Perceptron Neural Network Research Articles

Predicting air quality index and fine particulate matter levels in Bagdad city using advanced machine learning and deep learning techniques

Particulate matter pollution is recognized globally as one of the most hazardous forms of air pollution, profoundly impacting environmental integrity and public health. Key metrics for assessing this pollution include the Air Quality Index (AQI) and fine particulate matter with diameters ≤2.5 μm (PM2.5). These indicators are closely associated with severe health consequences, such as premature death from chronic exposure. While traditional statistical methods have been employed in some studies to evaluate AQI and PM2.5, the application of advanced machine learning techniques has been limited. This research employs deep learning and artificial neural networks (ANN) to forecast AQI and PM2.5 levels in Baghdad, Iraq. The study utilizes an extensive dataset from July 1, 2016, to December 12, 2021, comprising over 48,000 data points for AQI and PM2.5. Time serves as an independent input variable influencing these dependent variables. The analysis employs a diverse set of machine learning algorithms, including random forest, decision tree, K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), and Long Short-Term Memory networks (LSTM). The findings demonstrate that MLP and LSTM models outperform other methods, providing the most accurate predictions. The correlation coefficients were 0.977 and 0.983 for the prediction of AQI and 0.973 and 0.985 for the prediction of PM2.5 using MLP and LSTM, respectively. In addition, the outcomes showed that both AQI and PM2.5 were within the moderate to unhealthy ranges, and their distribution levels pointed to the need for addressing air quality in Baghdad city. Furthermore, this study contributes to the burgeoning field of machine learning applications in environmental science by establishing a robust and nuanced predictive framework for evaluating air quality. It highlights the potential of deep learning in public health applications and offers actionable insights for policymaking to mitigate air pollution and its adverse effects.

Enhancing left ventricular segmentation in echocardiography with a modified mixed attention mechanism in SegFormer architecture

Echocardiography is a key tool for the diagnosis of cardiac diseases, and accurate left ventricular (LV) segmentation in echocardiographic videos is crucial for the assessment of cardiac function. However, since semantic segmentation of video needs to take into account the temporal correlation between frames, this makes the task very challenging. This article introduces an innovative method that incorporates a modified mixed attention mechanism into the SegFormer architecture, enabling it to effectively grasp the temporal correlation present in video data. The proposed method processes each time series by encoding the image input into the encoder to obtain the current time feature map. This map, along with the historical time feature map, is then fed into a time-sensitive mixed attention mechanism type of convolution block attention module (TCBAM). Its output can serve as the historical time feature map for the subsequent sequence, and a combination of the current time feature map and historical time feature map for the current sequence. The processed feature map is then input into the Multilayer Perceptron (MLP) and subsequent networks to generate the final segmented image. Through extensive experiments conducted on two different datasets: Hamad Medical Corporation, Tampere University, and Qatar University (HMC-QU), Cardiac Acquisitions for Multi-structure Ultrasound Segmentation (CAMUS) and Sunnybrook Cardiac Data (SCD), achieving a Dice coefficient of 97.92 % on the SCD dataset and an F1 score of 0.9263 on the CAMUS dataset, outperforming all other models. This research provides a promising solution to the temporal modeling challenge in video semantic segmentation tasks using transformer-based models and points out a promising direction for future research in this field.

Unsupervised heterogeneous domain adaptation for EEG classification

Objective. Domain adaptation has been recognized as a potent solution to the challenge of limited training data for electroencephalography (EEG) classification tasks. Existing studies primarily focus on homogeneous environments, however, the heterogeneous properties of EEG data arising from device diversity cannot be overlooked. This motivates the development of heterogeneous domain adaptation methods that can fully exploit the knowledge from an auxiliary heterogeneous domain for EEG classification. Approach. In this article, we propose a novel model named informative representation fusion (IRF) to tackle the problem of unsupervised heterogeneous domain adaptation in the context of EEG data. In IRF, we consider different perspectives of data, i.e. independent identically distributed (iid) and non-iid, to learn different representations. Specifically, from the non-iid perspective, IRF models high-order correlations among data by hypergraphs and develops hypergraph encoders to obtain data representations of each domain. From the non-iid perspective, by applying multi-layer perceptron networks to the source and target domain data, we achieve another type of representation for both domains. Subsequently, an attention mechanism is used to fuse these two types of representations to yield informative features. To learn transferable representations, the maximum mean discrepancy is utilized to align the distributions of the source and target domains based on the fused features. Main results. Experimental results on several real-world datasets demonstrate the effectiveness of the proposed model. Significance. This article handles an EEG classification situation where the source and target EEG data lie in different spaces, and what’s more, under an unsupervised learning setting. This situation is practical in the real world but barely studied in the literature. The proposed model achieves high classification accuracy, and this study is important for the commercial applications of EEG-based BCIs.

