The 2nd International Electronic Conference on Applied Sciences

This book is focused on the works presented at the 2nd International Electronic Conference on Applied Sciences, organized by Applied Sciences from 15 to 31 October 2021 on the MDPI Sciforum platform. Two decades have passed since the start of the 21st century. The development of sciences and technologies is growing ever faster today than in the previous century. The field of science is expanding, and the structure of science is becoming ever richer. Because of this expansion and fine structure growth, researchers may lose themselves in the deep forest of the ever-increasing frontiers and sub-fields being created. This international conference on the Applied Sciences was started to help scientists conduct their own research into the growth of these frontiers by breaking down barriers and connecting the many sub-fields to cut through this vast forest. These functions will allow researchers to see these frontiers and their surrounding (or quite distant) fields and sub-fields, and give them the opportunity to incubate and develop their knowledge even further with the aid of this multi-dimensional network

Big data analytics for preventive medicine

© 2019, Springer-Verlag London Ltd., part of Springer Nature. Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations

Automatic BIRAD scoring of breast cancer mammograms

A computer aided diagnosis system (CAD) is developed to fully characterize and classify mass to benign and malignancy and to predict BIRAD (Breast Imaging Reporting and Data system) scores using mammographic image data. The CAD includes a preprocessing step to de-noise mammograms. This is followed by an active counter segmentation to deforms an initial curve, annotated by a radiologist, to separate and define the boundary of a mass from background. A feature extraction scheme wasthen used to fully characterize a mass by extraction of the most relevant features that have a large impact on the outcome of a patient biopsy. For this thirty-five medical and mathematical features based on intensity, shape and texture associated to the mass were extracted. Several feature selection schemes were then applied to select the most dominant features for use in next step, classification. Finally, a hierarchical classification schemes were applied on those subset of features to firstly classify mass to benign (mass with BIRAD score 2) and malignant mass (mass with BIRAD score over 4), and secondly to sub classify mass with BIRAD score over 4 to three classes (BIRAD with score 4a,4b,4c). Accuracy of segmentation performance were evaluated by calculating the degree of overlapping between the active counter segmentation and the manual segmentation, and the result was 98.5%. Also reproducibility of active counter 3 using different manual initialization of algorithm by three radiologists were assessed and result was 99.5%. Classification performance was evaluated using one hundred sixty masses (80 masses with BRAD score 2 and 80 mass with BIRAD score over4). The best result for classification of data to benign and malignance was found using a combination of sequential forward floating feature (SFFS) selection and a boosted tree hybrid classifier with Ada boost ensemble method, decision tree learner type and 100 learners’ regression tree classifier, achieving 100% sensitivity and specificity in hold out method, 99.4% in cross validation method and 98.62 % average accuracy in cross validation method. For further sub classification of eighty malignance data with BIRAD score of over 4 (30 mass with BIRAD score 4a,30 masses with BIRAD score 4b and 20 masses with BIRAD score 4c), the best result achieved using the boosted tree with ensemble method bag, decision tree learner type with 200 learners Classification, achieving 100% sensitivity and specificity in hold out method, 98.8% accuracy and 98.41% average accuracy for ten times run in cross validation method. Beside those 160 masses (BIRAD score 2 and over 4) 13 masses with BIRAD score 3 were gathered. Which means patient is recommended to be tested in another medical imaging technique and also is recommended to do follow-up in six months. The CAD system was trained with mass with BIRAD score 2 and over 4 also 4 it was further tested using 13 masses with a BIRAD score of 3 and the CAD results are shown to agree with the radiologist’s classification after confirming in six months follow up. The present results demonstrate high sensitivity and specificity of the proposed CAD system compared to prior research. The present research is therefore intended to make contributions to the field by proposing a novel CAD system, consists of series of well-selected image processing algorithms, to firstly classify mass to benign or malignancy, secondly sub classify BIRAD 4 to three groups and finally to interpret BIRAD 3 to BIRAD 2 without a need of follow up study

