scholarly journals Intelligent Approach to Network Device Migration Planning towards Software-Defined IPv6 Networks

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 143
Author(s):  
Babu R. Dawadi ◽  
Danda B. Rawat ◽  
Shashidhar R. Joshi ◽  
Pietro Manzoni
Keyword(s):  
Performance Metrics ◽  
Fuzzy Inference ◽  
Service Providers ◽  
Support Vector ◽  
Inference System ◽  
Telecom Service ◽  
Ipv6 Networks ◽  
Comparative Results ◽  
Intelligent Approach ◽  
Network Device

Internet and telecom service providers worldwide are facing financial sustainability issues in migrating their existing legacy IPv4 networking system due to backward compatibility issues with the latest generation networking paradigms viz. Internet protocol version 6 (IPv6) and software-defined networking (SDN). Bench marking of existing networking devices is required to identify their status whether the existing running devices are upgradable or need replacement to make them operable with SDN and IPv6 networking so that internet and telecom service providers can properly plan their network migration to optimize capital and operational expenditures for future sustainability. In this paper, we implement “adaptive neuro fuzzy inference system (ANFIS)”, a well-known intelligent approach for network device status identification to classify whether a network device is upgradable or requires replacement. Similarly, we establish a knowledge base (KB) system to store the information of device internetwork operating system (IoS)/firmware version, its SDN, and IPv6 support with end-of-life and end-of-support. For input to ANFIS, device performance metrics such as average CPU utilization, throughput, and memory capacity are retrieved and mapped with data from KB. We run the experiment with other well-known classification methods, for example, support vector machine (SVM), fine tree, and liner regression to compare performance results with ANFIS. The comparative results show that the ANFIS-based classification approach is more accurate and optimal than other methods. For service providers with a large number of network devices, this approach assists them to properly classify the device and make a decision for the smooth transitioning to SDN-enabled IPv6 networks.

Adaptive Neuro-Fuzzy Inference System-based Nonlinear Equalizer for CO-OFDM Systems

2019 ◽  
Vol 63 (2) ◽  
pp. 169-178
Author(s):  
Ajay Amrit Raj ◽  
Keyword(s):  
Optical Communications ◽  
Fuzzy Inference System ◽  
Orthogonal Frequency Division Multiplexing ◽  
Performance Metrics ◽  
Fuzzy Inference ◽  
Support Vector ◽  
Ofdm Systems ◽  
Ofdm System ◽  
Inference System ◽  
Neuro Fuzzy

Abstract The principle of orthogonal frequency-division multiplexing (OFDM) is to transmit the data through a large number of multiple orthogonal subcarriers. The coherent optical OFDM (CO-OFDM) is OFDM data that are being modulated to light frequency and being detected in coherent manner. CO-OFDM brings to optical communications the combination of two powerful techniques, coherent optical detection and OFDM. One of the primary challenges in the CO-OFDM system is to remove optical fiber nonlinear effects. This makes nonlinearity compensation a critical task of the CO-OFDM system. So a nonlinear equalizer (NLE) based on adaptive neuro-fuzzy inference system (ANFIS) is presented for CO-OFDM systems to mitigate nonlinearities on long-haul optical communications with high bit rate and bit error rate (BER)of the system. Various performance metrics were analyzed for the proposed ANFIS–NLE, and it is compared with existing techniques such as support vector machine and artificial neural network. From the experimental results, our proposed approach gives better performance in terms of BER and Q-factor on comparing with existing methods.

scholarly journals Modeling and predicting suspended sediment load under climate change conditions: a new hybridization strategy

2021 ◽  
Author(s):  
Saeed Farzin ◽  
Mahdi Valikhan Anaraki
Keyword(s):  
Climate Change ◽  
Suspended Sediment ◽  
General Circulation ◽  
Sediment Load ◽  
Fuzzy Inference ◽  
Circulation Model ◽  
Support Vector ◽  
Suspended Sediment Load ◽  
Inference System ◽  
New Strategy

Abstract In the present study, for the first time, a new strategy based on a combination of the hybrid least-squares support-vector machine (LS-SVM) and flower pollination optimization algorithm (FPA), average 24 general circulation model (GCM) output, and delta change factor method has been developed to achieve the impacts of climate change on runoff and suspended sediment load (SSL) in the Lighvan Basin in the period (2020–2099). Also, the results of modeling were compared to those of LS-SVM and adaptive neuro-fuzzy inference system (ANFIS) methods. The comparison of runoff and SSL modeling results showed that the LS-SVM-FPA algorithm had the best results and the ANFIS algorithm had the worst results. After the acceptable performance of the LS-SVM-FPA algorithm was proved, the algorithm was used to predict runoff and SSL under climate change conditions based on ensemble GCM outputs for periods (2020–2034, 2035–2049, 2070–2084, and 2085–2099) under three scenarios of RCP2.6, RCP4.5, and RCP8.5. The results showed a decrease in the runoff in all periods and scenarios, except for the two near periods under the RCP2.6 scenario for runoff. The predicted runoff and SSL time series also showed that the SSL values were lower than the average observation period, except for 2036–2039 (up to an 8% increase in 2038).