Exploring deep fully convolutional neural networks for surface defect detection in complex geometries

In this paper, we propose a machine learning approach for detecting superficial defects in metal surfaces using point cloud data. We compare the performance of two popular deep learning architectures, multilayer perceptron networks (MLPs) and fully convolutional networks (FCNs), with varying feature sets. Our results show that FCNs (F1=0.94) outperformed MLPs (F1=0.52) in terms of precision, recall, and F1-score. We found that transfer learning with pre-trained models can improve performance when the amount of available data is limited. Our study highlights the importance of considering the amount and quality of training data in developing machine learning models for defect detection in industrial settings with 3D images.

Forecasting Robot Movement with Sensor Readings and Multi-Layer Perceptron Models

Classification of sensor data plays a crucial role in different fields, aiding in important tasks like detecting faults, recognizing events and predicting maintenance needs. This research paper thoroughly explores the use of machine learning methods, in specific Multi-Layer Perceptron (MLP) networks, to classify sensor data. The dataset used in this study includes raw measurements from all 24 ultrasound sensors alongside their corresponding class labels indicating the robots actions (such as moving or turning left). The study tackles challenges like imbalanced classes, noisy signals, and striving for classification through a case study employing MLP models. Through conducting experiments and analysis, we fine-tuned the MLP models setup to achieve a 93.04% accuracy on the test dataset. Additionally evaluation metrics like precision, recall and F1 score highlighted the models effectiveness across different classes. A comparison with Support Vector Machines (SVM) and Logistic Regression models highlighted the performance of the MLP model. These results not only show the effectiveness of MLP networks but also provide valuable insights into best practices, for classifying sensor data. Through examining the intricacies of sensor data analysis and classification this study contributes to enhancing our knowledge of applying machine learning to real world challenges.

Analyzing speed-difference impact on freeway joint injury severities of Leading-Following vehicles using statistical and data-driven models

Rear-end (RE) crashes are notably prevalent and pose a substantial risk on freeways. This paper explores the correlation between speed difference among the following and leading vehicles (Δν) and RE crash risk. Three joint models, comprising both uncorrelated and correlated joint random-parameters bivariate probit (RPBP) approaches (statistical methods) and a cross-stitch multilayer perceptron (CS-MLP) network (a data-driven method), were estimated and compared against three separate models: Support Vector Machines (SVM), eXtreme Gradient Boosting (XGBoost), and MLP networks (all data-driven methods). Data on 15,980 two-vehicle RE crashes were collected over a two-year period, from January 1, 2021, to December 31, 2022, considering two possible levels of injury severity: no injury and injury/fatality for both drivers of following and leading vehicles. The comparative performance analysis demonstrates the superior predictive capability of the CS-MLP network over the uncorrelated/correlated joint RPBP model, SVM, XGBoost, and MLP networks in terms of recall, F-1 Score, and AUC. Significantly, numerous shared variables influence the injury severity outcomes for the following and leading vehicles across both statistical and data-driven approaches. Among these factors, the following vehicle (a truck) and the leading vehicle (a passenger car) demonstrate contrasting effects on the injury severity outcomes for both vehicles. Furthermore, the SHapley Additive exPlanations (SHAP) values from the CS-MLP network visually show the relationship between Δν and injury severity, revealing non-linear trends unlike the average effects shown by statistical methods. They indicate that the least injury outcomes for both following and leading vehicles occurs at a Δν of 0 to 10 mph, matching observed patterns in RE crash data. Additionally, a marked variation in the trend of SHAP values for the two vehicles is noted as the speed difference increases. Therefore, the findings affirm the superior performance of joint model development and substantiate the non-linear impacts of speed difference on injury outcomes. The adoption of dynamic speed control measures is recommended to mitigate the injury outcomes involved in two-vehicle RE crashes.