Predictive Modelling Approach to Data-driven Computational Psychiatry

This dissertation contributes with novel predictive modelling approaches to data-driven computational psychiatry and offers alternative analyses frameworks to the standard statistical analyses in psychiatric research. In particular, this document advances research in medical data mining, especially psychiatry, via two phases. In the first phase, this document promotes research by proposing synergistic machine learning and statistical approaches for detecting patterns and developing predictive models in clinical psychiatry data to classify diseases, predict treatment outcomes or improve treatment selections. In particular, these data-driven approaches are built upon several machine learning techniques whose predictive models have been pre-processed, trained, optimised, post-processed and tested in novel computationally intensive frameworks. In the second phase, this document advances research in medical data mining by proposing several novel extensions in the area of data classification by offering a novel decision tree algorithm, which we call PIDT, based on parameterised impurities and statistical pruning approaches toward building more accurate decision trees classifiers and developing new ensemblebased classification methods. In particular, the experimental results show that by building predictive models with the novel PIDT algorithm, these models primarily led to better performance regarding accuracy and tree size than those built with traditional decision trees. The contributions of the proposed dissertation can be summarised as follow. Firstly, several statistical and machine learning algorithms, plus techniques to improve these algorithms, are explored. Secondly, prediction modelling and pattern detection approaches for the first-episode psychosis associated with cannabis use are developed. Thirdly, a new computationally intensive machine learning framework for understanding the link between cannabis use and first-episode psychosis was introduced. Then, complementary and equally sophisticated prediction models for the first-episode psychosis associated with cannabis use were developed using artificial neural networks and deep learning within the proposed novel computationally intensive framework. Lastly, an efficient novel decision tree algorithm (PIDT) based on novel parameterised impurities and statistical pruning approaches is proposed and tested with several medical datasets. These contributions can be used to guide future theory, experiment, and treatment development in medical data mining, especially psychiatry

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

Incidencia de la calidad el aire en el desarrollo urbano sostenible. Metodología de pronóstico basado en herramientas de aprendizaje automático

