Android Mobile Application Research Articles

Time Efficiency and Cost-Benefit Analysis of New Student Admissions Mobile Application: A Case Study in A Vocational School Palembang

This study aims to analyze the effectiveness of using an Android mobile application in the new student admission system at a vocational school. The application is designed to replace the manual registration methods, which were previously conducted through Google Forms and WhatsApp communication. By integrating push notifications using Firebase Cloud Messaging (FCM) and a MySQL database, the system enables real-time communication between administrators and prospective students, as well as the automation of admission data management. The Time Efficiency method is used to measure the time saved in the registration and notification process compared to the manual method. Meanwhile, the Cost-Benefit Analysis method is applied to evaluate the ratio between the system implementation costs and the benefits produced, such as reduced operational costs, increased registration speed, and improved administrative staff efficiency.

Static Security Analysis of Government and Non-Government Android Mobile Applications in Malaysia: A Comparative Study Using MobSF and OWASP Mobile Top 10

Static Security Analysis of Government and Non-Government Android Mobile Applications in Malaysia: A Comparative Study Using MobSF and OWASP Mobile Top 10

Usability of a digital tool to support long-lasting insecticide net distribution in Northern Bahr el Ghazal State, South Sudan.

Long-lasting insecticidal nets (LLINs) have been a widely used malaria prevention method for decades. In South Sudan, LLINs are typically distributed by volunteers who use paper-based systems to collect distribution data. Paper-based systems are simple to use but have a higher occurrence of data inaccuracies and can hinder the timely use of data for decision-making. In 2022, a digital tool was introduced to collect data during the LLIN campaign in Northern Bahr el Ghazal (NBeG). The tool aimed to improve the accuracy of data entry and enable data to be used in real-time for decision making during the campaign. The digital tool was developed with offline functionality and interoperability with DHIS2 tracker version 2.8 in DHIS2 version 2.38. This study assessed the usability of the tool according to user perspectives. A questionnaire containing open- and closed-ended questions was conducted with users of the digital tool, supervisors and other key stakeholders in five counties of NBeG. The questionnaire was administered using Malaria Consortium's Projects Results System Android mobile application. Usability was determined through a modified and validated System Usability Scale (SUS) approach. A total of 93 participants responded to the usability questionnaire. The mean (± standard deviation) usability score across 10 SUS-scoring items was60.91 (12.87), indicating a modest level of usability. The majority of users agreed the tool was useful for managing the LLIN distribution workflow, was easy to use, reduced workload, and supported stock management and real-time campaign monitoring. There was no significant difference in the usability scores across genders, roles, and counties. Respondents with experience of both paper-based and the digital tool tended to express a preference for the digital tool over paper-based systems. The majority of respondents also reported they would recommend the digital tool to colleagues. Digital tools are perceived to improve data collection during LLIN campaigns, even in remote areas where network coverage is challenging. Additional improvements can be implemented to overcome operational challenges and improve usability of the tool. Further study is needed to assess the impact of the digital tool on data quality and real-time data use.

A Survey on Women Safety and Health Monitoring System

Women safety is a very big issue even in this modern world running with advanced technologies. After a history of times, women got a freedom of equality at the workplace, asset rights, family law, and education. Even Feminist Movement, claimed during 20th Century, Women are not safe anywhere and violence against women and girl child’s happening everywhere during the lonely travelling on roads, picnic spots, work and deserted places. Though we have many armies and super forces to protect everybody, the day to day crime rate against women has not been reduced. There are several safety devices available to protect the women during the violence, but need more human intervention such as entering the data, or shaking the device roughly. We propose a solution for a secure and peaceful environment for women with handbag safety hand-held devices with the aim to provide false proof women safety devices by overcoming the disadvantages in existing system. When we are talking more about Women empowerment, Women achievements it is also very important to think about women safety since a huge numbers (848) of women’s are Indian Women Are Harassed, Raped, Killed Every Day. The proposed work aims at IoT based women safety device by hardware controller attached to the handbag, android application and Bluetooth connectivity in Smartphone. By pressing the controller button, the device alerts the first holder, relatives stored in the database and police when a woman is not safe. The main advantage of the work is, the device works without internet connectivity. Additional features such as protecting the valuable things to be stolen by thieves in crowded places or buses by a separate alarming system, heartbeat sensor setup to monitor heart rate, fingerprint scanner for effecting accessing of the devices and also the mobile android application provide the victim’s location to reach the women and safeguard the women from any harassment at the right time. The main thought of this paper is that it will not only protect the women from physical harassment during odd hours but also safe the women health when it is found abnormal, as a single device comparatively.

