scholarly journals Predictive Algorithm for Handover Decisions between LTE and LTE-A Networks

2021 ◽  
Vol 9 (4) ◽  
pp. 110-126
Author(s):  
Wafa Benaatou ◽  
Adnane Latif ◽  
Vicent Pla
Keyword(s):  
Power Consumption ◽  
Mean Square Error ◽  
Fuzzy Inference ◽  
Hybrid Approach ◽  
Mobile Terminal ◽  
Base Station ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Statistical Parameters ◽  
Inference System

A heterogeneous wireless network needs to maintain seamless mobility and service continuity; for this reason, we have proposed an approach based on the combination of particle swarm optimization (PSO) and an adaptive neuro-fuzzy inference system (ANFIS) to forecast a handover during a movement of a mobile terminal from a serving base station to target base station. Additionally, the handover decision is made by considering several parameters, such as peak data rate, latency, packet loss, and power consumption, to select the best network for handover from an LTE to an LTE-A network. The performance efficiency of the new hybrid approach is determined by computing different statistical parameters, such as root mean square error (RMSE), coefficient of determination (R2), mean square error (MSE), and error standard deviation (StD). The execution of the proposed approach has been performed using MATLAB software. The simulation results show that the hybrid PSO-ANFIS model has better performance than other approaches in terms of prediction accuracy and reduction of handover latency and the power consumption in the network.  

Modelling response of infiltration loss toward water table depth using RBFN, RNN, ANFIS techniques

2021 ◽  
Vol 25 (2) ◽  
pp. 227-234
Author(s):  
Sandeep Samantaray ◽  
Abinash Sahoo
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Water Table ◽  
Environmental Sustainability ◽  
Model Development ◽  
Water Table Depth ◽  
Water Levels ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Inference System

Accurate prediction of water table depth over long-term in arid agricultural areas are very much important for maintaining environmental sustainability. Because of intricate and diverse hydrogeological features, boundary conditions, and human activities researchers face enormous difficulties for predicting water table depth. A virtual study on forecast of water table depth using various neural networks is employed in this paper. Hybrid neural network approach like Adaptive Neuro Fuzzy Inference System (ANFIS), Recurrent Neural Network (RNN), Radial Basis Function Neural Network (RBFN) is employed here to appraisal water levels as a function of average temperature, precipitation, humidity, evapotranspiration and infiltration loss data. Coefficient of determination (R2), Root mean square error (RMSE), and Mean square error (MSE) are used to evaluate performance of model development. While ANFIS algorithm is used, Gbell function gives best value of performance for model development. Whole outcomes establish that, ANFIS accomplishes finest as related to RNN and RBFN for predicting water table depth in watershed.

Development of an adaptive neuro-fuzzy inference system (ANFIS) model to predict sea surface temperature (SST)

2020 ◽  
Vol 49 (4) ◽  
pp. 354-373
Author(s):  
Semih Kale
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Mean Square Error ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Sea Surface ◽  
Mean Square ◽  
Anfis Model ◽  
Inference System ◽  
Neuro Fuzzy

Abstract An accurate estimation of the sea surface temperature (SST) is of great importance. Therefore, the objective of this work was to develop an adaptive neuro-fuzzy inference system (ANFIS) model to predict SST in the Çanakkale Strait. The observed monthly air temperature, evaporation and precipitation data from the Çanakkale meteorological observation station were used as input data. The Takagi–Sugeno fuzzy inference system was applied. The grid partition method (ANFIS-GP) and the subtractive clustering partitioning method (ANFIS-SC) were used with Gaussian membership functions to generate the fuzzy inference system. Six performance evaluation criteria were used to evaluate the developed SST prediction models, including mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), Nash-Sutcliffe efficiency (NSE) and correlation of determination (R2). The dataset was randomly divided into training and testing datasets for the machine learning process. Training data accounted for 75% of the dataset, while 25% of the dataset was allocated for testing in ANFIS. The hybrid algorithm was selected as a training algorithm for the ANFIS. Simulation results revealed that the ANFIS-SC4 model provided a higher correlation coefficient of 0.96 between the observed and predicted SST values. The results of this study suggest that the developed ANFIS model can be applied for predicting sea surface temperature around the world.

