Zobrazeno 1 - 10
of 12 484
pro vyhledávání: '"Alhassan, A."'
Autor:
Eya, I. O., Iyida, E. U., Okike, O., Ugwoke, R. E., Menteso, F. M., Ugwu, C. J., Simpemba, P., Simfukwe, J., Phiri, D. Silungwe S. P., Abbey, G. F, Alhassan, J. A., Chukwude, A. E.
Forbush decreases (Fd) are transient, short-term reductions in the intensity of galactic cosmic rays that reach the Earth's surface. When this reduction is observed at multiple locations at the same time, it is referred to as simultaneous Forbush dec
Externí odkaz:
http://arxiv.org/abs/2409.19612
Accurate identification of strawberries during their maturing stages is crucial for optimizing yield management, and pest control, and making informed decisions related to harvest and post-harvest logistics. This study evaluates the performance of YO
Externí odkaz:
http://arxiv.org/abs/2408.05661
Autor:
Mumuni, Fuseini, Mumuni, Alhassan
Grounding DINO and the Segment Anything Model (SAM) have achieved impressive performance in zero-shot object detection and image segmentation, respectively. Together, they have a great potential to revolutionize applications in zero-shot semantic seg
Externí odkaz:
http://arxiv.org/abs/2406.19057
Publikováno v:
Publication of the Astronomical Society of Nigeria (PASN) 07, pg 44 - 55 (2022)
The short-term rapid CR flux depressions, generally referred to as Forbush decreases (FDs), are the most spectacular time-intensity CR variation. The need for analytical transformation of the observational CR time series data, to account for FDs and
Externí odkaz:
http://arxiv.org/abs/2406.05160
Autor:
Eisner, Nora L., Grunblatt, Samuel K., Barragán, Oscar, Faridani, Thea H., Lintott, Chris, Aigrain, Suzanne, Johnston, Cole, Mason, Ian R., Stassun, Keivan G., Bedell, Megan, Boyle, Andrew W., Ciardi, David R., Clark, Catherine A., Hebrard, Guillaume, Hogg, David W., Howell, Steve B., Klein, Baptiste, Llama, Joe, Winn, Joshua N., Zhao, Lily L., Murphy, Joseph M. Akana, Beard, Corey, Brinkman, Casey L., Chontos, Ashley, Cortes-Zuleta, Pia, Delfosse, Xavier, Giacalone, Steven, Gilbert, Emily A., Heidari, Neda, Holcomb, Rae, Jenkins, Jon M., Kiefer, Flavien, Lubin, Jack, Martioli, Eder, Polanski, Alex S., Saunders, Nicholas, Seager, Sara, Shporer, Avi, Tyler, Dakotah, Van Zandt, Judah, Alhassan, Safaa, Amratlal, Daval J., Antonel, Lais I., Bentzen, Simon L. S., Bosch, Milton K. D., Bundy, David, Chitsiga, Itayi, Delaunay, Jérôme F., Doisy, Xavier, Ferstenou, Richard, Fynø, Mark, Geary, James M., Haynaly, Gerry, Hermes, Pete, Huten, Marc, Lee, Sam, Metcalfe, Paul, Pennell, Garry J., Puszkarska, Joanna, Schäfer, Thomas, Stiller, Lisa, Tanner, Christopher, Tarr, Allan, Wilkinson, Andrew
Publikováno v:
Published in AJ, 2024
We report on the discovery and validation of a transiting long-period mini-Neptune orbiting a bright (V = 9.0 mag) G dwarf (TOI 4633; R = 1.05 RSun, M = 1.10 MSun). The planet was identified in data from the Transiting Exoplanet Survey Satellite by c
Externí odkaz:
http://arxiv.org/abs/2404.18997
We present a novel approach to detecting noun abstraction within a large language model (LLM). Starting from a psychologically motivated set of noun pairs in taxonomic relationships, we instantiate surface patterns indicating hypernymy and analyze th
Externí odkaz:
http://arxiv.org/abs/2404.15848
Autor:
Mumuni, Alhassan, Mumuni, Fuseini
Modern approach to artificial intelligence (AI) aims to design algorithms that learn directly from data. This approach has achieved impressive results and has contributed significantly to the progress of AI, particularly in the sphere of supervised d
Externí odkaz:
http://arxiv.org/abs/2403.11395
Publikováno v:
A Survey of Synthetic Data Augmentation Methods in Machine Vision. Machine Intelligence Research, 1-39, (2024)
The standard approach to tackling computer vision problems is to train deep convolutional neural network (CNN) models using large-scale image datasets which are representative of the target task. However, in many scenarios, it is often challenging to
Externí odkaz:
http://arxiv.org/abs/2403.10075
Autor:
Mumuni, Alhassan, Mumuni, Fuseini
Data augmentation is arguably the most important regularization technique commonly used to improve generalization performance of machine learning models. It primarily involves the application of appropriate data transformation operations to create ne
Externí odkaz:
http://arxiv.org/abs/2403.08352
Autor:
Mumuni, Fuseinin, Mumuni, Alhassan
Publikováno v:
Cognitive Systems Research, 84 (2024)
We review current and emerging knowledge-informed and brain-inspired cognitive systems for realizing adversarial defenses, eXplainable Artificial Intelligence (XAI), and zero-shot or few-short learning. Data-driven deep learning models have achieved
Externí odkaz:
http://arxiv.org/abs/2403.07078