scholarly journals A Neuro-Fuzzy System to Detect IPv6 Router Alert Option DoS Packets

2019 ◽  
pp. 16-25
Author(s):  
Shubair Abdullah
Keyword(s):  
Performance Test ◽  
Fuzzy Inference ◽  
State Of The Art ◽  
Denial Of Service ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Training Dataset ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Ipv6 Networks

Detecting the denial of service attacks that solely target the router is a maximum security imperative in deploying IPv6 networks. The state-of-the-art Denial of Service detection methods aim at leveraging the advantages of flow statistical features and machine learning techniques. However, the detection performance is highly affected by the quality of the feature selector and the reliability of datasets of IPv6 flow information. This paper proposes a new neuro-fuzzy inference system to tackle the problem of classifying the packets in IPv6 networks in crucial situation of small-supervised training dataset. The proposed system is capable of classifying the IPv6 router alert option packets into denial of service and normal by utilizing the neuro-fuzzy strengths to boost the classification accuracy. A mathematical analysis from the fuzzy sets theory perspective is provided to express performance benefit of the proposed system. An empirical performance test is conducted on comprehensive dataset of IPv6 packets produced in a supervised environment. The result shows that the proposed system overcomes robustly some state-of-the-art systems.

scholarly journals A Hybrid System for Subjectivity Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Samir Rustamov
Keyword(s):  
Fuzzy Inference ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Training Dataset ◽  
Classification Problems ◽  
Lexical Knowledge ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Sentence Level ◽  
Subjectivity Analysis

We suggested different structured hybrid systems for the sentence-level subjectivity analysis based on three supervised machine learning algorithms, namely, Hidden Markov Model, Fuzzy Control System, and Adaptive Neuro-Fuzzy Inference System. The suggested feature extraction algorithm in our experiment computes a feature vector using statistical textual terms frequencies in a training dataset not having the use of any lexical knowledge except tokenization. Taking into consideration this fact, the above-mentioned methods may be employed in other languages as these methods do not utilize the morphological, syntactical, and lexical analysis in the classification problems.

scholarly journals The applicability of Generic Self-Evolving Takagi-Sugeno-Kang neuro-fuzzy model in modeling rainfall–runoff and river routing

2019 ◽  
Vol 50 (4) ◽  
pp. 991-1001 ◽  
Author(s):  
Mohammad Ashrafi ◽  
Lloyd H. C. Chua ◽  
Chai Quek
Keyword(s):  
Fuzzy Inference System ◽  
Hydrological Modeling ◽  
Fuzzy Inference ◽  
Training Dataset ◽  
Rule Base ◽  
Rainfall Runoff ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Takagi Sugeno ◽  
River Routing

Abstract Recent advancements in neuro-fuzzy models (NFMs) have made possible the implementation of dynamic rule base systems. This is in comparison with static applications commonly seen in global NFMs such as the Adaptive-Network-Based Fuzzy Inference System (ANFIS) model widely used in hydrological modeling. This study underlines key differences between local and global NFMs with an emphasis on rule base dynamics, in the context of two common flow forecast applications. A global NFM, ANFIS, and two local NFMs, Dynamic Evolving Neural-Fuzzy Inference System (DENFIS) and Generic Self-Evolving Takagi-Sugeno-Kang (GSETSK), were tested. Results from all NFMs compared favorably when benchmarked against physically based models. Rainfall–runoff modeling is a complex process which benefits from the advanced rule generation and pruning mechanisms in GSETSK, resulting in a more compact rule base. Although ANFIS resulted in the same number of rules, this came about at the expense of having the need for a large training dataset. All NFMs generated a similar number of rules for the river routing application, although local NFMs yielded better results for forecasts at longer lead times. This is attributed to the fact that the routing procedure is less complex and can be adequately modeled by static NFMs.

scholarly journals Proposed ANFIS Based Approach for Fault Tracking, Detection, Clearing and Rearrangement for Photovoltaic System

