Results 41 to 50 of about 267,347 (270)

DL-MRI: A Unified Framework of Deep Learning-Based MRI Super Resolution [PDF]

Journal of Healthcare Engineering, 2021
Magnetic resonance imaging (MRI) is widely used in the detection and diagnosis of diseases. High-resolution MR images will help doctors to locate lesions and diagnose diseases. However, the acquisition of high-resolution MR images requires high magnetic field intensity and long scanning time, which will bring discomfort to patients and easily introduce
Huanyu Liu, Jiaqi Liu, Junbao Li, Jeng-Shyang Pan, Xiaqiong Yu   +4 more
openaire   +2 more sources

Deep Learning for Software Vulnerabilities Detection Using Code Metrics

IEEE Access, 2020
Software vulnerability can cause disastrous consequences for information security. Earlier detection of vulnerabilities minimizes these consequences.
Mohammed Zagane, Mustapha Kamel Abdi, Mamdouh Alenezi   +2 more
doaj   +1 more source

Demystifying Deep Learning: A Geometric Approach to Iterative Projections

, 2018
Parametric approaches to Learning, such as deep learning (DL), are highly popular in nonlinear regression, in spite of their extremely difficult training with their increasing complexity (e.g. number of layers in DL).
Dai, Liyi, Krim, Hamid, Panahi, Ashkan
core   +1 more source

Enhancing prediction of supraspinatus/infraspinatus tendon complex injuries through integration of deep visual features and clinical information: a multicenter two-round assessment study

Insights into Imaging, 2023
Objective Develop and evaluate an ensemble clinical machine learning–deep learning (CML-DL) model integrating deep visual features and clinical data to improve the prediction of supraspinatus/infraspinatus tendon complex (SITC) injuries. Methods Patients
Yamuhanmode Alike, Cheng Li, Jingyi Hou, Yi Long, Jinming Zhang, Chuanhai Zhou, Zongda Zhang, Qi Zhu, Tao Li, Shinan Cao, Yuanhao Zhang, Dan Wang, Shuangqin Cheng, Rui Yang   +13 more
doaj   +1 more source

BPGrad: Towards Global Optimality in Deep Learning via Branch and Pruning

, 2017
Understanding the global optimality in deep learning (DL) has been attracting more and more attention recently. Conventional DL solvers, however, have not been developed intentionally to seek for such global optimality.
Wang, Guanghui, Wu, Yuanwei, Zhang, Ziming   +2 more
core   +1 more source

Learning Fast and Slow: PROPEDEUTICA for Real-time Malware Detection

, 2017
In this paper, we introduce and evaluate PROPEDEUTICA, a novel methodology and framework for efficient and effective real-time malware detection, leveraging the best of conventional machine learning (ML) and deep learning (DL) algorithms. In PROPEDEUTICA,
Chen, Aokun, Gregio, Andre, He, Pan, Li, Xiaolin, Oliveira, Daniela, Sun, Ruimin, Yuan, Xiaoyong, Zhu, Qile   +7 more
core   +1 more source

What does fault tolerant Deep Learning need from MPI?

, 2017
Deep Learning (DL) algorithms have become the de facto Machine Learning (ML) algorithm for large scale data analysis. DL algorithms are computationally expensive - even distributed DL implementations which use MPI require days of training (model learning)
Amatya, Vinay, Daily, Jeff, Siegel, Charles, Vishnu, Abhinav   +3 more
core   +1 more source

Deep learning for face detection using matlab [PDF]

, 2020
This project report presents face detection using Convolutional Neural Network algorithm and Deep Learning combination (DCT / DL) throughout MATLAB simulation and modeling.
Slim, Salim Adnan
core  

Deep Learning for Credit Card Fraud Detection: A Review of Algorithms, Challenges, and Solutions

IEEE Access
Deep learning (DL), a branch of machine learning (ML), is the core technology in today’s technological advancements and innovations. Deep learning-based approaches are the state-of-the-art methods used to analyse and detect complex patterns in ...
Ibomoiye Domor Mienye, Nobert Jere
doaj   +1 more source

Digital twins to accelerate target identification and drug development for immune‐mediated disorders

FEBS Open Bio, EarlyView.
Digital twins integrate patient‐derived molecular and clinical data into personalised computational models that simulate disease mechanisms. They enable rapid identification and validation of therapeutic targets, prediction of drug responses, and prioritisation of candidate interventions.
Anna Niarakis, Philippe Moingeon
wiley   +1 more source