Tesis por compendio[ES] La calidad del aire es un determinante de la salud y bienestar de las poblaciones; su mejora es parte de algunas metas de los objetivos de desarrollo sostenible (ODS) con la Agenda 2030. Al respecto, se han definido a nivel mundial protocolos, acuerdos, convenios y lineamientos de política para lograr avanzar en el cumplimiento de los ODS. Existen además reportes nacionales de avance en la implementación de metas específicas, según la agenda de cada país y en algunos casos en el ámbito de ciudad, cuyos indicadores pueden integrarse en las dimensiones más conocidas del desarrollo sostenible: la dimensión ambiental, la social y la económica. Existe información sobre el monitoreo del estado de la calidad de los recursos y de condiciones del territorio en diversos temas. Sin embargo, no en todos los territorios, en sus diferentes escalas espaciales, se realiza una continua evaluación de su desempeño sostenible y, además factores de deterioro ambiental como la contaminación del aire, son tratados como determinantes aislados con la generación de reportes de su comportamiento y el desarrollo de planes de monitoreo y de mitigación. Del mismo modo, para los diferentes temas que hacen parte de las dimensiones de la sostenibilidad, existen herramientas de modelación para evaluar el comportamiento de sus indicadores; sin embargo, no se cuenta con un instrumento que pronostique el nivel de avance en el desarrollo sostenible y además que identifique la influencia de la calidad del aire en su comportamiento. Las herramientas de aprendizaje automático pueden aportar en la respuesta a dicha situación, al ser instrumentos útiles en el pronóstico del comportamiento de un conjunto de datos. Por consiguiente, el objetivo central de este trabajo doctoral es establecer la incidencia de la calidad del aire sobre el desarrollo urbano sostenible, en sus dimensiones ambiental, social y económica, mediante el uso de herramientas de aprendizaje automático, como soporte para la toma de decisiones. Este objetivo involucra el diseño y ejecución de una metodología para identificar la influencia de indicadores en materia de calidad del aire, sobre el desarrollo urbano sostenible. Este trabajo doctoral se desarrolló como compendio de un conjunto de publicaciones que incluyen 1) la revisión del estado del arte para la identificación de las variables y parámetros que podrían calificar las dimensiones individuales del desempeño sostenible, 2) la evaluación del nivel de avance en el desarrollo sostenible de una zona urbana y el análisis estadístico de su desempeño sostenible según las variables analizadas; 3) la identificación, selección y aplicación de las herramientas de aprendizaje automático y por último 4) la identificación del grado de influencia de la calidad del aire en el pronóstico del nivel de sostenibilidad establecido. Para ello se hizo uso del software ArcGIS para el análisis espacial y del software de acceso libre R para los análisis estadísticos y la aplicación de las herramientas de aprendizaje automático. Esta investigación se realizó a partir de un estudio de caso en una localidad de la ciudad de Bogotá, en Colombia que es la capital del país, situada sobre una planicie altitudinal en la cordillera oriental y a 2625 metros sobre el nivel del mar. Bogotá es una de las ciudades más pobladas en América Latina y es una de las capitales mundiales que ha presentado altos niveles de contaminación por material particulado, siendo éste un factor de riesgo para su población. La metodología construida permite evaluar la influencia de la calidad del aire en el desarrollo urbano sostenible mediante herramientas de aprendizaje automático. Es aplicable a zonas urbanas y orienta el paso a paso para la determinación de los factores de mayor relevancia en cada una de las dimensiones de la sostenibilidad, constituyéndose en un instrumento de soporte para la toma de decisiones respecto a la implem[CA] La qualitat de l'aire és un determinant de la salut i benestar de les poblacions; la seua millora és part d'algunes metes dels objectius de desenvolupament sostenible (ODS) amb l'Agenda 2030. Sobre aquest tema, s'han definit a nivell mundial protocols, acords, convenis i alineaments de política per a aconseguir avançar en el compliment dels ODS. Existeixen reportes nacionals d'avanç sobre la implementació de metes específiques, segons l'agenda de cada país i en alguns casos en l'àmbit de ciutat, els indicadors de la qual poden integrar-se en les dimensions més conegudes del desenvolupament sostenible: la dimensió ambiental, la social i l'econòmica. Existeix informació sobre el monitoratge de l'estat de la qualitat dels recursos i de les condicions del territori en diversos temes. No obstant això, no en tots els territoris, en les seues diferents escales espacials, es realitza contínua avaluació del seu acompliment sostenible i, a més a més, factors de deterioració ambiental com la contaminació de l'aire, són tractats com a determinants aïllats amb la generació de reportes del seu comportament i el desenvolupament de plans de monitoratge i de mitigació. De la mateixa manera, per als diferents temes que fan part de les dimensions de la sostenibilitat, existeixen eines de modelatge per a avaluar el comportament dels seus indicadors; no obstant això, no es compta amb un instrument que pronostique el nivell d'avanç en el desenvolupament sostenible i a més que identifique la influència de la qualitat de l'aire en el seu comportament. Les eines d'aprenentatge automàtic poden aportar en la resposta a aquesta situació, en ser instruments útils en el pronòstic del comportament d'un conjunt de dades. Per consegüent, l'objectiu central d'aquest treball doctoral és establir la incidència de la qualitat de l'aire sobre el desenvolupament urbà sostenible, en les seues dimensions ambiental, social i