MCR-DL: Mix-and-Match Communication Runtime for Deep Learning
2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2023 In recent years, the training requirements of many state-of-the-art Deep Learning (DL) models have scaled beyond the compute and memory capabilities of a single processor, and necessitated distribution among processors. Training such massive models necessitates advanced parallelism strategies to maintain efficiency.
Quentin Anthony +7 more
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Deep Learning for Credit Card Fraud Detection: A Review of Algorithms, Challenges, and Solutions
IEEE AccessDeep 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
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A Comprehensive Review of Deep Learning: Architectures, Recent Advances, and Applications
InformationDeep learning (DL) has become a core component of modern artificial intelligence (AI), driving significant advancements across diverse fields by facilitating the analysis of complex systems, from protein folding in biology to molecular discovery in ...
Ibomoiye Domor Mienye, Theo G. Swart
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Deep-Learning-Based Segmentation of Cells and Analysis (DL-SCAN)
BiomoleculesWith 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 +6 more
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A survey of malware detection using deep learning [PDF]
Machine Learning with ApplicationsThe problem of malicious software (malware) detection and classification is a complex task, and there is no perfect approach. There is still a lot of work to be done. Unlike most other research areas, standard benchmarks are difficult to find for malware
Ahmed Bensaoud +2 more
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Machine and Deep Learning Approaches in Genome: Review Article [PDF]
Alfarama Journal of Basic & Applied Sciences, 2021 Throughout the years Machine Learning (ML) has increased a lot of consideration on ordinary products as search, filters, recognition and recently genomics.
Wael Awad +3 more
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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 +10 more
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TeachOpenCADD goes Deep Learning: Open-source Teaching Platform Exploring Molecular DL Applications [PDF]
, 2023 TeachOpenCADD is a free online platform that offers solutions to common computer-aided drug design (CADD) tasks using Python programming and open-source data and packages.
Verena, Wolf +10 more
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Convergence of Photovoltaic Power Forecasting and Deep Learning: State-of-Art Review
IEEE Access, 2021 Deep learning (DL)-based PV Power Forecasting (PVPF) emerged nowadays as a promising research direction to intelligentize energy systems. With the massive smart meter integration, DL takes advantage of the large-scale and multi-source data ...
Mohamed Massaoudi +4 more
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Deep Representation Learning for Speech Emotion Recognition [PDF]
, 2022 The success of machine learning (ML) algorithms generally depends on the quality of data representation or features. Good representations of the data make it easier to develop machine learning predictors or even deep learning (DL) classifiers.
Latif, Siddique
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