Explainable artificial intelligence approach towards classifying educational android app reviews using deep learning

Mobile application developers rely largely on user reviews for identifying issues in mobile applications and meeting the users' expectations. User reviews are unstructured, unorganized and very informal. Identifying and classifying issues by extracting required information from reviews is difficult due to a large number of reviews. To automate the process of classifying reviews many researchers have adopted machine learning approaches. Keeping in view, the rising demand for educational applications, especially during COVID-19, this research aims to automate Android application education reviews' classification and sentiment analysis using natural language processing and machine learning techniques. A baseline corpus comprising 13,000 records has been built by collecting reviews of more than 20 educational applications. The reviews were then manually labelled with respect to sentiment and issue types mentioned in each review. User reviews are classified into eight categories and various machine learning algorithms are applied to classify users' sentiments and issues of applications. The results demonstrate that our proposed framework achieved an accuracy of 97% for sentiment identification and an accuracy of 94% in classifying the most significant issues. Moreover, the interpretability of the model is verified by using the explainable artificial intelligence technique of local interpretable model-agnostic explanations. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Readwell: A Book Recommender Android Application with a Point-of-Sales System

The COVID-19 pandemic has given people much free time. With this, the researchers want to encourage these people to read instead of scrolling through social media. A barrier to reading for many people is not knowing what to read and disinterest in popular books that they would find when they search online. The existing websites that encourage book reading rely on social networking for their recommendations, while the collaborative filtering algorithms applied to books do not exist in the mobile application form. Readwell is a book recommender Android app with a Point-of-Sales System created using Java, Python, and SQLite databases. The information regarding the books was web scraped from the Goodreads website. It aims to apply the more efficient collaborative filtering algorithm to an accessible mobile application that allows users to directly buy the books they are interested in, thus encouraging the reading and buying of books. The researchers created unit test cases to validate the different functionalities of the application. © 2022 IEEE.

IoT Based Real Time Health Score Indicator

Every individual wishes and desire to be in good health. In the existence environment it has become exceedingly unusual to maintain a good health. The era of CORONA virus compiled every country of the world to give healthcare highest ion all respect. An ideal response to such an epidemic is an IoT-based health score indicator. Real-time health score indicators built by using Internet of Things (IoT) is intended to relieve the stress and movement of ill and fatigued patients. The patients can quickly perform many necessary or obligatory medical tests on-site. In today's hectic society, the vast majority of people as well as patients prefer medical testing to be done at home. In this study, various tools were combined including the thermometer, blood pressure, glucometer, pulse oximeter, heartbeat, and ECG on a single unit. The sensors along with Arduino and LCD were merged to create a single gadget. Moreover, a server-based Android app will be developed to upload information about all the testing results which can makes the patient life easier. © 2023 IEEE.

Android Application for Human Respiratory System Diagnosis: A Systematic Review

This paper presents a systematic review of android app respiratory system on smartphone. For some diseases, doctors have succeeded in inventing the necessary treatments that lasts for a short period, but in several cases, the treatment can stay for a lifetime. The goal of this system is to detect if a patient has any respiratory disease(s) by specifying the symptoms the patient encounters, schedules an appointement in the hospital for patient through the system to the linked specialist doctors to avoid contact in the case of Covid-19 patient. This research will help raise patient's awareness of the high risk of late discovery of having respiratory diseases (like Lung Cancer. corona virus etc), and also to develop a model that will help detect this disease early through mobile application. The focus of this review is to encourage medical institutions to adopt the health android app that can help patients in self-managing behavioral activities such as physical activities, using symptoms to determine the stage(early or critical) of the disease and drug suggestions with research evaluation using the app, this could help patients monitor and manage their health conditions. © 2022 IEEE.

No Salvation from Trackers: Privacy Analysis of Religious Websites and Mobile Apps

Many religious communities are going online to save costs and reach a large audience to spread their religious beliefs. Since the COVID-19 pandemic, such online transitions have accelerated, primarily to maintain the existence and continuity of religious communities. However, online religious services (e.g., websites and mobile apps) open the door to privacy and security issues that result from tracking and leakage of personal/sensitive information. While web privacy in popular sites (e.g., commercial and social media sites) is widely studied, privacy and security issues of religious online services have not been systematically studied. In this paper, we perform privacy and security measurements in religious websites and Android apps: 62,373 unique websites and 1454 Android apps, pertaining to major religions (e.g., Christianity, Buddhism, Islam, Hinduism). We identified the use of commercial trackers on religious websites—e.g., 32% of religious websites and 78% of religious Android apps host Google trackers. Session replay services (FullStory, Yandex, Inspectlet, Lucky Orange) on 198 religious sites sent sensitive information to third parties. Religious sites (14) and apps (7) sent sensitive information in clear text. Besides privacy issues, we also identify sites with potential security issues: 19 religious sites were vulnerable to various security issues;and 69 religious websites and 29 Android apps were flagged by VirusTotal as malicious. We hope our findings will raise awareness of privacy and security issues in online religious services. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Android App to Monitor the Position of a Group of Robots in a Hospital

