Probabilistic Neural Network Model Research Articles

Live cell imaging of cellular dynamics in poplar wood using computational cannula microscopy

This study presents significant advancements in computational cannula microscopy for live imaging of cellular dynamics in poplar wood tissues. Leveraging machine-learning models such as pix2pix for image reconstruction, we achieved high-resolution imaging with a field of view of 55µm using a 50µm-core diameter probe. Our method allows for real-time image reconstruction at 0.29 s per frame with a mean absolute error of 0.07. We successfully captured cellular-level dynamics in vivo, demonstrating morphological changes at resolutions as small as 3µm. We implemented two types of probabilistic neural network models to quantify confidence levels in the reconstructed images. This approach facilitates context-aware, human-in-the-loop analysis, which is crucial for in vivo imaging where ground-truth data is unavailable. Using this approach we demonstrated deep in vivo computational imaging of living plant tissue with high confidence (disagreement score ⪅0.2). This work addresses the challenges of imaging live plant tissues, offering a practical and minimally invasive tool for plant biologists.

Hybrid Aquila optimizer for efficient classification with probabilistic neural networks

The model of a probabilistic neural network (PNN) is commonly utilized for classification and pattern recognition issues in data mining. An approach frequently used to enhance its effectiveness is the adjustment of PNN classifier parameters through the outcomes of metaheuristic optimization strategies. Since PNN employs a limited set of instructions, metaheuristic algorithms provide an efficient way to modify its parameters. In this study, we have employed the Aquila optimizer algorithm (AO), a contemporary algorithm, to modify PNN parameters. We have proposed two methods: Aquila optimizer based probabilistic neural network (AO-PNN), which uses both local and global search capabilities of AO, and hybrid Aquila optimizer and simulated annealing based probabilistic neural network (AOS-PNN), which integrates the global search abilities of AO with the local search mechanism of simulated annealing (SA). Our experimental results indicate that both AO-PNN and AOS-PNN perform better than the PNN model in terms of accuracy across all datasets. This suggests that they have the potential to generate more precise results when utilized to improve PNN parameters. Moreover, our hybridization technique, AOS-PNN, is more effective than AO-PNN, as evidenced by classification experiments accuracy, data distribution, convergence speed, and significance. We have also compared our suggested approaches with three different methodologies, namely Coronavirus herd immunity optimizer based probabilistic neural network (CHIO-PNN), African buffalo algorithm based probabilistic neural network (ABO-PNN), and β-hill climbing. We have found that AO-PNN and AOS-PNN have achieved significantly higher classification accuracy rates of 90.68 and 93.95, respectively.

An Improved Identification Method of Pipeline Leak Using Acoustic Emission Signal

Pipelines constitute a vital component in offshore oil and gas operations, subjected to prolonged exposure to a range of alternating loads. Safeguarding their integrity, particularly through meticulous leak detection, is essential for ensuring safe and reliable operation. Acoustic emission detection emerges as an effective approach for monitoring pipeline leaks, demanding subsequent rigorous data analysis. Traditional analysis techniques like wavelet analysis, empirical mode decomposition (EMD), variational mode decomposition (VMD), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) often yield results with considerable randomness, adversely affecting leak detection accuracy. This study introduces an enhanced damage recognition methodology, integrating improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and probabilistic neural networks (PNN) for more accurate pipeline leak identification. This novel approach combines laboratory-acquired acoustic emission signals from leaks with ambient noise signals. Application of ICEEMDAN to these composite signals isolates eight intrinsic mode functions (IMFs), with subsequent time–frequency analysis providing insight into their frequency structures and feature vectors. These vectors are then employed to train a PNN, culminating in a robust neural network model tailored for leak detection. Conduct experimental research on pipeline leakage identification, focusing on the local structure of offshore platforms, experimental research validates the superiority of the ICEEMDAN–PNN model over existing methods like EMD, VMD, and CEEMDAN paired with PNN, particularly in terms of stability, anti-interference capabilities, and detection precision. Notably, even amidst integrated noise, the ICEEMDAN–PNN model maintains a remarkable 98% accuracy rate in identifying pipeline leaks.

