Skewness Research Articles

Study on canopy extraction method for narrowband spectral images based on superpixel color gradation skewness distribution features

BackgroundCrop phenotype extraction devices based on multiband narrowband spectral images can effectively detect the physiological and biochemical parameters of crops, which plays a positive role in guiding the development of precision agriculture. Although the narrowband spectral image canopy extraction method is a fundamental algorithm for the development of crop phenotype extraction devices, developing a highly real-time and embedded integrated narrowband spectral image canopy extraction method remains challenging owing to the small difference between the narrowband spectral image canopy and background.MethodsThis study identified and validated the skewed distribution of leaf color gradation in narrowband spectral images. By introducing kurtosis and skewness feature parameters, a canopy extraction method based on a superpixel skewed color gradation distribution was proposed for narrowband spectral images. In addition, different types of parameter combinations were input to construct two classifier models, and the contribution of the skewed distribution feature parameters to the proposed canopy extraction method was evaluated to confirm the effectiveness of introducing skewed leaf color skewed distribution features.ResultsLeaf color gradient skewness verification was conducted on 4200 superpixels of different sizes, and 4190 superpixels conformed to the skewness distribution. The intersection over union (IoU) between the soil background and canopy of the expanded leaf color skewed distribution feature parameters was 90.41%, whereas that of the traditional Otsu segmentation algorithm was 77.95%. The canopy extraction method used in this study performed significantly better than the traditional threshold segmentation method, using the same training set, Y1 (without skewed parameters) and Y2 (with skewed parameters) Bayesian classifier models were constructed. After evaluating the segmentation effect of introducing skewed parameters, the average classification accuracies Acc_Y1 of the Y1 model and Acc_Y2 of the Y2 model were 72.02% and 91.76%, respectively, under the same test conditions. This indicates that introducing leaf color gradient skewed parameters can significantly improve the accuracy of Bayesian classifiers for narrowband spectral images of the canopy and soil background.ConclusionsThe introduction of kurtosis and skewness as leaf color skewness feature parameters can expand the expression of leaf color information in narrowband spectral images. The narrowband spectral image canopy extraction method based on superpixel color skewness distribution features can effectively segment the canopy and soil background in narrowband spectral images, thereby providing a new solution for crop canopy phenotype feature extraction.

Numerical study of aluminum combustion with agglomerate size distribution in solid rocket motor

The aluminum agglomerate size distribution plays an important role in influencing the particle distributed combustion in motor, which subsequently affects the performance of solid rocket motor significantly. In this work, a three-phase model to describe distributed combustion of aluminum agglomerates is established based on the Eulerian-Lagrangian method. Then, the agglomerate size, including mono size and distributed size, is studied to reveal its effect on aluminum combustion and motor flow field. The simulated results indicate that the increasing agglomerate mono size observes the obvious decrease of the average temperature inside the motor combustion chamber, implying the low combustion efficiency of large agglomerate size. When considering the agglomerate size distribution, the size distribution mode and the mean size D43 determine the combustion efficiency together. In particular, even the mean size is similar, with different distribution mode, like skewed distribution, bimodal or trimodal distribution, the combustion efficiency and flow field parameters are nonnegligible different. However, when the size distribution mode is the same and the peak range is similar, the mean size D43 becomes the only and predominant factor as the mono size.

Association Between Nickel Exposure and Metabolic Syndrome: Data from NHANES 2017-2018.

