Missing Data Research Articles

Examining the relationship between Empowering leadership and Whistle Blowing: Testing a Moderated Mediated Model

The purpose of the present study examines the behaviour of media channels employees in Pakistan, test a theory , raise internal voice against worng doing with the partnership of empowering leadership.Pyschological empowerment reasoning the existence relationship between EL,IW, and an interactive variable. This research adopted cross-sectional research design, which is a design type under quantitative approach. In current study, convenience sampling technique was used. The study method forms use of tools such as distributed questionnaires with reply rate 81.7%, and .16 % missing values. The whole survey contains a total number of 41 scaled items that were adapted to measure the constructs of the study. The outcomes backing the supportive relationship of empowering leadership and internal whistleblowing in Pakistan's backgrounds.The research finding sustenance the objective of the study that employees of media channel do internal whistleblow against wrongdoing when they got empowered from their leaders, in the existence of psychological empowerment.The results displays the significant level of connection between motivating management power sharing and whistleblowing with the p-value support theory.The results of the assumption proved that PE and IW have important positive relationships.The lower and upper limits respectly indicated by the unstandardized regression coefficient are both positive results and no null prevails the indirect effect of the relationship of EL and IW, by PE supported proposition.The beta value provides evidendence relationship of psychological empowerment and whistleblowing is modified by psychological ownership.This study provides media groups with insights into how empowering leadership, Psychological empowerment, escalates internal whistleblowing reporting channels, in order to ensure that a framework is implemented so that company progress can be achieved.

Deep Learning Application as a Method for Classification Equipment Failure on Natural Gas Pipeline

Predicting maintenance and preventing failures in natural gas pipelines is critical for safety and efficiency in the oil and gas industry. This research utilizes machine learning (ML) and deep learning (DL) methods to predict failures in natural gas transmission pipelines. Using a dataset from the U.S. Department of Transportation’s Pipeline and Hazardous Materials Safety Administration (PHMSA), the study examines failure incidents from 2010-2019 and non-failure data from 2020-2023. The research involves comprehensive data preprocessing, addressing missing values and dataset imbalances. Key pipeline attributes such as diameter, wall thickness, age, and operating pressure are used for failure prediction. The ML approach employs logistic regression, optimized for binary classification tasks, to establish the probability of pipeline failures. For the DL approach, an Artificial Neural Network (ANN) with Feed Forward and Back Propagation algorithms is developed. Key parameters, including activation functions and hyperparameters, are finely tuned to enhance predictive performance. ReLU activation functions are used in input and hidden layers, while the sigmoid function is applied to the output layer. Performance evaluation of both ML and DL models is based on accuracy, F1 Score, and Area Under Curve (AUC). In summary, ML showed better performance than DL, with value of accuracy is 0.74, F1 Score is 0.85, and AUC is 0.55 while the value of accuracy, F1 score, AUC from DL is 0.61, 0.003, and 0.58 respectively. DL offers superior performance in handling complex and huge amounts of data. In the other hand, ML offers more robust models and simpler adjustable models than DL. Because of the amount of computed data in this research, it is better to have ML as a predictive algorithm.

아동·청소년기 생활시간과 학업성취 인과관계 종단분석

In this study, we explored the causal relationship between the daily activities and academic achievement of children and adolescents using the data from the Korean Children & Youth Panel Survey(KCYPS) 2018 provided by the National Youth Policy Institute. The analysis sample consisted of 1,740 participants from the 4th-grade panel (4th grade to 2nd-year of middle school) and 1,874 participants from the 1st-year of middle school panel (1st year of middle school to 2nd year of high school) who did not drop out from the 1st to the 5th wave and had no missing values for variables included in the research model. The independent variables consist of the daily activities of children and adolescents, including parental interaction time, extracurricular learning activities, and leisure time. The dependent variable is the academic achievement. The analysis method involved setting up a latent growth model to analyze the causal relationship between the daily activities and academic achievement of children and adolescents. The latent growth model analysis was conducted by estimating a linear model using the 5th wave data, with maximum likelihood estimation as the parameter estimation method. The results of the analysis indicate differences in daily activities and academic achievement between the 4th-grade panel and the 1st-year panel, with extracurricular learning time and leisure time showing contrasting results in the two panel groups. These findings contribute to understanding the context of the causal relationship between the daily activities and academic achievement of children.

