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Autism Spectrum Disorder (ASD) diagnostics requires specialized clinical expertise, posing accessibility and affordability barriers for many. To widen the availability of precise screening, this paper examines ensemble machine learning models that combine multiple algorithms for improved accuracy and generalizability. Specifically, this paper compares the performance of K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Naive Bayes, and a Voting Classifier that integrates Logistic Regression, Decision Trees, and KNN. The comparison was conducted on separate datasets of pediatric and adult ASD questionnaire responses. The ensemble model significantly outperforms individual techniques, achieving higher accuracy for both pediatrics and adults with balanced sensitivity and specificity maintained across groups, indicating the viability of accessible community-available screening to alleviate diagnostic bottlenecks. Before scale-up, further model optimization for interpretability and testing on more diverse multi-site data are warranted. Overall, findings demonstrate the feasibility of mobile distributed pre-screening systems leveraging optimized ensembles to predict ASD with high precision across ages, opening possibilities for explainable AI to lower costs and widen access compared to in-person evaluation. [ABSTRACT FROM AUTHOR]

In today's fast-paced technological landscape, advancements are happening at the speed of light. Every aspect of the surroundings is intricately linked to development and technology. AI, cloud computing, and the IoT reign supreme in the realm of technology. In the realm of cyberspace, everything is interconnected through computers and networking devices. With the increasing reliance on computers and the internet, ensuring safety becomes ever more critical. Safeguarding network architecture and the proper use of networking devices and tools play a vital role in cybersecurity. This study has developed a system called Network Intrusion Detection System using ML, which is suitable for home network environments. This paper has created an application that makes a significant impact on real-time network environments by providing security for a particular home network. Leveraging ML in networks has improved the results by providing accuracy and efficiency. The algorithm of Logistic Regression is used to demonstrate network behavior and classify network traffic as either in an "Attack" or "Benign" state. This helps in detecting suspicious activities across the network and can prevent them at a later stage. [ABSTRACT FROM AUTHOR]

This editorial summarizes the papers published in issue 17(2) (2022). The first paper evaluates logistic regression and machine-learning methods for predicting firm bankruptcy. The second paper demonstrates that machine learning outperforms existing tools to improve the estimation of regional input–output tables. The third paper investigates whether network centrality depends on the probability that a tie between two nodes is formed, as well as its intensity. The fourth paper sets out a Bayesian estimation technique to estimate a spatial autoregressive multinomial logit model. The fifth paper develops a statistic to test for several misspecification problems in spatial econometric models. The sixth paper compares the prediction accuracy of spatial and non-spatial econometric models explaining the number of tourist arrivals across countries. [ABSTRACT FROM AUTHOR]

Coauthorship prediction applies predictive analytics to bibliographic data to predict authors who are highly likely to be coauthors. In this study, we propose an approach for coauthorship prediction based on bibliographic network embedding through a graph‐based bibliographic data model that can be used to model common bibliographic data, including papers, terms, sources, authors, departments, research interests, universities, and countries. A real‐world dataset released by AMiner that includes more than 2 million papers, 8 million citations, and 1.7 million authors were integrated into a large bibliographic network using the proposed bibliographic data model. Translation‐based methods were applied to the entities and relationships to generate their low‐dimensional embeddings while preserving their connectivity information in the original bibliographic network. We applied machine learning algorithms to embeddings that represent the coauthorship relationships of the two authors and achieved high prediction results. The reference model, which is the combination of a network embedding size of 100, the most basic translation‐based method, and a gradient boosting method achieved an F1 score of 0.9 and even higher scores are obtainable with different embedding sizes and more advanced embedding methods. Thus, the strengths of the proposed approach lie in its customizable components under a unified framework. [ABSTRACT FROM AUTHOR]

Current electricity sectors will be unable to keep up with commercial and residential customers' increasing demand for data-enabled power systems. Therefore, next-generation power systems must be developed. It is possible for the smart grid, an advanced power system of the future, to make decisions, estimate loads, and execute other data-related jobs. Customers can adjust their needs in smart grid systems by monitoring bill information. Due to their reliance on data networks, smart grids are vulnerable to cyberattacks that could compromise billing data and cause power outages and other problems. A false data injection attack (FDIA) is a significant attack that targets the corruption of state estimation vectors. The primary goal of this paper is to show the impact of an FDIA attack on a power dataset and to use machine learning algorithms to detect the attack; to achieve this, the Python software is used. In the experiment, we used the power dataset from the IoT server of a 10 KV solar PV system (to mimic a smart grid system) in a controlled laboratory environment to test the effect of FDIA and detect this anomaly using a machine learning approach. Different machine learning models were used to detect the attack and find the most suitable approach to achieve this goal. This paper compares machine learning algorithms (such as random forest, isolation forest, logistic regression, decision tree, autoencoder, and feed-forward neural network) in terms of their effectiveness in detecting false data injection attacks (FDIAs). The highest F1 score of 0.99 was achieved by the decision tree algorithm, which was closely followed by the logistic regression method, which had an F1 score of 0.98. These algorithms also demonstrated high precision, recall, and model accuracy, demonstrating their efficacy in detecting FDIAs. The research presented in this paper indicates that combining logistic regression and decision tree in an ensemble leads to significant performance enhancements. The resulting model achieves an impressive accuracy of 0.99, a precision of 1, and an F1 score of 1. [ABSTRACT FROM AUTHOR]

