Zobrazeno 1 - 10
of 3 515
pro vyhledávání: '"ABSAR, A."'
A Novel Ensemble-Based Deep Learning Model with Explainable AI for Accurate Kidney Disease Diagnosis
Autor:
Arifuzzaman, Md., Ahmed, Iftekhar, Chowdhury, Md. Jalal Uddin, Sakib, Shadman, Rahman, Mohammad Shoaib, Hossain, Md. Ebrahim, Absar, Shakib
Chronic Kidney Disease (CKD) represents a significant global health challenge, characterized by the progressive decline in renal function, leading to the accumulation of waste products and disruptions in fluid balance within the body. Given its perva
Externí odkaz:
http://arxiv.org/abs/2412.09472
Autor:
Lachemat, Houssam Eddine-Othman, Abbas, Akli, Oukas, Nourredine, Kheir, Yassine El, Haboussi, Samia, Shammur, Absar Chowdhury
The paper introduces and publicly releases (Data download link available after acceptance) CAFE -- the first Code-switching dataset between Algerian dialect, French, and english languages. The CAFE speech data is unique for (a) its spontaneous speaki
Externí odkaz:
http://arxiv.org/abs/2411.13424
Autor:
Mousi, Basel, Durrani, Nadir, Ahmad, Fatema, Hasan, Md. Arid, Hasanain, Maram, Kabbani, Tameem, Dalvi, Fahim, Chowdhury, Shammur Absar, Alam, Firoj
Arabic, with its rich diversity of dialects, remains significantly underrepresented in Large Language Models, particularly in dialectal variations. We address this gap by introducing seven synthetic datasets in dialects alongside Modern Standard Arab
Externí odkaz:
http://arxiv.org/abs/2409.11404
This paper presents a novel Dialectal Sound and Vowelization Recovery framework, designed to recognize borrowed and dialectal sounds within phonologically diverse and dialect-rich languages, that extends beyond its standard orthographic sound sets. T
Externí odkaz:
http://arxiv.org/abs/2408.02430
Autor:
Hasan, Md. Arid, Hasanain, Maram, Ahmad, Fatema, Laskar, Sahinur Rahman, Upadhyay, Sunaya, Sukhadia, Vrunda N, Kutlu, Mucahid, Chowdhury, Shammur Absar, Alam, Firoj
Natural Question Answering (QA) datasets play a crucial role in evaluating the capabilities of large language models (LLMs), ensuring their effectiveness in real-world applications. Despite the numerous QA datasets that have been developed, there is
Externí odkaz:
http://arxiv.org/abs/2407.09823
Self-supervised models have revolutionized speech processing, achieving new levels of performance in a wide variety of tasks with limited resources. However, the inner workings of these models are still opaque. In this paper, we aim to analyze the en
Externí odkaz:
http://arxiv.org/abs/2406.16099
Children's speech recognition is considered a low-resource task mainly due to the lack of publicly available data. There are several reasons for such data scarcity, including expensive data collection and annotation processes, and data privacy, among
Externí odkaz:
http://arxiv.org/abs/2406.13431
A community needs assessment is a tool used by non-profits and government agencies to quantify the strengths and issues of a community, allowing them to allocate their resources better. Such approaches are transitioning towards leveraging social medi
Externí odkaz:
http://arxiv.org/abs/2403.13272
Current research concentrates on studying discussions on social media related to structural failures to improve disaster response strategies. However, detecting social web posts discussing concerns about anticipatory failures is under-explored. If su
Externí odkaz:
http://arxiv.org/abs/2402.13528
Supporting learners in introductory programming assignments at scale is a necessity. This support includes automated feedback on what learners did incorrectly. Existing approaches cast the problem as automatically repairing learners' incorrect progra
Externí odkaz:
http://arxiv.org/abs/2401.01416