Research on User Default Prediction Algorithm Based on Adjusted Homogenous and Heterogeneous Ensemble Learning

In the field of risk assessment, the traditional econometric models are generally used to assess credit risk. And with the introduction of the “dual-carbon” goals to promote the development of a low-carbon economy, the scale of green credit in China has rapidly expanded. But with the advent of the big data era, due to the poor interpretability of a traditional single machine learning model, it is difficult to capture nonlinear relationships, and there are shortcomings in prediction accuracy and robustness. This paper selects the adjusted ensemble learning model based on the homogeneous and heterogeneous factors for user default prediction, which can efficiently process large quantities of high-dimensional data. This article adjusts each model to adapt to the task and innovatively compares various models. In this paper, the missing value filling method, feature selection, and ensemble model are studied and discussed, and the optimal ensemble model is obtained. When comparing the predictions of single models and ensemble models, the accuracy, sensitivity, specificity, F1-Score, Kappa, and MCC of Categorical Features Gradient Boosting (CatBoost) and Random undersampling Boosting (RUSBoost) all reach 100%. The experimental results prove that the algorithm based on adjusted homogeneous and heterogeneous ensemble learning can predict the user default efficiently and accurately. This paper also provides some references for establishing a risk assessment index system.

Prediction of ovarian cancer using artificial intelligence tools.

Ovarian cancer is a common type of cancer and a leading cause of death in women. Therefore, accurate and fast prediction of ovarian tumors is crucial. One of the appropriate and precise methods for predicting and diagnosing this cancer is to build a model based on artificial intelligence methods. These methods provide a tool for predicting ovarian cancer according to the characteristics and conditions of each person. In this study, a data set included records related to 171 cases of benign ovarian tumors, and 178 records related to cases of ovarian cancer were analyzed. The data set contains the records of blood test results and tumor markers of the patients. After data preprocessing, including removing outliers and replacing missing values, the weight of the effective factors was determined using information gain indices and the Gini index. In the next step, predictive models were created using random forest (RF), support vector machine (SVM), decision trees (DT), and artificial neural network (ANN) models. The performance of these models was evaluated using the 10-fold cross-validation method using the indicators of specificity, sensitivity, accuracy, and the area under the receiver operating characteristic curve. Finally, by comparing the performance of the models, the best predictive model of ovarian cancer was selected. The most important predictive factors were HE4, CA125, and NEU. The RF model was identified as the best predictive model, with an accuracy of more than 86%. The predictive accuracy of DT, SVM, and ANN models was estimated as 82.91%, 85.25%, and 79.35%, respectively. Various artificial intelligence (AI) tools can be used with high accuracy and sensitivity in predicting ovarian cancer. Therefore, the use of these tools can help specialists and patients with early, easier, and less expensive diagnosis of ovarian cancer. Future studies can leverage AI to integrate image data with serum biomarkers, thereby facilitating the creation of novel models and advancing the diagnosis and treatment of ovarian cancer.

Crop recommendation and forecasting system for Maharashtra using machine learning with LSTM: a novel expectation-maximization technique

