Search Results (106)

Polyploidy, characterized by an increase in the number of whole sets of chromosomes in an organism, offers a promising avenue for cannabis improvement. Polyploid cannabis plants often exhibit altered morphological, physiological, and biochemical characteristics with a number of potential benefits compared to their diploid counterparts. The optimization of polyploidy induction, such as the level of antimitotic agents and exposure duration, is essential for successful polyploidization to maximize survival and tetraploid rates while minimizing the number of chimeric mixoploids. In this study, three classification-based machine learning algorithms—probabilistic neural network (PNN), support vector classification (SVC), and k-nearest neighbors (KNNs)—were used to model ploidy levels based on oryzalin concentration and exposure time. The results indicated that PNN outperformed both KNNs and SVC. Subsequently, PNN was combined with a genetic algorithm (GA) to optimize oryzalin concentration and exposure time to maximize tetraploid induction rates. The PNN-GA results predicted that the optimal conditions were a concentration of 32.98 µM of oryzalin for 17.92 h. A validation study testing these conditions confirmed the accuracy of the PNN-GA model, resulting in 93.75% tetraploid induction, with the remaining 6.25% identified as mixoploids. Additionally, the evaluation of morphological traits showed that tetraploid plants were more vigorous and had larger leaf sizes compared to diploid or mixoploid plants in vitro. Full article

Results change depending on the water quality evaluation methods used, and within good-quality water, many results still have parameters with concentrations exceeding the World Health Organization (WHO) desirable limits or national threshold values (TVs). Furthermore, there are few methods to classify the severity degree of contaminated water; most methods have problems in the parameter threshold boundary and in assigning weights. Aiming to solve the above problems, a water quality evaluation framework based on the single-indicator evaluation method (SIE), Weber–Fechner (W-F) law and Probabilistic Neural Network (PNN) is presented, named SIE&W-F&PNN. Forty-three confined water samples were collected for this research in Xi’an in September 2015. The SIE, water quality index (WQI) with three different weights (method weight, entropy weight and equal weight), comprehensive evaluation method (CEM) and SIE&W-F&PNN method were used, and the evaluation criteria for contaminated water were proposed based on the W-F law. The results of these methods were compared. The reasons for confined water pollution in Xi’an were analyzed. The results show that TC, NH₄-N, NO₂-N, β, As, Mn, F⁻, TH, Fe²⁺ and Turb were the contaminating parameters of the 43 confined water samples. In order, the results for the number or ratio of ‘Poor’ and even worse water samples by method are as follows: SIE-WHO (30, 69.77%) > SIE-GB = CEM (24, 55.81%) > WQI (entropy weight) (12, 27.91%) > WQI (method weight) (10, 23.26%) > WQI (equal weight) (9, 20.93%). These discrepancies highlight the influence of evaluation methods on the results. For this study, a water sample was classified as ‘contaminated (bad) water’ if any parameter exceeded either the national TV or the WHO’s desirable limit, prioritizing drinking water safety. The SIE&W-F&PNN results show that there were 10 excellent water samples and 33 bad water samples (among which 4 water samples were rated as VL (very lightly polluted), 14 as L (lightly polluted), 14 as M (moderately polluted) and 1 as H (heavily polluted)). The SIE&W-F&PNN method ensures that no parameters in ‘excellent’ or ‘good’ water samples exceed the WHO’s desirable limits or national TVs; can be used to classify the severity of contamination of contaminated water without assigning weights, avoiding the rate mutation near the threshold boundary; and can include any number of parameters and be applied to lakes, rivers, air, soil, etc. (i.e., it is not unique to groundwater). The primary causes of confined water pollution in Xi’an include historical pollution, contemporary anthropogenic activities, geological factors, excessive groundwater extraction, and the infiltration of contaminated surface and phreatic water. Full article

Noise profoundly affects the quality of electromagnetic data, and selecting the appropriate hyperparameters for machine learning models poses a significant challenge. Consequently, the current machine learning denoising techniques fall short in delivering precise processing of Wide Field Electromagnetic Method (WFEM) data. To eliminate the noise, this paper presents an electromagnetic data denoising approach based on the improved dung beetle optimized (IDBO) gated recurrent unit (GRU) and its application. Firstly, Spatial Pyramid Matching (SPM) chaotic mapping, variable spiral strategy, Levy flight mechanism, and adaptive T-distribution variation perturbation strategy were utilized to enhance the DBO algorithm. Subsequently, the mean square error is employed as the fitness of the IDBO algorithm to achieve the hyperparameter optimization of the GRU algorithm. Finally, the IDBO-GRU method is applied to the denoising processing of WFEM data. Experiments demonstrate that the optimization capacity of the IDBO algorithm is conspicuously superior to other intelligent optimization algorithms, and the IDBO-GRU algorithm surpasses the probabilistic neural network (PNN) and the GRU algorithm in the denoising accuracy of WFEM data. Moreover, the time domain of the processed WFEM data is more in line with periodic signal characteristics, its overall data quality is significantly enhanced, and the electric field curve is more stable. Therefore, the IDBO-GRU is more adept at processing the time domain sequence, and the application results also validate that the proposed method can offer technical support for electromagnetic inversion interpretation. Full article