Consumer price index has been in the centre of interest for many years, since being published in 1990s in the Czech Republic but recent price growth raised more questions on methodology and data sources used in price statistics. Users are interested not only in the figures itself but also in statistical issue influencing interpretation and the quality of consumer price index that is often used as an approximation of inflation rate. The paper introduces price statistics compiled by the Czech Statistical Office and it specifically focuses on data sources and in particular scanner data. The paper explains how advanced statistical methods such as machine learning are implemented in official statistical production. We think that the official statistics is being on the historical junction where modern methods are going to be implemented. Our paper shows the usage of machine learning procedures applied on scanner data within consumer price index. Used method is based on logistic regression and powerful Python solution and that provides fast and high quality results. [ABSTRACT FROM AUTHOR]

The main objective of this research paper is to apply logistic regression to estimate IPO subscription status in terms of oversubscription or under subscription. For this purpose, we used SMOTE (Synthetic Minority Over-sampling Technique) to generate minority class cases to rectify class imbalance problems and classification model logistic regression function to further classify the cases into majority class and minority class. KNIME (Konstanz Information Miner) and R Studio were used, as Integrated Development Environments (IDE), to develop the model. The results were quite encouraging with more than 90% accuracy levels for both training and testing datasets. The model was tested with different train-to-test ratios. The model and the results of the study can be used by firms and individuals involved in capital markets to predict the subscription status of a public offering. Further, there is ample scope to improvise the model by using different sets of variables and by applying different machine learning algorithms. [ABSTRACT FROM AUTHOR]

Early-stage prediction of a disease is an important and challenging task. The application of machine learning techniques is playing an important role in this era. Thyroid is one of the chronic endocrine diseases, and approximately 42 million people in India are affected by this disease. This paper presents a comprehensive investigation into the enhancement of classification performance through the novel 'FeatureBoostThyro' (FBT) model. The study evaluates various machine learning algorithms, including stochastic gradient descent (SGD), K nearest neighbor (KNN), logistic regression (LR), naive bayes (NB), and support vector machine (SVM), in conjunction with diverse feature selection methods. The research systematically explores the impact of feature selection techniques such as information gain, relief F, chi-square, gini index, forward selection, backward selection, recursive feature elimination, and LASSO on model performance across the chosen algorithms. The analysis reveals notable variations in performance metrics, including accuracy, precision, recall, and F1-score, providing valuable insights into the interplay between algorithm and feature selection. One main contribution of this research is the introduction of the FBT model, which consistently outperforms other models across various feature selection methods, making it a promising tool for addressing complex classification tasks. The findings contribute to a broader understanding of model selection and optimization in machine learning applications. The proposed model undergoes evaluation using two distinct datasets: the primary dataset acquired from Lata Mangeshkar Hospital in Nagpur and the secondary dataset obtained from the UCI dataset. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common and deadly type of cancer in the world. Based on machine learning algorithms such as XGBoost, random forest, logistic regression, and K-nearest neighbor, this paper establishes different models to classify and predict breast cancer, so as to provide a reference for the early diagnosis of breast cancer. Recall indicates the probability of detecting malignant cancer cells in medical diagnosis, which is of great significance for the classification of breast cancer, so this article takes recall as the primary evaluation index and considers the precision, accuracy, and F1-score evaluation indicators to evaluate and compare the prediction effect of each model. In order to eliminate the influence of different dimensional concepts on the effect of the model, the data are standardized. In order to find the optimal subset and improve the accuracy of the model, 15 features were screened out as input to the model through the Pearson correlation test. The K-nearest neighbor model uses the cross-validation method to select the optimal k value by using recall as an evaluation index. For the problem of positive and negative sample imbalance, the hierarchical sampling method is used to extract the training set and test set proportionally according to different categories. The experimental results show that under different dataset division (8 : 2 and 7 : 3), the prediction effect of the same model will have different changes. Comparative analysis shows that the XGBoost model established in this paper (which divides the training set and test set by 8 : 2) has better effects, and its recall, precision, accuracy, and F1-score are 1.00, 0.960, 0.974, and 0.980, respectively. [ABSTRACT FROM AUTHOR]

In this paper, the authors propose a model that uses text data to identify multiple mediators in a parallel mediation analysis. Text data are widely used in marketing research. In this paper, we propose a model that uses text data to identify multiple mediators in a parallel mediation analysis. Our model is based on the Latent Dirichlet Allocation (LDA) model that incorporates treatment and outcome variables. Treatment variables can affect topic composition in the text data, with topic probabilities used to predict outcomes via a logistic regression model. Lexical priors are introduced to seed topics that researchers consider relevant to an analysis, whereas non-seeded topics allow researchers to find other potential mediation paths. The resulting analysis of mediation replaces the use of rating scales with text that more flexibly reflects the reasons for respondent choices. The assessment of stimuli's effect on topic probabilities provides information on which aspects of stimuli contribute to the change in respondents' choices of words and their latent meanings behind these words. History: Olivier Toubia served as the senior editor for this article. Conflict of Interest Statement: All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or nonfinancial interest in the subject matter or materials discussed in this article. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mksc.2023.0045. [ABSTRACT FROM AUTHOR]

This paper investigated the factors in the technology–organization–environment (TOE) framework that affect the decision of whether to adopt electronic commerce (EC) or not within small‐ and medium‐sized enterprises (SMEs). To this end, a questionnaire‐based survey was conducted to collect data from 60 managers or owners of manufacturing SMEs in Tunisia. Unlike the traditional regression approaches, we referred to novel machine learning (ML) techniques and reveal that ML techniques reach a higher level of performance in forecasting driving factors to EC adoption compared to the traditional logistic regression approach. The achieved results also indicate that EC adoption within SMEs is significantly affected by eight factors, namely, IT vendors' support, the adopted technology complexity degree, chief executive officer (CEO) innovativeness, technology readiness, customers' pressure, firm size, infrastructure compatibility, and the innovative technology‐perceived relative advantage. [ABSTRACT FROM AUTHOR]

