Narrow Results

To meet the purpose of a multi-objective solution, a scheme is introduced into the optimal strategy-intensive training process. It supports a multi-task-driven computing model and has good reasoning accuracy, which makes the hydropower management system have efficient collaborative strategy prediction ability. Aiming at the trade-off problem between the cost-benefit and energy load of the hydropower control system, based on the neural network model to improve the stacked sparse denoising auto encoder second-order cone programming (SSDAE-SOCP) algorithm, a low-level backtracking depth uncertainty optimization scheduling model is proposed to obtain the optimal scheduling strategy. It intends to improve the poor accuracy of the global optimal results caused by the lack of priority sampling in the traditional strategy. The feature space without boundary conditions is set to solve the optimal solution for global/local organizations. Further, the flexibility is transferred according to the single-core function of task decomposition for improving the calculation accuracy of the whole solution data and the dynamic double-mutation balance. Simulation results have shown that the proposed algorithm enhances the training quality and performance by 9.5% and 15.2% compared with the traditional MOPSO and MOIMPA algorithms. Gradient descent and convergence speed, as well as the stability and security of intensive training, are better than the comparison methods. The training features verify the stability of the balanced prediction model, showing its importance in enhancing the anti-noise ability of the matrix. The research results can further enhance the multiple dispatching and command capabilities of the hydropower projects, providing technical support for the sustainable development of maximizing the benefits for the systems.

In order to achieve accurate and effective prediction of corporate financial risks, a financial risk prediction model based on news text and LS-SVM is proposed. The paper clarifies the targeted data standards for financial risk data collection by analyzing the financial risk prediction index system; combining news texts and using web crawling technology to extract unstructured enterprise financial risk data from financial websites; extracting news text topic features based on potential Dirichlet allocation, and achieving comprehensive financial risk feature fusion based on news text; based on the least squares support vector machine, a financial risk prediction model is constructed by taking the comprehensive financial risk features of the fused news text as input. The experimental results show that the highest accuracy of risk prediction generated by the design model is $98.2$%, the average accuracy of full sample prediction is $96.4$%, the minimum prediction time is $0.13$s, and the time efficiency reaches the highest value of $96$%. This indicates that the use of the design model can effectively capture the relationship between financial indicators and financial risks, ensure the accuracy of enterprise financial risk prediction, and have good scalability in large-scale data scenarios, with a relatively short overall risk prediction time.

Solar energy can be considered as an alternate solution to conventional energy sources. Short-term Photovoltaic (PV) Power Generation (PVPG) prediction methods are essential stabilize power integration among PV and smart grids. The PVPG generation process is highly dependent on climatic conditions and therefore high intermittent. Highly accurate PVPG prediction of PVPG acts based on the generation, transmission and dispersion of electricity, confirming the stability and dependability of power system. The current progress of Machine Learning (ML) and Deep Learning (DL) approaches enables for designing of accurate PVPG prediction models. In this view, this paper develops a new Badger Optimization with Deep Learning Enabled PV Power Generation Predictive (HBODL-PVPGP) model. The presented HBODL-PVPGP model enables to forecast of the PVPG process. To accomplish this, the HBODL-PVPGP model initially investigates the features depending upon intrinsic characteristics earlier in the learning process. In addition, the Bidirectional Gated Recurrent Unit (BiGRU) model is implemented for forecasting process. The performance of the BiGRU model can be improvised by the design of HBO-based hyperparameter tuning procedure. For ensuring the enhanced performance of the HBODL-PVPGP model, an extensive range of experimental study was effectuated and the results were investigated under various factors. The result highlighted the precipitated performance of the HBODL-PVPGP procedure on the current algorithms.