New continuum surgical robot based on hybrid concentric tube-tendon driven mechanism

2021 ◽  
pp. 095440622110424
Author(s):  
Mohammed Abdel-Nasser ◽  
Omar Salah
Keyword(s):  
Inverse Kinematics ◽  
High Performance ◽  
Analytical Study ◽  
Fuzzy Inference ◽  
Closed Form Solution ◽  
Form Solution ◽  
Robot Arm ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Intelligent Approach

Robotics technology is used widely in minimally invasive surgery (MIS) which provides high performance and accuracy. The most famous robot arm mechanisms, which are used in MIS, are tendon-driven mechanism (TDM), and concentric tube mechanism (CTM). Unfortunately, these mechanisms until now have some limitations, i.e. making friction with the tissue during extracting and retracting and strain limits, for TDM and CTM respectively. A new hybrid concentric tube-tendon driven mechanism (HCTDM) is proposed to overcome these limitations. HCTDM enables the end-effector to get close to and get away from the surgical area during the operation without harming the tissue and with more flexibility. In addition to that, the workspace increases as a result of this combination, too. This benefit serves MIS, especially endoscopic surgeries (ESs). We did an analytical study of this idea and got the forward kinematics. In the inverse kinematics, an intelligent approach which is called an adaptive neuro-fuzzy inference system (ANFIS) is used because the closed-form solution is more complicated for such these mechanisms. Finally, HCTDM is analyzed and evaluated by using a computer simulation. The simulation results show that the workspace becomes wider and has more dexterity than use TDM or CTM individually. Furthermore, various trajectories are used to test the mechanism and the kinematic analysis, which show the mechanism can follow and track the trajectories with maximum mean error 1.279, 0.7027, and [Formula: see text] for X, Y, and Z axes respectively.

scholarly journals Using Machine Learning-Based Algorithms to Analyze Erosion Rates of a Watershed in Northern Taiwan

2020 ◽  
Vol 12 (5) ◽  
pp. 2022 ◽  
Author(s):  
Kieu Anh Nguyen ◽  
Walter Chen ◽  
Bor-Shiun Lin ◽  
Uma Seeboonruang
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Fuzzy Inference ◽  
Absolute Error ◽  
Organic Content ◽  
Rank Test ◽  
Support Vector ◽  
Inference System ◽  
Erosion Rates ◽  
Northern Taiwan

This study continues a previous study with further analysis of watershed-scale erosion pin measurements. Three machine learning (ML) algorithms—Support Vector Machine (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Artificial Neural Network (ANN)—were used to analyze depth of erosion of a watershed (Shihmen reservoir) in northern Taiwan. In addition to three previously used statistical indexes (Mean Absolute Error, Root Mean Square of Error, and R-squared), Nash–Sutcliffe Efficiency (NSE) was calculated to compare the predictive performances of the three models. To see if there was a statistical difference between the three models, the Wilcoxon signed-rank test was used. The research utilized 14 environmental attributes as the input predictors of the ML algorithms. They are distance to river, distance to road, type of slope, sub-watershed, slope direction, elevation, slope class, rainfall, epoch, lithology, and the amount of organic content, clay, sand, and silt in the soil. Additionally, measurements of a total of 550 erosion pins installed on 55 slopes were used as the target variable of the model prediction. The dataset was divided into a training set (70%) and a testing set (30%) using the stratified random sampling with sub-watershed as the stratification variable. The results showed that the ANFIS model outperforms the other two algorithms in predicting the erosion rates of the study area. The average RMSE of the test data is 2.05 mm/yr for ANFIS, compared to 2.36 mm/yr and 2.61 mm/yr for ANN and SVM, respectively. Finally, the results of this study (ANN, ANFIS, and SVM) were compared with the previous study (Random Forest, Decision Tree, and multiple regression). It was found that Random Forest remains the best predictive model, and ANFIS is the second-best among the six ML algorithms.

