Applying Deep Learning Techniques for Big Data Analytics: A Systematic Literature Review

Muhammed, Memudu Tunde; Obidallah, Waeal J.; Raahemi, Bijan

doi:10.36959/863/756

Cited by 4 publications

(3 citation statements)

References 43 publications

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“…FP_rate = negatives incorrectly classified/total negatives (6) In binary classification and prediction problems, the precision metric is of utmost importance because it demonstrates how well the classifier predicts a label for each data point. Precision is the fraction of positively predicted points that are actually positive [58], or for the instance of this study the predicted outliers that are actually outliers.…”

Section: Evaluation Of Resultsmentioning

confidence: 99%

“…Anomaly and Cyber-Criminal Activities Detection Module: This module is responsible for applying advanced Big Data analytics techniques, based on machine learning, to the collected data [6]. The main purpose of this module is the identification of anomalies and behavioral indicators, as well as the revelation of unknown associations and rules that are linked with cyber-criminal activities.…”

Section: Semantic Information Representation and Fusion Modulementioning

confidence: 99%

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An Advanced Abnormal Behavior Detection Engine Embedding Autoencoders for the Investigation of Financial Transactions

et al. 2021

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Nowadays, (cyber)criminals demonstrate an ever-increasing resolve to exploit new technologies so as to achieve their unlawful purposes. Therefore, Law Enforcement Agencies (LEAs) should keep one step ahead by engaging tools and technology that address existing challenges and enhance policing and crime prevention practices. The framework presented in this paper combines algorithms and tools that are used to correlate different pieces of data leading to the discovery and recording of forensic evidence. The collected data are, then, combined to handle inconsistencies, whereas machine learning techniques are applied to detect trends and outliers. In this light, the authors of this paper present, in detail, an innovative Abnormal Behavior Detection Engine, which also encompasses a knowledge base visualization functionality focusing on financial transactions investigation.

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Section: Evaluation Of Resultsmentioning

confidence: 99%

Section: Semantic Information Representation and Fusion Modulementioning

confidence: 99%

An Advanced Abnormal Behavior Detection Engine Embedding Autoencoders for the Investigation of Financial Transactions

et al. 2021

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“…Various attractive approaches to conduct the survey studies have been implemented in (Singh and Singh 2020 ; Yu and Pan 2021 ; Yu and Sheng 2020 ). The guidelines followed in this paper are taken from systematic literature reviews published by Baashar et al ( 2020 ), Schön et al ( 2017 ), and Muhammed et al ( 2018 ), whereas, a revised study mapping process illustrated in Fig. 1 is designed for this review paper, which is a combination of preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement (Moher et al 2009 ; Safdar et al 2018 ); and a systematic mapping process presented by Hussain et al ( 2019 ).…”

Section: Methodsmentioning

confidence: 99%

Deep Neuro-Fuzzy System application trends, challenges, and future perspectives: a systematic survey

et al. 2022

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Deep neural networks (DNN) have remarkably progressed in applications involving large and complex datasets but have been criticized as a black-box. This downside has recently become a motivation for the research community to pursue the ideas of hybrid approaches, resulting in novel hybrid systems classified as deep neuro-fuzzy systems (DNFS). Studies regarding the implementation of DNFS have rapidly increased in the domains of computing, healthcare, transportation, and finance with high interpretability and reasonable accuracy. However, relatively few survey studies have been found in the literature to provide a comprehensive insight into this domain. Therefore, this study aims to perform a systematic review to evaluate the current progress, trends, arising issues, research gaps, challenges, and future scope related to DNFS studies. A study mapping process was prepared to guide a systematic search for publications related to DNFS published between 2015 and 2020 using five established scientific directories. As a result, a total of 105 studies were identified and critically analyzed to address research questions with the objectives: (i) to understand the concept of DNFS; (ii) to find out DNFS optimization methods; (iii) to visualize the intensity of work carried out in DNFS domain; and (iv) to highlight DNFS application subjects and domains. We believe that this study provides up-to-date guidance for future research in the DNFS domain, allowing for more effective advancement in techniques and processes. The analysis made in this review proves that DNFS-based research is actively growing with a substantial implementation and application scope in the future.

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Challenges and Opportunities on the Application of Heuristic Evaluations: A Systematic Literature Review

Lecaros

Paz

Moquillaza

2021

Design, User Experience, and Usability: UX Research and Design

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Applying Deep Learning Techniques for Big Data Analytics: A Systematic Literature Review

Cited by 4 publications

References 43 publications

An Advanced Abnormal Behavior Detection Engine Embedding Autoencoders for the Investigation of Financial Transactions

An Advanced Abnormal Behavior Detection Engine Embedding Autoencoders for the Investigation of Financial Transactions

Deep Neuro-Fuzzy System application trends, challenges, and future perspectives: a systematic survey

Challenges and Opportunities on the Application of Heuristic Evaluations: A Systematic Literature Review

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