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Accurate prediction of reservoir properties through linear transformation and regression methods are successful in limited cases but are often geologically unrealistic and have no concrete theoretical foundation. Artificial Neural Network's (ANN's) have emerged as an effective tool for deriving nonlinear mathematical relationships between seismic attributes and well logs that are theoretically plausible and may prove geologically realistic. In this paper, we devise a methodology to integrate rock physics analysis, seismic inversion, multi-attribute transformation, and Feedforward Neural Network (FNN) modeling for accurate inter-well reservoir property predictions. We test this methodology on well logs and seismic data from the Cretaceous sandstones of the Sembar Formation, Southern Indus Basin, Pakistan. Viable productive gas zones are identified through rock physics and Model Based Inversion (MBI) analyses. Five volume-based seismic attributes are sequentially calculated through forward stepwise regression and cross-validated for inter-well porosity prediction. When a Probabilistic Neural Network (PNN) is trained in a non-linear mode integrated with multi-attribute transformation, correlation (r 2) is improved from 72% to 88% between seismic attributes and porosity derived from logs. The PNN-derived porosity distribution is geologically more realistic than linear transformation and regression methods, supporting our model's validity. We suggest that it is theoretically possible for the ANN to make predictions about any attribute of the reservoir via bridging target logs and seismic data within a short computation time. [ABSTRACT FROM AUTHOR]

Conventional techniques, which depend on geostatistical modeling, frequently fail to capture reservoir variability, especially when well data are sparse. To overcome this limitation, we develop a combined approach that integrates Multi-Seismic Attribute Transformation (MSAT) and Probabilistic Neural Network (PNN) techniques. By leveraging data from eight wells and post-stack seismic data from the Poseidon 3D area, gas-saturated deltaic-fluvial settings, we analyzed twelve seismic attributes, among which acoustic impedance low-frequency attribute, relative geological time, amplitude envelope, and amplitude-weighted frequency emerge as the most significant. Using the MSAT approach, we established strong connections between these attributes and porosity. Six attributes provide a correlation coefficient of 0.65 with the target log. To enhance precision, PNN and fine-tuning the sigma factor using well drop-out cross-validation analysis was utilized. This leads to a significant enhancement in model accuracy, reaching 76%. In addition, when the model training is concentrated within a 10-millisecond range around the Plover reservoir zone, the accuracy increases significantly to 89%. Our approach has proven to be highly effective, with a success rate of 73% as evidenced by validation through well drop-out analysis. This demonstrates that our method surpasses traditional methods. This innovative integration of seismic attribute-driven methodologies has indicated a major leap forward in identifying and characterizing reservoir heterogeneity. The results demonstrate that it enables more efficient future well-planning strategies and deepens our comprehension of deltaic-fluvial reservoir dynamics. [ABSTRACT FROM AUTHOR]

In order to improve and optimize the advance classification and prediction method of tunnel surrounding rock, a prediction method based on Tunnel Seismic Prediction (TSP) and Probabilistic Neural Network (PNN) is proposed. Based on the characteristics of science, maneuverability and representativeness, several factors that greatly affect rock mass classification are selected as evaluation indices based on analysis of numerous TSP data, establishing an advance classification index system for surrounding rock, and designing the "Advance classification and prediction system for surrounding rock" to predict the classification. Engineering application of Jinpingyan Tunnel of Chenglan Railway in high altitude and high intensity area of China is taken as a case study, and proved that the evaluation indices are easy to obtain and the evaluation results are accurate and reliable, and compared with Back Propagation (BP) neural network prediction results, the results show that PNN has some advantages in predicting the calculation speed of surrounding rock classification, the ability to add samples and the classification accuracy in practical engineering applications. The PNN-TSP method can be further used for other tunnel engineering. [ABSTRACT FROM AUTHOR]

Abstract Conventional techniques, which depend on geostatistical modeling, frequently fail to capture reservoir variability, especially when well data are sparse. To overcome this limitation, we develop a combined approach that integrates Multi-Seismic Attribute Transformation (MSAT) and Probabilistic Neural Network (PNN) techniques. By leveraging data from eight wells and post-stack seismic data from the Poseidon 3D area, gas-saturated deltaic-fluvial settings, we analyzed twelve seismic attributes, among which acoustic impedance low-frequency attribute, relative geological time, amplitude envelope, and amplitude-weighted frequency emerge as the most significant. Using the MSAT approach, we established strong connections between these attributes and porosity. Six attributes provide a correlation coefficient of 0.65 with the target log. To enhance precision, PNN and fine-tuning the sigma factor using well drop-out cross-validation analysis was utilized. This leads to a significant enhancement in model accuracy, reaching 76%. In addition, when the model training is concentrated within a 10-millisecond range around the Plover reservoir zone, the accuracy increases significantly to 89%. Our approach has proven to be highly effective, with a success rate of 73% as evidenced by validation through well drop-out analysis. This demonstrates that our method surpasses traditional methods. This innovative integration of seismic attribute-driven methodologies has indicated a major leap forward in identifying and characterizing reservoir heterogeneity. The results demonstrate that it enables more efficient future well-planning strategies and deepens our comprehension of deltaic-fluvial reservoir dynamics.

