Classical Prediction Research Articles

Analysing Factors Influencing Women Unemployment Using a Random Forest Model

The unemployment crisis has been a persistent issue for both developed and developing countries, resulting in an economic indicator deficit. Women are at a disadvantage and continue to encounter significant obstacles to gaining employment. Nigeria, like many other developing countries with high unemployment rates, has a 33% unemployment rate. Consequently, there has been minimal research on the factors that affect women's unemployment. As a result, the purpose of this study investigates the factors that influence women's unemployment in Nigeria. Although the Random Forest model has been widely applied to classification issues, there is a gap in the literature's use of the random forest as a predictor for analyzing factors influencing women's unemployment. The random forest model was employed in this study because of its characteristics such as strong learning ability, robustness, and feasibility of the hypothesis space. As a result, the Random forest prediction model was benchmarked with seven different cutting-edge classical machine learning prediction models, which include the J48 pruned tree, Support Vector Machine, AdaBoost, Logistic Regression, Naive Bayes, Logistic Model Tree, Bagging, and Random Forest. The experimental results demonstrate that Random Forest outperformed the other seven machine learning classifier models using ten commonly used performance evaluation metrics. According to the study's findings, age groups, ethnicity, marital status, and religion were the essential factors affecting women's unemployment in Nigeria. Keywords: machine learning, National Demographic and Health Survey, random forest, influencing factors, women unemployment. DOI: https://doi.org/10.55463/hkjss.issn.1021-3619.60.38

Genome-Wide RNA Secondary Structure Prediction.

The information of RNA secondary structure has been widely applied to the inference of RNA function. However, a classical prediction method is not feasible to long RNAs such as mRNA due to the problems of computational time and numerical errors. To overcome those problems, sliding window methods have been applied while their results are not directly comparable to global RNA structure prediction. In this chapter, we introduce ParasoR, a method designed for parallel computation of genome-wide RNA secondary structures. To enable genome-wide prediction,ParasoR distributes dynamic programming (DP) matrices required for structure prediction to multiple computational nodes. Using the database of not the original DP variable but the ratio of variables, ParasoR can locally compute the structure scores such as stem probability or accessibility on demand. A comprehensive analysis of local secondary structures by ParasoR is expected to be a promising way to detect the statistical constraints on long RNAs.

An effective veracity management for sentiment analysis in social media using crowdsourcing and multi‐set feature learning

An effective veracity management for sentiment analysis in social media using crowdsourcing and multi‐set feature learning

Daily resting-state intracranial EEG connectivity for seizure risk forecasts.

Forecasting seizure risk aims to detect proictal states in which seizures would be more likely to occur. Classical seizure prediction models are trained over long-term electroencephalographic (EEG) recordings to detect specific preictal changes for each seizure, independently of those induced by shifts in states of vigilance. A daily single measure-during a vigilance-controlled period-to estimate the risk of upcoming seizure(s) would be more convenient. Here, we evaluated whether intracranial EEG connectivity (phase-locking value), estimated from daily vigilance-controlled resting-state recordings, could allow distinguishing interictal (no seizure) from preictal (seizure within the next 24 h) states. We also assessed its relevance for daily forecasts of seizure risk using machine learning models. Connectivity in the theta band was found to provide the best prediction performances (area under the curve ≥.7 in 80% of patients), with accurate daily and prospective probabilistic forecasts (mean Brier score and Brier skill score of .13 and .72, respectively). More efficient ambulatory clinical application could be considered using mobile EEG or chronic implanted devices.

Predicting the future with humans and AI

AbstractWe review the classic clinical versus statistical prediction debate as well as related modern work on humans versus. algorithms. Despite the successes of statistical prediction over clinical prediction, there is still widespread resistance to algorithms. We discuss recent attempts to understand that resistance. Current research focuses on when people use algorithmic predictions, how people perceive algorithms, and how algorithms can be made more appealing. We also examine attempts to boost human forecasting accuracy, either by spotting talent, cultivating talent via training, or developing algorithms that aggregate individual forecasts. We hypothesize that hybrid models with both human and algorithmic predictions may encounter less resistance than algorithms alone, especially when the algorithm is “humanized” (with anthropomorphic features) and the human is “algorithmized” (by reducing noise, decreasing bias and increasing signal).

