Название: Algorithms in Advanced Artificial Intelligence
Автор: R.N.V. Jagan Mohan, B.H.V.S. Rama Krishnam Raju, V. Chandra Sekhar, T.V.K.P. Prasad
Издательство: CRC Press
Год: 2025
Страниц: 786
Язык: английский
Формат: pdf (true)
Размер: 86.4 MB

Algorithms in Advanced Artificial Intelligence is a collection of papers on emerging issues, challenges, and new methods in Artificial Intelligence, Machine Learning, Deep Learning, Cloud Computing, Federated Learning, Internet of Things, and Blockchain technology. It addresses the growing attention to advanced technologies due to their ability to provide “paranormal solutions” to problems associated with classical Artificial Intelligence frameworks. AI is used in various subfields, including learning, perception, and financial decisions. It uses four strategies: Thinking Humanly, Thinking Rationally, Acting Humanly, and Acting Rationally. The authors address various issues in ICT, including Artificial Intelligence, Machine Learning, Deep Learning, Data Science, Big Data Analytics, Vision, Internet of Things, Security and Privacy aspects in AI, and Blockchain and Digital Twin Integrated Applications in AI.

Integrating RF Signal Analysis and Deep Learning for Effective Drone Classification and Detection of Drones: Drones are utilized for drug dealing, weapon sneaking, and jeopardizing air terminals and atomic power offices, in spite of their many advantages. Existing robot limitation and arrangements expect the robot was distinguished and grouped. Over the course of the last 10 years, sensor innovation has progressed, yet no detection and classification of Drone techniques has been proposed. This exploration utilizes radio frequency (RF) signal recurrence trademark to perceive and sort Drones. A novelty robot Radio Frequency dataset is made and looked at a two-staged and incorporated Detection as well as Classification structure utilizing business drones. The two systems identification and order results are displayed for single-signal and concurrent multi-signal circumstances. We show that the “You Only Look Once” (YOLO) system beats the “Goodness-of-Fit” (GoF) range detecting structure into synchronous multi-signal circumstances & the “Deep Residual Neural Network” (DRNN) system into arrangement.

Sky Guard: A Vision Based Drone Vs. Bird Detection and Alert System Using Deep Learning: As the number of drones continues to rise, the challenges associated with them and the hazards they pose were being increasing day by day. Even though Drones have some uses like delivering the items, capturing the videos, etc, Drones will cause several threats to the people. Drones can interrupt the privacy of the people, by capturing them secretly. Drones can easily enter into the restricted areas. Drones can be used to transfer the illegal items. So, The Drones need to be detected to ensure privacy, safety, security, and operational integrity. This paper presents an optimized detection framework that integrates the MobileNetv2 architecture with the YOLOv8 backbone to achieve high-performance drone and bird detection. The system was trained on a comprehensive dataset of over 10,000 labelled images representing diverse environments and conditions. To get the desired results, the dataset which consists of various scenarios of backgrounds, foregrounds and different weather conditions was used. Experimental results demonstrate a 95% overall accuracy, with a mean Average Precision (mAP) score of 0.93 for drone detection and 0.91 for bird detection. This paper also presents an alert system deployed using Flask, which provides instant notifications upon detecting drones.






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