scholarly journals Construction and evaluation of a web application for the educational process on Normal Distribution considering the science of data and machine learning

2019 ◽  
Vol 27 (0) ◽  
Author(s):  
Ricardo-Adán Salas-Rueda
Keyword(s):  
Machine Learning ◽  
Normal Distribution ◽  
Web Application ◽  
Educational Process

scholarly journals Diseño y uso de una aplicación web para el campo de la estadística considerando el modelo ASSURE y la ciencia de datos

2019 ◽  
Vol 12 (1) ◽  
pp. 48-71
Author(s):  
Ricardo-Adán Salas-Rueda ◽  
Érika-Patricia Salas-Rueda ◽  
Rodrigo-David Salas-Rueda
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Data Science ◽  
Hypothesis Test ◽  
Web Design ◽  
Positive Influence ◽  
Educational Process ◽  
Data Simulation ◽  
Teaching Learning ◽  
The Impact

RESUMEN: Esta investigación mixta tiene como objetivo analizar el impacto de laAplicación web para el Proceso Educativo sobre la Prueba de Hipótesis (APEPH)en la asignatura Instrumentación estadística para los negocios durante el ciclo escolar 2018. El modelo instruccional ASSURE permite la organización, construcción e implementación de la aplicación APEPH. Los resultados del aprendizaje automático (regresión lineal) con 60%, 70% y 80% de entrenamiento indican que el contenido, el diseño web y la simulación de datos en la aplicación APEPH influyen positivamente en el aprendizaje y la motivación del estudiante. Asimismo, la ciencia de datos permite la construcción de 6 modelos predictivos sobre el uso de la aplicación APEPH en el proceso educativo por medio de la técnica árbol de decisión. Por último, la aplicación APEPH facilita el proceso de enseñanza-aprendizaje sobre la estadística por medio del contenido, el diseño web y la simulación de datos. PALABRAS CLAVE: tecnología educativa; modelo ASSURE; enseñanza superior; ciencia de datos; aprendizaje automático.   ABSTRACT: This mixed research aims at analyzing the impact of the Web Application for the Educational Process on the Hypothesis Test (APEPH) in the Statistical Instrumentation for Business subject during the 2018 school year. ASSURE model allows the organization, construction and implementation of the APEPH application. The results of machine learning (linear regression) with 60%, 70% and 80% of training indicate that the content, web design and simulation of data in the APEPH application have a positive influence on the student's learning and motivation. Likewise, data science allows the construction of 6 predictive models on the use of the APEPH application in the educational process by means of the decision tree technique. Finally, the APEPH application facilitates the teaching-learning process on statistics through the content, web design and data simulation. KEYWORDS: educational technology; ASSURE model; higher education; data science; machine learning.

A Database for Counterfeit Electronics and Automatic Defect Detection Based on Image Processing and Machine Learning

2016 ◽  
Author(s):  
Navid Asadizanjani ◽  
Sachin Gattigowda ◽  
Mark Tehranipoor ◽  
Domenic Forte ◽  
Nathan Dunn
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Integrated Circuits ◽  
Web Application ◽  
The United States ◽  
Global Market ◽  
Online Database ◽  
Ongoing Effort ◽  
Time Period ◽  
Physical Defects

Abstract Counterfeiting is an increasing concern for businesses and governments as greater numbers of counterfeit integrated circuits (IC) infiltrate the global market. There is an ongoing effort in experimental and national labs inside the United States to detect and prevent such counterfeits in the most efficient time period. However, there is still a missing piece to automatically detect and properly keep record of detected counterfeit ICs. Here, we introduce a web application database that allows users to share previous examples of counterfeits through an online database and to obtain statistics regarding the prevalence of known defects. We also investigate automated techniques based on image processing and machine learning to detect different physical defects and to determine whether or not an IC is counterfeit.

Development of the geoinformation system for hunting farms.

