PNN Model Research Articles

A Novel Brain-Machine Safety System Based on Drowsiness Detection using the PNN and MLP Algorithms

This work aims to develop a safety system capable of detecting drowsiness, in order to reduce the accident rate, the main idea is based on the classification of the cerebral signal captured by the electroencephalograph, after processing to eliminate all noise, the resulting data will then be segmented into two classes, the first category is devoted to the state of wakefulness and the second will be devoted to drowsiness. This categorization is based on the combination of features such as mean and standard deviation, and performed by two neural network algorithms, the probabilistic neural network and the multilayer perceptron, whose performance is evaluated according to machine learning metrics such as accuracy, error, specificity, sensitivity, F1 indicator and ROC curve representation. The binary classification results are highly significant in terms of class discrimination, with both models achieving excellent performance, with the MLP classifier outperforming the PNN model with an accuracy of 96.10% and an accuracy rate of 98.43%. This suggests that the MLP model can be effectively applied to critical tasks where good discrimination is required, such as detecting drowsiness in drivers to avoid accidents.

A Study on Factors Associated with Child Sexual Abuse and Recognizing the Severity: Special Reference to Galle District

Child Sexual Abuse has been a global epidemic with devastating consequences. One in four girls and one in six boys have been experienced some form of sexual abuse in their tender age in the world. According to Police statistics, Child Sexual Abuse (CSA) cases is growing in recent years in Sri Lanka too. Galle is among the four districts where the reported child abuse cases high and the reported CSA complaints are increasing extraordinarily. Also, there is no previous research have been done in the Southern part of the country regarding the crisis of CSA. So, main objective of this study is to determine the key risk factors that affected to a CSA in Galle Police Division, and to develop suitable regression and machine learning models to predict the severity of CSA. 225 CSA cases reported to Police Child and Women Bureau of Galle Police Division during the period 2017 – 2020 were treated for this study. Out of twenty-one risk which were found from literature and knowledge of domain experts, sixteen variables showed a significant relationship with response variable severity of CSA according to chi-square test of association. Traditional OLR model was performed to predict severity of CSA and to detect key risk factors to a CSA with two different data selection methods. Next, machine learning techniques: Decision Tree, SVM, and PNN were trained to classify severity of CSA. Random over-sampling technique was used to overcome the class imbalanced problem persists in the dataset. Finally, bagging technique was executed to conserve robustness of models and to improve performance. The OLR model classified the severity of CSA with 68.85% accuracy. Machine learning techniques, Decision Tree, SVM and PNN model classified the severity of CSA with an accuracy of 82.15%, 77.68% and 85.25% respectively. PNN model performed with higher accuracy better than other fitted models. The results obtained from this study can be used to take precautions and to arrange awareness sessions for adults to reduce CSA in Galle Police Division. Also, the study can be extended to the whole island to reduce CSA and to make it a better place for children.

Predicting Sliding Angles on Random Pit-Distributed Textures Using Probabilistic Neural Networks

The three-phase contact line best reflects the sliding ability of droplets on solid surfaces. Most studies on the sliding angle (SA) of superhydrophobic surfaces are limited to regularly arranged microtextured surfaces, lacking definite models and effective methods for a complex surface of a random texture. In this study, random pits with an area ratio of 19% were generated on 1 mm × 1 mm subregions, and the subregions formed arrays on a sample surface of 10 mm × 10 mm to obtain a randomly distributed microtexture surface with no pit overlaps. Although the contact angle (CA) of randomly pitted texture was the same, the SA was different. The SA of surfaces was affected by the pit location. The location of random pits increased the complexity of the three-phase contact line movement. The continuity of the three-phase contact angle (T) can reveal the rolling mechanism of the random pit texture and predict the SA, but the relationship between the T and SA is a relatively poor linear relation (R2 = 74%), and the SA of the random pit texture can only be roughly estimated. The quantized pit coordinates and SA were used as the input and output labels for the PNN model, respectively, and the accuracy of the model convergence was 90.2%.