PREDIKSI DEFLEKSI BALOK KAYU TUMPUAN SEDERHANA DENGAN MODEL ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS)

Konstruksia ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 127
Author(s):  
Novia Hilda Silviani ◽  
Buan Anshari ◽  
Ngudiyono Ngudiyono
Keyword(s):  
Mean Square Error ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Mean Square ◽  
Inference System ◽  
Neuro Fuzzy

Defleksi merupakan parameter penting untuk mengontrol elemen struktur balok elemen pada kondisi layan. Beberapa cara untuk menghitung defleksi diantaranya dengan metode matematis seperti luas momen, balok konjugasi, Castigliano's, prinsip kerja virtual dan metode numerik seperti metode beda hingga, elemen hingga dan lain lain. Dalam naskah ini, telah dibangun model Adaptive Neuro Fuzzy Inference System (ANFIS), untuk memprediksi defleksi balok kayu tumpuan sederhana dengan beban terdistribusi merata. Data proses pembelajaran terdiri dari input dan output (target). Input pada penelitian ini meliputi modulus elastisitas (E), lebar (b), tinggi (h), bentang (L) dan beban terdistribusi merata (W) sedangkan output adalah defleksi balok. Hasil analisis menunjukkan bahwa model ANFIS mempunyai tingkat akurasi yang baik, jika dibandingkan dengan teori dimana koefisien korelasi (R2) untuk data pengujian 0.995 dan Mean Square Error (MSE) 0.13 mm. Hal ini menunjukkan bahwa model ANFIS yang dibangun dapat diandalkan untuk memprediksi lendutan balok kayu tumpuan sederhana.

scholarly journals Modeling of Carbon Dioxide Fixation Rate by Micro Algae Using Hybrid Artificial Intelligence and Fuzzy Logic Methods and Optimization by Genetic Algorithm

2021 ◽  
Author(s):  
Omkar Singh Kushwaha ◽  
Haripriyan Uthayakumar ◽  
Karthigaiselvan Kumaresan
Keyword(s):  
Carbon Dioxide ◽  
Genetic Algorithm ◽  
Fuzzy Inference ◽  
Scale Up ◽  
Hybrid Approach ◽  
Coefficient Of Determination ◽  
Anfis Model ◽  
Fixation Rate ◽  
Inference System ◽  
Prediction Capability

Abstract In this study we are reporting a prediction model for the estimation of carbon dioxide (CO2) fixation based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithm (GA) hybrid approach. The experimental parameters such as temperature and pH conditions of the micro-algae-based carbon dioxide uptake process were taken as the input variables and the CO2 fixation rate was taken as the output variable. The optimization of ANFIS parameters and formation of the model structure were performed by genetic algorithm (GA) algorithm in order to achieve optimum prediction capability and industrial applicability. The best-fitting model was figured out using statistical analysis parameters such as RMSE, R2 and AARD. According to the analysis, GA-ANFIS model depicted a superior prediction capability over ANFIS optimized model. The Root Mean Square Error (RMSE), coefficient of determination (R2) and AARD for GA-ANFIS were determined as 0.000431, 0.97865 and 0.044354 in the training phase and 0.00056, 0.98457 and 0.032156 in the testing phase, respectively for the GA-ANFIS Model. As a result, it can be concluded that the proposed GA-ANFIS model is an efficient technique having very high potential to accurately calculate CO2 fixation rate and the exploration of the industrial scale-up process for commercial activities.

scholarly journals ANFIS Prediction Model for the Mechanical Properties of Soil and Activated Rice Husk Ash Blend for Sustainable Construction

2021 ◽  
Author(s):  
Kennedy C Onyelowe ◽  
Elvis M Mbadike ◽  
Michael E Onyia ◽  
George U Alaneme ◽  
M. U. Dimonyeka ◽  
...  
Keyword(s):  
Mean Square Error ◽  
Rice Husk ◽  
Rice Husk Ash ◽  
Clayey Soil ◽  
Model Development ◽  
Construction Materials ◽  
Sustainable Construction ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Inference System