To compensate for the reactive power, inverter-based conditioners have been utilized in recent years due to their faster response. Distribution static synchronous compensator (DSTATCOM) has been utilized to enhance power quality in power system that is an inverter-based device that is widely utilized. To control this type of equipment, a proportional integrated (PI) controller has been utilized to control most of the equipment with respect to certain parameters. The performance of the controller basically does not meet the expectations because of the dynamics and nonlinearity of a system parameters. In this present paper, a probabilistic neural network has been used in a controller with a fully informed particle swarm optimization (FIPSO) algorithm to generate a suitable weight for controlling the axes of various parameters of DSTATCOMs. Using MATLAB/Simulink software, simulations were performed, and the responses were monitored with particular regard to the reference reactive parameter. The results are compared. DSTATCOM improves power system damping. [ABSTRACT FROM AUTHOR]

The present study addresses the segmentation and the 3D reconstruction of the corpus callosum from MRI scans. Accurate segmentation of the corpus callosum is essential in order to enable its reconstruction and 3D visualization to facilitate early diagnosis. In fact, many studies have established a strong correlation between the shape of the corpus callosum and several pathological conditions. However, the segmentation is made difficult by regions of similar intensity within the MRI images. To overcome this challenge, we propose an automated method that relies mainly on a probabilistic neural network applied to superpixels. The proposed scheme involves segmenting the corpus callosum within the MRI scans, followed by the application of the marching cubes technique in order to generate 3D volumes. The effectiveness of the proposed method has been extensively validated on four challenging datasets (OASIS, ABIDE, MIRIAD, and SBD), and the obtained results demonstrate its superior performance compared to other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Oil and gas pipelines are the lifelines of the energy market, but due to long-term use and environmental factors, these pipelines are prone to corrosion and leaks. Offshore oil and gas pipeline leaks, in particular, can lead to severe consequences such as platform fires and explosions. Therefore, it is crucial to accurately and swiftly identify oil and gas leaks on offshore platforms. This is of significant importance for improving early warning systems, enhancing maintenance efficiency, and reducing economic losses. Currently, the efficiency of identifying leaks in offshore platform pipelines still needs improvement. To address this, the present study first established an experimental platform to simulate pipeline leaks in a marine environment. Laboratory leakage signal data were collected, and on-site noise data were gathered from the "Liwan 3-1" offshore oil and gas platform. By integrating leakage signals with on-site noise data, this study aimed to closely mimic real-world application scenarios. Subsequently, several neural network-based leakage identification methods were applied to the integrated dataset, including a probabilistic neural network (PNN) combined with time-domain feature extraction, a Backpropagation Neural Network (BPNN) optimized with simulated annealing and particle swarm optimization, and a Long Short-Term Memory Network (LSTM) combined with Mel-Frequency Cepstral Coefficients (MFCC). Corresponding models were constructed, and the effectiveness of leak detection was validated using test sets. Additionally, this paper proposes an improved convolutional neural network (CNN) leakage detection technology named SART-1DCNN. This technology optimizes the network architecture by introducing attention mechanisms, transformer modules, residual blocks, and combining them with Dropout and optimization algorithms, which significantly enhances data recognition accuracy. It achieves a high accuracy rate of 99.44% on the dataset. This work is capable of detecting pipeline leaks with high accuracy. [ABSTRACT FROM AUTHOR]

Accurate prediction of reservoir properties through linear transformation and regression methods are successful in limited cases but are often geologically unrealistic and have no concrete theoretical foundation. Artificial Neural Network’s (ANN’s) have emerged as an effective tool for deriving nonlinear mathematical relationships between seismic attributes and well logs that are theoretically plausible and may prove geologically realistic. In this paper, we devise a methodology to integrate rock physics analysis, seismic inversion, multi-attribute transformation, and Feedforward Neural Network (FNN) modeling for accurate inter-well reservoir property predictions. We test this methodology on well logs and seismic data from the Cretaceous sandstones of the Sembar Formation, Southern Indus Basin, Pakistan. Viable productive gas zones are identified through rock physics and Model Based Inversion (MBI) analyses. Five volume-based seismic attributes are sequentially calculated through forward stepwise regression and cross-validated for inter-well porosity prediction. When a Probabilistic Neural Network (PNN) is trained in a non-linear mode integrated with multi-attribute transformation, correlation (r2) is improved from 72% to 88% between seismic attributes and porosity derived from logs. The PNN-derived porosity distribution is geologically more realistic than linear transformation and regression methods, supporting our model’s validity. We suggest that it is theoretically possible for the ANN to make predictions about any attribute of the reservoir via bridging target logs and seismic data within a short computation time.