The Effects of Negative Regulation on the Dynamical Transition in Epileptic Network

The transiting mechanism of abnormal brain functional activities, such as the epileptic seizures, has not been fully elucidated. In this study, we employ a probabilistic neural network model to investigate the impact of negative regulation, including negative connections and negative inputs, on the dynamical transition behavior of network dynamics. It is observed that negative connections significantly influence the transition behavior of the network, intensifying the oscillation of discharge probability, corresponding to uneven discharge and epileptic states. Negative inputs, within a certain range, exhibited a similar impact on the dynamic state of the network as negative connections, enhancing network oscillations and resulting in higher fragility. However, larger negative inputs can led to the disappearance of oscillations in the discharge probability, indicating a maintenance of lower fragility. We speculate that negative regulation may be an indispensable factor in the occurrence of epileptic seizures, and future research should give it due consideration.

Rapid identification of bacteria in water by multi-wavelength transmittance spectroscopy and the artificial neural network

Background: Multi-wavelength transmittance spectroscopy, in combination with the artificial neural network, has been a novel tool used to identify and classify microorganisms in recent years.Methods: In our work, the transmittance spectra in the region from 200 to 900 nm for four bacterial species of interest, Escherichia coli (E. coli), Staphylococcus aureus (S. aureus), Klebsiella pneumoniae (K.pneumoniae), and Salmonella typhimurium (S. typhi), were recorded using an ultraviolet–visible spectrophotometer. Considering too much redundant data on the full-wave band spectra, the characteristic wavelength variables were selected using the competitive adaptive reweighting sampling (CARS) algorithm. Spectra of the initial training set of these targeted microorganisms were used to create identification models representing the spectral variability of each species using four kinds of neural networks, namely, backpropagation (BP), radial basis function network (RBF), generalized regression neural network (GRNN), and probabilistic neural network (PNN).Results: The blinded isolate spectra of targeted species were identified using the four identification models given above. Compared to fullband modeling, after using CARS to screen the wavelength variables, four identification models are established for the 35 preferred characteristic wavelengths, and the prediction performance of the four models is notably improved. Among them, the CARS–PNN model is the best, and the identification rates of all targeted bacteria were achieved with 100% accuracy; the calculation time is just approximately 0.04 s.Discussion: The use of CARS can effectively remove useless information from the spectra, reduce model complexity, and enhance model prediction performance. Multi-wavelength transmission spectroscopy, combined with the CARS–PNN method, can provide a new method for the rapid detection of bacteria in water and could be readily extended for bacterial microbiological detection in blood and food.

Optimizing the Probabilistic Neural Network Model with the Improved Manta Ray Foraging Optimization Algorithm to Identify Pressure Fluctuation Signal Features.

To improve the identification accuracy of pressure fluctuation signals in the draft tube of hydraulic turbines, this study proposes an improved manta ray foraging optimization (ITMRFO) algorithm to optimize the identification method of a probabilistic neural network (PNN). Specifically, first, discrete wavelet transform was used to extract features from vibration signals, and then, fuzzy c-means algorithm (FCM) clustering was used to automatically classify the collected information. In order to solve the local optimization problem of the manta ray foraging optimization (MRFO) algorithm, four optimization strategies were proposed. These included optimizing the initial population of the MRFO algorithm based on the elite opposition learning algorithm and using adaptive t distribution to replace its chain factor to optimize individual update strategies and other improvement strategies. The ITMRFO algorithm was compared with three algorithms on 23 test functions to verify its superiority. In order to improve the classification accuracy of the probabilistic neural network (PNN) affected by smoothing factors, an improved manta ray foraging optimization (ITMRFO) algorithm was used to optimize them. An ITMRFO-PNN model was established and compared with the PNN and MRFO-PNN models to evaluate their performance in identifying pressure fluctuation signals in turbine draft tubes. The evaluation indicators include confusion matrix, accuracy, precision, recall rate, F1-score, and accuracy and error rate. The experimental results confirm the correctness and effectiveness of the ITMRFO-PNN model, providing a solid theoretical foundation for identifying pressure fluctuation signals in hydraulic turbine draft tubes.

