Results 1 to 10 of about 264,274 (268)

DL-LA: Deep Learning Leakage Assessment

Transactions on Cryptographic Hardware and Embedded Systems, 2021
In recent years, deep learning has become an attractive ingredient to side-channel analysis (SCA) due to its potential to improve the success probability or enhance the performance of certain frequently executed tasks.
Thorben Moos, Felix Wegener, Amir Moradi
doaj   +4 more sources

Deep Learning (DL)-Enabled System for Emotional Big Data [PDF]

IEEE Access, 2021
Emotion care for human well-being is important for all ages. In this paper, we propose an emotion care system based on big data analysis for autism disorder patient training, where emotion is detected in terms of facial expression.
Haopeng Wang, Diana P. Tobon V., M. Shamim Hossain, Abdulmotaleb El Saddik   +3 more
doaj   +2 more sources

Electroencephalographic (EEG) based Deep Learning (DL): A Comparative Review

Al-Iraqia Journal for Scientific Engineering Research, 2023
Deep learning (DL) has recently shown great promise in supporting knowledge of electroencephalographic (EEG) as a result of its ability to discover visual features (feature representation) from original (raw) data.
Riyadh Salam Mohammed, Ammar A. Al-Hamadani   +1 more
doaj   +3 more sources

DL-RMD: a geophysically constrained electromagnetic resistivity model database (RMD) for deep learning (DL) applications [PDF]

Earth System Science Data, 2023
Deep learning (DL) algorithms have shown incredible potential in many applications. The success of these data-hungry methods is largely associated with the availability of large-scale datasets, as millions of observations are often required to achieve ...
M. R. Asif, M. R. Asif, M. R. Asif, N. Foged, N. Foged, T. Bording, T. Bording, J. J. Larsen, J. J. Larsen, A. V. Christiansen, A. V. Christiansen   +10 more
doaj   +3 more sources

DL-TODA: A Deep Learning Tool for Omics Data Analysis

Biomolecules, 2023
Metagenomics is a technique for genome-wide profiling of microbiomes; this technique generates billions of DNA sequences called reads. Given the multiplication of metagenomic projects, computational tools are necessary to enable the efficient and ...
Cecile M. Cres, Andrew Tritt, Kristofer E. Bouchard, Ying Zhang   +3 more
doaj   +5 more sources

Evaluation of deep learning tools in medical diagnosis and treatment of cancer: research analysis of clinical and randomized clinical trials [PDF]

Frontiers in Network Physiology
Artificial Intelligence and machine learning tools have brought a revolution in the healthcare sector. This has allowed healthcare providers, patients, and public to be at pole position -amidst the key consideration and barriers-to attain precision and ...
Rawad Hodeify
doaj   +2 more sources

DL-AMDet: Deep learning-based malware detector for android

Intelligent Systems with Applications
The Android operating system, with its market share leadership and open-source nature in smartphones, has become the primary target of malware. However, detecting malicious Android processes has become a significant challenge because of the complexity of
Ahmed R. Nasser, Ahmed M. Hasan, Amjad J. Humaidi   +2 more
doaj   +2 more sources

Integrating CT-based radiomics and deep learning for invasive prediction of ground-glass nodules in lung adenocarcinoma: a multicohort study [PDF]

Insights into Imaging
Objectives This study aimed to explore a multiple-instance learning (MIL) framework incorporating radiomics features and deep learning representations to predict the invasiveness of ground-glass nodules (GGNs) in lung adenocarcinoma (LUAD) using ...
Hai Du, Jing Shen, Feng Chen, Kaifeng Wang, Lili Qin, Yijiang Hu, Yue Xiao, Xiulin Wang, Jianlin Wu   +8 more
doaj   +2 more sources

Deep-Learning-Based Segmentation of Cells and Analysis (DL-SCAN)

Biomolecules
With the recent surge in the development of highly selective probes, fluorescence microscopy has become one of the most widely used approaches to studying cellular properties and signaling in living cells and tissues.
Alok Bhattarai, Jan Meyer, Laura Petersilie, Syed I. Shah, Louis A. Neu, Christine R. Rose, Ghanim Ullah   +6 more
doaj   +3 more sources

How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset [PDF]

Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021
Deep Learning Hard (DL-HARD) is a new annotated dataset designed to more effectively evaluate neural ranking models on complex topics. It builds on TREC Deep Learning (DL) topics by extensively annotating them with question intent categories, answer types, wikified entities, topic categories, and result type metadata from a commercial web search engine.
Mackie, Iain, Dalton, Jeff, Yates, Andrew   +2 more
openaire   +5 more sources