Agriculture in Maharashtra has immense importance in India, acting as the back-bone of the economy and a primary livelihood source for a significant population. Being the third largest state in India, Maharashtra has a high scale crop production in the country which also has an important impact on the economy. Initially the study focus on developing predictive models that guide farmers in selecting suitable crops for the divisions in the state of Maharashtra. This study presents a Crop Recommendation System (CRS) designed to support Maharashtra’s agricultural sector by utilizing a comprehensive dataset from 2001 to 2022 provided by the India Meteorological Department. This study helps in improvising technical efficiency and productivity of the farmers. Harvesting crops in optimal condition can help to produce efficient harvest hence the research concentrates on providing best crop recommendation system (CRS) with the help of Machine Learning and Deep Learning techniques. The data, enhanced for accuracy using expectation-maximization optimization, underpins predictive models that guide crop selection. EM contributes to a more robust and reliable dataset for subsequent analyses and modeling by iterative estimating and updating missing values based on probabilistic expectations. Key findings show that the Random Forest algorithm excels in predicting suitable crops with 92% accuracy. Further precision is achieved through a Long Short-Term Memory network forecasting weather patterns three months ahead, accommodating temporal data variations. Subsequently, the proposed system leverages these forecasts to recommend five ideal crops per division within Maharashtra, aiding farmers’ decision-making and adapting to regional climatic conditions. A supplementary crop calendar offers monthly district-specific planting guidance. An intuitive Graphical User Interface delivers this information effectively, ensuring practical and informed agricultural choices across the state. In essence, the study provides an innovative tool for enhancing economic stability and sustenance in Maharashtra through technology-driven agriculture recommendations aligned with future weather expectations.

Understanding and predicting pregnancy termination in Bangladesh: A comprehensive analysis using a hybrid machine learning approach.

Reproductive health issues, including unsafe pregnancy termination, remain a significant concern for women in developing nations. This study focused on investigating and predicting pregnancy termination in Bangladesh by employing a hybrid machine learning approach. The analysis used data from the Bangladesh Demographic and Health Surveys conducted in 2011, 2014, and 2017 to 2018. Ten independent variables, encompassing factors such as age, residence, division, wealth index, working status, BMI, total number of children ever born, recent births, and number of living children, were examined for their potential associations with pregnancy termination. The dataset undergoes preprocessing, addressing missing values and balancing class distributions. To predict pregnancy termination, 8 classical machine learning models and hybrid models were used in this study. The models' performance was evaluated based on the area under the curve, precision, recall, and F1 score. The results highlighted the effectiveness of the hybrid models, particularly the Voting hybrid model (area under the curve: 91.97; precision: 84.14; recall: 83.87; F1 score: 83.84), in accurately predicting pregnancy termination. Notable predictors include age, division, and wealth index. These findings hold significance for policy interventions aiming to reduce pregnancy termination rates, emphasizing the necessity for tailored approaches that consider regional disparities and socioeconomic factors. Overall, the study demonstrates the efficacy of hybrid machine learning models in comprehending and forecasting pregnancy termination, offering valuable insights for reproductive health initiatives in Bangladesh and similar contexts.

Editorial

Dear Readers, It gives me great pleasure to announce the sixth regular issue of 2024. In this issue, 6 papers cover various topical aspects of computer science by 19 authors from 7 countries: Brazil, Chile, France, India, Saudi Arabia, Tunisia, and Turkey. In an ongoing effort to further strengthen our journal, I would like to expand the editorial board: If you are a tenured associate professor or above with a strong publication record, you are welcome to apply to join our editorial board. We are also interested in high-quality proposals for special issues on new topics and trends. As always, I would like to thank all authors for their sound research and the editorial board and our guest reviewers for their extremely valuable review effort and suggestions for improvement. These contributions, together with the generous support of the consortium members, help to maintain the quality of our journal.  In this regular issue, I am very pleased to introduce the following 6 accepted articles: George Marsicano, Edna Dias Canedo, Glauco V. Pedrosa, Cristiane S. Ramos, and Rejane M. da C. Figueiredo from Brazil look in their study into digital transformation of public services in a startup-based environment by 23 focus groups and 175 participants in total. Mauricio Solar and Pablo Aguirre from Chile discuss their research on 3D chest CT processes applying a ResNet-50 model to which a new dimension of information has been added, namely a simple autoencoder. In a collaborative work between researchers from Tunisia and France, Rakia Saidi, Fethi Jarray, and Didier Schwab propose in their article a cross-encoder neural network (Cross-BERT-GRU) to deal with the semantic similarity of Arabic sentences that benefits from both the strong contextual understanding of BERT and the sequential modeling capabilities of GRU. Also in a collaborative research between institutions from Tunisia and Saudi Arabia, Nozha Jlidi, Sameh Kouni, Olfa Jemai, and Tahani Bouchrika present their research on MediaPipe with GNN for human activity recognition. G.V.Vidya Lakshmi and S. Gopikrishnan from India look into missing values research for the IoT domain and in particular present IMD-MP technique that improves imputation accuracy for big data analysis in IoT applications based on spatial-temporal correlations. Last but not least, F. Didem Alay, Nagehan İlhan, and M. Tahir Güllüoğlu address in their article a comparative study of data mining methods for solar radiation and temperature forecasting models. Enjoy Reading Cordially,  Christian Gütl, Managing Editor Graz University of Technology, Graz, Austria