Abstract Aiming at the demagnetization fault problem of the permanent magnet synchronous motor (PMSM), a demagnetization fault diagnosis method based on the combination of the principal component analysis (PCA) algorithm, the improved sparrow search algorithm (ISSA), and the probabilistic neural network (PNN) algorithm is proposed. First, the principal components of phase currents are extracted using PCA. Second, ISSA is used to optimize the smoothing coefficients of the PNN algorithm, and the optimized PNN algorithm is combined with PCA to obtain the PCA-ISSA-PNN fault diagnosis model. Finally, the established fault diagnosis model was tested using the current data collected from the experiments and compared with the fault diagnosis indexes and optimization performance of the conventional PNN, PCA-PNN, PCA-GA (genetic algorithm)-PNN, PCA-DA (dragonfly algorithm)-PNN, PCA-GTO (artificial gorilla troop optimizer)-PNN, PCA-AHA-PNN, and PCA-SSA-PNN. The test results show that the fault diagnosis accuracy of PCA-ISSA-PNN reaches 95.83%, and the fault diagnosis indexes are significantly higher than those of PNN, PCA-PNN, PCA-GA-PNN, and PCA-DA-PNN; its optimization performance is also significantly better than that of PCA-GTO-PNN, PCA-AHA-PNN, and PCA-SSA-PNN, which verifies the accuracy and efficiency of the proposed method.

Aiming at the demagnetization fault problem of the permanent magnet synchronous motor (PMSM), a demagnetization fault diagnosis method based on the combination of the principal component analysis (PCA) algorithm, the improved sparrow search algorithm (ISSA), and the probabilistic neural network (PNN) algorithm is proposed. First, the principal components of phase currents are extracted using PCA. Second, ISSA is used to optimize the smoothing coefficients of the PNN algorithm, and the optimized PNN algorithm is combined with PCA to obtain the PCA-ISSA-PNN fault diagnosis model. Finally, the established fault diagnosis model was tested using the current data collected from the experiments and compared with the fault diagnosis indexes and optimization performance of the conventional PNN, PCA-PNN, PCA-GA (genetic algorithm)-PNN, PCA-DA (dragonfly algorithm)-PNN, PCA-GTO (artificial gorilla troop optimizer)-PNN, PCA-AHA-PNN, and PCA-SSA-PNN. The test results show that the fault diagnosis accuracy of PCA-ISSA-PNN reaches 95.83%, and the fault diagnosis indexes are significantly higher than those of PNN, PCA-PNN, PCA-GA-PNN, and PCA-DA-PNN; its optimization performance is also significantly better than that of PCA-GTO-PNN, PCA-AHA-PNN, and PCA-SSA-PNN, which verifies the accuracy and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

In view of the lack of considering index weight and less nutritional status classification in maize crop’s nutritional status identification, an identification method of maize crop’s nutritional status based on index weight is studied. Based on the five aspects of Agronomic and soil properties, 15 identification indexes such as plant height and soil available phosphorus content are selected to construct the identification index system of maize crop’s nutritional status. Through the evidence fusion process, the subjective weight calculation method is combined with the objective weight calculation method to calculate each identification index system. The nutritional status of maize crops is divided into nine grades: extreme poor nutrition to extreme severe eutrophication. Samples are generated by random interpolation between the values of grade standard domain. The probabilistic neural network recognition model is constructed, and the randomly generated samples are used to train and test the model to obtain the recognition model architecture that meets the accuracy requirements. The weight of each index and the normalized sample index matrix are calculated and input into the trained recognition model to obtain the recognition results of nutritional status of corn crop samples. The test results show that the index weight obtained by this method has higher reliability and can meet the application needs of maize crop’s nutritional status identification. [ABSTRACT FROM AUTHOR]