Data-Driven Water Monitoring System with Descriptive Analytics for Aquaculture Prawn Farms

This study aimed to develop a data-driven water monitoring system with descriptive analytics for aquaculture prawn farms. This system was designed to automate the monitoring of the water quality of the prawn farm so as to minimize labor and time spent for manually testing the water. Utilizing a rapid prototyping methodology, which is a rapid or quick creation of a prototype or a physical model of a device used in the development of a project, the said monitoring system was evaluated through a comparative analysis between the data gathered manually and the data collected by the monitoring system. To ensure reliability and validity of this monitoring system, it was used various times over several consecutive days. Also, a t-test was used to determine the significant differences between manual and system readings. Findings of the study showed that the designed water monitoring system provided accurate results, and the objectives of the study were met, particularly in terms of integrating a Raspberry Pi 3 microprocessor to automate the system in monitoring the water quality. It was also found out that the use of different sensors to test the water condition in terms of its temperature, pH level, salinity, dissolved oxygen, and turbidity was accurate. Furthermore, the system also met its objective of developing an Android mobile application to monitor the status remotely. With this, it is recommended for prawn farmers to utilize the system (with internet connectivity) for their efficient water quality management and to improve their remote monitoring capabilities.

Development and Validation of a Mobile Phone Application for Measuring Knee Range of Motion.

Knee range of motion (ROM) is an important indicator of knee function. Outside the clinical setting, patients may not be able to accurately assess knee ROM, which may impair recovery following trauma or surgery. This study aims to validate a smartphone mobile application developed to measure knee ROM compared to visual and goniometer ROM measurements. A knee ROM Android mobile application was developed to measure knee ROM. Patients ≥ 18 years old presenting to an orthopaedic clinic with native knee complaints were approached to participate. Knee ROM was measured bilaterally by an arthroplasty-trained surgeon using (1) vision, (2) goniometer, and (3) the mobile application. Measurements were compared in flexion and extension using a one-way analysis of variance with post hoc Tukey test (alpha = 0.05). Eighty-four knee ROM measurements (40 left, 44 right) were obtained in 47 patients. Median Kellgren-Lawrence grade from available radiographs was grade 3. In flexion, mobile application (117.6 ± 14.7 degrees) measurements were not significantly different from visual (116.1 ± 13.6 degrees) or goniometer (116.2 ± 13.6 degrees) measurements. In extension, mobile application (4.8 ± 7.3 degrees) measurements were significantly different from visual (1.9 ± 4.1 degrees) measurements on post hoc analysis (p < 0.01), while no differences were present compared to goniometer (3.1 ± 5.8 degrees) measurements. Our study found that a mobile application for evaluating knee ROM was noninferior to goniometer-based measurements performed by an arthroplasty-trained surgeon. Future studies will investigate this application's utility in (1) remote patient care, (2) accelerating recovery during rehabilitation, (3) detecting early postoperative complications including arthrofibrosis, and (4) adding additional functionalities to the application to provide more detail-oriented descriptive analyses of patient knee function.

A Comparative Study of Maze Generation Algorithms in a Game-Based Mobile Learning Application for Learning Basic Programming Concepts

This study evaluates several maze generation algorithms applied to generate mazes in a game-based Android mobile application designed to support children in learning basic programming concepts and computational thinking. Each algorithm is assessed for its ability to generate solvable and educationally effective mazes, varying in complexity and size. Key findings indicate that Wilson’s and Aldous–Broder algorithms were identified as the most time inefficient. In comparison, Sidewinder and Binary Tree algorithms perform best for smaller mazes due to their straightforward traversal methods. The Hunt-and-Kill and Recursive backtracker algorithms maintain higher ratios of longest paths, making them suitable for the more complex maze generation required for advanced game levels. Additionally, the study explores various maze-solving algorithms, highlighting the efficiency of the recursive algorithm for simpler mazes and the reliability of Dijkstra’s algorithm across diverse maze structures. This research underscores the importance of selecting appropriate maze generation and solving algorithms to balance generation speed, path complexity, and navigational characteristics. While the study demonstrates the practical applicability of these algorithms in a mobile educational application, it also identifies limitations and suggests directions for future research.

AI-Powered Obstacle Detection for Safer Human-Machine Collaboration

Abstract This article deals with ensuring and increasing the safety of mobile robotic systems in human-machine collaboration. The goal of the research was to design and implement an artificial intelligence application that recognizes obstacles, including humans, and increases safety. The resulting mobile Android application uses a MiDaS model to generate a depth map of the environment from the drone’s camera to approximate the distance from all obstacles to avoid the drone’s collision. Besides, this work introduced us to DJI Mobile SDK and neural network optimizations for their use on smartphones.