A pre-training model based on CFD for open-channel velocity field prediction with small sample data

Abstract Accurately obtaining the distribution of the open-channel velocity field in hydraulic engineering is extremely important, which is helpful for better calculation of open-channel flow and analysis of open-channel water flow characteristics. In recent years, machine learning has been used for open-channel velocity field prediction. However, effective training of data-driven models in machine learning heavily depends on the diversity and quantity of data. In this paper, a CFD-based pre-training neural network model (CFD–PNN) is proposed for accurate open-channel velocity field prediction, allowing the adaption to the task with small sample data. Also, a cross-sectional velocity field prediction method combining the computational fluid dynamics (CFD) and machine learning is established. By comparing CFD–PNN with six other neural network algorithm models and the CFD model, the results show that, in the case of small sample data, the CFD–PNN model can predict a more reasonable open-channel velocity field with higher prediction accuracy than other models. The average error of the velocity calculation for the trapezoidal open-channel cross-section is about 3.62%. Compared with other models, the accuracy is improved by 0.3–2.8%.

Political Optimizer with Probabilistic Neural Network-Based Arabic Comparative Opinion Mining

Opinion Mining (OM) studies in Arabic are limited though it is one of the most extensively-spoken languages worldwide. Though the interest in OM studies in the Arabic language is growing among researchers, it needs a vast number of investigations due to the unique morphological principles of the language. Arabic OM studies experience multiple challenges owing to the poor existence of language sources and Arabic-specific linguistic features. The comparative OM studies in the English language are wide and novel. But, comparative OM studies in the Arabic language are yet to be established and are still in a nascent stage. The unique features of the Arabic language make it essential to expand the studies regarding the Arabic text. It contains unique features such as diacritics, elongation, inflection and word length. The current study proposes a Political Optimizer with Probabilistic Neural Network-based Comparative Opinion Mining (POPNN-COM) model for the Arabic text. The proposed POPNN-COM model aims to recognize comparative and non-comparative texts in Arabic in the context of social media. Initially, the POPNN-COM model involves different levels of data pre-processing to transform the input data into a useful format. Then, the pre-processed data is fed into the PNN model for classification and recognition of the data under different class labels. At last, the PO algorithm is employed for fine-tuning the parameters involved in this model to achieve enhanced results. The proposed POPNN-COM model was experimentally validated using two standard datasets, and the outcomes established the promising performance of the proposed POPNN-COM method over other recent approaches.

Application of Computational Model Based Probabilistic Neural Network for Surface Water Quality Prediction

Applications of artificial intelligence (AI) models have been massively explored for various engineering and sciences domains over the past two decades. Their capacity in modeling complex problems confirmed and motivated researchers to explore their merit in different disciplines. The use of two AI-models (probabilistic neural network and multilayer perceptron neural network) for the estimation of two different water quality indicators (namely dissolved oxygen (DO) and five days biochemical oxygen demand (BOD5)) were reported in this study. The WQ parameters estimation based on four input modelling scenarios was adopted. Monthly water quality parameters data for the duration from January 2006 to December 2015 were used as the input data for the building of the prediction model. The proposed modelling was established utilizing many physical and chemical variables, such as turbidity, calcium (Ca), pH, temperature (T), total dissolved solids (TDS), Sulfate (SO4), total suspended solids (TSS), and alkalinity as the input variables. The proposed models were evaluated for performance using different statistical metrics and the evaluation results showed that the performance of the proposed models in terms of the estimation accuracy increases with the addition of more input variables in some cases. The performances of PNN model were superior to MLPNN model with estimation both DO and BOD parameters. The study concluded that the PNN model is a good tool for estimating the WQ parameters. The optimal evaluation indicators for PNN in predicting BOD are (R2 = 0.93, RMSE = 0.231 and MAE = 0.197). The best performance indicators for PNN in predicting Do are (R2 = 0.94, RMSE = 0.222 and MAE = 0.175).

Modulation Recognition of Communication Signals Based on Multimodal Feature Fusion.