The air quality in children's living spaces has a significant impact on their health, and ultrafine particulate matter (PM2.5), as one of the main pollutants in the air, poses a particularly prominent threat to children's respiratory and immune systems. Therefore, it important to conduct health risk assessment on ultrafine particle exercise in children's living spaces. In this article, an intelligent risk assessment model of ultrafine atmospheric particles in children's sports space in residential areas based on probabilistic neural network (PNN) is studied. This model utilizes probabilistic neural networks to monitor and predict ultrafine particles in residential spaces, evaluating their potential risks to children's health. By collecting relevant data such as particulate matter concentration and air quality index in residential spaces, and using probabilistic neural networks for training and prediction, accurate assessment of the health risks of ultrafine particulate matter during exercise can be achieved. This model can be applied to densely populated places such as families and schools, providing parents and relevant institutions with scientific risk warning and prevention measures to ensure the health and safety of children. Probabilistic neural networks can learn and simulate complex nonlinear relationships, making the model more accurate in predicting the concentration of ultrafine particles and health risks. So as to construct a method system of health risk assessment of atmospheric particles, and the model is heavy metal elements in atmospheric fine particles, and corresponding improvement measures are put forward according to the assessment results. For circulatory system diseases, the degree of harm of atmospheric temperature and fine particles to children's health is basically close, and with the continuous decline of temperature, the synergistic effect of the two is more obvious. The concentration of atmospheric ultrafine particles significantly affects the health risk of children's sports activities space. All the experiments in this article are quite good. Among them, the average accuracy of the PNN health risk identification model is 0.840, and the average recall rate is 0.837. The health risk assessment model in this article strengthens the correlation of data information through scientific methods, grasps the basic information of children's sports activity spaces in residential areas, and formulates targeted sports health risk assessments for children's sports activity spaces in residential areas. Compared with the comparison method, the response speed is significantly improved, with an accuracy rate of over 90%.

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For knowledge acquisition and social engagement, reading comprehension is essential. However, 20% or so of younger students have trouble with it. In order to predict the effects of consanguineous marriage on reading handicap and customize adaptive learning experiences, the study proposes an Intelligent Adaptive Learning and Prediction Framework (IALPF). This framework is proposed as a transformative solution that smoothly combines cutting-edge AI approaches. IALPF provides precise predictions and individualized learning pathways by utilizing extensive cognitive profiling, data gathering, and hybrid neural network design. It includes early warning systems, flexible content distribution, and ongoing development based on active learning and feedback loops. The IALPF represents a significant change in education that has wide-ranging effects. We evaluated reading skills among 770 students in a study that included two experimental groups, a control group, and 22 pupils from first-cousin marriages and 21 children of unrelated parents, respectively. Tests were given for word identification and reading comprehension, among other things. The findings showed that children of first cousin parents had a higher chance of reading difficulties than those of parents from other families. The outstanding performance of IALPF, which outperformed conventional techniques like Back Propagation (BP) and General Regression Neural Network (GRNN), was further supported by empirical evaluation. This demonstrates IALPF's success in reinventing personalized learning and predictive analysis, strengthening its potential to improve education in a variety of scenarios. The seamless integration of cutting-edge AI methods into IALPF, which forecasts the effect of consanguineous marriage on reading handicap, is a significant innovation. To set it apart from conventional approaches, this special framework integrates cognitive profile, information gathering, and hybrid neural networks for accurate predictions. The empirical analysis demonstrates the revolutionary potential of IALPF by demonstrating its improved predictive accuracy when compared to Back Propagation (BP) and General Regression Neural Network (GRNN). [ABSTRACT FROM AUTHOR]

In this new age, because of the exceptional achievement of the global computer network, the trading of digital media over the web has turned out to be unbelievably easy. However, securing the exclusive rights of the owner while trading digital media is an important issue. Digital image watermarking is a method to ensure copyright protection, security, and authenticity of data. In this paper, a novel technique is presented which is optimized, secure, and robust. The Watermark is set solidly in the discrete cosine transform domain. It is also encrypted based on the proposed block shuffling algorithm to increase security. The method was examined against various attacks, and it succeeded in maintaining statistical significance in terms of robustness and imperceptibility, as the average Peak Signal to Noise Ratio value is 65 dB, and the average Normalized Correlation value is close to one after apply all possible attacks. The results have been tested on MATLAB 2020a. [ABSTRACT FROM AUTHOR]

