Android Mobile Devices Research Articles

Measuring the Effectiveness of the ‘Batch Operations’ Energy Design Pattern to Mitigate the Carbon Footprint of Communication Peripherals on Mobile Devices

The Internet of Things (IoT) is set to play a significant role in the future development of smart cities, which are designed to be environmentally friendly. However, the proliferation of these devices, along with their frequent replacements and the energy required to power them, contributes to a significant environmental footprint. In this paper we provide scientific evidences on the advantages of using an energy design pattern named ‘Batch Operations’ (BO) to optimize energy consumption on mobile devices. Big ICT companies like Google already batch multiple API calls instead of putting the device into an active state many times. This is supposed to save tail energy consumption in communication peripherals. To confirm this, we set up an experiment where we compare energy consumption and carbon emission when BO is applied to two communication peripherals on Android mobile device: 4G and GPS. Results show that (1) BO can save up to 40% energy when sending HTTP requests, resulting in an equivalent reduction in CO2 emissions. (2) no advantages for the GPS interface.

An open-source, QGIS-based solution for digital geological mapping: GEOL-QMAPS

Digital geological mapping has experienced significant growth over the past three decades due to the advent of commercial geographical information systems, advances in global positioning systems, and the availability of portable hand-held devices, such as mobile personal computers (PCs), smartphones, and tablets. Numerous software packages have been developed to collect, combine, organise, visualise, publish, and share field data with enhanced spatial accuracy and minimal post-field processing. However, many of these tools are not open-source or are not made available to the geoscientific community, remaining specific to given mapping projects or organisations.In this contribution we introduce GEOL-QMAPS, an open-source, QGIS-based solution promoting digital geological mapping in a harmonised, comprehensive and flexible way. It can be used in the field with a tablet PC or via the QGIS-based QField app on iOS or Android mobile devices, enabling synchronisation with desktop QGIS and the creation of field databases. Designed as a general solution, the GEOL-QMAPS solution consists of a QGIS field data entry template and a custom QGIS plugin, both available on free-access online repositories. The plugin allows for the adaptation of dictionaries (i.e., lists of attributes describing geological features), initially set to international nomenclatures, to the guidelines of different mapping projects. The solution also facilitates the loading and consultation of relevant legacy geodatasets (e.g., preexisting field data, geochemical, geophysical maps or punctual datasets). A fact map, created from field data collected across the Archean Sula-Kangari greenstone belt in Sierra Leone, demonstrates the solution's advantages in terms of post-field processing and raw field data sharing.

Perancangan Aplikasi Kamus Air Traffic Control Berbasis Mobile Android

This study discusses the Air Traffic Controller dictionary where this dictionary will discuss all the initials that have developed in the world of air police, which so far all information about the rules in regulating flights is only in written form (Manual Dictionary). The purpose of this study is to help air transportation speed directors recall some of the existing definitions in the world of aviation, so that it is hoped that there will be no more misunderstandings between pilots and air transportation speed controllers. The method used by the author is the waterfall method, where the flow is Analysis, Design, Implementation and testing. The Unified Modeling Language design will help readers understand this research. The results of this study illustrate that the application can run on an Android mobile device.

DEVELOPMENT OF MARKERLESS BASED AUGMENTED REALITY INTERACTIVE LEARNING MEDIA OF 3D GEOMETRY SHAPES

Education is one sector that continues to experience significant development, especially in terms of the use of information and communication technology (ICT) in the learning process. This research is intended as a development in terms of creating an Augmented Reality 3D geometry shapes interactive learning based on the previous researches featuring a Markerless Based, that is expected to be more effective to use. The conclusion of this research is that Markerless Based Augmented Reality Interactive Learning media of 3D Geometry Shapes has been be developed succesfully. The application development stage was successfully carried out by applying the waterfall model and obtained successful results in the testing plan on the Black Box Testing of application prototypes on mobile android devices, indicates that the developed application has fulfilled all the functional requirements.

