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

Abstract

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.

Systematic Literature Review of Information Extraction From Textual Data: Recent Methods, Applications, Trends, and Challenges

“…The methodology used in this study was adopted based on the SLR guidelines [31], [32]. Additionally, this systematic study followed the reporting style and flow of comprehensive studies [33], [34]. The modified mapping process employed in this study is shown in Figure 1 and further explained in Section III-A.…”

“…Additionally, these documents may come from various domains, such as healthcare, science, chemistry, law, safety, and finance. Consequently, creating annotated corpora or labeled data for these human-generated documents is highly resource-intensive and time-consuming as it requires many rules to be generated for IE task purposes [16], [34], [171].…”

Deep Learning in Diverse Intelligent Sensor Based Systems

“…A FDNN can be comprised by a broad category of DNNs and fuzzy inference systems in different architectures. Current architectures in the literature can be classified into three categories: sequential FDNN, parallel FDNN, and cooperative FDNN [ 189 ].…”

FLORA: Fuzzy Logic - Objective Risk Analysis for Intrusion Detectionand Prevention

“…Although it is difficult to collect evidence to support IoT and Cloud Computing cybercrime cases without deductive knowledge, and even more challenging to connect various pieces of evidence into a chain of custody, it is critically important to consider the classification of the crime, the methods of collecting evidence, and all relevant laws and regulations in an extremely consistent manner use of blockchain technology [20]. The systematic classification of data, using advanced data mining techniques to categorize data [21], to aid in a more accurate analysis should be the norm. Such classifications should also assist analysts to predict the target class for each new case in the data and information flow.…”