Previous studies have found a possible association between nickel and metabolic syndrome (MetS), but with conflicting results. No studies have determined whether nickel exposure increases the prevalence of MetS in the general U.S. population. Therefore, we used data from the National Health and Nutrition Examination Survey (NHANES) to assess the association between urinary nickel and MetS. Since urinary nickel levels were presented as a skewed distribution, they were normalized using a logarithmic transformation. Weighted multivariate logistic models, restricted cubic spline, threshold effect analysis, and subgroup analyses were used to examine the association between urinary nickel concentration and the risk of MetS and its components. Based on data from 1577 participants, individuals in the second, third, and fourth quartiles of urinary nickel had an adjusted OR for MetS of 1.42 (95% CI: 0.88, 2.28), 2.00 (95% CI: 1.22, 3.28), and 1.68 (95% CI: 1.05, 2.70), respectively, representing an inverted "L"-shaped nonlinear dose-response relationship with an inflection point at 0.2141ng/L. Patients over the age of 40, males, less educated, and smokers are more susceptible to nickel exposure. In addition, there were significant associations between nickel and most components of the MetS, with the strongest to weakest correlations being high fasting glucose, reduced high-density lipoprotein, abdominal obesity, and elevated blood pressure; however, there was no significant correlation between nickel and hyperlipidemia. In conclusion, environmental nickel exposure increases the prevalence of MetS in U.S. adults, particularly in males over 40years of age, those with less education, and smokers.

Influence of tidal asymmetry on local scour near the offshore platform

An in-situ mooring measurement was conducted using a profiling sound of navigation and ranging (SONAR) system to reveal the influence of tidal asymmetry on the local scour near an artificial offshore platform, Southwest Offshore Windfarm Complex, Korea. During the mooring period, the flood tide was significantly dominated by positive tidal duration asymmetry and skewness. The lengths of scour hole were 9.8m and 5.1m along the flood and ebb directions, respectively. Flood-dominated currents intensified the horseshoe vortex, resulting in an approximately 10% deeper scour hole on the flood face than the ebb face. The periodic occurrence of bidirectional currents generated vortices that facilitated the mixing and redistribution of seabed sediments, forming a gentle slope within the scour hole. The average slope angles ranged from 14° to 16° on the flood face and 6° to 8° on the ebb face. Despite the strong influence of local scour, variations in scour depth (Ds) consistently remained around −2.15m, suggesting the exposure of the underlying consolidated sediment layer. During the intermediate period from spring to neap tide, the Ds on the lower slope of scour hole increased by 0.12m, while it decreased by 0.11m from neap to spring tide, suggesting that the net Ds gradually approached zero over the tidal cycle. These findings underscore the importance of understanding tidal impacts on the local scour morphology to enhance the stability and design of offshore wind turbine foundations.

Hedging political risk in international portfolios

We show that internationally diversified portfolios carry sizeable political risk premia and expose investors to tail risk. We obtain political efficient frontiers with and without hedging political risk using a portfolio selection model for skewed distributions and develop a new asymptotic inference test to compare portfolio performance. Politically hedged portfolios outperform a broad market index and the equally weighted portfolio for US, Eurozone, and Japanese investors. Political risk hedging is not subsumed by currency hedging, and the diversification gains of politically hedged portfolios persist under currency hedging and transaction cost frictions. Hedging political risk induces equity home bias but does not fully explain the puzzle.

Enhancing aspect category detection in imbalanced online reviews: An integrated approach using Select-SMOTE and LightGBM

Aspect category detection (ACD) is a pivotal subtask within the field of sentiment analysis in natural language processing, aiming to identify implicit aspect category information in online review texts. In real-world scenarios of online review category detection tasks, data imbalance often arises, leading to skewed distributions among distinct review categories. This phenomenon poses substantial challenges for accurately recognizing minority categories through modeling. To address this, we propose a method for detecting imbalanced aspect categories by combining the selective synthetic over-sampling (Select-SMOTE) algorithm with the light gradient boosting machine (LightGBM). Our approach commences with text data representation through features, followed by a strategy involving joint sample partitioning and boundary optimization within the feature space to generate minority class samples. This partitioning strategy aligns generated data more closely with the original distribution, while the boundary optimization module enhances classification performance by eliminating samples near boundaries. Subsequently, the balanced dataset is input to the LGB model, enabling the extraction of aspect category information through parameter optimization and class weight assignment. Finally, our method is evaluated using the SemEval and SentiHood datasets and compared with prevailing sampling methods and classification models. Empirical results manifestly demonstrate the method’s superiority across diverse metrics, reflecting robustness and effective mitigation of imbalanced data challenges in ACD.