This study examines the determinants of life insurance ownership with a focus on rural areas and farming households in the United States. Utilizing data from online surveys conducted in 2019 and 2021, this paper explores how psychological factors, financial knowledge, and household characteristics influence life insurance ownership. Traditional indicators like wealth, income, and age were evaluated alongside less frequently discussed variables such as farm loans and rural residency. Machine learning techniques, including neural networks, Support Vector Machine modeling, Gradient Boosting, and logistic regression, were employed to identify the most robust predictors of life insurance demand. The findings reveal that farming-associated factors, particularly holding a farm loan and living in a farming household, significantly predict life insurance ownership. The study also highlights the complexity of life insurance demand, showing that financial education and management practices are critical determinants. This research underscores the need for tailored financial risk management strategies for rural and farming households and contributes to a nuanced understanding of life insurance demand in varying contexts. [ABSTRACT FROM AUTHOR]

The primary concern of modern technology is cyber attacks targeting the Internet of Things. As it is one of the most widely used networks today and vulnerable to attacks. Real-time threats pose with modern cyber attacks that pose a great danger to the Internet of Things (IoT) networks, as devices can be monitored or service isolated from them and affect users in one way or another. Securing Internet of Things networks is an important matter, as it requires the use of modern technologies and methods, and real and up-to-date data to design and train systems to keep pace with the modernity that attackers use to confront these attacks. One of the most common types of attacks against IoT devices is Distributed Denial-of-Service (DDoS) attacks. Our paper makes a unique contribution that differs from existing studies, in that we use recent data that contains real traffic and real attacks on IoT networks. And a hybrid method for selecting relevant features, And also how to choose highly efficient algorithms. What gives the model a high ability to detect distributed denial-of-service attacks. the model proposed is based on a two-stage process: selecting essential features and constructing a detection model using the K-neighbors algorithm with two classifier algorithms (logistic regression and Stochastic Gradient Descent classifier (SGD), combining these classifiers through ensemble machine learning (stacking), and optimizing parameters through Grid Search-CV to enhance system accuracy. Experiments were conducted to evaluate the effectiveness of the proposed model using the CIC-IoT2023 and CIC-DDoS2019 datasets. Performance evaluation demonstrated the potential of our model in robust intrusion detection in IoT networks, achieving an accuracy of 99.965% and a detection time of 0.20 s for the CIC-IoT2023 dataset, and 99.968% accuracy with a detection time of 0.23 s for the CIC-DDoS 2019 dataset. Furthermore, a comparative analysis with recent related works highlighted the superiority of our methodology in intrusion detection, showing improvements in accuracy, recall, and detection time. [ABSTRACT FROM AUTHOR]

The governance of cross-border data flows around the digital economy, data security, and data sovereignty has become a crucial global governance issue. This paper evaluates the legitimacy of data exit rules of CPTPP countries based on machine learning algorithm models under the perspective of cross-border data flow governance. In this study, four machine learning algorithms, namely, logistic regression, decision tree, random forest, and GBDT, are used to build an outbound data assessment and evaluation model. The confusion matrix is used to classify the outbound data legitimacy dichotomously. The recall, precision, and F1 scores are evaluated to compare the empirical results of each model. Based on this, a logistic regression-based outbound data risk scoring model is introduced to quantify the outbound data risk at a deeper level and to classify the outbound data risk level for the reference of regulators to make more scientific and reasonable decisions. The experimental results show that the machine learning models can meet the needs and applications of practical work and make accurate predictions of outbound data risks. [ABSTRACT FROM AUTHOR]

Questioned document examination aims to assess if a document of interest has been forged. Spectroscopy-based methods are the gold standard for this type of evaluation. In the past 15 years, Matrix-Assisted Laser Desorption Ionisation–Mass Spectrometry Imaging (MALDI-MSI) has emerged as a powerful analytical tool for the examination of finger marks, blood, and hair. Therefore, this study intended to explore the possibility of expanding the forensic versatility of this technique through its application to questioned documents. Specifically, a combination of MALDI-MSI and chemometric approaches was investigated for the differentiation of seven gel pens, through their ink composition, over 44 days to assess: (i) the ability of MALDI MSI to detect and image ink chemical composition and (ii) the robustness of the combined approach for the classification of different pens over time. The training data were modelled using elastic net logistic regression to obtain probabilities for each pen class and assess the time effect on the ink. This strategy led the classification model to yield predictions matching the ground truth. This model was validated using signatures generated by different pens (blind to the analyst), yielding a 100% accuracy in machine learning cross-validation. These data indicate that the coupling of MALDI-MSI with machine learning was robust for ink discrimination within the dataset and conditions investigated, which justifies further studies, including that of confounders such as paper brands and environmental factors. [ABSTRACT FROM AUTHOR]

Knowledge discovery from various perspectives has become a crucial asset in almost all fields. Sentimental analysis is a classification task used to classify the sentence based on the meaning of their context. This paper addresses class imbalance problem which is one of the important issues in sentimental analysis. Not much works focused on sentimental analysis with imbalanced class label distribution. The paper also focusses on another aspect of the problem which involves a concept called "Code Mixing". Code mixed data consists of text alternating between two or more languages. Class imbalance distribution is a commonly noted phenomenon in a code-mixed data. The existing works have focused more on analyzing the sentiments in a monolingual data but not in a code-mixed data. This paper addresses all these issues and comes up with a solution to analyze sentiments for a class imbalanced code-mixed data using sampling technique combined with levenshtein distance metrics. Furthermore, this paper compares the performances of various machine learning approaches namely, Random Forest Classifier, Logistic Regression, XGBoost classifier, Support Vector Machine and Naïve Bayes Classifier using F1- Score. [ABSTRACT FROM AUTHOR]

