Global Journal of Computer Science and Technology, D: Neural & Artificial Intelligence, Volume 23 Issue 2

40. Oil and Gas: The oil and gas industry has utilized artificial intelligence techniques to automate tasks, anticipate equipment problems, and boost production. 41. Transport Automotive: AI in transportation is anticipated to offer reliable, safe, and efficient transportation with minimal adverse effects on the environment and local populations. The complex transport networks, including numerous separate parts and parties with potentially competing goals, are a significant development problem. AI-based fuzzy logic controllers run gearboxes. Autonomous parking and adaptive cruise control are two examples of driver assistance features based on AI. Additionally, autonomous rail transportation and electric minibusses are prototypes of autonomous automotive public vehicles. There are other autonomous delivery vehicle prototypes, some of which contain delivery robots. Due to the intricacy of transportation, it is frequently impractical to train an AI in a real-world driving context, and On-road training can be safer when done in a simulator. AI supports self-driving cars. Among the businesses utilizing AI are Tesla, Waymo, and General Motors. AI-based systems oversee choreographing actions, including braking, lane switching, collision avoidance, navigation, and mapping. Testing for autonomous trucks is now underway. After the UK government passes legislation, 2018 will see the start of testing for autonomous truck platoons. Uncrewed trucks follow each other closely in a queue. The Freightliner Inspiration is being tested by the German company Daimler. For autonomous vehicles to navigate between destinations, precise maps are a need. Some autonomous vehicles (which lack steering wheels and pedals) do not allow human drivers. 42. Traffic Management: Wait times, energy use, and emissions have all been reduced by up to 25% thanks to the application of AI in traffic management. Using predictive algorithms and cameras with radar and ultrasonic acoustic location sensors, artificially intelligent traffic lights can be created to improve traffic flow. 43. Military: The Air Operations Division (AOD) of the Royal Australian Air Force (RAAF) employs AI in its expert systems. AIs serve as stand-in operators for humans in training and battle simulators, mission management tools, tactical decision support systems, and post-processing simulator data into symbolic summaries. AI is used in aircraft simulators to train pilots. It is possible to replicate flight conditions that let pilots make mistakes without endangering themselves or expensive aircraft. Simulated air combat is another option. Like how AI is used to drive ground vehicles, it can also be used to fly airplanes. Drones with autonomy can fly alone or in groups. Drones can receive vocal instructions from traffic controllers thanks to speech recognition. AIDA, or artificial intelligence-supported aircraft design, is a tool used by designers to assist in the development of conceptual designs for airplanes. Thanks to this program, the designers may concentrate more on the design itself and less on the design process. The software enables the user to lessen their attention to the software tools. The AIDA computes its data using rule-based systems. This diagram shows how the AIDA modules are set up. Despite being straightforward, the program is working well. 44. NASA: The software was developed in 2003 as part of a Dryden Flight Research Centre study to allow a damaged aircraft to fly on until a safe landing is possible. The software used the remaining undamaged components to compensate for the broken ones. The 2016 Intelligent Autopilot System integrated behavioral cloning with apprenticeship learning, allowing the Autopilot to monitor both the high-level strategy and low-level actions needed to control the airplane. 45. Environmental Monitoring: Machine learning is used in passive acoustics, remote sensing, AI-driven satellite data analysis, autonomous ocean monitoring ships, and other environmental monitoring applications. In order to reduce plastic pollution, especially ocean pollution, "Global Plastic Watch" is an AI-based satellite surveillance platform that analyses and tracks plastic waste sites. It identifies who and where improperly disposes of plastic garbage and dumps it into the ocean. 46. Early-Warning Systems: Machine learning can identify early warning signals of natural disasters and environmental problems, such as pandemics, landslides, heavy rain, long-term water supply vulnerability, tipping points of ecosystem collapse, cyanobacterial bloom epidemics, and droughts. 47. Computer Science: GitHub Copilot is a piece of artificial intelligence that GitHub and OpenAI created that can autocomplete code in various programming languages. 48. Neural Network Design: Other AIs can be made using AI. For instance, Google's AutoML project, which develops novel neural network topologies, produced NASNet, a system tailored for ImageNet and POCO F1, around November 2017. NASNet surpassed all previously published performances on ImageNet. g) Applications based on Artificial Intelligence: Chatbots are used for consumer interactions on corporate websites and social media platforms, such as answering frequently requested inquiries. Chatbots give round-the-clock assistance and replace humans, helping businesses reduce costs. © 2023 Global Journals Global Journal of Computer Science and Technology Volume XXIII Issue II Version I 27 ( )D Year 2023 Journey of Artificial Intelligence Frontier: A Comprehensive Overview