Titlebook: Deep Learning and Big Data for Intelligent Transportation; Enabling Technologie Khaled R. Ahmed,Aboul Ella Hassanien Book 2021 The Editor(s

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Estimating Crowd Size for Public Place Surveillance Using Deep Learningbanks, political rallies, religious events etc. The risk of any abnormal behavior or any security concern arises with the number of people in the crowd. Most of the public places are overcrowded and require crowd count monitoring to avoid any mishappening. It is impossible to appoint personnel at di

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IoT Based Regional Speed Restriction Using Smart Sign Boardss also gets declined while moving at higher pace, which causes judgment mistakes and leads to a crash. Around 30% of road accidents are due to over speeding. There have been various ways to avoid accidents due to over speeding, but none of them can automatically control the speed and customize the r

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Synergy of Internet of Things with Cloud, Artificial Intelligence and Blockchain for Empowering Autoeal world Scenario to intelligent virtual form. The term Internet of Things is coined from two phrases such as Internet and Thing which states that the physical objects such as computing devices acts through the network of connection. This technological ecosystem allows the object or thing to collec

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Athanasios Karapantelakis,Yonghui Guog is the improper management of land resources, which leads to a great shortage in parking space causing chaos in our daily lives. Parking is one of the biggest challenges that we need to tackle in the years to come. Using smart parking, there is proper utilization of the parking space and makes the experience pleasant.

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Enhanced End-to-End System for Autonomous Driving Using Deep Convolutional Networkson, is a facilitated version of an autonomous steering system over just considering a single-blind end-to-end architecture. The benefits of proposing such an algorithm for the makeover of existing cars include reduced costs, increased safety, and increased mobility.

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Deep Learning Technologies to Mitigate Deer-Vehicle Collisionsoses an intelligent deer detection system using computer vision and deep learning techniques. It warns the driver to avoid collision with deer. The generated deer detection model achieves 99.3% mean average precision (mAP@0.5) and 78.4% mAP@0.95 at 30 frames per second on the test dataset.