2017 ◽  
Author(s):  
М.Р.  Вагизов ◽  
Д.А. Дубов
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Organizational Structure ◽  
Spatial Data ◽  
Web Application ◽  
Geographic Information ◽  
Educational Process ◽  
Information Support ◽  
Quality Level ◽  
Strategy Of Development

Рассматривается необходимость разработки специализированной геоинформационной системы для отрасли охотничьего хозяйства. Указаны основные принципы проектирования и разработки приложения. Определены необходимые требования к проектируемой ГИС и задачи, которые способна решать система. Проведено описание организационной структуры геоинформационной системы и функции, подлежащие визуализации. Составлено схемотехнические решение и визуализация графического интерфейса взаимодействия пользователя с системой. В соответствии со Стратегией развития охотничьего хозяйства РФ до 2030 года, требуется повышать программно-информационное обеспечение охотпользователей. Обоснованием разработки является отсутствие единой системы сбора, хранения и систематизации данных о состоянии охотхозяйств, численности популяций и проведении необходимых мероприятий. Разработка специальной ГИС способна обеспечить поддержку в системе принятия решений человеком в интересах управления территориями охотхозяйств. Разработка продуманного интерактивного интерфейса позволит вывести ГИС на наиболее качественный уровень взаимодействия. Общеизвестно, что пространственные данные являются основой информационного обеспечения геоинформационных систем, в том числе локальных ГИС. Для непрерывного обновления геоданных требуется своевременная актуализация информации на сервере, в которой данная система развернута. Поэтому в качестве технологической основы выбрано проектирование именно веб-приложения, работающего через предустановленный браузер пользователя. Реализация функций данной ГИС, продуманный человеко-машинный интерфейс, включенный в ее состав, позволит использовать данную систему не только охотпользователям, но и заинтересованным специалистам в смежных отраслях: лесное хозяйство, картография, геоинформатика, зоогегография, охотоведение. Разработанная система может использоваться в учебном процессе при подготовке студентов по различным направлениям в высших образовательных учреждениях. Discusses the necessity of developing a specialized geographic information system for hunting groung. Carried out a description of the organizational structure of geoinformation systems and functions to be rendering. Determined necessary requirements for the design of the GIS and the tasks which the system are able to solve. The organizational structure of the geographic information system and the functions to be visualized are described. Compiled technical scheme and visualization GUI. In accordance with the strategy of development of the hunting industry of the Russian Federation until 2030, it is required to increase the software and information support for hunting users. The basis for development is the lack of a unified system of collection, storage and systematization of data on the state of hunting, population and carry out the necessary measures. The development of a special GIS is able to provide support in the system of decision-making by a person in the interests of managing the territories of hunting farms. The development of a thoughtful interactive interface will bring the GIS to the most high-quality level of interaction. It is well known that the spatial data are the basis for information support of geographic information systems, including local GIS. For continuous updates of the geodata requires a timely update information on the server in which the system is deployed. Therefore, as the technological base of the selected design it is the web application running through a preset user's browser. The implementation of the functions of this GIS, intelligent man-machine interface, included in its composition, will allow you to use this system not only hunters, but also to interested professionals in related industries, forestry, cartography, geoinformatics, zoogeography, and hunting. The developed system can be used in educational process for training students in various areas in higher educational institutions.

Prediction of aircraft estimated time of arrival using machine learning methods

2021 ◽  
pp. 1-15
Author(s):  
O. Basturk ◽  
C. Cetek
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Weather Data ◽  
Time Of Arrival ◽  
Learning Models ◽  
Trajectory Data ◽  
Different Sources ◽  
Machine Learning Models

ABSTRACT In this study, prediction of aircraft Estimated Time of Arrival (ETA) is proposed using machine learning algorithms. Accurate prediction of ETA is important for management of delay and air traffic flow, runway assignment, gate assignment, collaborative decision making (CDM), coordination of ground personnel and equipment, and optimisation of arrival sequence etc. Machine learning is able to learn from experience and make predictions with weak assumptions or no assumptions at all. In the proposed approach, general flight information, trajectory data and weather data were obtained from different sources in various formats. Raw data were converted to tidy data and inserted into a relational database. To obtain the features for training the machine learning models, the data were explored, cleaned and transformed into convenient features. New features were also derived from the available data. Random forests and deep neural networks were used to train the machine learning models. Both models can predict the ETA with a mean absolute error (MAE) less than 6min after departure, and less than 3min after terminal manoeuvring area (TMA) entrance. Additionally, a web application was developed to dynamically predict the ETA using proposed models.

