Android Devices Research Articles

Design and Development of an Intuitive Android Application for Smart Farming

Farmers face challenges in adapting to environmental changes, yet with decreasing implementation costs and a growing accessibility to technical expertise, progress is evident. This research introduces KisanVishwa, an Android app designed for smart farming. KisanVishwa optimizes precision agriculture systems, enabling farmers to monitor their fields in real time and address agricultural constraints. Tailored to meet the specific needs of today's farmers, the app provides user-friendly tools and the latest information on farm equipment, fertilizers, chemicals, technological advancements, and local distributors and retailers. By integrating KisanVishwa into their operations, farmers can overcome challenges associated with the adoption and maintenance of precision agriculture. KisanVishwa adopts a user-centric design for compatibility across diverse Android devices, integrating monitoring tools and collaborating with agricultural research institutions. Empowering farmers, the app addresses contemporary challenges, fostering informed decisions and efficiency. With its user-friendly interface, KisanVishwa promotes the adoption of precision agriculture, essential resources, and sustainable practices in the agricultural sector. Convincing traditional farmers to adopt advanced technologies may pose challenges. Functionality may depend on reliable internet, posing challenges in areas with limited connectivity.

Evaluating the use of smartphones for processing computational tasks: "A case study for the annotation of prokaryotic genomes"

Currently, there is a growing dependence on smartphones in the daily lives of thousands of people, noting that the number of users continues to rise, surpassing the number of computers in use globally. In addition, the performance of smartphone hardware has become an essential factor when choosing these devices, along with price, brand and operating system. Constant innovations in smartphones have made these devices increasingly powerful, even rivaling the performance of many computers. The example of the Samsung S23 5G, which has features comparable to a low-performance notebook, illustrates the advancement of the hardware in these devices. Despite the growing demand for advanced hardware in smartphones, many users may not fully understand the technical specifications of the processors. The article proposes an evaluation of the use of this hardware in the execution of specific tasks, with a focus on the annotation of prokaryotic genomes. The methodology of the study involved selecting popular smartphones from Samsung and Motorola, choosing the Android operating system due to its efficiency in resource management, using the Prokka tool for genomic annotation due to its minimal resource requirements and using Termux, a terminal emulator that allows users to run a full Linux distribution on Android devices. To evaluate performance, the article uses the Linux 'time' command to measure execution time, memory consumption, CPU utilization and other relevant metrics

Validity of an android device for assessing mobility in people with chronic stroke and hemiparesis: a cross-sectional study

BackgroundIncorporating instrument measurements into clinical assessments can improve the accuracy of results when assessing mobility related to activities of daily living. This can assist clinicians in making evidence-based decisions. In this context, kinematic measures are considered essential for the assessment of sensorimotor recovery after stroke. The aim of this study was to assess the validity of using an Android device to evaluate kinematic data during the performance of a standardized mobility test in people with chronic stroke and hemiparesis.MethodsThis is a cross-sectional study including 36 individuals with chronic stroke and hemiparesis and 33 age-matched healthy subjects. A simple smartphone attached to the lumbar spine with an elastic band was used to measure participants’ kinematics during a standardized mobility test by using the inertial sensor embedded in it. This test includes postural control, walking, turning and sitting down, and standing up. Differences between stroke and non-stroke participants in the kinematic parameters obtained after data sensor processing were studied, as well as in the total execution and reaction times. Also, the relationship between the kinematic parameters and the community ambulation ability, degree of disability and functional mobility of individuals with stroke was studied.ResultsCompared to controls, participants with chronic stroke showed a larger medial-lateral displacement (p = 0.022) in bipedal stance, a higher medial-lateral range (p < 0.001) and a lower cranio-caudal range (p = 0.024) when walking, and lower turn-to-sit power (p = 0.001), turn-to-sit jerk (p = 0.026) and sit-to-stand jerk (p = 0.001) when assessing turn-to-sit-to-stand. Medial-lateral range and total execution time significantly correlated with all the clinical tests (p < 0.005), and resulted significantly different between independent and limited community ambulation patients (p = 0.042 and p = 0.006, respectively) as well as stroke participants with significant disability or slight/moderate disability (p = 0.024 and p = 0.041, respectively).ConclusionThis study reports a valid, single, quick and easy-to-use test for assessing kinematic parameters in chronic stroke survivors by using a standardized mobility test with a smartphone. This measurement could provide valid clinical information on reaction time and kinematic parameters of postural control and gait, which can help in planning better intervention approaches.

