Abstract
Human-computer interaction (HCI) is a prominent development that provides autonomous and pervasive service broadcasting for in-person communications. Computer vision techniques are assimilated with HCI for human detection and object classification in autonomous communication environments. This article introduces an application dependable interaction module (ADIM) that is optimal in recognizing humans and other autonomous systems for communication. This recognition helps to precisely detect the application demands of the user/system for flawless service broadcast. In this process, a deep belief network is used for the persistent analysis of the behavior of the system/human in the interacting end. The behavior is characterized using touch, voice, and commands/requests for identifying the object at the initial stage. The metrics sharing delay, response latency, interaction failures, recognition ratio, and error are employed to verify the proposed module's performance.