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La emergencia sanitaria por la pandemia de COVID-19 y ha sido necesario que sea tratada con respuesta inmediatas e integradas a todas las organizaciones involucradas para lograr intercambio de datos sobre ésta y futuras pandemias globales de rápida propagación. Este estudio se enfoca en predecir la incidencia del COVID-19 en Ecuador mediante minería de datos de los registros proporcionados por instituciones públicas del estado ecuatoriano con información oficial del COVID-19 en el Ecuador. Se experimentó con modelos de regresión y memoria a largo plazo obteniendo como resultado el modelo óptimo para estimar el número de casos positivos de COVID-19. Para los modelos empleados se hizo uso del error cuadrático como métrica del rendimiento. Del análisis de los datos sobre el COVID-19 en Ecuador el modelo de regresión polinomial predijo la incidencia con un error cuadrático de 0.86. siendo los factores más efectivos la incidencia de días anteriores y el número de población de cada una de las provincias afectadas.

Data analytics in the healthcare sector has experienced an exponential surge in recent years, driven by advances in data science and the growing volume of digitized medical information. In this landscape of rapid technological transformation, the present research conducts a bibliometric analysis to discern key patterns that have shaped this field. Utilizing cutting-edge specialized software such as VOSviewer, Bibliometrics, and ScientoPy, this work delves deeply into critical indicators such as researchers' productivity over time, geographical distribution of publications, impact of contributions, and emerging trends. Findings obtained through these tools will provide a comprehensive insight into the historical evolution and future trajectories in the field of healthcare data analytics. [...]this article represents a valuable contribution for researchers, healthcare professionals, and policymakers, offering a detailed map of the current state and potential directions of this rapidly evolving discipline.

A business data analyst, being assigned to the accounting department, is responsible for reporting and analyses, and this especially requires software user skills as well as analytical thinking skills and communication and presentation skills in order to prepare data as a basis for decision-making. The business data scientist is responsible for data management and big data; he builds mathematical-statistical models in order to increase evaluation and planning accuracy and to support decisions related to business models. Auch Machine-Learning (5 %) und Data-Mining (5 %), um mittels mathematisch-statistischer Algorithmen auch aus unstrukturierten Datenbeständen Muster, Trends oder Zusammenhänge zu extrahieren, ist angesichts der seltenen Nennung in den Anzeigen offenbar für Business Data Analysts von geringer Relevanz. Stellenprofil Business Data Analysts im Fachbereich »Rechnungswesen« Der Literatur zufolge wird der Business Data Analyst überwiegend in den jeweiligen Fachbereichen (Marketing, Accounting oder Produktion) eingesetzt.17 Die vorliegende Analyse der Stellenanzeigen aus dem Bereich Rechnungswesen zeigt, dass in der betrieblichen Praxis der Business Data Analyst organisatorisch der Controlling-Abteilung im Fachbereich Rechnungswesen oder aber auch einer bereichsübergreifenden IT-Abteilung zugeordnet wird.

The brain–computer interface (BCI) is an emerging technology that has the potential to revolutionize the world, with numerous applications ranging from healthcare to human augmentation. Electroencephalogram (EEG) motor imagery (MI) is among the most common BCI paradigms that have been used extensively in smart healthcare applications such as post-stroke rehabilitation and mobile assistive robots. In recent years, the contribution of deep learning (DL) has had a phenomenal impact on MI-EEG-based BCI. In this work, we systematically review the DL-based research for MI-EEG classification from the past ten years. This article first explains the procedure for selecting the studies and then gives an overview of BCI, EEG, and MI systems. The DL-based techniques applied in MI classification are then analyzed and discussed from four main perspectives: preprocessing, input formulation, deep learning architecture, and performance evaluation. In the discussion section, three major questions about DL-based MI classification are addressed: (1) Is preprocessing required for DL-based techniques? (2) What input formulations are best for DL-based techniques? (3) What are the current trends in DL-based techniques? Moreover, this work summarizes MI-EEG-based applications, extensively explores public MI-EEG datasets, and gives an overall visualization of the performance attained for each dataset based on the reviewed articles. Finally, current challenges and future directions are discussed.