The photoelectric Factor (PEF) log is a powerful tool for distinguishing between siliciclastic and carbonate lithofacies in well-log analysis and 2D correlations. However, its application in complex reservoirs has some challenges due to well spacing. We present a workflow to extend its capabilities into a 3D environment to characterize the Pennsylvanian Strawn and Canyon reef complex in the Salt Creek field, Kent County, West Texas. The productive zones within this reservoir are composed of porous oolitic grainstones and skeletal packstones. However, there are some porous shale beds within the reef complex that are indistinguishable from the porous limestone zones on the neutron porosity log that have posed major challenges to hydrocarbon production. To address these problems, we used a machine-learning procedure involving multiattribute analysis and probabilistic neural network (PNN) to predict photoelectric factor (PEF) volume to characterize the reservoir and identify the shale beds. By combining neutron porosity, gamma ray, and the predicted PEF logs, we found that (1) these shale beds, hereby referred to as shale-influenced carbonates, are characterized by photoelectric factor values ranging from 4 to 4.26 B/E. (2) Based on the PEF values, the least porous interval is the Canyon System, having <1% porosity and characterized by PEF values of >4.78 B/E; while the most porous interval is the Strawn System, composed mostly of zones with porosity ranging from 3% to 28%, characterized by PEF values varying from 4.26 to 4.78 B/E. Full article

Direct-drive permanent magnet synchronous machines (DDPMSMs) have recently become an ideal candidate for applications such as military, robotics, electric vehicles, etc. These machines eliminate the need for a transmission mechanism and excitation coil circuits, which enhances the system’s overall efficiency and decreases the likelihood of failures. However, it may incur demagnetization faults. Due to the characteristic of having a large number of pole pairs, this type of machine exhibits numerous demagnetization fault modes, which poses a challenge in locating demagnetization faults. This paper proposed a probabilistic neural network (PNN)-based diagnostic system to detect and locate demagnetization faults in DDPMSMs, using information obtained through three toroidal-yoke-type search coils arranged at the bottom of the stator slot. A rotor partition method is proposed to solve the problem of demagnetization fault location difficulty caused by various fault modes. Demagnetization fault location is achieved by sequentially diagnosing the condition of each partition of permanent magnets. Three demagnetization fault identified signals (DFISs) are constructed by the voltage of the three toroidal-yoke coils, which are used as inputs of PNNs. Three PNNs have been designed to map the extracted features and their corresponding types of demagnetization faults. The database for training and testing the PNNs is generated from a DDPMSM with different demagnetization conditions and different operating conditions, which are established through an experimentally validated mathematical model, an FEM model, and experiments. The simulation and experimental test results showed that the accuracy in diagnosing the location of the demagnetization fault is 99.2% when the demagnetization severity is 10%, which demonstrates the effectiveness of the proposed three PNN-based diagnostic approach. Full article

A diagnostic method for gas-path faults of turbofan engines, relying on a Probabilistic Neural Network (PNN) coupled with a thermodynamic model of the engine, is presented. The novel aspect of the method is that its training information is generated dynamically by an accompanying Engine Performance Model. In the proposed approach, the PNN efficiently addresses the first step of a diagnostic process (i.e., detection of the faulty component at the current operating point), while with the aid of an adaptive engine model, the fault is then further isolated and identified. A description of the proposed method and training aspects of the PNN are presented. The method is applied to the case of a mixed-flow turbofan engine to diagnose common gas-path faults in compressors and turbines (i.e., fouling, FOD, erosion, and tip clearance). Its performance is evaluated using realistic fault data that may be acquired at various operating conditions within a flight envelope. Full article