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2269
Author(s):  
Ahmed F. Bendary ◽  
Almoataz Y. Abdelaziz ◽  
Mohamed M. Ismail ◽  
Karar Mahmoud ◽  
Matti Lehtonen ◽  
...  
Keyword(s):  
Fault Detection ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Detection Methods ◽  
Photovoltaic Systems ◽  
Inference System ◽  
Photovoltaic Array ◽  
Neuro Fuzzy ◽  
Effective Manner ◽  
Simulation Results

In the last few decades, photovoltaics have contributed deeply to electric power networks due to their economic and technical benefits. Typically, photovoltaic systems are widely used and implemented in many fields like electric vehicles, homes, and satellites. One of the biggest problems that face the relatability and stability of the electrical power system is the loss of one of the photovoltaic modules. In other words, fault detection methods designed for photovoltaic systems are required to not only diagnose but also clear such undesirable faults to improve the reliability and efficiency of solar farms. Accordingly, the loss of any module leads to a decrease in the efficiency of the overall system. To avoid this issue, this paper proposes an optimum solution for fault finding, tracking, and clearing in an effective manner. Specifically, this proposed approach is done by developing one of the most promising techniques of artificial intelligence called the adaptive neuro-fuzzy inference system. The proposed fault detection approach is based on associating the actual measured values of current and voltage with respect to the trained historical values for this parameter while considering the ambient changes in conditions including irradiation and temperature. Two adaptive neuro-fuzzy inference system-based controllers are proposed: (1) the first one is utilized to detect the faulted string and (2) the other one is utilized for detecting the exact faulted group in the photovoltaic array. The utilized model was installed using a configuration of 4 × 4 photovoltaic arrays that are connected through several switches, besides four ammeters and four voltmeters. This study is implemented using MATLAB/Simulink and the simulation results are presented to show the validity of the proposed technique. The simulation results demonstrate the innovation of this study while proving the effective and high performance of the proposed adaptive neuro-fuzzy inference system-based approach in fault tracking, detection, clearing, and rearrangement for practical photovoltaic systems.

scholarly journals Damage Detection of Irregular Plates and Regular Dams by Wavelet Transform Combined Adoptive Neuro Fuzzy Inference System

2018 ◽  
Vol 4 (2) ◽  
pp. 305 ◽  
Author(s):  
D. Hamidian ◽  
J. Salajegheh ◽  
E. Salajegheh
Keyword(s):  
Finite Element ◽  
Wavelet Transform ◽  
Damage Detection ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Damage Zone ◽  
Detection Methods ◽  
Mode Shapes ◽  
Inference System ◽  
Neuro Fuzzy

This paper presents a technique for irregular plate and regular dam damage detection based on combination of wavelet with adoptive neuro fuzzy inference system (ANFIS). Many damage detection methods need response of structures (such as the displacements, stresses or mode shapes) before and after damage, but this method only requires response of structures after damage, otherwise many damage detection methods study regular plate but this method also studies irregular plate. First, the structure (irregular plate or regular dam) is modelled by using ANSYS software, the model is analysed and structure’s responses with damage are obtained by finite element approach. Second, the responses at the finite element points with regular distances are obtained by using ANFIS. The damage zone is represented as the elements with reduced elasticity modules. Then these responses of structures are analysed with 2D wavelet transform. It is shown that matrix detail coefficients of 2D wavelet transform can specified the damage zone of plates and regular dams by perturbation in the damaged area.

scholarly journals Optimizing an Adaptive Neuro-Fuzzy Inference System for Spatial Prediction of Landslide Susceptibility Using Four State-of-the-art Metaheuristic Techniques

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1723 ◽  
Author(s):  
Mohammad Mehrabi ◽  
Biswajeet Pradhan ◽  
Hossein Moayedi ◽  
Abdullah Alamri
Keyword(s):  
Fuzzy Inference System ◽  
Landslide Susceptibility ◽  
Fuzzy Inference ◽  
State Of The Art ◽  
Spatial Prediction ◽  
Inference System ◽  
Conditioning Factors ◽  
Susceptibility Maps ◽  
Neuro Fuzzy ◽  
Landslide Susceptibility Maps