Abstract Adaptive neuro-fuzzy inference system (ANFIS), which integrates both Takagi-Sugeno fuzzy logic and neural network principles and also captures their benefits in a single framework was deployed for the modelling of the mechanical strength behaviour of expansive clayey soil treated with hydrated-lime activated rice husk ash (HARHA). The compaction properties, consistency limits and the activated ash (HARHA) were the predictors while CBR and UCS were the targets in this evolutionary model. The advantages of artificial intelligence techniques deployment in geotechnical research is to deal with the complex challenges associated with effectiveness in construction materials’ utilization so as to achieve optimal assessment of geotechnical materials’ behaviour and sustainable engineering design. ANFIS model development were carried out with 35 data sets derived from the experimental responses with respect to varying proportions of HARHA treatment from 0% to 12%. 25 and 10 datasets were used for training and testing the network respectively. The California bearing ratio (CBR) and unconfined compressive strength (UCS) were the target response while the HARHA replacement ratio, compaction and consistency limits properties were the input variables of the developed model. The model evaluation results obtained using statistical tools showed mean absolute error (MAE) of 0.582 and 0.7196, root mean square error (RMSE) of 0.6198 and 0.9004, mean square error (MSE) of 0.384 and 0.811, and coefficient of determination (CoD) value of 0.9973 and 0.9992 for CBR and UCS response parameters respectively. The results obtained indicates a very good performance in terms of prediction accuracy. This shows that ANFIS provides the flexibility in achieving sustainable geotechnical materials integration in civil works.

scholarly journals IndoorPlant: A Model for Intelligent Services in Indoor Agriculture Based on Context Histories

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1631
Author(s):  
Bruno Guilherme Martini ◽  
Gilson Augusto Helfer ◽  
Jorge Luis Victória Barbosa ◽  
Regina Célia Espinosa Modolo ◽  
Marcio Rosa da Silva ◽  
...  
Keyword(s):  
Technology Acceptance ◽  
Mean Square Error ◽  
Ease Of Use ◽  
Coefficient Of Determination ◽  
Square Root ◽  
Mean Square ◽  
Use Context ◽  
The Mean ◽  
Acceptance Model ◽  
The Internet Of Things

The application of ubiquitous computing has increased in recent years, especially due to the development of technologies such as mobile computing, more accurate sensors, and specific protocols for the Internet of Things (IoT). One of the trends in this area of research is the use of context awareness. In agriculture, the context involves the environment, for example, the conditions found inside a greenhouse. Recently, a series of studies have proposed the use of sensors to monitor production and/or the use of cameras to obtain information about cultivation, providing data, reminders, and alerts to farmers. This article proposes a computational model for indoor agriculture called IndoorPlant. The model uses the analysis of context histories to provide intelligent generic services, such as predicting productivity, indicating problems that cultivation may suffer, and giving suggestions for improvements in greenhouse parameters. IndoorPlant was tested in three scenarios of the daily life of farmers with hydroponic production data that were obtained during seven months of cultivation of radicchio, lettuce, and arugula. Finally, the article presents the results obtained through intelligent services that use context histories. The scenarios used services to recommend improvements in cultivation, profiles and, finally, prediction of the cultivation time of radicchio, lettuce, and arugula using the partial least squares (PLS) regression technique. The prediction results were relevant since the following values were obtained: 0.96 (R2, coefficient of determination), 1.06 (RMSEC, square root of the mean square error of calibration), and 1.94 (RMSECV, square root of the mean square error of cross validation) for radicchio; 0.95 (R2), 1.37 (RMSEC), and 3.31 (RMSECV) for lettuce; 0.93 (R2), 1.10 (RMSEC), and 1.89 (RMSECV) for arugula. Eight farmers with different functions on the farm filled out a survey based on the technology acceptance model (TAM). The results showed 92% acceptance regarding utility and 98% acceptance for ease of use.

Optimized neural network and adaptive neuro-fuzzy controlled dynamic voltage restorer for power quality performance

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Prashant Kumar ◽  
Sabha Raj Arya ◽  
Khyati D. Mistry
Keyword(s):  
Neural Network ◽  
Power Distribution ◽  
Fuzzy Inference ◽  
Dynamic Performance ◽  
Hybrid Approach ◽  
Voltage Regulation ◽  
Dynamic Voltage Restorer ◽  
Inference System ◽  
Intelligence System ◽  
Neuro Fuzzy

