Validating an Access Cost Model for Wide Area Applications

The influence of vegetation cover on the spectroscopic estimation of soil properties

Offline constraint-based causal feature selection (OC-CFS) algorithms are essential for identifying causal relationships from observational data. However, existing methods often suffer from limitations such as low prediction accuracy or high computational cost, particularly when sample sizes vary. To address these limitations, we propose Triplet, a novel framework that leverages the HITON-MB Parents and Children (PC) strategy to identify strongly relevant PC nodes while eliminating irrelevant and redundant features. It concurrently employs the BAMB strategy to detect relevant spouses and discard irrelevant ones, and applies the STMB non-Markov Blanket (non-MB) strategy to identify and exclude non-MB descendants. Through this integration, the proposed T-OCD[Formula: see text] overcomes these limitations, accurately identifying the true MB with high prediction accuracy and reduced runtime. To validate its effectiveness, we evaluated T-OCD[Formula: see text] on benchmark Bayesian networks (BNs) and real-world datasets. Extensive experimental results demonstrate that T-OCD[Formula: see text] achieves significant improvements in both prediction accuracy and computational efficiency compared to existing methods. On small sample sizes (n=500), T-OCD[Formula: see text] achieved the highest recall in 5 out of 7 datasets, with an average improvement of over 20% compared to rivals. On large sample sizes (n=5000), it excelled in precision, achieving the top score in 4 out of 7 datasets with an average precision of 94%. Computationally, T-OCD[Formula: see text] is highly efficient, operating as the second-fastest method overall. It ran over 55% faster than half of the benchmarks and a remarkable 35% faster than the average competitor on large datasets. The source code for this research is available at the following repository: https://github.com/vickykhan89/T-OCDmb.

A novel MP-LSTM method for ship trajectory prediction based on AIS data

The current prediction model for the system efficiency of pumping units primarily relies on a mechanistic approach. However, this approach incorporates numerous unnecessary factors, thereby, increasing the cost associated with predictions. With the improvement of the oil field database, the available information is increasing. Some scholars propose a prediction model based on a single neural network, however, such models face challenges in effectively capturing complex data, resulting in lower prediction accuracy and limited resistance to interference. To solve the above problems, the study proposes a novel stacking integrated learning prediction model, which incorporates fivefold cross‐validation. First, the magnitude of the correlation coefficient was quantified using the Pearson correlation coefficient. Second, the impact and predictive features were normalized. Final, convolutional neural network (CNN), recurrent neural network (RNN), Long Short‐Term Memory network (LSTM), gated recurrent unit (GRU), and transformer are used as the base models, and fully connected neural network (FNN) is used as the metamodel. Each base model was trained by fivefold cross‐validation, and the predicted values of each fold were stacked by rows. Next, the predicted values of each base model are stacked by columns as input variables to the metamodel and metamodel learning is performed, and the stacking integrated learning prediction model based on fivefold crossover validation is established. To validate the accuracy of the model, we selected 5,000 actual well parameters, including 26 impact features and one predictive feature, for comparative analysis. This analysis presents the maximum percentage reduction in mean square error (MSE), mean absolute error (MAE), and root‐mean‐square error (RMSE) of our proposed integrated learning model concerning a single neural network prediction model as 28.26%, 24.40%, and 15.66%, respectively. The maximum percentage improvement in R2 is 17.74%. It shows that our proposed integrated learning prediction model has high prediction accuracy.

In the non-medical model physiological parameter monitoring system, learning the monitoring parameters can improve the diagnostic and prediction accuracy. Aiming at the problems of insufficient information mining and low prediction accuracy in multi-task time series, the supervised and semi-supervised learning methods in machine learning are combined to predict the physiological status of remote health monitoring objects. This method uses the K-means algorithm to cluster the same type of data and use the Multitasking Least Squares Support Vector Machine (MTLS-SVM) to train historical data for trend prediction. In order to evaluate the effectiveness of the method, the MTLS-SVM method is compared with the K-means and MTLS-SVM methods. It can be seen from the experimental results that the body temperature data measured by the GY-MCU90615 is close to that of the digital thermometer. Moreover, the body temperature speed collected by the GY-MCU90615 can reach the millisecond level, which can well meet the needs of the system. The research shows that the method has higher prediction accuracy and has a breakthrough significance for the monitoring of athletes’ physiological parameters.