Somatosensory evoked potential (SEP) has been commonly used as intraoperative monitoring to detect the presence of neurological deficits during scoliosis surgery. However, SEP usually presents an enormous variation in response to patient-specific factors such as physiological parameters leading to the false warning. This study proposes a prediction model to quantify SEP amplitude variation due to noninjury-related physiological changes of the patient undergoing scoliosis surgery. Based on a hybrid network of attention-based long-short-term memory (LSTM) and convolutional neural networks (CNNs), we develop a deep learning-based framework for predicting the SEP value in response to variation of physiological variables. The training and selection of model parameters were based on a 5-fold cross-validation scheme using mean square error (MSE) as evaluation metrics. The proposed model obtained MSE of 0.027$\mu {\mathrm{\text{V}}}^2$ on left cortical SEP, MSE of 0.024$\mu {\mathrm{\text{V}}}^2$ on left subcortical SEP, MSE of 0.031$\mu {\mathrm{\text{V}}}^2$ on right cortical SEP, and MSE of 0.025$\mu {\mathrm{\text{V}}}^2$ on right subcortical SEP based on the test set. The proposed model could quantify the affection from physiological parameters to the SEP amplitude in response to normal variation of physiology during scoliosis surgery. The prediction of SEP amplitude provides a potential varying reference for intraoperative SEP monitoring.

Nonlinear spiking neural P (NSNP) systems are one of neural-like membrane computing models, abstracted by nonlinear spiking mechanisms of biological neurons. NSNP systems have a nonlinear structure and can show rich nonlinear dynamics. In this paper, we introduce a variant of NSNP systems, called gated nonlinear spiking neural P systems or GNSNP systems. Based on GNSNP systems, a recurrent-like model is investigated, called GNSNP model. Moreover, exchange rate forecasting tasks are used as the application background to verify its ability. For the purpose, we develop a prediction model based on GNSNP model, called ERF-GNSNP model. In ERF-GNSNP model, the GNSNP model is followed by a “dense” layer, which is used to capture the correlation between different sub-series in multivariate time series. To evaluate the prediction performance, nine groups of exchange rate data sets are utilized to compare the proposed ERF-GNSNP model with 25 baseline prediction models. The comparison results demonstrate the effectiveness of the proposed ERF-GNSNP model for exchange rate forecasting tasks.

Ti-6Al-4V alloy is widely used for the deep-sea manned submersible. In addition to the normal cyclic loading, the manned cabin will experience a period of dwell time in each cycle during their service life. In this research, the fatigue and dwell-fatigue crack growth behavior of Ti-6Al-4V alloy under different dwell time were studied experimentally. The mechanism of dwell-fatigue crack growth was investigated. The acceleration phenomenon of the dwell-fatigue crack growth can be directly observed in the experiment. The relationship between the crack length and the dwell time was captured under different $\mathrm{\Delta }K$ within one cycle. The results presented that there is a saturation time for the dwell-fatigue crack growth. A prediction model is proposed to predict the dwell-fatigue crack growth behavior considering the effects of dwell time.

The field of ionic liquid has gained rapid growth in recent years and occupied a forefront in green chemistry. As a useful instrument to the research and development of novel ionic liquids, the physical properties are of utmost importance. Thus, great efforts have been made to obtain these important data, as it is far from getting sufficient physiochemical information for intensive and extensive investigation which consequently becomes a bottleneck for the theoretical and applicable research of ionic liquids. Additionally, with the given immeasurable possible ionic liquids by various cation and anion combinations, it is an impossible task to find an ideal ionic liquid with desired physical properties using conventionally "try and error" process. For these reasons, exploration of novel prediction models for physical properties of ionic liquid is imperative. This paper gives an overview of recent progress of various prediction models.

Traffic prediction is a classical time series prediction which has been investigated in different domains, but most existing models are proposed based on limited time or spatial scale. Mobile cellular network traffic prediction is of paramount importance for quality-of-service (QoS) and power management of the cellular base stations, especially in the 5G era. Through the statistical analysis of the real historical traffic data obtained in a city scale spanning across multiple months, this paper makes an in-depth study of the temporal characteristics and behavior rules of the model data traffic. Considering that the time series data show different changing rules under the different time dimensions, spatial dimensions and independent dimensions, a multi-dimensional recurrent neural network (MDRNN) prediction model is established to predict the future cell traffic volume over various temporal and spatial dimensions. The data of this paper are trained and tested over real data of a city, and the granularity of the proposed prediction model can be drilled down to the cell level. Compared with the traditional trend fitting method, the proposed model achieves mean absolute percentage error (MAPE) reduction of 6.56%, and provides guidance for energy efficiency optimization and power consumption reduction of base stations in various temporal and spatial dimensions.