Joint probabilistic modeling approach for harmonic and three-phase unbalanced disturbance sources

Harmonics and three-phase imbalance are two common disturbances in power systems that interact with and affect each other, leading to more complex impacts on power system performance. To comprehensively and accurately analyze the distribution and interaction mechanisms of harmonics and three-phase imbalance, this study investigated the joint emission level of harmonic and three-phase unbalanced disturbance sources (H-TU-DS). This paper proposes a joint probabilistic emission level modeling approach. The power, harmonic, and three-phase unbalanced data of H-TU-DS are multidimensional, and the feature dataset is filtered by calculating multiple correlation coefficients to achieve dimensionality reduction without losing data information. Considering the different operating characteristics of the H-TU-DS, an improved fuzzy C-means (FCM) algorithm is applied to classify operating conditions, enabling a comprehensive and accurate analysis of the joint emission level. Based on the nonparametric kernel density function, joint probability modeling of H-TU-DS is performed using an adaptive bandwidth improvement method, effectively enhancing the accuracy of the modeling process. Finally, the effectiveness and accuracy of the proposed methods for feature dataset filtering, operating condition classification, and joint probability modeling are demonstrated through comparisons with both simulated and actual data. The proposed model facilitates the prediction and assessment of the impact of H-TU-DS on power quality after grid connection, enabling the effective prevention of power-quality problems through advanced treatment.

Addressing Class Imbalance of Health Data: A Systematic Literature Review on Modified Synthetic Minority Oversampling Technique (SMOTE) Strategies

The Synthetic Minority Oversampling Technique (SMOTE) method is the baseline for solving unbalanced data problems. The working concept of the SMOTE method is to generate new synthetic data patterns by performing linear interpolation between minority class samples based on k-nearest neighbors. However, the SMOTE method has weaknesses, namely the problem of overgeneralization due to excessive sampling of sample noise and increased overlapping between classes in the decision boundary area, which has the potential for noise data. Based on the weaknesses of the Smote method, the purpose of this research is to conduct a systematic literature review on the Smote method modification approach in solving unbalanced data. This systematic literature review method comprises keyword identification, article search process, determination of selection criteria, and selection results based on criteria. The results of this study showed that the SMOTE modification approach was based on filtering, clustering, and distance modification to reduce the resulting noise data. The filtering approach removed the noise data before SMOTE, positively impacting resolving unbalanced data. Meanwhile, the use of a clustering approach in SMOTE can minimize the overlapping artificial minority data that has noise potential. The most used datasets are Pima 60% and Haberman 50%. The most used performance evaluation on unbalanced data is f1-measure 57%, accuracy 55%, recall 43%, and AUC 27%. The implication of the results of this literature review is to provide opportunities for further research in modifying SMOTE in addressing health data imbalances, especially handling noise and overlapping data. The thoroughness of our literature review should instill confidence in the research community.

Adaptive Quotient Filters

Filters trade off accuracy for space and occasionally return false positive matches with a bounded error. Numerous systems use filters in fast memory to avoid performing expensive I/Os to slow storage. A fundamental limitation in traditional filters is that they do not change their representation upon seeing a false positive match. Therefore, the maximum false positive rate is only guaranteed for a single query, not for an arbitrary set of queries. We can improve the filter's performance on a stream of queries, especially on a skewed distribution, if we can adapt after encountering false positives. Adaptive filters, such as telescoping quotient filters and adaptive cuckoo filters, update their representation upon detecting a false positive to avoid repeating the same error in the future. Adaptive filters require an auxiliary structure, typically much larger than the main filter and often residing on slow storage, to facilitate adaptation. However, existing adaptive filters are not practical and have not been adopted in real-world systems for two main reasons. First, they offer weak adaptivity guarantees, meaning that fixing a new false positive can cause a previously fixed false positive to come back. Secondly, the sub-optimal design of the auxiliary structure results in adaptivity overheads so substantial that they can actually diminish overall system performance compared to a traditional filter. In this paper, we design and implement the \sysname, the first practical adaptive filter with minimal adaptivity overhead and strong adaptivity guarantees, which means that the performance and false-positive guarantees continue to hold even for adversarial workloads. The \sysname is based on the state-of-the-art quotient filter design and preserves all the critical features of the quotient filter such as cache efficiency and mergeability. Furthermore, we employ a new auxiliary structure design which results in considerably low adaptivity overhead and makes the \sysname practical in real systems. We evaluate the \sysname by using it to filter queries to an on-disk B-tree database and find no negative impact on insert or query performance compared to traditional filters. Against adversarial workloads, the \sysname preserves system performance, whereas traditional filters incur 2× slowdown from adversaries representing as low as 1% of the workload. Finally, we show that on skewed query workloads, the \sysname can reduce the false-positive rate 100× using negligible (1/1000th of a bit per item) space overhead.