The air quality in children's living spaces has a significant impact on their health, and ultrafine particulate matter (PM2.5), as one of the main pollutants in the air, poses a particularly prominent threat to children's respiratory and immune systems. Therefore, it important to conduct health risk assessment on ultrafine particle exercise in children's living spaces. In this article, an intelligent risk assessment model of ultrafine atmospheric particles in children's sports space in residential areas based on probabilistic neural network (PNN) is studied. This model utilizes probabilistic neural networks to monitor and predict ultrafine particles in residential spaces, evaluating their potential risks to children's health. By collecting relevant data such as particulate matter concentration and air quality index in residential spaces, and using probabilistic neural networks for training and prediction, accurate assessment of the health risks of ultrafine particulate matter during exercise can be achieved. This model can be applied to densely populated places such as families and schools, providing parents and relevant institutions with scientific risk warning and prevention measures to ensure the health and safety of children. Probabilistic neural networks can learn and simulate complex nonlinear relationships, making the model more accurate in predicting the concentration of ultrafine particles and health risks. So as to construct a method system of health risk assessment of atmospheric particles, and the model is heavy metal elements in atmospheric fine particles, and corresponding improvement measures are put forward according to the assessment results. For circulatory system diseases, the degree of harm of atmospheric temperature and fine particles to children's health is basically close, and with the continuous decline of temperature, the synergistic effect of the two is more obvious. The concentration of atmospheric ultrafine particles significantly affects the health risk of children's sports activities space. All the experiments in this article are quite good. Among them, the average accuracy of the PNN health risk identification model is 0.840, and the average recall rate is 0.837. The health risk assessment model in this article strengthens the correlation of data information through scientific methods, grasps the basic information of children's sports activity spaces in residential areas, and formulates targeted sports health risk assessments for children's sports activity spaces in residential areas. Compared with the comparison method, the response speed is significantly improved, with an accuracy rate of over 90%. [ABSTRACT FROM AUTHOR]

Thanks to various European directives, individuals are empowered to share and trade electricity within Renewable Energy Communities, enhancing the operational efficiency of local energy systems. The digital transformation of the energy market enables the integration of decentralized energy resources using cloud computing, the Internet of Things, and artificial intelligence. In order to assess the feasibility of new business models based on data-driven solutions, various electricity consumption time series are necessary at this level of aggregation. Since these are currently not yet available in sufficient quality and quantity, and due to data privacy reasons, synthetic time series are essential in the strategic planning of smart grid energy systems. By enabling the simulation of diverse scenarios, they facilitate the integration of new technologies and the development of effective demand response strategies. Moreover, they provide valuable data for assessing novel load forecasting methodologies that are essential to manage energy efficiently and to ensure grid stability. Therefore, this research proposes a methodology to synthesize electricity consumption time series by applying the Box–Jenkins method, an intelligent sampling technique for data augmentation and a probabilistic forecast model. This novel approach emulates the stochastic nature of electricity consumption time series and synthesizes realistic ones of Renewable Energy Communities concerning seasonal as well as short-term variations and stochasticity. Comparing autocorrelations, distributions of values, and principle components of daily sequences between real and synthetic time series, the results exhibit nearly identical characteristics to the original data and, thus, are usable in designing and studying efficient smart grid systems. [ABSTRACT FROM AUTHOR]

Optical coherence tomography (OCT) has been widely used for the diagnosis of dental demineralization. Most methods rely on extracting optical features from OCT echoes for evaluation or diagnosis. However, due to the diversity of biological samples and the complexity of tissues, the separability and robustness of extracted optical features are inadequate, resulting in a low diagnostic efficiency. Given the widespread utilization of entropy analysis in examining signals from biological tissues, we introduce a dental demineralization diagnosis method using OCT echoes, employing multiscale entropy analysis. Three multiscale entropy analysis methods were used to extract features from the OCT one-dimensional echo signal of normal and demineralized teeth, and a probabilistic neural network (PNN) was used for dental demineralization diagnosis. By comparing diagnostic efficiency, diagnostic speed, and parameter optimization dependency, the multiscale dispersion entropy-PNN (MDE-PNN) method was found to have comprehensive advantages in dental demineralization diagnosis with a diagnostic efficiency of 0.9397. Compared with optical feature-based dental demineralization diagnosis methods, the entropy features-based analysis had better feature separability and higher diagnostic efficiency, and showed its potential in dental demineralization diagnosis with OCT.