This article presents the design of an Application in Android operating system, with the aim of monitoring movements in real time through a 2D graphic representation system for a group of Mobile Robots with medical assistance capabilities, in a portable and economical way. In addition, to provide information about the robots positioning in Cartesian coordinates, percentage of current battery and state of operation during their navigation. The robots are link to an Android mobile device through wireless communication in MQTT or WiFi protocol, depending on the technical case available. As a basis of application there is in the first instance a Covid hospital, the need arises as a means of medical assistance between the patient with Covid symptoms and the doctor, nurses and support staff in their treatment and isolation, in order to avoid contact, contagion and the additional expense of wearing special clothing to treat the patient. © 2022 IEEE.

Sentiment Analysis Model for KlikIndomaret Android App During Pandemic Using Vader and Transformers NLTK Library

COVID-19 has changed the Indonesian people's shopping habits for consumer goods. The online retail application came as a response to social distancing and stay-at-home advice. KlikIndomaret is an online retail application that uses the omnichannel concept. As the number of downloads increased, the number of various comments and sentiments on that application also increased. In this study, the researcher did a sentiment analysis aimed to improve the quality of application experiences and retail services. The result of the analysis reflected the services given to customers thus far. The data included reviews and star ratings derived from 4,066 reviews which went under the process of data pre-processing. The methods used in this study were VADER and NLTK, improved by Transformer, without pre-training data. These methods could filter the users' reviews with sarcasm tone. The results were sentiment labels that were appropriate based on the score comparison of positive and negative sentiments in one user's review. This approach made the review sentiment process of thousands of data faster and more accurate. © 2022 IEEE.

Android App based on CNN for Covid-19 Detection using Chest X-ray Images

Covid_19 pandemic is considered a global emergency that has caused great human as well as economic loss. To attack this pandemic, we developed a convolutional neural network model (CNN) the detection of COVID-19 using Chest X-Ray images (CXR). The model is then deployed on the Android app RT2022 which is tested on real CXR images and the first results obtained, after experiments, testify to the performance of the RT2022 model. In COVID-19 detection, we achieved an accuracy of 90.60% © 2022 IEEE.

Smarter Movement Control Application

The coronavirus disease 2019 (Covid-19) has devastated the entire globe in a short period of time and was declared a global pandemic by the World Health Organization (WHO) on March 11th, 2020. It rapidly increased in fatality rate and has become an international public health crisis, culminating in social and economic calamity. However, mobile applications are being introduced globally to minimize the coronavirus's continuous spread by tracing people's circulation or mobility using digital software and smartphones. There is no digitally equipped tool that monitors the movement of the people, particularly in the public places. This work on Smarter Movement Control Application (SMCApp) aims to develop methods that will assist Malaysian people to move around with the aid of mobile tracing application. Therefore, the SMCApp was designed and developed as a mobile application software which stands to improve compliance with the mandated SOP measures across the country, as well as to provide digital support to those who wish to travel to various parts of the country. As a result, it is concluded that the usage of Smarter Movement Control Application (SMCApp) is hoped to bring about safe and effective movement of people throughout these two regions, as it will constantly alert individuals of any suspicious close-contact or the state of the location. Once the application is in effect, with at least 90% of users installing the App on their smartphones, it is projected to increase tranquility and elevate compliance with SOP measures. © 2022 IEEE.

Investigating the Usability and Quality of Experience of Mobile Video-Conferencing Apps Among Bandwidth-Constrained Users in South Africa

During the COVID-19 pandemic and mandated global lockdowns, people and businesses started the extensive use of video-conferencing applications for staying connected. This surge in demand and the usability of video-conferencing services has been severely overlooked in developing countries like South Africa, where one-third of adults rely on mobile devices to access the internet, and the per-gigabyte data cost is among the highest in Africa. Considering these numbers, we conduct a two-pronged study where 1) we measure data consumption of different Android apps through data measurement experiments and 2) we conduct interviews and usability assessments with bandwidth-constrained users to better understand the usability and Quality of Experience (QoE) of mobile video-conferencing apps. Usability is the degree to which specified users can use a product to achieve specified goals. In contrast, QoE measures the subjective perception of the quality of an application and the level of delight or annoyance with a service. The key benefit of this study will be to inform organisations that seek to be inclusive about these tools’ relative usability by letting them know about the factors influencing users’ QoE. © 2022, EasyChair. All rights reserved.

