Lower Average Error Research Articles

A Study on Canopy Volume Measurement Model for Fruit Tree Application Based on LiDAR Point Cloud

The accurate measurement of orchard canopy volume serves as a crucial foundation for wind regulation and dosage adjustments in precision orchard management. However, existing methods for measuring canopy volume fail to satisfy the high precision and real-time requirements necessary for accurate variable-rate applications in fruit orchards. To address these challenges, this study develops a canopy volume measurement model for orchard spraying using LiDAR point cloud data. In the domain of point cloud feature extraction, an improved Alpha Shape algorithm is proposed for extracting point cloud contours. This method improves the validity judgment for contour line segments, effectively reducing contour length errors on each 3D point cloud projection plane. Additionally, improvements to the mesh integral volume method incorporate the effects of canopy gaps in height difference calculations, significantly enhancing the accuracy of canopy volume estimation. For feature selection, a random forest-based recursive feature elimination method with cross-validation was employed to filter 10 features. Ultimately, five key features were retained for model training: the number of point clouds, the 2D point cloud contour along the X- and Z-projection directions, the 2D width in the Y-projection direction, and the 2D length in the Z-projection direction. During model construction, the study optimized the hyperparameters of partial least squares regression (PLSR), backpropagation (BP) neural networks, and gradient boosting decision trees (GBDT) to build canopy volume measurement models tailored to the dataset. Experimental results indicate that the PLSR model outperformed other approaches, achieving optimal performance with three principal components. The resulting canopy volume measurement model achieved an R2 of 0.9742, an RMSE of 0.1879, and an MAE of 0.1161. These results demonstrate that the PLSR model exhibits strong generalization ability, minimal prediction bias, and low average prediction error, offering a valuable reference for precision control of canopy spraying in orchards.

Efficient face anti-spoofing identifier network (FASIN) with depth and near infrared deep learning methods

Face anti-spoofing (FAS) is a crucial task in the field of face recognition practices, which aims to detect and prevent attempts to spoof/attack a facial recognition system using fake or manipulated images. In this work, we aimed to develop a novel Face Anti-Spoofing Identifier Network (FASIN) with Depth and Near Infrared (NIR) embeddings, trained on multi modalities using multi stream Convolutional Neural Networks (CNNs). The proposed FASIN model is capable of processing RGB, Depth, and NIR images to extract discriminative features and effectively distinguish between genuine and fake faces. The depth maps and the near infrared (NIR) images are acquired from RGB images by constructing the depth map construction network (DMCN) and near infrared construction network (NIRCN) respectively. The FASIN model comprises three sub-networks: one for processing RGB images, a second for processing depth images, and the other sub-network for processing NIR images. All the sub-networks consist of multiple CNN layers, which extract features at different scales and levels of abstraction. The inherent noise and other variables may reduce the efficacy of CNN, the wavelet spatial attention mechanism has been proposed to support the RGB CNN stream and it is named wavelet attention CNN (WA-CNN). The extracted features are then concatenated using a multi modal feature fusion module to obtain a robust feature representation that is used to classify real and fake faces. An ensemble learning mechanism has been attached to the model to learn the concatenated features effectively. Experimental results obtained on four benchmark datasets (namely, CelebA-Spoof, CASIA-SURF, WMCA, and MSU-MFSD) demonstrate the efficacy of the proposed FASIN model collated with the state-of-the-art methods. The proposed FASIN model achieves high accuracy and low average classification error rates (ACER), indicating its potential for real-world applications in face anti spoofing identification systems.