Modelling Environmental Tree Species’ Volume using some selected Skewed Distributions

Ecological requirements' knowledge in determining tree species' distributions is a precondition for sustainable forest management. Tree species in all regions are threatened by climate change but some are more vulnerable than others. Rightly skewed distributions were used to take care of the environmental data set obtained from FRIN using five species which are: Beech Wood, White Afara, Opepe, Afon and Teak. Appropriate statistical tools distributions like descriptive analysis, Akaike Information Criterion (AIC), Goodness of Fit, Probability Functions, Kolmogorov Smirnovt-test, Gamma, Weibull, Log-normal and Beta-weibull were carried out to determine the best distribution for each selected specie in this research. It was established from the distributional pattern of the tree species' volume used in this research that the Gamma distribution was a better fit for the Beech wood with the AIC of 617.21, Beta-weibull distribution was a better _t for the White Afara, Opepe and Teak Species with the AIC values of 580.772, 630.84 and 733.60 respectively while the Weibull distribution was a better fit for the Afon specie with the AIC value of 752.07. Conclusively, the implication of the analysis is that the Beta-weibull distribution described the tree species' volume best among the four distributions used. In line with the findings, it is recommended that Beta-weibull should be the appropriate distribution to model forest specie when it comes to modelling because of its four (4) parameters and its goodness when fitting.

An Optimal Multi-Modal Approach for Stock Market Price Forecasting with Fused Sentiment Analysis for Real Time Data

Abstract: This study presents an innovative fusion-based methodology that integrates real-time stock market technical indicators with news sentiment analysis from financial news feeds to enhance stock selection decisions. The proposed framework employs a Bidirectional Long Short-Term Memory (Bi-LSTM) model for forecasting stock prices and a Deep Neural Network (DNN) used in conjunction with transformer-based model for sentiment classification, both optimized through the incorporation of real-time datasets. To further refine feature selection, Artificial Bee Colony (ABC) and Firefly algorithms are utilized, significantly improving the accuracy of price trend predictions and sentiment analysis. Technical indicators from stock market data are processed using the Bi-LSTM model to predict future stock prices. Concurrently, sentiment data from the Economic Times is pre-processed through Term Frequency-Inverse Document Frequency (TF-IDF) vectorization and pre-trained transformer models to extract key sentiment scores. The ABC algorithm enhances textual feature selection, while the Firefly algorithm reduces skewness in the error distribution of technical indicators, thereby improving the alignment between forecasted and actual stock prices. The fusion mechanism integrates predicted price movements with sentiment analysis to provide actionable insights for stock selection. The decision-making framework classifies stocks based on the consistency between price movements and sentiment: stocks predicted to rise with positive sentiment are labelled as "Sure Selection"; rising prices with negative sentiment are categorized as "Dicey Selection"; declining prices with positive sentiment result in a "Risky Selection"; and negative sentiment with falling prices trigger an "Avoid Selection". The proposed approach achieved a prediction accuracy of 67.00% for the Bi-LSTM model, with a precision of 0.714, recall of 0.695, and an F1 score of 0.677, demonstrating the model's robustness. The mean absolute error (MAE) for the Bi-LSTM model was 4.30, indicating strong predictive performance. The combined use of ABC and Firefly algorithms optimizes both technical and sentiment features, significantly contributing to the overall performance of the prediction model. By fusing multiple data sources and employing advanced optimization techniques, this methodology offers a unique and effective solution for stock market prediction and decision-making

