Zobrazeno 1 - 10
of 66 195
pro vyhledávání: '"A Hafiz"'
Autor:
Muqtadir, Abdul, Bilal, Hafiz Syed Muhammad, Yousaf, Ayesha, Ahmed, Hafiz Farooq, Hussain, Jamil
This research work delves into the manifestation of hallucination within Large Language Models (LLMs) and its consequential impacts on applications within the domain of mental health. The primary objective is to discern effective strategies for curta
Externí odkaz:
http://arxiv.org/abs/2410.10853
Autor:
Shahid, Hafiz Faheem, Harjula, Erkki
The rapid growth of the Internet of Things (IoT) applications inflicts high requirements for computing resources and network bandwidth. A growing number of service providers are applying edge-cloud computing to improve the quality of their services.
Externí odkaz:
http://arxiv.org/abs/2412.03181
This survey provides an examination of the use of Deep Neural Networks (DNN) in Collaborative Filtering (CF) recommendation systems. As the digital world increasingly relies on data-driven approaches, traditional CF techniques face limitations in sca
Externí odkaz:
http://arxiv.org/abs/2412.01378
In this study, we introduce Convolutional Transformer Neural Collaborative Filtering (CTNCF), a novel approach aimed at enhancing recommendation systems by effectively capturing high-order structural information in user-item interactions. CTNCF repre
Externí odkaz:
http://arxiv.org/abs/2412.01376
Autor:
Islam, Md. Touhidul, Chowdhury, Md. Abtahi M., Salekin, Sumaiya, Maung, Aye T., Taki, Akil A., Imtiaz, Hafiz
In contrast to non-medical image denoising, where enhancing image clarity is the primary goal, medical image denoising warrants preservation of crucial features without introduction of new artifacts. However, many denoising methods that improve the c
Externí odkaz:
http://arxiv.org/abs/2411.19549
Autor:
Liu, Rong, Liu, Tie, Jiménez-Serra, Izaskun, Li, Jin-Zeng, Martín-Pintado, Jesús, Liu, Xunchuan, Lee, Chang Won, Sanhueza, Patricio, Chibueze, James O., Rivilla, Víctor M., Juvela, Mika, Colzi, Laura, Bronfman, Leonardo, Liu, Hong-Li, Sanz-Novo, Miguel, López-Gallifa, Álvaro, Li, Shanghuo, Megías, Andrés, Andrés, David San, Garay, Guido, Hwang, Jihye, Zhou, Jianwen, Xu, Fengwei, Martínez-Henares, Antonio, Saha, Anindya, Nazeer, Hafiz
The production of silicon monoxide (SiO) can be considered as a fingerprint of shock interaction. In this work, we use high-sensitivity observations of the SiO (2-1) and H$^{13}$CO$^{+}$ (1-0) emission to investigate the broad and narrow SiO emission
Externí odkaz:
http://arxiv.org/abs/2411.19489
Autor:
Dastagir, R. B., Jami, J. T., Chanda, S., Hafiz, F., Rahman, M., Dey, K., Rahman, M. M., Qureshi, M., Chowdhury, M. M.
Rapid diagnostic tests are crucial for timely disease detection and management, yet accurate interpretation of test results remains challenging. In this study, we propose a novel approach to enhance the accuracy and reliability of rapid diagnostic te
Externí odkaz:
http://arxiv.org/abs/2411.18007
In recent years, the growth of data across various sectors, including healthcare, security, finance, and education, has created significant opportunities for analysis and informed decision-making. However, these datasets often contain sensitive and p
Externí odkaz:
http://arxiv.org/abs/2411.16121
Autor:
Ige, Tosin, Kiekintveld, Christopher, Piplai, Aritran, Waggler, Amy, Kolade, Olukunle, Matti, Bolanle Hafiz
Phishing is one of the most effective ways in which cybercriminals get sensitive details such as credentials for online banking, digital wallets, state secrets, and many more from potential victims. They do this by spamming users with malicious URLs
Externí odkaz:
http://arxiv.org/abs/2411.16751
The rapid spread of misinformation, particularly through online platforms, underscores the urgent need for reliable detection systems. This study explores the utilization of machine learning and natural language processing, specifically Support Vecto
Externí odkaz:
http://arxiv.org/abs/2411.12703