A Novel Fitting Polynomial Approach for An Accurate Soc Estimation In Li-Ion Batteries Considering Temperature Hysteresis

Lithium-ion batteries are essential to modern technology, requiring accurate estimation of the state of charge (SOC) for optimal performance. Traditional methods such as Coulomb Counting (CC) are ineffective in the face of temperature variations, leading to inaccuracies in SOC estimation, which in turn cause obvious deformation of hysteresis curves. To address this, this paper introduces a novel method called Polynomial Fit State of Charge (FPSOC), for effective SOC estimation. This method incorporates a fifth-degree polynomial fitting that accounts for a wide range of temperature variations (from -10°C to +80°C), a feature that, according to the authors, has not been offered by all previously published methods. A series of simulation tests using the MATLAB/Simulink tool are conducted under various temperature profiles to evaluate the effectiveness of the FPSOC method. The results demonstrate the notable superiority of the FPSOC model compared to the CC method, with a significantly reduced RMSE of only 0.93% compared to 6.77% of the CC model. Particularly effective at low SOC levels (30%), the FPSOC model demonstrates precision up to six times higher compared to the CC model. Additionally, when evaluated against other recent SOC estimation techniques such as CM, RLSF, EKF, DST, BBDST, ASMO, LPM_H, LSTM-SA Group A and B, and baseline ECM-ID, The FPSOC method proves extremely accurate, with the lowest average error under different temperature conditions.

Advancing Mix Design Prediction in 3D Printed Concrete: Predicting Anisotropic Compressive Strength and Slump Flow

Introducing 3D-concrete printing has started a revolution in the construction industry, presenting unique opportunities alongside undeniable challenges. Among these, the major challenge is the iterative process associated with mix design formulation, which results in significant material and time consumption. This research uses machine learning (ML) techniques such as Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Decision Tree Regression (DTR), Gaussian Process Regression (GPR), and Artificial Neural Network (ANN) to overcome these challenges. A dataset containing 21 mix constituent features and 4 output properties (cast and printed compressive strength, and slump flow) was extracted from the literature to investigate the relationship between mix design and performance. The models were assessed using a range of evaluation metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and R-squared value. Gaussian Process Regression (GPR) yielded more favorable results. In the case of cast strength, GPR achieved an R2 value of 0.9069, along with RMSE, MSE, and MAE values of 13.04, 170.12, and 9.40, respectively. A similar trend was observed for printed strengths in directions 1, 2, and 3. GPR achieved R-squared values exceeding 0.91 for all directions, accompanied by significantly lower RMSE values (below 4.1). The machine learning models were also validated using four unique mix designs. These mixes were 3D printed and tested for compressive strength and slump flow. GPR's average error was 10.55%, while SVM achieved a slightly lower average error of 9.38%. Overall, this work presents a novel approach for optimizing 3D-printed concrete by enabling the prediction of slump flow and compressive strength directly from the mix design. This approach can facilitate the design and fabrication of 3D-printed concrete structures that fulfill the necessary strength and printability requirements.

Experiments and modeling of the combined tension-shear ultra-low-cycle fatigue of structural steel sheet regarding loading histories

This paper presents the experimental and modeling studies of the combined tension-shear ultra-low-cycle fatigue (ULCF) of Q345 structural steel sheet with thickness of 8 mm, considering the loading histories and stress state dependencies. Butterfly-shaped cyclic shear specimens, along with fixtures designed for adjustable shear angles, are used to explore ULCF damage evolution subjected to various combinations of shear and normal stress. The cyclic test programs consist of loading cases with shear angles of 0°(CS00), 30°(CS30), and 45°(CS45), all subjected to cyclic loading with constant-deformation-range (CR), varying-deformation-range (VR), and random-deformation (RD). Testing results confirm dependencies on deformation range and stress state. The combined tension-shear ULCF life increases as the deformation range decreases, while the higher triaxiality induced by the increase in shear angle reduces the ULCF life. Subsequently, the cyclic shear-void damage model (CSVDM) is proposed to comprehensively characterize the evolution of combined tension-shear ULCF damage. The kinematic equivalent plastic strain is introduced to determine dependency on plastic deformation histories. A new damage locus related to stress state is proposed to consider the matrix-shear mechanism under plane strain and the void-growth-collapse mechanism under axisymmetric loading. Compared to the conventional stress-weighted ductile fracture model (SWDFM) and the Lode parameter-enhanced cyclic void growth model (LCVGM), the proposed model exhibits advantages in accurately predicting ULCF failure under different loading protocols and shear angles, as indicated by impressively low average errors of 0.92 % for CS00, 2.08 % for CS30, and 1.42 % for CS45 specimens.