IGSA-PNN-based Methods for Power Transformer Fault Diagnosis

To enhance the precision of power transformer fault diagnosis, it is necessary to make improvements. Aiming at the shortcomings of Probabilistic Neural Network (PNN) network experience selection smoothing factor and avoiding the shortcomings of traditional Gravitational Search Algorithm (GSA) easy 0to fall into local optimum and convergence speed slow, a Probabilistic Neural Network (PNN) model using chaos sequence to improved GSA for power transformer fault diagnosis is proposed. Firstly, chaos sequence is used to increase the diversity of gravitational particles to avoid falling into local optimum during the training process. Then, the improved GSA algorithm is used to optimize the parameters of the PNN model itself to improve the prediction accuracy of the model. Finally, the prediction results are compared with the prediction results of other traditional diagnostic models. The results show that IGSA-PNN fault diagnosis model performs better in generalization ability and classification accuracy.

A data-driven method for modelling dissipation rates in stratified turbulence

We present a deep probabilistic convolutional neural network (PCNN) model for predicting local values of small-scale mixing properties in stratified turbulent flows, namely the dissipation rates of turbulent kinetic energy and density variance, $\varepsilon$ and $\chi$ . Inputs to the PCNN are vertical columns of velocity and density gradients, motivated by data typically available from microstructure profilers in the ocean. The architecture is designed to enable the model to capture several characteristic features of stratified turbulence, in particular the dependence of small-scale isotropy on the buoyancy Reynolds number $Re_b:=\varepsilon /(\nu N^2)$ , where $\nu$ is the kinematic viscosity and $N$ is the background buoyancy frequency, the correlation between suitably locally averaged density gradients and turbulence intensity and the importance of capturing the tails of the probability distribution functions of values of dissipation. Empirically modified versions of commonly used isotropic models for $\varepsilon$ and $\chi$ that depend only on vertical derivatives of density and velocity are proposed based on the asymptotic regimes $Re_b\ll 1$ and $Re_b\gg 1$ , and serve as an instructive benchmark for comparison with the data-driven approach. When trained and tested on a simulation of stratified decaying turbulence which accesses a range of turbulent regimes (associated with differing values of $Re_b$ ), the PCNN outperforms assumptions of isotropy significantly as $Re_b$ decreases, and additionally demonstrates improvements over the fitted empirical models. A differential sensitivity analysis of the PCNN facilitates a comparison with the theoretical models and provides a physical interpretation of the features enabling it to make improved predictions.

Evaluation of disaster-bearing capacity for natural gas pipeline under third-party damage based on optimized probabilistic neural network

Third-party damage has become a significant factor leading to pipeline failures and leaks, resulting in severe ecological damage and human casualties. Traditional safety risk analysis methods in the past have focused on severing the causal chain of accidents, while neglecting the unique characteristics of different pipeline segments and environmental factors on their ability to bearing disasters. This paper presents a framework for evaluating the disaster-bearing capacity of natural gas pipeline third-party damage based on probabilistic neural networks. A comprehensive disaster-bearing capacity index system for evaluating third-party damage to natural gas pipelines has been constructed by analyzing the causal factors and potential consequences of third-party damage risk incidents. Next, the cluster analysis and indicator weighting information is used for achieving the comprehensive disaster-bearing capacity assessment. Finally, the data samples are constructed with the index levels and comprehensive disaster-bearing capacity levels of each evaluation unit. An optimization algorithm, Particle Swarm Optimization (PSO), is utilized to improve the parameter selection process of the Probabilistic Neural Network (PNN) model, in order to evaluate the disaster-bearing level of each unit. A typical long-distance natural gas pipeline in Zhejiang Province was selected as the case study, and a total of 200 research units were obtained through pipeline analysis. Furthermore, 14 indicator data were obtained by leveraging multiple sources of data surrounding the pipeline. With the utilization of cluster analysis, it was determined that Gaussian mixed model clustering method ultimately identified a total of 5 different levels of disaster-bearing capacity. The optimal smoothing factor can be accurate from 0.5 to 0.588 by using the PSO-PNN model, and the final accuracy rate is improved from 88% to 98%, which is obviously the introduction of PSO optimization algorithm in the PNN model can improve the accuracy of the model. This represents a significant improvement in accuracy when compared to traditional PNN models. The proposed evaluation method can provide scientific basis for the prevention and control of third-party damage in oil and gas pipelines and pipeline planning.