Considering the persistent challenge of early heart attack detection in patients, despite significant advancements in medical systems, this research project is motivated by the imperative need to develop effective predictive machine learning models. The central problem addressed here in is the identification of individuals at risk of experiencing a heart attack. In response to this problem, two distinct models have been devised and meticulously evaluated, namely decision trees and logistic regression, each designed to fulfil the primary objective of this research. Through a rigorous analysis and thorough evaluation of the results, we have scrutinised the performance of these models. The comparison between decision trees and logistic regression provides valuable insights into their efficacy in predicting heart attacks. The culmination of this endeavor not only contributes to the growing body of knowledge in heart attack prediction and provides healthcare professionals with powerful tools for early diagnosis, potentially saving lives and improving patient outcomes. [ABSTRACT FROM AUTHOR]

The most common reason for women's deaths worldwide is Breast Cancer, which can be controlled if detected at an early stage. There are numerous techniques based on Machine Learning as well as Deep Learning can be used for the diagnoses of the cancer. This paper employs Convolutional Neural Network (CNN), which also performs feature extraction for the Machine Learning algorithms in comparison with Support Vector Machine (SVM), Naïve Bayes classifier (NB), K-Nearest Neighbor (KNN), Logistic Regression (LR) and Random Forest (RF). The system is experimented on BreaK-Hist Dataset obtained from Kaggle. The train_test_split method is used to bifurcate the raw data to distinct sets in order to achieve appropriate results. Softmax activation function predicts the outcomes based on probabilities. The main objective is to correctly classify the data and asses the performance of the models in comparison based on precision and recall rate. Further it also asses based on f1-score and as well as accuracy. When experimented the results show that CNN gives maximum accuracy with the lower most loss/error rate. [ABSTRACT FROM AUTHOR]

In recent years, the latest maintenance modelling techniques that adopt the data-based method, such as machine learning (ML), have brought about a broad range of useful applications. One of the major challenges in the automotive industry is the early detection of component failure for quick response, proper action, and minimizing maintenance costs. A vital component of an automobile system is an air pressure system (APS). Failure of APS without adequate and quick responses may lead to high maintenance costs, loss of lives, and component damages. This paper addresses classification problem where we detect whether a fault does or does not belong to APS. If a failure occurs in APS, it is classified as positive class; otherwise, it is classified as negative class. Hence, in this paper, we propose broad embedded logistic regression (BELR). The proposed BELR is applied to predict APS failure. It combines a broad learning system (BLS) and logistic regression (LogR) classifier as a fusion model. The proposed approach capitalizes on the strength of BLS and LogR for a better APS failure prediction. Additionally, we employ the BLS's feature-mapped nodes for extracting features from the input data. Additionally, we use the enhancement nodes of the BLS to enhance the features from feature-mapped nodes. Hence, we have features that can assist LogR for better classification performances, even when the data is skewed to the positive class or negative class. Furthermore, to prevent the curse of dimensionality, a common problem with high-dimensional data sets, we utilize principal component analysis (PCA) to reduce the data dimension. We validate the proposed BELR using the APS data set and compare the results with the other robust machine learning classifiers. The commonly used evaluation metrics, namely Recall, Precision, an F1-score, to evaluate the model performance. From the results, we validate that performance of the proposed BELR. [ABSTRACT FROM AUTHOR]

The impact of common and rare variants in COVID-19 host genetics has been widely studied. In particular, in Fallerini et al. (Human genetics, 2022, 141, 147-173), common and rare variants were used to define an interpretable machine learning model for predicting COVID-19 severity. First, variants were converted into sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. After that, the Boolean features, selected by these logistic models, were combined into an Integrated PolyGenic Score (IPGS), which offers a very simple description of the contribution of host genetics in COVID-19 severity. IPGS leads to an accuracy of 55%-60% on different cohorts, and, after a logistic regression with both IPGS and age as inputs, it leads to an accuracy of 75%. The goal of this paper is to improve the previous results, using not only the most informative Boolean features with respect to the genetic bases of severity but also the information on host organs involved in the disease. In this study, we generalize the IPGS adding a statistical weight for each organ, through the transformation of Boolean features into "Boolean quantum features," inspired by quantum mechanics. The organ coefficients were set via the application of the genetic algorithm PyGAD, and, after that, we defined two new integrated polygenic scores (IPGS¹ph and IPGS²ph). By applying a logistic regression with both IPGS, (IPGS²ph (or indifferently IPGS¹ph) and age as inputs, we reached an accuracy of 84%-86%, thus improving the results previously shown in Fallerini et al. (Human genetics, 2022, 141, 147-173) by a factor of 10%. [ABSTRACT FROM AUTHOR]

To investigate pedestrian route choice mechanisms from a perspective distinct from that employed in discrete choice models (DCMs), this study utilizes machine learning models and employs SHapley Additive exPlanations (SHAP) for model interpretation. The data used in this paper come from several pedestrian flow experiments with two routes, which were recorded by UAV. Our findings indicate that logistic regression (similar to a binary logit model) exhibits good computational efficiency but falls short in predictive accuracy when compared to other machine learning models. Among the 12 machine learning models assessed, by calculating the new indicator named OP, we find that eXtreme Gradient Boosting (XGB) and Light Gradient Boosting (LGB) strike the best balance between accuracy and computational efficiency. Regarding feature contribution, our analysis reveals that bottlenecks exert the most significant influence on pedestrian route choice behavior, followed by the time it takes pedestrians to return from the end of the route to the origin (reflecting pedestrian characteristics and attitudes). While the pedestrian density of the shorter route contributes less compared to bottlenecks and return time, it exhibits a threshold effect, meaning that once the density of the shorter route surpasses a certain threshold, most pedestrians opt for the longer route. [ABSTRACT FROM AUTHOR]