Tool Wear Prediction in Machining of Aluminum Matrix Composites with the Use of Machine Learning Models.

This paper discusses the diagnostic models of tool wear during face milling of Aluminum Matrix Composite (AMC), classified as a difficult-to-cut material. Prediction and classification models were considered. The models were based on one-dimensional simple regression or on multidimensional regression trees, random forest, nearest neighbor and multilayer perceptron neural networks. Measures of diagnostic signals obtained from measurements of cutting forces and vibration accelerations of the workpiece were used. The study demonstrated that multidimensional models outperformed one-dimensional models in terms of prediction accuracy and classification performance. Specifically, multidimensional predictive models exhibited lower maximum and average absolute prediction errors (0.036 mm vs. 0.050 mm and 0.026 mm vs. 0.045 mm, respectively), and classification models recorded fewer Type I and Type II errors. Despite the increased complexity, the higher predictive accuracy (up to 0.97) achieved with multidimensional models was shown to be suitable for industrial applications. However, simpler one-dimensional models offered the ad-vantage of greater reliability in signal acquisition and processing. It was also highlighted that the advantage of simple models from a practical point of view is the reduced complexity and consequent greater reliability of the system for acquiring and processing diagnostic signals.

A 4IR-Driven operational risk model for CO2 storage in deepwater abandoned hydrocarbon reservoirs

This study presents the development of an advanced operational risk model that leverages Fourth Industrial Revolution (4IR) technologies to optimize carbon dioxide (CO2) storage within deepwater abandoned hydrocarbon reservoirs. The model systematically combines Artificial Neural Networks (ANN) with the optimization capabilities of Genetic Algorithms (GA) and the probabilistic analysis strengths of a Bayesian Network (BN) to perform dynamic and comprehensive risk assessments. By applying the model to a dataset covering a 200-year timeframe, it effectively forecasts CO2 storage capacities while simultaneously evaluating associated risks across different operational scenarios. One of the key innovations of this model is the introduction of a novel loss function designed to precisely manage forecast deviations and enhance the efficiency of operational processes. This function is critical in ensuring that the model remains robust and accurate in real-time risk assessments, allowing for more reliable decision-making in CO2 storage operations. In addition, the study conducts an economic evaluation that underscores the crucial role of 45Q tax credits in bolstering the financial sustainability of carbon sequestration projects. The analysis highlights how these credits significantly reduce the economic barriers to adopting carbon utilization, storage, and sequestration (CUSS) technologies, making large-scale implementation more feasible. The model's performance is underscored by its ability to achieve a 49% CO2 retention rate over two centuries, with an impressively low average error margin of 0.249%. These results highlight the model's impressive efficiency and accuracy, while also demonstrating its capacity to markedly improve the predictability of CO2 storage outcomes. The findings suggest that this model could play a pivotal role in advancing global sustainability efforts by optimizing CO2 storage processes, thereby contributing to the reduction of atmospheric CO2 levels and supporting long-term climate goals.

Equipment Sounds’ Event Localization and Detection Using Synthetic Multi-Channel Audio Signal to Support Collision Hazard Prevention

Construction workplaces often face unforeseen collision hazards due to a decline in auditory situational awareness among on-foot workers, leading to severe injuries and fatalities. Previous studies that used auditory signals to prevent collision hazards focused on employing a classical beamforming approach to determine equipment sounds’ Direction of Arrival (DOA). No existing frameworks implement a neural network-based approach for both equipment sound classification and localization. This paper presents an innovative framework for sound classification and localization using multichannel sound datasets artificially synthesized in a virtual three-dimensional space. The simulation synthesized 10,000 multi-channel datasets using just fourteen single sound source audiotapes. This training includes a two-staged convolutional recurrent neural network (CRNN), where the first stage learns multi-label sound event classes followed by the second stage to estimate their DOA. The proposed framework achieves a low average DOA error of 30 degrees and a high F-score of 0.98, demonstrating accurate localization and classification of equipment near workers’ positions on the site.

