Article
Computer Science, Information Systems
Luis Bote-Curiel, Sergio Ruiz-Llorente, Sergio Munoz-Romero, Monica Yague-Fernandez, Arantzazu Barquin, Jesus Garcia-Donas, Jose Luis Rojo-Alvarez
Summary: This study explores the relationships between clinical and genetic factors and disease progression in ovarian cancer using data science techniques. Results show individual differences in certain features between platinum resistant and platinum sensitive groups.
Article
Chemistry, Multidisciplinary
Janka Kabathova, Martin Drlik
Summary: This research focused on predicting student dropout using machine learning classifiers, emphasizing the importance of data understanding and collection, highlighting the limitations of the educational dataset, and demonstrating the performance comparison of several machine learning classifiers.
APPLIED SCIENCES-BASEL
(2021)
Article
Meteorology & Atmospheric Sciences
Bradley Wade Bishop, Ashley Marie Orehek, Hannah R. Collier
Summary: This study aimed to capture the skills of Earth science data managers and librarians through interviews with current job holders, revealing a focus on communication and data handling tasks, as well as the importance of formal education and on-the-job experience. Future professionals in these careers may benefit from tailored education informed by job analyses.
BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY
(2021)
Article
Multidisciplinary Sciences
Alba Llauro, David Fonseca, Susana Romero, Marian Alaez, Jorge Torres Lucas, Maria Martinez Felipe
Summary: This study uses a multidisciplinary approach to parameterize the factors that define the entry profile of undergraduates at the national level in Spain, aiming to reduce the dropout rate in the first year of study. The research results indicate that there may be differences in the weighting systems for personal variables by tutors and the main variables according to discipline, university, and/or region.
Article
Biochemistry & Molecular Biology
Fergal Casey, Soumya Negi, Jing Zhu, Yu H. Sun, Maria Zavodszky, Derrick Cheng, Dongdong Lin, Sally John, Michelle A. Penny, David Sexton, Baohong Zhang
Summary: With advancements in NGS technologies, there is a growing need for bench scientists to access and analyze the petabytes of transcriptional profiling data deposited in public repositories. OmicsView is an open source analytics and visualization platform that allows users to easily explore and mine expression data across various disease areas. The platform comes preloaded with thousands of samples from the GTEx database, providing researchers with the capability to uncover disease pathology and identify robust biomarkers.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2022)
Article
Business
Awad Elsayed Awad Ibrahim, Ahmed A. Elamer, Amr Nazieh Ezat
Summary: This study explores the potential convergence points between big data and accounting and presents exciting research questions for future studies. By reviewing literature and proposing new ideas, the research develops new points of convergence between big data and accounting. The conclusion indicates that big data has the potential to overcome data limitations in accounting techniques and shows significant convergence with three accounting theories.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2021)
Article
Computer Science, Interdisciplinary Applications
John Darrell Van Horn
Summary: Brain scientists now have the capability to collect more data than ever before, which is being increasingly shared and made openly available for analysis. Advances in data processing technology and cloud computing have enabled large-scale processing of brain science results, fundamentally changing how information is communicated. Despite challenges in ethics, professionalism, and motivation, government investments and community pressure are driving the continued growth of large-scale brain and data science.
Article
Computer Science, Artificial Intelligence
Chia-Yen Lee, Chen-Fu Chien
Summary: This study aims to develop a five-phase analytics framework to investigate pitfalls for intelligent manufacturing and suggest protocols for the practical application of data science methodologies in various contexts.