scholarly journals PredNTS: Improved and Robust Prediction of Nitrotyrosine Sites by Integrating Multiple Sequence Features

2021 ◽  
Vol 22 (5) ◽  
pp. 2704
Author(s):  
Andi Nur Nilamyani ◽  
Firda Nurul Auliah ◽  
Mohammad Ali Moni ◽  
Watshara Shoombuatong ◽  
Md Mehedi Hasan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Web Application ◽  
Computational Prediction ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Recursive Feature Elimination ◽  
Post Translational Modification ◽  
Multiple Sequence ◽  
Sequence Features

Nitrotyrosine, which is generated by numerous reactive nitrogen species, is a type of protein post-translational modification. Identification of site-specific nitration modification on tyrosine is a prerequisite to understanding the molecular function of nitrated proteins. Thanks to the progress of machine learning, computational prediction can play a vital role before the biological experimentation. Herein, we developed a computational predictor PredNTS by integrating multiple sequence features including K-mer, composition of k-spaced amino acid pairs (CKSAAP), AAindex, and binary encoding schemes. The important features were selected by the recursive feature elimination approach using a random forest classifier. Finally, we linearly combined the successive random forest (RF) probability scores generated by the different, single encoding-employing RF models. The resultant PredNTS predictor achieved an area under a curve (AUC) of 0.910 using five-fold cross validation. It outperformed the existing predictors on a comprehensive and independent dataset. Furthermore, we investigated several machine learning algorithms to demonstrate the superiority of the employed RF algorithm. The PredNTS is a useful computational resource for the prediction of nitrotyrosine sites. The web-application with the curated datasets of the PredNTS is publicly available.

scholarly journals Short-Term Prediction of COVID-19 Cases Using Machine Learning Models

2021 ◽  
Vol 11 (9) ◽  
pp. 4266
Author(s):  
Md. Shahriare Satu ◽  
Koushik Chandra Howlader ◽  
Mufti Mahmud ◽  
M. Shamim Kaiser ◽  
Sheikh Mohammad Shariful Islam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Learning Models ◽  
Short Term ◽  
Infected People ◽  
Infected Case ◽  
First Case ◽  
Novel Coronavirus ◽  
Short Term Prediction ◽  
Machine Learning Models

The first case in Bangladesh of the novel coronavirus disease (COVID-19) was reported on 8 March 2020, with the number of confirmed cases rapidly rising to over 175,000 by July 2020. In the absence of effective treatment, an essential tool of health policy is the modeling and forecasting of the progress of the pandemic. We, therefore, developed a cloud-based machine learning short-term forecasting model for Bangladesh, in which several regression-based machine learning models were applied to infected case data to estimate the number of COVID-19-infected people over the following seven days. This approach can accurately forecast the number of infected cases daily by training the prior 25 days sample data recorded on our web application. The outcomes of these efforts could aid the development and assessment of prevention strategies and identify factors that most affect the spread of COVID-19 infection in Bangladesh.

scholarly journals Signature-based and Machine-Learning-based Web Application Firewalls: A Short Survey

2021 ◽  
Vol 189 ◽  
pp. 359-367
Author(s):  
Simon Applebaum ◽  
Tarek Gaber ◽  
Ali Ahmed
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Short Survey

scholarly journals Algorithm for detecting attacks on Web applications based on machine learning methods and attributes queries

2022 ◽  
Vol 2 (14) ◽  
pp. 26-34
Author(s):  
Nguyen Manh Thang ◽  
Tran Thi Luong
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Cloud Storage ◽  
Web Applications ◽  
Detection Algorithm ◽  
Learning Methods ◽  
Google Docs ◽  
Effective Work ◽  
Web Resources ◽  
Machine Learning Methods