Since the internal heat transfer is a complicated process, the heat pipe heat exchanger of the engine has not been fully understood yet, which is originated from its extreme complexity. In theoretical studies, the involvement of two-phase flow and phase change processes usually simplifies the processing very much, and the model built differs too much from the actual one, resulting in reduced simulation accuracy. In this study, the prediction model of heat transfer and heat resistance of the heat pipe intercooler is established based on artificial neural networks (ANNs). Then the performance of the heat pipe intercooler from heat transfer and heat resistance aspects is investigated. The average relative error between the heat transfer prediction model and the test value is 3.6%, and the average relative error between the resistance prediction model and the test value is 12.68%, which shows that the prediction model can predict the thermal performance of heat pipe intercooler more accurately. Finally, the proposed model is applied to optimize the structural parameters of the heat pipe intercooler, and the optimal parameters are obtained accordingly. These optimal design parameters can provide the basis for further investigation and development of the heat pipe intercooler in diverse applications.

The distribution transformer voltage may be overloaded, which may lead to the aging of distribution transformer components, shorten the service life of distribution transformer components and even affect the daily life of community residents and the operation of enterprises. A large amount of real data are collected, and the factors that affect the heavy overload of distribution transformer are comprehensively considered from multiple angles, so as to establish a model for future prediction and early maintenance to reduce losses. First, the collected data is analyzed by attributes and preprocessed to improve the quality of the data. Then, the time attributes are generalized according to seasons, months, holidays and weekends. The test results show that the data prediction value is more accurate when generalized according to seasons. For the prediction model, the gradient lifting decision tree algorithm is selected to establish the model, and then the parameters are further optimized, and finally the model is evaluated. Lastly, the prediction accuracy of the model reaches a high level, and it can be determined that the prediction is close to the objective fact. The model can be used to predict the heavy overload of distribution transformer voltage, so as to reduce the loss caused by abnormal conditions of relevant equipment for the enterprises.

Object-oriented software (OOS) is dominating the software development world today and thus, has to be of high quality and maintainable. However, their recent size and complexity affects the delivering of software products with high quality as well as their maintenance. In the perspective of software maintenance, software change impact analysis (SCIA) is used to avoid performing change in the “dark”. Unfortunately, OOS classes are not without faults and the existing SCIA techniques only predict impact set. The intuition is that, if a class is faulty and change is implemented on it, it will increase the risk of software failure. To balance these, maintenance should incorporate both impact and fault-proneness (FP) predictions. Therefore, this paper propose an extended approach of SCIA that incorporates both activities. The goal is to provide important information that can be used to focus verification and validation efforts on the high risk classes that would probably cause severe failures when changes are made. This will in turn increase maintenance, testing efficiency and preserve software quality. This study constructed a prediction model using software metrics and faults data from NASA data set in the public domain. The results obtained were analyzed and presented. Additionally, a tool called Class Change Recommender (CCRecommender) was developed to assist software engineers compute the risks associated with making change to any OOS class in the impact set.

This study provides an analytical model to predict the fixing pattern of issues in the open-source software (OSS) packages to assist developers in software development and maintenance. Moreover, the continuous evolution of software due to bugs removal, new features addition or existing features modification results in the source code complexity. The proposed model quantifies the complexity in the source code using the Shannon entropy measure. In addition, the issues fixing growth behavior is viewed as a function of continuation time of the software in the field environment and amount of uncertainty or complexity present in the source code. Therefore, a two-dimensional function called Cobb–Douglas production function is applied to model the intensity function of the issues fixing rate. Furthermore, the rate of fixing the different issue types is considered variable that may alter after certain time points. Thus, this study incorporates the concept of multiple change-points to predict and assess the fixing behavior of issues in the software system. The performance of the proposed model is validated by fitting the proposed model to the actual issues data of three open-source projects. Findings of the data analysis exhibit excellent prediction and estimation capability of the model.

Recent advancements in Artificial Intelligence techniques, including machine learning models, have led to the expansion of prevailing and practical prediction simulations for various fields. The quality of teachers’ performance mainly influences the quality of educational services in universities. One of the major challenges of higher education institutions is the increase of data and how to utilize them to enhance the academic program’s quality and administrative decisions. Hence, in this paper, Artificial Intelligence assisted Multi-Objective Decision-Making model (AI-MODM) has been proposed to predict the instructor’s performance in the higher education systems. The proposed AI-assisted prediction model analyzes the numerical values on various elements allocated for a cluster of teachers to evaluate an overall quality evaluation representing the individual instructor’s performance level. Instead of replacing teachers, AI technologies would increase and motivate them. These technologies would reduce the time necessary for routine tasks to enable the faculty to focus on teaching and analysis. The usage for administrative decision-making of artificial intelligence and associated digital tools. The experimental results show that the suggested AI-MODM method enhances the accuracy (93.4%), instructor performance analysis (96.7%), specificity analysis (92.5%), RMSE (28.1 %), and precision ratio (97.9%) compared to other existing methods.

