Zobrazeno 1 - 10
of 6 731
pro vyhledávání: '"Haque, Md."'
Plant diseases significantly impact agricultural productivity, resulting in economic losses and food insecurity. Prompt and accurate detection is crucial for the efficient management and mitigation of plant diseases. This study investigates advanced
Externí odkaz:
http://arxiv.org/abs/2408.15289
We have discussed in detail how neutrinos produced from inflaton solely through gravitational interaction can successfully reheat the universe. For this, we have introduced the well-known Type-I seesaw neutrino model. Depending on seesaw model parame
Externí odkaz:
http://arxiv.org/abs/2408.12450
Autor:
Chandrashekar, Mayanka, Goethert, Ian, Haque, Md Inzamam Ul, McMahon, Benjamin, Dhaubhadel, Sayera, Knight, Kathryn, Erdos, Joseph, Reagan, Donna, Taylor, Caroline, Kuzmak, Peter, Gaziano, John Michael, McAllister, Eileen, Costa, Lauren, Ho, Yuk-Lam, Cho, Kelly, Tamang, Suzanne, Fodeh-Jarad, Samah, Ovchinnikova, Olga S., Justice, Amy C., Hinkle, Jacob, Danciu, Ioana
Objectives: This study aims to assess the impact of domain shift on chest X-ray classification accuracy and to analyze the influence of ground truth label quality and demographic factors such as age group, sex, and study year. Materials and Methods:
Externí odkaz:
http://arxiv.org/abs/2407.21149
Autor:
Maity, Suvashis, Haque, Md Riajul
One of the fundamental characteristics of slow roll inflation is its generation of tensor perturbations, which manifest as stochastic gravitational waves (GWs). Slow roll inflation results in a nearly scale-invariant GW spectrum that maintains its sc
Externí odkaz:
http://arxiv.org/abs/2407.18246
The increasing security and privacy concerns in the Smart Grid sector have led to a significant demand for robust intrusion detection systems within critical smart grid infrastructure. To address the challenges posed by privacy preservation and decen
Externí odkaz:
http://arxiv.org/abs/2407.15879
Autor:
Haque, Md. Zahurul
Algorithms are used in eCommerce product recommendation systems. These systems just recently began utilizing machine learning algorithms due to the development and growth of the artificial intelligence research community. This project aspires to tran
Externí odkaz:
http://arxiv.org/abs/2407.21026
Autor:
Shi, Shanghao, Haque, Md Shahedul, Parida, Abhijeet, Linguraru, Marius George, Hou, Y. Thomas, Anwar, Syed Muhammad, Lou, Wenjing
Federated learning (FL) allows a set of clients to collaboratively train a machine-learning model without exposing local training samples. In this context, it is considered to be privacy-preserving and hence has been adopted by medical centers to tra
Externí odkaz:
http://arxiv.org/abs/2407.09972
We explore the possibility of explaining the observed dark matter (DM) relic abundance, along with matter-antimatter asymmetry, entirely from the evaporation of primordial black holes (PBH) beyond the semi-classical approximation. We find that, depen
Externí odkaz:
http://arxiv.org/abs/2405.15858
Exploring the Potential of the Large Language Models (LLMs) in Identifying Misleading News Headlines
In the digital age, the prevalence of misleading news headlines poses a significant challenge to information integrity, necessitating robust detection mechanisms. This study explores the efficacy of Large Language Models (LLMs) in identifying mislead
Externí odkaz:
http://arxiv.org/abs/2405.03153
We compute the relic abundance of dark matter in the presence of Primordial Black Holes (PBHs) beyond the semiclassical approximation. We take into account the quantum corrections due to the memory burden effect, which is assumed to suppress the blac
Externí odkaz:
http://arxiv.org/abs/2404.16815