Deep Learning Techniques in Big Data Analytics

Najafabadi, Maryam M.; Villanustre, Flavio; Khoshgoftaar, Taghi M.; Seliya, Naeem; Wald, Randall; Muharemagc, Edin

doi:10.1007/978-3-319-44550-2_5

Cited by 13 publications

(10 citation statements)

References 28 publications

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“…Deep learning algorithms can also take advantage a huge amount of unsupervised data to automatically learn complex representation [3]. The best results obtained on supervised learning tasks often involve an unsupervised feature learning step [87].…”

Section: Deep Learningmentioning

confidence: 99%

“…In addition, big data provides opportunities to make causality inference based on chains of sequence. Nevertheless, big data also introduces major challenges to ML such as high data dimensionality, model scalability, distributed computing, streaming data [3], adaptability, and usability. In this paper, we introduce a framework of ML on big data (MLBiD) to guide the discussion of its opportunities and challenges.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning on big data: Opportunities and challenges

et al. 2017

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Section: Deep Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine learning on big data: Opportunities and challenges

et al. 2017

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“…is key benefit makes DL an extremely valuable tool for big data analytics since the available raw data are largely unlabeled, unannotated, and uncategorized. Najafabadi et al [116,117] explore how DL is utilized for big data analytics by extracting complex patterns from massive volumes of data, semantic indexing, data tagging, fast information retrieval, and simplifying discriminative tasks. Najafabadi et al [116,117] also investigate DL in terms of analyzing the streaming data, high-dimensional data, scalability of models, and distributed computing.…”

Section: For Big Datamentioning

confidence: 99%

WearableDL: Wearable Internet-of-Things and Deep Learning for Big Data Analytics—Concept, Literature, and Future

Dargazany

Stegagno

Mankodiya

2018

Mobile Information Systems

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This work introduces Wearable deep learning (WearableDL) that is a unifying conceptual architecture inspired by the human nervous system, offering the convergence of deep learning (DL), Internet-of-things (IoT), and wearable technologies (WT) as follows: (1) the brain, the core of the central nervous system, represents deep learning for cloud computing and big data processing. (2) The spinal cord (a part of CNS connected to the brain) represents Internet-of-things for fog computing and big data flow/transfer. (3) Peripheral sensory and motor nerves (components of the peripheral nervous system (PNS)) represent wearable technologies as edge devices for big data collection. In recent times, wearable IoT devices have enabled the streaming of big data from smart wearables (e.g., smartphones, smartwatches, smart clothings, and personalized gadgets) to the cloud servers. Now, the ultimate challenges are (1) how to analyze the collected wearable big data without any background information and also without any labels representing the underlying activity; and (2) how to recognize the spatial/temporal patterns in this unstructured big data for helping end-users in decision making process, e.g., medical diagnosis, rehabilitation efficiency, and/or sports performance. Deep learning (DL) has recently gained popularity due to its ability to (1) scale to the big data size (scalability); (2) learn the feature engineering by itself (no manual feature extraction or hand-crafted features) in an end-to-end fashion; and (3) offer accuracy or precision in learning raw unlabeled/labeled (unsupervised/supervised) data. In order to understand the current state-of-the-art, we systematically reviewed over 100 similar and recently published scientific works on the development of DL approaches for wearable and person-centered technologies. The review supports and strengthens the proposed bioinspired architecture of WearableDL. This article eventually develops an outlook and provides insightful suggestions for WearableDL and its application in the field of big data analytics.

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“…employing the Image Classification Models (SVM, ELM, CNN, OSELM, ACNNELM, etc.) [2]. Industrial Revolution 4.0 demands more robust and scalable Image Classification models.…”

Section: Introductionmentioning

confidence: 99%

Accuracy Performance Degradation in Image Classification Models due to Concept Drift

Hashmani¹,

Jameel²,

Alhussain³

et al. 2019

IJACSA

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Big data is playing a significant role in the current computing revolution. Industries and organizations are utilizing their insights for Business Intelligence by using Deep Learning Networks (DLN). However, dynamic characteristics of BD introduce many critical issues for DLN; Concept Drift (CD) is one of them. CD issue appears frequently in Online Supervised Learning environments in which data trends change over time. The problem may even worsen in a BD environment due to the veracity and variability factors. The CD issue may render the DLN inapplicable by degrading the accuracy of classification results in DLN which is a very serious issue that needs to be addressed. Therefore, these DLN need to quickly adapt to changes for maintaining the accuracy level of the results. To overcome classification accuracy, we need some dynamical changes in the existing DLN. Therefore, in this paper, we examine some of the existing Shallow Learning and Deep Learning models and their behavior before and after the Concept Drift (in experiment 1) and validate the pre-trained Deep Learning network (ResNet-50). In future work, this experiment will examine the most recent pre-trained DLN (Alex Net, VGG16, VGG19) and identify their suitability to overcome Concept Drift using fine-tuning and transfer learning approaches.

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Deep Learning Techniques in Big Data Analytics

Cited by 13 publications

References 28 publications

Machine learning on big data: Opportunities and challenges

Machine learning on big data: Opportunities and challenges

WearableDL: Wearable Internet-of-Things and Deep Learning for Big Data Analytics—Concept, Literature, and Future

Accuracy Performance Degradation in Image Classification Models due to Concept Drift

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