Fuzzy Deep Neural Network for Classification of Overlapped Data

Dabare, Rukshima; Wong, Kok Wai; Shiratuddin, Mohd Fairuz; Koutsakis, P.

doi:10.1007/978-3-030-36708-4_52

Cited by 7 publications

(3 citation statements)

References 29 publications

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“…The approximation of the inverse dynamics of the system is a typical problem of regression; thus, the mean square error was set as a cost function in this study for both offline and online training. Various other examples of similar deep neuro-fuzzy structure designs can be found in the literature (An et al 2019 ; Aviles et al 2016 ; Dabare et al 2019 ; El Hatri and Boumhidi 2018 ; Korshunova 2018 ; Liu et al 2020a , b ; Ramasamy and Hameed 2019 ; Yeganejou and Dick 2018 , 2019 ).…”

Section: Analysis and Synthesis Of Datamentioning

confidence: 92%

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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Section: Analysis and Synthesis Of Datamentioning

confidence: 92%

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

et al. 2022

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“…Class overlap occurs when samples from multiple classes share a common region in the data space. These samples share feature values that tend to overlap despite being in different classes or are extremely close to one another, complicating classification with traditional ML and DL techniques [46], [47]. In an ideal classification, the data should be organized so that the inter-class distances are greater than the intra-class distances.…”

Section: Methodsmentioning

confidence: 99%

Machine learning analysis of lung adenocarcinoma and squamous cell carcinoma microbiome datasets reveals biomarkers for early diagnosis

Kashyap,

Raj,

Dutt

et al. 2023

Preprint

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IntroductionLung cancer (LC) is the second most frequent cancer worldwide with high incidences and mortality rates and non-small-cell-lung cancer (NSCLC) accounts for 80-85% cases of LC. It is further majorly sub-classified into adenocarcinoma (AC), and squamous-cell carcinoma (SCC). A late diagnosis at an advanced stage, a high rate of metastasis, and the development of therapy resistance are responsible for approximately 95% of mortality. Owing to high heterogeneity and variances in subtypes, it is important to precisely classify them for treatment. However, it poses a challenge in clinical practices, as it requires accurate quantification of each proportion subtype which is time-consuming and sometimes erroneous. The lower airways are home to a dynamic bacterial population sustained by the immigration, elimination, and migration of microbes from the gastrointestinal tract and upper airway tracts. The disruption in the homeostasis of microbiome compositions was found to be correlated with the increased risk of LC. Artificial intelligence (AI) techniques are used extensively in the early screening, and treatment of NSCLC have made significant strides in recent years. Recently, the use of CT/MRI scan image data in prediction models also results in false-positive rates and requires subsequent tests for further exploration which delays the prognostication. Therefore, early diagnosis, prevention, and treatment are critical to enhance survival and reduce death. Here, we aim for the classification of AC and SCC using the lung microbiome of lung tissue samples, implementing AI-based algorithms.MethodsWe have obtained raw sequencing data from the NCBI online database, and 149 AC samples and 145 SCC samples in patients were analyzed for their lung microbiome present in the lung tissue samples. The metadata such as patient age, sex, smoking history, and environmental material (malignant or not) were also analyzed. Using these data, machine learning algorithms were applied to select the best microbiome features for the classification of subtypes.ResultsA supervised ML and DL based model was developed that can discriminate NSCLC subtypes based on their microbial information, exploring the microbiome as predictive information for early screening. Consequently, 17 features were identified as a biomarker, and they showed good performance in distinguishing AC from SCC with an accuracy of 81% in KNN and 71% in DNN when demonstrated on the validation dataset.ConclusionThis study proposed a supervised machine learning framework where we can rely on taxonomic features and AI techniques to classify overlapped AC and SCC metagenomic data providing lung microbiome as a predictive and diagnostic biomarker in LC. Moreover, our framework will also be very helpful to other researchers to obtain further biomarkers and perform analysis in overlapped subtypes in different diseases.

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“…What the user faces in both classes 1 and 2 is the stalling of the video. This type of problem, called class overlapping, is one of the toughest classification problems [21]. Some research studies emphasize that class overlapping has a more severe negative impact on classification compared to imbalanced class data [22].…”

Section: The Datasetmentioning

confidence: 99%

Deep Learning-Based Fault Localization in Video Networks Using Only Client-Side QoE

Dinaki

Shirmohammadi

Janulewicz

et al. 2020

IEEE Trans. Artif. Intell.

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Maintaining a satisfactory customer Quality of Experience (QoE) is of vital importance for video service providers such as Netflix or Amazon Prime Video. Network faults degrade QoE and must therefore be detected, isolated, and fixed. However, this is difficult because each part of the end-to-end path belongs to a different autonomous system (AS) that is typically owned by a different entity, such as the video streaming provider, the internet service provider (ISP), and the client's local network operator. Although the video service provider (VSP) is usually blamed by the customer when there is poor QoE, the VSP does not have access to many parts of the network to localize the issue. In this paper, we show that with the aid of AI, it is possible for the VSP to localize the network fault without having access to the faulty part and using only QoE metrics. We collected a dataset from an actual video streaming testbed, where multiple videos are streamed from a video server through a simplified ISP network to a client network. Actual faults were generated in both the ISP and the client networks. Using only the QoE metrics measured at the client side, we use the deep learning methods of multi-layer perceptron (MLP) and long-short-term memory (LSTM) to detect and localize the fault with an accuracy of 93-97%, depending on the situation.Impact Statement-Technologically, our work impacts video/game streaming service providers such as Netflix, YouTube, Amazon Prime, Google Stadia, Sony PlayStation Now, Nvidia GeForce Now, and videoconferencing providers such as Zoom and Skype. Our work enables these providers to train similar AI systems that can localize network problems using only the video quality of experience (QoE) recorded by their client software. They can then take an appropriate action, such as rerouting traffic using Open Connect Appliances (OCA) if available, using another network provider if they have contracts with more than one, or informing the owner of the network segment with the fault, so they can fix the problem and maintain their customers' QoE at a satisfactory level. Economically, our work can contribute to the market expansion of any video streaming solution because it will lead to better QoE, which is synonymous with more customers.

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Fuzzy Deep Neural Network for Classification of Overlapped Data

Cited by 7 publications

References 29 publications

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

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

Machine learning analysis of lung adenocarcinoma and squamous cell carcinoma microbiome datasets reveals biomarkers for early diagnosis

Deep Learning-Based Fault Localization in Video Networks Using Only Client-Side QoE

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