As a fast detection method, Fourier transform infrared attenuated total reflection (ATR-FTIR) spectroscopy is seldom used for monitoring soluble sugars in crops. This study aimed to demonstrate the feasibility of leveraging ATR-FTIR coupled with chemometrics to quantify and sort the contents of soluble sugar in tomatoes. Firstly, 192 tomato samples were scanned using ATR-FTIR; subsequently, a quantitative model was developed using PLSR with selected wavelength variables as inputs. Finally, a classification model was estimated through probabilistic neural network (PNN) to determine the samples. The results indicated that ATR-FTIR had successfully captured the spectra from the cellular layers of tomatoes, resulting in a robust PLSR model created by 468 selected variables with a R² value of 0.86, a RMSEP of 0.71%, a ratio of performance to relative percent deviation (RPD) of 1.87, and a ratio of prediction to interquartile range (RPIQ) of 2.1. Meanwhile, the PNN model demonstrated a high rate correct (RC) of 92.17% in identifying whether the samples with a higher soluble sugar content than the limit of detection (LOD at 2.1%). Overall, ATR-FTIR coupled with chemometrics has proven effective for non-destructive determination of soluble sugars in tomatoes, offering new insights into internal monitoring techniques for crop quality assurance. Full article

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. Full article

The key point in the process of agent-based management in e-service for malware detection (according to accuracy criteria) is a decision-making process. To determine the optimal e-service for malware detection, two concepts were investigated: Fuzzy Logic (FL) and Probabilistic Neural Networks (PNN). In this study, three evolutionary variants of fuzzy partitioning, including regular, hierarchical fuzzy partitioning, and k-means, were used to automatically process the design of the fuzzy partition. Also, this study demonstrates the application of a feature selection method to reduce the dimensionality of the data by removing irrelevant features to create fuzzy logic in a dataset. The behaviors of malware are analyzed by fuzzifying relevant features for pattern recognition. The Apriori algorithm was applied to the fuzzified features to find the fuzzy-based rules, and these rules were used for predicting the output of malware detection e-services. Probabilistic neural networks were also used to find the ideal agent-based model for numerous classification problems. The numerical results show that the agent-based management performances trained with the clustering method achieve an accuracy of 100% with the PNN-MCD model. This is followed by the FL model, which classifies on the basis of linguistic variables and achieves an average accuracy of 82%. Full article

The fault diagnosis of rotating machinery is vital in industry but traditionally depends on manual expertise, requiring substantial resources. To improve diagnostic accuracy, enable effective condition monitoring, and minimize the impact of faults on operations, advanced diagnostic techniques are essential. Hence, we propose an advanced fault diagnosis framework that leverages improved particle swarm optimization (IPSO), variational mode decomposition (VMD), and probabilistic neural networks (PNN) to accurately diagnose faults in rotating machinery using gear and rolling bearing vibration signals. Initially, the vibration signals are decomposed into intrinsic mode functions via VMD, enabling the capture of subtle but critical fault features. To address parameter selection challenges in VMD, we employed IPSO to optimize the VMD parameters, ensuring the optimal decomposition effect. Further, we refined the feature set by applying Laplace fraction optimization and feature dimensionality reduction, isolating sensitive features that serve as input to a PNN-based fault classification model. Experimental results demonstrated that this IPSO-VMD-PNN framework achieves high diagnostic accuracy for various fault types, establishing it as an effective tool for fault identification in rotating machinery. Full article

Climate change has emerged as one of the most significant challenges in modern agriculture, with potential implications for global food security. The impact of changing climatic conditions on crop yield, particularly for staple crops like wheat, has raised concerns about future food production. By integrating historical climate data, GCM (CMIP3) projections, and wheat-yield records, our analysis aims to provide significant insights into how climate change may affect wheat output. This research uses advanced machine learning models to explore the intricate relationship between climate change and wheat-yield prediction. Machine learning models used include multiple linear regression (MLR), boosted tree, random forest, ensemble models, and several types of ANNs: ANN (multi-layer perceptron), ANN (probabilistic neural network), ANN (generalized feed-forward), and ANN (linear regression). The model was evaluated and validated against yield and weather data from three Punjab, Pakistan, regions (1991–2021). The calibrated yield response model used downscaled global climate model (GCM) outputs for the SRA2, B1, and A1B average collective CO₂ emissions scenarios to anticipate yield changes through 2052. Results showed that maximum temperature (R = 0.116) was the primary climate factor affecting wheat yield in Punjab, preceding the $T_{min}$ (R = 0.114), while rainfall had a negligible impact (R = 0.000). The ensemble model (R = 0.988, nRMSE= 8.0%, MAE = 0.090) demonstrated outstanding yield performance, outperforming Random Forest Regression (R = 0.909, nRMSE = 18%, MAE = 0.182), ANN(MLP) (R = 0.902, MAE = 0.238, nRMSE = 17.0%), and boosting tree (R = 0.902, nRMSE = 20%, MAE = 0.198). ANN(PNN) performed inadequately. The ensemble model and RF showed better yield results with $R^2$ = 0.953, 0.791. The expected yield is 5.5% lower than the greatest average yield reported at the site in 2052. The study predicts that site-specific wheat output will experience a significant loss due to climate change. This decrease, which is anticipated to be 5.5% lower than the highest yield ever recorded, points to a potential future loss in wheat output that might worsen food insecurity. Additionally, our findings highlighted that ensemble approaches leveraging multiple model strengths could offer more accurate and reliable predictions under varying climate scenarios. This suggests a significant potential for integrating machine learning in developing climate-resilient agricultural practices, paving the way for future sustainable food security solutions. Full article

