prevalent. For example, customer segmentation, i.e., the process of grouping related customers based on common activities and behaviors, could be a data-driven and knowledge-intensive process. In this paper, we present an intelligent data-driven pipeline composed of a set of processing elements to move customers' data from one system to another, transforming the data into the contextualized data and knowledge along the way. The goal is to present a novel intelligent customer segmentation process which automates the feature engineering, i.e., the process of using (banking) domain knowledge to extract features from raw data via data mining techniques, in the banking domain. We adopt a typical scenario for analyzing customer transaction records, to highlight how the presented approach can significantly improve the quality of risk-based customer segmentation in the absence of feature engineering.As result, our proposed method is able to achieve accuracy of %91 compared to classical approaches in terms of detecting, identifying and classifying transaction to the right classification.

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