Traffic noise in urban areas is increasing day by day, owing to addition in the number of vehicles on road in developing and developed countries. The increase of noise level reduces the wellbeing of the exposed people. Exposure to high noise results in development of ill health including annoyance, high blood pressure, headache and other physiological and psychological problems. Noise not only affects the human life but also affects animals and birds in the ecosystem. In this experimental study, the evaluation of traffic noise in Berhampur city has been done. Eleven important locations covering the whole city were considered for traffic noise evaluation. Other than the evaluation of traffic noise, the well-known traffic noise prediction models have been tested here. It has been found that, in this situation, such established models do not function well and have a low coefficient of correlation value. A new befitting model has been formulated using multiple regression analysis to predict the traffic noise level and later on, it is also tested as well as validated at different locations.

A nonlinear fuzzy linguistic prediction (NFLP) model for acute hyperglycemia prediction is proposed in this paper. The model used IF–THEN expressions which are human-readable and easy to understand. Using cardiac electrophysiological signals as the input, the model can predict actuation durations and concentrations of acute hyperglycemia. The prediction results are compared with the ones of four classical models which are partial least squares (PLS), least-square support vector machine (LSSVM), back-propagation neural network (BPNN) and Takagi–Sugeno (T–S) model. The results show that the proposed method has high prediction accuracy. The method can provide support for clinical diagnosis of acute hyperglycemia.

This study used knee MR imaging features to quantify the severity of knee injury in patients and analyzed the predictive value of knee MR imaging features in their risk of knee replacement. A total of 120 patients with knee arthritis were included from a public knee arthritis database FNIH OAI. First, univariate logistic regression was used to screen clinical features to obtain the clinical risk factors. Then the minimum redundancy maximum correlation (mRMR) method was used to reduce the image features, and the LASSO method was used to further screen the retained image features to construct a Rad model. Next, the multivariate logistic regression method was used to combine the Rad model and the screened clinical risk factors to construct a combined model and its corresponding nomogram. Finally, ROC curve and its related metrics, Hosmer–Lemeshow test and Delong test were used to evaluate and compare the accuracy and consistency of the model performance. Age and body mass index (BMI) were found as significant clinical risk factors for knee replacement. After using mRMR and LASSO methods, 147 image features were reduced to 30 and 7 features, respectively, then these 7 features were linearly combined to construct the Rad model. Age, BMI, and the Rad model were combined to establish the combined model and its corresponding nomogram. The resulted two models showed high accuracy ($\mathrm{\text{AUC}}\sim 0.8$) and consistency (H-L test: $p>0.05$) on both the training set and test set. Finally, the comparison results showed that the prediction performance of the combined model was better than that of the Rad model, but their difference was not significant (Delong test: $p>0.05$). This paper studied the predictive performance of MR imaging features on the risk of knee replacement and built a model that can be used to predict the potential risk of knee replacement in patients with knee arthritis. The resulting models showed good predictive accuracy and consistency. The drawn nomogram could be used as a useful tool to personalize the prognosis of patients with knee arthritis and guide clinical decisions on knee replacement.