Multi-scale convolutional networks for traffic forecasting with spatial-temporal attention

• Capturing complex spatial-temporal correlations for both short and long term traffic prediction. • Applying soft thresholding fusion to eliminate unimportant spatial-temporal features. • Designing a trainable module to automatically determine the spatial and temporal thresholds. • Constructing multiple convolutions networks and forming groups of units to extract multi-scale features. Traffic forecasting is a classical spatial-temporal data prediction problem with extensive applications, such as dynamic traffic management, route planning and traffic congestion alleviation. However, accurate traffic forecasting is challenging due to complex correlations of traffic data. Few approaches are satisfied with both long-term and short-term prediction tasks. In this paper, we propose a novel Multi-Scale Convolutional Networks (MSCN), an end-to-end solution to solve traffic forecasting problem. MSCN first employs an encoder with spatial-temporal attention mechanism to model both spatial and temporal correlations. Then, the decoder utilizes multiple convolutions and form groups of units to to extract spatial-temporal features for different resolutions. In particular, we propose a soft thresholding fusion mechanism to adaptively adjust the conditions of spatial and temporal correlations. Experiments on two real traffic datasets demonstrate that the proposed MSCN obtains improvements over the state-of-the-art baselines.

Geographical spatial analysis and risk prediction based on machine learning for maritime traffic accidents: A case study of Fujian sea area

Safety analysis according to the spatial distribution characteristics of maritime traffic accidents is critical to maritime traffic safety management. An accident analysis framework based on the geographic information system (GIS) is proposed to characterize the spatial distribution of maritime traffic accidents occurring in the Fujian sea area in 2007–2020 by employing kernel density estimation and spatial autocorrelation techniques. The sea area is divided into various grids, and in each grid, the mapping relationships between the number and severity of the traffic accidents and the traffic characteristics are established. Machine learning (ML) technology is used to assess whether a grid area is an accident-prone area and to predict accident severity in each grid. The accident prediction of different ML models, including random forest (RF) model, Adaboost model, gradient boosting decision tree (GBDT) model, and Stacking combined model, were compared. The optimality of the Stacking combined model was verified by comparing the experimental results of this model with those of classical prediction models, convolutional neural network (CNN), long short term memory (LSTM), and support vector machine (SVM). According to the results, the maritime accident data set of the entire Fujian sea area shows typical clustering characteristics and positive spatial correlation. That is, the kernel density estimation indicates that subareas, including the Ningde sea area, Fuzhou sea area, and Xiamen sea area, generally have high densities of maritime accidents and the highest risk value within the whole Fujian sea area. High-high accident clustering, that is high cluster areas neighbored by other areas of high cluster, is mainly seen in the Ningde and Fuzhou sea areas, while the Xiamen, Putian, and Zhangzhou subareas show low-low clustering, which are low clusters neighbored by low clusters. Among the ML models, the Stacking combined model shows high accuracy, precision, recall, and F1-score values of 0.912, 0.910, 0.912, and 0.904 in predicting whether a grid area is an accident-prone area and 0.750, 0.745, 0.750, and 0.746 in predicting the accident severity in the grid, indicating its superior maritime traffic accident prediction performance. Based on our analysis of the distribution characteristics and geospatial data, our proposed method demonstrates effective and reliable risk prediction.

Experimental investigation on RP‐3 aviation kerosene large‐scale pool fires at high altitude

AbstractRP‐3 aviation kerosene is a standard fuel that is used in civil aircraft and is a substitute for Jet‐A fuel. This paper investigates the combustion behaviour of RP‐3 aviation kerosene in an open space in the low‐pressure environment of a plateau. A series of large‐scale pools with diameters ranging from 0.4 m to 2.82 m combustion experiments are performed. Typical combustion characteristic parameters, including combustion rate, flame height and axial plume temperature distribution is measured. Results show that a low‐pressure environment significantly affects pool fires. With combustion rate, there is a linear variation law between oil pool size and combustion rate. The experimental data are fitted as a means of obtaining the large‐scale combustion rate relationship equation at low pressure. Experimental flame height data is compared using classical prediction models. Prediction model coefficients at low pressure are determined. The axial plume temperature distribution is greater than in the literature findings due to flames becoming longer in a low‐pressure environment. The three regimes in McCaffery's model are redefined and correlate well with the vertical temperature profile of the pool centreline. The results of the experimental investigation will help research fire hazards in low‐pressure environments while also contributing to the provision of basic data for the development of firefighting operation strategies.