A population-based study on trajectories of HER2 status during neoadjuvant chemotherapy for early breast cancer and metastatic progression

BackgroundThis study aimed to investigate the distribution and changes of HER2 status in untreated tumours, in residual disease and in metastasis, and their long-term prognostic implications.MethodsThis is a population-based cohort study of patients treated with neoadjuvant chemotherapy for breast cancer during 2007–2020 in the Stockholm–Gotland region which comprises 25% of the entire Swedish population. Information was extracted from the National Breast Cancer Registry and electronic patient charts to minimize data missingness and misclassification.ResultsIn total, 2494 patients received neoadjuvant chemotherapy, of which 2309 had available pretreatment HER2 status. Discordance rates were 29.9% between primary and residual disease (kappa = 0.534), 31.2% between primary tumour and metastasis (kappa = 0.512) and 33.3% between residual disease to metastasis (kappa = 0.483). Adjusted survival curves differed between primary HER2 0 and HER2-low disease (p < 0.001), with the former exhibiting an early peak in risk for death which eventually declined below the risk of HER2-low. Across all disease settings, increasing the number of biopsies increased the likelihood of detecting HER2-low status.ConclusionHER2 status changes during neoadjuvant chemotherapy and metastatic progression, and the long-term behaviours of HER2 0 and HER2-low disease differ, underscoring the need for obtaining tissue biopsies and for extended follow-up in breast cancer studies.

An Imputation Method for Simulating 3D Well Screen Locations from Limited Regional Well Log Data.

In groundwater modeling studies, accurate spatial and intensity identification of water sources and sinks is of critical importance. Precise construction data about wells (water sinks) are particularly difficult to obtain. The collection of well log data is expensive and laborious, and government records of historic well log data are often imprecise and incomplete with respect to the precise location or pumping rate. In many groundwater modeling studies, such as groundwater quality assessments, a precise representation of the horizontal and vertical distribution of well screens is required to accurately estimate contaminant breakthrough curves. The number of wells under consideration may be very large, for example, in the assessment of nonpoint source pollution. In this paper, we propose an imputation framework that allows for proper reconstruction of missing well data. Our approach exploits available information and tolerates data gaps and imprecisions. We demonstrate the value of this method for a subregion of the Central Valley aquifer (California, USA). We show that our framework imputes missing values that preserve statistical properties of available data and that remain consistent with the known spatial distribution of well screens and pumping rates in the three-dimensional aquifer system.

Evaluating a clinical mentorship intervention on maternal and neonatal complications in primary health facilities in Blantyre district, Malawi: A longitudinal analysis of the Global Action in Nursing program

