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This paper reviews the advances, challenges, and approaches of artificial intelligence (AI) and machine learning (ML) in the banking sector. The use of these technologies is accelerating in various industries, including banking. However, the literature on banking is scattered, making a global understanding difficult. This study reviewed the main approaches in terms of applications and algorithmic models, as well as the benefits and challenges associated with their implementation in banking, in addition to a bibliometric analysis of variables related to the distribution of publications and the most productive countries, as well as an analysis of the co-occurrence and dynamics of keywords. Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework, forty articles were selected for review. The results indicate that these technologies are used in the banking sector for customer segmentation, credit risk analysis, recommendation, and fraud detection. It should be noted that credit analysis and fraud detection are the most implemented areas, using algorithms such as random forests (RF), decision trees (DT), support vector machines (SVM), and logistic regression (LR), among others. In addition, their use brings significant benefits for decision-making and optimizing banking operations. However, the handling of substantial amounts of data with these technologies poses ethical challenges.

The use of data mining to address the issue of people who consume tobacco and other harmful substances for their health has led to a significant dependence among smokers, which over time causes illnesses that may result in the addict's death. As a result, the research's goal is to apply a data mining study whose findings showed that the confidence intervals are less than 0.355. However, the lift and conviction in the last three rules are also lower, making it unlikely that these rules will be followed. On the other hand, the knowledge discovery in data bases method was used. It consists of the following stages: data selection, preparation, data mining, and evaluation and interpretation of the results. To that end, comparisons of agile data mining methodologies like crisp-dm, knowledge discovery in data, and Semma are also done. As a result, using specific criteria, dimensions are segmented to allow for the differentiation of these methodologies. As a result, a comparison graph of models such as naive Bayes, decision trees, and rule induction is used. To sum up, it can be said that the rules of association apply to men, the number of admissions, and the cancers that can be brought on by smoking. Also, the percentage of male patients admitted with cancers that can be brought on by smoking Last but not least, the number of admissions and cancers that can be brought on by smoking

Industrial hygiene is a preventive technique that tries to avoid professional illnesses and damage to health caused by several possible toxic agents. The purpose of this study is to simultaneously analyze different risk factors (body vibration, lighting, heat stress and noise), to obtain an overall risk assessment of these factors and to classify them on a scale of levels of Unacceptable, Not recommended or Acceptable. In this work, an artificial intelligence model based on the grey clustering method was applied to evaluate the quality of industrial hygiene. The grey clustering method was selected, as it enables the integration of objective factors related to hazards present in the workplace with subjective employee evaluations. A case study, in the three warehouses of a beer industry in Peru, was developed. The results obtained showed that the warehouses have an acceptable level of quality. These results could help industries to make decisions about conducting evaluations of the different occupational agents and determine whether the quality of hygiene represents a risk, as well as give certain recommendations with respect to the factors presented.

This bibliometric study analyzes the evolution of research in artificial intelligence (AI) applied to medicine from 2015 to September 2023. Using the Scopus database and keywords related to AI, machine learning, and deep learning in medicine, tools such as VOSviewer and Bibliometrix were used to explore publication trends, subject areas, co-authorship networks, and the most productive countries, among others. 2,064 articles were analyzed, and a significant increase in global academic production has been evident in the last five years. International collaboration was notable, with China and the United States leading in knowledge contribution. The keyword analysis highlights the breadth of topics and applications of AI in medicine, with particular emphasis on cancer detection, dengue diagnosis, and medical image analysis, among others. In conclusion, this study highlights the growing academic interest in the application of AI in medicine and the need for collaborative research. The findings underscore the relevance of these technologies in key areas of health care, contributing significantly to advances in medical diagnosis and prognosis.

Diabetes is a persistent health condition that impacts millions of people globally. Early and accurate prediction of this disease is critical for prevention and effective management. Machine learning models have emerged as promising tools for this task; however, the variability in the performance of different algorithms requires a thorough evaluation to identify the most effective ones. The main objective of this study was to assess several machine learning models using different performance metrics to identify the most robust and consistent approaches to diabetes prediction. Nine machine learning models were evaluated using the Pima Indian dataset, with data balancing performed via Synthetic Minority Over-sampling Technique (SMOTE) and performance assessed through cross-validation and test data. Among the models, Random Forest and AdaBoost produced the most robust and consistent results across key metrics, such as the AUC-ROC and AUPRC. These findings highlight their potential use in clinical decision support systems for early risk detection and improved patient management. In conclusion, the study emphasizes the significance of utilizing various evaluation metrics to obtain a thorough insight into the performance of machine learning models in predicting diabetes.