Copyright of Ingeniería Solidaria is the property of Universidad Cooperativa de Colombia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The smart grid (SG) ensures the flow of electricity and data between suppliers and consumers. The reliability and security of data also play an important role in the overall management. This can be achieved with the help of adaptive energy management (AEM). This research aims to highlight the big data issues and challenges faced by AEM employed in SG networks. In this paper, we will discuss the most commonly used data processing methods and will give a detailed comparison between the outputs of some of these methods. We consider a dataset of 50,000 instances from consumer smart meters and 10,000 attributes from previous fault data and 12 attributes. The comparison will tell us about the reliability, stability, and accuracy of the system by comparing the output of the various graphical plots of these methods. The accuracy percentage of the linear regression method is 98%; for the logistic regression method, it is 96%; and for K-Nearest Neighbors, it is 92%. The results show that the linear regression method applied gives the highest accuracy compared to logistic regression and K-Nearest Neighbors methods for prediction analysis of big data in SGs. This will ensure their use in future research in this field. [ABSTRACT FROM AUTHOR]

Social media platforms like Instagram have become a haven for online scams, employing various deceptive tactics to exploit unsuspecting users. This paper investigates advanced algorithmic approaches to combat this growing threat. We explore various machine learning models for scam profile detection on Instagram. Our methodology involves collecting a comprehensive dataset from a trusted source and meticulously preprocessing the data for analysis. We then evaluate the effectiveness of a suite of machine learning algorithms, including decision trees, logistic regression, SVMs, and other ensemble methods. Each model's performance is measured using established metrics like accuracy, precision, recall, and F1-scores. Our findings indicate that ensemble methods, particularly random forest, XGBoost, and gradient boosting, outperform other models, achieving accuracy of 90%. The insights garnered from this study contribute significantly to the body of knowledge in social media forensics, offering practical implications for the development of automated tools to combat online deception. [ABSTRACT FROM AUTHOR]

The paper presents research, the purpose of which is to define a machine learning model for express analysis of network traffic in a distributed computer system for managing decentralized generation facilities based on renewable energy. The available tools for monitoring processes in the computer network are considered. The problem of network traffic anomaly detection is presented as a binary classification problem. The input data of the model is represented by 10 features, which are determined on the basis of the RFE method based on the results of computational experiments on classification based on the examples of the Network Intrusion Detection dataset. To determine the optimal model, both neural network models: MLP, RNN, LSTM, and traditional machine learning models: KNN, Logistic Regression, Decision Tree, GBM were investigated. Implementations of these models from Scikit-learn and TensorFlow resources were configured and trained. According to the results of computational experiments, the most accurate models for detecting network traffic anomalies in a distributed computer system were determined: LSTM (F1 score: 0.97 on the test sample, 0.95 - in working mode) and RNN (F1 score: 0.97 on the test sample, 0.94 - in working mode). Tests have shown that RNN or LSTM anomaly prediction when transmitting a packet does not change the time order relative to transmission without prediction. The use of defined machine learning models to build subsystems for detecting network traffic anomalies in systems of distributed generation of electric energy based on renewable sources will allow to increase resistance to failures and force majeure situations, the efficiency of their work, especially taking into account the challenges to the operation of the energy system of Ukraine during the war. [ABSTRACT FROM AUTHOR]

Refactoring is a maintenance task that aims at enhancing the quality of a software's source code by restructuring it without affecting the external behavior. Move method refactoring (MMR) involves reallocating a method by moving it from one class to the class in which the method is used most. Several studies have been performed to explore the impact of MMR on several quality attributes. However, these studies have several limitations related to the applied approaches, considered quality attributes, and size of the selected datasets. This paper reports an empirical study that applies statistical and machine learning (ML) approaches to explore the impact of MMR on code quality. The study overcame the limitations of the existing studies, and this improvement is expected to make the results of this study more reliable and trustworthy. We considered eight quality attributes and thirty quality measures, and a total of approximately 4 K classes from seven Java open-source systems were involved in the study. The results provide evidence that most of the quality attributes were significantly improved by MMR in most cases. In addition, the results show that a limited number of measures, when considered individually, have a significant ability to predict MMR, whereas most of the considered measures, when considered together, significantly contribute to the MMR prediction model. The constructed ML-based prediction model has an area under curve (AUC) value of 96.6%. [ABSTRACT FROM AUTHOR]

Discrete choice models (DCMs) are applied in many fields and in the statistical modelling of consumer behavior. This paper focuses on a form of choice experiment, best–worst scaling in discrete choice experiments (DCEs), and the transition probability of a choice of a consumer over time. The analysis was conducted by using simulated data (choice pairs) based on data from Flynn's (2007) 'Quality of Life Experiment'. Most of the traditional approaches assume the choice alternatives are mutually exclusive over time, which is a questionable assumption. We introduced a new copula-based model (CO-CUB) for the transition probability, which can handle the dependent structure of best–worst choices while applying a very practical constraint. We used a conditional logit model to calculate the utility at consecutive time points and spread it to future time points under dynamic programming. We suggest that the CO-CUB transition probability algorithm is a novel way to analyze and predict choices in future time points by expressing human choice behavior. The numerical results inform decision making, help formulate strategy and learning algorithms under dynamic utility in time for best–worst DCEs. [ABSTRACT FROM AUTHOR]