Four state-of-the-art metaheuristic algorithms including the genetic algorithm (GA), particle swarm optimization (PSO), differential evolutionary (DE), and ant colony optimization (ACO) are applied to an adaptive neuro-fuzzy inference system (ANFIS) for spatial prediction of landslide susceptibility in Qazvin Province (Iran). To this end, the landslide inventory map, composed of 199 identified landslides, is divided into training and testing landslides with a 70:30 ratio. To create the spatial database, thirteen landslide conditioning factors are considered within the geographic information system (GIS). Notably, the spatial interaction between the landslides and mentioned conditioning factors is analyzed by means of frequency ratio (FR) theory. After the optimization process, it was shown that the DE-based model reaches the best response more quickly than other ensembles. The landslide susceptibility maps were developed, and the accuracy of the models was evaluated by a ranking system, based on the calculated area under the receiving operating characteristic curve (AUROC), mean absolute error, and mean square error (MSE) accuracy indices. According to the results, the GA-ANFIS with a total ranking score (TRS) = 24 presented the most accurate prediction, followed by PSO-ANFIS (TRS = 17), DE-ANFIS (TRS = 13), and ACO-ANFIS (TRS = 6). Due to the excellent results of this research, the developed landslide susceptibility maps can be applied for future planning and decision making of the related area.

scholarly journals Analysis and Prediction of COVID-19 Pandemic in Bangladesh by using Long short-term memory network (LSTM) and Adaptive neuro fuzzy inference system(ANFIS)

2020 ◽  
Author(s):  
Anjir Ahmed Chowdhury ◽  
Khandaker Tabin Hasan ◽  
Khadija Kubra Shahjalal Hoque
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Fuzzy Inference ◽  
Machine Learning Techniques ◽  
Short Term ◽  
Inference System ◽  
Learning Techniques ◽  
Neuro Fuzzy ◽  
The World ◽  
Long Short Term Memory

Abstract Objectives: The dangerously contagious virus named \newline SARS-CoV-2 has hit the world hard that has locked downed billion people in their homes for stopping further spread. All the researchers and scientists in various fields are working around the clock to come up with a vaccine and prevention methods to save the world from this invisible pathogen. However, reliable prediction of the epidemic may help contain the contagion until cure becomes available. The machine learning techniques is one of the frontier in predicting the future trend and behavior of this outbreak. Our research is focused on finding a suitable machine learning model that can predict on small dataset with higher accuracy.Methods: In this research, we have used the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the long short-term memory[LSTM] to foresee the newly infected cases in Bangladesh. We have compared both the results of the experiments and it can be forenamed that LSTM has shown more satisfactory results.Results: Upon study and testing on several models, we have showed that LSTM works better on scenario based model for Bangladesh with MAPE 4.51, RMSE 6.55 and Correlation Coefficient 0.75. Conclusion: This study is expected to shade light on Covid-19 prediction models for researchers working with machine learning techniques and help avoid proven failures specially for small imprecise dataset.

scholarly journals Analysis and Prediction of COVID-19 Pandemic in Bangladesh by using Long short-term memory network (LSTM) and Adaptive neuro fuzzy inference system(ANFIS)

2020 ◽  
Author(s):  
Anjir Ahmed Chowdhury ◽  
Khandaker Tabin Hasan ◽  
Khadija Kubra Shahjalal Hoque
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Fuzzy Inference ◽  
Machine Learning Techniques ◽  
Short Term ◽  
Inference System ◽  
Learning Techniques ◽  
Neuro Fuzzy ◽  
The World ◽  
Long Short Term Memory