The impact of the Le-Diabet application on self-efficacy and blood glucose levels in diabetes mellitus patients

Le-Diabet is an Android mobile application developed for diabetes management whose effectiveness has remained unknown. This research aimed to investigate its impact on self-efficacy and blood glucose levels in patients with diabetes. Employing a quasi-experimental approach, the study utilized a pretest and posttest control group design. The sample included 28 respondents in the control group and 34 in the intervention group, selected through purposive sampling based on criteria such as diagnosed diabetes, smartphone usage, and 6 weeks of using the Le-Diabet application. Self-efficacy was measured using the Diabetes Management Self-Efficacy Scale, while blood glucose levels were monitored with a glucometer. Data analysis involved paired and unpaired t-tests. The results revealed a significant increase in self-efficacy scores by 3.1 points [p=0.000, 95% confidence interval (CI) = -6.006 to -1.876] in the intervention group, whereas the control group experienced a decrease of 1.9 points. Both groups exhibited an increase in blood glucose levels, with a significant rise of 35.6 mg/dL (p=0.035, 95% CI = -68.578 to -2.636) in the control group and a non-significant increase of 3.59 mg/dL (p=0.076, 95% CI = -22.759 to 15.582) in the intervention group. The research concludes that the use of the Le-Diabet application enhances self-efficacy and maintains blood glucose level stability; however, it has not shown an impact on metabolic syndrome indicators in diabetic patients. Further research is needed, utilizing a larger and more diverse sample from various age groups and extending the duration of the study to evaluate the long-term impact of the applied intervention.

Pengembangan Aplikasi Mobile Android untuk Deteksi Otomatis Mata Katarak Menggunakan CNN dan Tensorflow

The development of an Android mobile application for automatic cataract detection using Convolutional Neural Network (CNN) and TensorFlow has been conducted. The aim of this research is to provide an easily accessible solution for the public to detect cataracts early, thereby reducing the negative impact of this eye condition. The CNN method is utilized to recognize cataract patterns through image data, with TensorFlow serving as the primary development platform. Preprocessing steps and data processing are implemented to enhance the detection accuracy and address variations in eye images. Evaluation indicates that the application is capable of detecting cataracts with satisfactory accuracy, making it a potential tool for cataract prevention and early management. In conclusion, this application enables rapid and efficient cataract detection, improving the accessibility of eye care and contributing to enhancing overall quality of life for communities by providing early intervention and treatment options.

Sistem Informasi Pelaporan Perkembangan Pasien (SI-PELPASI) berbasis Mobile Android

The use of Android mobile applications in the healthcare sector as tools for patient progress reporting has become a significant innovation. The mobile Android-based patient progress reporting information system (SI-PELPASI) serves as a solution to enhance efficiency and accuracy in patient monitoring. This study aims to develop a mobile Android-based patient progress reporting information system at the Bukit doa rehabilitation centre, an Institution for Mandatory Reporting (IPWL). The system development in this study employs the System Development Life Cycle (SDLC) method, which consists of systematic stages: the planning stage aims to plan the system, the analysis stage involves reviewing existing and current systems and collecting data through interviews and observations, the design stage includes designing Unified Modelling Language (UML) diagrams and the user interface, the implementation stage uses the Kodular app creation platform, and the testing stage uses the black box method to test system functionality. Our findings resulted in the Patient Progress Reporting Information System in the form of a Mobile Android application named SI-PELPASI. The application runs smoothly and can be operated on Android-based mobile devices. With this application, the process of reporting patient progress becomes faster and real-time.

Design of an Android-Based Dam Flood and Drought Early Warning Information System (SI BANKER) in West Nusa Tenggara

Dam construction in Indonesia is currently very developed, namely 235 dams that have been built and West Nusa Tenggara Province contributes 30% of the number of dams in Indonesia, namely 70 dams spread across the islands of Lombok and Sumbawa, where 32 dams are in the Lombok River Basin and 38 dams in the Sumbawa River Basin. The current problem for the Europeanization and maintenance of existing dams in West Nusa Tenggara province is that there is no information system that can make it easier for officers at each dam to report the latest condition of the water level of each dam and provide early warning of the condition of the dam's water level whether it will be close to flood or dry conditions. Currently, to report water level conditions in each dam, officials at each dam can only report the results of water level monitoring under normal circumstances, floods or droughts only manually. The purpose of this study is to provide early warning information / notifications on flood and drought conditions in dams in West Nusa Tenggara province based on android. The research method used in the process of developing this system is the waterfall development method which consists of 5 stages, namely defining needs, designing, coding and implementation, testing and documentation. The results of this study are in accordance with the description in the previous chapter including interface implementation, system testing and evaluating the system. Evaluation is carried out to determine the shortcomings of the system, so that it can be better for future development. The conclusion of this study from the design and testing that has been carried out shows that all functions have run in accordance with the proposed system. For further research, it is recommended to create a similar Android mobile application but using a more complete framework and having many references to perfect some features in the application that are still not perfect.

