ABSTRACT
The Flipped Classroom methodology encourages students to interact with content in multiple ways and professors, who provide active learning strategies to create a super engaging group space that can extend beyond the classroom walls. The purpose of the study was to generate learning scenarios that ensure the good performance of students to achieve the skills in two programming courses at a private university in Peru, making its implementation sustainable over the years. The educational proposal presented in this research made use of the flipped classroom methodology and the Discord platform as an agile means of communication. The results are very encouraging because it allowed students to participate in their own learning in an active and self-directed way so that they self-regulate their own learning path individually and in groups;based on flipped classroom and successfully deployed on the Discord platform. © 2023 IEEE.
ABSTRACT
Healthcare monitoring frameworks emerged as one of the most essential frameworks and innovations established over the last decade. As a result of failing to provide adequate clinical attention to patients at the appropriate time, many people are facing the possibility of an untimely death. Ultimately, the goal was to develop an IoT-based integrated healthcare monitoring framework that could be relied upon by healthcare professionals to screen their patients, whether they were in the hospital or at home, to ensure that they were being well-cared for. A mobile phone-based remote healthcare monitoring framework has been constructed with the help of sensors, an information acquisition unit, a microcontroller (such as Arduino), and a product modification. This framework has the potential to provide continuous web-based data regarding a patient's physiological states (i.e., JAVA). Before transmitting it to the specialist's portable device along with the application, the framework examines the patient's temperature, heart rate, and EEG data. It then displays and saves this information. An Internet of Things-based patient monitoring framework may monitor a patient's health condition in an efficient manner and save the patient's life at the appropriate moment. © 2023 IEEE.
ABSTRACT
Service providers from the informal sectors in the Philippines are left unemployed or want to look for part-time jobs due to the sudden COVID-19 pandemic. With the country on lockdown and as strict restrictions are implemented, people started adopting the e-commerce and m-commerce market. The rise in the Filipino masses using smartphones and participating in mobile commerce to purchase products and services over the Internet has given researchers a potential solution for the arising problem. Thus, the primary objective of the research is to design and develop a user-friendly mobile application that will give a platform where potential home service providers can offer their services to potential clients. HanAPP Buhay, a mobile application that is created in Android Studio with Java as a programming language, is a platform where service providers can offer a variety of home services including laundry, plumbing, cleaning, and electrical works to potential clients. As for the Application Programming Interface (API), Stripe and Firebase are the tools utilized for databases and transaction purposes. The researchers conducted a series of surveys and experiments and have determined that in the 38 trials, the HanAPP Buhay mobile application is functioning 100% accurately as expected, 99.995382330563% and 99.994941213182% working at real-time booking of appointments in the 16 trials for clients and 22 trials for workers respectively, have a reliable user's interface and secured data of both users through the Scrypt algorithm, and effective in its overall specifications in terms of customer's satisfaction. © 2022 IEEE.
ABSTRACT
Virtual laboratories have successfully proven to be very versatile and intuitive when simulating experiments in science, biotechnology, and engineering. These tools must complement the experiments carried out in real labs or pilot plants. This study describes the creation of a virtual laboratory through the Easy JavaScript Simulation platform. A web‐based simulation of an enzymatic stirred‐tank bioreactor has been built using a dynamic model. This simulation reproduces the behavior of a continuous bioreactor, including the deviations of ideal mixing conditions as by the use of an in tanks‐in‐series model for nonideal flow. This article describes the continuous dynamic model in a stirred tank bioreactor, as well as the operation of a tool capable of carrying out virtual practice with students. Practice scripts have been developed that should be used by students during the practical classes. This interactive tool is powerful and useful to develop many experiments by varying the different input parameters, saving time and resources. In addition, the tool allows following teaching sessions in specific situations such as the health situation derived from the pandemic caused by COVID‐19.
ABSTRACT
Since the outbreak of the novel coronavirus at the end of 19, the competition among SMEs has become increasingly fierce. Based on their traditional management model and low level of informatization, SMEs have poor ability to cope with the impact of the epidemic and cannot quickly meet the new needs of target customers and new market opportunities. In the context of the impact of the epidemic situation, small and medium-sized enterprises need to optimize the inventory management system by introducing computer technology, use big data analysis technology to adapt to market demand, reduce the problem of enterprise inventory backlog, improve the efficiency of enterprise resource allocation, and maximize the avoidance of resource mismatch. This paper uses Java EE programming technology and the Spring Boot framework to design an enterprise inventory management system, which can comprehensively and directly display the enterprise inventory management situation, achieve data recording, storage, and modification. At the same time, it uses enterprise inventory management data to achieve the portrait analysis of target users, improve the efficiency of enterprise inventory management, optimize the enterprise management system, and quickly meet the new needs of customers. © 2022 Association for Computing Machinery.
ABSTRACT
Steganography is the set of techniques aiming to hide information in messages as images. Recently, stenographic techniques have been combined with polyglot attacks to deliver exploits in Web browsers. Machine learning approaches have been proposed in previous works as a solution for detecting stenography in images, but the specifics of hiding exploit code have not been systematically addressed to date. This paper proposes the use of deep learning methods for such detection, accounting for the specifics of the situation in which the images and the malicious content are delivered using Spatial and Frequency Domain Steganography algorithms. The methods were evaluated by using benchmark image databases with collections of JavaScript exploits, for different density levels and steganographic techniques in images. A convolutional neural network was built to classify the infected images with a validation accuracy around 98.61% and a validation AUC score of 99.75%.
