Výsledky vyhledávání - "Ahmed, Khaled R"

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Real Time Deep Learning Weapon Detection Techniques for Mitigating Lone Wolf Attacks

Autor: Akhila, Kambhatla, Ahmed, Khaled R

Firearm Shootings and stabbings attacks are intense and result in severe trauma and threat to public safety. Technology is needed to prevent lone-wolf attacks without human supervision. Hence designing an automatic weapon detection using deep learnin

Externí odkaz: http://arxiv.org/abs/2405.14148

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Gasformer: A Transformer-based Architecture for Segmenting Methane Emissions from Livestock in Optical Gas Imaging

Autor: Sarker, Toqi Tahamid, Embaby, Mohamed G, Ahmed, Khaled R, AbuGhazaleh, Amer

Methane emissions from livestock, particularly cattle, significantly contribute to climate change. Effective methane emission mitigation strategies are crucial as the global population and demand for livestock products increase. We introduce Gasforme

Externí odkaz: http://arxiv.org/abs/2404.10841

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Cannabis Seed Variant Detection using Faster R-CNN

Autor: Sarker, Toqi Tahamid, Islam, Taminul, Ahmed, Khaled R

Publikováno v: 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2024, pp. 1403-1408

Analyzing and detecting cannabis seed variants is crucial for the agriculture industry. It enables precision breeding, allowing cultivators to selectively enhance desirable traits. Accurate identification of seed variants also ensures regulatory comp

Externí odkaz: http://arxiv.org/abs/2403.10722

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Advancing Generative Model Evaluation: A Novel Algorithm for Realistic Image Synthesis and Comparison in OCR System

Autor: Memari, Majid, Ahmed, Khaled R., Rahimi, Shahram, Golilarz, Noorbakhsh Amiri

This research addresses a critical challenge in the field of generative models, particularly in the generation and evaluation of synthetic images. Given the inherent complexity of generative models and the absence of a standardized procedure for thei

Externí odkaz: http://arxiv.org/abs/2402.17204

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FedSiKD: Clients Similarity and Knowledge Distillation: Addressing Non-i.i.d. and Constraints in Federated Learning

Autor: Alsenani, Yousef, Mishra, Rahul, Ahmed, Khaled R., Rahman, Atta Ur

In recent years, federated learning (FL) has emerged as a promising technique for training machine learning models in a decentralized manner while also preserving data privacy. The non-independent and identically distributed (non-i.i.d.) nature of cl

Externí odkaz: http://arxiv.org/abs/2402.09095

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