BackgroundWhile Malawi has made great strides increasing the number of facility-based births, maternal and neonatal mortality remains high. An intervention started in 2019 provided short-course training followed by year-long longitudinal bedside mentorship for nurse midwives at seven health facilities in Blantyre district. The intervention was initiated following invitation from the district to improve outcomes for patients during childbirth. This study examined the impact of the intervention on the reporting of obstetric and neonatal complications and related care. MethodsPatient level data were collected from the District Health Information System 2 database from intervention and non-intervention facilities. Bivariate analysis explored the impact of longitudinal bedside mentorship on select District Health Information System 2 variables at six-month intervals. Outcomes were then analyzed using nonlinear quantile mixed models to better account for the impact of time and clustering at the facility level. ResultsSignificant changes were found in the reporting of obstetric and neonatal complications over time at intervention facilities compared to non-intervention facilities. Intervention facilities showed statistically significant increases in the reporting of prolonged labor, pre/eclampsia, fetal distress, retained placenta, and premature labor. There was also a statistically significant decrease in the reporting of no complications in the multivariate model (95%CI: −0.8 to −0.2). In both the bivariate and multivariate models, the reporting of ‘None’ significantly decreased (0.8 % median), while the reporting of prematurity (0.2 % median) and asphyxia (0.3 % median) both significantly increased. The missingness of data at intervention facilities decreased to almost zero compared to non-intervention facilities. DiscussionThe increase in reported maternal and neonatal complications suggests improved early identification of complications at the facility level. The improved accuracy of patient data from intervention facilities shows the impact mentorship has on data quality which is crucial for the allocation of resources. By highlighting the apparent dose–response relationship of longitudinal bedside mentorship, this study will inform the broader use of mentorship in training programs. Future research is needed to explore the impact of longitudinal mentorship on quality of care.

Electronic medical records imputation by temporal Generative Adversarial Network

The loss of electronic medical records has seriously affected the practical application of biomedical data. Therefore, it is a meaningful research effort to effectively fill these lost data. Currently, state-of-the-art methods focus on using Generative Adversarial Networks (GANs) to fill the missing values of electronic medical records, achieving breakthrough progress. However, when facing datasets with high missing rates, the imputation accuracy of these methods sharply deceases. This motivates us to explore the uncertainty of GANs and improve the GAN-based imputation methods. In this paper, the GRUD (Gate Recurrent Unit Decay) network and the UGAN (Uncertainty Generative Adversarial Network) are proposed and organically combined, called UGAN-GRUD. In UGAN-GRUD, it highlights using GAN to generate imputation values and then leveraging GRUD to compensate them. We have designed the UGAN and the GRUD network. The former is employed to learn the distribution pattern and uncertainty of data through the Generator and Discriminator, iteratively. The latter is exploited to compensate the former by leveraging the GRUD based on time decay factor, which can learn the specific temporal relations in electronic medical records. Through experimental research on publicly available biomedical datasets, the results show that UGAN-GRUD outperforms the current state-of-the-art methods, with average 13% RMSE (Root Mean Squared Error) and 24.5% MAPE (Mean Absolute Percentage Error) improvements.

Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning

Imputation techniques provide means to replace missing measurements with a value and are used in almost all downstream analysis of mass spectrometry (MS) based proteomics data using label-free quantification (LFQ). Here we demonstrate how collaborative filtering, denoising autoencoders, and variational autoencoders can impute missing values in the context of LFQ at different levels. We applied our method, proteomics imputation modeling mass spectrometry (PIMMS), to an alcohol-related liver disease (ALD) cohort with blood plasma proteomics data available for 358 individuals. Removing 20 percent of the intensities we were able to recover 15 out of 17 significant abundant protein groups using PIMMS-VAE imputations. When analyzing the full dataset we identified 30 additional proteins (+13.2%) that were significantly differentially abundant across disease stages compared to no imputation and found that some of these were predictive of ALD progression in machine learning models. We, therefore, suggest the use of deep learning approaches for imputing missing values in MS-based proteomics on larger datasets and provide workflows for these.

Two-stage strategy using denoising autoencoders for robust reference-free genotype imputation with missing input genotypes.