Research on the Design of Financial Management Model Based on SOM-PNN Driven by Digital Economy

This study proposes a novel financial risk prediction methodology by harnessing the power of self-organizing mapping (SOM) neural network and probabilistic neural network (PNN). The amalgamation of SOM and PNN's advantageous characteristics is seamlessly integrated into the algorithm posited within this paper. In order to collate and prognosticate data, the SOM network employs a two-dimensional topological framework comprising of two layers of neurons. Subsequently, the PNN model expeditiously furnishes the final classification outcomes by processing the output results obtained from the SOM model. The technique developed atop this composite model offers accelerated computation, effectively mitigates the impact of noisy samples, and significantly augments model accuracy. Finally, the effectiveness of the proposed method was demonstrated through a comprehensive financial risk analysis of listed companies from 2016 to 2020. The experimental results show that the SOM-PNN method has achieved high accuracy in predicting the financial difficulties experienced by traditional companies in the selected company samples, exceeding 85%. Especially when the sample data is insufficient, its accuracy reaches 80%, surpassing other algorithms. Statement: In the modern era, financial institutions use big data to perform background analysis and review, continuously optimize, and adjust, in order to introduce quantitative analysis methods into every link of risk management as far as possible. This allows financial institutions to quickly achieve balance in the game process of risk and income, and achieve Profit maximization in local or even more space.

Probabilistic generative transformer language models for generative design of molecules

Self-supervised neural language models have recently found wide applications in the generative design of organic molecules and protein sequences as well as representation learning for downstream structure classification and functional prediction. However, most of the existing deep learning models for molecule design usually require a big dataset and have a black-box architecture, which makes it difficult to interpret their design logic. Here we propose the Generative Molecular Transformer (GMTransformer), a probabilistic neural network model for generative design of molecules. Our model is built on the blank filling language model originally developed for text processing, which has demonstrated unique advantages in learning the “molecules grammars” with high-quality generation, interpretability, and data efficiency. Benchmarked on the MOSES datasets, our models achieve high novelty and Scaf compared to other baselines. The probabilistic generation steps have the potential in tinkering with molecule design due to their capability of recommending how to modify existing molecules with explanation, guided by the learned implicit molecule chemistry. The source code and datasets can be accessed freely at https://github.com/usccolumbia/GMTransformer

Oral Cancer Prediction Using a Probability Neural Network (PNN).

In India, usually, oral cancer is mostly identified at a progressive stage of malignancy. Hence, we are motivated to identify oral cancer in its early stages, which helps to increase the lifetime of the patient, but this early detection is also more challenging. The proposed research work uses a probabilistic neural network (PNN) for the prediction of oral malignancy. The recommended work uses PNN along with the discrete wavelet transform to predict the cancer cells accurately. The classification accuracy of the PNN model is 80%, and hence this technique is best for the prediction of oral cancer. Due to heterogeneity in the appearance of oral lesions, it is difficult to identify the cancer region. This research work explores the different computer vision techniques that help in the prediction of oral cancer. Oral screening is important in making a decision about oral lesions and also in avoiding delayed referrals, which reduces mortality rates.