Feature selection via robust weighted score for high dimensional binary class-imbalanced gene expression data

In this paper, a robust weighted score for unbalanced data (ROWSU) is proposed for selecting the most discriminative features for high dimensional gene expression binary classification with class-imbalance problem. The method addresses one of the most challenging problems of highly skewed class distributions in gene expression datasets that adversely affect the performance of classification algorithms. First, the training dataset is balanced by synthetically generating data points from minority class observations. Second, a minimum subset of genes is selected using a greedy search approach. Third, a novel weighted robust score, where the weights are computed by support vectors, is introduced to obtain a refined set of genes. The highest scoring genes based on this approach are combined with the minimum subset of genes selected by the greedy search approach to form the final set of genes. The novel method ensures the selection of the most discriminative genes, even in the presence of skewed class distribution, thereby improving the performance of the classifiers. The performance of the proposed ROWSU method is evaluated on 7 gene expression datasets. Classification accuracy, sensitivity and F1-score are used as performance metrics to compare the proposed ROWSU algorithm with several other state-of-the-art methods. Boxplots and stability plots are also constructed for a better understanding of the results. The results show that the proposed method outperforms the existing feature selection procedures based on classification performance from k nearest neighbors (kNN) and random forest (RF) classifiers.

Cosmogenic Radionuclide Beryllium-7 Skewed Data Preprocessing for Northeast Monsoon Forecasting in Malaysia

Cosmogenic Radionuclide Beryllium-7 Skewed Data Preprocessing for Northeast Monsoon Forecasting in Malaysia

Investigation of wettability and IFT alteration during hydrogen storage using machine learning

Reducing the environmental impact caused by the production or use of carbon dioxide (CO2) and other greenhouse gases (GHG) has recently attracted the attention of scientific, research, and industrial communities. In this context, oil production and enhanced oil recovery (EOR) have also focused on using environmentally friendly methods. CO2 has been studied as a significant gas in reducing harmful environmental effects and preventing its release into the atmosphere. This gas, along with methane (CH4) and nitrogen (N2), is recognized as a ‘cushion gas’. Given that hydrogen (H2) is considered a green and environmentally friendly gas, its storage for altering wettability (contact angle (CA) and interfacial tension (IFT)) has recently become an intriguing topic. This study examines how H2 can be utilized as a novel cushion gas in EOR systems. In this research, the role of H2 and its storage in altering wettability in the presence of other cushion gases has been investigated. The performance of H2 in changing the CA and IFT with other gases has also been compared using machine learning (ML) models. During this process, ML and experimental data were used to predict and report the values of IFT and CA. The data used underwent statistical and quantitative preprocessing, processing, evaluation, and validation, with outliers and skewed data removed. Subsequently, ML models such as Random Forest (RF), Random Tree, and LSBoost were implemented on training and testing data. During this process of modeling and predicting IFT and CA, the hyperparameters were optimized using Bayesian algorithms and random search (RS) methods. Finally, the results and performance of the modeling were evaluated, with the LSBoost modeling method using Bayesian optimization reporting R2 values of 0.998614 for IFT and 0.986999 for CA.

Exploring Customer Segmentation in E-Commerce using RFM Analysis with Clustering Techniques

The proliferation of big data and the growth of e-commerce have intensified the challenges associated with extracting actionable data for personalised recommendations and decision-making. With data-driven marketing strategies, understanding and predicting customer behaviour has become paramount for maintaining competitive advantage. This study leverages business analytics tools, focusing on Recency, Frequency, and Monetary (RFM) Analysis, alongside K-Means and Hierarchical (Agglomerative) Clustering algorithms, to segment customer transactional data. Data normalisation, a critical step for accurate clustering, was performed using log transformation and the Power Transformer technique with the Yeo-Johnson parameter, the latter proving more effective for handling both positively and negatively skewed data, enhancing data normalisation and suitability for analysis. This study reveals that RFM Analysis with Hierarchical Clustering outperforms K-Means Clustering, achieving a Silhouette Score of 0.47 and a Calinski–Harabasz Index of 3787.1, indicating a more accurate identification of customer segments. RFM Analysis alone generated eight clusters, while integrating RFM Analysis with both Hierarchical Clustering and K-Means generated three similar-sized clusters with interchanged labels. These metrics highlight the proficiency of Hierarchical Clustering in identifying unique customer segments and customising marketing strategies. The findings indicate that the RFM-Hierarchical Clustering approach enhances segmentation precision and facilitates more refined and effective marketing strategies.

