Zobrazeno 1 - 10
of 698
pro vyhledávání: '"Ovi, A"'
Autor:
Ovi, Md Sultanul Islam, Anjum, Nafisa, Bithe, Tasmina Haque, Rahman, Md. Mahabubur, Smrity, Mst. Shahnaj Akter
With the increasing adoption of AI-driven tools in software development, large language models (LLMs) have become essential for tasks like code generation, bug fixing, and optimization. Tools like ChatGPT, GitHub Copilot, and Codeium provide valuable
Externí odkaz:
http://arxiv.org/abs/2409.19922
Phishing attacks are a growing cybersecurity threat, leveraging deceptive techniques to steal sensitive information through malicious websites. To combat these attacks, this paper introduces PhishGuard, an optimal custom ensemble model designed to im
Externí odkaz:
http://arxiv.org/abs/2409.19825
Autor:
Paul, Ovi, Nayem, Abu Bakar Siddik, Sarker, Anis, Ali, Amin Ahsan, Amin, M Ashraful, Rahman, AKM Mahbubur
Land Use Land Cover (LULC) analysis on satellite images using deep learning-based methods is significantly helpful in understanding the geography, socio-economic conditions, poverty levels, and urban sprawl in developing countries. Recent works invol
Externí odkaz:
http://arxiv.org/abs/2406.05912
Autor:
Ovi, Tareque Bashar, Bashree, Nomaiya, Mukherjee, Protik, Mosharrof, Shakil, Parthima, Masuma Anjum
Building extraction is an essential component of study in the science of remote sensing, and applications for building extraction heavily rely on semantic segmentation of high-resolution remote sensing imagery. Semantic information extraction gap con
Externí odkaz:
http://arxiv.org/abs/2310.06847
Autor:
Ovi, Tareque Bashar, Mosharrof, Shakil, Bashree, Nomaiya, Islam, Md Shofiqul, Islam, Muhammad Nazrul
The segmentation of satellite images is crucial in remote sensing applications. Existing methods face challenges in recognizing small-scale objects in satellite images for semantic segmentation primarily due to ignoring the low-level characteristics
Externí odkaz:
http://arxiv.org/abs/2310.06848
Autor:
Turzo, Asif Kamal, Faysal, Fahim, Poddar, Ovi, Sarker, Jaydeb, Iqbal, Anindya, Bosu, Amiangshu
Publikováno v:
Proceedings of the 17th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), 2023
Background: As improving code review (CR) effectiveness is a priority for many software development organizations, projects have deployed CR analytics platforms to identify potential improvement areas. The number of issues identified, which is a cruc
Externí odkaz:
http://arxiv.org/abs/2307.03852
Autor:
Md. Adil Hossain, Asif Hosen, Heider A. Abdulhussein, Ahmad A. Mousa, Md. Muneef Hasan, Istiak Ahmed Ovi, Md. Riazul Islam, Redi Kristian Pingak, Mohammed S. Abu-Jafar
Publikováno v:
Results in Engineering, Vol 24, Iss , Pp 103340- (2024)
This study discusses the feasibility of diversifying the scope of application of lead-free Sr3ZBr3 (Z = As, Sb) as promising materials for their enhanced electronic, mechanical, optical, thermodynamic, and thermoelectric properties using first-princi
Externí odkaz:
https://doaj.org/article/8964d071a2a5431fa22f90ed1ecc407e
Publikováno v:
AIP Advances, Vol 14, Iss 11, Pp 115226-115226-15 (2024)
This study depicts the physical characteristics, including electronic, structural, mechanical, magnetic, and optical properties, of the lead-free, inorganic, non-toxic cubic perovskite compound FrCdX3 (where X = Br, Cl, and F). The main goal is to ev
Externí odkaz:
https://doaj.org/article/845eceeb3c16423f817caedffac0c8ff
Autor:
Sayed Hasan Mahmud, Md. Washim Akram, Sayed Md. Redwan Ferdous, Dedarul Islam, Kaneez Fatema, Md. Showkat Akbar Chowdhury, Avi Das, Shazed Muntashir Ovi
Publikováno v:
Next Materials, Vol 5, Iss , Pp 100236- (2024)
The green, biodegradable, and cost-effective properties of natural fiber-reinforced polymer composite materials have attracted significant interest when compared to synthetic polymer composites. Jute is a naturally occurring bast fiber that is inexpe
Externí odkaz:
https://doaj.org/article/b401c876aceb4165bfe035d30a027c7b
Federated Learning (FL) enables collaborative model building among a large number of participants without the need for explicit data sharing. But this approach shows vulnerabilities when privacy inference attacks are applied to it. In particular, in
Externí odkaz:
http://arxiv.org/abs/2210.13457