Background and objective: Heart failure (HF) is a lethal public health problem in the field of cardiovascular diseases with high incidence, rehospitalization, and mortality rates. Therefore, the prediction of in-hospital mortality of patients with HF is of paramount significance in providing clinical information to doctors. To improve the accuracy of prediction, this study constructed a prediction model for in-hospital mortality of patients with HF based on machine learning algorithms. Methods: We obtained the medical data of 1901 patients with HF from a public database and performed preprocessing to extract 19 variables as inputs of the prediction model. A prediction model was constructed based on the light gradient boosting machine (LightGBM) and its performance was improved using the Optuna framework to optimize the LightGBM hyperparameters. To evaluate the proposed algorithm, five machine learning algorithms widely used in the field of biomedicine were selected for comparison: support vector machine, classification and regression tree, random forests, gradient boosting decision tree, and LightGBM. Further, we explained the proposed model based on Deep SHapley Additive exPlanations. We also quantified the importance of each variable and analyzed its correlation with the results. Results: The accuracy rate of Optuna–LightGBM was $92\pm 1.44\%$, the precision rate was $83.71\pm 8.34\%$, the recall rate was $69.46\pm 11.18\%$, the $F$-measure was $74.81\pm 6.55\%$, and the area under the receiver operating characteristic curve was $83.14\pm 5\%$. The results show that this model outperformed other models on all evaluation indicators. Conclusions: The proposed method can be used to construct a prediction model for in-hospital mortality of patients with HF. Optuna–LightGBM can assist clinicians to quickly classify the high-risk patients with HF so that the clinicians can provide timely care and optimize hospital resources.

Information systems (IS) and data analytics-focused academic disciplines remained surprisingly silent in attempting to contribute to a public understanding of critical societal challenges such as foreclosures. This paper tackles the gap by presenting a framework for building foreclosure prediction models by integrating publicly-available census-tract demographic data and readily-available technology (geographic IS (GIS) and machine learning (ML)). The framework is tested and validated using over 19,000 foreclosures from Cuyahoga County (OH) using J48 decision tree, artificial neural network, and Naive Bayes algorithms. The framework’s empirical test identifies nine critical demographic attributes to successfully predict foreclosures, confirming the findings of prior studies while offering several new, highly predictive variables that were missed by prior research. This research is a call to broader IS, CS, and data science communities to assist society in understanding critical societal issues that may need deploying and integrating more advanced technologies.

Post-translational modifications (PTMs) occur in the vast majority of proteins, and they are essential for many protein functions. Computational prediction of the residue location of PTMs enhances the functional characterization of proteins. ADP-Ribosylation is an important type of PTM, because it is implicated in apoptosis, DNA repair, regulation of cell proliferation, and protein synthesis. However, mass spectrometric approaches have difficulties in identifying a vast number of protein ADP-Ribosylation sites. Therefore, a computational method for predicting ADP-Ribosylation sites of human proteins seems useful and necessary. Four types of sequence features and an incremental feature selection technique are utilized to predict protein ADP-Ribosylation sites. The final feature set for ADPR prediction modeling is optimized, based on a minimum redundancy maximum relevance criterion, so as to make more accurate predictions on aspartic acid ADPR modified residues. Our prediction model, ADPRtool, is capable to predict Asp-ADP-Ribosylation sites with a total accuracy of 85.45%, which is as good as most computational PTM site predictors. By using a sequence-based computational method, a new ADP-Ribosylation site prediction model — ADPRtool, is developed, and it has shown great accuracies with total accuracy, Matthew's correlation coefficient and area under receiver operating characteristic curve.

Traditional Chinese medicine (TCM) is characterized by synergistic therapeutic effect involving multiple compounds and targets, which provide potential new therapy for the treatment of complex cancer conditions. However, the main contributors and the underlying mechanisms of synergistic TCM cancer therapies remain largely undetermined. Machine learning now provides a new approach to determine synergistic compound combinations from complex components of TCM. In this study, a prediction model based on extreme gradient boosting (XGBoost) algorithm was constructed by integrating gene expression data of different cancer cell lines, targets information of natural compounds and drug response data. Radix Paeoniae Rubra (RPR) was selected as a model herbal sample to evaluate the reliability of the constructed model. The optimal XGBoost prediction model achieved a good performance with Mean Square Error (MSE) of 0.66, Mean Absolute Error (MAE) of 0.61, and the Root Mean Squared Error (RMSE) of 0.81 on test dataset. The superior synergistic anti-tumor combinations of D15 (Paeonol$+$Ethyl gallate) and D13 (Paeoniflorin$+$Paeonol) were successfully predicted from RPR and experimentally validated on MCF-7 cells. Moreover, the combination of D13 could work as a main contributor to a synergistic anti-proliferative activity in the compatibility of RPR and Cortex Moutan (CM). Our XGBoost model could be a reliable tool for the efficient prediction of synergistic anti-tumor multi-compound combinations from TCM.