Tolerance Intervals Under a Class of Unbalanced Linear Mixed Models

The study of tolerance intervals for linear mixed models under unbalanced data has been mainly focused on one-way random-effects models. In this article, we present a method to compute both one- and two-sided ( β , γ ) -tolerance intervals that is applicable to a more general class of unbalanced linear mixed models. The proposed method is based on the concept of generalized pivotal quantities and thus relies on the derivation of pivotal quantities that are shown to be independent within this particular class of models. The method employs Monte Carlo sampling methods to obtain realizations of the quantities needed to compute the tolerance intervals of interest. Extensive simulation studies confirm that coverage probabilities of the computed tolerance intervals are consistently close to their nominal levels. Furthermore, to showcase the application of our method in real-world scenarios, we provide illustrative examples of case studies that examine models falling within the specified class. Lastly, motivated by the prospective application of our method in drug development, we implement the proposed procedures to estimate shelf life of a drug product through tolerance intervals and unbalanced stability data.

Genetic variability and diversity analysis for some agronomic traits of a sweet potato (Ipomoea batatas L.) collection: Insights for breeding superior genotypes

A study was conducted during the 2017 cropping season to assess the genetic variability of selected agronomic traits in sweet potato. A total of 355 sweet potato genotypes (351 from different countries of the world and four check varieties) were evaluated at Tuber Crops Research Sub Centre, Bangladesh Agricultural Research Institution, Bogura, Bangladesh. The experiment was conducted using an augmented randomized complete block design. Findings revealed positive correlations among five agronomic traits, namely the weight of marketable storage root per plant (MRW), weight of storage root per plant (RW), fresh weight of foliage per plant (FW), number of marketable storage root per plant (MRN) and number of storage root per plant (RN) suggesting their suitability for phenotypic selection. There was a positive skewness in most studied traits (FW, MRW, MRW and RW), indicating the presence of a few genotypes with exceptionally high values for those traits. By employing principal component analysis, the most influential traits in sweet potato genotypes were identified for first two components (Dim) showing significant contributions to Dim-1 and Dim-2 collectively accounting for 84.7 % of the total variance. Through cluster analysis, two main clusters (Cluster I and Cluster II) were identified among the studied genotypes, each characterized by distinct trait patterns. Cluster I demonstrated superior performance in all traits, while Cluster II exhibited lower values for all traits. Further sub-clustering within Cluster II (sub-clusters IIA and IIB) revealed additional variations of the collection. The Euclidean distance for inter-cluster (ranging from 7.94 to 11.39) and intra-cluster (ranging from 3.17 to 10.38) were determined utilizing the complete distance method. In restricted maximum likelihood (REML) model, all the traits were significantly influenced by genetic effects, with broad-sense heritability ranging from 32 % to 70 %. The REML displayed high selection accuracy, with values exceeding 91 % for studied traits. The Multi-Trait Genotype-Ideotype Distance Index (MGIDI) identified 18 genotypes, with Moz1.15 emerging as the best-performing genotype under a 5 % selection pressure. Among the selected genotypes, trait uniqueness was highest for FW (54 %), followed by RN (50 %), MRN (18 %), MRW (14 %), and RW (5 %). The selection gain for these top-ranked genotypes varied between 23.2 % and 69.2 %. Findings established the existence of useful genetic variability for selected agronomic traits of sweet potato that could be exploited for breeding, genetics study and conservation.