Widely used genotype imputation methods are based on the Li and Stephens model, which assumes that new haplotypes can be represented by modifying existing haplotypes in a reference panel through mutations and recombinations. These methods use genotypes from SNP arrays as inputs to estimate haplotypes that align with the input genotypes by analyzing recombination patterns within a reference panel, and then infer unobserved variants. While these methods require reference panels in an identifiable form, their public use is limited due to privacy and consent concerns. One strategy to overcome these limitations is to use de-identified haplotype information, such as summary statistics or model parameters. Advances in deep learning (DL) offer the potential to develop imputation methods that use haplotype information in a reference-free manner by handling it as model parameters, while maintaining comparable imputation accuracy to methods based on the Li and Stephens model. Here, we provide a brief introduction to DL-based reference-free genotype imputation methods, including RNN-IMP, developed by our research group. We then evaluate the performance of RNN-IMP against widely-used Li and Stephens model-based imputation methods in terms of accuracy (R2), using the 1000 Genomes Project Phase 3 dataset and corresponding simulated Omni2.5 SNP genotype data. Although RNN-IMP is sensitive to missing values in input genotypes, we propose a two-stage imputation strategy: missing genotypes are first imputed using denoising autoencoders; RNN-IMP then processes these imputed genotypes. This approach restores the imputation accuracy that is degraded by missing values, enhancing the practical use of RNN-IMP.

Solving the missing at random problem in semi‐supervised learning: An inverse probability weighting method

We propose an estimator for the population mean under the semi‐supervised learning setting with the Missing at Random (MAR) assumption. This setting assumes that the probability of observing , denoted by , depends on the total sample size and satisfies . To efficiently estimate , we introduce an adaptive estimator based on inverse probability weighting and cross‐fitting. Theoretical analysis reveals that our proposed estimator is consistent and efficient, with a convergence rate of , slower than the typical rate, due to the diminishing proportion of labelled data as the sample size increases in the semi‐supervised setting. We also prove the consistency of inverse probability weighting (IPW)–Nadaraya–Watson density function estimators. Extensive simulations and an application to the Los Angeles homeless data validate the effectiveness of our approach.

Predicting and analyzing the cementing quality of oil well reservoirs based on Bayesian-random forest model

Cementing quality crucially influences the safety, efficiency, and costs of oil and gas wells. Traditional evaluations rely on empirical and qualitative methods, introducing uncertainty. This study analyzed nearly 2000 wells casing and cementing data from 2016 to 2021 in an eastern China oil field. The original data was analyzed using multiple data cleaning techniques, and effectively dealing with missing values and anomalies. Additionally, the application of feature selection techniques such as random forest successfully identified key elements within the data. Three machine learning models and hyperparameter optimization methods were compared. A Bayesian random forest (BS-RF) model was established with hyperparameter optimization for cementing-quality evaluation. Finally, the relationship between the influencing factors and well-cementing quality were determined by the SHapley Additive exPlanations (SHAP) method. This enabled the evaluation and prediction of well cementing quality. The results indicate that the model achieved an accuracy of 93.21%. When the validation set was used for verification, the predictive accuracy was 95%. This indicates that the model is relatively stable and exhibits strong generalization capabilities across diverse datasets. Performance numeric of cement slurry (SPN), drilling fluid density before cementing, deviation angle, and flushing time are the four key features that influence cementing quality. Each has a SHAP value above 0.12. This article provides guidance for future well-cemented construction schemes.

A novel clinical prediction model for in-hospital mortality in sepsis patients complicated by ARDS: A MIMIC IV database and external validation study

BackgroundSepsis complicated by ARDS significantly increases morbidity and mortality, underscoring the need for robust predictive models to enhance patient management. MethodsWe collected data on 6390 patients with ARDS-complicated sepsis from the MIMIC IV database. Following rigorous data cleaning, including outlier management, handling missing values, and transforming variables, we conducted univariate analysis and logistic multivariate regression. We employed the LASSO machine learning algorithm to identify risk factors closely associated with patient outcomes. These factors were then used to develop a new clinical prediction model. The model underwent preliminary assessment and internal validation, and its performance was further tested through external validation using data from 225 patients at a major tertiary hospital in China. This validation assessed the model's discrimination, calibration, and net clinical benefits. ResultsThe model, illustrated by a concise nomogram, demonstrated significant discrimination with an area under the curve (AUC) of 0.711 in the internal validation set and 0.771 in the external validation set, outperforming conventional severity scores such as the SOFA and SAPS II. It also showed good calibration and net clinical benefits. ConclusionsOur model serves as a valuable tool for identifying sepsis patients with ARDS at high risk of in-hospital mortality. This could enable the implementation of personalized treatment strategies, potentially improving patient outcomes.