ANDROID MALWARE CLASSIFICATION USING BASIC MACHINE LEARNING METHODS

Android malware attacks grow in both sophistication and volume day by day, thus android users are vulnerable to cyber-attacks. Researchers have developed many machine learning techniques to detect, block or mitigate these attacks. However, technological advancements, increase in Android mobile devices and the applications used on these devices, also increase problems in terms of user privacy due to malware. In this study, a comprehensive study is presented on the detection and classification of malicious applications using an up-to-date dataset containing 241 attributes. First, incorrect and missing data are detected and the relevant lines are removed, then normalization-based scaling is performed. After this preprocessing step, the data set is randomly divided into 70% training and 30% testing using hold-out cross validation. Finally, classification is carried out using 6 different machine learning methods: Multilayer Perceptron (MLP), Logistic Regression (LOGR), K-Nearest Neighbor (KNN), Decision Tree Classifier (DTC), Random Forest (RF). The comparison of modeling results demonstrates that RF machine learning technique can achieve the best performance with the level of 97% accuracy and the various other metrics for Android malware detection in real-world Android application sets.

A novel method of detecting malware on Android mobile devices with explainable artificial intelligence

The increasing prevalence of malware targeting android mobile devices has raised significant concerns regarding user privacy and security. In response, effective methods for malware classification and detection are crucial to protect users from malicious applications. This paper presents an approach that leverages deep learning techniques and explainable artificial intelligence (XAI) for android mobile malware classification and detection. Convolutional neural networks (CNNs) are deep learning model that has shown impressive performance in several application areas, including image and text classification. In the context of android mobile malware, CNNs have shown promising results in capturing intricate patterns and features inherent in malware samples. By training these models on large datasets of benign and malicious applications, accurate classification can be achieved. To enhance transparency and interpretability, XAI techniques are integrated into the classification process. These techniques provide insights into the decision-making process of the deep learning models, enabling the identification of critical features and characteristics that contribute to the classification results. This research, by combining deep learning and XAI methods, presents a fresh strategy for identifying and categorizing Android malware. This research paper will focus on a fascinating CNN-based malware categorization technique.

Waste management improvement with software application at Wringinsongo Village, Tumpang Regency, Malang

Wringinsongo Village, Tumpang Subdistrict, Malang Regency maintains a clean environment through environmentally friendly waste management. One of Wringinsongo's BUMDes (Village-Owned Enterprises) is the Waste Bank, which uses waste as a potential sector. It needs to get special attention related to its governance because the recording is still less effective and there are often calculation errors caused by the recording system using paper. Based on these conditions, it is necessary to develop an application to help record transaction activities at the Waste Bank. The Wringinsongo Village Waste Bank application can be accessed on both the web platform and Android mobile devices, based on the ease of access to the system by village managers and officials. After developing the application, socialization, and training were conducted to the Waste Bank manager and village officials to provide information on how to use the application. After three months of use, feedback was taken to get responses related to the use of the application. The result is that BUMDes managers find it easy to record and obtain accurate and faster calculation results.

INTEGRATING IOT AND MOBILE APPLICATIONS FOR EFFICIENT PLANT WATERING: THE IOT SMART WATERING SYSTEM (SWS)

The Internet of Things (IoT) is the connection with networks and devices that can exchange data through the Internet without human intervention. A mobile application is a software system that runs on a mobile device. The absence of smart detection in the existing system is insufficient for watering the plantation. This brought some problems, as no one knew about the condition of the plantation, and caused overwatering and under-watering problems. Therefore, combining IoT concepts and mobile applications has adapted to make the system a more innovative automated system. The IoT Smart Watering System (SWS)” focuses on building an intelligent watering system that will sense the humidity of the soil and the temperature of the environment using the sensor. Then, the data will be sent to the user’s mobile application using the internet to allow users to view it. The objective of this project is to let the user know about the condition of the plant and smart watering based on the condition. Compared with the existing system, the advantage of this system is that the user can automatically monitor the status of the data from mobile phones. This system is developed for everybody who wishes to have this system, such as homeowners, farmers, and students using an Android mobile device. In this project, the waterfall method was implemented as a software development process to ensure the successful development of the system and meet the user’s requirements. The existence of the IoT Smart Watering System will benefit society by preventing the waste of water and minimizing human intervention. This could save users time and enhance the watering plant's effectiveness. In the future, a notification and view data graph feature will be integrated into this system.