Analytical GNI Analysis for Lateral-torsional Behavior of Thin-walled Beams with Doubly-symmetric I-Sections

In this paper elastic lateral-torsional behavior of simple beams is discussed. The motivation of the presented research is the observation that classic analytical prediction and finite element prediction are, typically, considerably different, when the second-order nonlinear behavior of beams with initial imperfections is analyzed. In order to understand and explain the observed differences, a novel analytical solution is presented for the geometrically nonlinear analysis of beams with initial geometric imperfection. The presented analytical solution is derived for doubly-symmetric cross-sections, but with the novelty that it takes into consideration the changing geometry as the load is increasing. The most important steps of the derivations are summarized, and the resulted formulae are briefly discussed. Numerical studies are performed, too: the results of the new analytical formulae are compared to those from shell finite element analysis. The results suggest that the new formulae are able to capture the most important elements of the behavior. By the analytical and numerical results, it is proved that classic analytical solutions for the geometrically nonlinear analysis of beams with geometric imperfections are necessarily different from the numerical results obtained by incremental-iterative procedures.

A comparison of classical and machine learning-based phenotype prediction methods on simulated data and three plant species.

Genomic selection is an integral tool for breeders to accurately select plants directly from genotype data leading to faster and more resource-efficient breeding programs. Several prediction methods have been established in the last few years. These range from classical linear mixed models to complex non-linear machine learning approaches, such as Support Vector Regression, and modern deep learning-based architectures. Many of these methods have been extensively evaluated on different crop species with varying outcomes. In this work, our aim is to systematically compare 12 different phenotype prediction models, including basic genomic selection methods to more advanced deep learning-based techniques. More importantly, we assess the performance of these models on simulated phenotype data as well as on real-world data from Arabidopsis thaliana and two breeding datasets from soy and corn. The synthetic phenotypic data allow us to analyze all prediction models and especially the selected markers under controlled and predefined settings. We show that Bayes B and linear regression models with sparsity constraints perform best under different simulation settings with respect to explained variance. Further, we can confirm results from other studies that there is no superiority of more complex neural network-based architectures for phenotype prediction compared to well-established methods. However, on real-world data, for which several prediction models yield comparable results with slight advantages for Elastic Net, this picture is less clear, suggesting that there is a lot of room for future research.

A Radiomics-Integrated Deep Learning Model for Identifying Radionecrosis Following Brain Metastasis Stereotactic Radiosurgery (SRS)

<h3>Purpose/Objective(s)</h3> To develop a radiomics-integrated deep learning (RIDL) model for identifying radionecrosis in brain metastasis patients with post-SRS radiographic progression. Such a novel model is hypothesized to outperform classic radiomics and deep learning approaches in radionecrosis/recurrence differentiation. <h3>Materials/Methods</h3> The model was developed based on a 51-patient cohort at our institution with post-SRS radiographic progression and known biopsy outcome (37 radionecrosis, 14 recurrence) from Laser Interstitial Thermal Therapy (LITT). Utilizing the 3-month post-SRS high resolution T1+c volume, the RIDL model comprises three key steps: 1) 184 radiomics features (RFs) are extracted from within the SRS planning target volume (PTV) and 60% isodose volume (V₆₀) followed with Z-score normalization; 2) a deep neural network (DNN) mimicking the encoding path of U-net is trained for radionecrosis/recurrence prediction using the 3D volume. Prior to the binary prediction output, latent variables in the DNN are extracted as 512 deep features (DFs); and 3) all extracted features are synthesized as an input of support vector machine (SVM) execution. Key features with higher linear kernel weighting factor values are identified by clustering analysis, and these key features are utilized by SVM to generate the final radionecrosis/recurrence prediction result. During the model training, a 7:3 training/test data sample ratio was adopted, and 50 model versions were acquired with random validation sample assignments. Sensitivity, specificity, accuracy, and ROC of the model were evaluated, and these results were compared with 1) classic radiomics-based prediction (i.e., radiomics features-only for SVM input) and 2) DNN prediction results. Potential associations between the identified key radiomics/deep features and patients' genetic profiles were studied. <h3>Results</h3> As seen in the table, while radiomics-based prediction achieved acceptable accuracy but very low sensitivity, DNN prediction achieved similar accuracy with an improved sensitivity; in contrast, the RIDL achieved the best prediction accuracy and sensitivity results with 32 identified key features (3 RFs+29 DFs), and it also demonstrated superior ROC results. For patients with NSCLC primary disease, 2 RFs extracted in SRS V₆₀ exhibited significant correlation with ALK mutation (R>0.6), and 1 DF exhibited significant correlation with EGFR mutation (R>0.7). <h3>Conclusion</h3> The developed RIDL can accurately differentiate brain metastasis radionecrosis/recurrence using a single post-SRS MR scan. Future work that investigates its integration with genomics as a comprehensive radiogenomic tool is indicated.