JOURNAL OF INTELLIGENT MANUFACTURING
(2022)
Article
Computer Science, Artificial Intelligence
Zhaojing Luo, Shaofeng Cai, Gang Chen, Jinyang Gao, Wang-Chien Lee, Kee Yuan Ngiam, Meihui Zhang
Summary: The paper introduces an adaptive regularization method that learns the best regularization function using Gaussian Mixture, integrates EM with SGD for update algorithm, and includes lazy update and sparse update algorithms to reduce computational costs. Experimental results demonstrate significant improvement over existing regularization methods on 14 standard benchmark datasets and three types of deep learning/machine learning models.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Computer Science, Interdisciplinary Applications
Alessia Calafiore, Gregory Palmer, Sam Comber, Daniel Arribas-Bel, Alex Singleton
Summary: This study develops a Geographic Data Science framework that transforms Foursquare data into knowledge about the emerging forms and characteristics of cities' neighbourhoods. By employing mobile data from ten global cities and utilizing social media data to reveal intangible aspects of urban life, the framework combines network science and geospatial analysis methods to uncover functional neighbourhoods.
COMPUTERS ENVIRONMENT AND URBAN SYSTEMS
(2021)
Article
Education & Educational Research
Paul T. Von Hippel, Alvaro Hofflinger
Summary: Enrolment in higher education has significantly increased in Latin America, particularly in Chile, but graduation and persistence rates are low. Using data and analytics to identify at-risk students, intervene, and evaluate effectiveness can help improve student success. Predictors such as financial aid and choice of major impact persistence rates.
JOURNAL OF HIGHER EDUCATION POLICY AND MANAGEMENT
(2021)
Article
Computer Science, Artificial Intelligence
Minh Phan, Arno De Caigny, Kristof Coussement
Summary: Managing student dropout in higher education is crucial, and predictive modeling combined with student segmentation can enhance the accuracy of predictions. This study proposes a hybrid decision support framework that incorporates student textual feedback data, confirming its superior performance compared to various benchmarks. The research contributes by introducing a new framework for data-driven student segmentation, highlighting the improved predictive performance by incorporating student textual feedback, and providing useful insights for decision-makers to anticipate and manage student dropout behaviors.
DECISION SUPPORT SYSTEMS
(2023)
Article
Computer Science, Interdisciplinary Applications
Serhat Simsek, Abdullah Albizri, Marina Johnsosn, Tyler Custis, Stephan Weikert
Summary: Predictive analytics and artificial intelligence are crucial for improving organizational performance and managerial decision-making. This study focused on identifying MLB free agents likely to receive a contract, using a design science research paradigm and CAM theory to develop a framework. The research found that a player's statistical performance and factors like age, Wins above Replacement, and last team played for are significant in predicting contract signings.
JOURNAL OF ENTERPRISE INFORMATION MANAGEMENT
(2021)
Article
Computer Science, Software Engineering
Dongyun Han, Abdullah-Al-Raihan Nayeem, Jason Windett, Isaac Cho
Summary: Sub-national governments in the US implement policies to address societal problems, and these policies are often adopted by other states, a process known as policy diffusion. PDViz is a visual analytics approach that allows social scientists to analyze the history and patterns of policy diffusion.
COMPUTER GRAPHICS FORUM
(2023)
Review
Agriculture, Dairy & Animal Science
Luis O. Tedeschi
Summary: The emergence of hybrid intelligent systems combining AI algorithms with classical modeling paradigms can enhance the predictive ability of existing models. However, the lack of transparency and reporting of AI code reproducibility is still a challenge.
JOURNAL OF ANIMAL SCIENCE
(2022)
Article
Engineering, Multidisciplinary
Victor Leiva, Rafael A. dos Santos, Helton Saulo, Carolina Marchant, Yuhlong Lio
Summary: This work proposes a methodology for monitoring a shift in the quantile of a distribution belonging to the log-symmetric family. The parametric bootstrap method is used to determine the sampling distribution and establish control limits. Monte Carlo simulations are conducted to assess the performance of the proposed bootstrap control charts. An application in the field of reliability data is presented. The research also provides an R package named chartslogsym for public use.
QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL
(2023)
Article
Engineering, Environmental
Iqra Sardar, Muhammad Azeem Akbar, Victor Leiva, Ahmed Alsanad, Pradeep Mishra
Summary: This article proposes an autoregressive modeling framework based on machine learning and statistical methods to predict confirmed COVID-19 cases in SAARC countries. By comparing different forecasting models, it is found that the ARIMA model performs well in predicting confirmed cases in these countries.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2023)
Article
Mathematics
Helton Saulo, Roberto Vila, Giovanna V. Borges, Marcelo Bourguignon, Victor Leiva, Carolina Marchant
Summary: Income modeling is crucial in determining workers' earnings and is an important research topic in labor economics. Traditional regressions based on normal distributions are widely used but not suitable for asymmetric income data. This study proposes parametric quantile regressions based on two asymmetric income distributions: Dagum and Singh-Maddala. Monte Carlo simulation studies and empirical data analysis show that both models perform well in model fitting for positively asymmetrically distributed income data. The economic implications of this investigation are discussed, and the proposed models are valuable tools for statisticians and econometricians.
Article
Infectious Diseases
Santiago Ortiz, Alexandra Catano-Lopez, Henry Velasco, Juan P. Restrepo, Andres Perez-Coronado, Henry Laniado, Victor Leiva
Summary: This article retrospectively analyzes confirmed dengue cases in the Antioquia region of Colombia from 2015 to 2020, distinguishing by subregions and dengue severity. The authors conducted exploratory analysis of epidemic data and performed statistical survival analysis using a Cox regression model. The findings identify the hazard and socio-demographic patterns of dengue infections in Antioquia, Colombia from 2015 to 2020.
TROPICAL MEDICINE AND INFECTIOUS DISEASE
(2023)
Article
Biology
Muhammad Zia Rahman, Muhammad Azeem Akbar, Victor Leiva, Abdullah Tahir, Muhammad Tanveer Riaz, Carlos Martin-Barreiro
Summary: The study aims to design and implement an intelligent health monitoring and diagnosis system for critical cardiac arrhythmia COVID-19 patients. The system utilizes artificial intelligence tools, including IoT-based health monitoring and fuzzy logic-based medical diagnosis, and provides intelligent diagnosis and health surveillance by doctors for critical COVID-19 patients or patients in remote locations. Communication with doctors in case of emergency is achieved through sensors, cloud storage, as well as a global system for mobile texts and emails.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Chemistry, Physical
Muhammad Zia Ur Rahman, Mohsin Rizwan, Rabia Liaquat, Victor Leiva, Muhammad Muddasar
Summary: This article focuses on the control problem of microbial electrolysis cell (MEC) systems, develops a robust controller to achieve fast and stable response, and proposes an anti-integral windup control strategy to address the issue of increasing control effort due to error accumulation.
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
(2023)
Article
Statistics & Probability
Yonghui Liu, Jing Wang, Victor Leiva, Alejandra Tapia, Wei Tan, Shuangzhe Liu
Summary: This article proposes a skew-t autoregressive model and estimates its parameters using the expectation-maximization (EM) method. It also develops an influence methodology based on local perturbations for validation. The study identifies influential observations using normal curvatures for four perturbation strategies and assesses their performance through Monte Carlo simulations. An example of financial data analysis on Brent crude futures daily log-returns is presented to investigate the possible impact of the COVID-19 pandemic.
JOURNAL OF APPLIED STATISTICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Yonghui Liu, Jing Wang, Dawei Shi, Victor Leiva, Shuangzhe Liu
Summary: In this paper, a score test is proposed to study a vector autoregressive model and detect extreme values. Maximum likelihood estimators and information matrix are derived using a likelihood approach. The score statistic for the vector autoregressive model is established to identify influential cases or outliers. The effectiveness of the diagnostics is examined through simulation study. The model is applied to analyze monthly log-returns of IBM stock and the S&P 500 index. Comparisons between the score test and the local influence method are made, revealing that the score test is more effective while the local influence analysis can identify more influential cases.