Predicting diabetes in adults: identifying important features in unbalanced data over a 5-year cohort study using machine learning algorithm

BackgroundImbalanced datasets pose significant challenges in predictive modeling, leading to biased outcomes and reduced model reliability. This study addresses data imbalance in diabetes prediction using machine learning techniques. Utilizing data from the Fasa Adult Cohort Study (FACS) with a 5-year follow-up of 10,000 participants, we developed predictive models for Type 2 diabetes.MethodsWe employed various data-level and algorithm-level interventions, including SMOTE, ADASYN, SMOTEENN, Random Over Sampling and KMeansSMOTE, paired with Random Forest, Gradient Boosting, Decision Tree and Multi-Layer Perceptron (MLP) classifier. We evaluated model performance using F1 score, AUC, and G-means—metrics chosen to provide a comprehensive assessment of model accuracy, discrimination ability, and overall balance in performance, particularly in the context of imbalanced datasets.Resultsour study uncovered key factors influencing diabetes risk and evaluated the performance of various machine learning models. Feature importance analysis revealed that the most influential predictors of diabetes differ between males and females. For females, the most important factors are triglyceride (TG), basal metabolic rate (BMR), and total cholesterol (CHOL), whereas for males, the key predictors are body Mass Index (BMI), serum glutamate Oxaloacetate Transaminase (SGOT), and Gamma-Glutamyl (GGT). Across the entire dataset, BMI remains the most important variable, followed by SGOT, BMR, and energy intake. These insights suggest that gender-specific risk profiles should be considered in diabetes prevention and management strategies. In terms of model performance, our results show that ADASYN with MLP classifier achieved an F1 score of 82.17 ± 3.38, AUC of 89.61 ± 2.09, and G-means of 89.15 ± 2.31. SMOTE with MLP followed closely with an F1 score of 79.85 ± 3.91, AUC of 89.7 ± 2.54, and G-means of 89.31 ± 2.78. The SMOTEENN with Random Forest combination achieved an F1 score of 78.27 ± 1.54, AUC of 87.18 ± 1.12, and G-means of 86.47 ± 1.28.ConclusionThese combinations effectively address class imbalance, improving the accuracy and reliability of diabetes predictions. The findings highlight the importance of using appropriate data-balancing techniques in medical data analysis.

Multivariate skew-normal distribution for modelling skewed spatial data

Multivariate spatial data are commonly modelled using the shared spatial component and multivariate intrinsic conditional autoregressive (MICAR) models where the spatial random variables are assumed to be normally distributed. However, the normality assumption may not be always right as the spatially structured component may show non-normal distributions. We present, multivariate skew-normal spatial distribution in the modelling of multivariate conditional autoregressive models. Simulations and an application to estimate district HIV rates in South Africa are used for illustrating the capabilities of the proposed multivariate skewed spatial model. The estimation is done in a Bayesian framework. A comparison between our suggested approach and the common MICAR model is made using conditional predictive ordinate (CPO). The CPO values indicate that our suggested approach is better than the MICAR model for predicting the outcome variables of both the simulated and HIV data.

The impact of customer concentration on the provision of reverse trade credit

PurposeThe purpose of this study is to investigate the impact of customer concentration on the provision of reverse trade credit at the firm level.Design/methodology/approachUtilizing unbalanced panel data of Chinese A-share listed firms from 2007 to 2022 as the study sample, this paper employs a fixed-effects model to investigate the association between customer concentration and firms’ reverse trade credit.FindingsThis study finds that firms with higher customer concentration receive less reverse trade credit. Heterogeneity tests reveal a significant amplification of reverse trade credit in high-tech firms but a detrimental impact in large-sized, competitive and high-analyst-following firms. Further studies conclude that firms’ motivations, including bargaining power, financing and transaction guarantee motivations, collectively influence the extent of reverse trade credit acquisition.Originality/valueTo our knowledge, this paper represents the first attempt to conduct a comprehensive investigation of reverse trade credit, specifically through the lens of customer concentration, utilizing firm-level panel data sourced from a singular country.