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Navid Khalili Dizaji and Mustafa Dogan
Brain tumors are one of the deadliest types of cancer. Rapid and accurate identification of brain tumors, followed by appropriate surgical intervention or chemotherapy, increases the probability of survival. Accurate determination of brain tumors in MRI ...
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Kaziwa Saleh, Sándor Szénási and Zoltán Vámossy
Although current computer vision systems are closer to the human intelligence when it comes to comprehending the visible world than previously, their performance is hindered when objects are partially occluded. Since we live in a dynamic and complex envi...
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Saqib Ali, Sana Ashraf, Muhammad Sohaib Yousaf, Shazia Riaz and Guojun Wang
The successful outcomes of deep learning (DL) algorithms in diverse fields have prompted researchers to consider backdoor attacks on DL models to defend them in practical applications. Adversarial examples could deceive a safety-critical system, which co...
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Smita Mahajan, Shruti Patil, Moinuddin Bhavnagri, Rashmi Singh, Kshitiz Kalra, Bhumika Saini, Ketan Kotecha and Jatinderkumar Saini
This paper aims at analyzing the performance of reinforcement learning (RL) agents when trained in environments created by a generative adversarial network (GAN). This is a first step towards the greater goal of developing fast-learning and robust RL age...
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Dmitry Namiot,Eugene Ilyushin
Pág. 101 - 118
This article, written for the Robust Machine Learning Curriculum, discusses the so-called Generative Models in Machine Learning. Generative models learn the distribution of data from some sample data set and then can generate (create) new data instances....
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Dmitry Namiot,Eugene Ilyushin
Pág. 101 - 118
This article, written for the Robust Machine Learning Curriculum, discusses the so-called Generative Models in Machine Learning. Generative models learn the distribution of data from some sample data set and then can generate (create) new data instances....
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James Msughter Adeke, Guangjie Liu, Junjie Zhao, Nannan Wu and Hafsat Muhammad Bashir
Machine learning (ML) models are essential to securing communication networks. However, these models are vulnerable to adversarial examples (AEs), in which malicious inputs are modified by adversaries to produce the desired output. Adversarial training i...
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Sapdo Utomo, Adarsh Rouniyar, Hsiu-Chun Hsu and Pao-Ann Hsiung
Smart city applications that request sensitive user information necessitate a comprehensive data privacy solution. Federated learning (FL), also known as privacy by design, is a new paradigm in machine learning (ML). However, FL models are susceptible to...
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Ajay Bandi, Pydi Venkata Satya Ramesh Adapa and Yudu Eswar Vinay Pratap Kumar Kuchi
Generative artificial intelligence (AI) has emerged as a powerful technology with numerous applications in various domains. There is a need to identify the requirements and evaluation metrics for generative AI models designed for specific tasks. The purp...
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Huidong Tang, Sayaka Kamei and Yasuhiko Morimoto
Text classification is widely studied in natural language processing (NLP). Deep learning models, including large pre-trained models like BERT and DistilBERT, have achieved impressive results in text classification tasks. However, these models? robustnes...
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Woonghee Lee, Mingeon Ju, Yura Sim, Young Kul Jung, Tae Hyung Kim and Younghoon Kim
Deep learning-based segmentation models have made a profound impact on medical procedures, with U-Net based computed tomography (CT) segmentation models exhibiting remarkable performance. Yet, even with these advances, these models are found to be vulner...
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Valeria Mercuri, Martina Saletta and Claudio Ferretti
As the prevalence and sophistication of cyber threats continue to increase, the development of robust vulnerability detection techniques becomes paramount in ensuring the security of computer systems. Neural models have demonstrated significant potential...
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Shruti Patil, Vijayakumar Varadarajan, Devika Walimbe, Siddharth Gulechha, Sushant Shenoy, Aditya Raina and Ketan Kotecha
Cyber security is used to protect and safeguard computers and various networks from ill-intended digital threats and attacks. It is getting more difficult in the information age due to the explosion of data and technology. There is a drastic rise in the ...
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Everton Jose Santana, Ricardo Petri Silva, Bruno Bogaz Zarpelão and Sylvio Barbon Junior
With data collected by Internet of Things sensors, deep learning (DL) models can forecast the generation capacity of photovoltaic (PV) power plants. This functionality is especially relevant for PV power operators and users as PV plants exhibit irregular...
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Angelo Casolaro, Vincenzo Capone, Gennaro Iannuzzo and Francesco Camastra
A time series is a sequence of time-ordered data, and it is generally used to describe how a phenomenon evolves over time. Time series forecasting, estimating future values of time series, allows the implementation of decision-making strategies. Deep lea...
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Zahid Masood, Muhammad Usama, Shahroz Khan, Konstantinos Kostas and Panagiotis D. Kaklis
Generative models offer design diversity but tend to be computationally expensive, while non-generative models are computationally cost-effective but produce less diverse and often invalid designs. However, the limitations of non-generative models can be...
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Biprodip Pal, Debashis Gupta, Md. Rashed-Al-Mahfuz, Salem A. Alyami and Mohammad Ali Moni
The COVID-19 pandemic requires the rapid isolation of infected patients. Thus, high-sensitivity radiology images could be a key technique to diagnose patients besides the polymerase chain reaction approach. Deep learning algorithms are proposed in severa...
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Han Zheng, Zanyang Cui and Xingchen Zhang
Driving modes play vital roles in understanding the stochastic nature of a railway system and can support studies of automatic driving and capacity utilization optimization. Integrated trajectory data containing information such as GPS trajectories and g...
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William Villegas-Ch, Angel Jaramillo-Alcázar and Sergio Luján-Mora
This study evaluated the generation of adversarial examples and the subsequent robustness of an image classification model. The attacks were performed using the Fast Gradient Sign method, the Projected Gradient Descent method, and the Carlini and Wagner ...
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Albatul Albattah and Murad A. Rassam
Deep learning (DL) models are frequently employed to extract valuable features from heterogeneous and high-dimensional healthcare data, which are used to keep track of patient well-being via healthcare monitoring systems. Essentially, the training and te...
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