Results 131 to 140 of about 33,606 (255)

Artificial intelligence for adaptive neuromodulation in drug‐resistant epilepsy

Epilepsia, EarlyView.
Abstract Drug‐resistant epilepsy (DRE) affects nearly one third of people with epilepsy and is associated with substantial cognitive, psychiatric, and mortality burdens. For patients who are not candidates for resection or laser interstitial thermal therapy, neuromodulation therapies such as vagus nerve stimulation, deep brain stimulation, and ...
Amir Hossein Daraie, Arianna Damiani, Mahsa Khoshkhou, Luis A. Sanchez, Rachel J. Smith, Shruti H. Agashe, Jorge A. Gonzalez‐Martinez, Jennifer Hopp, Adam S. Charles, Sridevi V. Sarma, Joon‐Yi Kang   +10 more
wiley   +1 more source

AI‐based localization of the epileptogenic zone using intracranial EEG

Epilepsia Open, EarlyView.
Abstract Artificial intelligence (AI) is rapidly transforming our lives. Machine learning (ML) enables computers to learn from data and make decisions without explicit instructions. Deep learning (DL), a subset of ML, uses multiple layers of neural networks to recognize complex patterns in large datasets through end‐to‐end learning.
Atsuro Daida, Yipeng Zhang, Sotaro Kanai, Richard Staba, Vwani Roychowdhury, Hiroki Nariai   +5 more
wiley   +1 more source

GAN‐LSTM‐3D: An efficient method for lung tumour 3D reconstruction enhanced by attention‐based LSTM

CAAI Transactions on Intelligence Technology, EarlyView., 2023
Abstract Three‐dimensional (3D) image reconstruction of tumours can visualise their structures with precision and high resolution. In this article, GAN‐LSTM‐3D method is proposed for 3D reconstruction of lung cancer tumours from 2D CT images. Our method consists of three phases: lung segmentation, tumour segmentation, and tumour 3D reconstruction. Lung
Lu Hong, Mohammad Hossein Modirrousta, Mohammad Hossein Nasirpour, Mohammadreza Mirshekari Chargari, Fardin Mohammadi, Seyed Vahid Moravvej, Leila Rezvanishad, Mohammadreza Rezvanishad, Ivan Bakhshayeshi, Roohallah Alizadehsani, Imran Razzak, Hamid Alinejad‐Rokny, Saeid Nahavandi   +12 more
wiley   +1 more source

Meta-IDS-GAN: Utilizing Meta Learning to Enhance GAN-based Adversarial Traffic Generation

This thesis proposes Meta-IDS-GAN, a novel adversarial attack framework that combines Generative Adversarial Networks (GAN) with Model-Agnostic Meta-Learning (MAML) to generate highly transferable and statistically realistic adversarial network traffic ...
Xu, Yufeng
core  

Application of GAN–CNN in Risk Assessment of Pipeline Failures in Multiphase Pipeline With Image Information Encoding Approach

Energy Science &Engineering, EarlyView.
Field data from multiphase pipelines are transformed into grayscale images via Image Information Encoding, preserving feature values and interparameter relationships. A GAN–CNN model generates synthetic images that are decoded to expand the original database.
Sihang Chen, Na Zhang, Biyuan Shui
wiley   +1 more source

Explainable human‐in‐the‐loop healthcare image information quality assessment and selection

CAAI Transactions on Intelligence Technology, EarlyView., 2023
Abstract Smart healthcare applications cannot be separated from healthcare data analysis and the interactive interpretability between data and model. A human‐in‐the‐loop active learning approach is introduced to reduce the cost of healthcare data labelling by evaluating the information quality of unlabelled medical data and then screening the high ...
Yang Li, Sezai Ercisli
wiley   +1 more source

A Review of Overcurrent Protection in Smart Grids Under Cyber‐Physical Threats With a Cyber‐Physical Evaluation Framework

Energy Science &Engineering, EarlyView.
By manipulating current and voltage measurements, an assailant can induce unwanted relay action while attempting to avoid detection. Detecting advanced cyber intrusions in power protection environments requires specialised data analysis and anomaly detection methods.
Feras Alasali, Anas AlMajali, Naser Naily, Hassen Loukil, Salah AbuGhalyon, William Holderbaum, Abdelaziz Salah Saidi   +6 more
wiley   +1 more source

Domain‐adapted driving scene understanding with uncertainty‐aware and diversified generative adversarial networks

CAAI Transactions on Intelligence Technology, EarlyView., 2023
Abstract Autonomous vehicles are required to operate in an uncertain environment. Recent advances in computational intelligence techniques make it possible to understand driving scenes in various environments by using a semantic segmentation neural network, which assigns a class label to each pixel.
Yining Hua, Jie Sui, Hui Fang, Chuan Hu, Dewei Yi   +4 more
wiley   +1 more source

Deep generative models as an adversarial attack strategy for tabular machine learning

peer reviewedDeep Generative Models (DGMs) have found application in computer vision for generating adversarial examples to test the robustness of machine learning (ML) systems.
Giunchiglia, Eleonora, Cătălina Stoian, Mihaela, Cordy, Maxime, DYRMISHI, Salijona   +3 more
core