Modulation recognition is the indispensable part of signal interception analysis, which has always been the research hotspot in the field of radio communication. With the increasing complexity of the electromagnetic spectrum environment, interference in signal propagation becomes more and more serious. This paper proposes a modulation recognition scheme based on multimodal feature fusion, which attempts to improve the performance of modulation recognition under different channels. Firstly, different time- and frequency-domain features are extracted as the network input in the signal preprocessing stage. The residual shrinkage building unit with channel-wise thresholds (RSBU-CW) was used to construct deep convolutional neural networks to extract spatial features, which interact with time features extracted by LSTM in pairs to increase the diversity of the features. Finally, the PNN model was adapted to make the features extracted from the network cross-fused to enhance the complementarity between features. The simulation results indicated that the proposed scheme has better recognition performance than the existing feature fusion schemes, and it can also achieve good recognition performance in multipath fading channels. The test results of the public dataset, RadioML2018.01A, showed that recognition accuracy exceeds 95% when the signal-to-noise ratio (SNR) reaches 8dB.

Fault monitoring and diagnosis of high-pressure heater system based on improved particle swarm optimization and probabilistic neural network

The high-pressure heater system is an important part of the return heat system of thermal power units, which can significantly reduce the boiler fuel consumption and is of great significance to the safe and economic operation of the units. Taking into consideration the issues that the high-pressure heater system data has strong non-linear characteristics and the fault diagnosis accuracy is low, this paper proposes a hybrid model-based fault monitoring and diagnosis method for a high-pressure heater system. Firstly, an improved particle swarm optimization algorithm (IEDPSO) is proposed. A differential evolution operation is introduced to enhance particle diversity, and the inertia weight coefficients and learning factor parameters are improved to optimize the particle position and velocity update process. The problem that PSO tends to fall into local optimum at the late stage of iterative optimization search is solved. Numerical simulation experiments demonstrate that IDEPSO has high convergence speed and accuracy in the optimization process of the test function. Secondly, a fault monitoring and diagnosis method based on a hybrid kernel principal component analysis (KPCA)-IDEPSO-probabilistic neural network (PNN) model is proposed. Non-linear features are extracted using KPCA for fault monitoring. The IDEPSO algorithm is used to iteratively find the best PNN to improve the fault diagnosis accuracy. Simulation experiments prove that compared with the traditional PNN model, the fault diagnosis accuracy of the KPCA-IDEPSO-PNN model is improved by 4.9% and the number of fault misclassifications is reduced by 34, effectively improving the fault diagnosis accuracy of the high-pressure heater system and ensuring the safe and stable operation of thermal power units.

Two-level Noise Robust and Block Featured PNN Model for Speaker Recognition in Real Environment

Two-level Noise Robust and Block Featured PNN Model for Speaker Recognition in Real Environment

Probabilistic Neural Network-based Model for Identification of Parkinson’s Disease by using Voice Profile and Personal Data

Parkinson’s disease (PD) is an aging neurological disease deficiencies dopamine and occupies the second position among the neurological disease after the Alzheimer’s in the world. The identification of PD in the early stage is extremely advanced and expensive. Many researchers investigated on PD in divergent ways and different approaches to identifying the PD in the early stage with low cost. One of the effective approaches such as PD voice analysis is an important topic in the current decade. In this paper, a novel probabilistic neural network-based approach is proposed for analyzing the PD. The major objective of this paper is to develop a highly accurate probabilistic neural network-based intelligent approach for the identification and classification of PD diseases. The inputs are considered as 1200 sound records as vowel vocalizations ‘a’, ‘e’, ‘i’, ‘o’, and ‘u’ in different timings (morning, mid-day, and night) of the day from 62 PD and 51 non-PD individuals. From the experimental analysis, it is evident that the performance of the dataset with PNN is increased proportionally to the incremental neurons in the hidden layer of PNN up to seven and it is found 100% accuracy with minimum time and gradient values. The projected PNN model with seven hidden layer neurons is a very powerful tool for predicting the PD in early detections with minimum cost. Comparative analysis with other standard machine learning approaches is evident towards the superiority of the proposed PNN model performance for successful identification of PD through voice analysis.

