Zobrazeno 1 - 10
of 340
pro vyhledávání: '"Rahman, Md Mahmudur"'
This study proposes a modification of the yield condition that overcomes the mathematical constraints of the Directional Distortional Hardening models developed by Feigenbaum and Dafalias. This modified model surpasses the mathematical inconsistency
Externí odkaz:
http://arxiv.org/abs/2406.02446
Autor:
Roy, Kashob Kumar, Moon, Md Hasibul Haque, Rahman, Md Mahmudur, Ahmed, Chowdhury Farhan, Leung, Carson Kai-Sang
Publikováno v:
Information Sciences 582 (2022): 865-896
Due to the rapid development of science and technology, the importance of imprecise, noisy, and uncertain data is increasing at an exponential rate. Thus, mining patterns in uncertain databases have drawn the attention of researchers. Moreover, frequ
Externí odkaz:
http://arxiv.org/abs/2404.00746
Autor:
Roy, Kashob Kumar, Moon, Md Hasibul Haque, Rahman, Md Mahmudur, Ahmed, Chowdhury Farhan, Leung, Carson K.
In this uncertain world, data uncertainty is inherent in many applications and its importance is growing drastically due to the rapid development of modern technologies. Nowadays, researchers have paid more attention to mine patterns in uncertain dat
Externí odkaz:
http://arxiv.org/abs/2404.01347
Autor:
Rahman, Md. Mahmudur, Islam, Sajid, Chowdhury, Showren, Zeba, Sadia Jahan, Karmaker, Debajyoti
This study delves into the flight behaviors of Budgerigars (Melopsittacus undulatus) to gain insights into their flight trajectories and movements. Using 3D reconstruction from stereo video camera recordings, we closely examine the velocity and accel
Externí odkaz:
http://arxiv.org/abs/2312.00597
Autor:
Sultana, Sharmin, Rahman, Md Mahmudur, Mahi, Atqiya Munawara, Liu, Shao-Hsien, Alam, Mohammad Arif Ul
The combination of diverse health data (IoT, EHR, and clinical surveys) and scalable-adaptable Artificial Intelligence (AI), has enabled the discovery of physical, behavioral, and psycho-social indicators of pain status. Despite the hype and promise
Externí odkaz:
http://arxiv.org/abs/2307.05333
Deep learning advancements have revolutionized scalable classification in many domains including computer vision. However, when it comes to wearable-based classification and domain adaptation, existing computer vision-based deep learning architecture
Externí odkaz:
http://arxiv.org/abs/2307.00883
Domain adaptation for sensor-based activity learning is of utmost importance in remote health monitoring research. However, many domain adaptation algorithms suffer with failure to operate adaptation in presence of target domain heterogeneity (which
Externí odkaz:
http://arxiv.org/abs/2210.09499
We present a new semi-supervised domain adaptation framework that combines a novel auto-encoder-based domain adaptation model with a simultaneous learning scheme providing stable improvements over state-of-the-art domain adaptation models. Our framew
Externí odkaz:
http://arxiv.org/abs/2210.09486
Multi-state survival analysis (MSA) uses multi-state models for the analysis of time-to-event data. In medical applications, MSA can provide insights about the complex disease progression in patients. A key challenge in MSA is the accurate subject-sp
Externí odkaz:
http://arxiv.org/abs/2207.05291
Survival analysis, time-to-event analysis, is an important problem in healthcare since it has a wide-ranging impact on patients and palliative care. Many survival analysis methods have assumed that the survival data is centrally available either from
Externí odkaz:
http://arxiv.org/abs/2207.05247