Classification of different types of DDR RAM using transfer learning in convolutional learning networks

Technology, specifically computers play an important role in modern society. People who are new to computers can determine what type of RAM they have, which can be used to avoid confusion on what type of RAM their computer needs with the help of an Android device. For this study, a powerful computer with a Graphics Processing Unit (GPU) needed to be used to shorten the amount of time that the deep learning process takes. The study gathered images of 4 types of Random Access Memory for a RAM classification system. There were 1000 images in total for DDR1, DDR2, DDR3, and DDR4 RAM. The study utilized transfer learning to RAM type classification with pre-trained models such as VGG16, VGG19, Inception V3, and Xception. The data that was gathered showed that Xception is the best classifier with an initial average accuracy of 85.034% and a 100% Val_Accuracy even though the model had the longest loading time with 12 seconds.

Timbang App: An Operation Timbang Monitoring System

Malnutrition is one of the leading causes of diseases and infections that result in infant deaths, especially during the first five years of life. To mitigate this issue, the Rural Health Unit (RHU) of San Miguel, Bulacan, conducts Operation Timbang Plus (OPT) to identify the status of each child and locate families with malnourished children. However, the data collection is extensive and has delayed publication of results. Therefore, this project was created to help the RHU with the OPT, thus providing an IT solution that calculates, manages, and generates reports of OPT data. The project was developed using V-Model, a type of Waterfall model. Moreover, the ISO 9126 Quality Model was used to determine if the system functions were created. Lastly, unit testing, compatibility testing, stress testing, system testing, and integration testing are conducted to ensure the quality of the IT solution. The tests show that the system is compatible with Android devices and browsers such as Google Chrome, Microsoft Edge, and Mozilla Firefox. The integration test shows that mobile and web applications worked as a unified system. Lastly, the survey results show that the system's functionality and accuracy are 100% acceptable for mobile and web applications. However, the usability of web and mobile applications varies. Offering 99.5% for mobile application and 99.34% for web application. These imply that web applications can effectively organize, store, and retrieve OPT data. The mobile application can effectively collect, calculate, and report data and provide SMS notifications to parents about nutrition programs.

Who Should We Blame for Android App Crashes? An In-Depth Study at Scale and Practical Resolutions

Android system has been widely deployed in energy-constrained IoT devices for many practical applications, such as smart phone, smart home, healthcare, fitness, and beacons. However, Android users oftentimes suffer from app crashes, which directly disrupt user experience and could lead to data loss. Till now, the community have limited understanding of their prevalence, characteristics, and root causes. In this article, we make an in-depth study of the crash events regarding ten very popular apps of different genres, based on fine-grained system-level traces crowd-sourced from 93 million Android devices. We find that app crashes occur prevalently on the various hardware models studied, and better hardware does not seem to essentially relieve the problem. Most importantly, we unravel multi-fold root causes of app crashes, and pinpoint that the most crashes stem from the subtle yet crucial inconsistency between app developers’ supposed memory/process management model and Android’s actual implementations. We design practical approaches to addressing the inconsistency; after large-scale deployment, they reduce 40.4% of the app crashes with negligible system overhead. In addition, we summarize important lessons learned from this study, and have released our measurement code/data to the community.

