ABSTRACT
The COVID-19 pandemic has caused disruption to the economy due to the increasing infection that affects the workforce in different sectors. The Philippine government has imposed lockdowns to control the spread of infection. This urged the different sectors to implement flexible work schedules or work from home setup. A work-from-home (WFH) setup burdens both the employee and employer by installing different equipment set-ups such as WiFi-equipped laptops, computers, tablets, or smartphones. However, the internet stability in some of the areas in the Philippines is not yet reliable. In this study, an application is used collect survey information and provide an estimate of the telework internet cost requirement of a given government employee or a given government employee implementing a work-from-home set up in their respective household. This involves survey results from different respondents who are currently on a work-from-home setup and significant factors from the survey have been analyzed using machine learning (ML) algorithms. Among the machine learning algorithms used, the ensemble bagged trees model outperformed the other ML models. This work can be extended by incorporating a wider scope of datasets from different industry doing work from home set-up. In addition, in terms of education, it is also recommended to determine the WFH set up not just with the government employee and employer but to also extend this into the education side. © 2022 IEEE.
ABSTRACT
By early 2020, COVID-19 has caused a global pandemic which led to an enormous number of challenges worldwide in various sectors. The Philippine government has implemented multiple quarantine guidelines and travel restrictions to ensure the people's health and safety. However, the International Labour Organization projected an initial economic and labor market disruption affecting 11 million workers, or about 25% of the Philippine workforce, due to the pandemic. Therefore, the government, thru the concerned agencies continues to encourage employers to implement alternative work plans such as a work-from-home (WFH) operation in compliance with the established regulations in line with existing laws and policies. In line with the telecommuting concept, various research has already been performed, however, some were regarded inconclusive and require further study. Hence, in this study, a Web application was developed along with an embedded fuzzy model to evaluate the telecommuting capability assessment of employees. The proposed web application with embedded fuzzy model is capable of providing capability assessment using the four main input variables which are also relatively characterized for possible telecommuting cost assessment. © 2022 IEEE.
ABSTRACT
A horrifying number of people died because of the COVID-19 pandemic. There was an unexpected threat to food systems, public health, and the workplace. The pandemic has severely disturbed society and there was a serious impediment to the economy. The world went through an unprecedented state of chaos during this period. To avoid anything similar, we can only be cautious. The project aims to develop a web application for the preliminary detection of COVID-19 using Artificial Intelligence(AI). This project would enable faster coordination, secured data storage, and normalized statistics. First, the available chest X-ray datasets were collected and classified as Covid, Non-Covid, and Normal. Then they were trained using various state-of-the-art pre-trained Convolutional Neural Network (CNN) models with the help of Tensor-flow. Further, they were ranked based on their accuracy. The best-performing models were ensembled into a single model to improve the performance. The model with the highest accuracy was transformed into an application programming interface (API) and integrated with the Decentralized application (D-App). The user needs to upload an image of their chest X-ray, and the D-App then suggests if they should take a reverse transcription-polymerase chain reaction (RT-PCR) test for confirmation. © 2022 IEEE.
ABSTRACT
Before Covid, we introduced our own classroom response system to improve the effectiveness of our teaching. To this end, we adopted an open-source technique, SignalR, which provides a framework for building real-time web applications. Overnight, due to the emergency situation starting in 2019, education was moved to the virtual space. Both students and professors had to learn how to teach or learn using only online facilities, without a testing period. During the emergency, a synchronous online teaching mode was required by our university, so the choice was made to use Microsoft Teams, implemented with SignalR for real-time functionality. After the emergency, we were all happy to have our 'old life' back and return to our personal teaching style, but is it possible, is it possible to continue teaching in the same way as before Covid-19 - is it possible to step into the same river twice? Students have become accustomed to convenient, modern, digital options during the online education period and now that we are back in school, they insist that we continue to use the new tools. In this essay, we want to describe the changes in students' attitudes that we can usefully build on in the future and that will influence the further development of our project. © 2023 IEEE.