Machine Learning-Based Predictions of Flow and Heat Transfer Characteristics in a Lid-Driven Cavity with a Rotating Cylinder

Machine learning-based predictions of heat transfer characteristics in lid-driven cavities are transforming the field of computational fluid dynamics (CFD). Lid-driven cavities are a fundamental problem in fluid mechanics, characterized by the motion of a fluid inside a square cavity driven by the motion of one of its walls. The goal of this study was to develop multiple machine-learning regression models and highlight the discrepancies between the predicted and actual average Nusselt numbers. Additionally, the study utilized physics-informed neural networks (PINNs) to model the flow and thermal behavior at both low and high Reynolds numbers. The results were compared among actual data from computational fluid dynamics (CFD) simulations, PINN models trained with CFD data, and purely PINN models created without any prior data input. The findings of this study showed that the random forest model exhibited an exceptional stability in its predictions, consistently maintaining low errors even as the Reynolds number increased compared with other machine-learning regression models. Further, the results of this study in terms of flow and thermal behavior within the cavity were found to depend significantly on the PINN method. The data-driven PINN exhibited a much lower mean average errors at both Reynolds numbers, while the physics-based PINN showed lower physics loss.

Hybrid learning-based visual path following for an industrial robot

Abstract This study proposes a novel hybrid learning approach for developing a visual path-following algorithm for industrial robots. The process involves three steps: data collection from a simulation environment, network training, and testing on a real robot. The actor network is trained using supervised learning for 500 epochs. A semitrained network is then obtained at the $250^{th}$ epoch. This network is further trained for another 250 epochs using reinforcement learning methods within the simulation environment. Networks trained with supervised learning (500 epochs) and the proposed hybrid learning method (250 epochs each of supervised and reinforcement learning) are compared. The hybrid learning approach achieves a significantly lower average error (30.9 mm) compared with supervised learning (39.3 mm) on real-world images. Additionally, the hybrid approach exhibits faster processing times (31.7 s) compared with supervised learning (35.0 s). The proposed method is implemented on a KUKA Agilus KR6 R900 six-axis robot, demonstrating its effectiveness. Furthermore, the hybrid approach reduces the total power consumption of the robot’s motors compared with the supervised learning method. These results suggest that the hybrid learning approach offers a more effective and efficient solution for visual path following in industrial robots compared with traditional supervised learning.

Kilometer-Scale Precipitation Forecasting Utilizing Convolutional Neural Networks: A Case Study of Jiangsu’s Coastal Regions

High-resolution precipitation forecasts play a pivotal role in formulating comprehensive disaster prevention and mitigation plans. As spatial resolution enhances, striking a balance between computation, storage, and simulation accuracy becomes imperative to ensure optimal cost-effectiveness. Convolutional neural networks (CNNs), a cornerstone of deep learning, are examined in this study for their downscaling capabilities in precipitation simulation. During a precipitation event on 23 June 2022, in Jiangsu Province, China, distinct rain belts emerged in both southern and northern Jiangsu, precisely captured by a numerical model (the Weather Research and Forecasting, WRF) with a 3 km spatial resolution. Specifically, precipitation was prevalent in northern Jiangsu from 00:00 to 11:00 Beijing Time (BJT), transitioning to southern Jiangsu from 12:00 to 23:00 BJT on the same day. Upon dynamic downscaling, the model reproduced precipitation in these periods with an average error of 12.35 mm at 3 km and 12.48 mm at 1 km spatial resolutions. Employing CNN technology for statistical downscaling to a 1 km spatial resolution, samples from the initial period were utilized for training, while those from the subsequent period served for validation. Following dynamic downscaling, CNNs with four, five, six, and seven layers exhibited average errors of 8.86 mm, 8.93 mm, 9.71 mm, and 9.70 mm, respectively, accompanied by correlation coefficients of 0.550, 0.570, 0.574, and 0.578, respectively. This analysis indicates that for this precipitation event, a shallower CNN depth yields a lower average error and correlation coefficient, whereas a deeper architecture enhances the correlation coefficient. By employing deep network architectures, CNNs are capable of capturing nonlinear patterns and subtle local features from complex meteorological data, thereby providing more accurate predictions during the downscaling process. Leveraging faster computation and reduced storage requirements, machine learning has demonstrated immense potential in high-resolution forecasting research. There is significant scope for advancing technologies that integrate numerical models with machine learning to achieve higher-resolution numerical forecasts.

