Zobrazeno 1 - 10
of 33 307
pro vyhledávání: '"Tahir, P"'
This study addresses the challenge of analyzing the growth kinetics of carbon nanotubes using in-situ homodyne polarization microscopy (HPM) by developing an automated deep learning (DL) approach. A Mask-RCNN architecture, enhanced with a ResNet-50 b
Externí odkaz:
http://arxiv.org/abs/2410.13594
Autor:
Alamu, Opeyemi Sheu, Choque, Bismar Jorge Gutierrez, Rizvi, Syed Wajeeh Abbs, Hammed, Samah Badr, Medani, Isameldin Elamin, Siam, Md Kamrul, Tahir, Waqar Ahmad
Breast cancer remains a significant global health challenge, with prognosis and treatment decisions largely dependent on clinical characteristics. Accurate prediction of patient outcomes is crucial for personalized treatment strategies. This study em
Externí odkaz:
http://arxiv.org/abs/2410.13404
With the emergence of large-scale pre-trained neural networks, methods to adapt such "foundation" models to data-limited downstream tasks have become a necessity. Fine-tuning, preference optimization, and transfer learning have all been successfully
Externí odkaz:
http://arxiv.org/abs/2410.08194
Autor:
Zafar, Roohi, Kamran, Muhammad, Malik, Tahir, Karera, Kashish, Tariq, Humayon, Mustafa, Ghulam, Khan, Muhammad Mubashir
Random numbers are central to various applications such as secure communications, quantum key distribution theory (QKD), statistics, and other tasks. One of today's most popular generators is quantum random numbers (QRNGs). The inherent randomness an
Externí odkaz:
http://arxiv.org/abs/2409.20492
Publikováno v:
European Physical Journal A 60:75 (2024)
The beta-decay log ft values for 210 215Pb 210 215Bi and 210 215Bi 210 215Po transitions in the north east region of 208Pb nuclei are estimated using the proton neutron quasiparticle random phase approximation model. The pn-QRPA equations were solved
Externí odkaz:
http://arxiv.org/abs/2409.12565
Autor:
Nakao, Masato, Hamamoto, Kensei, Tsunoda, Masateru, Tahir, Amjed, Toda, Koji, Monden, Akito, Nakasai, Keitaro, Matsumoto, Kenichi
Developers must select a high-performance fault localization (FL) technique from available ones. A conventional approach is to try to select only one FL technique that is expected to attain high performance before debugging activity. In contrast, we
Externí odkaz:
http://arxiv.org/abs/2409.06268
Autor:
Hamamoto, Kensei, Tsunoda, Masateru, Tahir, Amjed, Bennin, Kwabena Ebo, Monden, Akito, Toda, Koji, Nakasai, Keitaro, Matsumoto, Kenichi
Ensemble learning methods have been used to enhance the reliability of defect prediction models. However, there is an inconclusive stability of a single method attaining the highest accuracy among various software projects. This work aims to improve
Externí odkaz:
http://arxiv.org/abs/2409.06264
Autor:
Xue, Tengfei, Li, Xuefeng, Azim, Tahir, Smirnov, Roman, Yu, Jianhui, Sadrieh, Arash, Pahlavan, Babak
Large language models (LLMs) have significantly improved code generation, particularly in one-pass code generation. However, most existing approaches focus solely on generating code in a single programming language, overlooking the potential of lever
Externí odkaz:
http://arxiv.org/abs/2409.04114
Autor:
Tahir, Ghalib Ahmed
This paper aims to shed light on the ethical problems of creating and deploying computer vision tech, particularly in using publicly available datasets. Due to the rapid growth of machine learning and artificial intelligence, computer vision has beco
Externí odkaz:
http://arxiv.org/abs/2409.10533
The growing demand for road use in urban areas has led to significant traffic congestion, posing challenges that are costly to mitigate through infrastructure expansion alone. As an alternative, optimizing existing traffic management systems, particu
Externí odkaz:
http://arxiv.org/abs/2408.15751