Mini Medical Assistant: A Handheld Emergency Medical Support Program During COVID19 Pandemic

Android phones are one of the most common accessories used all over the world. Although once a luxury, it has now become a basic need for all generations. It is a multipurpose tool that can be used for all sorts of necessities and entertainment. Through our android app corona care, a mobile phone can be a helping hand for health care. This app can help prevent the deadly virus known as COVID-19 through plasma donation, consultation with doctors, setting up appointments, predicting corona risk assessment from symptoms using the Gaussian Naive Bayes method of predicting the risk percentage, providing emergency health services and updating users about the safety instructions about Covid-19. Our application consists of most features needed in a mHealth application that can provide necessary medical assistance to each and every household. © 2021 IEEE.

Do You Have the Energy for This Meeting?: An Empirical Study on the Energy Consumption of the Google Meet and Zoom Android apps

Context. With 'work from home' policies becoming the norm during the COVID-19 pandemic, videoconferencing apps have soared in popularity, especially on mobile devices. However, mobile devices only have limited energy capacities, and their batteries degrade slightly with each charge/discharge cycle. Goal. With this research we aim at comparing the energy consumption of two Android videoconferencing apps, and studying the impact that different features and settings of these apps have on energy consumption. Method. We conduct an empirical experiment by utilizing as subjects Google Meet and Zoom. We test the impact of multiple factors on the energy consumption: number of call participants, microphone and camera use, and virtual backgrounds. Results. Zoom results to be more energy efficient than Google Meet, albeit only to a small extent. Camera use is the most energy greedy feature, while the use of virtual background only marginally impacts energy consumption. Number of participants affect differently the energy consumption of the apps. As exception, microphone use does not significantly affect energy consumption. Conclusions. Most features of Android videoconferencing apps significantly impact their energy consumption. As implication for users, selecting which features to use can significantly prolong their mobile battery charge. For developers, our results provide empirical evidence on which features are more energy-greedy, and how features can impact differently energy consumption across apps. © 2022 ACM.

Customization and testing of a mobile reader app for an open-access SARS-CoV-2 antigen lateral flow assay

Lateral flow assays (LFA’s) are a common diagnostic test form, particularly in low-to-middle income countries (LMIC’s). Visual interpretation of LFA’s can be subjective and inconsistent, especially with faint positive results, and commercial readers are expensive and challenging to implement in LMIC’s. We report a phone-agnostic Android app to acquire images and interpret results of a variety of LFA’s with no additional hardware. Starting from the open-source “rdt-scan” codebase, we integrated new features and revamped the peak detection method. This included improved perspective corrections, phone level check to eliminate shadows, high resolution still-image capture besides existing video frame capture, and new peak detection method. This peak detection incorporated smoothing and baseline removal from the one-dimensional profiles of a given color channel’s intensity averaged across the read window’s width, with location and relative size constraints to correctly report locations and peak heights of control and test lines. The app was tested in a real-world setting in conjunction with an open-access LFA for SARS-CoV-2 antigen developed by GH Labs. The app acquired 155 images of LFA cassettes, and results were compared against both visual interpretation by trained clinical staff and PCR results from the same patients. With an appropriate setting for test line intensity threshold, the app matched visual read for all cases but one missed visual positive. From ROC analyses against PCR, the app outperformed visual read by 1-3% across sensitivity, specificity, and AUC. The app thus demonstrated promise for accurate, consistent interpretation of LFA’s while generating digital records that could also be useful for health surveillance. © 2022 SPIE

Development of Acoustically Optimized, 3D-Printed Stethoscope for Telehealth

The need for telehealth accelerated by the COVID-19 pandemic has changed the way doctors interact with patients. Recently, Internet-connected stethoscopes have been developed;however, they have not been adopted as far as many companies have hoped. The high cost of branded, high-quality stethoscopes may serve as a barrier to the adaptation, especially in low-and middle-income countries. With the above in mind, a 3D-printed stethoscope that preserves a long air column but still maintains a small form factor has been developed. Inserting a microphone into the stethoscope enables us to connect the stethoscope to our smartphone, turning it into a digital device. To take advantage of smartphone capabilities, we have also developed an Android app that enables us to record, share, and listen to heart sounds in real-time via Bluetooth headphones. Our experimental results show that the stethoscope made of thermoplastic polyurethane exhibits harmonic resonances comparable to commercially available selected acoustic stethoscopes. © 2022 IEEE.

