Hand gesture recognition is one of the very active research areas in the Computer Vision field. It provides the easiness to interact with machines without using any extra device and if the users don’t have much technical knowledge about the system, they still will be able to use the system with their normal hands. Gestures communicate the meaning of statement said by the human being. They come naturally with the words to help the receiver to understand the communication. It allows individuals to communicate feelings and thoughts with different emotions with words or without words. This paper presents a parallel implementation of hand gesture recognition. Gesture made by human being can be any but few have a special meaning. Human hand can have movement in a direction and can bend to any angle in all available coordinates
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To address the challenges of providing quick and plausible explanations in Explainable AI (XAI) for object detection models, we introduce the Gaussian Class Activation Mapping Explainer (G-CAME).
The rising use of Artificial Intelligence (AI) in human detection on Edge camera systems has led to accurate but complex models, challenging to interpret and debug.
In recent years, reinforcement learning (RL) has emerged as a new, promising way to solve old problems. The algorithms’ role in finding approximate solutions in NP-hard complexity became crucial for developing modern intelligent decisions.
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