Design and Construction of Voice Controlled Home Automation using Arduino

System automation has been widely researched in the twenty-first century due to its essential role in daily life. The fundamental advantage of an automated system is its ability to reduce human stress and minimize errors. Over the past few years, there has been a swift shift from traditional switches to switches equipped with remote controls. Currently, the presence of conventional wall switches distributed around the house poses challenges in terms of use, especially for individuals who are elderly or have physical disabilities. Due to technological advancements, mobile smartphones are now affordable for all individuals. Android devices are becoming equipped with applications that aid in multiple ways. Another new technology is the Google speech recognition APIs, which enable voice-based system command and control. This study demonstrates the implementation of voice-controlled home automation using the Arduino Uno microcontroller. Users of this system will be able to exercise full authority over every domestic device through spoken commands. The control circuit consists of an Arduino Uno microcontroller that receives and interprets voice commands from an Android smartphone equipped with the corresponding application. While the Bluetooth module shares signal data after establishing a wireless link between the microcontroller and the smartphone, the relay regulates device switching.

Towards Building a Faster and Incentive Enabled Privacy-Preserving Proof of Location Scheme from GTOTP

In recent years, there has been significant growth in location-based services (LBSs) and applications. These services empower users to transmit their location data to location service providers, thereby facilitating the provisioning of pertinent resources and services. However, in order to prevent malicious users from sending fake location data, users must attest to their location for service providers, namely, through a proof of location (PoL). Such a proof should additionally prevent attackers from being able to obtain users’ identity and location information through it. In this paper, we propose an efficient privacy-preserving proof of location (PPPoL) scheme. The scheme is based on the standard cryptographic primitives, including Group Time-based One-Time Password (GTOTP) and public key encryption, which achieves entity privacy, location privacy, and traceability. Unlike the previous GTOTP-based PPPoL scheme, our scheme enables instant location verification with additional hash operations. To encourage the active participation of witnesses in location proofs, we propose an incentive mechanism based on smart contracts. Additionally, we implement a proof of concept of our PPPoL scheme on an Android device. Our experimental results show that proof generation and verification time are on the order of milliseconds. Meanwhile, the total overhead for the incentive mechanism amounts to 0.0011 ETH. This result is practical for mobile device-based LBSs.

Redesigning Android Development: Using Reactive Programming to Retrofit REST APIs and Concurrency

In this paper the current techniques and tools used by developers to create applications are described in detail and the use of REST APIs in Android applications as a case study. The first component is the installation of the APIs, which we will focus on the Retrofit library for network management, and the API Clients to manage requests. This study evaluates the nuances of JSON parsing, serialization, and deserialization through the Retrofit API client. We study the technique of data searching, the introduction of Tap Drawer using Firebase to improve the app functionality, and the string transformation method of Scalar with JSON Converter Factory.The tutorial explains how to set up an API, design an interface for Product ID query, use a Binding Adaptor for handling images and a Glide dependency to load images. As well, we consider how to create a Product Adaptor for a Recycler View which leverages Fragments to implement complex user interfaces. According to the plan of Android development, we absorb width, wrap content and verticality alignment as layout factors.The building of the “Adapter List” based on the “Create View Holder” function and the creation of Product Model class — which consists of fields for ratings and product details — as well as the JSON integration are presented in the article. Asynchronous processes are applied in the application as well as the `enqueue` technique has been used to provide effective network connection.We intend to acquire an experimental data by app scanning on the Android platform, by referring to Stack Overflow's most favourite pages and through a survey of mobile application developers in order to build a code collection associated with REST mobile client technologies. From our findings it follows that application communicating through the Internet phenomenon associated with JSON syntax implementation in comparison to XML data processing execution rather widespread and acceptable in terms of Android developers. We include the HTTP libraries standardized by third-party organizations in the paper which also explains how to use the practices.In unity, we discuss the perks of Android system uniqueness which facilitates the application customization, suppliers and the consumers, in terms of security, usability, and usefulness. Our app framework for Android offers coronary adjust to existing apps through behaviour simply without accessing or modifying app source code but sticks to the universal app-agnostic transformation objectives. This architecture is thus designed to allow the modified software version to function without any hindrance on any normal Android device. Keywords- REST API, Android, Retrofit, Firebase, Gson, Recycler View, JSON parsing, HTTP libraries, refactoring, Android programming principles.

