Zobrazeno 1 - 10
of 255
pro vyhledávání: '"Sadeghi, Zahra"'
A Review of Global Sensitivity Analysis Methods and a comparative case study on Digit Classification
Autor:
Sadeghi, Zahra, Matwin, Stan
Global sensitivity analysis (GSA) aims to detect influential input factors that lead a model to arrive at a certain decision and is a significant approach for mitigating the computational burden of processing high dimensional data. In this paper, we
Externí odkaz:
http://arxiv.org/abs/2406.16975
In this paper, we propose leveraging causal generative learning as an interpretable tool for explaining image classifiers. Specifically, we present a generative counterfactual inference approach to study the influence of visual features (i.e., pixels
Externí odkaz:
http://arxiv.org/abs/2401.11394
Autor:
Sadeghi, Zahra, Alizadehsani, Roohallah, Cifci, Mehmet Akif, Kausar, Samina, Rehman, Rizwan, Mahanta, Priyakshi, Bora, Pranjal Kumar, Almasri, Ammar, Alkhawaldeh, Rami S., Hussain, Sadiq, Alatas, Bilal, Shoeibi, Afshin, Moosaei, Hossein, Hladik, Milan, Nahavandi, Saeid, Pardalos, Panos M.
XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased
Externí odkaz:
http://arxiv.org/abs/2304.01543
Self-supervised Learning (SSL) is a machine learning algorithm for pretraining Deep Neural Networks (DNNs) without requiring manually labeled data. The central idea of this learning technique is based on an auxiliary stage aka pretext task in which l
Externí odkaz:
http://arxiv.org/abs/2303.01584
Autor:
Sadeghi Zahra, Matwin Stan
Publikováno v:
Journal of Intelligent Systems, Vol 32, Iss 1, Pp 1-26 (2023)
Anomaly detection is a fundamental problem in data science and is one of the highly studied topics in machine learning. This problem has been addressed in different contexts and domains. This article investigates anomalous data within time series dat
Externí odkaz:
https://doaj.org/article/09851cce627f47e2a14982d9713a0d5b
Autor:
Mahan, Fatemeh, Moridi Farimani, Mahdi, Alilou, Mostafa, Sadeghi, Zahra, Eidi, Maryam, Ramak, Parvin, Omrani, Marzieh
Publikováno v:
In South African Journal of Botany May 2024 168:9-15
Autor:
Sadeghi, Zahra, Alizadehsani, Roohallah, CIFCI, Mehmet Akif, Kausar, Samina, Rehman, Rizwan, Mahanta, Priyakshi, Bora, Pranjal Kumar, Almasri, Ammar, Alkhawaldeh, Rami S., Hussain, Sadiq, Alatas, Bilal, Shoeibi, Afshin, Moosaei, Hossein, Hladík, Milan, Nahavandi, Saeid, Pardalos, Panos M.
Publikováno v:
In Computers and Electrical Engineering August 2024 118 Part A
Autor:
Almosawy, Wala, Alizadeh, As'ad, Koosha, Naser, Najafi, Nahid, Abdi, Negar, Najafi, Mohammad, Sadeghi, Zahra, Ardalan, Aram
Publikováno v:
In Journal of Drug Delivery Science and Technology March 2024 93
Autor:
Sadeghi, Zahra
This study is concerned with the top-down visual processing benefit in the task of occluded object recognition. To this end, a psychophysical experiment is designed and carried out which aimed at investigating the effect of consistency of contextual
Externí odkaz:
http://arxiv.org/abs/2007.10232
Autor:
Anantrasirichai, Nantheera, Biggs, Juliet, Kelevitz, Krisztina, Sadeghi, Zahra, Wright, Tim, Thompson, James, Achim, Alin, Bull, David
The large volumes of Sentinel-1 data produced over Europe are being used to develop pan-national ground motion services. However, simple analysis techniques like thresholding cannot detect and classify complex deformation signals reliably making prov
Externí odkaz:
http://arxiv.org/abs/2005.03221