A study on the application of artificial neural network to predict k-eff and peaking factor of a small modular PWR

Machine learning (ML) using artificial neural network (ANN) methods is being applied to predict required parameters for nuclear reactors based on learning from big data sets. The ML models usually give faster calculation speed while the accuracy is good agreement with physical simulators. In this work, a multi-layer perceptron network was built and trained to predict k-eff and peaking factor of a small modular pressurized water reactor (PWR). The results are compared with those obtained by using a reactor physics code system, i.e. SRAC2006. The comparison shows good agreement accuracy and higher performance of the ML models.

Understanding Childhood Victimization Experiences and Mental Health Outcomes in a Sample of Prison Inmates in Northern Chile

In this paper, the weak points have been investigated, the proposal to solve them, the application of the solution and the comparison of the results in the mode of simulation and experimental testing have been discussed. For this purpose, a four-wheel mobile robot has been considered for simulation and implementation, and a linear quadratic regulator controller and a nonlinear model predictive controller have been used to control it. But the combination of these classic and modern controllers with machine learning can greatly help to make these controllers work more accurately; As a result, in order to increase the accuracy of the performance of these controllers, by training neural networks of multilayer perceptrons, the controllers have been made intelligent. Controllers with cost function have coefficients as weighting to the matrix of system state variables and control input, which are greatly affected by changing these two weighting matrices of problem solving and optimization. For this reason, it is necessary to extract these two matrices for each separate path in order to improve the performance of the controller by trial and error. But by applying the proposed network which is trained with a new algorithm, not only the performance accuracy has increased, but the network extracts these two matrices without the need to spend human energy. Also, in order to reduce the existing time delays, especially in the implementation of the nonlinear controller on the robot in the experimental mode, by training other neural networks to optimally extract the benefit of the forecast horizon, reducing the calculations and increasing the speed of the solution has been achieved. In the hardware part, by examining and using operators such as the pixy camera and the U2D2 interface, which are faster than the usual method, the solution time has been reduced.

Variational temporal convolutional networks for I-FENN thermoelasticity

Machine learning (ML) has been used to solve multiphysics problems like thermoelasticity through multi-layer perceptron (MLP) networks. However, MLPs have high computational costs and need to be trained for each prediction instance. To overcome these limitations, we introduced an integrated finite element neural network (I-FENN) framework to solve transient thermoelasticity problems in Abueidda and Mobasher (2024). This approach used a physics-informed temporal convolutional network (PI-TCN) within a finite element scheme for solving transient thermoelasticity problems. In this paper, we introduce an I-FENN framework using a new variational TCN model trained to minimize the thermoelastic variational form rather than the strong form of the energy balance. We mathematically prove that the I-FENN setup based on minimizing the variational form of transient thermoelasticity still leads to the same solution as the strong form. Introducing the variational form to the ML model brings the advantages of lower requirement for the differentiability of the basis function and, thus, lower memory requirement and higher computational efficiency. Also, it automatically satisfies zero Neumann boundary conditions, thus reducing the complexity of the loss function. The formulation based on the variational form complies with thermodynamic requirements. The proposed loss function reduces the difference between predicted and target data while minimizing the variational form of thermoelasticity equations, combining the benefits of both data-driven and variational methods. In addition, this study uses finite element shape functions for spatial gradient calculations and compares their performance against automatic differentiation. Our results reveal that models leveraging shape functions exhibit higher accuracy in capturing the behavior of the thermoelasticity problem and faster convergence. Adding the variational term and using shape functions for gradient calculations ensure better adherence to the underlying physics. We demonstrate the capabilities of this I-FENN framework through multiple numerical examples. Additionally, we discuss the convergence of the proposed variational TCN model and the impact of hyperparameters on its performance. The proposed approach offers a well-founded and flexible platform for solving fully coupled thermoelasticity problems while retaining computational efficiency, where the efficiency scales proportional to the model size.

Ultra-fast GST-based optical neuron for the implementation of integrated photonic neural networks

A fast-integrated optical neuron is proposed based on a micro-ring resonator structure. The GST phase change material is used in the MRR structure, and neuron switching is achieved by the electrical actuation of this material. The nonlinear activation function of the neuron is extracted through three-dimensional electrical and optical simulations. The neuron is then employed in a three-layer perceptron network using the extracted activation function, and its performance is examined in the classification of handwritten digits. The overall performance of the proposed neuron is compared with the previous optical neural networks. Simulations show an ultra-fast switching time of 200fs, which is one order of magnitude faster than the best-reported value, and an accuracy of 98.9% (in the classification of MNIST handwritten digits dataset) which is among the best results, while the estimated neuron footprint is relatively small.