Comparative Evaluation of Python Integrated Development Environments (IDEs) on Android Mobile Devices: Implications for Teaching Programming Concepts

Comparative Evaluation of Python Integrated Development Environments (IDEs) on Android Mobile Devices: Implications for Teaching Programming Concepts

MOBILE ATTENDANCE SYSTEM USING GPS AND FACIAL AUTHENTICATION

Student attendance in any educational institutions is a critical content of the administration of school and the major task of the course lecturer who maintain attendance. The conventional method of monitoring students' attendance in the classroom typically involves requiring students to physically record their attendance. Initially, this process was carried out manually using pen and paper. However, as the student population grows, this approach becomes increasingly burdensome, time-consuming, and susceptible to mistakes. To avoid these challenges, this paper proposes a Mobile attendance system using GPS locator and facial authentication algorithm to monitor students’ attendance using android mobile devices. The mobile attendance app is based on Client-Server Architecture where the server is hosted in the cloud and clients are with mobile devices with in-built GPS Locator. To secure the integrity of the attendance, the GPS locator helps in locating the precise geographical position of the students present, a mock detector is also integrated into the app to detect fake location of the students in the classroom meeting and the facial image used by the student at runtime support startup camera.

Association Rules Elicitation for Customer’s Shopping on E-commerce Applications

This research provides an approach for eliciting association rules for customers’ shopping on e-commerce applications based on the Apriori algorithm. The proposed system shows and displays some offers and deals from various branches. The system provides the analytics for the seller as the demand for some varieties and the weakness in other varieties and the whole application is organized on the Cloud. The architecture includes three levels; the front-end, middle, and back-end. The front-end level of the site-based Mobile shopping application is made up of Android Mobile devices, to buy miscellaneous products from various nearby branches. The front-end level also displays the link between items purchased. The middle repository level provides a Web service to generate returns from a relational database. The Exchanged information and data between application and servers is stored in the Cloud. The background level offers a Web server and a MySQL database. In this paper, we propose an architecture that reduces the communication overhead in existing Mobile Agent-based Distributed Association Rule Mining (MAD-ARM).

Detection and Prevention of Malware in Android Mobile Devices: A Literature Review

Detection and Prevention of Malware in Android Mobile Devices: A Literature Review

Malware Detection in Android Mobile Devices by Applying Swarm Intelligence Optimization and Machine Learning for API Calls

Malware Detection in Android Mobile Devices by Applying Swarm Intelligence Optimization and Machine Learning for API Calls

Bayesian networks for the development of an interdisciplinary decision support system in screening for the risk of laryngotracheal aspiration

Screening for the risk of laryngotracheal aspiration in hospitalized adults is a complex and interdisciplinary task that involves multiple variables and causes. The objective of this study was to develop and assess the applicability of a clinical decision support system based on Bayesian Network to assist the interdisciplinary hospital team. Methods: A Bayesian network was constructed using a systematic literature review to identify and categorize the main variables required for assessing the risk of laryngotracheal aspiration. These variables were grouped into the patient’s current diagnosis, history of swallowing disorders, swallowing-related aspects, and basic epidemiological characteristics. An application was developed and subsequently evaluated by nine specialists from an interdisciplinary hospital team. The evaluation focused on both the internal and external quality of the application. Results: The system was developed for Android mobile devices and was evaluated by a group of specialists (n=9) from various disciplines including physicians, nurses, nutritionists, and speech pathologists. The specialists considered the software product to be functional (86.1%), reliable (83.3%), usable (84.4%), efficient (92.6%), maintainable (90.4%), and portable (88.9%). Conclusions: The interdisciplinary screening system developed in this study was found to be valuable and feasible for use by specialists. Study registration: The validation of this study involved specialists from the fields of medicine, nursing, nutrition, and speech pathology. As a result, it was necessary to obtain evaluation and approval from the Ethics Committee in Research with Human Beings of the Federal University of Health Sciences of Porto Alegre – UFCSPA (# 44920121.0.0000.5345).