For a millimeter wave (mmWave) intelligent re-configurable surface (IRS)-MIMO system, if it can correctly detect the interference occurrence and their locations, the patterns of interference signal can be collected and learned using machine learning for the prediction of interference arrival. With the information of interference location and activity pattern, the capacity of the system can be largely improved using many techniques such as beamforming, interference cancellation, and transmission scheduling. This paper aims to detect interference occurrence using a low-complexity MUSIC (MUSIC: multiple signal classification) spectrum-based method, and then localize their sources for mmWave IRS-MIMO system. The MUSIC spectrum of wireless system can be regarded as somehow the ‘signature’ related to the signals transmitted from different users or interference. We utilize such property to detect the occurrence of interference, and then localize their sources in a low-complexity way. Finally, the pattern of interference occurrence can be learned to predict the interference arrival from the collected data. This paper also proposed an efficient probabilistic neural network (PNN)-based predictor for the interference arrival prediction and showed its prediction accuracy. From simulated results, our proposed method can achieve the correct results with the accuracy near to 100% when the fingerprint samples is over 10. In addition, the localization error can be within 1 m with more than 65% and 43% for Y-axis and X-axis, respectively. Finally, based on the results of the interference occurrence, the proposed PNN-based predictor for the interference arrival prediction can capture correctly the similar distribution function of the coming continuous idle status.

Results from numerical simulations play a vital role in the decision process of everyday groundwater management. However, these simulations can be time-consuming for large-scale investigations, and it can be necessary to apply approximate methods instead.This study investigates the abilities of a neural network to replicate simulated drawdown from groundwater abstraction in a numerical groundwater model of the Egebjerg catchment, Denmark. We follow a generalised methodology that uses the information within the deterministic numerical model to create a training set for the neural network to learn from and extend the method to work in a 3D Danish groundwater model case. We compare the abilities of the trained neural network with the results of conventional computations in terms of speed and accuracy and argue that this approach has the potential to improve decision support for decision-makers within groundwater management.

In order to accurately predict the risk level of coal mine gas explosion accidents, based on the feature vector in line with the actual situation, the particle swarm optimization probabilistic neural network (RWPSO-PNN) is improved to realize the prediction model of gas explosion risk level. Firstly, the cause of coal mine gas explosion accident is extracted by Chinese word segmentation, and the input feature vector of the model is selected by grey correlation analysis (GRA). Aiming at the problem of low recognition rate caused by smoothing factor in probabilistic neural network (PNN), RWPSO-PNN is proposed to adjust the smoothing factor adaptively. Finally, RWPSO-PNN is analyzed and compared with extreme learning machine algorithm, BP neural network and support vector machine algorithm. The results show that the prediction accuracy of RWPSO-PNN is 90 %, and the average absolute error is 0.133, which is obviously better than the comparison algorithm.

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. [ABSTRACT FROM AUTHOR]

Bio-inspired models based on the lobula giant movement detector (LGMD) in the locust's visual brain have received extensive attention and application for collision perception in various scenarios. These models offer advantages such as low power consumption and high computational efficiency in visual processing. However, current LGMD-based computational models, typically organized as four-layered neural networks, often encounter challenges related to noisy signals, particularly in complex dynamic environments. Biological studies have unveiled the intrinsic stochastic nature of synaptic transmission, which can aid neural computation in mitigating noise. In alignment with these biological findings, this paper introduces a probabilistic LGMD (Prob-LGMD) model that incorporates a probability into the synaptic connections between multiple layers, thereby capturing the uncertainty in signal transmission, interaction, and integration among neurons. Comparative testing of the proposed Prob-LGMD model and two conventional LGMD models was conducted using a range of visual stimuli, including indoor structured scenes and complex outdoor scenes, all subject to artificial noise. Additionally, the model's performance was compared to standard engineering noise-filtering methods. The results clearly demonstrate that the proposed model outperforms all comparative methods, exhibiting a significant improvement in noise tolerance. This study showcases a straightforward yet effective approach to enhance collision perception in noisy environments. [ABSTRACT FROM AUTHOR]

Emotions are fundamental part of human interactions. Emotion recognition is the approach of discovering human emotions. Generally, emotion recognition is done using text, speech, audio, face, etc. In this research, Speech emotion recognition is used, Speech Emotion Recognition is the mission of identifying the emotional facets of speech. In this paper, we identify five basic emotions such as happy, sad, angry, fear and bored. The proposed pre-processing stage helps in silence removal, noise removal and normalization. In feature extraction stage pitch and frequency features are explored. Based on these features, the wave plot of amplitude vs time is plotted for all considered emotions. EMO_DB and RAVDESS databases are used for training and testing the Probabilistic Neural Network (PNN) classifier. Results show 95.76% accuracy for EMO_DB database and 84.64% accuracy for RAVDESS database. Comparison of other algorithms such as Gaussian Mixture Model, Recurrent Neural Network, Hidden Markov Model and Convolutional Neural Network is also presented. The highest accuracy is of 95.76% by using PNN with EMO_DB database. Results also reveal that PNN is a superior option as classifier for emotion classification than other classifiers. [ABSTRACT FROM AUTHOR]

