With the development and application of modern information and communication equipment and services (ICES), global society has undergone significant development and progress in everyday life. Future studies should contribute to strengthen the theoretical production, while AI is being continuously reinforced with new empirical evidence. This article argues that AI technologies are driving the service industry and have had promising results in reducing the service lead time while is being more cost-effective and error-free. To this end, we have systematically reviewed the literature to identify and synthesize the existing body of knowledge and update academics and practitioners regarding the latest AI developments on the SDS’s. This study provides an overview of the existing theory concerning the next generation of AI technologies that are revolutionizing the service delivery systems (SDS). A notable example is Amazon, which is reshaping itself with AI-based technologies, relying on robot service delivery systems, either through faster inventory checks or product delivery that reached unprecedented speed. With increased availability of virtual channels, new approaches to resource management are required for effective service delivery. Our results show that the safety response mechanism successfully generates paths without obstacles to the closest safety exits from all the factory locations.Īrtificial intelligence (AI) is transforming the 21st century service industries. We simulate a simple and small manufacturing environment overview to test our safety procedure. The factory emergency signal can be given by an ESTOP or a voice command sent directly to the factory central controller: an S7-1200 Siemens programmable logic controller (PLC) in this experiment. We also program a speech recognition system for operators to react timeously, with a voice command, to an emergency that requires stopping all plant activities even when they are far away from emergency stops (ESTOPs) button. After obtaining the robot optimal path selection options with Q-learning, we code the outcome as a rule-based system for the safety response. We implement a reinforcement learning (RL) algorithm, Q-learning, to enable the path learning abilities of the robot. Our research proposes a safety response mechanism for a small manufacturing plant, through which an autonomous robot learns the obstacle-free trajectory to the closest safety exit in emergencies. Operators are subject to frequent safety inductions to react in emergencies but very little is done for robots. Safety becomes crucial for humans and robots to ensure a smooth production run in such environments. Under this revolution, known as Industry 4.0 (I40), a robot is no longer a static equipment but an active workforce to the factory production alongside human operators. The industrial manufacturing sector is currently undergoing a tremendous revolution moving from traditional production processes to intelligent techniques. Furthermore, all of this will be analyzed primarily in the transport(car) environment with emphasis on the potential advantages and disadvantages of such tools and principles. The purpose of the paper is to show the possibilities of human voice recognition using smart terminal devices. Given the current trends in the use of smart terminal devices and technical and technological opportunities that they offer, it is inevitable to study the potential of these devices in theĪrea of human voice recognition. The aim of voice recognition systems is to provide links and ease of communication between theĭevice and the man and the development of further possible applications. The primary purpose of recognizing of human speech is the ability to customize information (which are intended to people) for using with devices. Speech recognition enables devices to adapt voice information in understandable form, which means complete identification and understanding of The development of science and technology made visible improvements in the capabilities and the quality of recognition of the human voice/speech using some kind of terminal devices. The ability to recognize human speech has always been an area of interest of people primarily because of the large range of applications in almost every segment of society.
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