Antiviral drugs preformulation: thermal energy effects by multilayer perceptron network approach and Hirshfeld surface properties

Antiviral drugs preformulation: thermal energy effects by multilayer perceptron network approach and Hirshfeld surface properties

Fast Prediction of Airfoil Aerodynamic Characteristics Based on a Combined Autoencoder

Aircraft airfoils are classified into two main categories: symmetrical and asymmetrical. Both types of airfoils have a significant impact on the flight performance and safety of the aircraft. The fast prediction of the aerodynamic coefficients and pressure distributions of airfoils is crucial for the design of aircraft. The traditional wind tunnel test and CFD methods have the disadvantages of high test cost and high time consumption. To solve these problems, a combined autoencoder (CAE) network is proposed in this paper, which can achieve the fast prediction of airfoil aerodynamic coefficients and pressure distributions. The network consists of an airfoil shape autoencoder (AE) network and a multilayer perceptron (MLP) network. Firstly, an autoencoder network reflecting the characteristics of the airfoil shape is established, and the effects of different latent variables on the performance of the autoencoder network are investigated. Then, the latent variables obtained from the autoencoder are concatenated with the inflow conditions such as the Reynolds number and the angle of attack to be used as inputs to the MLP network, and the aerodynamic coefficients of different airfoils in different inflow conditions are predicted. The effects of various latent variable inputs, as well as the direct input of the airfoil shape into the MLP network, on the prediction performance of aerodynamic coefficients are compared and analyzed. The optimal aerodynamic coefficient prediction network is then obtained. Finally, the CAE network is also applied to predict the pressure distributions of different airfoils in different inflow conditions and the effects of different latent variables and input conditions on the prediction performance of the pressure distributions are analyzed and compared with the advantages and disadvantages of the CAE network and the conditional variational autoencoder (CVAE) network. The results demonstrate that the proposed method is capable of accurately predicting aerodynamic characteristics in a shorter time, offering a valuable reference for the fast and efficient design of aircraft airfoils.

Analyzing the effects of streetscape and land use on urban accidents and predicting future accidents by using machine learning algorithms (case study: Mashhad)

In general, land use and layout of streets can have a significant impact on the behavior of drivers and pedestrians. In particular, streetscape has often been overlooked that recognizing the role of streetscape on street accident in urban areas is important. The aim of this research is to investigate the influence of streetscape and land use on urban accidents that occurred in Mashhad between the years 2017 and 2021. To achieve this objective, the study focused on analyzing accidents in three different urban zones. It also considered the land use types adjacent to both closed and open streets, including residential, commercial, and mixed land uses. The research employed various surveys to gather the necessary data and insights related to the targeted areas. Statistics on accident in three zones show that among the mentioned land uses, commercial areas have experienced the highest number of accidents, with their share being approximately three times that of accidents in residential areas. Additionally, 75 % of all accidents took place in areas with open streetscape, whereas accidents in areas with enclosed view accounted for one third of the number of accidents in open streetscape areas. In this research, analysis and modeling were conducted using machine learning algorithms implemented in the Python programming language. Several models were employed, and the best models were selected based on their performance and accuracy, which include Random Forest Regression (RFR), Multilayer Neural Network Perceptron Regression (MLP) and Extreme Boost Gradient Regression (XGBoost). The accuracy of the machine learning models which successfully predicted future outcomes was as follows: Random Forest Regression (RFR) achieved 85 % accuracy, Extreme Boost Gradient Regression (XGBoost) achieved 81 % accuracy, and finally, Neural Network Multilayer Perceptron Regression (MLP) achieved 75 % accuracy.