Impact monitoring on complex structure using VMD-MPE feature extraction and transfer learning

Impacts are common damage events in aviation scenarios that can cause damage to the structural integrity ofan aircraft and pose a threat to its safe operation. Therefore, it is crucial to monitor impact events. A region-to-point monitoring method is proposed to address the challenges posed by the large area of monitored aircraft structures and the long distance between sensors. Firstly, to fully use the information in the original impact signal and reduce the aliasing effect caused by the reinforced structure, the original signal is decomposed into several modes with different frequency bands by Variational Mode Decomposition (VMD). The Multi-scale Permutation Entropy (MPE) value is then calculated to reflect the various characteristics of each mode, which is used as a basis for classification. Secondly, Transfer Component Analysis (TCA) is selected as a transfer learning method to reduce the difference between the features of the source domain and the target domains' features. Thirdly, the TCA-transformed source domain data are used to train the Probabilistic Neural Network model (PNN), and the unfamiliar target domain data are used to verify the impact area identification. Finally, based on regional location, the system identification technology and weighted centroid algorithm can be used to obtain the history of impact force and the precise coordinates of the impact location.

Material Classification and Aging Time Prediction of Structural Metals Using Laser-Induced Breakdown Spectroscopy Combined with Probabilistic Neural Network.

In this paper, laser-induced breakdown spectroscopy (LIBS) combined with a probabilistic neural network (PNN) was applied to classify engineering structural metal samples (valve stem, welding material, and base metal). Additionally, utilizing data from the plasma emission spectrum generated by laser ablation of samples with different aging times, an aging time prediction model based on a firefly optimized probabilistic neural network (FA-PNN) was established, which can effectively evaluate the service performance of structural materials. The problem of insufficient features obtained by principal component analysis (PCA) for predicting the aging time of materials is addressed by the proposal of a time-frequency feature extraction method based on short-time Fourier transform (STFT). The classification accuracy (ACC) of time-frequency features and principal component features was compared under PNN. The results indicate that, in comparison to the PCA feature extraction approach, the time-frequency feature extraction method based on STFT demonstrates higher accuracy in predicting the time of aging materials. Then, the relationship between classification accuracy (ACC) and settings of PNN was discussed. The ACC of the PNN model for both the material classification test set and the aging time test set achieved 100% with Firefly (FA) optimization algorithms. This result was also compared with the ACC of ANN, KNN, PLS-DA, and SIMCA for the aging time test set (95%, 87.5%, 85%, and 62.5%, respectively). The experimental results demonstrated that the classification model using LIBS combined with FA-PNN could realize better classification accuracy.

Probabilistic neural network-based diagnosis of blockage faults in air conditioning and refrigeration units

Abstract The refrigeration unit is a core component of the air conditioner, and its typical faults are pipe scaling and blockage, which can cause refrigerant leakage in serious cases, and these faults can affect the cooling effect of the air conditioner and even affect the safety of using the air conditioner in serious cases. Therefore, it is necessary to study the fault diagnosis system of the refrigeration unit. The traditional fault diagnosis system for refrigeration units, which generally uses wireless sensor networks to transmit data, is a challenge because of its slow diagnosis speed and the low accuracy rate of fault diagnosis for refrigeration units. This paper uses a probabilistic neural network (PNN) to debug a blockage fault diagnosis algorithm system for simulated fault training. The system successfully achieves four levels of pre-warning for blockage faults: “uncertainty, micro-blockage, blockage, and severe blockage”, with the fastest pre-warning time of “uncertainty, micro-blockage” being less than 2 seconds. The experimental results of this paper show that the PNN system can take timely measures against blockage faults, and the fault diagnosis accuracy of the model reaches 97.97%, which is 13.34% higher than the traditional fault identification algorithm. The PNN model can pinpoint the blockage faults of the refrigeration unit, which enables the working state of the refrigeration unit to be continuously optimized, and the air conditioner to maintain energy-efficient operation. The fault simulation of the air conditioning refrigeration unit in automatic control mode verifies the blockage fault diagnosis’s accuracy and the algorithm’s feasibility, providing reference and learning for the development of fault diagnosis algorithms.