PNN and BP Neural Network Fault Diagnosis Research on Electronic Accelerator Pedal Detection

Background: The working state of electronic accelerator pedal directly affects the safety of vehicles and drivers. Effective fault detection and judgment for the working state of the accelerator pedal can prevent accidents. Methods: Aiming at different working conditions of electronic accelerator pedal, this paper used PNN and BP diagnosis model to detect the state of electronic accelerator pedal according to the principle and characteristics of PNN and BP neural network. The fault diagnosis test experiment of electronic accelerator pedal was carried out to get the data acquisition. Results: After the patents for electronic accelerator pedals are queried and used, the first measured voltage, the upper limit of first voltage, the first voltage lower limit, the second measured voltage, the upper limit of second voltage and the second voltage lower limit are tested to build up the data samples. Then the PNN and BP fault diagnosis models of electronic accelerator pedal are established. Six fault samples are defined through the design of electronic accelerator pedal fault classifier and the fault diagnosis processes are executed to test. Conclusion: The fault diagnosis results were analyzed and the comparisons between the PNN and the BP research results show that BP neural network is an effective method for fault detection of electronic throttle pedal, which is obviously superior to PNN neural network based on the experiment data.

Gear Fault Diagnosis Based on Genetic Mutation Particle Swarm Optimization VMD and Probabilistic Neural Network Algorithm

The decomposition number $K$ and penalty factor $\alpha $ in the variational mode decomposition (VMD) algorithm have a great influence on the decomposition effect and the accuracy of subsequent fault diagnosis. Therefore, a gear fault diagnosis method based on genetic mutation particle swarm optimization VMD and probabilistic neural network (GMPSO-VMD-PNN) algorithm is proposed in this paper. Firstly, the GMPSO algorithm is used to optimize the $[K,\alpha]$ parameter combination in the VMD algorithm, and the optimal $[K,\alpha]$ parameter combination of each gear fault vibration signal to be decomposed is selected. Then, the gear fault vibration signal is decomposed into several intrinsic mode functions (IMFs) by VMD, and the sample entropy value of each IMFs is extracted to form the feature vector of subsequent fault diagnosis. Finally, the characteristic vector of gear fault vibration signal is input into PNN model, and gear fault is accurately classified. By comparing with fixed parameter VMD algorithm, empirical mode decomposition (EMD) and complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) algorithm, the superiority of this method in gear fault diagnosis is verified. Therefore, the GMPSO-VMD-PNN algorithm proposed in this paper has certain application value for gear fault diagnosis.

An Empirical Comparison on Malicious Activity Detection Using Different Neural Network-Based Models

The internet is growing at a rapid pace offering multiple web-based applications catering to the changing needs and demands of customers. Nevertheless, extensive use of internet services has potentially exposed the threats of data security and reliability. With technological advancements, cyber threats have also become more sophisticated with the blend of distinctive forms of attacks to cause potential damage. The increase in the number and variety of cyber attacks is inevitable; hence it is imperative to improve the efficiency of the cyber security systems. This research aims to compare different neural network models to distinguish malicious acts from non-malicious ones. The examined models are trained, validated, and tested using two datasets(cyber-physical subsystem dataset and KDD dataset). The performance of the studied models is measured using the confusion matrix. For the cyber-physical subsystem dataset, binary classification and multi-class classification are used for evaluating the models. In the KDD dataset, binary classification is the only classification approach because the dataset contains two classes, regular (normal actions) and harmful (malicious actions). In general, the results in binary classification are more encouraging than in multi-class classification. Among all the models, the PNN model achieves the best performance, while the GRNN model is the fastest one. Although PNN's runtime is slightly higher than the GRNN model, we can claim that the PNN is the best model for our data because a trade-off between the performance and run time can be obtained.