ABSTRACT
MNLTour is a virtual tour system for selected tourist spots situated within the city of Manila. It utilizes 360-degree images, 2D images, voice recordings, and virtual reality technology to offer an immersive user experience of the virtual environment. The virtual tour system was developed using the Unity3D software and was then integrated into web and mobile applications accessible through web browsers and android smartphones, respectively. MNLTour aims to promote the wonders of Manila city by showcasing some of its historical tourist spots that have been severely affected by the outbreak of the COVID-19 pandemic. The developed web and mobile applications were tested and later evaluated to assess the overall quality of the software in accordance with ISO 9126 standard. The evaluation statements primarily focus on the aspects of functionality, efficiency, usability, effectiveness, and user satisfaction in using the application. Descriptive statistics was used to analyze and summarize the data gathered from the evaluation respondents. The evaluation of the application in both platforms turned out to have admirable evaluation results;hence, it's safe to say that the developed software has an acceptable overall quality. © 2022 IEEE.
ABSTRACT
This paper presents a web application to control personnel access to a work area without contact;this makes it ideal to help combat the Covid-19 health emergency. For its implementation, deep learning and computer vision techniques have been used for face detection and recognition. The system consists of four phases, the first one aimed at detecting and aligning the face with deep learning algorithms. The second phase obtains the facial features to recognize different people. The third phase consists of implementing a module that detects face impersonation, and significantly prevents possible attacks on the system by identifying whether the face is real or fake;and the last phase is the design and development of the web interface. This interface performs the communication of the algorithms, the users and the administration. In order to evaluate this proposal, several experiments have been carried out under diverse real conditions. The main results to correctly identify the user show that it has an accuracy of 99 %, in an estimated time of 3 seconds, in the range of 20 cm to 90 cm away, with respect to the camera. In addition, the system is capable of identifying users wearing masks or glasses, in this case with an accuracy of 95% in 4 seconds. © 2022 IEEE.
ABSTRACT
In the early months of 2020, pandemic covid-19 hit many parts of the world. Especially developing countries like India observed a negative growth rate in few quarters of last financial year. Retailing is one of the key sectors that contribute to Indian GDP with a share of nearly 10 percent. Hence there is a need for the retail sector to bounce back which is possible with the efficient use of new digital technologies. Market basket analysis is used here to extract the association rules which can be directly used for formulating discount and combo offers. Along with that, these rules can be used to decide the product positioning in the retail store. Items which are bought together can be placed next to each other to increase sales. Recommendation systems are most commonly used in ecommerce websites like Amazon, Flipkart, etc, and streaming platforms like Netflix to recommend the items that are to be purchased by users. Although recommendation engines are implemented in multiple web and mobile applications, these are not in the implementation stage in offline retail stores due to many implications associated with them like infrastructure, cost, etc. In this project, we have used market basket analysis and recommendation systems to propose a model to implement in retail stores to increase sales revenues and enhance customer experience. © 2022 IEEE.
ABSTRACT
Web RTC can provide real time capabilities for multimedia applications like voice, video and data between peers by utilizing the open standards. With the onset of covid, video conferencing has become a need of the day. Optimization of bandwidth, and other features have become the necessity. In the current work, WebRTC protocols are built upon, to improve the connection and success rate, optimize the bitrate and reduce the frame rate. This improvement is carried out without visible or audible loss of clarity in the video sessions. The Session Description Protocol is utilized to accomplish this, and this would not have been possible using WebRTC APIs alone. N-to-N connection among peers is established in an optimized manner, so that the application does not engage an intermediate server to transfer media streams which has resulted in multi-fold improvement in bandwidth performance and also maximized the number of participants, without incurring the cost for an intermediate media server. Conventionally, an intermediate media server is used to stitch streams from various senders into a single stream and then sent to the receivers. Bandwidth utilization is reduced close to 100x with good visibility in the stream. Robust web application is achieved using the TURN (Traversal Using Relays around NAT) server. The proposed work has addressed multiple ways of optimizing for the video conferencing using WebRTC. © 2022 IEEE.
