Prediction of Computer Vision Syndrome in Health Personnel by Means of Genetic Algorithms and Binary Regression Trees

One of the major consequences of the digital revolution has been the increase in the use of electronic devices in health services. Despite their remarkable advantages, though, the use of computers and other visual display terminals for a prolonged time may have negative effects on vision, leading to a greater risk of Computer Vision Syndrome (CVS) among their users. In this study, the importance of ocular and visual symptoms related to CVS was evaluated, and the factors associated with CVS were studied, with the help of an algorithm based on regression trees and genetic algorithms. The performance of this proposed model was also tested to check its ability to predict how prone a worker is to suffering from CVS. The findings of the present research confirm a high prevalence of CVS in healthcare workers, and associate CVS with a longer duration of occupation and higher daily computer usage

IoT in smart communities, technologies and applications.

Internet of Things is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The Internet of Things (IoT) for Smart Cities has many different domains and draws upon various underlying systems for its operation, in this work, we provide a holistic coverage of the Internet of Things in Smart Cities by discussing the fundamental components that make up the IoT Smart City landscape, the technologies that enable these domains to exist, the most prevalent practices and techniques which are used in these domains as well as the challenges that deployment of IoT systems for smart cities encounter and which need to be addressed for ubiquitous use of smart city applications. It also presents a coverage of optimization methods and applications from a smart city perspective enabled by the Internet of Things. Towards this end, a mapping is provided for the most encountered applications of computational optimization within IoT smart cities for five popular optimization methods, ant colony optimization, genetic algorithm, particle swarm optimization, artificial bee colony optimization and differential evolution. For each application identified, the algorithms used, objectives considered, the nature of the formulation and constraints taken in to account have been specified and discussed. Lastly, the data setup used by each covered work is also mentioned and directions for future work have been identified. Within the smart health domain of IoT smart cities, human activity recognition has been a key study topic in the development of cyber physical systems and assisted living applications. In particular, inertial sensor based systems have become increasingly popular because they do not restrict users’ movement and are also relatively simple to implement compared to other approaches. Fall detection is one of the most important tasks in human activity recognition. With an increasingly aging world population and an inclination by the elderly to live alone, the need to incorporate dependable fall detection schemes in smart devices such as phones, watches has gained momentum. Therefore, differentiating between falls and activities of daily living (ADLs) has been the focus of researchers in recent years with very good results. However, one aspect within fall detection that has not been investigated much is direction and severity aware fall detection. Since a fall detection system aims to detect falls in people and notify medical personnel, it could be of added value to health professionals tending to a patient suffering from a fall to know the nature of the accident. In this regard, as a case study for smart health, four different experiments have been conducted for the task of fall detection with direction and severity consideration on two publicly available datasets. These four experiments not only tackle the problem on an increasingly complicated level (the first one considers a fall only scenario and the other two a combined activity of daily living and fall scenario) but also present methodologies which outperform the state of the art techniques as discussed. Lastly, future recommendations have also been provided for researchers

Síndrome Visual Informático en trabajadores que usan computador

Introduction: Exposure to computer screens for long periods of time leads to visual efforts, changes in the ocular surface and tear film, and a set of signs and symptoms called Visual Computer Syndrome. Objective: to synthesize scientific knowledge on Computer Visual Syndrome in computer user workers, according to year, country, type of study, the worker, instrument used to measure the syndrome, and signs and symptoms. Methods: Systematic review (PROSPERO, CRD42020216218) of original studies in Spanish and English published between 2010 and June 2020 in the PubMed, SciELO, Scopus, BVS, Dialnet, Science Direct and Google Scholar databases. The methodological quality was evaluated according to the STROBE criteria and a qualitative synthesis of the results was carried out. Results: Of 962 articles, 17 complied with the protocol. The average score of the quality assessment was 16.3 ± 2.06. 76.47% were published from 2016 to June 2020 and Spain presented the highest number of publications (23.5%). The vast majority were cross-sectional studies (94.1%), office and IT workers were the most studied (29.4%), the most widely used instrument was the Computer Vision Syndrome Questionnaire (CVS-Q) (11.8%), and headache was the most frequent symptom. Conclusions: The visual computer syndrome has been investigated very little in the last decade, showing great information on its prevalence but not on its diagnosis, intervention and treatment. It is necessary to design other instruments for its detection.Introducción: La exposición a las pantallas de los computadores durante largos períodos de tiempo conlleva a esfuerzos visuales, cambios en la superficie ocular y en la película lagrimal, y un conjunto de signos y síntomas denominado Síndrome Visual Informático. Objetivo: Sintetizar el conocimiento científico sobre el Síndrome Visual Informático en trabajadores usuarios de computadores, según año, país, tipo de estudio, el trabajador, instrumento utilizado para medir el síndrome, y los signos y síntomas. Métodos: Revisión sistemática (PROSPERO, CRD42020216218) de estudios originales en español e inglés publicados entre el 2010 y junio del 2020 en las bases de datos PubMed, SciELO, Scopus, BVS, Dialnet, Science Direct y Google Scholar. La calidad metodológica se evaluó según los criterios de STROBE y se realizó la síntesis cualitativa de los resultados. Resultados: De 962 artículos, 17 cumplieron con el protocolo. El puntaje promedio de la valoración de calidad fue de 16.3 ±2.06. El 76.5% fue publicado del 2016 a junio del 2020 y España presentó el mayor número de publicaciones (23.5%). La gran mayoría fueron estudios transversales (94.1%), los trabajadores de oficina y de informática fueron los más estudiados (29.4%), el instrumento más empleado fue el Computer Vision Syndrome Questionnaire (CVS-Q) (11.8%), y el dolor de cabeza fue el síntoma más frecuente. Conclusiones:  El síndrome visual informático ha sido investigado muy poco en la última década, evidenciando gran información sobre su prevalencia pero no en su diagnóstico, intervención y tratamiento. Es necesario diseñar otros instrumentos para su detección