Abstract—Almost developed applications tend to become as accessible as possible to the user on the Internet. Different applications often store their data in cyberspace for more effective work and entertainment, such as Google Docs, emails, cloud storage, maps, weather, news,... Attacks on Web resources most often occur at the application level, in the form of HTTP/HTTPS-requests to the site, where traditional firewalls have limited capabilities for analysis and detection attacks. To protect Web resources from attacks at the application level, there are special tools - Web Application Firewall (WAF). This article presents an anomaly detection algorithm, and how it works in the open-source web application firewall ModSecurity, which uses machine learning methods with 8 suggested features to detect attacks on web applications. Tóm tắt—Hầu hết các ứng dụng được phát triển có xu hướng trở nên dễ tiếp cận nhất có thể đối với người dùng qua Internet. Các ứng dụng khác nhau thường lưu trữ dữ liệu trên không gian mạng để làm việc và giải trí hiệu quả hơn, chẳng hạn như Google Docs, email, lưu trữ đám mây, bản đồ, thời tiết, tin tức,... Các cuộc tấn công vào tài nguyên Web thường xảy ra nhất ở tầng ứng dụng, dưới dạng các yêu cầu HTTP/HTTPS đến trang web, nơi tường lửa truyền thống có khả năng hạn chế trong việc phân tích và phát hiện các cuộc tấn công. Để bảo vệ tài nguyên Web khỏi các cuộc tấn công ở tầng ứng dụng, xuất hiện các công cụ đặc biệt - Tường lửa Ứng dụng Web (WAF). Bài viết này trình bày thuật toán phát hiện bất thường và cách thức hoạt động của tường lửa ứng dụng web mã nguồn mở ModSecurity khi sử dụng phương pháp học máy với 8 đặc trưng được đề xuất để phát hiện các cuộc tấn công vào các ứng dụng web.

scholarly journals About the Usage of the Augmented Reality Technology in Mathematics and Physics Learning

2020 ◽  
Vol 24 (3) ◽  
pp. 44-55
Author(s):  
Yu. Yu. Dyulicheva
Keyword(s):  
Augmented Reality ◽  
Technology Implementation ◽  
Mobile Application ◽  
Web Application ◽  
Mathematics Learning ◽  
Educational Process ◽  
Dynamic Problems ◽  
Physics Learning ◽  
Augmented Reality Technology ◽  
Mathematics And Physics

The purpose of the work is the investigation of the modern approaches to augmented reality usage in mathematics and physics learning and the development of mobile application with graphical tips in the augmented reality mode for solving the dynamics typical problems.Materials and methods. The review of the modern articles on the augmented reality usage for mathematics and physics learning is provided; the iOS applications usage in mathematics learning such as GeoGebraAR for second order surfaces learning; the game MultiplicationAR for the multiplication table studying in an fascinating way; VectorAR application for vectors, cross and dot products, Cartesian and skew coordinate systems learning and iOS apps for physics learning such as Physics-Lab for conducting of the experiments with electrical circuits connections in the augmented reality, astrophysics and electromagnetism learning; Galileo application for accompanying of the theoretical material in physics with experiments to demonstrate paradoxes in mechanics, gravitational slingshot etc.; Arious application for students’ acquaintance with famous physicists and their discoveries in the augmented reality are considered. The analysis of tools for lecturers that can be used for the augmented reality content creation, such as HP Reveal and web-application Augment is performed. It should be noted the high personal interests of lecturers for the augmented reality technology implementation.Results. In the course of the work, the difficulties encountered by students in the study of mathematics and physics and possibilities of the augmented reality usage to overcome these difficulties are studied. A mobile application with the ability to receive graphical tips for solving dynamics problems that aimed at students with difficulties of forces and their projections understanding is developed. The methods for the mobile application are developed based on the basic classes of ARKit framework such as SCNNode, SCNBox, SCNPlane, SCNText, SCNGeometry, SCNGeometryElement, SCNShape, SCNMaterial etc. To conduct the experiments a pilot group of 14 random students is created, 9 of which experienced difficulties in solving some types of dynamic problems. The results of the experiment show a positive attitude of students to use of the augmented reality. 7 of 9 students who had difficulties got the skills to solve typical dynamic problems. Conclusion. Based on results of the research, we can conclude about the effectiveness of the augmented reality usage for studying of the abstract concepts in mathematics and physics. The developed mobile application with graphical tips in the augmented reality mode has improved the students’ performance in the pilot group. The lecturers play an important role in the implementation of AR-technology in the educational process. They help to maintain the student’s interest to AR-technology throughout the lesson. The disadvantages of the augmented reality applications include their narrow focus on studying of specific process, phenomenon or concept. The results of research can be applied in the educational process for mathematics and physics learning in order to increase the motivation and interest of students.

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