An adaptive and hybrid artificial bee colony algorithm (aABC) for ANFIS training

Karaboğa, Derviş; Kaya, Ebubekir

doi:10.1016/j.asoc.2016.07.039

Cited by 114 publications

(38 citation statements)

References 61 publications

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“…The ANFIS is perhaps the first integrated neuro-fuzzy model, and its typical architecture illustrated in Fig. 3. There are two types of fuzzy inference system models: the Mamdani and the Sugeno models (Karaboga and Kaya, 2016).…”

Section: Why Use An Adaptive Neuro-fuzzy Inference System?mentioning

confidence: 99%

Intelligent web-phishing detection and protection scheme using integrated features of Images, frames and text

Adebowale

Lwin

Sánchez

et al. 2019

Expert Systems with Applications

108

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A phishing attack is one of the most significant problems faced by online users because of its enormous effect on the online activities performed. In recent years, phishing attacks continue to escalate in frequency, severity and impact. Several solutions, using various methodologies, have been proposed in the literature to counter the web-phishing threats. Notwithstanding, the existing technology cannot detect the new phishing attacks accurately due to the insufficient integration of features of the text, image and frame in the evaluation process. The use of related features of images, frames and text of legitimate and non-legitimate websites and associated artificial intelligence algorithms to develop an integrated method to address these together. This paper presents an Adaptive Neuro-Fuzzy Inference System (ANFIS) based robust scheme using the integrated features of the text, images and frames for web-phishing detection and protection. The proposed solution achieves 98.3% accuracies. To our best knowledge, this is the first work that considers the best-integrated text, image and frame feature based solution for phishing detection scheme.

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Section: Why Use An Adaptive Neuro-fuzzy Inference System?mentioning

confidence: 99%

Intelligent web-phishing detection and protection scheme using integrated features of Images, frames and text

Adebowale

Lwin

Sánchez

et al. 2019

Expert Systems with Applications

108

View full text Add to dashboard Cite

show abstract

“…Karaboga and Kaya [13] proposed an Adaptive and Hybrid Artificial Bee Colony (aABC) algorithm to train all parameters of ANFIS. Najafi [14] employed PSO with ANFIS to optimize and train parameters for prediction of viscosity of mixed oils.…”

Section: Comparative Study Of Anfismentioning

confidence: 99%

A Modified Neuro-Fuzzy System Using Metaheuristic Approaches for Data Classification

Salleh¹,

Talpur²,

HussainTalpur³

2018

Artificial Intelligence - Emerging Trends and Applications

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The impact of innovated Neuro-Fuzzy System (NFS) has emerged as a dominant technique for addressing various difficult research problems in business. ANFIS (Adaptive Neuro-Fuzzy Inference system) is an efficient combination of ANN and fuzzy logic for modeling highly non-linear, complex and dynamic systems. It has been proved that, with proper number of rules, an ANFIS system is able to approximate every plant. Even though it has been widely used, ANFIS has a major drawback of computational complexities. The number of rules and its tunable parameters increase exponentially when the numbers of inputs are large. Moreover, the standard learning process of ANFIS involves gradient based learning which has prone to fall in local minima. Many researchers have used meta-heuristic algorithms to tune parameters of ANFIS. This study will modify ANFIS architecture to reduce its complexity and improve the accuracy of classification problems. The experiments are carried out by trying different types and shapes of membership functions and meta-heuristics Artificial Bee Colony (ABC) algorithm with ANFIS and the training error results are measured for each combination. The results showed that modified ANFIS combined with ABC method provides better training error results than common ANFIS model.

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“…Congestion will increase if the traffic flow is so large that the vehicle will be very close to one vehicle with other vehicles. Total congestion occurs when the vehicle must stop or move very slowly and be added with other vehicles that keep coming from behind [2].…”

Section: A R T I C L E I N F Omentioning

confidence: 99%

Calculating vehicle intensiveness increase on eid al-fitr day with anfis method

Pratama¹,

Utami²,

Wibowo³

2018

IJARLIT

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the number of motorized vehicles increases every year, especially private vehicles and is not offset by inadequate access until the road becomes more crowded, even traffic jams occur, especially during public holidays and national holidays. for example, during eid holidays there is a density of traffic flow when going back and forth every year, with the development of current technology the density of traffic flows that occur can be calculated so that it will be easier to anticipate in the future. but in this study only will examine the parameter values that cause the vehicle to occur density and accumulation, because it can be developed with parameter values so that the results can be obtained efficiently in solving traffic density. From the results of the anfis method, efficiency is obtained, namely on h-1 and h days of 2014, and 2017 can use Parameters with magins of 6,3% and 4,32%, while 2015 and 2016 can use parameters with margins 1,79% and 0,79%. and for the h + 1 day of 2014, 2016, and 2017, it is more efficient to use parameters with margins 1,4% and only parameters in 2015 which have the efficiency value using parameters with margin -6,17. anfis application in this calculation can be developed in a prediction system.

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An adaptive and hybrid artificial bee colony algorithm (aABC) for ANFIS training

Cited by 114 publications

References 61 publications

Intelligent web-phishing detection and protection scheme using integrated features of Images, frames and text

Intelligent web-phishing detection and protection scheme using integrated features of Images, frames and text

A Modified Neuro-Fuzzy System Using Metaheuristic Approaches for Data Classification

Calculating vehicle intensiveness increase on eid al-fitr day with anfis method

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