Considering the problem of residual noise and spurious modes in the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), a rolling element bearing malfunction diagnostic method based on improved CEEMDAN (ICEEMDAN) is proposed. First, different from the CEEMDAN, which directly adds Gaussian white noise with a mean of zero, the proposed method adds the k th component obtained from white noise decomposed by empirical mode decomposition (EMD) to the vibration signal, and then the ICEEMDAN is employed to decompose the signal into several intrinsic mode functions (IMFs). Second, aiming at the uncertainty problem of entropy estimation in multi-scale fuzzy entropy (MFE), a refined composite multi-scale fuzzy entropy (RCMFE) is proposed to obtain the characteristic from the selected IMFs. Finally, smoothing factor of PNN is determined by fruit fly optimization algorithm (FOA), and the extracted features are input into the FOA-PNN model to achieve condition identification. Experimental results illustrate that the identification accuracy is more than 99%, which indicates its high effectiveness and superiority. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The gas content in the oil is used as the fault input characteristic for the power transformer. Still, the accuracy of the diagnosis results is not ideal, and such a model is unstable. This research proposes a hybrid intelligent fault diagnosis method based on the improved grey wolf algorithm and an optimized probabilistic neural network. Firstly, a strategy of three nonlinear control factors is introduced to fit the grey wolves’ search process. The weighted distance was modified to update the position information of grey wolf elements to avoid the algorithm falling into the local optimum. Secondly, the performance of the improved grey wolf algorithm was tested through six commonly used functions. The results show that the improved grey wolf algorithm has high convergence accuracy and stability in both multimodal and unimodal functions. Finally, the improved grey wolf algorithm and the probabilistic neural network were combined to diagnose the oil-immersed power transformer through hybrid intelligent algorithms. As a result, the fault diagnosis model proved valid for transformer fault diagnosis.

Oil and gas pipelines are the lifelines of the energy market, but due to long-term use and environmental factors, these pipelines are prone to corrosion and leaks. Offshore oil and gas pipeline leaks, in particular, can lead to severe consequences such as platform fires and explosions. Therefore, it is crucial to accurately and swiftly identify oil and gas leaks on offshore platforms. This is of significant importance for improving early warning systems, enhancing maintenance efficiency, and reducing economic losses. Currently, the efficiency of identifying leaks in offshore platform pipelines still needs improvement. To address this, the present study first established an experimental platform to simulate pipeline leaks in a marine environment. Laboratory leakage signal data were collected, and on-site noise data were gathered from the “Liwan 3-1” offshore oil and gas platform. By integrating leakage signals with on-site noise data, this study aimed to closely mimic real-world application scenarios. Subsequently, several neural network-based leakage identification methods were applied to the integrated dataset, including a probabilistic neural network (PNN) combined with time-domain feature extraction, a Backpropagation Neural Network (BPNN) optimized with simulated annealing and particle swarm optimization, and a Long Short-Term Memory Network (LSTM) combined with Mel-Frequency Cepstral Coefficients (MFCC). Corresponding models were constructed, and the effectiveness of leak detection was validated using test sets. Additionally, this paper proposes an improved convolutional neural network (CNN) leakage detection technology named SART-1DCNN. This technology optimizes the network architecture by introducing attention mechanisms, transformer modules, residual blocks, and combining them with Dropout and optimization algorithms, which significantly enhances data recognition accuracy. It achieves a high accuracy rate of 99.44% on the dataset. This work is capable of detecting pipeline leaks with high accuracy.

Classification, segmentation, and the identification of the infection region in MRI images of brain tumors are labor-intensive and iterative processes. Numerous anatomical structures of the human body may be envisioned using an image processing theory. With basic imaging methods, it is challenging to see the aberrant human brain's structure. The neurological structure of the human brain may be distinguished and made clearer using the magnetic resonance imaging technique. The MRI approach uses a number of imaging techniques to evaluate and record the human brain’s interior features. In this study, we focused on strategies for noise removal, gray-level co-occurrence matrix (GLCM) extraction of features, and segmentation of brain tumor regions based on Discrete Wavelet Transform (DWT) to minimize complexity and enhance performance. In turn, this reduces any noise that could have been left over after segmentation due to morphological filtering. Brain MRI scans were utilized to test the accuracy of the classification and the location of the tumor using probabilistic neural network classifiers. The classifier's accuracy and position detection were tested using MRI brain imaging. The efficiency of the suggested approach is demonstrated by experimental findings, which showed that normal and diseased tissues could be distinguished from one another from brain MRI scans with about 100% accuracy.

