Mohamed Torky

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About

Mohamed Torky is a researcher at the forefront of artificial intelligence and its application to real-world security challenges. His work centers on deep learning, computer vision, and autonomous systems, with a particular focus on developing intelligent detection methodologies. Torky’s most notable contribution is his pioneering study on "Drones and Birds Detection Based on InceptionV3-CNN Model," a deep learning framework that addresses the critical need for distinguishing between drones and birds in surveillance systems. This work, published in 2024, has already garnered 2 citations, signaling its emerging impact in the fields of aerial security and wildlife monitoring. By leveraging the InceptionV3 convolutional neural network, Torky has advanced the accuracy and efficiency of object detection in complex environments, offering a robust solution for airspace management and threat assessment. His research bridges the gap between theoretical AI models and practical deployment, making him a key figure in the development of next-generation autonomous surveillance technologies. For students and researchers, Torky’s work exemplifies how deep learning can be harnessed to solve pressing societal problems, from drone regulation to ecological conservation.