Abstract Objectives: The dangerously contagious virus named SARS-CoV-2 has hit the world hard that has locked downed billion people in their homes for stopping fur- ther spread. All the researchers and scientists in various fields are working around the clock to come up with a vaccine and prevention methods to save the world from this invisible pathogen. However, reliable prediction of the epidemic may help contain the contagion until cure becomes available. The machine learning techniques is one of the frontier in predicting the future trend and behavior of this outbreak. Our research is focused on finding a suitable machine learning model that can pre- dict on small dataset with higher accuracy.Methods: In this research, we have used the Adap- tive Neuro-Fuzzy Inference System (ANFIS) and the long short-term memory[LSTM] to foresee the newly infected cases in Bangladesh. We have compared both the results of the experiments and it can be forenamed that LSTM has shown more satisfactory results.Results: Upon study and testing on several models, we have showed that LSTM works better on scenario based model for Bangladesh with MAPE 4.51, RMSE 6.55 and Correlation Coefficient 0.75.Conclusion: This study is expected to shade light on Covid-19 prediction models for researchers working with machine learning techniques and help avoid proven failures specially for small imprecise dataset.

scholarly journals Analysis and Prediction of COVID-19 Pandemic in Bangladesh by using Long short-term memory network (LSTM) and Adaptive neuro fuzzy inference system(ANFIS)

2020 ◽  
Author(s):  
Anjir Ahmed Chowdhury ◽  
Khandaker Tabin Hasan ◽  
Khadija Kubra Shahjalal Hoque
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Fuzzy Inference ◽  
Machine Learning Techniques ◽  
Short Term ◽  
Inference System ◽  
Learning Techniques ◽  
Neuro Fuzzy ◽  
The World ◽  
Long Short Term Memory

Abstract Objectives: The dangerously contagious virus named SARS-CoV-2 has hit the world hard that has locked downed billion people in their homes for stopping fur- ther spread. All the researchers and scientists in various fields are working around the clock to come up with a vaccine and prevention methods to save the world from this invisible pathogen. However, reliable prediction of the epidemic may help contain the contagion until cure becomes available. The machine learning techniques is one of the frontier in predicting the future trend and behavior of this outbreak. Our research is focused on finding a suitable machine learning model that can pre- dict on small dataset with higher accuracy.Methods: In this research, we have used the Adap- tive Neuro-Fuzzy Inference System (ANFIS) and the long short-term memory[LSTM] to foresee the newly infected cases in Bangladesh. We have compared both the results of the experiments and it can be forenamed that LSTM has shown more satisfactory results.Results: Upon study and testing on several models, we have showed that LSTM works better on scenario based model for Bangladesh with MAPE 4.51, RMSE6.55 and Correlation Coefficient 0.75.Conclusion: This study is expected to shade light on Covid-19 prediction models for researchers working with machine learning techniques and help avoid proven failures specially for small imprecise dataset.

Forecasting East Asian Tourist Arrivals to Thailand with Adaptive Neuro-Fuzzy Inference System

2019 ◽  
Vol 4 (1) ◽  
pp. 1-8
Author(s):  
Chawalsak Phetchanchai ◽  
Chuthawuth Chantaramalee ◽  
Napatsarun Chatchawalanont ◽  
Piyapong Phatcha
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
East Asian ◽  
Time Lag ◽  
Training Dataset ◽  
Inference System ◽  
Fuzzy C Means ◽  
Neuro Fuzzy ◽  
Fuzzy C Means Clustering ◽  
Takagi Sugeno

Objective - This research aims to propose the approach of forecasting tourist arrivals to Thailand. Methodology/Technique – Adaptive Neuro-Fuzzy Inference System (ANFIS) was used as our forecasting method by using fuzzy C-means clustering as a technique for the partitioning training dataset Findings - The appropriate parameter of time lag was found for each dataset of East Asian tourist arrivals to Thailand. Novelty - The forecasting procedure with the appropriate parameter of time lag was represented our work as a novelty idea. Type of Paper: Empirical. Keywords: Tourist arrivals forecasting, East Asian countries, adaptive neuro-fuzzy inference system, fuzzy C-means clustering, Takagi–Sugeno fuzzy inference system.

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