The Effects of Missing Data Handling Methods on Reliability Coefficients: A Monte Carlo Simulation Study

This study holds significant implications as it examines the impact of different missing data handling methods on the internal consistency coefficients. With Monte Carlo simulations, we manipulated number of items, true reliability, sample size, missing data ratio and mechanisms, to compare relative bias of reliability coefficients. The reliability coefficients under scrutiny in this study encompass Cronbach's Alpha, Heise &amp; Bohrnsted's Omega, Hancock &amp; Mueller's H, Gölbaşı-Şimşek &amp; Noyan's Theta G, Armor's Theta, and Gilmer-Feldt coefficients. Our arsenal of techniques includes single imputation methods like zero, mean, median, and regression imputation, as well as multiple imputation approaches like expectation maximization and random forest. We also employ the classic deletion method known as listwise deletion. The findings suggest that, for missing completely at random (MCAR) or missing at random (MAR) data, single imputation approaches (excluding zero imputation) may still be preferable to expectation maximization and random forest imputation, thereby underscoring the importance of our research.

Comparing preprocessing strategies for 3D-Gene microarray data of extracellular vesicle-derived miRNAs

BackgroundExtracellular vesicle-derived (EV)-miRNAs have potential to serve as biomarkers for the diagnosis of various diseases. miRNA microarrays are widely used to quantify circulating EV-miRNA levels, and the preprocessing of miRNA microarray data is critical for analytical accuracy and reliability. Thus, although microarray data have been used in various studies, the effects of preprocessing have not been studied for Toray’s 3D-Gene chip, a widely used measurement method. We aimed to evaluate batch effect, missing value imputation accuracy, and the influence of preprocessing on measured values in 18 different preprocessing pipelines for EV-miRNA microarray data from two cohorts with amyotrophic lateral sclerosis using 3D-Gene technology.ResultsEighteen different pipelines with different types and orders of missing value completion and normalization were used to preprocess the 3D-Gene microarray EV-miRNA data. Notable results were suppressed in the batch effects in all pipelines using the batch effect correction method ComBat. Furthermore, pipelines utilizing missForest for missing value imputation showed high agreement with measured values. In contrast, imputation using constant values for missing data exhibited low agreement.ConclusionsThis study highlights the importance of selecting the appropriate preprocessing strategy for EV-miRNA microarray data when using 3D-Gene technology. These findings emphasize the importance of validating preprocessing approaches, particularly in the context of batch effect correction and missing value imputation, for reliably analyzing data in biomarker discovery and disease research.

A Novel Dataset and Approach for Adversarial Attack Detection in Connected and Automated Vehicles

Adversarial attacks have received much attention as communication network applications rise in popularity. Connected and Automated Vehicles (CAVs) must be protected against adversarial attacks to ensure passenger and vehicle safety on the road. Nevertheless, CAVs are susceptible to several types of attacks, such as those that target intra- and inter-vehicle networks. These harmful attacks not only cause user privacy and confidentiality to be lost, but they also have more grave repercussions, such as physical harm and death. It is critical to precisely and quickly identify adversarial attacks to protect CAVs. This research proposes (1) a new dataset comprising three adversarial attacks in the CAV network traffic and normal traffic, (2) a two-phased adversarial attack detection technique named TAAD-CAV, where in the first phase, an ensemble voting classifier having three machine learning classifiers and one separate deep learning classifier is trained, and the output is used in the next phase. In the second phase, a meta classifier (i.e., Decision Tree is used as a meta classifier) is trained on the combined predictions from the previous phase to detect adversarial attacks. We preprocess the dataset by cleaning data, removing missing values, and adjusting the Z-score normalization. Evaluation metrics such as accuracy, recall, precision, F1-score, and confusion matrix are employed to evaluate and compare the performance of the proposed model. Results reveal that TAAD-CAV achieves the highest accuracy with a value of 70% compared with individual ML and DL classifiers.