Given the urgent need for early detection of learning disorders such as dysgraphia, dyslexia, and dyscalculia in children, this study aimed to evaluate an expert system developed in Python to facilitate early diagnosis of these disorders. The background highlights the importance of providing parents, educators, and health professionals with an effective tool for early detection of these disorders. In 21 simulated cases, the system showed impressive performance with an accuracy rate of 95%, a precision of 100%, a sensitivity of 93%, and a specificity of 100%. Furthermore, the acceptability evaluation, conducted with 15 parents selected by convenience sampling, showed a high level of satisfaction, with an overall mean of 4.78 and a standard deviation of 0.45, indicating consistency in responses. In conclusion, this study confirms the effectiveness of the expert system in the early diagnosis of learning disabilities, providing parents, educators, and health professionals with a valuable tool. Despite these encouraging results, the need for additional research is recognized to address limitations and improve the external validity of the system to ensure its widespread utility and adaptability in real clinical settings.

In this work, the results of the analysis on total electron content (TEC) data before, during and after the geomagnetic storm of 8 September 2017 are reported. One of the responses to geomagnetic storms due to the southern vertical interplanetary magnetic field (Bz) is the enhancement of the electron density in the ionosphere. Vertical TEC (VTEC) from the Center for Orbit determination in Europe (CODE) along with a statistical method were used to identify positive and/or negative ionospheric storms in response to the geomagnetic storm of 8 September 2017. When analyzing the response to the storm of 8 September 2017 it was indeed possible to observe an enhancement of the equatorial ionization anomaly (EIA); however, what was unexpected was the identification of a local TEC enhancement (LTE) to the south of the EIA (∼40∘ S, right over New Zealand and extending towards the southeastern coast of Australia and also eastward towards the Pacific). This was a very transitory LTE that lasted approximately 4 h, starting at ∼ 02:00 UT on 8 September where its maximum VTEC increase was of 241.2 %. Using the same statistical method, comparable LTEs in a similar category geomagnetic storm, the 2015 St. Patrick's Day storm, were looked for. However, for the aforementioned storm no LTEs were identified. As also indicated in a past recent study for a LTE detected during the 15 August 2015 geomagnetic storm, an association between the LTE and the excursion of Bz seen during the 8 September 2017 storm was observed as well. Furthermore, it is very likely that a direct impact of the super-fountain effect along with traveling ionospheric disturbances may be playing an important role in the production of this LTE. Finally, it is indicated that the 8 September 2017 LTE is the second one to be detected since the year 2016.

Cardiopathy is one of the most serious diseases worldwide with its high morbidity and mortality rates posing a latent risk over time. The objective of this research focuses on evaluating Machine Learning (ML) models such as Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Logistic Regression (LR) for the prediction of coronary heart disease (CHD), with the aim of identifying the most efficient model for this prediction. The model construction followed the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, which comprises five stages: business understanding, data understanding, data preparation, modeling, and evaluation. The modeling results revealed the superior predictive capability of the XGBoost algorithm for detecting coronary heart disease, compared to Random Forest and Logistic Regression. The assessment of performance metrics (Accuracy, Precision, Sensitivity, and F1 Score) established XGBoost as the reference model, highlighting an F1 Score of approximately 90.8%. This superiority is attributed to its robustness in capturing nonlinear interactions among clinical variables. Consequently, the XGBoost model is selected as the optimal tool for integration into future medical decision support systems. In summary, this ML-based approach provides a highly predictive tool capable of identifying subtle risk patterns from real clinical data. The XGBoost model is a promising candidate for integration into decision support systems and for the optimization of primary prevention protocols for coronary heart disease.

This study addressed the challenge of accurate and timely diagnosis of respiratory diseases such as influenza, asthma, and pneumonia by developing and evaluating a web-based expert system. The objective was to develop and assess both the usability and diagnostic efficiency of a web- based expert system adaptable to mobile devices. A combined methodological approach was used, using the rapid application development (RAD) model to build the system and the user usability system (SUS) to evaluate the usability with the participation of 15 users and 21 simulated cases with a confusion matrix to determine the precision, accuracy, sensitivity, and specificity of the system in diagnosing respiratory diseases. The results showed that the expert system has a considerable capacity to identify and differentiate these diseases, with a precision of 86%, an accuracy of 76%, a sensitivity of 80%, and a specificity of 67%. Furthermore, the usability evaluation using the SUS method yielded an average of 82, indicating a positive perception and good usability by the users. In conclusion, although the results suggest a promising potential to improve the diagnostic process in clinical and community settings, the need for future studies to validate its performance in real clinical settings is recognized.