ABSTRACT
This paper proposes pandemic support system design exercises from both hardware and software perspective as constituent part of higher education computer science courses. Two case studies in context of computer science and automation study programmes at University of Niš, Faculty of Electronic Engineering in Serbia ae covered: Intelligent Information Systems and Microcontroller Programming. In case of the first one, the topics cover information system implementation relying on Java Enterprise Edition (JEE) technology with presence of machine learning elements provided by Weka API, so smart vaccination process support information system is presented as example. On the other side, the focus on the second course is on PIC16 family microcontrollers and RTOS-based system implementation using CCS C compiler and presented example represents control unit for indoor coronavirus safety control. © 2022 IEEE.
ABSTRACT
We present in this paper a suite of four software applications that will help a user to learn the basic vocabulary (about 6000 words) of the English language. The software program asks the user to choose a word from a list of other words. The originality of these applications consists mainly in the modality in which the choices for a tested word are selected. The software applications are graphical applications written in Java programming language. Applications can be modified so that the user can learn other foreign languages. They can also be integrated on e-learning platforms. In the context of the COVID pandemic, online learning has grown. © 2022 IEEE.
ABSTRACT
In order to quickly and accurately collect the massive commodities and corresponding transaction data of large-scale e-commerce platforms, and improve the ability of data analysis and mining, this paper proposes a platform commodity information collection system based on splash technology. The system prerenders the javascript code in the product page, combined with the Scrapy crawler framework, to realize a system that quickly and effectively collects product data from different platforms, and uses “mobile phone” as the retrieval keyword to verify the designed system, respectively. The experimental results show that the system can effectively collect up to 60,000 comments and 6,000 system requests. Conclusion. The platform commodity information collection system based on splash technology has certain application value and promotion for the commodity data collection of different platforms of e-commerce.
ABSTRACT
With the growing popularity of cryptocurrencies, which are an important part of day-to-day transactions over the Internet, the interest in being part of the so-called cryptomining service has attracted the attention of investors who wish to quickly earn profits by computing powerful transactional records towards the blockchain network. Since most users cannot afford the cost of specialized or standardized hardware for mining purposes, new techniques have been developed to make the latter easier, minimizing the computational cost required. Developers of large cryptocurrency houses have made available executable binaries and mainly browser-side scripts in order to authoritatively tap into users’ collective resources and effectively complete the calculation of puzzles to complete a proof of work. However, malicious actors have taken advantage of this capability to insert malicious scripts and illegally mine data without the user’s knowledge. This cyber-attack, also known as cryptojacking, is stealthy and difficult to analyze, whereby, solutions based on anti-malware extensions, blocklists, JavaScript disabling, among others, are not sufficient for accurate detection, creating a gap in multi-layer security mechanisms. Although in the state-of-the-art there are alternative solutions, mainly using machine learning techniques, one of the important issues to be solved is still the correct characterization of network and host samples, in the face of the increasing escalation of new tampering or obfuscation techniques. This paper develops a method that performs a fingerprinting technique to detect possible malicious sites, which are then characterized by an autoencoding algorithm that preserves the best information of the infection traces, thus, maximizing the classification power by means of a deep dense neural network.
ABSTRACT
With the diffusion of technology worldwide, almost every individual is starting to have at least one mobile device, making mobile applications very popular and an important sector. Given the direction in which many sectors, including education, are moving, there is a clear and evident need for communication on-line rather than in-person. Concerning universities, the communication gap between teachers and students was particularly compromised in light of the Covid-19 pandemic. This study is aimed to fill in this gap by developing a social platform where students and teachers can share their posts and create their personal profiles. The Firebase database and Android Studio are the chosen technologies for this paper due to their compatibility as well as popularity as a chat application, along with Java as the programming language. The developed platform, named 'SeniorHouse', is focused on educational content to provide both faculty and students with a two-way, real-time communication to share updates, posts, documents, project ideas, and personal profiles, to name a few. The SeniorHouse application has significant potentials to be expanded into a major hub for future on-line education. © 2021 IEEE.
ABSTRACT
The post-COVID-19 era will create major financial losses in organizational resources as a result of fraudulent activities by malicious agents existing at the edge and cloud domains. Most transactional systems from the edge-to-cloud layers lack the robust platform integration (such as application program interface (API) microservices) needed for fraud mitigation in networks and systems. This paper presents an AI containerization API system based on JAVA-SQL container (JSR-233) for fraud prediction and prevention in telecommunication networks. Pipeline modeling involving the Bayesian software implementation paradigm is introduced using the AI-JasCon model. A demonstration of how the AI engine works with a complex network system for observation of some calls, call frequency, and hidden activities for predictive classification (analytics) at the network backend is discussed. Robust network architecture is introduced for deterministic data mining while creating Bayesian computation to determine fraud potentials through prior, posterior, and joint probability distributions. AI-JasCon framework achieves predictive fraud detection with containerization and modularization using class models and data structures. At the network core layer, an enterprise management backend uses linear discriminant via fog controllers that processes identified fraud subscribers in the network. Also, a standard Java middleware container for distributed transaction management, directory services, and messaging is used to test the application. AI-JasCon framework provides a successful standard for determining fraudulent interactions in edge-to-cloud networks while providing a pipeline application programming model for continuous integration and continuous delivery (CI/CD). © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.