ABSTRACT
This research develops the TB/non-TB detection and drug-resistant categorization diagnosis decision support system (TB-DRC-DSS). The model is capable of detecting both TB-negative and TB-positive samples, as well as classifying drug-resistant strains and also providing treatment recommendations. The model is developed using a deep learning ensemble model with the various CNN architectures. These architectures include EfficientNetB7, mobileNetV2, and Dense-Net121. The models are heterogeneously assembled to create an effective model for TB-DRC-DSS, utilizing effective image segmentation, augmentation, and decision fusion techniques to improve the classification efficacy of the current model. The web program serves as the platform for determining if a patient is positive or negative for tuberculosis and classifying several types of drug resistance. The constructed model is evaluated and compared to current methods described in the literature. The proposed model was assessed using two datasets of chest X-ray (CXR) images collected from the references. This collection of datasets includes the Portal dataset, the Montgomery County dataset, the Shenzhen dataset, and the Kaggle dataset. Seven thousand and eight images exist across all datasets. The dataset was divided into two subsets: the training dataset (80%) and the test dataset (20%). The computational result revealed that the classification accuracy of DS-TB against DR-TB has improved by an average of 43.3% compared to other methods. The categorization between DS-TB and MDR-TB, DS-TB and XDR-TB, and MDR-TB and XDR-TB was more accurate than with other methods by an average of 28.1%, 6.2%, and 9.4%, respectively. The accuracy of the embedded multiclass model in the web application is 92.6% when evaluated with the test dataset, but 92.8% when evaluated with a random subset selected from the aggregate dataset. In conclusion, 31 medical staff members have evaluated and utilized the online application, and the final user preference score for the web application is 9.52 out of a possible 10.
ABSTRACT
The pandemic caused by COVID-19 impacted the entire world, but the significant challenges to be faced during this crisis opened an opportunity for organizations to evolve toward a digital transformation. Educational institutions were a concrete example of the use of technologies, which were abruptly incorporated into the teaching–learning model. Although this initiative was initially a challenge for teachers and students, it has now become a tool for new innovative teaching models, such as hybrid, online, and flexible models. The impact of technology used in education has been beneficial due to emerging technologies (virtual reality, augmented reality, games, web applications, mobile applications, etc.), which have served as tools to facilitate and motivate studying. These educational trends contribute directly to the fourth Sustainable Development Goal (SDG). This research analyzes whether the use of a web application, as a support in the educational model, can make students better understand the subjects of network infrastructure and be more efficient when configuring equipment in a data network. Therefore, this research is based on the design of an educational web application based on Python libraries, which allows the configuration of networking equipment based on the concept of network automation with the application of a graphical user interface (GUI). The web application can be deployed with communication equipment or in conjunction with the GNS3 simulator. This versatility allows this web tool to be applied to the teaching of network equipment configuration in any mode of study (classroom, online, hybrid, or flexible). The results obtained in this research are encouraging and open the way for the implementation of network automation and Python libraries for educational applications that can be important tools within the teaching and learning models of higher education. © 2023 by the authors.
ABSTRACT
An elderly squire indicated the importance of medication intake in everyday life. Some individuals forget to take medicine on time. Also, monitor using the scheduled medicine to help people with an illness. In addition, technology has increased for utilization in healthcare. According to Harvard Medical School by Stephanie Watson about the technology advancement is flourishing in the Philippines, and telehealth stays with innovation to improve digital health platforms. In addition, healthcare services are facing challenges with Covid-19. On the other hand, telehealth gives good service, and about 91% of consumers use digital healthcare services. Also, A reminder system should be simple, familiar, flexible, and recognizable.Technology can positively impact the lives of older people, including their physical and mental health and daily activities. Technology can help people become active. It also increases awareness and motivation to increase physical activity. © 2022 IEEE.
ABSTRACT
The year 2020 was an unprecedented time for all, combating COVID, following precautionary measures and finding a cure for the virus was of utmost importance. As the COVID-19 is here to stay, it is imperative to detect it as early as possible. Our web application (COVID RayScan) is a prediction-based Machine Learning application which can be used by technicians, doctors at hospitals to understand a X-ray or CT-Scan and hence quickly detect if a patient suffers from Covid or not. According to NCBI, it takes 17.4 minutes for a doctor to treat every patient and that metric has increased exponentially with increase in COVID. COVID RayScan with the help of Deep Learning CNN Networks like ResNet50,VGG16,Inception and Xception helps a technician to run the X-Ray/CT-Scan image through our web application to get the desired result which in turn saves the doctor's as well as patients time and make the process much more efficient. © 2022 IEEE.