Antie-theft Device Tracking System

This project's goal is to develop an Android application that offers SMS-based location tracking capabilities for Android devices. We can also follow the images of the individual who took our Android phone. This function supports Android OS and sends and receives emails and SMS in response to calls. With simple SMS commands, you can track your Android device. Tracing a location: We can use basic SMS commands to locate the misplaced phone. In a theft gadget, the camera can be remotely manipulated to take pictures. It is said of mobile gadgets that they are portable and that a person can carry them from one place to another. Mobile device theft occurs frequently, and the current system watches users' walking patterns and movements.

Time Efficiency, Reliability, and User Satisfaction of the Tooth Memo App for Recording Oral Health Information: Cross-Sectional Questionnaire Study.

Digitalizing oral health data through an app can help manage the extensive data obtained through oral health surveys. The Tooth Memo app collects data from oral health surveys and personal health information. This study aims to evaluate the evaluate the time efficiency, reliability, and user satisfaction of the Tooth Memo app. There are 2 sections in the Tooth Memo app: oral health survey and personal oral health record. For the oral health survey section of the Tooth Memo app, different data entry methods were compared and user satisfaction was evaluated. Fifth-year dental students had access to the oral health survey section in the Tooth Memo app during their clinical work. The time required for data entry, analysis, and summary of oral health survey data by 3 methods, that is, pen-and-paper (manual), Tooth Memo app on iOS device, and Tooth Memo app on Android device were compared among 3 data recorders who entered patients' information on decayed, missing, and filled permanent teeth (DMFT) index and community periodontal index (CPI), which were read aloud from the database of 103 patients by another dental personnel. The interobserver reliability of the 3 different data-entering procedures was evaluated by percent disagreement and kappa statistic values. Laypeople had access to the personal oral health record section of this app, and their satisfaction was evaluated through a Likert scale questionnaire. The satisfaction assessments for both sections of the Tooth Memo app involved the same set of questions on the app design, usage, and overall satisfaction. Of the 103 dental records on DMFT and CPI, 5.2% (177/3399) data points were missing in the manual data entries, but no data on tooth status were missing in the Android and iOS methods. Complete CPI information was provided by all 3 methods. Transferring data from paper to computer took an average of 55 seconds per case. The manual method required 182 minutes more than the iOS or Android methods to clean the missing data and transfer and analyze the tooth status data of 103 patients. The users, that is, 109 fifth-year dental students and 134 laypeople, expressed high satisfaction with using the Tooth Memo app. The overall satisfaction with the oral health survey ranged between 3 and 10, with an average (SD) of 7.86 (1.46). The overall satisfaction with the personal oral health record ranged between 4 and 10, with an average (SD) of 8.09 (1.28). The Tooth Memo app was more efficacious than manual data entry for collecting data of oral health surveys. Dental personnel as well as general users reported high satisfaction when using this app.

HertDroid: Android Malware Detection Method with Influential Node Filter and Heterogeneous Graph Transformer

The explosive growth of malware targeting Android devices has resulted in the demand for the acquisition and integration of comprehensive information to enable effective, robust, and user-friendly malware detection. In response to this challenge, this paper introduces HertDroid, an innovative Android malware detection method that leverages the hidden contextual information within application entities. Specifically, we formulate a heterogeneous graph encapsulating rich semantics of entities and their interactions to model the behavior of Android applications. To alleviate computational burdens, a filter is implemented to identify nodes containing crucial information. The Transformer architecture is then deployed for efficient information aggregation across diverse entities. In our experiments, HertDroid demonstrates superior performance by achieving the highest F1 scores when compared to baseline methods on a dataset comprising 10,361 benign and 11,043 malicious apps. Notably, HertDroid excels in maintaining a lightweight profile, and its performance is achieved without the necessity of manual meta-path configuration.