Thermal error modelling of motorised spindle in large-sized gear grinding machine

2017 ◽  
Vol 231 (5) ◽  
pp. 768-778 ◽  
Author(s):  
He Dai ◽  
Shilong Wang ◽  
Xin Xiong ◽  
Baocang Zhou ◽  
Shouli Sun ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Machine Tools ◽  
Fuzzy Inference ◽  
Thermal Error ◽  
Support Vector ◽  
Inference System ◽  
Thermal Errors ◽  
Gear Grinding ◽  
Neuro Fuzzy ◽  
Grinding Machine

Thermal errors are one of the most significant factors that influence the machining precision of machine tools. For large-sized gear grinding machine tools, thermal errors of beds, columns and rotary tables are decreased by their huge heat capacity. However, different from machine tools of normal sizes, thermal errors increase with greater power in motorised spindles. Thermal error compensation is generally considered as a relatively effective, convenient and cost-efficient approach in thermal error control and reduction. This article proposes two thermal error prediction models for motorised spindles based on an adaptive neuro-fuzzy inference system and support vector machine, respectively. In the adaptive neuro-fuzzy inference system–based model, the temperature values are divided into different groups using subtractive clustering. A hybrid learning scheme is adopted to adjust membership functions so as to learn from the input data. In the particle swarm optimisation support vector machine–based model, particle swarm optimisation is used to optimise the hyperparameters of the established model. Thermal balance experiments are conducted on a large-sized computer numerical control gear grinding machine tool to establish the prediction models. Comparative results show that the adaptive neuro-fuzzy inference system model has higher prediction accuracy (with residual errors within ±2.5 μm in the radial direction and ±3 μm in the axial direction) than the support vector machine model.

scholarly journals Time Series Prediction of Electricity Demand Using Adaptive Neuro-Fuzzy Inference Systems

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Amevi Acakpovi ◽  
Alfred Tettey Ternor ◽  
Nana Yaw Asabere ◽  
Patrick Adjei ◽  
Abdul-Shakud Iddrisu
Keyword(s):  
Urban Areas ◽  
Fuzzy Inference ◽  
Predictive Accuracy ◽  
Resource Planning ◽  
Electricity Demand ◽  
Least Square ◽  
Quality Data ◽  
Support Vector ◽  
Inference System ◽  
Neuro Fuzzy

This paper is concerned with the reliable prediction of electricity demands using the Adaptive Neuro-Fuzzy Inference System (ANFIS). The need for electricity demand prediction is fundamental and vital for power resource planning and monitoring. A dataset of electricity demands covering the period of 2003 to 2018 was collected from the Electricity Distribution Company of Ghana, covering three urban areas namely Mallam, Achimota, and Ga East, all in Ghana. The dataset was divided into two parts: one part covering a period of 0 to 500 hours was used for training of the ANFIS algorithm while the second part was used for validation. Three scenarios were considered for the simulation exercise that was done with the MATLAB software. Scenario one considered four inputs sampled data, scenario two considered an additional input making it 5, and scenario 3 was similar to scenario 1 with the exception of the number of membership functions that increased from 2 to 3. The performance of the ANFIS algorithm was assessed by comparing its predictions with other three forecast models namely Support Vector Regression (SVR), Least Square Support Vector Machine (LS-SVM), and Auto-Regressive Integrated Moving Average (ARIMA). Findings revealed that the ANFIS algorithm can perform the prediction accurately, the ANFIS algorithm converges faster with an increase in the data used for training, and increasing the membership function resulted in overfitting of data which adversely affected the RMSE values. Comparison of the ANFIS results to other previously used methods of predicting electricity demands including SVR, LS-SVM, and ARIMA revealed that there is merit to the potentials of the ANFIS algorithm for improved predictive accuracy while relying on a quality data for training and reliable setting of tuning parameters.

scholarly journals Driver Drowsiness Detection Based on Steering Wheel Data Applying Adaptive Neuro-Fuzzy Feature Selection

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 943 ◽  
Author(s):  
Sadegh Arefnezhad ◽  
Sajjad Samiee ◽  
Arno Eichberger ◽  
Ali Nahvi
Keyword(s):  
Feature Selection ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Detection System ◽  
Steering Wheel ◽  
Support Vector ◽  
Inference System ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
Neuro Fuzzy

This paper presents a novel feature selection method to design a non-invasive driver drowsiness detection system based on steering wheel data. The proposed feature selector can select the most related features to the drowsiness level to improve the classification accuracy. This method is based on the combination of the filter and wrapper feature selection algorithms using adaptive neuro-fuzzy inference system (ANFIS). In this method firstly, four different filter indexes are applied on extracted features from steering wheel data. After that, output values of each filter index are imported as inputs to a fuzzy inference system to determine the importance degree of each feature and select the most important features. Then, the selected features are imported to a support vector machine (SVM) for binary classification to classify the driving conditions in two classes of drowsy and awake. Finally, the classifier accuracy is exploited to adjust parameters of an adaptive fuzzy system using a particle swarm optimization (PSO) algorithm. The experimental data were collected from about 20.5 h of driving in the simulator. The results show that the drowsiness detection system is working with a high accuracy and also confirm that this method is more accurate than the recent available algorithms.

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