Abstract In this article, a hybrid approach is implemented namely, neural network training (NNT) based machine learning (ML) estimator inspired by artificial neural network (ANN) and self-adaptive neuro-fuzzy inference system (ANFIS) to tackle the voltage aggravations in the power distribution network (DN). In this work, potential of swarm intelligence technique namely particle swam optimization (PSO) is analysed to obtain an optimum prediction model with certain modifications in training algorithm parameters. In practice, when the systems are continuously subjected to parametric changes or external disturbances, then ample time is dedicated to tune the system to regain its stable performance. To improve the dynamic performance of the system intelligence-based techniques are proposed to overcome the shortcomings of conventional controllers. So, gain tuning process based on the intelligence system is a desirable choice. The statistical tools are used to proclaim the effectiveness of the controllers. The obtained MSE, RMSE, ME, SD and R were evaluated as 0.0015959, 0.039949, −0.00089838, 0.039941 and 1 in the training phase and 0.0015372, 0.039207, −0.0005657, 0.039203 and 1 in the testing phase, respectively. The results revealed that the ANFIS-PSO network model could accomplish a better DC voltage regulation performance when it is compared to the conventional PI. The proposed intelligence strategies confirm that the predicted DVR model based on NNT-ML and ANFIS has faster convergence speed and reliable prediction rate. Moreover, the simulation results show that the dynamic response is improved with proposed PSO based NNT based ML and ANFIS (Takagi-Sugeno) that significantly compensates the voltage based PQ issues. The proposed DVR is actualized in MATLAB/SIMULINK platform.

scholarly journals Multi-State Load Demand Forecasting Using Hybridized Support Vector Regression Integrated with Optimal Design of Off-Grid Energy Systems—A Metaheuristic Approach

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1166
Author(s):  
Bashir Musa ◽  
Nasser Yimen ◽  
Sani Isah Abba ◽  
Humphrey Hugh Adun ◽  
Mustafa Dagbasi
Keyword(s):  
Support Vector Regression ◽  
Mean Square Error ◽  
Prediction Accuracy ◽  
Demand Forecasting ◽  
Load Forecasting ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Mean Square ◽  
Hybrid Renewable Energy System

The prediction accuracy of support vector regression (SVR) is highly influenced by a kernel function. However, its performance suffers on large datasets, and this could be attributed to the computational limitations of kernel learning. To tackle this problem, this paper combines SVR with the emerging Harris hawks optimization (HHO) and particle swarm optimization (PSO) algorithms to form two hybrid SVR algorithms, SVR-HHO and SVR-PSO. Both the two proposed algorithms and traditional SVR were applied to load forecasting in four different states of Nigeria. The correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used as indicators to evaluate the prediction accuracy of the algorithms. The results reveal that there is an increase in performance for both SVR-HHO and SVR-PSO over traditional SVR. SVR-HHO has the highest R2 values of 0.9951, 0.8963, 0.9951, and 0.9313, the lowest MSE values of 0.0002, 0.0070, 0.0002, and 0.0080, and the lowest MAPE values of 0.1311, 0.1452, 0.0599, and 0.1817, respectively, for Kano, Abuja, Niger, and Lagos State. The results of SVR-HHO also prove more advantageous over SVR-PSO in all the states concerning load forecasting skills. This paper also designed a hybrid renewable energy system (HRES) that consists of solar photovoltaic (PV) panels, wind turbines, and batteries. As inputs, the system used solar radiation, temperature, wind speed, and the predicted load demands by SVR-HHO in all the states. The system was optimized by using the PSO algorithm to obtain the optimal configuration of the HRES that will satisfy all constraints at the minimum cost.

scholarly journals Authentication of Patin Fish Oil (Pangasius micronemus) using FTIR Spectroscopy Combined with Chemometrics

2020 ◽  
pp. 23
Author(s):  
Anggita Rosiana Putri ◽  
Abdul Rohman ◽  
Sugeng Riyanto ◽  
Widiastuti Setyaningsih
Keyword(s):  
Root Mean Square Error ◽  
Fish Oil ◽  
Ftir Spectroscopy ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square

Authentication of Patin fish oil (MIP) is essential to prevent adulteration practice, to ensure quality, nutritional value, and product safety. The purpose of this study is to apply the FTIR spectroscopy combined with chemometrics for MIP authentication. The chemometrics method consists of principal component regression (PCR) and partial least square regression (PLSR). PCR and PLSR were used for multivariate calibration, while for grouping the samples using discriminant analysis (DA) method. In this study, corn oil (MJ) was used as an adulterate. Twenty-one mixed samples of MIP and MJ were prepared with the adulterate concentration range of 0-50%. The best authentication model was obtained using the PLSR technique using the first derivative of FTIR spectra at a wavelength of 650-3432 cm-1. The coefficient of determination (R2) for calibration and validation was obtained 0.9995 and 1.0000, respectively. The value of root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were 0.397 and 0.189. This study found that the DA method can group the samples with an accuracy of 99.92%.

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