ABSTRACT
With the arrival of Covid-19, several preventive measures were implemented to limit the spread of this virus. Among these measures is the use of masks, both in open and closed public spaces. This measure has forced commercial establishments, workplaces, schools, hospitals, to maintain constant vigilance, upon entering their facilities, of the proper use of the mask, which should completely cover the nose, mouth and chin. However, this manual control is tedious and ineffective since most of the population is not able to correctly identify when a person has the mask on properly, with high error rates in the manual detection of the correct use of the mask according to surveys carried out. For this reason, this work proposes the automation of the detection of the proper use of the mask at the entrance to the work areas, also providing a follow-up panel of the recorded incidents. The effectiveness of the proposal was evaluated through the detection and categorization of a data set of more than 3000 images, resulting in an accuracy of 98.6%. © 2022 IEEE.
ABSTRACT
As the world is entering its 3rd year of the COVID-19 pandemic, the number of COVID-19 patients are increasing. So as the number of post-COVID patients who need rehabilitation. This paper proposes a web-based telerehabilitation system with the aims to aid COVID-19 rehabilitation research and clinical trial management. Our proposed system allows researchers to conduct various experiments such as physical therapeutic treatment and herbal treatment on COVID-19 outpatients. The web-based system is chosen for its ubiquity and cost effectiveness where patients can easily participate in the rehabilitation program remotely from any tablet devices. Stakeholder involvement is crucial to the long-term success of this work. Therefore, user experience methodology is used to gain user adoption at the beginning of the project. Initial testing has shown satisfactory results. The developed system is expected to be used in an actual rehabilitation research. © 2022 IEEE.
ABSTRACT
The requirement for remote examination had emerged along with remote learning during the COVID-19 pandemic as the unprecedented situation had brought the world to halt. The pandemic had forced many educational institutions to move towards the online mode of assessment to assess the caliber of the students. This paper focuses on the ways that an online examination system can be prepared and can be used for conducting exams remotely in a secure way. It also emphasizes on various test cases that are essential for an efficient and useful examination system that can benefit both students and faculty by saving them time and effort. Due to the challenges in the existing mode of online assessment such as the use of digital forms that are usually used for conducting surveys, scanning and uploading answer sheets using phone with poor camera quality, the problem of engaging in the different kinds of misconduct, it was important to understand the user requirements at an examiner and examinee level and prepare a web application that addresses them and makes it convenient to conduct and attempt. We propose different methodologies that can be implemented in a Python based web application with the help of JavaScript such as switching the browser window to full-screen in order to restrict access to other applications, limited exits from full-screen, easy management of examiner and candidate data along with visualization of exam data that help to better understand and draw quick conclusions at the time of exam. It is also focused on the continuously evolving distance education system and finding the best software solution possible for online examinations. Additionally, an automated grading system may help to reduce human error and declare results easily reducing fatigue. © 2022 IEEE.
ABSTRACT
With the advent of the e-commerce markets, the small businesses in India are experiencing a major hit and a loss of customers. Since the medieval times, India is known for its street markets. It is so prominent that it is a cultural representation, and this prompts a considerable number of people to opt for establishment of business on the streets. During the pandemic, the street vendors are experiencing losses to an extent that they are unable to support their families. Our solution to the problem is a web application called 'Street Vendor Mart'. This aims at helping the hard-working street vendors by marketing their business. Say a citizen is walking on the road and finds a street vendor who is toiling under the sun, seeking to earn even the minimum wage. This citizen can help this street vendor through our application, 'Street vendor mart'. The advertisement posted by the citizen will now be recorded on the site and visible to any person who wishes to do some street shopping. If a user wants to shop for some item for cheaper prices, the user can log in to our site and find a list of street vendors around his location to buy the products. The users can visit the vendors near them, shop from them. Thus our 'Street Vendor Mart' is essentially a virtual mall filled with stores by street vendors If two or more vendors are selling the same category of products in the same location, then the gains will not be up to the mark because of the reduction in demands. As a solution to this, our web application runs Data Analytics to find the optimal location for the vendor to sell his/her category of goods which will maximize their profit. © 2022 IEEE.