EMarket for Local Farmers

The Covid-19 pandemic is a global health crisis that has brought an impact on everyone. A negative impact has made local farmers struggle to find their means of income other than selling their crops to their customers. The customers have had difficulties searching for good quality fresh produce other than from the supermarket. The purpose of this paper is twofold: firstly, to assess the manual and existing system on how consumers acquire their fresh produce during the Covid-19 pandemic, to design and implement a delivery system for local farmers to vend their fresh produce through a mobile application;and secondly, to test and evaluate the usability and functionality of the online farmers' market application. The existing mobile applications have changed everyone's approach in acquiring the daily essentials that the pandemic has brought. Therefore, the idea of developing an online marketplace for local farmers resulted in the proposed application, which will be explained in this paper. Rapid Application Development (RAD) was used as the methodology for the development of the eMarket application. A survey was conducted via Google form for twenty local farmers and another twenty potential customers during the pre-development stage. It was to collect data on the users' opinions regarding the proposed application. We then conducted the usability testing through Google form to collect the users' feedback on the eMarket application. Ten local farmers from Matang's market have participated in the testing phase. Additionally, ten students from a local university participated as potential customers in the usability testing. As a result of these testings, we have developed a usable farmer's market application that has been well-received by local farmers and customers. © 2021 IEEE.

Beyond the virus: a first look at coronavirus-themed Android malware.

As the COVID-19 pandemic emerged in early 2020, a number of malicious actors have started capitalizing the topic. Although a few media reports mentioned the existence of coronavirus-themed mobile malware, the research community lacks the understanding of the landscape of the coronavirus-themed mobile malware. In this paper, we present the first systematic study of coronavirus-themed Android malware. We first make efforts to create a daily growing COVID-19 themed mobile app dataset, which contains 4,322 COVID-19 themed apk samples (2,500 unique apps) and 611 potential malware samples (370 unique malicious apps) by the time of mid-November, 2020. We then present an analysis of them from multiple perspectives including trends and statistics, installation methods, malicious behaviors and malicious actors behind them. We observe that the COVID-19 themed apps as well as malicious ones began to flourish almost as soon as the pandemic broke out worldwide. Most malicious apps are camouflaged as benign apps using the same app identifiers (e.g., app name, package name and app icon). Their main purposes are either stealing users' private information or making profit by using tricks like phishing and extortion. Furthermore, only a quarter of the COVID-19 malware creators are habitual developers who have been active for a long time, while 75% of them are newcomers in this pandemic. The malicious developers are mainly located in the US, mostly targeting countries including English-speaking countries, China, Arabic countries and Europe. To facilitate future research, we have publicly released all the well-labelled COVID-19 themed apps (and malware) to the research community. Till now, over 30 research institutes around the world have requested our dataset for COVID-19 themed research.

A first look at Android applications in Google Play related to COVID-19.

Due to the convenience of access-on-demand to information and business solutions, mobile apps have become an important asset in the digital world. In the context of the COVID-19 pandemic, app developers have joined the response effort in various ways by releasing apps that target different user bases (e.g., all citizens or journalists), offer different services (e.g., location tracking or diagnostic-aid), provide generic or specialized information, etc. While many apps have raised some concerns by spreading misinformation or even malware, the literature does not yet provide a clear landscape of the different apps that were developed. In this study, we focus on the Android ecosystem and investigate Covid-related Android apps. In a best-effort scenario, we attempt to systematically identify all relevant apps and study their characteristics with the objective to provide a first taxonomy of Covid-related apps, broadening the relevance beyond the implementation of contact tracing. Overall, our study yields a number of empirical insights that contribute to enlarge the knowledge on Covid-related apps: (1) Developer communities contributed rapidly to the COVID-19, with dedicated apps released as early as January 2020; (2) Covid-related apps deliver digital tools to users (e.g., health diaries), serve to broadcast information to users (e.g., spread statistics), and collect data from users (e.g., for tracing); (3) Covid-related apps are less complex than standard apps; (4) they generally do not seem to leak sensitive data; (5) in the majority of cases, Covid-related apps are released by entities with past experience on the market, mostly official government entities or public health organizations.