A Systematic Literature Review on Digital Forensic Investigation on Android Devices

Mobile forensics, particularly in the Android ecosystem, is a rapidly evolving field that demands continuous advancements to address the growing complexity and diversity of mobile devices. This article emphasizes the importance of developing techniques for digitally analyzing Android smartphones, which dominate the smartphone market. The primary objective of this research is to contribute to the development of effective forensic investigation strategies tailored specifically for Android mobile devices, providing insights into the tools and methods used for this purpose. The objective of this study is to improve the precision and effectiveness of forensic examinations pertaining to Android mobile phones. It discusses the fundamental functionality of mobile devices as a source of digital evidence and provides an overview of tools and methodologies for collecting and analyzing such evidence. The importance of comprehending the hardware and software architecture of Android handsets in order to choose the right forensic tools is also highlighted in the article. Furthermore, it proposes future enhancements for Andriller, a popular digital forensic tool, to improve its effectiveness in Android forensic investigations. These enhancements include advancements in data extraction techniques, compatibility with new Android versions, support for additional data types, integration with advanced analysis methods, and addressing identified limitations. Additionally, the paper stresses the need for robust methodologies for conducting cloud forensics on Android devices, particularly in the context of data stored in cloud storage services. The proposed work aims to enhance the capabilities of Andriller and improve the efficiency of digital forensic investigations on Android devices.

Impact of Remote Patient Monitoring Platform on Patients With Moderate to Severe Persistent Asthma: Observational Study.

Asthma is one of the most common respiratory diseases, with an ever-growing health care burden. Remote patient monitoring (RPM) has gained increasing importance in the respiratory care area with the outbreak of the COVID-19 pandemic. In this pilot study, we introduced a novel platform that remotely monitors patients with chronic respiratory illnesses using Centers for Disease Control and Prevention guidelines to reduce hospitalizations and emergency department visits. This study aimed to understand patient and physician engagement with a new virtual care solution (KevaTalk app and Keva365 platform) and the value, for both patients and providers, of using an RPM tool. We assessed real-world use of the platform from both physician and patient perspectives and the impact of devices on engagement and monitoring. Participants with a history of moderate to severe persistent asthma, seen by a pulmonologist at a hospital, were included in this study. The inclusion criteria involved being aged ≥18 years and having access to an Android or iOS mobile device with internet. We provided patient questionnaires to assess the app's usefulness and evaluate its features. We monitored remote spirometry and oximetry data, app check-ins, alerts, and escalations during this study's time window. Data were reviewed daily and predetermined criteria were set to escalate for physician review based on the patient's symptoms and objective data. Overall, 25 patients were included in this pilot. The mean age was 57 (SD 10.7) years and a majority (n=23, 92%) were female. A baseline questionnaire, which was used to rate the app, indicated that the ease of check-in and ease of modification to the patient's asthma plan were the 2 highest rated features. In total, 2066 check-ins (1550 green, 506 yellow, and 10 red check-ins) and 1155 spirometry sessions were recorded during this 3-month period. Further, 64% (14/22) and 91% (20/22) of patients were found to have peak flows in their red and yellow zones at least once, respectively. During the course of this study, 484 alerts were recorded and evaluated by the team, of which 37.2% (n=180) required an escalation to the physician; this included a transfer to a medical facility, change in respiratory medication, or further education. In this pilot study, we demonstrated the feasibility of implementing a novel RPM platform in patients with asthma. Our platform showed high patient engagement and satisfaction and provided physicians with real-time subjective data to evaluate patients remotely that aids in clinical decision-making. The escalations prevented patients from having an exacerbation or flare up, which led to the prevention of an emergency department visit. Continuous monitoring of chronic disease has benefits over episodic monitoring. It allows for improved quality of life, better outcomes, and huge health care savings.

Supervisory Monitoring and Control Solution on Android Mobile Devices for the Water Industry 4.0

The capacity for using mobile devices for monitoring and controlling local processes has seen rapid growth in industry for maintenance operations before and after deployment. This is especially important in the case of geographically widely-dispersed locations, such as in the case of the water sector, where processes, technologies, and local automation solutions are widely spread. Usually, the available mobile solutions are dependent on Supervisory Control and Data Acquisition (SCADA) software installed in the control rooms of water and wastewater facilities, usually without configuration possibilities. Considering the various SCADA control rooms, each focusing on a specific system, and hundreds of smaller locations accessible only with PLC and eventually a small touch screen, the dependence on local SCADA software is proving increasingly impractical. This paper presents the implementation of an easy-to-use SCADA system for the Android operating system, conceived following Industry 4.0 concepts. An OPC UA client-based architecture is proposed to cope with current interoperability standards, mobility and security across industrial processes in various domains. The design relies on a foreground service for uninterrupted communication between the application and the OPC UA client. The system is envisaged to provide notifications to alert the user when alarms are triggered, including both an independent application level alarming module and a new Alarms and Conditions based protocol level module, increasing visibility and response time for technical issues or faults, and being adaptable to both legacy and modern OPC UA specifications. The solution was tested first in the laboratory to validate the communication system with as many OPC UA structures as possible, and then in real scenarios with drinking water and wastewater systems interfacing PLC, HMI and SCADA level OPC UA servers. The tests in the real scenarios included a second-level test for water operators and engineers which accessed and monitored various processes with the developed solution, and all results proved to be satisfactory.

