Zobrazeno 1 - 10
of 433 197
pro vyhledávání: '"Rahman, A A"'
Autor:
Rahman, Md. Sohanur, Chowdhury, Muhammad E. H., Rahman, Hasib Ryan, Ahmed, Mosabber Uddin, Kabir, Muhammad Ashad, Roy, Sanjiban Sekhar, Sarmun, Rusab
In this study, we propose a novel and robust framework, Self-DenseMobileNet, designed to enhance the classification of nodules and non-nodules in chest radiographs (CXRs). Our approach integrates advanced image standardization and enhancement techniq
Externí odkaz:
http://arxiv.org/abs/2410.12584
Exterior painting of high-rise buildings is a challenging task. In our country, as well as in other countries of the world, this task is accomplished manually, which is risky and life-threatening for the workers. Researchers and industry experts are
Externí odkaz:
http://arxiv.org/abs/2409.05153
Autor:
Rahman, Mustazee, Virag, Balint
A basic question about the directed landscape is how much of it can be reconstructed simply by knowing the shapes of its geodesics. We prove that the directed landscape can be reconstructed from the shapes of its semi-infinite geodesics. In order to
Externí odkaz:
http://arxiv.org/abs/2410.19070
Consider the math problem: "Lily received 3 cookies from her best friend yesterday and ate 5 for breakfast. Today, her friend gave her 3 more cookies. How many cookies does Lily have now?" Many large language models (LLMs) in previous research approa
Externí odkaz:
http://arxiv.org/abs/2410.18921
Climate change poses critical challenges globally, disproportionately affecting low-income countries that often lack resources and linguistic representation on the international stage. Despite Bangladesh's status as one of the most vulnerable nations
Externí odkaz:
http://arxiv.org/abs/2410.17225
Autor:
Hedar, Abdel-Rahman, Abdel-Hakim, Alaa E., Deabes, Wael, Alotaibi, Youseef, Bouazza, Kheir Eddine
Metaheuristic search methods have proven to be essential tools for tackling complex optimization challenges, but their full potential is often constrained by conventional algorithmic frameworks. In this paper, we introduce a novel approach called Dee
Externí odkaz:
http://arxiv.org/abs/2410.17042
Autor:
Hua, Jiang, Bewong, Michael, Kwashie, Selasi, Rahman, MD Geaur, Hu, Junwei, Guo, Xi, Fen, Zaiwen
Data imputation addresses the challenge of imputing missing values in database instances, ensuring consistency with the overall semantics of the dataset. Although several heuristics which rely on statistical methods, and ad-hoc rules have been propos
Externí odkaz:
http://arxiv.org/abs/2410.15747
Autor:
Kmainasi, Mohamed Bayan, Shahroor, Ali Ezzat, Hasanain, Maram, Laskar, Sahinur Rahman, Hassan, Naeemul, Alam, Firoj
Large Language Models (LLMs) have demonstrated remarkable success as general-purpose task solvers across various fields, including NLP, healthcare, finance, and law. However, their capabilities remain limited when addressing domain-specific problems,
Externí odkaz:
http://arxiv.org/abs/2410.15308
The financial sector's dependence on digital infrastructure increases its vulnerability to cybersecurity threats, requiring strong IT security protocols with other entities. This collaboration, however, is often identified as the most vulnerable link
Externí odkaz:
http://arxiv.org/abs/2410.15194
Autor:
Ahasan, Md Mubtasim, Fahim, Md, Mohiuddin, Tasnim, Rahman, A K M Mahbubur, Chadha, Aman, Iqbal, Tariq, Amin, M Ashraful, Islam, Md Mofijul, Ali, Amin Ahsan
Recent advancements in speech-language models have yielded significant improvements in speech tokenization and synthesis. However, effectively mapping the complex, multidimensional attributes of speech into discrete tokens remains challenging. This p
Externí odkaz:
http://arxiv.org/abs/2410.15017