Cyber learning with Dr.Vanitha

  Introduction: In recent years, unmanned aerial vehicles, commonly known as drones, have gained significant popularity across various industries for their versatility and potential to revolutionize traditional operations. India, a rapidly developing nation with a bustling economy and a growing technology sector, is also exploring the immense potential of drones. In this blog post, we will delve into the various applications of drones in India and the opportunities they present. Delivery and Logistics: One of the most notable use cases of drones in India is their application in delivery and logistics. With its vast geographical expanse and significant logistical challenges, the country's e-commerce giants, such as Flipkart and Amazon, are piloting drone delivery services. Drones offer the potential to overcome infrastructure constraints and deliver packages efficiently, especially in remote or congested areas, reducing delivery times and costs. Agriculture and Crop Monitoring: Indi

  Biomimicry is about valuing nature for what we can learn, not what we can extract, harvest, or domesticate. In the process, we learn about ourselves, our purpose, and our connection to each other and our home on earth

 The world is moving to an autonomous era. Autonomous vehicles take a major role in day-to-day activity, helping human personnel do work quickly and independently. Unmanned aerial vehicles (UAVs) are autonomous vehicles controlled using remotes in ground station by human personnel. These UAVs act as a network that plays a vital role in the digital era. There are different architectures of UAV networks available. This chapter concentrates on centralized UAV network. Because of wireless and autonomy characteristics, these networks are prone to various security issues, so it's very important to monitor and analyze the traffic of the UAV network in order to identify the intrusions. This chapter proposes enhanced deep feed forward neural network (EDFFNN) in order to monitor and analyze the traffic of the UAV network to detect the intrusions with maximum detection rate of 94.4%. The results have been compared with the previous method of intrusion detection.