Zobrazeno 1 - 10
of 3 266
pro vyhledávání: '"A., Yunusa"'
Autor:
Yunusa, Muhammad, Schulz, Andrew K., Parker, Tim, Schneider, Felix, Elibol, Kenan, Predel, Marius, Dzíbelová, Jana, Rebmann, Michel, Gorkan, Taylan, van Aken, Peter A., Meixner, Alfred J., Durgun, Engin, Kotakoski, Jani, Zhang, Dai, Sitti, Metin
Gallenene is a promising low-dimensional material with a structure down to the thickness of a single atom, similar to graphene. However, van der Waals stacking of two-dimensional (2D) gallenene under confinement remain poorly understood. In this stud
Externí odkaz:
http://arxiv.org/abs/2412.00461
Multimodal aspect-based sentiment analysis (MABSA) enhances sentiment detection by combining text with other data types like images. However, despite setting significant benchmarks, attention mechanisms exhibit limitations in efficiently modelling lo
Externí odkaz:
http://arxiv.org/abs/2408.15379
Crop field detection is a critical component of precision agriculture, essential for optimizing resource allocation and enhancing agricultural productivity. This study introduces KonvLiNA, a novel framework that integrates Convolutional Kolmogorov-Ar
Externí odkaz:
http://arxiv.org/abs/2408.13160
Autor:
Chukkol, Abdulrahman Hamman Adama, Luo, Senlin, Sharif, Kashif, Haruna, Yunusa, Abdullahi, Muhammad Muhammad
Binary program vulnerability detection is critical for software security, yet existing deep learning approaches often rely on source code analysis, limiting their ability to detect unknown vulnerabilities. To address this, we propose VulCatch, a bina
Externí odkaz:
http://arxiv.org/abs/2408.07181
Aspect-based Sentiment Analysis (ABSA) evaluates sentiments toward specific aspects of entities within the text. However, attention mechanisms and neural network models struggle with syntactic constraints. The quadratic complexity of attention mechan
Externí odkaz:
http://arxiv.org/abs/2407.10347
The recent emergence of hybrid models has introduced another transformative approach to solving computer vision tasks, slowly shifting away from conventional CNN (Convolutional Neural Network) and ViT (Vision Transformer). However, not enough effort
Externí odkaz:
http://arxiv.org/abs/2407.07603
Malaria is a life-threatening infectious disease caused by Plasmodium parasites, which poses a significant public health challenge worldwide, particularly in tropical and subtropical regions. Timely and accurate detection of malaria parasites in bloo
Externí odkaz:
http://arxiv.org/abs/2405.14242
Aspect-Based Sentiment Analysis (ABSA) is increasingly crucial in Natural Language Processing (NLP) for applications such as customer feedback analysis and product recommendation systems. ABSA goes beyond traditional sentiment analysis by extracting
Externí odkaz:
http://arxiv.org/abs/2405.13013
Detecting objects across various scales remains a significant challenge in computer vision, particularly in tasks such as Rice Leaf Disease (RLD) detection, where objects exhibit considerable scale variations. Traditional object detection methods oft
Externí odkaz:
http://arxiv.org/abs/2402.16291
Autor:
Yunusa, Haruna, Qin, Shiyin, Chukkol, Abdulrahman Hamman Adama, Yusuf, Abdulganiyu Abdu, Bello, Isah, Lawan, Adamu
The hybrid of Convolutional Neural Network (CNN) and Vision Transformers (ViT) architectures has emerged as a groundbreaking approach, pushing the boundaries of computer vision (CV). This comprehensive review provides a thorough examination of the li
Externí odkaz:
http://arxiv.org/abs/2402.02941