econòmica, mitjançant l'ús d'eines d'aprenentatge automàtic, com a suport per a la presa de decisions. Aquest objectiu involucra el disseny i execució d'una metodologia per a identificar la influència d'indicadors en matèria de qualitat de l'aire, sobre el desenvolupament urbà sostenible. Aquest treball doctoral es va desenvolupar com a compendi d'un conjunt de publicacions que inclouen 1) la revisió de l'estat de l'art per a la identificació de les variables i paràmetres que podrien qualificar les dimensions individuals de l'acompliment sostenible, 2) l'avaluació del nivell d'avanç en el desenvolupament sostenible d'una zona urbana i l'anàlisi estadística del seu acompliment sostenible segons les variables analitzades; 3) la identificació, selecció i aplicació de les eines d'aprenentatge automàtic i finalment 4) la identificació del grau d'influència de la qualitat de l'aire en el pronòstic del nivell de sostenibilitat establit. Per a això es va fer ús del programari ArcGIS per a l'anàlisi espacial i del programari d'accés lliure R per a les anàlisis estadístiques i l'aplicació de les eines d'aprenentatge automàtic. Aquesta investigació es va realitzar a partir d'un estudi de cas en una localitat de la ciutat de Bogotà, a Colòmbia que és la capital del país, situada sobre una planícia altitudinal en la serralada oriental i a 2625 metres sobre el nivell de la mar. Bogotà és una de les ciutats més poblades a Amèrica Llatina i és una de les capitals mundials que ha presentat alts nivells de contaminació per material particulat, sent aquest un factor de risc per a la seua població. La metodologia construïda permet avaluar la influència de la qualitat de l'aire en el desenvolupament urbà sostenible mitjançant l'ús d'eines d'aprenentatge automàtic. És aplicable a zones urbanes i orienta el pas a pas per a la determinació dels factors de major rellevància en cadascuna de les dimensions de la sostenibilitat, constituint-se en un instrument de suport per a la presa d[EN] Air quality is a determinant to the health and well-being of populations; its improvement is part of some of the targets of the Sustainable Development Goals (SDGs) with the 2030 Agenda. In this regard, protocols, agreements, pacts, and policy guidelines have been defined worldwide to progress in the SDGs' achievement. Additionally, there are national progress reports on reaching specific goals, based on each country's agenda. In certain cases, these include city-level reports, whose indicators, both at the national and city levels, can be integrated into the central and best-known dimensions of sustainable development, namely the environmental, social, and economic dimensions. There is information concerning the monitoring of the state of resource quality and territorial conditions in various areas. However, not all territories in their different spatial scales are continuously evaluated for their sustainable performance. Moreover, environmental deterioration factors such as air pollution are handled as isolated determinants with reports generated on their behavior, in addition to developing monitoring and mitigation plans. Likewise, there are modeling tools to evaluate the behavior of different components that are part of the dimensions of sustainability. However, there is no instrument that forecasts the level of progress in sustainable development that also identifies the influence of air quality on its behavior. Machine learning tools can contribute to responding to this situation, as they are able to predict the behavior of a data set. Therefore, the primary objective of this doctoral work is to establish the incidence of air quality on urban sustainable development, in its environmental, social, and economic dimensions, through the use of machine learning tools to support decision-making. This objective entails designing and implementing a methodology to identify the influence of air quality indicators on urban sustainable development. This doctoral thesis was developed as a compendium of a set of publications which include: 1) the review of the state of the art for identifying variables and parameters that could qualify the individual dimensions of sustainable performance; 2) the evaluation of the level of progress of the sustainable development of an urban area, and the statistical analysis of its sustainable performance based on the variables analyzed; 3) the identification, selection, and use of machine learning tools, and lastly 4) the identification of the influence of air quality on the prediction of the established sustainability level. The ArcGIS program was used for the spatial analysis, and the free-access software R for the statistical analysis, and the use of the machine learning tools. This research was performed based on a case study of a locality in the capital of Colombia; Bogotá, which is located on an altitudinal plain in the eastern mountain range at 2625 meters above sea level. Bogotá is one of the most populated cities in Latin America and is one of the world capitals with the highest levels of air pollution from particulate matter, which is a risk factor for its population. The methodology developed enables evaluating the influence of air quality on urban sustainable development with machine learning tools. This methodology is valid in urban areas, and through a step-by-step approach, determines the most relevant factors for each sustainability dimension. It has become a tool to support decision-making regarding the implementation and progress of the SDGs from the micro-territory level.Molina Gómez, NI. (2021). Incidencia de la calidad el aire en el desarrollo urbano sostenible. Metodología de pronóstico basado en herramientas de aprendizaje automático [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/168398TESISCompendi

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC