Traffic Analysis has been a problem that city planners have dealt with for years. Smarter ways are being developed to analyze traffic and streamline the process. Analysis of traffic may account for the number of vehicles in an area per some arbitrary time period and the class of vehicles. People have designed such mechanism for decades now but most of them involve use of sensors to detect the vehicles i.e. a couple of proximity sensors to calculate the direction of the moving vehicle and to keep the vehicle count. Even though over the time these systems have matured and are highly effective, they are not very budget friendly. The problem is such systems require maintenance and periodic calibration. Therefore, this study has purposed a vision based vehicle counting and classification system. The system involves capturing of frames from the video to perform background subtraction in order detect and count the vehicles using Gaussian Mixture Model (GMM) background subtraction then it classifies the vehicles by comparing the contour areas to the assumed values. The substantial contribution of the work is the comparison of two classification methods. Classification has been implemented using Contour Comparison (CC) as well as Bag of Features (BoF) and Support Vector Machine (SVM) method.

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Interconnectivity of smart devices such as mobile technology adoption in healthcare holds humongous impacts. Yet, medical professionals are reluctant to reap potential benefits of technology and the reason behind this phenomenon is ambiguous. This study aims to highlight current critical conditions in public healthcare hospitals in Pakistan, and how IoT will add value in healthcare services effectiveness through mobile computing and also to indicate current concepts that may add value in over-all smart healthcare system. According to available information, study to on AI enabled M-IoT network-based healthcare system specially in developing countries to address healthcare problems are rarely known. This study empirically analyzed the factors that influence IoT based smart healthcare devices adoption in Pakistan. In understand the phenomenon, Partial Least Square Equation Model (PLS SEM) was used to understand the relational influence of performance expectance, effort expectance, and social influence over behavior to use through intention to use the technology supported by Unified Theory of Acceptance Technology (UTAUT) assumptions. The results show that clinicians are reluctant to use technology though the results also reveal that same clinicians have positive influence of performance and effort expectations on their intention to use technology that leads the actual behavior of using the technology. Though, this research is among few to beacon upon urgent focus of public healthcare management in developing countries. Yet, new research is lacking far behind to facilitate methods to opt for M-IoT healthcare devices powered by AI.

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