Читать книгу Cyberphysical Smart Cities Infrastructures. Optimal Operation and Intelligent Decision Making онлайн

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AI definitely pushed all the science a step forward by making the systems and processes of scientific inquiry as smart as possible, for example, autonomous transportation systems [24] in smart mobility. Making a decision about whether the object seen is a human being or not is challenging [45]. Object detection is one of the challenging issues in smart mobility that surely boosts and facilitates automation in transportation systems. Consequently, this positively enhances and improves smart mobility in smart cities. Researchers in [45] explored analysis of decent object detection solutions like DL [46]. The scientists leveraged a well‐known object detection system, namely, YOLO (You Only Look Once), which was developed earlier by Redmon et al. [47] and assessed its performance on real‐time data.

In this section, we address possible challenges and solutions in the world of data analysis. The first and foremost problem that researchers tackle is lack of data for rare classes within the dataset that are used to make a model. The less number of samples we have in the dataset, the higher chance of ignoring that sample while we train and make the model. To handle this problem, meta‐learning has come to play an essential role to make a model only using few samples. It has three important promises: ZSL, one‐shot learning, and few‐shot learning [7]. ZSL is a certain type of meta‐learning when a training dataset does not have any samples for classes, and we still can predict them during a test process. For instance, a research work [44] was conducted to not use any annotation for processing vehicles tracklets. This study established a route understanding system based on zero‐shot theory for intelligent transportation, namely, Zero‐virus, which obtains high effectiveness with zero samples of annotation of vehicle tracklets. Further, another research work [40] established a new technique, namely MetaSense, which is the process of learning to sense rather than sensing to learn. This process takes advantage of the lack of samples of classes by learning from learning rather than learning from samples.