In the structure of ANFIS, there are two different parameter groups: premise and consequence. Training ANFIS means determination of these parameters using an optimization algorithm. In the first ANFIS model developed by Jang, a hybrid learning approach was proposed for training. In this approach, while premise parameters are determined by using gradient descent (GD), consequence parameters are found out with least squares estimation (LSE) method. Since ANFIS has been developed, it is used in modelling and identification of numerous systems and successful results have been achieved. The selection of optimization method utilized in training is very important to get effective results with ANFIS. It is seen that derivate based (GD, LSE etc.) and non-derivative based (heuristic algorithms such us GA, PSO, ABC etc.) algorithms are used in ANFIS training. Nevertheless, it has been observed that there is a trend toward heuristic based ANFIS training algorithms for better performance recently. At the same time, it seems to be proposed in derivative and heuristic based hybrid algorithms. Within the scope of this study, the heuristic and hybrid approaches utilized in ANFIS training are examined in order to guide researchers in their study. In addition, the final status in ANFIS training is evaluated and it is aimed to shed light on further studies related to ANFIS training.

Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey

Despite the extensive use of mortars materials in constructions over the last decades, there is not yet a reliable and robust method, available in the literature, which can estimate its strength based on its mix parameters. This limitation is due to the highly nonlinear relation between the mortar’s compressive strength and the mixed components. In this paper, the application of artificial intelligence techniques toward the prediction of the compressive strength of cement-based mortar materials with or without metakaolin has been investigated. Specifically, surrogate models (such as artificial neural network, ANN and adaptive neuro-fuzzy inference system, ANFIS models) have been developed to the prediction of the compressive strength of mortars trained using experimental data available in the literature. The comparison of the derived results with the experimental findings demonstrates the ability of both ANN and ANFIS models to approximate the compressive strength of mortars in a reliable and robust manner. Although ANFIS was able to obtain higher performance prediction to estimate the compressive strength of mortars compared to ANN model, it was found through the verification process of some other additional data, the ANFIS model has overfitted the data. Therefore, the developed ANN model has been introduced as the best predictive technique for solving problem of the compressive strength of mortars. Furthermore, using the optimum developed model an ambitious attempt to reveal the nature of mortar materials has been made.

A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength

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.

https://research.tees.ac.uk/ws/files/7373224/Journal_Manuscript_final.pdf

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

Landslide, one of the most critical natural hazards, is caused due to specific compositional slope movement In the past decades, due to inflation of urbanized area and climate change, a c

Review on remote sensing methods for landslide detection using machine and deep learning

Computation offloading is one of the important application in Internet of Things (IoT) ecosystem. Computational offloading provides assisted means of processing large amounts of data generated by abundant IoT devices, speed up processing of intensive tasks and save battery life. In this paper, we propose a secure computation offloading scheme in Fog-Cloud-IoT environment (SecOFF-FCIoT). Using machine learning strategies, we accomplish efficient, secure offloading in Fog-IoT setting. In particular, we employ Neuro-Fuzzy Model to secure data at the smart gateway, then the IoT device selects an optimal Fog node to which it can offload its workload using Particle Swarm Optimization(PSO) via the smart gateway. If the fog node is not capable of handling the workload, it is forwarded to the cloud after being classified as either sensitive or non-sensitive. Sensitive data is maintained in private cloud. Whereas non-sensitive data is offloaded using dynamic offloading strategy. In PSO, the availability of fog node is computed using two metrics; i) Available Processing Capacity (APC), and ii) Remaining Node Energy (RNE). Selection of cloud is based on Reinforcement Learning. Our proposed approach is implemented for smart city applications using NS-3 simulator with JAVA Programming. We compare our proposed secure computation offloading model with previous approaches which include DTO-SO, FCFS, LOTEC, and CMS-ACO. Simulation results show that our proposed scheme minimizes latency as compared to selected benchmarks.

SecOFF-FCIoT: Machine learning based secure offloading in Fog-Cloud of things for smart city applications

paper, we presented the architecture and basic learning process underlying ANFIS (adaptive-network-based fuzzy inference system) which is a fuzzy inference system implemented in the framework of adaptive networks. Soft computing approaches including artificial neural networks and fuzzy inference have been used widely to model expert behavior. Using given input/output data values, the proposed ANFIS can construct mapping based on both human knowledge (in the form of fuzzy if-then rules) and hybrid learning algorithm. In modeling and simulation, the ANFIS strategy is employed to model nonlinear functions, to control one of the most important parameters of the induction machine and predict a chaotic time series, all yielding more effective, faster response or settling times.

/pdf/anfis-adaptive-neuro-fuzzy-inference-system-a-survey-19mm6rjdtu.pdf

ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey

In this paper we presented an architecture and basic learning process underlying in fuzzy inference system and adaptive neuro fuzzy inference system which is a hybrid network implemented in framework of adaptive network. In real world computing environment, soft computing techniques including neural network, fuzzy logic algorithms have been widely used to derive an actual decision using given input or output data attributes, ANFIS can construct mapping based on both human knowledge and hybrid learning algorithms. This study involves study of ANFIS strategy ANFIS strategy is employed to model nonlinear functions, to control one of the most important parameters of the induction machine and predict a chaotic time series, all yielding more effective, faster response or settling times.

/pdf/a-survey-on-applications-of-adaptive-neuro-fuzzy-inference-h7462m6qr6.pdf

A Survey on Applications of Adaptive Neuro Fuzzy Inference System

Received Mar 3, 2016 Revised May 7, 2016 Accepted May 24, 2016 Soft Computing is conglomerate of methodologies which works together and provides an ability to make a decision from reliable data or expert’s experience. Nowadays different types of soft computing techniques such as neural network, fuzzy logic, genetic algorithm and hybrid system are largely used in medical areas. In this paper, an algorithm for analysis of lung infection is presented. The main focus is to develop system architecture to find probable disease stage patient may have. Severity level of disease is determined by using rule base method. The algorithm uses an output of Rulebase entered by the user to determine a level of infection. Keyword:

/pdf/effective-analysis-of-lung-infection-using-fuzzy-rules-1o761vxyff.pdf

Effective Analysis of Lung Infection using Fuzzy Rules

Soft Computing is conglomerate of methodologies which works together and provides an ability to make a decision from reliable data or expert’s experience. Nowadays different types of soft computing techniques such as neural network, fuzzy logic, genetic algorithm and hybrid system are largely used in medical areas. In this paper, an algorithm for analysis of lung infection is presented. The main focus is to develop system architecture to find probable disease stage patient may have. Severity level of disease is determined by using rule base method. The algorithm uses an output of Rulebase entered by the user to determine a level of infection. Normal 0 false false false EN-MY X-NONE X-NONE /* Style Definitions */
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 Soft Computing is conglomerate of methodologies which works together and provides an ability to make decision from reliable data or expert’s experience. Nowadays different types of soft computing techniques such as neural network, fuzzy logic, genetic algorithm and hybrid system are largely used in medical areas. In this paper, algorithm for analysis of lung infection is presented. The main focus is to develop system architecture to find probable disease stage patient may have. Severity level of disease is determined by using rule base method. The algorithm uses output of Rulebase entered by user to determine level of infection.

https://ijai.iaescore.com/index.php/IJAI/article/download/1409/pdf

Navneet Walia

Papers

ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey

A Survey on Applications of Adaptive Neuro Fuzzy Inference System

Effective Analysis of Lung Infection using Fuzzy Rules

Effective Analysis of Lung Infection using Fuzzy Rules