The study of big data analytics (BDA) methods for the data-driven industries is gaining research attention and implementation in today’s industrial activities, business intelligence, and rapidly changing the perception of industrial revolutions. The uniqueness of big data and BDA has created unprecedented new research calls to solve data generation, storage, visualization, and processing challenges. There are significant gaps in knowledge for researchers and practitioners on the right information and BDA tools to extract knowledge in large significant industrial data that could help to handle big data formats. Notwithstanding various research efforts and scholarly studies that have been proposed recently on big data analytic processes for industrial performance improvements. Comprehensive review and systematic data-driven analysis, comparison, and rigorous evaluation of methods, data sources, applications, major challenges, and appropriate solutions are still lacking. To fill this gap, this paper makes the following contributions: presents an all-inclusive survey of current trends of BDA tools, methods, their strengths, and weaknesses. Identify and discuss data sources and real-life applications where BDA have potential impacts. Other main contributions of this paper include the identification of BDA challenges and solutions, and future research prospects that require further attention by researchers. This study provides an insightful recommendation that could assist researchers, industrial practitioners, big data providers, and governments in the area of BDA on the challenges of the current BDA methods, and solutions that would alleviate these challenges.

Palabras-clave: Software, metodología, base de datos Abstract: The project \"Management system and analysis of climate variables using Data Warehouse and Business Intelligence\" aims to create a web system that allows the user to obtain information on the behavior, trends and history of various climate variables such as temperature, humidity and pressure. Knowledge about the behavior of climatic variables is a basis for decision-making in various areas, such as agronomy, tourism, and construction, among others. The use of business intelligence tools in the organization of data that facilitate interpretation and compression correction, aids decisionmaking, giving a competitive advantage to the user. Introducción El presente trabajo de Sistema de gestión y análisis de variables climáticas mediante la utilización de Data Warehouse (Inmon, 2002) y Business Intelligence (Curto Díaz, 2010), representa un diseño web, que es escalable y sigue las especificaciones de desarrollo de software, donde se registran los datos de la estación climática, con el procesamiento de los datos en tableau para los respectivos análisis. 2.

In the era of \"Internet plus,\" the world economy is becoming more and more globalized and informationalized. China's enterprises are facing unprecedented opportunities for their operation and development. However, it is also facing the financial uncertainties brought about by the fluctuations of the general economic environment, and the company is facing increasing financial risks. The reason why most enterprises encounter a serious financial crisis or even close down in the later stage is that they do not pay full attention to the initial financial problems and do not take effective measures to deal with the crisis in time. Financial risk warning has become an important part of modern enterprise financial management. This paper mainly puts forward the optimized BP neural system as the financial early warning model and ensures its high prediction accuracy. In the research, the operation principle and related reasoning process of the model are described, its shortcomings are analyzed, and solutions are put forward. Through the financial risk analysis of listed companies from 2017 to 2020, we find that the correct rate of the prediction results of the financial distress of normal companies in the selected companies based on the optimized BPNN has reached more than 80%, which proves the effectiveness of the optimized BPNN.

With the rapid development of information technology, the conception of smart healthcare has progressively come to the fore. Smart healthcare utilizes next-generation technologies, such as artificial intelligence, the Internet of Things (IoT), big data and cloud computing to transform intelligently the existing medical system-making it more efficient, more reliable, and personalized. In this work, skin data are collected using dedicated hardware from mobile health units-working as nodes. The collected samples are uploaded to the cloud for further processing using a novel multi-modal information fusion framework, which performs skin lesion segmentation, followed by classification. The proposed framework has two main functional blocks: Segmentation and classification. In each block, we have a performance booster, which works on the principle of information fusion. For lesion segmentation, a hybrid framework is proposed, which utilizes the complementary strengths of two convolutional neural network (CNN) architectures to generate the segmented images. The resultant binary images are later fused using joint probability distribution and marginal distribution function. For lesion classification, a 30-layered CNN architecture is designed, which is trained on the HAM10000 dataset. A novel summation discriminant correlation analysis technique is used to fuse the extracted features from two fully connected layers. To avoid feature redundancy, a feature selection method “Regular Falsi” is developed, which down samples the extracted features into the lower dimensions. The selected features are finally classified using an extreme learning machine classifier. Five skin benchmark datasets (ISBI2016, ISIC2017, ISBI2018, ISIC2019, and HAM10000) are used to evaluate both segmentation and classification frameworks using average accuracy, false-negative rate, sensitivity, and computational time, whose results are impressive compared to state-of-the-art methods.