Identification of Reasons for Culpable Homicides and Attempted Murders: A Case Study for the Kelaniya Police Division

Culpable Homicides and attempted murders are ultimate crimes that could create ripple effects on a society which could go far beyond the original loss of human life. Owing to the unpredictable nature of such crimes that require complex investigations the objective of this study was to come up with an appropriate model to identify the reason for a culpable homicide or an attempted murder using a statistical approach. This study use data collected from 12 Police stations in Kelaniya Police Division relating to the incidents happened between 2010 and 2020. The Pearson Chi-square test was used in identifying the influential explanatory variables. Out of the 18 variables, 8 predictors including Weapon used, Relationship, Location, Civil Status of the perpetrator were statistically associated with the identified reasons at 5 % level of significance. Multinomial logistic regression followed by four data mining models including classification tree, support vector machine (SVM), k-nearest neighbour (KNN), and probabilistic neural network (PNN) were employed initially with a training and testing set which was randomly selected in the ratio 90:10. The 4 data mining models were then fitted separately by using the bagging technique. The accuracies were compared using the confusion matrixes and rates of misclassifications of the critical classes. Out of the fitted models, the highest accuracy of 93.75 % was shown by the PNN model with a spread of 0.6. The identified model can be used as a decision support tool by crime investigators and relevant authorities for wise decision making.

Deep Boosted Molecular Dynamics: Accelerating Molecular Simulations with Gaussian Boost Potentials Generated Using Probabilistic Bayesian Deep Neural Network.

We have developed a new deep boosted molecular dynamics (DBMD) method. Probabilistic Bayesian neural network models were implemented to construct boost potentials that exhibit Gaussian distribution with minimized anharmonicity, thereby allowing for accurate energetic reweighting and enhanced sampling of molecular simulations. DBMD was demonstrated on model systems of alanine dipeptide and the fast-folding protein and RNA structures. For alanine dipeptide, 30 ns DBMD simulations captured up to 83-125 times more backbone dihedral transitions than 1 μs conventional molecular dynamics (cMD) simulations and were able to accurately reproduce the original free energy profiles. Moreover, DBMD sampled multiple folding and unfolding events within 300 ns simulations of the chignolin model protein and identified low-energy conformational states comparable to previous simulation findings. Finally, DBMD captured a general folding pathway of three hairpin RNAs with the GCAA, GAAA, and UUCG tetraloops. Based on a deep learning neural network, DBMD provides a powerful and generally applicable approach to boosting biomolecular simulations. DBMD is available with open source in OpenMM at https://github.com/MiaoLab20/DBMD/.

Model application for monitoring and locating leakages in rural area water pipeline networks

ABSTRACT Monitoring and locating leaks in water supply pipelines are critical to the safety of rural drinking water, which is a highlighted issue in China. To meet this need, an XGBoost-based model was developed and applied to the rural water supply network in Dingyuan, China. It could diagnose water leakage while overcoming the obstacles caused by the limited scale and incompleteness of data. In a comparative case study, the proposed model outperformed the probabilistic neural network models, which require large-scale data, in terms of both F1-score and accuracy, thus demonstrating its capability to accurately locate leakage in rural water supply pipelines.

Classification and Authentication of Induction Motor Faults using Time and Frequency Feature Dependent Probabilistic Neural Network Model

Classification and Authentication of Induction Motor Faults using Time and Frequency Feature Dependent Probabilistic Neural Network Model

The Vehicle Intention Recognition with Vehicle-Following Scene Based on Probabilistic Neural Networks

In the vehicle-following scenario of autonomous driving, the change of driving style in the front vehicle will directly affect the decision on the rear vehicle. In this paper, a strategy based on a probabilistic neural network (PNN) for front vehicle intention recognition is proposed, which enables the rear vehicle to obtain the driving intention of the front vehicle without communication between the two vehicles. First, real vehicle data with different intents are collected and time—frequency domain variables are extracted. Secondly, Principal Component Analysis (PCA) is performed on the variables in order to obtain comprehensive features. Meanwhile, two cases are classified according to whether the front vehicle can transmit data to the rear vehicle. Finally, two recognition models are trained separately according to a PNN algorithm, and the two models obtained from the training are verified separately. When the front vehicle can communicate with the rear vehicle, the recognition accuracy of the corresponding PNN model reaches 96.39% (simulation validation) and 95.08% (real vehicle validation). If it cannot, the recognition accuracy of the corresponding PNN model reaches 78.18% (simulation validation) and 73.74% (real vehicle validation).