Motor Insurance Claim Frequency Prediction Using XGBoost

Insurance claim frequency modelling is an important task for non-life insurers, this together with other variables forms an important part of product pricing and risk management. The traditional frequency models such as Poisson, Negative Binomial and Zero Inflated models have several weakness such as scalibility issues, overdispersion and independence assumptions for large datasets and therefore not ideal to use when dealing with complex and unstructured data. Extreme gradient Boosting Algorithm (XGBoost) is an ensemble learning which has the capacity to effectively handle big complex and unstructured insurance data. XGBoost creates tree-based models by iteratively fitting decision trees to the residuals of the previous predictions, effectively reducing the error in each iteration. This research utilized and explored the XGBoost algorithm to process motor insurance claims large dataset in-order to predict the frequencies of insurance claims, that is 0,1,2,and 3. Using this algorithm we aim to enhance the accuracy of predictions that will yield better estimates for improved risk assessment and pricing of insurance products. Cross validation was performed to assess the true performance of our model. Cross validation results showed that XGBoost models for the claim frequency had a RMSE estimate of 0.949, MAE of 0.7741 and RSQ 0.781. This demonstrated a strong predictive performance, with an RMSE of 0.949 and an MAE of 0.7741, indicating a low average error in the predictions.The RSQ of 0.781 suggests further that the model explained a significant proportion of variability of the insurance data. Our model was evaluated with a confusion matrix. The results of the confusion matrix showed that for the frequency 0 99.59% of cases were correctly predicted, frequency 1 94.01% were correctly predicted and frequency 2 84.80% and finally frequency 3 only 40.96% of the observations were correctly predicted.These results highlights the potential of XGBoost as a robust modeling technique for handling big data and accurately predicting insurance claim frequency. The results corroborate with other studies that XGBoost is an invaluable tool for insurance companies.

Machine Learning-Empowered Real-Time Acoustic Trapping: An Enabling Technique for Increasing MRI-Guided Microbubble Accumulation

Acoustic trap, using ultrasound interference to ensnare bioparticles, has emerged as a versatile tool for life sciences due to its non-invasive nature. Bolstered by magnetic resonance imaging’s advances in sensing acoustic interference and tracking drug carriers (e.g., microbubble), acoustic trap holds promise for increasing MRI-guided microbubbles (MBs) accumulation in target microvessels, improving drug carrier concentration. However, accurate trap generation remains challenging due to complex ultrasound propagation in tissues. Moreover, the MBs’ short lifetime demands high computation efficiency for trap position adjustments based on real-time MRI-guided carrier monitoring. To this end, we propose a machine learning-based model to modulate the transducer array. Our model delivers accurate prediction of both time-of-flight (ToF) and pressure amplitude, achieving low average prediction errors for ToF (−0.45 µs to 0.67 µs, with only a few isolated outliers) and amplitude (−0.34% to 1.75%). Compared with the existing methods, our model enables rapid prediction (<10 ms), achieving a four-order of magnitude improvement in computational efficiency. Validation results based on different transducer sizes and penetration depths support the model’s adaptability and potential for future ultrasound treatments.

Comprehensive input models and machine learning methods to improve permeability prediction

This study investigates the use of machine learning techniques and the proper selection of input data to estimate permeability in geosciences, using six types of input logs: gamma ray (GR), resistivity (RT), effective porosity (PHIE), density (RHO), sonic (DT), and compensated neutron porosity (NPHI). A total of 57 models were constructed using combinations of these logs and tested using five machine learning methods: Extreme Learning Machine (ELM), Random Forest (RF), Gradient Boosting (GB), K-Nearest Neighbor (KNN), and Multilayer Perceptron (MLP). This approach produced 285 unique permeability predictions. RF had the highest correlation coefficient (0.925) and average error (0.196), indicating a precision-correlation trade-off. The ELM approach had the lowest average error, 0.083, and a correlation value of 0.871. Testing on a blind well revealed that the GB and RF approaches were highly effective in predicting permeability, with R² values of 0.92 and 0.90, respectively, even in untested settings. The findings emphasize the need of using appropriate machine learning algorithms and input data to improve model accuracy and reliability.