The parameter κ is a general agreement structure used across many fields, such as medicine, machine learning and the pharmaceutical industry. A popular estimator for κ is Cohen's κ ; however, this estimator does not account for multiple influential factors. The primary goal of this paper is to propose an estimator of agreement for a binary response using a logistic regression framework. We use logistic regression to estimate the probability of a positive evaluation while adjusting for factors. These predicted probabilities are then used to calculate expected agreement. It is shown that ignoring needed adjustment measures, as in Cohen's κ , leads to an inflated estimate of κ and a situation similar to Simpson's paradox. Simulation studies verified mathematical relationships and confirmed estimates are inflated when necessary covariates are left unadjusted. Our method was applied to an Alzheimer's disease neuroimaging study. The proposed approach allows for inclusion of both categorical and continuous covariates, includes Cohen's κ as a special case, offers an alternative interpretation, and is easily implemented in standard statistical software. [ABSTRACT FROM AUTHOR]

Background: Machine Learning models have been applied in various healthcare fields, including Audiology, to predict disease outcomes. The prognosis of sudden sensorineural hearing loss is difficult to predict due to the variable course of the disease. Hence, researchers have attempted to utilize ML models to predict the outcome of patients with sudden sensorineural hearing loss. The objectives of this study were to review the performance of these machine learning models and assess their applicability in real-world settings. Methods: A systematic search was conducted in PubMed, Web of Science and Scopus. Only studies that built machine learning prediction models were included, and studies that used algorithms such as logistic regression only for the purpose of adjusting for confounding variables were excluded. The risk of bias was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST). Results: After screening, a total of 7 papers were eligible for synthesis. In total, these studies built 48 ML models. The most common utilized algorithms were Logistic Regression, Support Vector Machine (SVM) and boosting. The area under the curve of the receiver operating characteristic curve ranged between 0.59 and 0.915. All of the included studies had a high risk of bias; hence there are concerns regarding their applicability. Conclusion: Although these models showed great performance and promising results, future studies are still needed before these models can be applied in a real-world setting. Future studies should employ multiple cohorts, different feature selection methods, and external validation to further validate the models' applicability. [ABSTRACT FROM AUTHOR]

It is an important deployment of the Party Central Committee and the State Council to fully promote the employment of college graduates with higher quality, and salary is an important indicator of quality measurement. This paper takes the cross-sectional data of the employment of graduates from a financial and economic university in 2020 as the sample; whether the actual starting salary is a high salary as the dependent variable; and human capital, social capital, labor market as the explanatory variables and uses R to establish a logistic regression model to analyze the determinants of the high salary of graduates. Five machine learning methods, SVM, naive Bayes, CART, random forest, and XGBoost, are used to predict whether graduates can get a high starting salary, compare the advantages and disadvantages of various methods horizontally, optimize the parameters at the same time, and further enhance the performance of the model. Based on the employment data of graduate students in a university of finance and economics in 2020, this paper makes an empirical study. The study shows that academic qualifications, professional disciplines, employment regions, employment industries, the nature of employment units, gender, and whether they have served as student cadres have a significant impact on whether graduates can get "high salaries." The main factors affecting the starting salary of graduates are the accumulation of human capital and social capital, but the segmentation of labor market is also the main reason affecting the starting salary of graduates. The prediction results of several models show that the integrated models have better performance than single models, and the XGBoost model is the best, which can help predict whether graduates get high salary. [ABSTRACT FROM AUTHOR]

Although the Chinese government has shifted from a one-child policy to a two-child policy (allowing a couple to have up to two children) since 2016 in response to the aging population, the policy results have been unsatisfactory. This is the first paper to systematically investigate the factors influencing residents' intentions to have a second child. The research focuses on the perspective of individual, family, and social characteristics based on the Chinese General Social Survey (CGSS) from 2017 to 2018. Three machine learning methods are used in conjunction with logistic regression to reveal that the intention of having a second child increases heavily with age, more siblings in the family of origin, and better health. The family income, which is currently the focus of the literature and is statistically significant, is only sixth most important. This study further reveals differences between genders: Women with a lower level of education and religious beliefs prefer to have a second child, whereas for men, non-agricultural hukou and marriage are the position factors. The results of this study also illustrate the importance of future research focusing on the relationship of individuals to their family of origin and districts. [ABSTRACT FROM AUTHOR]

Heart disease is becoming the leading cause of death in worldwide. In today's world due to work load and changes in the human lifestyle lead to many diseases. Early detection of any disease prevents human life and save it from other damage also, artificial intelligence techniques provide the greatest results for every sector, health care or medical science is one of them. In this paper, we present cardiovascular disease prediction using machine learning techniques such as logistic regression, random forest, and k-nearest neighbor classifier, our result shows that better than existing techniques, here results are evaluated in the form of quality of performance parameters measured such as accuracy, precision, recall, and F1-score, all the results are based on respective confusion matrix which is based on some parameters like true positive rate, false positive rate, true negative rate, and false negative rate. [ABSTRACT FROM AUTHOR]

