Zobrazeno 1 - 10
of 6 056
pro vyhledávání: '"P. Santosh Kumar"'
Autor:
Goda Srinivasa Rao, P. Santosh Kumar Patra, V.A. Narayana, Avala Raji Reddy, G.N.V. Vibhav Reddy, D. Eshwar
Publikováno v:
Egyptian Informatics Journal, Vol 27, Iss , Pp 100526- (2024)
The Internet of Things (IoT) network infrastructures are becoming more susceptible to distributed denial of service (DDoS) attacks because of the proliferation of IoT devices. Detecting and predicting such attacks in this complex and dynamic environm
Externí odkaz:
https://doaj.org/article/0a057e9274b34f2a935c66f5a34b358c
The rapid adaptation of data driven AI models, such as deep learning inference, training, Vision Transformers (ViTs), and other HPC applications, drives a strong need for runtime precision configurable different non linear activation functions (AF) h
Externí odkaz:
http://arxiv.org/abs/2412.11702
Autor:
Khan, MD Raqib, Negi, Anshul, Kulkarni, Ashutosh, Phutke, Shruti S., Vipparthi, Santosh Kumar, Murala, Subrahmanyam
Quality degradation is observed in underwater images due to the effects of light refraction and absorption by water, leading to issues like color cast, haziness, and limited visibility. This degradation negatively affects the performance of autonomou
Externí odkaz:
http://arxiv.org/abs/2412.01456
Autor:
Chaudhary, Santosh Kumar, Gupta, Nitin
In this paper, we investigate inaccuracy measures based on record values, focusing on the relationship between the distribution of the n-th upper and lower k-record values and the parent distribution. We extend the classical Kerridge inaccuracy measu
Externí odkaz:
http://arxiv.org/abs/2410.17658
Autor:
Radha, Santosh Kumar, Goktas, Oktay
Human learning thrives on the ability to learn from mistakes, adapt through feedback, and refine understanding-processes often missing in static machine learning models. In this work, we introduce Composite Learning Units (CLUs) designed to transform
Externí odkaz:
http://arxiv.org/abs/2410.08037
Iterative human engagement is a common and effective means of leveraging the advanced language processing power of large language models (LLMs). Using well-structured prompts in a conversational manner, human users can effectively influence an LLM to
Externí odkaz:
http://arxiv.org/abs/2409.12618
Deep neural networks (DNNs) offer plenty of challenges in executing efficient computation at edge nodes, primarily due to the huge hardware resource demands. The article proposes HYDRA, hybrid data multiplexing, and runtime layer configurable DNN acc
Externí odkaz:
http://arxiv.org/abs/2409.04976
We show in this letter how a heavy $(\mathcal{O}(TeV))$ invisible $Z'$ gauge boson that will practically be out of reach of the Large Hadron Collider (LHC), can be discovered at the future muon collider. The new force carrier has a relatively stronge
Externí odkaz:
http://arxiv.org/abs/2408.14396
This paper introduces a Scalable Hierarchical Aware Convolutional Neural Network (SHA-CNN) model architecture for Edge AI applications. The proposed hierarchical CNN model is meticulously crafted to strike a balance between computational efficiency a
Externí odkaz:
http://arxiv.org/abs/2407.21370
Memory management is necessary with the increasing number of multi-connected AI devices and data bandwidth issues. For this purpose, high-speed multi-port memory is used. The traditional multi-port memory solutions are hard-bounded to a fixed number
Externí odkaz:
http://arxiv.org/abs/2407.20628