Based on dislocation superposition method and improved probabilistic neural network, a fault diagnosis method of centrifugal pumps was proposed to solve the problems of online fault diagnosis using acoustic radiation signals of centrifugal pumps under strong background noise. Firstly, the acoustic radiation signals of centrifugal pumps were denoised by dislocation superposition method to enhance the fault information in acoustic radiation signals and improve the signal-to-noise ratio. The time domain features of acoustic signals were extracted to construct the time domain feature matrix. After dimensionality reduction of the obtained time domain feature matrix through principal component analysis, which was used as the inputs of machine learning probabilistic neural network. At the same time, Harris hawk optimization algorithm was used to optimize the parameters of the probabilistic neural network to get the diagnosis model, and then the improved probabilistic neural network was used to recognize the patterns of the centrifugal pump faults, and compared with a variety of diagnostic methods. The experimental results show that the dislocation superposition method may highlight the signal characteristics and realize signal enhancement, and the improved probabilistic neural network has a good ability of online fault diagnosis of centrifugal pump acoustic radiation signals. [ABSTRACT FROM AUTHOR]

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The agricultural sector is one of the most important sectors that has a key role in development, because it is one of the most important sources in providing foodstuffs to the population and raw materials for industries. The purpose of this research is to analyze the reality of agricultural production in Iraq by studying the effect of each of the factors (cultivated area, water resources, rainfall amounts, agricultural mechanization, fertilizer quantities, investment expenditures in the agricultural sector and labor force) and their impact on agricultural product. Artificial intelligence methods represented by Probabilistic Neural Network (PNN) were used for the purpose of analysis, where the network is based on a statistical algorithm called Kernel Discrimination Analysis, and the probabilistic network gave good results. [ABSTRACT FROM AUTHOR]

This paper introduces a novel hybrid algorithm for emotion classification based on electroencephalogram (EEG) signals. The proposed hybrid model consists of two layers: the first layer includes three parallel adaptive neuro-fuzzy inference systems (ANFIS), and the second layer called the adaptive network comprises various models such as radial basis function neural network (RBFNN), probabilistic neural network (PNN), and ANFIS. It is examined that the feature distribution graphs of the dataset, which includes three emotion classes: positive, negative, and neutral, and selected the most appropriate features for classification. The three parallel ANFIS structures were trained using the selected features as input vectors, and the outputs of these models were combined to obtain a new feature vector. This feature vector was then used as the input to the adaptive network, which produced the output of emotion prediction. In addition, it is evaluated the accuracy of the network trained using only the first features of the dataset. The hybrid structure was designed to enhance the system's performance, and the best accuracy result of 96.51% was achieved using the ANFIS-ANFIS model. Overall, this study provides a promising approach for emotion classification based on EEG signals. [ABSTRACT FROM AUTHOR]

Rail corrugation is a common problem in metro lines, and its efficient recognition is always an issue worth studying. To recognize the wavelength and amplitude of rail corrugation, a particle probabilistic neural network (PPNN) algorithm is developed. The PPNN is incorporated with the particle swarm optimization algorithm and the probabilistic neural network. On the basis of the above, the in-vehicle noise characteristics measured in the field are used to recognize normal rail wavelengths of 30 and 50 mm. A stepwise moving window search algorithm suitable for selecting features with a fixed order was developed to select in-vehicle noise features. Sound pressure levels at 400, 500, 630 and 800 Hz of in-vehicle noise are fed into the PPNN, and the average accuracy can reach 96.43%. The bogie acceleration characteristics calculated by the multi-body dynamics simulation model are used to recognize normal rail amplitudes of 0.1 and 0.2 mm. The bogie acceleration is decomposed by the complete ensemble empirical mode decomposition with adaptive noise, and a reconstructional signal is obtained. The energy entropy of the reconstructional signal is fed into the PPNN, and the average accuracy can reach 95.40%. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'. [ABSTRACT FROM AUTHOR]

Artificial neural networks can solve various tasks in computer vision, such as image classification, object detection, and general recognition. Our comparative study deals with four types of artificial neural networks—multilayer perceptrons, probabilistic neural networks, radial basis function neural networks, and convolutional neural networks—and investigates their ability to classify 2D matrix codes (Data Matrix codes, QR codes, and Aztec codes) as well as their rotation. The paper presents the basic building blocks of these artificial neural networks and their architecture and compares the classification accuracy of 2D matrix codes under different configurations of these neural networks. A dataset of 3000 synthetic code samples was used to train and test the neural networks. When the neural networks were trained on the full dataset, the convolutional neural network showed its superiority, followed by the RBF neural network and the multilayer perceptron. [ABSTRACT FROM AUTHOR]

Copyright of Geology & Exploration is the property of Geology & Exploration Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)