A user experience survey of an augmented reality android application for learning Coulomb’s law

In recent years, the COVID-19 pandemic has necessitated social distancing measures, prompting a greater interest in augmented reality (AR) as a means to facilitate accessible physics experiments for students. Recognizing this, we have recently developed an Android mobile application that harnesses AR technology. The application named CoulombsLawAR allows users to investigate how electrical charges and their distance influence the electrical force between them. The primary objective of our study was to assess students’ experiences while using this AR application. To achieve this, we engaged 156 undergraduate students from the Faculty of Chemical Engineering and Technology from University of Zagreb in the evaluation process. They were requested to utilize our AR application to complete a task specifically designed to enhance their understanding of Coulomb’s law. Subsequently, the participants were given a series of questionnaires, comprising five open-ended questions and 30 close-ended questions. These questionnaires included the System Usability Scale, User Experience, and Handheld Augmented Reality Usability Scale. In-depth analysis of the students’ feedback provided valuable insights. The majority of the participants found the application to be user-friendly, consistent in its performance, self-explanatory, and enjoyable to use. Additionally, students provided constructive feedback regarding potential enhancements that could be made to the application. Overall, the study findings indicate that the AR application we developed was well-received by undergraduate students, highlighting its potential as an effective tool for teaching and learning physics concepts.

LeakPred: An Approach for Identifying Components with Resource Leaks in Android Mobile Applications

Context: Mobile devices contain some resources, for example, the camera, battery, and memory, that are allocated, used, and then deallocated by mobile applications. Whenever a resource is allocated and not correctly released, a defect called a resource leak occurs, which can cause crashes and slowdowns. Objective: In this study, we intended to demonstrate the usefulness of the LeakPred approach in terms of the number of components with resource leak problems identified in applications. Method: We compared the approach’s effectiveness with three state-of-the-art methods in identifying leaks in 15 Android applications. Result: LeakPred obtained the best median (85.37%) of components with identified leaks, the best coverage (96.15%) of the classes of leaks that could be identified in the applications, and an accuracy of 81.25%. The Android Lint method achieved the second best median (76.92%) and the highest accuracy (100%), but only covered 1.92% of the leak classes. Conclusions: LeakPred is effective in identifying leaky components in applications.

A View on a PHP-Based Project for A Donation and Waste Food Management System

Food waste is a problem that affects everyone. People are impacted anywhere it is present, including in our homes, schools, restaurants, grocery stores, places of business, and even in transportation. With the help of this software, hotels can give leftover food to those in need while reducing food waste. With the help of this software, users may sign up, log in, see, add, and remove products from their carts, and then log out of the system. Additionally, this software contained a real-time database. Through this app, food donors may enter information about their donations, and NGO volunteers can see the photographs of the food that each donor has contributed. Food waste is a widespread issue in our culture. Management of food waste is essential since it may increase our sustainability both economically and environmentally. We have determined how mobile technology may be used to minimize food waste management, and we have developed an android mobile application that enables restaurants or individual users to share and donate their leftover food with those in need. We intended to complete this project in order to use an Android application to lessen food waste. The visitor may log in and input the kind, quantity, and location of food that is offered in this project. The agent is then sent a brief notice. The agent at that location may log in and get the donor's data after receiving the notice. The software allows the donor to create an account, and he may log in to add the location and food data anytime there is food waste. The agent is also capable of retrieving the data and holding an account. Once the information has been retrieved, the agent may go get food from the donor and provide it to the orphans or other needy people. Food redistribution is an incredibly effective social innovation initiative that addresses food poverty and waste. Because it has a distinct account for every user, the user's information is kept private

A Mobile-Based System for Detecting Plant Leaf Diseases Using Deep Convolutional Neural Network