The effective seismic attributes in porosity prediction for different rock types: Some implications from four case studies

The present study evaluates the performance of PNN models for porosity prediction using seismic attributes. Four seismic datasets and more than 20 wells from different sedimentary basins located in Libya, Iraq, Egypt and USA are employed to characterize the effective attributes for porosity prediction. Verification and testing error analysis is adopted for evaluating the prediction performance. Results indicated that the porosity prediction models are primarily dependent to the propagation related attributes with frequency attributes as the most effective parameters in porosity prediction. In addition, the data quality and processing history strongly control the prediction model performance with relatively limited effects to dataset dimensionality (2D versus 3D) and the number of wells utilized in model construction. Such results are important to better understand and evaluate the performance of PNN porosity prediction models using various seismic attributes.

Load State Identification Method for Ball Mills Based on Improved EWT, Multiscale Fuzzy Entropy and AEPSO_PNN Classification

To overcome the difficulty of accurately determining the load state of a wet ball mill during the grinding process, a method of mill load identification based on improved empirical wavelet transform (EWT), multiscale fuzzy entropy (MFE), and adaptive evolution particle swarm optimization probabilistic neural network (AEPSO_PNN) classification is proposed. First, the concept of a sliding frequency window is introduced based on EWT, and the adaptive frequency window EWT algorithm, which is used to decompose the vibration signals recorded under different load states to obtain the intrinsic mode components, is proposed. Second, a correlation coefficient threshold is used to select the sensitive mode components that characterize the state of the original signal for signal reconstruction. Finally, the MFE of the reconstructed signal is used as the characteristic vector to characterize the load state of the mill, and the partial mean value of MFE is calculated. The results show that the mean value of MFE under different load states varies. To further identify the load state, a characteristic mill load vector is constructed from the MFE values of the reconstructed signal under different load conditions and is used as the input of the AEPSO_PNN model, which then outputs the predicted ball mill load state. Thus, a load state identification model is established. The feasibility of the method is verified based on grinding experiments. The results show that the overall recognition rate of the proposed method is as high as 97.3%. Compared with the back propagation (BP) neural network, Bayes discriminant method, and PNN classification, AEPSO_PNN classification increases the overall recognition rate by 8%, 5.3%, and 3.3%, respectively, which indicates that this method can be used to accurately identify the different load states of a ball mill.

A General Hybrid GMDH–PNN Model to Predict Thermal Conductivity for Different Groups of Nanofluids

In this study, a general model for estimating the nanofluids (NFs) thermal conductivity by using a hybrid group method of data handling polynomial neural network (GMDH–PNN) has been investigated. NFs thermal conductivity was modeled as a function of nanoparticle size and volume fraction, nanoparticle and base fluid thermal conductivity, and base fluid temperature. For this purpose, a network that contains 6 hidden layers with 2 inputs in each layer and with training algorithm of least squares regression has been applied. The results showed a good accuracy for estimating the thermal conductivity of NFs with a root mean squared error (RMSE) of 0.03027 for 118 systems containing 1929 training data sets. Furthermore, the RMSE for 27 systems containing 244 data as the validation sets was 0.02843 and also mean absolute percentage errors (MAPE) for training and validation data sets were 4.47 and 4.59%, respectively. Moreover, the proposed hybrid GMDH–PNN model was compared with different models from literature for different groups of NFs. The results indicated an improvement in prediction of thermal conductivity with lower errors compared to the previous models.

Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition

The era of artificial neural network (ANN) began with a simplified application in many fields and remarkable success in pattern recognition (PR) even in manufacturing industries. Although significant progress achieved and surveyed in addressing ANN application to PR challenges, nevertheless, some problems are yet to be resolved like whimsical orientation (the unknown path that cannot be accurately calculated due to its directional position). Other problem includes; object classification, location, scaling, neurons behavior analysis in hidden layers, rule, and template matching. Also, the lack of extant literature on the issues associated with ANN application to PR seems to slow down research focus and progress in the field. Hence, there is a need for state-of-the-art in neural networks application to PR to urgently address the above-highlights problems for more successes. The study furnishes readers with a clearer understanding of the current, and new trend in ANN models that effectively addresses PR challenges to enable research focus and topics. Similarly, the comprehensive review reveals the diverse areas of the success of ANN models and their application to PR. In evaluating the performance of ANN models, some statistical indicators for measuring the performance of the ANN model in many studies were adopted. Such as the use of mean absolute percentage error (MAPE), mean absolute error (MAE), root mean squared error (RMSE), and variance of absolute percentage error (VAPE). The result shows that the current ANN models such as GAN, SAE, DBN, RBM, RNN, RBFN, PNN, CNN, SLP, MLP, MLNN, Reservoir computing, and Transformer models are performing excellently in their application to PR tasks. Therefore, the study recommends the research focus on current models and the development of new models concurrently for more successes in the field.

Efficient moving vehicle detection for intelligent traffic surveillance system using optimal probabilistic neural network

The vehicle detection system plays an essential role in the traffic video surveillance system. Video communication of these traffic cameras over real-world limited bandwidth networks can frequently suffer network congestion. The objective of this paper is to develop an effective method for moving vehicle detection problems that can find high quality solutions (with respect to detection accuracy) at a high convergence speed. To achieve this objective, we propose a method that hybridises the cuckoo search (CS) with Opposition-based learning (OBL), where OBL is improve the performance of the CS algorithm while optimising the weights of the standard PNN model. The proposed system mainly consists of two modules such as: 1) design novel OCS-PNN model; 2) moving vehicle detection using OCS-PNN model. The algorithm is tested on three standard video dataset. For instance, the proposed method achieved the maximum precision of 94%, F-measure of 94% and similarity of 94%.

Efficient Moving Vehicle Detection for Intelligent Traffic Surveillance System Using Optimal Probabilistic Neural Network

The vehicle detection system plays an essential role in the traffic video surveillance system. Video communication of these traffic cameras over real-world limited bandwidth networks can frequently suffer network congestion. The objective of this paper is to develop an effective method for moving vehicle detection problems that can find high quality solutions (with respect to detection accuracy) at a high convergence speed. To achieve this objective, we propose a method that hybridises the cuckoo search (CS) with Opposition-based learning (OBL), where OBL is improve the performance of the CS algorithm while optimising the weights of the standard PNN model. The proposed system mainly consists of two modules such as: 1) design novel OCS-PNN model; 2) moving vehicle detection using OCS-PNN model. The algorithm is tested on three standard video dataset. For instance, the proposed method achieved the maximum precision of 94%, F-measure of 94% and similarity of 94%.

Neural network approaches for noisy language modeling.

Text entry from people is not only grammatical and distinct, but also noisy. For example, a user's typing stream contains all the information about the user's interaction with computer using a QWERTY keyboard, which may include the user's typing mistakes as well as specific vocabulary, typing habit, and typing performance. In particular, these features are obvious in disabled users' typing streams. This paper proposes a new concept called noisy language modeling by further developing information theory and applies neural networks to one of its specific application-typing stream. This paper experimentally uses a neural network approach to analyze the disabled users' typing streams both in general and specific ways to identify their typing behaviors and subsequently, to make typing predictions and typing corrections. In this paper, a focused time-delay neural network (FTDNN) language model, a time gap model, a prediction model based on time gap, and a probabilistic neural network model (PNN) are developed. A 38% first hitting rate (HR) and a 53% first three HR in symbol prediction are obtained based on the analysis of a user's typing history through the FTDNN language modeling, while the modeling results using the time gap prediction model and the PNN model demonstrate that the correction rates lie predominantly in between 65% and 90% with the current testing samples, and 70% of all test scores above basic correction rates, respectively. The modeling process demonstrates that a neural network is a suitable and robust language modeling tool to analyze the noisy language stream. The research also paves the way for practical application development in areas such as informational analysis, text prediction, and error correction by providing a theoretical basis of neural network approaches for noisy language modeling.