ABSTRACT
Currently, there is no effective cure for SARS-COVID-19 diseases. The identification of novel therapeutic targets and drug-like compounds is required for the development of anti-COVID-19 drugs. Virtual screening is currently the most significant component for identifying drug-like molecules from large datasets for drug design and development. However, there are no effective easily available and user-friendly applications for virtual screening of drug leads against SARS-COV-2. Therefore, we have developed a user-friendly web-app named ‘AIDrugApp' for the virtual screening of inhibitor molecules against SARS-CoV-2. AIDrugApp is a novel open-access, deep learning AI-based inhibitory activity prediction and data statistics visualisation platform. Users can predict the inhibitory activities (Active/Inactive) and pIC-50 values of new compounds against SARS-CoV-2 replicase polyprotein, 3CLpro and human angiotensin-converting enzymes. It is also useful for virtual screening of chemical features of molecules towards SARS-COVID-19 clinical trial bioactivities. This paper presents the development and architecture of AIDrugApp. We also present two case studies where large sets of molecules were screened using the ‘Bioactivity Prediction' module of our app. Screened molecules were analysed further for validation by molecular docking and ADME analysis to identify the potential drug candidates.
ABSTRACT
According to the supply chain disruptions induced by the COVID-19 pandemic and the situation between NATO and Russia that led to the rising of fuel costs. Rising fuel costs are a massive problem for business and consumers. People must pay more money for their living because higher fuel costs affect costs of products, services, and transportation. This paper aims to develop the vehicle management system to track the fuel usage, mileage, cost, and maintenance of cars in an organization. The system is developed in a form of a web application for reporting a fuel usage and requesting permission to reserve a car. We used HTML, PHP, and Laravel Jetstream to develop this web application for employees and administrators. Employee can choose a car and submit a reservation form through a website. While administrator can manage cars information through the dashboard. User can use the mobile application to check in to get a car when a car is ready to use and check out a car when he/she wants to return a car to a company. © 2023 IEEE.
ABSTRACT
IoT technology is emerging as a fully developed automation that could be integrated in various web applications, which will be present in upcoming generations of the World Wide Web. Blockchain, like IoT, is a burgeoning field whereby every system associated in the blockchain incorporates a disseminated ledger that improves safety and consistency. Due to the blockchain network abilities to accomplish smart contracts and consensus, unauthorized users are unable to undertake any fault transactions. The IoT and blockchain can be aggregated to improve application performance dynamically at run time. However, controlling and monitoring the machines linked to sensors in an IoT background and mining the blockchain will always be a technical challenge to the researchers. With this context, this paper enables to review the fundamentals of IoT, blockchain field, and its topographies. In this paper, design architecture, namely, IoT Blockchain Assurance-Based Compliance to COVID Quarantine, is proposed and concluded up with novel architectural framework that improves the efficiency of data safety and data transparency. Unlicensed users are not permitted to conduct any erroneous transactions within the blockchain network, which has the capability to engage in smooth contracts and agreement, thus extending the safekeeping between clinicians and chronically ill patients. This methodology was created with immobile elderly chronically ill patients in mind who are suffering from COVID that require on-the-spot treatment and continuous monitoring by a doctor in mind. This paper is designed to analyze the performance of proposed IoT Blockchain Assurance-Based Compliance to COVID Quarantine with Ethereum private blockchain network beneath a genesis block and the results are conferred. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
ABSTRACT
The discrete-event system specification (DEVS) formalism has been recognized to be able to enable a formal and complete description of the components and subsystems of hybrid models. What is missing for accelerated adoption of DEVS-based methodology is to offer a way to design web apps to interact with a simulation model and to automatically deploy it on an online server which is remotely accessible from web app. The deployment of DEVS simulation models is the process of making models available in production where web applications, enterprise software, and APIs can consume the simulation by providing new inputs and generating outputs. This paper proposes a framework allowing one to simplify the DEVS simulation model building and deployment on the web by the modeling and simulation engineers with minimal web development knowledge. A case study on the management of COVID-19 epidemic surveillance is presented. © 2022 IEEE.