Detecting Android Malware by Mining Enhanced System Call Graphs

The persistent threat of malicious applications targeting Android devices has been growing in numbers and severity. Numerous techniques have been utilized to defend against this thread, including heuristic-based ones, which are able to detect unknown malware. Among the many features that this technique uses are system calls. Researchers have used several representation methods to capture system calls, such as histograms. However, some information may be lost if the system calls as a feature is only represented as a 1-dimensional vector. Graphs can represent the interaction of different system calls in an unusual or suspicious way, which can indicate malicious behavior. This study uses machine learning algorithms to recognize malicious behavior represented in a graph. The system call graph was fed into machine learning algorithms such as AdaBoost, Decision Table, Naïve Bayes, Random Forest, IBk, J48, and Logistic regression. We further employ a series feature selection method to improve detection accuracy and eliminate computational complexity. Our experiment results show that the proposed method has reduced feature dimension to 91.95% and provides 95.32% detection accuracy.

A lightweight improved YOLOv5s model and its deployment for detecting pitaya fruits in daytime and nighttime light-supplement environments

Precise detection and low-cost deployment are the technological basis of intelligent fruit picking. This study proposes a lightweight improved YOLOv5s model to detect pitaya fruits in daytime and nighttime light-supplement environments, and make it successfully deploy in an Android device. This model first uses the module of shufflenetv2 to reconstruct the YOLOv5s backbone network. Then, the study proposes a Concentrated-Comprehensive Convolution Receptive Field Enhancement (C3RFE) module to improve the detection precision of pitaya fruits. Furthermore, a Bidirectional Feature Pyramid Network(BiFPN) feature fusion method is used to enhance the multi-scale feature fusion. Moreover, three optimized Squeeze-and-Excitation (SE) attention modules are added to make full use of the image feature information. Finally, a dynamic label allocation strategy simple Optimal Transport Assignment (simOTA) is used to optimize the YOLOv5s model original label allocation strategy. The experimental results show that the improved model achieves an average precision rate of 97.80 %, with frames per second (FPS) of 139 FPS in a GPU run environment. The model size is only 2.5 MB. Compared to the state-of-the-art SSD, Faster RCNN, YOLOv4, YOLOv4 tiny, YOLOv5s, YOLOv5Lite-s, YOLOXs, YOLOv7, YOLOv7-tiny, YOLOv8n and YOLOv8s, the improved YOLOv5s achieve preferred comprehensive performance in average precision rate, FPS and model size. When deploying this model on the Realme GT Android mobile phone by developing an application based on an NCNN framework, such an application accomplishes real-time pitaya fruit detection with an FPS exceeding 30 FPS. This study can provide technological support for a precise and effective pitaya fruit intelligent picking.

Smart Home Automation System

In fast moving world, where people are very busy in their own things, there is a need for automation in all activities, not only industries and machineries; even home automation is of prime importance. As of now, for automation purposes we use different wireless technologies and that process uses a PC, voice processor etc., these have their own limitations and results in decreased accuracy. In order to provide more reliable and convenient automation we use android applications. Smart phones are becoming more popular now a days and each of us are having a smart phone in present situation. So by using dedicated applications on an android device complete automation can be done and this to a great level will decrease the drawbacks caused by other conventional wireless technological processes. It has a variety of advantages over conventional old microcontrollers such as RISC architecture, larger memory word, fast operation and a host of inbuilt features such as ADC, communication protocols etc., The project can be used to control any external environment where in the person may like to control all his surroundings such as fans, lights, TVs, etc; He may also like to open the door for someone or close the water tap.

