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No-code machine learning (ML) tools provide an avenue for individuals who lack advanced ML skills to develop ML applications. Extant literature indicates that by using such tools, individuals can acquire relevant ML skills. However, no explanation has been provided of how the use of no-code ML tools leads to the generation of these skills. Using the theory of technology affordances and constraints, this article undertakes a qualitative evaluation of publicly available no-code ML tools to explain how their usage can lead to the formation of relevant ML skills. Subsequently, the authors show that no-code ML tools generate familiarization affordances, utilization affordances, and administration affordances. Subsequently, they provide a conceptual framework and process model that depicts how these affordances lead to the generating of ML skills.

Accidents related with vulnerable pedestrians around crosswalks are continued so that proactive safety support system is required. Pedestrian detection from frames captured by a camera is a significant and yet challenging task. An autonomous road crossing surveillance system would be ideal for tracking pedestrians who want to cross the road and assist them. A practical solution for aiding pedestrians regularly, a road crossing surveillance with real-time Pedestrian Detection. Since the background subtraction from videos and images is still a persistent problem and difficult to accomplish. A Haar Cascade Classifier with the full-body detection algorithm is used to detect people in real-time captured by a camera. Keywords: Pedestrian crossing, surveillance, road crossing surveillance, surveillance system, autonomous, Haar Cascade Classifier.

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