Advancing Quantum Temperature Sensors for Ultra-Precise Measurements (UPMs): A Comparative Study

In this study, we compared the performance of quantum temperature sensors (QTSs) with conventional sensors (CSs), highlighting differences in measurement accuracy and stability. Quantum sensors (QSs), known for their ability to provide ultra-precise measurements (UPMs), were tested across a temperature range of −10 to 40 °C. The results indicate that QSs offer superior accuracy, with a lower average error and a smaller standard deviation compared to CSs, indicating better measurement stability. For this comparison, we utilized Python scripts to conduct simulations and statistical analyses, leading to precise and reproducible results. The sensor performance was simulated in a controlled environment, and the obtained data were compared with experimental results. This comparison reveals that QSs are more reliable for applications requiring high precision, such as those in the Internet of Things (IoT) domain. These findings underscore the potential advantage of QSs in critical systems where measurement accuracy is paramount.

Experiment and prediction of the strain-range dependent ultra-low-cycle fatigue based on micromechanical cyclic void growth model

Under severe seismic action, the local stress concentration region in steel structures is prone to the ultra-low-cycle fatigue (ULCF) under high triaxiality and irregular cyclic deformation with intensively varying strain amplitudes. It emphasizes the necessity of the jointed effects of the stress triaxiality and strain range incorporated in the ULCF life prediction. Given the limited relevant studies in terms of the underlying micro-mechanisms, this paper focuses on experimental and modeling studies of the combined effects of stress triaxiality and strain range on the ULCF failure, based on Gurson-Tvergaard-Needleman (GTN) microvoid evolution theories. Monotonic tensile tests on three types of notched round bars suggest that fracture strain decreases with increasing triaxiality. Cyclic tests on two types of notched bars under various strain ranges confirm the combined effects of stress triaxiality and strain range. Given a specific triaxiality, the ULCF damage accumulation rate increases linearly as the tensile plastic strain range ramps up. For the same tensile plastic strain range, higher triaxiality results in a greater damage accumulation rate. In the modeling studies, the consistency of the GTN and uncoupled damage models has been proven in terms of ULCF failure of structural steel. Based on GTN theories, a new micromechanical cyclic void growth model (GCVGM) is proposed, considering the detailed processes of void nucleation, growth, and coalescence under monotonic and cyclic loadings. Moreover, the joint effects of stress triaxiality and strain range can be explicitly described within a unified framework, without additional empirical assumptions. Based on experimental and simulation results, the proposed GCVGM shows significant advantages over conventional models in accurately predicting ULCF failure under various loading protocols, with low average errors of 2.73 % and −0.91 % for two types of notched bar specimens, respectively.

Efficient Jamming Policy Generation Method Based on Multi-Timescale Ensemble Q-Learning

With the advancement of radar technology toward multifunctionality and cognitive capabilities, traditional radar countermeasures are no longer sufficient to meet the demands of countering the advanced multifunctional radar (MFR) systems. Rapid and accurate generation of the optimal jamming strategy is one of the key technologies for efficiently completing radar countermeasures. To enhance the efficiency and accuracy of jamming policy generation, an efficient jamming policy generation method based on multi-timescale ensemble Q-learning (MTEQL) is proposed in this paper. First, the task of generating jamming strategies is framed as a Markov decision process (MDP) by constructing a countermeasure scenario between the jammer and radar, while analyzing the principle radar operation mode transitions. Then, multiple structure-dependent Markov environments are created based on the real-world adversarial interactions between jammers and radars. Q-learning algorithms are executed concurrently in these environments, and their results are merged through an adaptive weighting mechanism that utilizes the Jensen–Shannon divergence (JSD). Ultimately, a low-complexity and near-optimal jamming policy is derived. Simulation results indicate that the proposed method has superior jamming policy generation performance compared with the Q-learning algorithm, in terms of the short jamming decision-making time and low average strategy error rate.