Association between NMR metabolomic signatures of healthy lifestyle and incident coronary artery disease.

To identify metabolites associated with a healthy lifestyle and explore the possible mechanisms of lifestyle in coronary artery disease (CAD). The nuclear magnetic resonance metabolomics platform was applied to perform metabolomic profiling of baseline plasma samples from a randomly selected subset of 121 733 UK Biobank participants. Cox proportional hazards models with covariate adjustments were used to investigate the associations between validated lifestyle-associated metabolites and incident CAD and to estimate the accuracy of the inclusion of metabolites to predict CAD compared with traditional prediction models. The discriminatory ability of each model was evaluated using Harrell's C statistic, integrated discrimination improvement (IDI), and continuous net reclassification improvement (NRI) indexes. During a median of 8.6 years of follow-up, 5513 incident CAD cases were documented. Among the 111 lifestyle-associated metabolites, 65 were significantly associated with incident CAD after multivariate adjustment (Bonferroni P < 3.11 × 10-04). The addition of these metabolites to classic risk prediction models [Framingham Risk Score (FRS) using lipids; FRS using body mass index] improved CAD prediction accuracy as assessed by the C statistic (increasing to 0.739 [95% CI, 0.731-0.747] and 0.752 [95% CI, 0.746-0.758]), respectively; continuous NRI (0.274 [0.227-0.325] and 0.266 [0.223-0.317]) and IDI (0.003 [0.002-0.004] and 0.003 [0.002-0.004]). Healthy lifestyle-associated metabolites are associated with the incidence of CAD and may help improve the prediction of CAD risk. The use of metabolite information combined with the FRS model warrants further investigation before clinical implementation.

A probabilistic perspective on nearest neighbor for implicit recommendation

Over the past years, the recommender systems community invented several novel approaches that reached better and better prediction accuracy. Sequential recommendation, such as music recommendation, has seen large improvements from neural network-based models such as recurrent neural networks or transformers. When no sequential information is available or not relevant, such as for book, movie, or product recommendation, however, the classic k-nearest neighbor algorithm appears to remain competitive, even when compared to much more sophisticated methods. In this paper, we attempt to explain the inner workings of the nearest neighbor using probabilistic tools, treating similarity as conditional probability and presenting a novel model for explaining and removing popularity bias. First, we provide a probabilistic formulation of similarity and the classic prediction formula. Second, by modeling user behavior as a combination of personal preference and global influence, we are able to explain the presence of popularity bias in the predictions. Finally, we utilize Bayesian inference to construct a theoretically grounded variant of the widely used inverse frequency scaling, which we use to mitigate the effect of popularity bias in the predictions. By replacing the formerly ad hoc choices of nearest neighbor with probabilistically founded counterparts, we are able to improve prediction accuracy over a variety of data sets and gain an increased understanding of the theory behind the method.

Ancient homomorphy of molluscan sex chromosomes sustained by reversible sex-biased genes and sex determiner translocation.

Contrary to classic theory prediction, sex-chromosome homomorphy is prevalent in the animal kingdom but it is unclear how ancient homomorphic sex chromosomes avoid chromosome-scale degeneration. Molluscs constitute the second largest, Precambrian-originated animal phylum and have ancient, uncharacterized homomorphic sex chromosomes. Here, we profile eight genomes of the bivalve mollusc family of Pectinidae in a phylogenetic context and show 350 million years sex-chromosome homomorphy, which is the oldest known sex-chromosome homomorphy in the animal kingdom, far exceeding the ages of well-known heteromorphic sex chromosomes such as 130-200 million years in mammals, birds and flies. The long-term undifferentiation of molluscan sex chromosomes is potentially sustained by the unexpected intertwined regulation of reversible sex-biased genes, together with the lack of sexual dimorphism and occasional sex chromosome turnover. The pleiotropic constraint of regulation of reversible sex-biased genes is widely present in ancient homomorphic sex chromosomes and might be resolved in heteromorphic sex chromosomes through gene duplication followed by subfunctionalization. The evolutionary dynamics of sex chromosomes suggest a mechanism for 'inheritance' turnover of sex-determining genes that is mediated by translocation of a sex-determining enhancer. On the basis of these findings, we propose an evolutionary model for the long-term preservation of homomorphic sex chromosomes.