Prediction of BLEVE-induced response of road tunnel using Transformer network with modified self-attention (SAMT)

Road tunnels might be exposed to Boiling Liquid Expansion Vapor Explosion (BLEVE) due to the transportation of liquified gas tankers passing through road tunnels. An efficient and accurate prediction of the response of road tunnels under internal BLEVEs can facilitate the reliable BLEVE-resistant design and risk assessment of road tunnels. This study introduces an advanced deep-learning model that employs a Transformer-based architecture with a modified self-attention mechanism, termed as Self-Attention Modified Transformer (SAMT), to predict BLEVE-induced support rotation of tunnel structure, which is a common criterion in assessing reinforced concrete structure damage to blast loads. Unlike the Transformer with the traditional self-attention mechanism, the proposed SAMT effectively aggregates global information across all variables while mitigating undue dependencies among uncorrelated variables. Consequently, the proposed SAMT is better suited for processing tabular data with uncorrelated variables. The feasibility and advantages of the proposed SAMT are verified by extensive data generated using calibrated numerical models of box-shaped road tunnels subjected to internal BLEVEs. By comparing the performance of the proposed SAMT with the non-modified Transformer network (FT-Transformer) as well as two other typical deep learning networks, i.e., Multi-layer Perceptron (MLP) and Residual Network (ResNet), it is found that the SAMT offers higher prediction accuracy and robustness than the other three models in predicting BLEVE-induced support rotations of box-shaped road tunnels. The study demonstrates that the proposed SAMT is an effective tool for the prediction of BLEVE-induced support rotations of road tunnels.

A decision-level sensor fusion scheme integrating ultrasonic guided wave and vibration measurements for damage identification

Structural health monitoring is designed to detect and analyze damage in mechanical, aerospace, and industrial structures to prevent potential catastrophic failures. The effectiveness of identifying structural faults and operational damage significantly relies on the sensing technology. Multisensory data fusion, as one of the promising techniques, combines data from multiple sensor modalities for reliable and accurate fault diagnosis. In this paper, we propose a novel decision-level multisensory data fusion approach. This approach combines data from an ultrasonic guided-wave sensor modality and a vibration sensor modality for the first time, aiming to achieve precise and stable damage identification. The Bayesian probabilistic distributions from each sensor modality are obtained using a multi-level Metropolis–Hastings algorithm. Subsequently, a multilayer perceptron network is trained to automatically allocate confidence scores for each sensor modality. The final fusion results are derived from the proposed decision-level fusion approach, combining the Bayesian probabilistic distributions from each sensor modality with allocated confidence scores. Simulation and experiment results indicate that the decision-level fusion approach produces more accurate and reliable outcomes for damage identification compared to relying on a single modality, especially in the case of progressive damage.

A Metadata-Driven Approach to Malignant Tumor Identification in Dermoscopy Images based on Genetic-Inspired Crow Search Optimized Convolutional Multilayer Perceptron Network

A Metadata-Driven Approach to Malignant Tumor Identification in Dermoscopy Images based on Genetic-Inspired Crow Search Optimized Convolutional Multilayer Perceptron Network

An enhanced non-iterative real-time solver via multilayer perceptron for on-board component-level models

The real-time on-board component-level model (CLM) of gas turbine engines (GTEs) is an important foundation for fault-tolerant control and health management. However, traditional iterative solving algorithms typically rely on the multiple computations of gas-path thermodynamic parameters, which seriously limits their practical application. In this paper, aiming to improve the real-time performance of CLM, an enhanced non-iterative real-time solver (ENRS) method is proposed. It has combined an input selection strategy, an under-sampling supplemental enhancement training (USET) technique, and a multilayer perceptron (MLP) network. Compared with the existing CLM solving methods, its innovations are as follows: (1) a novel framework for directly solving CLM is proposed, which helps get rid of the dilemma of increased time consumption due to multiple iterations. (2) an enhanced training process is designed for the MLP network, which helps improve the adaptivity to the full flight envelope and all engine states, especially to reduce the maximum error. Simulation tests eventually show that the ENRS effectively improves the real-time performance of CLM in practical applications, and maintains solution accuracy and robustness.

MvMRL: a multi-view molecular representation learning method for molecular property prediction.

