Machine learning is a branch of AI, and it gives computers the capacity to learn automatically from data and prior experiences to recognize patterns and make predictions with the least amount of human intervention. Large data sets can yield useful information through machine learning, which uses algorithms to find patterns and learn through iterations. ML algorithms do not rely on any fixed equation that may be used as a model; instead, they use computation techniques to learn directly from data. As the number of samples accessible increases during the “learning” processes, the performance of machine learning algorithms improves adaptively. For instance, one kind of machine learning called deep learning teaches computers to mimic human behaviors, like learning from examples.
Top1. Introduction
Smart technologies are ubiquitous throughout the environment, allowing for applications with improved efficiency and performance. In a smart city, it offers improved capabilities through object configuration. Recently, drone technology has aided in the creation of tiny drones like microdrones and quadcopters (K.D. Atherton). The benefit of using these tiny drones for tracking multiple areas, such as industrial surveillance and disaster response, search and rescue, military use, precise agriculture, shipping, and delivery, is that they can be effortlessly incorporated into any infrastructure. Uncrewed aerial vehicles (UAVs), sometimes known as drones, are aircraft that are in the air without human operators (V.S. Campos,2016, D.M. Marshall, 2017). Aerial photography and weather forecasting are just two uses for them. Aerodynamic forces usually use unmanned aerial vehicles (UAVs) to give them remote machine control capabilities (R. Altawy, 2017, A. Hamza, 2020). Similar commercial applications have affected everyone and changed the characteristics of different businesses. Unmanned Aerial Vehicles (UAVs) are valuable tools for surveillance and monitoring since they can easily collect data from the air and transmit it back to the base station. Figure 1 Overview of chapter title.
Figure 1. Overview of chapter title
The surge in e-commerce has brought numerous delivery-related problems in the supply chain (SC) to light. Last-mile deliveries account for most delivery-related problems. Last-mile delivery issues have been regarded as more costly, environmentally damaging, and inefficient than any other issues in SC since last-mile delivery logistics demand a variety of components to guarantee quicker and more effective delivery circumstances (L. H. Hernandez, 2014; N. Jumaat, 2018; J.-P.A, 2019). Today's unmanned aerial vehicles (UAVs), also known as drones, have the potential to revolutionize traditional delivery methods in SCs due to technological improvements and the decreasing cost of sensors. Drone deployment for last-mile delivery has numerous advantages for SC, including lower travel expenses, contact-free delivery, avoiding gridlock, and expedited delivery times. UPS, DHL, and Amazon are among the prominent names in logistics and transportation that are looking into using drones for last-mile deliveries. Drones can quickly and efficiently deliver medical supplies to difficult-to-reach locations (C.A. Thiels, 2015; M. Lipsitch, 2020; Abdullah, 2018). When carrying little packages, they are significantly lighter and use less electricity. Drone delivery can prevent human interaction during disease outbreaks like COVID-19 and ensure that medical facilities receive timely vaccines. Drone delivery has the potential to revolutionize logistics and boost customer happiness (Aiello, G.,2018; Abbas, S., 2017). Drones have two fundamental technical limitations: restricted flight range and limited payload, notwithstanding their benefits. Adopting autonomous drone delivery as a last-mile substitute for vehicle delivery is hampered by these limitations. To overcome these limitations, researchers have devised several different truck-drone system designs.