A Supervised Link Prediction Method Using Optimized Vertex Collocation Profile

Classical link prediction methods mainly utilize vertex information and topological structure to predict missing links in networks. However, accessing vertex information in real-world networks, such as social networks, is still challenging. Moreover, link prediction methods based on topological structure are usually heuristic, and mainly consider common neighbors, vertex degrees and paths, which cannot fully represent the topology context. In recent years, network embedding models have shown efficiency for link prediction, but they lack interpretability. To address these issues, this paper proposes a novel link prediction method based on an optimized vertex collocation profile (OVCP). First, the 7-subgraph topology was proposed to represent the topology context of vertexes. Second, any 7-subgraph can be converted into a unique address by OVCP, and then we obtained the interpretable feature vectors of vertexes. Third, the classification model with OVCP features was used to predict links, and the overlapping community detection algorithm was employed to divide a network into multiple small communities, which can greatly reduce the complexity of our method. Experimental results demonstrate that the proposed method can achieve a promising performance compared with traditional link prediction methods, and has better interpretability than network-embedding-based methods.

A novel reliability prediction with input transients for an LLC converter

Utility applications demand highly reliable power converters to match market quality needs. The classical reliability prediction methods do not account for the sudden transients involved with the power converter. This work envisages a new reliability prediction procedure for LLC converter which accounts for the input transients’ impact on the failure rate. Experiments are conducted to collect the actual stress values at input transient and fault conditions, which aids to predict the failure rate with more accuracy. The reliability prediction has been performed using the collected experimental data from the component level to the system level at different mission profiles with transient operating conditions. The impact of various faults and transients on the converter failure rate prediction has been clearly projected from the quantitative analysis presented in this paper. To have a clear picture, the effect of reliability with respect to other stress factors like temperature stress, environmental stress, electrical voltage, current, and power stress on failure rates are also compiled and tabulated. Failure rate and Mean Time Between Failures (MTBF) have been calculated for an LLC converter using the experimental data. The proposed reliability model can be used in the design phase to have an optimal design, planning, and operation of a power electronic converter in the field. This enables to reach out power converters with better reliability profile to cater the industrial needs for real time applications. From the results, it is evident that the reliability prediction is more realistic when the input transients are considered using the experimental data.

A Prediction Model of Relativistic Electrons at Geostationary Orbit Using the EMD‐LSTM Network and Geomagnetic Indices

AbstractIn this study, the Empirical Mode Decomposition algorithm (EMD) and the Long Short Term Memory neural network (LSTM) are combined into an EMD‐LSTM model, to predict the variation of the &gt;2 MeV electron fluxes 1 day ahead. Input parameters include the Pc5 power, AP, AE, Kp, &gt;0.6 MeV, and historical electron flux values, are used for predictions. All the time resolution of parameters are daily integral values. As compared the prediction results of the EMD‐LSTM model with other classical prediction models, the results show that the 1 day ahead prediction efficiency of the &gt;2 MeV electron fluxes possesses a prediction efficiency of 0.80, and the highest prediction efficiency can reach 0.93. These results are superior to the prediction accuracy of more previous models. Using two high‐energy electron flux storm events for validation, the results indicate that the performance of the EMD‐LSTM model in the period of the high‐energy electron flux storm is also relatively good, especially for the prediction of high‐energy electron fluxes at extreme points, and the predictions are closer to actual observations.

An Accurate Maritime Radio Propagation Loss Prediction Approach Employing Neural Networks