In order to solve the problems of high battery capacity detection error and low life prediction accuracy existing in traditional lithium-ion battery cycle life prediction methods, based on the battery capacity detection results, the cycle life prediction of lithium-ion batteries for new energy electric vehicles was carried out. Firstly, the principle of charge and discharge of lithium-ion battery is analyzed. Based on this, the idea of differential equation is introduced to detect the capacity of lithium-ion battery in real time. Secondly, according to the battery capacity test results, the exponential function of lithium-ion battery cycle life decline is constructed, and the calculation result of life influence factor is obtained. Finally, the lithium-ion battery cycle life prediction model is constructed, and the final prediction results are obtained. The experimental results show that the proposed method can always keep a low battery capacity detection error in multiple charging and discharging cycles, and the battery cycle life prediction accuracy can reach 97%.

Large amount of hydrological data set is a kind of big data, which has much hidden and potentially useful knowledge. Hydrological prediction is important for the state flood control and drought relief. How to forecast accurately and timely with hydrological big data becomes a big challenge. There are some forecasting techniques used widely. However, they are limited by their adaptability, the data volume and the data feature. The most important problems are the high time consumption, low accuracy and bad adaptability of prediction. In this paper, a new forecasting approach based on an integration of two tasks of data mining is put forward. This approach which is called S LMDBP combines similarity search and Levenberg-Marquardt(LM) algorithm improved Double-hidden layer Back Propagation(BP) neural network. A specialized data pretreatment including three parts is applied to process the hydrological data. The results of similarity search are then input into the improved BP neural network, which not only reduces dimensionality of training data without losing important patterns, but also improves the accuracy of the prediction. A set of experiments are conducted to validate the proposed approach. The data in the experiment is the daily water level data of three stations in Jiangxi province of China from 1950 to 2010. The experimental results demonstrate the real-timing, accuracy and robustness of our approach.

Everyday information systems collect a different kind of process instances of a business flow. As time goes on, the size of the collected data builds up speedily and constitutes a huge amount of data. It is a very challenging task to obtain valuable information and features of processes from such big data. Considering in advance, the trend and different features of the ongoing process are essential. Especially, time management is crucial in designing and conducting business processes. In this article, a novel process miner algorithm is proposed for time prediction, named time-oriented İnteractive process miner (T-IPM), which predicts the remaining and completion time of each process in a business workflow. The goal is to develop a process miner algorithm for time prediction that is able to work on a huge amount of event logs, and to incorporate the execution records of ongoing processes into the discovered process model instantly. In this study, three datasets from different domains that include event logs of repair, hospital, and traffic process flows were used to carry out the experimental studies in order to show the effectiveness of the proposed time prediction algorithm. The experimental studies show that the proposed algorithm is capable of performing process analysis on a huge amount of event logs with low memory consumption and high prediction accuracy.

This study investigates various machine learning models, namely multi-layer perceptron (MLP) and generalized regression neural network (GRNN), for predicting the mechanical properties of high compressive strength geopolymer mortars. Both classification (MLPC and GRNNC) and regression (MLPR and GRNNC) based models, with MLP architectures comprising 1 and 2 hidden layers, are developed. Furthermore, three optimization algorithms, namely Levenberg–Marquardt (LM), momentum (M), and resilient backpropagation (R), are utilized. The models’ inputs are alkali concentrations, heat-curing temperatures, and curing periods. The results showed that the classification-based MLP with one hidden layer and resilient optimizer (MLPC-1-R) outperformed the other models by recording lower prediction deviations and high prediction accuracy. On the other hand, the regression-based models showed promising results and less sensitivity to the optimization type, unlike the classification-based ones. Finally, the resilient backpropagation (R) optimizer tends to provide consistent and high performance for both classification and regression-based models.

In order to overcome the problems of low accuracy of current system tourism route prediction and poor accuracy of route recommendation, this paper proposes a tourism route recommendation system design based on fast Spark artificial intelligence architecture. In the hardware part, the search process structure of tourist route, web page information classification and extraction module and tourist document classification process structure are designed. For the software part, firstly the problem is defined for the tourist route recommendation, secondly the Markov model is constructed, and finally the tourist route is mined. By ranking the transfer probability of tourist attractions, the tourist route can be obtained, and finally the tourist route recommendation is realised. The experimental results show that the designed system has a prediction accuracy of more than 70% and a recommendation accuracy of more than 80%, with high prediction accuracy and route recommendation accuracy.