Anomalous Network Behaviour Detection in Interoperable Health Systems using Machine Learning in Resource Limited Areas

The connection of devices in distributed environments produces and shares a vast amount of data useful for different organisational decision-making. In healthcare service organisations, for example, multiple e-health systems from different departments or facilities connect and share health data and information. During sharing, proper management is important to ensure the information is secure against intruders. Machine learning as a non-conventional security technique can be used along conventional techniques like firewalls, antivirus and intrusion detection systems to predict future network threats and other anomalies using historical backgrounds and other features. However, some machine learning algorithms have complex computation thus requiring resourceful systems in terms of network bandwidth, CPU power, memory, and storage capacity. In resource-constrained environments, therefore, special consideration is needed to ensure that the analysis of the big data is successful and that the benefits associated with them are effectively obtained. In this paper, a Machine Learning algorithm was selected among four algorithms whose performance was compared through various performance metrics. Classification accuracy, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Relative Absolute Error (RAE) among other performance metrics were used to compare the ANN, Random forest, Decision trees, and Naïve byes classification algorithms using an extract from CICDDOS2019 dataset. Using the Weka version 3.8.6, the algorithms were compared to choose the best one to classify the data. By using three computers with different resources, the experiments were carried out to determine the performance of those machine learning algorithms. The result revealed that the random forest produced a good average classification performance in resource-limited systems since it surpassed other algorithms in classifying the data at an average of 99 per cent with a low average mean absolute error of 0.0001. Furthermore, as an ensemble that classifies with multiple decision trees algorithm, it likewise uses reasonable time to build and test the model therefore recommended for resource-limited systems.

تحسين علاقة المرهون لضغط نقطة الفقاعة للنفط الخام الليبي

Accurate estimation of crude oil bubble point pressure plays a vital role in many petroleum engineering calculations, such as reserve estimation, material balance, reservoir simulation, production equipment design, and optimization of well performance. The Pb can be measured in the pressure-volume-temperature (PVT) experiments. Nonetheless, the PVT measurements have limitations, such as being costly and time-consuming. In this paper, the Al-Marhoun correlation for bubble point pressure for black oil reservoirs is improved using the linearization of non-linear regression techniques to fit Libyan crudes accurately. A total of 62 PVT data reports taken from various Libyan oil fields were used in the study. The PVT data consist of an oil gravity range of 24.7 ºAPI to 46.8 ºAPI and bubble point pressures of 123 psig to 6100 psig. Statistical error analyses and graphical methods were used to evaluate the original and modified Al-Marhoun correlations. The results showed that the improved Al-Marhoun correlation exhibits significantly lower average absolute error and deviation than the published ones. The correlation coefficient of R2 of the improved Al-Marhoun correlation is 96.70%, and the average percent error (APE) is 0.70% with a standard deviation (SD) of 14.70.

Ventricular segmentation algorithm for echocardiography based on transfer learning and GAN

With the swift progression of computer technology, utilizing deep learning for left ventricular image segmentation in echocardiography is of great significance for automated cardiac function assessment. In this study, a left ventricular segmentation algorithm for echocardiography based on transfer learning and generative adversarial networks (GAN) was proposed. A hierarchical fine-tuning strategy was employed to adaptively adjust network parameters, thereby improving segmentation accuracy. This strategy was based on combining multi-scale GAN and attention mechanisms to enhance segmentation performance, and incorporating the idea of transfer learning into the segmentation network. The algorithm demonstrated efficacy in heart image segmentation tasks, achieving high levels of classification accuracy and producing satisfactory segmentation outcomes. In terms of image segmentation of dual and four chamber images, this method achieved high Dice similarity coefficients and average intersection values. Furthermore, this method demonstrated excellent performance in image segmentation experiments on clinical data, with low average absolute errors in the calculated end diastolic volume, end systolic volume, and ejection fraction. The experimental results demonstrate that the algorithm exhibits satisfactory performance in left ventricular segmentation in echocardiography and is suitable for use in medical clinical data. The study presents a novel approach to ventricular segmentation in echocardiography, which enhances the precision and efficiency of automated cardiac function assessment and has significant clinical applicability.