The radio propagation loss prediction model is essential for maritime communication. The oceanic tropospheric duct is much more complicated than the atmospheric structure on land due to the rough sea surface influence, and it leads to difficulties in loss prediction. Classical radio wave propagation loss prediction models are either based on complicated electromagnetic wave theories or rely on empirical data. Consequently, they suffer from low accuracy and a limited range of application. To address this issue, a novel maritime propagation loss prediction approach is proposed, which fully exploits the data for training. In this new approach, 3D sea surface contour profiles are generated based on the Pierson–Moskowitz spectrum theory and the direction expansion formula Stereo Wave Observation Program (SWOP), by sweeping the parameters. The full-wave propagation procedure of radio signals over the sea surface is simulated by commercial EM analysis software CST Studio Suite. Based on the large quantity of simulated data, the BP neural network is employed to fit the radio propagation loss and obtain the sea surface radio wave propagation prediction model. Other classical machine learning methods are also compared to validate the proposed approach. Traditional empirical model construction relies on observation data. This approach, for the first time, proposed an automatic scheme which covers the whole procedure from the data generation to prediction model training. It avoids the requirements for on-site observation, as well as significantly decreases the cost of experiments. The application scope of the prediction model such as propagation distance range and working frequency range could be adaptive through adjustments for simulated sea size and simulated working frequency. This approach is validated to save 99% of prediction time in comparison with the full-wave simulations. The prediction model trained via our proposed method could obtain the coefficient of determination R2 which is over 0.92, demonstrating the superiority of this method.

Possible quantum effects at the transition from cosmological deceleration to acceleration

The recent transition from decelerated to accelerated expansion can be seen as a reflection (or ``bounce'') in the connection variable, defined by the inverse comoving Hubble length ($b=\stackrel{\ifmmode \dot{}\else \textperiodcentered \fi{}}{a}$, on shell). We study the quantum cosmology of this process. We use a formalism for obtaining relational time variables either through the demotion of the constants of nature to integration constants, or by identifying fluid constants of motion. We extend its previous application to a toy model (radiation and $\mathrm{\ensuremath{\Lambda}}$) to the realistic setting of a transition from dust matter to $\mathrm{\ensuremath{\Lambda}}$ domination. In the dust and $\mathrm{\ensuremath{\Lambda}}$ model two time variables may be defined, conjugate to $\mathrm{\ensuremath{\Lambda}}$ and to the dust constant of motion, and we work out the monochromatic solutions to the Schr\"odinger equation representing the Hamiltonian constraint. As for their radiation and $\mathrm{\ensuremath{\Lambda}}$ counterparts, these solutions exhibit ``ringing,'' whereby the incident and reflected waves interfere, leading to oscillations in the amplitude. In the semiclassical approximation we find that, close to the bounce, the probability distribution becomes double peaked, one peak following a trajectory close to the classical limit but with a Hubble parameter slightly shifted downwards, the other with a value of $b$ stuck at its minimum $b={b}_{\ensuremath{\star}}$. Still closer to the transition, the distribution is better approximated by an exponential distribution, with a single peak at $b={b}_{\ensuremath{\star}}$, and a (more representative) average $b$ biased towards a value higher than the classical trajectory. Thus, we obtain a distinctive prediction for the average Hubble parameter with redshift: slightly lower than its classical value when $z\ensuremath{\approx}0$, but potentially much higher than the classical prediction around $z\ensuremath{\sim}0.64$, where the bounce most likely occurred. The implications for the ``Hubble tension'' have not escaped us.

Prioritizing potential circRNA biomarkers for bladder cancer and bladder urothelial cancer based on an ensemble model

Bladder cancer is the most common cancer of the urinary system. Bladder urothelial cancer accounts for 90% of bladder cancer. These two cancers have high morbidity and mortality rates worldwide. The identification of biomarkers for bladder cancer and bladder urothelial cancer helps in their diagnosis and treatment. circRNAs are considered oncogenes or tumor suppressors in cancers, and they play important roles in the occurrence and development of cancers. In this manuscript, we developed an Ensemble model, CDA-EnRWLRLS, to predict circRNA-Disease Associations (CDA) combining Random Walk with restart and Laplacian Regularized Least Squares, and further screen potential biomarkers for bladder cancer and bladder urothelial cancer. First, we compute disease similarity by combining the semantic similarity and association profile similarity of diseases and circRNA similarity by combining the functional similarity and association profile similarity of circRNAs. Second, we score each circRNA-disease pair by random walk with restart and Laplacian regularized least squares, respectively. Third, circRNA-disease association scores from these models are integrated to obtain the final CDAs by the soft voting approach. Finally, we use CDA-EnRWLRLS to screen potential circRNA biomarkers for bladder cancer and bladder urothelial cancer. CDA-EnRWLRLS is compared to three classical CDA prediction methods (CD-LNLP, DWNN-RLS, and KATZHCDA) and two individual models (CDA-RWR and CDA-LRLS), and obtains better AUC of 0.8654. We predict that circHIPK3 has the highest association with bladder cancer and may be its potential biomarker. In addition, circSMARCA5 has the highest association with bladder urothelial cancer and may be its possible biomarker.