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
(2023)
Article
Mathematics, Interdisciplinary Applications
Prasantha Bharathi Dhandapani, Victor Leiva, Carlos Martin-Barreiro, Maheswari Rangasamy
Summary: In this paper, a SIVR model using the Laplace Adomian decomposition is introduced, which focuses on studying the characteristics of vaccination in infected communities. The epidemiological parameters are analyzed using equilibrium stability and numerical analysis techniques. The model establishes the chance for the next wave of any pandemic disease and demonstrates that a consistent vaccination strategy could control it. This work is important for future research on COVID-19 and pandemic diseases as it considers the vaccinated population.
FRACTAL AND FRACTIONAL
(2023)
Review
Mathematics
Raydonal Ospina, Joao A. M. Gondim, Victor Leiva, Cecilia Castro
Summary: This article focuses on the issues presented by the COVID-19 pandemic and examines the use of ARIMA models for short-term forecasting. The study highlights the importance of accurate and timely predictions for public health strategies and interventions. The research also emphasizes the limitations of ARIMA models for long-term predictions.
Article
Mathematics
Jorge Figueroa-Zuniga, Juan G. Toledo, Bernardo Lagos-Alvarez, Victor Leiva, Jean P. Navarrete
Summary: Extensive research has examined models utilizing the Kumaraswamy distribution for describing continuous variables with bounded support. This study focuses on the trapezoidal Kumaraswamy model and proposes a parameter estimation method using the stochastic expectation maximization algorithm, which overcomes challenges faced by the traditional expectation maximization algorithm. The results are applied to modeling daily COVID-19 cases in Chile.
Article
Biochemistry & Molecular Biology
Raydonal Ospina, Adenice G. O. Ferreira, Helio M. de Oliveira, Victor Leiva, Cecilia Castro
Summary: This research aims to improve the classification and prediction of ischemic heart diseases using machine learning techniques. Novel non-invasive indicators called Campello de Souza features were introduced and evaluated with a comprehensive dataset. The study demonstrates the potential of machine learning algorithms in streamlining diagnostic procedures and reducing errors and dependency on extensive clinical testing.
Article
Biology
Jeniffer D. Sanchez, Leandro C. Rego, Raydonal Ospina, Victor Leiva, Christophe Chesneau, Cecilia Castro
Summary: In this study, sensitivity analysis in similarity-based predictive models is performed, using computational simulations and two distinct methodologies, with a focus on a biological application. A linear regression model is used as a reference point, and the coefficient of variation of parameter estimators is calculated to gauge sensitivity. Results show that the first approach outperforms the second one when dealing with categorical variables and offers the advantage of being more parsimonious. Predictive models based on empirical similarity are crucial in biology and data science, and this study provides insights into how to handle categorical variables effectively.
Article
Mathematics, Applied
Carlos Martin-Barreiro, Xavier Cabezas, Victor Leiva, Pedro Ramos-De Santis, John A. Ramirez-Figueroa, Erwin J. Delgado
Summary: This study analyzes the number of vaccinated cases and deaths due to COVID-19 in ten South American countries using principal component analysis and K-means analysis. The countries are classified into groups based on these variables, which reveal common properties and differences. Factors such as political decisions, availability of resources, bargaining power with suppliers, and health infrastructure can affect the vaccination process and timely care. Most countries acted promptly in terms of vaccination, with the exception of two countries. All countries experienced peaks in the number of deaths at some point during the study period.
Article
Mathematical & Computational Biology
Francisco J. Perdomo-Arguello, Estelina Ortega-Gomez, Purificacion Galindo-Villardon, Victor Leiva, Purificacion Vicente-Galindo
Summary: This paper presents a methodology based on multivariate three-way methods to assess the real change in vision-related quality of life (QoL) for myopic patients before and after corneal surgery. The study conducted in Costa Rica found a statistically significant difference in perceived QoL levels after surgery and identified recalibration and reconceptualization.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2023)