Tahap pengawalan pihak pengurusan politeknik dalam mengurangkan gejala sosial di kalangan pelajar

Kebelakangan ini gejala sosial di kalangan pelajar sarna ada di sekolah mahupun di Institusi Pengajian Tinggi semakin membimbangkan kita. Benta-berita dan laporan media massa dan elektronik tentang gejala sosial seperti berdua-duaan (coupling), herpeleseran, bersekedudukan, membuang bayi dan pergaulan bebas kemp kah dilaporkan dan semacam sudah menjadi perkara biasa Oleh itu kajian ini dijalankan bertujuan untuk mengenalpasti tahap pengawalan dan tahap kejayaan pengawalan yang dilakukan oleh pihak pengurusan politeknik dalam menangani gejala sosia1 di kalangan pelajar. Kajian ini dijalankan di Politeknik Kota Bharu dan 100 orang pensyarahnya dikenalpasti sebagai responden kajian. Data-data dianalisis menggunakan perisian Statistical Package of Social Science (.SPSS) Versi 10.0 melibatkan peratusan dan purata min. Dapatan kajian mendapati bahawa pihak pengurusan politeknik sudah melakukan pengawalan yang tinggi dalam menangani masalah ini. Narnun mungkin disebabkan faktor-faktor di luar kawalan maka gejala sosial dllihat masih berlaku di politeknik walaupun ianya tidaklah berapa sen us

Computational Intelligence in Healthcare

This book is a printed edition of the Special Issue Computational Intelligence in Healthcare that was published in Electronic

Підсистема прийняття рішень на базі нечітких нейронних мереж

Робота публікується згідно наказу ректора від 29.12.2020 р. №580/од "Про розміщення кваліфікаційних робіт здобувачів вищої освіти в репозиторії НАУ".Керівник дипломної роботи: д.т.н., проф., завідувач кафедри авіаційних комп’ютерно-інтегрованих комплексів, Синєглазов Віктор МихайловичThe purpose of scientific work: development of a subsystem for decision-making on the basis of fuzzy neural networks, improvement of existing algorithms. The thesis considers theoretical and software part of the development of the decision-making subsystem for solving the classification problem. The author substantiates the relevance of using fuzzy neural networks to solve the problem of classification, analyzes the existing topologies of fuzzy neural networks and fuzzy classifiers, basic algorithms to improve results and combine them into a single structure, identified their shortcomings and proposed a solution to eliminate them An optimization and improvement algorithm for solving the classification problem based on the creation of an ensemble of fuzzy neural networks, namely, a fuzzy TSK classifier, is proposed. This software architecture allows you to create a neural classifier that improves the results of an existing solution. And expands the range of calculations performed to classify the input data.Мета наукової роботи: розробка підсистеми для прийняття рішень на базі нечітких нейронних мереж, покращення існуючих алгоритмів. В дипломній роботі розглядається теоретична та програмна частина розробки підсистеми прийняття рішень для розв’язання задачі класифікації. Автором обґрунтовано актуальність використання нечітких нейронних мереж для вирішення задачі класифікації, проведено аналіз існуючих топологій нечітких нейронних мереж та нечітких класифікаторів, основних алгоритмів для покращення результатів та поєднання їх в єдину структуру, виявлено їх недоліки та запропоноване рішення, що дозволяє їх усунути Запропоновано алгоритм оптимізації та покращення для вирішення задачі класифікації на основі створення ансамблю з нечітких нейронних мереж а саме, нечіткого класифікатора TSK. Дана програмна архітектура дозволяє створити нейронний класифікатор який покращує результати уже існуючого рішення. Та розширює спектр виконуваних обчислювань для класифікації вхідних даних

Computational Intelligence in Healthcare

The number of patient health data has been estimated to have reached 2314 exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such a vast quantity of data. Thus, intelligent data analysis methods combining human expertise and computational models for accurate and in-depth data analysis are necessary. The technological revolution and medical advances made by combining vast quantities of available data, cloud computing services, and AI-based solutions can provide expert insight and analysis on a mass scale and at a relatively low cost. Computational intelligence (CI) methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods, have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice. CI-based systems can learn from data and evolve according to changes in the environments by taking into account the uncertainty characterizing health data, including omics data, clinical data, sensor, and imaging data. The use of CI in healthcare can improve the processing of such data to develop intelligent solutions for prevention, diagnosis, treatment, and follow-up, as well as for the analysis of administrative processes. The present Special Issue on computational intelligence for healthcare is intended to show the potential and the practical impacts of CI techniques in challenging healthcare applications