A Novel Optimization Algorithm: Cascaded Adaptive Neuro-Fuzzy Inference System

Rathnayake, Namal; Dang, Tuan Linh; Hoshino, Yukinobu

doi:10.1007/s40815-021-01076-z

Cited by 23 publications

(18 citation statements)

References 47 publications

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“…ANFIS combines two different algorithms, such as NN and FL. Therefore, ANFIS showcases the advantages of both NN and FL algorithms [26]. ANFIS has six layers in its structure.…”

Section: The Cascaded-anfis Algorithmmentioning

confidence: 99%

“…The Cascaded-ANFIS algorithm is made up of two major parts: the pair selection method and the training method. More information and technical details about Cascaded-ANFIS can be found in Rathnayake et al [26,28]. ANFIS poses a major disadvantage when it is used with higher dimensional data.…”

Section: The Cascaded-anfis Algorithmmentioning

confidence: 99%

“…Therefore, this research study proposes a new algorithm based on the Cascaded Adaptive Neuro Fuzzy Inference System (Cascaded-ANFIS) [26] to fill the above-identified research gaps in the literature and then to present a much enhanced and robust model to identify the fruits based on their properties. The major contributions of the presented research can be listed as follows.…”

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confidence: 99%

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An Efficient Automatic Fruit-360 Image Identification and Recognition Using a Novel Modified Cascaded-ANFIS Algorithm

Rathnayake

Dang

et al. 2022

Sensors

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Automated fruit identification is always challenging due to its complex nature. Usually, the fruit types and sub-types are location-dependent; thus, manual fruit categorization is also still a challenging problem. Literature showcases several recent studies incorporating the Convolutional Neural Network-based algorithms (VGG16, Inception V3, MobileNet, and ResNet18) to classify the Fruit-360 dataset. However, none of them are comprehensive and have not been utilized for the total 131 fruit classes. In addition, the computational efficiency was not the best in these models. A novel, robust but comprehensive study is presented here in identifying and predicting the whole Fruit-360 dataset, including 131 fruit classes with 90,483 sample images. An algorithm based on the Cascaded Adaptive Network-based Fuzzy Inference System (Cascaded-ANFIS) was effectively utilized to achieve the research gap. Color Structure, Region Shape, Edge Histogram, Column Layout, Gray-Level Co-Occurrence Matrix, Scale-Invariant Feature Transform, Speeded Up Robust Features, Histogram of Oriented Gradients, and Oriented FAST and rotated BRIEF features are used in this study as the features descriptors in identifying fruit images. The algorithm was validated using two methods: iterations and confusion matrix. The results showcase that the proposed method gives a relative accuracy of 98.36%. The Fruit-360 dataset is unbalanced; therefore, the weighted precision, recall, and FScore were calculated as 0.9843, 0.9841, and 0.9840, respectively. In addition, the developed system was tested and compared against the literature-found state-of-the-art algorithms for the purpose. Comparison studies present the acceptability of the newly developed algorithm handling the whole Fruit-360 dataset and achieving high computational efficiency.

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“…ANFIS combines two different algorithms, such as NN and FL. Therefore, ANFIS showcases the advantages of both NN and FL algorithms [26]. ANFIS has six layers in its structure.…”

Section: The Cascaded-anfis Algorithmmentioning

confidence: 99%

Section: The Cascaded-anfis Algorithmmentioning

confidence: 99%

mentioning

confidence: 99%

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An Efficient Automatic Fruit-360 Image Identification and Recognition Using a Novel Modified Cascaded-ANFIS Algorithm

Rathnayake

Dang

et al. 2022

Sensors

Self Cite

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“…This method has the advantage of multi-objective prediction but reduces the number of parameters [ 18 ]. Rathnayake et al have improved the ANFIS model by using optimization techniques to determine the parameters for the ANFIS model [ 19 ]. Recently, research-based techniques of using artificial intelligence in forecasting problems have attracted much attention from scientists.…”