Effective molecular representation learning is very important for Artificial Intelligence-driven Drug Design because it affects the accuracy and efficiency of molecular property prediction and other molecular modeling relevant tasks. However, previous molecular representation learning studies often suffer from limitations, such as over-reliance on a single molecular representation, failure to fully capture both local and global information in molecular structure, and ineffective integration of multiscale features from different molecular representations. These limitations restrict the complete and accurate representation of molecular structure and properties, ultimately impacting the accuracy of predicting molecular properties. To this end, we propose a novel multi-view molecular representation learning method called MvMRL, which can incorporate feature information from multiple molecular representations and capture both local and global information from different views well, thus improving molecular property prediction. Specifically, MvMRL consists of four parts: a multiscale CNN-SE Simplified Molecular Input Line Entry System (SMILES) learning component and a multiscale Graph Neural Network encoder to extract local feature information and global feature information from the SMILES view and the molecular graph view, respectively; a Multi-Layer Perceptron network to capture complex non-linear relationship features from the molecular fingerprint view; and a dual cross-attention component to fuse feature information on the multi-views deeply for predicting molecular properties. We evaluate the performance of MvMRL on 11 benchmark datasets, and experimental results show that MvMRL outperforms state-of-the-art methods, indicating its rationality and effectiveness in molecular property prediction. The source code of MvMRL was released in https://github.com/jedison-github/MvMRL.

Effectiveness of an ensemble technique based on the distributivity equation in detecting suspicious network activity

With the growing complexity and frequency of cyber threats, there is a pressing need for more effective defense mechanisms. Machine learning offers the potential to analyze vast amounts of data and identify patterns indicative of malicious activity, enabling faster and more accurate threat detection. Ensemble methods, by incorporating diverse models with varying vulnerabilities, can increase resilience against adversarial attacks. This study covers the usage and evaluation of the relevance of an innovative approach of ensemble classification for identifying intrusion threats on a large CICIDS2017 dataset. The approach is based on the distributivity equation that appropriately aggregates the underlying classifiers. It combines various standard supervised classification algorithms, including Multilayer Perceptron Network, k-Nearest Neighbors, and Naive Bayes, to create an ensemble. Experiments were conducted to evaluate the effectiveness of the proposed hybrid ensemble method. The performance of the ensemble approach was compared with individual classifiers using measures such as accuracy, precision, recall, F-score, and area under the ROC curve. Additionally, comparisons were made with widely used state-of-the-art ensemble models, including the soft voting method (Weighted Average Probabilities), Adaptive Boosting (AdaBoost), and Histogram-based Gradient Boosting Classification Tree (HGBC) and with existing methods in the literature using the same dataset, such as Deep Belief Networks (DBN), Deep Feature Learning via Graph (Deep GFL). Based on these experiments, it was found that some ensemble methods, such as AdaBoost and Histogram-based Gradient Classification Tree, do not perform reliably for the specific task of identifying network attacks. This highlights the importance of understanding the context and requirements of the data and problem domain. The results indicate that the proposed hybrid ensemble method outperforms traditional algorithms in terms of classification precision and accuracy, and offers insights for improving the effectiveness of intrusion detection systems.

Measuring volume fractions of a three-phase flow without separation utilizing an approach based on artificial intelligence and capacitive sensors.

Many different kind of fluids in a wide variety of industries exist, such as two-phase and three-phase. Various combinations of them can be expected and gas-oil-water is one of the most common flows. Measuring the volume fraction of phases without separation is vital in many aspects, one of which is financial issues. Many methods are utilized to ascertain the volumetric proportion of each phase. Sensors based on measuring capacity are so popular because this kind of sensor operates seamlessly and autonomously without necessitating any form of segregation or disruption for measuring in the process. Besides, at the present moment, Artificial intelligence (AI) can be nominated as the most useful tool in several fields, and metering is no exception. Also, three main type of regimes can be found which are annular, stratified, and homogeneous. In this paper, volume fractions in a gas-oil-water three-phase homogeneous regime are measured. To accomplish this objective, an Artificial Neural Network (ANN) and a capacitance-based sensor are utilized. To train the presented network, an optimized sensor was implemented in the COMSOL Multiphysics software and after doing a lot of simulations, 231 different data are produced. Among all obtained results, 70 percent of them (161 data) are awarded to the train data, and the rest of them (70 data) are considered for the test data. This investigation proposes a new intelligent metering system based on the Multilayer Perceptron network (MLP) that can estimate a three-phase water-oil-gas fluid's water volume fraction precisely with a very low error. The obtained Mean Absolute Error (MAE) is equal to 1.66. This dedicates the presented predicting method's considerable accuracy. Moreover, this study was confined to homogeneous regime and cannot measure void fractions of other fluid types and this can be considered for future works. Besides, temperature and pressure changes which highly temper relative permittivity and density of the liquid inside the pipe can be considered for another future idea.