Zobrazeno 1 - 10
of 13 225
pro vyhledávání: '"SHAFIQUE, A."'
The proliferation of smartphones and other mobile devices provides a unique opportunity to make Advanced Driver Assistance Systems (ADAS) accessible to everyone in the form of an application empowered by low-cost Machine/Deep Learning (ML/DL) models
Externí odkaz:
http://arxiv.org/abs/2410.19336
Autor:
Minhas, Mishal Fatima, Putra, Rachmad Vidya Wicaksana, Awwad, Falah, Hasan, Osman, Shafique, Muhammad
To adapt to real-world dynamics, intelligent systems need to assimilate new knowledge without catastrophic forgetting, where learning new tasks leads to a degradation in performance on old tasks. To address this, continual learning concept is propose
Externí odkaz:
http://arxiv.org/abs/2410.09218
Disruptions to medical infrastructure during disasters pose significant risks to critically ill patients with advanced chronic kidney disease or end-stage renal disease. To enhance patient access to dialysis treatment under such conditions, it is cru
Externí odkaz:
http://arxiv.org/abs/2410.02956
Autor:
Duan, Minxuan, Qian, Yinlong, Zhao, Lingyi, Zhou, Zihao, Rasheed, Zeeshan, Yu, Rose, Shafique, Khurram
Existing methods for anomaly detection often fall short due to their inability to handle the complexity, heterogeneity, and high dimensionality inherent in real-world mobility data. In this paper, we propose DeepBayesic, a novel framework that integr
Externí odkaz:
http://arxiv.org/abs/2410.01011
Autor:
Guesmi, Amira, Shafique, Muhammad
Autonomous vehicles (AVs) rely heavily on LiDAR (Light Detection and Ranging) systems for accurate perception and navigation, providing high-resolution 3D environmental data that is crucial for object detection and classification. However, LiDAR syst
Externí odkaz:
http://arxiv.org/abs/2409.20426
Autor:
Abramovich, Talor, Udeshi, Meet, Shao, Minghao, Lieret, Kilian, Xi, Haoran, Milner, Kimberly, Jancheska, Sofija, Yang, John, Jimenez, Carlos E., Khorrami, Farshad, Krishnamurthy, Prashanth, Dolan-Gavitt, Brendan, Shafique, Muhammad, Narasimhan, Karthik, Karri, Ramesh, Press, Ofir
Although language model (LM) agents are demonstrating growing potential in many domains, their success in cybersecurity has been limited due to simplistic design and the lack of fundamental features for this domain. We present EnIGMA, an LM agent for
Externí odkaz:
http://arxiv.org/abs/2409.16165
Optimizing Deep Learning-based Simultaneous Localization and Mapping (DL-SLAM) algorithms is essential for efficient implementation on resource-constrained embedded platforms, enabling real-time on-board computation in autonomous mobile robots. This
Externí odkaz:
http://arxiv.org/abs/2409.14515
The field of medical diagnostics has witnessed a transformative convergence of artificial intelligence (AI) and healthcare data, offering promising avenues for enhancing patient care and disease comprehension. However, this integration of multimodal
Externí odkaz:
http://arxiv.org/abs/2409.13115
Autor:
Dutta, Siddhant, Karanth, Pavana P, Xavier, Pedro Maciel, de Freitas, Iago Leal, Innan, Nouhaila, Yahia, Sadok Ben, Shafique, Muhammad, Neira, David E. Bernal
The widespread deployment of products powered by machine learning models is raising concerns around data privacy and information security worldwide. To address this issue, Federated Learning was first proposed as a privacy-preserving alternative to c
Externí odkaz:
http://arxiv.org/abs/2409.11430
Autor:
Stanford, Chris, Adari, Suman, Liao, Xishun, He, Yueshuai, Jiang, Qinhua, Kuai, Chenchen, Ma, Jiaqi, Tung, Emmanuel, Qian, Yinlong, Zhao, Lingyi, Zhou, Zihao, Rasheed, Zeeshan, Shafique, Khurram
Collecting real-world mobility data is challenging. It is often fraught with privacy concerns, logistical difficulties, and inherent biases. Moreover, accurately annotating anomalies in large-scale data is nearly impossible, as it demands meticulous
Externí odkaz:
http://arxiv.org/abs/2409.03024