The purpose of this study was to clarify whether the current scoring systems and single serum markers used in pancreatitis remain applicable for the early prediction of infected pancreatic necrosis (IPN) and the severity and mortality of acute pancreatitis (AP) in accordance with the revised Atlanta and determinant-based classifications. Demographic, clinical, and laboratory data from 708 consecutive patients with AP were prospectively collected between January 2011 and December 2012. The severity was classified using the revised Atlanta and determinant-based classification systems. The predictive accuracies for moderately severe AP (MSAP), severe AP (SAP), critically severe AP (CAP), IPN, and mortality were measured using area under the receiver operating characteristic curves. The receiver operating characteristic analysis showed that the multifactor scoring systems and single serum markers had a low predictive accuracy regarding moderately severe AP. The Acute Physiology and Chronic Health Evaluation (APACHE) II score had the highest accuracy in predicting SAP with area under the curve (AUC) values of 0.75 (95% CI=0.71-0.79) and 0.77 (95% CI=0.73-0.81) at 24 and 48h after admission, respectively. Procalcitonin was the most accurate predictor for CAP and IPN, with respective AUCs of 0.86 (95% CI=0.82-0.89) and 0.83 (95% CI=0.78-0.87) at 48h after admission. In predicting mortality, both the APACHE II score and blood urea nitrogen had the highest accuracy. The APACHE II score had the highest predictive accuracy for SAP and mortality as defined by the revised Atlanta classification, whereas procalcitonin was the most accurate predictor for CAP and IPN.

Accuracy of Intraocular Lens Calculation Formulas in Patients Undergoing Combined Phakic Intraocular Lens Removal and Cataract Surgery

The end-point carbon content and temperature in the steelmaking process of AOD furnace are important factors affecting product quality, and the traditional algorithms have the problems of slow convergence, being easy to fall into the local optimal solution and lack a unified parameter selection criterion, which leads to the problems of slow convergence and low prediction accuracy. To solve the above problems, the research group adopts the improved arithmetic optimization algorithm and deep stochastic configuration networks (LAOA-DeepSCNs) to predict the end-point carbon content and temperature of the AOD furnace. First, correlation analysis was performed with SPSS to identify the seven factors as model inputs. Second, to verify the prediction effect of the model, the algorithm is compared with three typical algorithms: BP, RBF and SCN. The results show that LAOA-DeepSCNs have the fastest convergence speed, the highest prediction accuracy, and the strongest generalization ability. Finally, the model was applied to the actual production of a steel mill, and the results showed that the hit rate is 90.8%, 86.4%; and 92.6%, 88.1% for refining end-point carbon content and end-point temperature error within ±0.015%, ±0.01%; and ±10 °C, ±5 °C, respectively. Which can well meet the practical needs of a steel mill. It also provides theoretical guidance for the control of carbon content and temperature at the end-point of the AOD furnace.

Software defect prediction could improve the reliability of software and reduce development costs. Traditional prediction models usually have a lower prediction accuracy. In order to solve this problem, a new model for software defect prediction using Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) named P-SVM model is proposed in this paper, which takes advantage of non-linear computing capability of SVM and parameters optimization capability of PSO. Firstly, P-SVM model uses PSO algorithm to calculate the best parameters of SVM, and then it adopts the optimized SVM model to predict software defect. P-SVM model and other three different prediction models are used to predict the software defects in JM1 data set as an experiment, the results show that P-SVM model has a higher prediction accuracy than BP Neural Network model, SVM model, GA-SVM model.

In the context of global warming and the dramatic increase in greenhouse gas emissions, the power industry is the largest source of carbon emissions. Adjusting and optimizing the carbon dioxide emissions from the power industry will help China achieve its “dual carbon” goals and is of great significance for mitigating global carbon dioxide emissions. This paper takes six power plants in Jilin Province as the research objects, and firstly accounts for the carbon emission production data between January 2020 and December 2023 according to the "Accounting Methods and Reporting Guidelines for Greenhouse Gas Emissions from Enterprises - Power Generation Facilities". Then, LSTM was used to establish carbon emission prediction models for six different power plants in Jilin Province, and the analysis of each model showed that that the single-step prediction RMSEs are all less than one, with higher prediction accuracy, but only can used in short-term prediction, the multi-step prediction RMSEs are bigger than one, with lower prediction accuracy, but can used in long-term carbon emission trend prediction can be achieved. The carbon emission trend prediction of six power plants in Jilin province between January 2024 and August 2031 confirms that the carbon emissions of power plants will be affected by seasons and shown cyclical changes. Finally, reasonable policy recommendations are provided for the successful realisation of the "double carbon" target for electricity in Jilin Province.