1,720,959 research outputs found
An Improved Model Ensembled of Different Hyper-parameter Tuned Machine Learning Algorithms for Fetal Health Prediction
Get PDFFetal health is a critical concern during pregnancy as it can impact the
well-being of both the mother and the baby. Regular monitoring and timely
interventions are necessary to ensure the best possible outcomes. While there
are various methods to monitor fetal health in the mother's womb, the use of
artificial intelligence (AI) can improve the accuracy, efficiency, and speed of
diagnosis. In this study, we propose a robust ensemble model called ensemble of
tuned Support Vector Machine and ExtraTrees (ETSE) for predicting fetal health.
Initially, we employed various data preprocessing techniques such as outlier
rejection, missing value imputation, data standardization, and data sampling.
Then, seven machine learning (ML) classifiers including Support Vector Machine
(SVM), XGBoost (XGB), Light Gradient Boosting Machine (LGBM), Decision Tree
(DT), Random Forest (RF), ExtraTrees (ET), and K-Neighbors were implemented.
These models were evaluated and then optimized by hyperparameter tuning using
the grid search technique. Finally, we analyzed the performance of our proposed
ETSE model. The performance analysis of each model revealed that our proposed
ETSE model outperformed the other models with 100% precision, 100% recall, 100%
F1-score, and 99.66% accuracy. This indicates that the ETSE model can
effectively predict fetal health, which can aid in timely interventions and
improve outcomes for both the mother and the baby.Comment: 23 pages, 6 Tables, 5 Figure
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Influence of artificial intelligence in higher education; impact, risk and counter measure
No full textArtificial Intelligence (AI) is an emerging field that seeks to replicate or emulate human-like cognitive abilities using artificial means. As the world changes, the development and application of AI tools and technologies in areas such as agriculture, medicine, healthcare, and education are growing at an unprecedented pace. This chapter presents a review study on the impact, risks, and countermeasures of artificial intelligence in higher education (AIHE). The chapter begins by discussing the journey of AI in education from its beginning to the present day. It then examines the existing AI tools and technologies in education and explores their potential applications. The chapter goes on to analyze the influences of these tools in education and the challenges and risks they face in higher education. Additionally, it highlights the limitations of AI tools and proposes ways to overcome these gaps. The purpose of this study is to provide updated information to students, teachers, professors, national policymakers, and researchers, as well as to explore the scope of research on AI in higher education. By offering a comprehensive analysis of the impact of AI on higher education (HE), this chapter aims to inform and inspire the academic community to embrace AI as a transformative technology in education
An efficient and explainable machine learning framework for Alzheimer's disease detection
No full textAlzheimer's disease detection is of paramount importance as it is a global health problem in terms of early detection, interventions, and management. The study applies prominent machine learning classification to Alzheimer's disease by integrating longitudinal and cross-sectional data analysis. With careful correlation analysis, we derived the following important predictive features: 'M/F', 'Age', 'Education', 'SES', 'CDR', 'MMSE', 'eTIV', 'nWBV', 'ASF', and 'Hand'. In this study, the entire dataset was divide into training (80%) and testing (20%) sets, with eight machine learning models implemented on it for classification: CatBoost-CB, Logistic Regression-LR, Extreme Gradient Boosting-XGB, Support Vector Machine-SVM, Random Forest-RF, K-Nearest Neighbors-KNN, Decision Tree-DT, and Gradient Boosting-GB. Our results demonstrated superior performance from ensemble models, with RF, XGB, GB, DT, and CB achieving 98.1% accuracy, 98.2% precision, and 98.1% recall. These models showed exceptional performance with a Kappa score of 0.95, indicating strong reliability in classification. We have also used LIME explainability to enhance model transparency, providing insight into the contribution of different features in support of the interpretability of the model. In this study, we stress the power of machine learning for early detection of Alzheimer's disease, and provide future research directions on improving the predictive accuracy and transparency of predictive models
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