Plant diseases pose a significant challenge to agriculture in India, causing substantial losses in crop production each year. Limited-resource farmers often struggle with diagnosing these diseases accurately, especially when relying on visual observation of leaf symptoms. Hence, there is an urgent need to enhance the detection, monitoring, and prediction of crop diseases to mitigate agricultural losses effectively. To address this challenge, we propose a mobile-based system empowered by Machine Learning (ML) and computer vision tailored for the Indian context. Our system utilizes Convolutional Neural Networks (CNNs) as the core deep learning engine to classify 37 disease categories commonly found in Indian crops. We have curated a comprehensive dataset comprising 96,206 images of plant leaves, encompassing both healthy and infected, for training, validation, and testing purposes. The user interface of our system is designed as an Android mobile application, enabling farmers to capture images of diseased plant leaves effortlessly. Upon image capture, the system swiftly identifies the disease category and presents it to the user along with a confidence percentage. This functionality empowers farmers to take timely action to protect their crops, thereby reducing reliance on incorrect fertilizers that may exacerbate plant stress. Furthermore, we evaluated the performance of our system using various metrics, including classification accuracy and processing time. Our results indicate an impressive overall classification accuracy of 94% across the 37 disease classes prevalent in 14 different crop species commonly cultivated in India. In essence, our ML-powered mobile-based solution offers Indian farmers a robust tool for efficient crop disease diagnosis and management, ultimately contributing to the enhancement of agricultural productivity and sustainability in the country.

Practical Model of Mobile Learning Applications

The mobile device has been supported by the operating system that makes the phone has the potential to be a learning tool. The Android operating system is the mobile platform of high potential in education, plus it is open source, it can support independent learning. Availability of facilities and applications that can be installed offline, allowing an independent learning process starts from the students themselves. The purpose of this research is to find a practical model that can be applied in learning. Make mobile learning a practical model that can be used teachers, students and schools take advantage of the resources that already exist. The research method uses research and development, to mobile learning models that later developed into a model factual. Results The study concluded that: Android mobile application that is filled with interesting teaching materials can continue to invite progress within teacher as creator and students as users. Availability of Internet applications, making new teaching participants, can catch up with learning by opening apps without worry about the royalty claims. Android mobile application that supports online messaging greatly help communication between members. Results of the study are: a practical mobile learning model consists of four aspects, namely: Four Key Roles, Learning Tasks Accommodation, Interactive Content and the Modular Learning Outcomes.

Real Estate System

Abstract: This system will provide facility to the user to search Residential and Commercial property and view property. This system will provide facility to view the property by admin and user. User will able to upload the property information to the site and able to manage it. This system will provide facility to the user to fill up their requirement and according to their Requirement Admin can add the Requirement property. This system will provide facility to the user to publish advertise to the Site and view. This system will provide facility to the user to feedback to the site. The system's key features include an intelligent property listing module that allows real estate agents to create and update property listings with detailed descriptions, images, and pricing information. This article studies the importance of establishing a real estate information system in the real estate market. A web application and an Android mobile application allow different types of registered users to access the REMS. The mobile based application is proposed for collecting house and land data with geographical position at the real place. Meanwhile, the web application is created to handle the backend services, and provides all functions of the property management. The evaluation of user satisfaction shows that our proposed system is practically used and satisfied.

Facial Expression Based Restaurant Scoring System

Abstract: Automated becoming unmanned restaurants have become more and more common in recent years. Customers' opinions cannot be directly seen in order to learn about their encounters with the idea of a restaurant because staff members are not there. This research provides a rating system based on already trained convolutional neural network (CNN) algorithms for face expression recognition. It consists of an AI server that has been trained, a web server, and an Android mobile application. Ratings are intended for both the cuisine and the setting. At the moment, the scoring system offers three expressions: neutral, disappointed, and satisfied.

Food Wastage Management and Tracking System

Abstract: In recent years, the issue of food wastage has garnered significant attention due to its adverse environmental, social, and economic implications. To address this challenge, technological innovations have emerged as powerful tools. This abstract provides a comprehensive overview of a Food Wastage Management Android App, offering an integrated solution to reduce food waste. The app leverages cutting-edge technologies to efficiently manage surplus food, promoting responsible consumption and minimizing waste generation. Food wastage is a pervasive issue in our society, and effective management is crucial for improving environmental and economic sustainability. Recognizing the potential of mobile technology in mitigating food waste, we have developed an Android mobile application. This app enables individual users or restaurants to donate and share their surplus food with those in need. Users can register, login, view available items, add new items, access donor details, and logout seamlessly. Utilizing a real-time database, the app allows users in need to browse through donated food images from various contributors and easily contact donors. By facilitating the redistribution of surplus food, the app aims to combat food wastage and promote a more sustainable food ecosystem.