Sistem Monitoring Suhu Lab Komputer Universitas Bina Darma Berbasis IOT Mobile Android Menggunakan Arduino

This study aims to create a computer lab temperature monitoring tool using the DHT11 sensor and the NodeMCU ESP8266 device based on the internet of things integrated with Android mobile devices. The system research and development method used in this research is the R&D (Research and Development) method. Research and Development (R&D) is a research method used to produce certain products, and test the effectiveness of these products. This system will be designed and developed to monitor temperature via an Android smartphone. The system consists of several components, the main component is NodeMC ESP8266 which will obtain an input signal from the DHT11 temperature sensor, after which the data is stored in a database using the internet network via wifi. The increase in temperature when the engine is operating is recorded by the sensor and will be sent to the microcontroller which can be accessed via an android smartphone mobile device. Testing this system will be carried out into two parts, namely the first is testing on hardware such as the functionality of the tools that have been made, especially the sensitivity of the DHT-11 sensor in reading temperature and humidity, the second is Blackbox testing on software, namely testing whether there is a software failure in reading sensor values. sent

Android ransomware detection using binary Jaya optimization algorithm

AbstractRansomware is a serious security concern to mobile devices, as it prevents the use of the device and its contents until a ransom is paid, resulting in considerable financial losses for both people and corporations. The existing anti‐malware measures have shown to be inadequate in combatting new malware variants that utilize advanced evasion strategies like Polymorphic, Metamorphic, Dynamic Code Loading, Time‐based evasion, and Reflection. Furthermore, these primary defences have also suffered from low detection rates, significant false positives, high processing times, and excessive processing and power consumption that is inappropriate for smartphones. This paper offers the binary JAYA (BJAYA) for ransomware detection in Android mobile devices using the BJAYA optimization‐based algorithm. The developed algorithm's effectiveness has been assessed against two datasets, the 0–1 knapsack, and real ransomware dataset. The proposed BJAYA method surpassed the other algorithms on 85% of the 0–1 knapsack datasets. The suggested BJAYA method was also tested on a ransomware dataset in two phases. In the first stage of testing, BJAYA outperformed other standard classifiers with sensitivity and Gmean values of 97% and 98.2%, respectively. In the second stage of testing, BJAYA outperformed other GA, FPA, and PSO metaheuristic algorithms in terms of specificity, sensitivity, and Gmean. These findings indicate the applicability of the suggested BJAYA algorithm for ransomware detection.

An Integrated Approach for Analysis of Electronic Health Records Using Blockchain and Deep Learning

Abstract: Blockchain is used to assess health records digitally, preserving the security and immutability of the records. The goal of this study is to make it easier for patients to access their medical records and to send them alert messages about important dates for their check-ups, healthy diet, and appointments. To achieve the above-mentioned objective, an integrated approach using Blockchain and Deep learning is initiated. The first approach is Hyperledger Fabric in Blockchain, i.e., private Blockchain, for storing the data in the medically documented ledger, which can be shared among hospitals as well as Health organizations. The second approach is incorporated with a deep learning algorithm. With the help of algorithms, we can analyse the ledger, after which an alert i.e. consultation, health diet, medication, etc., will be sent to the patient’s registered mobile device. The proposed work uses nine features from the dataset; the features are identification number, age, person gender, disease, weight, consultation date, medication, diagnosis, and diet specification. The study is conducted with several features to give accurate results. The integrated model used in this suggested piece of work automates the patient's alert system for a variety of activities. In terms of precision, recall, and F1 score, testing data demonstrate that the LSTM performs better than the other models. By working together with the calendar software on Android mobile devices, alert systems can be improved in the future.