Background: Teachers often rely on the use of open‐ended questions to assess students' conceptual understanding of assigned content. Particularly in the context of mathematics; teachers use these types of questions to gain insight into the processes and strategies adopted by students in solving mathematical problems beyond what is possible through more close‐ended problem types. While these types of problems are valuable to teachers, the variation in student responses to these questions makes it difficult, and time‐consuming, to evaluate and provide directed feedback. It is a well‐studied concept that feedback, both in terms of a numeric score but more importantly in the form of teacher‐authored comments, can help guide students as to how to improve, leading to increased learning. It is for this reason that teachers need better support not only for assessing students' work but also in providing meaningful and directed feedback to students. Objectives: In this paper, we seek to develop, evaluate, and examine machine learning models that support automated open response assessment and feedback. Methods: We build upon the prior research in the automatic assessment of student responses to open‐ended problems and introduce a novel approach that leverages student log data combined with machine learning and natural language processing methods. Utilizing sentence‐level semantic representations of student responses to open‐ended questions, we propose a collaborative filtering‐based approach to both predict student scores as well as recommend appropriate feedback messages for teachers to send to their students. Results and Conclusion: We find that our method outperforms previously published benchmarks across three different metrics for the task of predicting student performance. Through an error analysis, we identify several areas where future works may be able to improve upon our approach. Lay Description: What is already known about this topic: Open‐ended questions are used by teachers in the domain of mathematics to assess their students understanding but automated support for these types of questions are limited in online learning platforms. Recent advancements in areas of machine learning and natural language processing have led to promising results in a range of domains and applications. What this paper adds: Emulating how teachers identify similar student answers can be used to build tools that support teachers in assessing and providing feedback to student open‐ended work. Implications for practice: Developing better automated supports can increase the amount of direct feedback students receive to guide their learning. [ABSTRACT FROM AUTHOR]

English expression, language characteristics and usage norms are quite special, which is quite different from Chinese. This has special requirements for auxiliary teaching tools that use computer technology for English text processing. Based on neural network algorithm, this paper combines the actual needs of English grammar error correction to construct an English grammar error correction model based on neural network. In data processing, after feature selection, logistic regression model is used to analyze the influence of different features on article error correction. The article error correction incorporating word vector features mainly explores how to effectively express the features in English grammar error correction. In addition, this paper proposes two methods to optimize the feature representation in article error correction. One is to directly use the word vector corresponding to the word as a feature, replacing the original One-hot encoding, and the other uses a clustering method to compress the article features. Finally, this paper designs experiments to study the performance of the model constructed in this paper. The results obtained show that the model constructed in this paper has a certain effect. [ABSTRACT FROM AUTHOR]

The ease with which mobile money is used to facilitate cross‐border payments presents a global threat to law enforcement in the fight against money laundering and terrorist financing. This paper aims to utilize machine learning classifiers to predict transactions flagged as a fraud in mobile money transfers. The data for this study were obtained from real‐time transactions that simulate a well‐known mobile transfer fraud scheme. Logistic regression is used as the baseline model and is compared with ensemble and gradient descent models. The results indicate that the logistic regression model still showed reasonable performance while not performing as well as the other models. Among all the measures, the random forest classifier exhibited outstanding performance. The amount of money transferred emerged as the top feature for predicting money laundering transactions in mobile money transfers. These findings suggest that further research is needed to enhance the logistic regression model, and the random forest classifier should be explored as a potential tool for law enforcement and financial institutions to detect money laundering activities in mobile money transfers. [ABSTRACT FROM AUTHOR]

The prime purpose of the proposed study is to construct a novel predictive scheme for assisting in the prognosis of criticality using the MIMIC-III dataset. With the adoption of various analytics and advanced computing in the healthcare system, there is an increasing trend toward developing an effective prognostication mechanism. Predictive-based modeling is the best alternative to work in this direction. This paper discusses various scientific contributions using desk research methodology towards the Medical Information Mart for Intensive Care (MIMIC-III). This open-access dataset is meant to help predict patient trajectories for various purposes ranging from mortality forecasting to treatment planning. With a dominant machine learning approach in this perspective, there is a need to discover the effectiveness of existing predictive methods. The resultant outcome of this paper offers an inclusive discussion about various available predictive schemes and clinical diagnoses using MIMIC-III in order to contribute toward better information associated with its strengths and weaknesses. Therefore, the paper provides a clear visualization of existing schemes for clinical diagnosis using a systematic review approach. [ABSTRACT FROM AUTHOR]

Milk is considered as complete food as it contains rich set of proteins and vitamins. Therefore determining quality of cow milk plays an important role in today's research. In this paper four methods are implemented to check quality of cow milk using dataset consists of 1059 milk samples taken from various cows. Three grades of milk grade A, B, C are considered based on different features of cow milk. Various machine learning methods K Nearest neighbors, Logistic regression, Support Vector machine and ANN are implemented. Accuracy of these methods is then compared. It has been observed that the results of KNN (n=3) is more accurate amongst all four methods implemented in the proposed research work. [ABSTRACT FROM AUTHOR]

In such an advancing world, telecommunication companies confront the relentless problems of customer churn. Churning can be defined as cancelling the present company's subscription and buying a new service plan from another company. Churn Prediction is the technique that is used to detect these customers before they churn. For example, when JIO was launched, there was a significant change in the number of customers of Airtel, Idea, etc. Many of them churned and shifted to Jio as they offered economical plans. This paper focuses on utilizing machine learning algorithms like logistic regression, Extreme Gradient Boosting (XGBoost), Random Forest, etc to predict the customers who are likely to churn. We initiated the project by thoroughly exploring a Telecom Customer Churn dataset (WA_Fn-UseC_-Telco-Customer- Churn) available on Kaggle. This dataset includes several features like customer characteristics, service usage patterns and contract details. The insights from these features are important for the prediction of customer churn. Furthermore, we emphasize the development of a user-friendly front-end interface using StreamLit. This tool will enable telecom service providers to input customer information and receive real-time churn predictions, improving decision-making processes. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a privacy-preserving collective machine learning paradigm. Vertical federated learning (VFL) deals with the case where participants share the same sample ID space but have different feature spaces, while label information is owned by one participant. Early studies of VFL supported two participants and focused on binary-class logistic regression problems, while recent studies have put more attention on specific aspects such as communication efficiency and data security. In this paper, we propose the multi-participant multi-class vertical federated learning (MMVFL) framework for multi-class VFL problems involving multiple parties. By extending the idea of multi-view learning (MVL), MMVFL enables label sharing from its owner to other VFL participants in a privacy-preserving manner. To demonstrate the effectiveness of MMVFL, a feature selection scheme is incorporated into MMVFL to compare its performance against supervised feature selection and MVL-based approaches. The proposed framework is capable of quantifying feature importance and measuring participant contributions. It is also simple and easy to combine with other communication and security techniques. The experiment results on feature selection for classification tasks on real-world datasets show that MMVFL can effectively share label information among multiple VFL participants and match the multi-class classification performance of existing approaches. [ABSTRACT FROM AUTHOR]

