Adaptive Neuro Fuzzy Inference System for Diagnosing Coronavirus Disease 2019 (COVID-19)

Ukaoha, Kingsley Chiwuike; Ademiluyi, Oluwadamilola; Ndunagu, Juliana Ngozi; Daodu, S.S.; Osang, F. B.

doi:10.21608/ijicis.2020.40518.1027

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…Computational intelligence techniques have been heavily involved in numerous applications related to various research fields such as face recognition through deep learning [2], Image retrieval through knowledge-based techniques [3]. Additionally, there are applications of AI in the field of medical research [4] and even in Covid-19 related research [5]. Computer music composition is a rather novel field of research.…”

Section: International Journal Of Intelligent Computing and Informatimentioning

confidence: 99%

Applications of Computational Intelligence in Computer Music Composition

Siphocly

Salem

El-Horabty

2021

IJICIS

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Engaging computers in composing musical pieces is a challenging and trending field of research. The musical tasks that can be performed or aided by computers' computational powers, are numerous. This paper is concerned with applications of computational intelligence in music composition. Its main objective is to survey various computational intelligence techniques for performing miscellaneous music composition tasks. To achieve this objective, we first define each music composition task, then we discuss the recent applications of each, and the techniques adopted in them. We also highlight the most suitable techniques for performing each task. Our study shows that the most suitable techniques for human composers imitative systems are case-based reasoning and artificial neural networks. It is also shown that Markov models are more suitable for predicting musical notes based on the given previous notes. Genetic algorithms excel in chord progressions generation. Deep neural networks are clever at capturing temporal information of a musical piece. The state-of-the-art generative adversarial networks produce music as close as possible to real compositions. At the end of this study, we shed the light on many future research directions in the field of computer music composition.

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Section: International Journal Of Intelligent Computing and Informatimentioning

confidence: 99%

Applications of Computational Intelligence in Computer Music Composition

Siphocly

Salem

El-Horabty

2021

IJICIS

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“…It is a fuzzy inference system (FIS) constructed in an adaptive network framework that combines the benefits of neural networks and fuzzy logic [4]. It is based on the Takagi-Sugeno FIS, which was created in the early 1990s [5]. This model can learn new values on its own and may be used to construct membership functions and IF-THEN rules from the data.…”

Section: Introductionmentioning

confidence: 99%

Medical Image-Based Diagnosis Using a Hybrid Adaptive Neuro-Fuzzy Inferences System (ANFIS) Optimized by GA with a Deep Network Model for Features Extraction

Rashed,

Popescu

2024

Mathematics

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Predicting diseases in the early stages is extremely important. By taking advantage of advances in deep learning and fuzzy logic techniques, a new model is proposed in this paper for disease evaluation depending on the adaptive neuro-fuzzy inference system (ANFIS) with a genetic algorithm (GA) for classification, and the pre-trained DenseNet-201 model for feature extraction, in addition to the whale optimization algorithm (WOA) for feature selection. Two medical databases (chest X-ray and MRI brain tumor) for the diagnosis of two disease types were used as input in the suggested model. The optimization of ANFIS parameters was performed by GA to achieve the optimum prediction capability. DenseNet-201 for feature extraction was employed to obtain better classification accuracy. Having more features sometimes leads to lower accuracy, and this issue can be rectified using a feature selection strategy WOA which gave good results. The proposed model was evaluated utilizing statistical metrics root mean square error (RMSE), mean square error (MSE), standard deviation (STD), and coefficient of determination (R2), and it was compared with the conventional ANFIS model, with the proposed model (ANFIS-GA) showing a superior prediction capability over the ANFIS model. As a result, it can be concluded that the proposed ANFIS-GA model is efficient and has the potential for a robust diseases evaluation with good accuracy. Also, we conclude from this work that integrating optimization algorithms with ANFIS boosts its performance, resulting in a more accurate and reliable model.

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“…Rauf ve arkadaşları çalışmaların d a COVID-19 vaka sayısını tahmin etmek için derin öğrenme tekniklerinden LSTM, RNN ve GRU yöntemlerin i kullanmışlar ve %90 doğruluk ile vaka sayısını tahmin ettiklerini belirtmişlerdir [24]. Ukaoha ve arkadaşları çalışmalarında COVID-19 teşhisi için 600 verinin bulunduğu veri setini ANFIS ile eğitimin i gerçekleştirdiklerini ve bu model ile %96,6 doğruluğa sahip bir sonuç elde ettiklerini belirtmişlerdir [25].…”

Section: Gi̇ri̇ş (Introduction)unclassified

Uyarlamalı Ağ Tabanlı Bulanık Mantık Çıkarım Sistemi ve Yapay Sinir Ağları ile Türkiye’deki COVID-19 Vefat Sayısının Tahmin Edilmesi

TURAN

Köse

2022

Bilişim Teknolojileri Dergisi

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COVID-19, küresel pandemi olarak ilan edilmiş, kritik ve ölümcül bir hastalıktır. Küresel salgın tehlikesinin sürmekte olduğu bu süreçte, pandemi ile yüzleşmek için özel politikalar ve planlar yapmak oldukça önemlidir. COVID-19 doğrulanmış verilerini kullanarak, yapay zeka teknikleri ile tahminlerde bulunmak ise gelecek planlarının geliştirilmesine yardımcı olduğu için önemli bir konudur. Bu çalışmada, Türkiye Sağlık Bakanlığı COVID-19 bilgilendirme sayfasında yayınlanan veriler kullanılarak, uyarlamalı ağ tabanlı bulanık mantık çıkarım sistemi (ANFIS) ve yapay sinir ağları (YSA) ile tahmin modelleri geliştirilmiştir. Bu modeller farklı istatistiksel değerlendirme kriterleri kullanılarak karşılaştırılmıştır. Değerlendirme sonucunda ANFIS modeli 0.0247 ortalama mutlak hata (MAE) değeri, 0.0012 ortalama kare hata (MSE) değeri, 0.0351 kök ortalama kare hata (RMSE) değeri ve 0.9847 regresyon katsayısı R2 sonucu ile en iyi model sonucunu elde etmiştir. Ayrıca ANFIS ile oluşturulan modelin gelecek beş günlük vefat sayılarını %96 doğruluk oranı ile tahmin ettiği görülmüştür.

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Adaptive Neuro Fuzzy Inference System for Diagnosing Coronavirus Disease 2019 (COVID-19)

Cited by 6 publications

References 11 publications

Applications of Computational Intelligence in Computer Music Composition

Applications of Computational Intelligence in Computer Music Composition

Medical Image-Based Diagnosis Using a Hybrid Adaptive Neuro-Fuzzy Inferences System (ANFIS) Optimized by GA with a Deep Network Model for Features Extraction

Uyarlamalı Ağ Tabanlı Bulanık Mantık Çıkarım Sistemi ve Yapay Sinir Ağları ile Türkiye’deki COVID-19 Vefat Sayısının Tahmin Edilmesi

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