Accurate prognosis and diagnosis are crucial for selecting and planning lung cancer treatments. As a result of the rapid development of medical imaging technology, the use of computed tomography (CT) scans in pathology is becoming standard practice. An intricate interplay of requirements and obstacles characterizes computer-assisted diagnosis, which relies on the precise and effective analysis of pathology images. In recent years, pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection have witnessed the considerable potential of artificial intelligence, especially deep learning techniques. In this context, an artificial intelligence (AI)-based methodology for lung cancer diagnosis is proposed in this research work. As a first processing step, filtering using the Butterworth smooth filter algorithm was applied to the input images from the LUNA 16 lung cancer dataset to remove noise without significantly degrading the image quality. Next, we performed the bi-level feature selection step using the Chaotic Crow Search Algorithm and Random Forest (CCSA-RF) approach to select features such as diameter, margin, spiculation, lobulation, subtlety, and malignancy. Next, the Feature Extraction step was performed using the Multi-space Image Reconstruction (MIR) method with Grey Level Co-occurrence Matrix (GLCM). Next, the Lung Tumor Severity Classification (LTSC) was implemented by using the Sparse Convolutional Neural Network (SCNN) approach with a Probabilistic Neural Network (PNN). The developed method can detect benign, normal, and malignant lung cancer images using the PNN algorithm, which reduces complexity and efficiently provides classification results. Performance parameters, namely accuracy, precision, F-score, sensitivity, and specificity, were determined to evaluate the effectiveness of the implemented hybrid method and compare it with other solutions already present in the literature. Full article

In the industrial sector, accurate fault identification is paramount for ensuring both safety and economic efficiency throughout the production process. However, due to constraints imposed by actual working conditions, the motor state features collected are often limited in number and singular in nature. Consequently, extending and extracting these features pose significant challenges in fault diagnosis. To address this issue and strike a balance between model complexity and diagnostic accuracy, this paper introduces a novel motor fault diagnostic model termed FSCL (Fourier Singular Value Decomposition combined with Long and Short-Term Memory networks). The FSCL model integrates traditional signal analysis algorithms with deep learning techniques to automate feature extraction. This hybrid approach innovatively enhances fault detection by describing, extracting, encoding, and mapping features during offline training. Empirical evaluations against various state-of-the-art techniques such as Bayesian Optimization and Extreme Gradient Boosting Tree (BOA-XGBoost), Whale Optimization Algorithm and Support Vector Machine (WOA-SVM), Short-Time Fourier Transform and Convolutional Neural Networks (STFT-CNNs), and Variational Modal Decomposition-Multi Scale Fuzzy Entropy-Probabilistic Neural Network (VMD-MFE-PNN) demonstrate the superior performance of the FSCL model. Validation using the Case Western Reserve University dataset (CWRU) confirms the efficacy of the proposed technique, achieving an impressive accuracy of 99.32%. Moreover, the model exhibits robustness against noise, maintaining an average precision of 98.88% and demonstrating recall and F1 scores ranging from 99.00% to 99.89%. Even under conditions of severe noise interference, the FSCL model consistently achieves high accuracy in recognizing the motor’s operational state. This study underscores the FSCL model as a promising approach for enhancing motor fault diagnosis in industrial settings, leveraging the synergistic benefits of traditional signal analysis and deep learning methodologies. Full article

In order to improve the reliability and life of the wind turbine, this paper takes the rolling bearing in the experimental platform of the wind turbine as the research object. In order to obtain the intrinsic mode function (IMF) of each fault type, the original signals of different fault states of the rolling bearing on the experimental platform are decomposed by using the overall average empirical mode decomposition method (EEMD) and the wavelet packet decomposition method (WPD), respectively. Then the energy ratio of the IMF component of the different types of faults to the total energy value is calculated and the eigenvectors of different types of faults are constructed. The extreme learning machine (ELM) and probabilistic neural network (PNN) are used to learn fault types and eigenvector samples to identify the faults of the rolling bearing. It is found that the bearing fault characteristics obtained by the WPD method are more obvious, and the results obtained by the same recognition method are ideal; and the PNN method is obviously superior to the extreme learning machine method in bearing fault recognition rate. Full article

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. Full article