This work develops a new method for vector data augmentation. The proposed method applies principal component analysis (PCA), determines the eigenvectors of a set of training vectors for a machine learning (ML) method and uses them to generate the distilled vectors. The training and PCA-distilled vectors have the same dimension. The user chooses the number of vectors to be distilled and augmented to the set of training vectors. A statistical approach determines the lowest number of vectors to be distilled such that when augmented to the original vectors, the extended set trains an ML classifier to achieve a required accuracy. Hence, the novelty of this study is the distillation of vectors with the PCA method and their use to augment the original set of vectors. The advantage that comes from the novelty is that it increases the statistics of ML classifiers. To validate the advantage, we conducted experiments with four public databases and applied four classifiers: a neural network, logistic regression and support vector machine with linear and polynomial kernels. For the purpose of augmentation, we conducted several distillations, including nested distillation (double distillation). The latter notion means that new vectors were distilled from already distilled vectors. We trained the classifiers with three sets of vectors: the original vectors, original vectors augmented with vectors distilled by PCA and original vectors augmented with distilled PCA vectors and double distilled by PCA vectors. The experimental results are presented in the paper, and they confirm the advantage of the PCA-distilled vectors increasing the classification statistics of ML methods if the distilled vectors augment the original training vectors. [ABSTRACT FROM AUTHOR]

Randomized prospective studies represent the gold standard for experimental design. In this paper, we present a randomized prospective study to validate the benefits of combining rule-based and data-driven natural language understandingmethods in a virtual patient dialogue system. The system uses a rule-based pattern matching approach together with a machine learning (ML) approach in the form of a text-based convolutional neural network, combining the two methods with a simple logistic regression model to choose between their predictions for each dialogue turn. In an earlier, retrospective study, the hybrid system yielded a nearly 50% error reduction on our initial data, in part due to the differential performance between the two methods as a function of label frequency. Given these gains, and considering that our hybrid approach is unique among virtual patient systems, we compare the hybrid system to the rule-based system by itself in a randomized prospective study. We evaluate 110 unique medical student subjects interacting with the system over 5,296 conversation turns, to verify whether similar gains are observed in a deployed system. This prospective study broadly confirms the findings from the earlier one but also highlights important deficits in our training data. The hybrid approach still improves over either rule-based or ML approaches individually, even handling unseen classes with some success. However, we observe that live subjects ask more out-of-scope questions than expected. To better handle such questions, we investigate several modifications to the system combination component. These show significant overall accuracy improvements and modest F1 improvements on out-of-scope queries in an offline evaluation. We provide further analysis to characterize the difficulty of the out-of-scope problem that we have identified, as well as to suggest future improvements over the baseline we establish here. [ABSTRACT FROM AUTHOR]

Early detection of diabetes is crucial because of its incurable nature. Several diabetes prediction models have been developed using machine learning techniques (MLTs). The performance of MLTs varies for different accuracy measures. Thus, selecting appropriate MLTs for diabetes prediction is challenging. This paper proposes a multicriteria decision-making (MCDM) based framework for evaluating MLTs applied to diabetes prediction. Initially, three MCDM methods--WSM, TOPSIS, and VIKOR--are used to determine the individual ranks of MLTs for diabetes prediction performance by using various comparable performance measures (PMs). Next, a fusion approach is used to determine the final rank of the MLTs. The proposed method is validated by assessing the performance of 10 MLTs on the Pima Indian diabetes dataset using eight evaluation metrics for diabetes prediction. Based on the final MCDM rankings, logistic regression is recommended for diabetes prediction modeling. [ABSTRACT FROM AUTHOR]

Globally, heart disease is the primary cause of death. Early detection of this disease enables cardiologists to make more accurate judgments regarding the health of their patients. Due to machine learning's ability to identify patterns in data, its use in the medical industry has increased. Many heart disease prediction models have been developed by various researchers utilizing machine learning techniques (MLTs). The performance of MLTs on heart disease prediction may vary for different accuracy measures. Thus, the choice of the appropriate machine-learning technique for heart disease prediction is a challenging task. This paper proposes a multi-criteria decision-making (MCDM)-based method to evaluate the MLTs for heart disease prediction considering various performance measures taken into account altogether. The proposed approach uses the concept of a combined compromise solution (CoCoSo)- an MCDM method. For validation of the proposed approach, an experimental study was conducted to evaluate the performance of fifteen machine learning techniques for predicting heart disease over three heart disease datasets considering six performance measures taken into account altogether. Results show that the logistic regression and support vector machine are recommended as the most suitable MLTs for heart disease prediction modeling with respect to six performance measures considered simultaneously. [ABSTRACT FROM AUTHOR]