Section: Introductionmentioning

confidence: 99%

“…Type-2 fuzzy sets are called an interval type-2 fuzzy sets if the secondary membership function i.e., A type-2 fuzzy set is defined as follows: An interval type-2 fuzzy set [ 19 ] is characterized by an interval type-2 membership function where and , i.e., Uncertainty of , denoted FOU, is union of primary functions, i.e., . Upper/lower bounds of membership function (UMF/LMF), denoted and , of are two type-1 membership function and bounds of FOU (see Fig.…”

Section: Introductionmentioning

confidence: 99%

Application of Interval Type-2 Fuzzy Logic System and Ant Colony Optimization for Hydropower Dams Displacement Forecasting

Mai

Bui

Doan

2023

Int. J. Fuzzy Syst.

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Forecasting the dam displacement is an important problem in dam monitoring. By using multi-temporal monitoring data to make predictions and forecast dam structure health, it is possible to detect abnormalities over time of the dam so that remedial measures can be taken promptly to minimize risks. The paper proposes a method of applying the interval type-2 fuzzy logic system (IT2FLS) and ant colony optimization (ACO) technique to forecast hydroelectric dams’ displacement. The method consists of two stages: The first is to design an IT2FLS according to the input and output data with base rules and newly constructed membership functions. The second is to find the optimal parameters for an interval type-2 fuzzy logic system based on the ACO technique and give the final fuzzy forecasting system. Experimental data are measured from hydroelectricity monitoring data of Ialy, Gia Lai Province, Vietnam, in the period from 2004 to 2016. The proposed method is compared with some other methods to compare prediction efficiency and accuracy. The experimental results show that the proposed model has the best performance compared to other standard methods in both accuracy and stability of prediction. This shows that a predictive model based on the IT2FLS is a promising method and can be used for forecasting time-series data. Supplementary Information The online version contains supplementary material available at 10.1007/s40815-022-01452-3.

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A novel‐cascaded ANFIS‐based deep reinforcement learning for the detection of attack in cloud IoT‐based smart city applications

Almasri

Alajlan

2023

Concurrency and Computation

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SummaryVast usages of Internet of Things (IoT) devices in various smart applications have laid a foundation for the evolution of modern smart cities. The increasing dependency of smart city applications on communication and information technologies enhances operational efficiency, sustainability, and automation of city services. However, due to the heterogeneous nature of IoT devices, the network faces critical security issues while executing continued network operations and services, particularly by cyber‐attacks. One of the predominant and rampant cyber‐attacks in smart city applications is botnet attacks. Therefore, a novel deep learning model for the detection and isolation of cyber‐attacks is proposed in the cloud IoT‐based smart city applications to protect against such cyber‐attacks. The proposed framework utilizes two different modules to automatically detect and isolate the malicious traffic emanating from compromised IoT devices with more efficiency. Here, two different datasets namely the IoT network intrusion and the ISCX 2012 IDs datasets are utilized for the evaluation of the proposed framework. In the first phase, the compromised device which communicates malicious network traffics through the network is identified using a cascaded adaptive neuro‐fuzzy inference system (CANFIS). After detection, IP address of abnormal traffic is recorded and informed to the system administrator. In the second phase, communication pathways of compromised devices with other normal devices are blocked and the compromised devices are isolated from the network using the modified deep reinforcement learning (MDRL) approach. The analytic result shows that the proposed framework achieves a greater accuracy rate of about 98.7% as compared to other state‐of‐art methods.

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A Novel Optimization Algorithm: Cascaded Adaptive Neuro-Fuzzy Inference System

Cited by 23 publications

References 47 publications

An Efficient Automatic Fruit-360 Image Identification and Recognition Using a Novel Modified Cascaded-ANFIS Algorithm

An Efficient Automatic Fruit-360 Image Identification and Recognition Using a Novel Modified Cascaded-ANFIS Algorithm

Application of Interval Type-2 Fuzzy Logic System and Ant Colony Optimization for Hydropower Dams Displacement Forecasting

A novel‐cascaded ANFIS‐based deep reinforcement learning for the detection of attack in cloud IoT‐based smart city applications

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