Machine learning approach to detect android malware using feature-selection based on feature importance score

The hazards posed by malware are proliferating along with technology’s rapid advancement and the use of online services. Specifically, attacks on Android devices are growing enormously because of the boost in the popularity of Smartphones. Existing research confirms that identifying benign or malware applications on the Android platform is possible by analysing the permissions through the machine-learning classifier. There are machine-learning techniques that create models to detect Android malware using permission-based attributes. However, further research is still needed to develop effective feature selection strategies for malware detection mechanisms in Android. In this study, a machine-learning-based Android malware detection mechanism is proposed, and standard machine-learning algorithms are used on multiple permission-based datasets to classify malware. This study suggests a feature selection method that uses the feature importance score computed using Gradient boosting to identify the essential permissions. The proposed methodology decreases the feature vector’s dimension, reducing the model’s training time. We also compare the classifier’s performance with the complete feature set and with the reduced feature set. Examining the results, we notice that the algorithm’s execution time improved significantly for all datasets with negligible loss in accuracy.

OFEI: A Semi-Black-Box Android Adversarial Sample Attack Framework Against DLaaS

With the growing popularity of Android devices, Android malware is seriously threatening the safety of users. Although such threats can be detected by deep learning as a service (DLaaS), deep neural networks as the weakest part of DLaaS are often deceived by the adversarial samples elaborated by attackers. In this paper, we propose a new semi-black-box attack framework called one-feature-each-iteration (OFEI) to craft Android adversarial samples. This framework modifies as few features as possible and requires less classifier information to fool the classifier. We conduct a controlled experiment to evaluate our OFEI framework by comparing it with the benchmark methods JSMF, GenAttack and pointwise attack. The experimental results show that our OFEI has a higher misclassification rate of 98.25%. Furthermore, OFEI can extend the traditional white-box attack methods in the image field, such as fast gradient sign method (FGSM) and DeepFool, to craft adversarial samples for Android. Finally, to enhance the security of DLaaS, we use two uncertainties of the Bayesian neural network to construct the combined uncertainty, which is used to detect adversarial samples and achieves a high detection rate of 99.28%.

The Hearing Test App for Android Devices: Distinctive Features of Pure-Tone Audiometry Performed on Mobile Devices.

The popularity of mobile devices, combined with advances in electronic design and internet technology, has enabled home-based hearing tests in recent years. The purpose of this article is to highlight the distinctive aspects of pure-tone audiometry performed on a mobile device by means of the Hearing Test app for Android devices. The first version of this app was released a decade ago, and since then the app has been systematically improved, which required addressing many issues common to the majority of mobile apps for hearing testing. The article discusses techniques for mobile device calibration, outlines the testing procedure and how it differs from traditional pure-tone audiometry, explores the potential for bone conduction testing, and provides considerations for interpreting mobile audiometry including test duration and background noise. The article concludes by detailing clinically relevant aspects requiring special attention during testing and interpretation of results which are of substantial value to the hundreds of thousands of active users of the Hearing Test app worldwide, as well as to users of other hearing test apps.

An efficient cloud-integrated distributed deep neural network framework for IoT malware classification

The proliferation of interconnected devices in the Internet of Things (IoT) landscape has introduced significant security concerns. With the integration of android devices, the potential for attackers to exploit vulnerabilities becomes a crucial issue. Timely detection of malware attacks has emerged as a critical challenge, especially in industrial IoT (IIoT), to prevent permanent damage. The slow inference speed of the current algorithms causes limited performance and a significant delay in the detection and mitigation of malware, especially in large-scale environments such as IIoT. High resource utilization can limit the size of the model (number of parameters), which could be implemented in embedded devices, which causes lower accuracy of the model. Hence, we proposed a HierarchicalCloudDNN, a distributed malware detection framework that used modified state-of-the-art deep learning algorithms in a boosting ensemble architecture. The proposed method could scale efficiently from IIoT devices up to the edge and cloud to speed up malware detection, simultaneously reduce resource utilization, and maintain a controllable level of accuracy compared to rival methods in malware detection. The objective is to minimize both run-time and resource consumption while maintaining performance characteristics. The performance analysis of the HierarchicalCloudDNN method was evaluated using standard performance measures on the frequently used BIG 2015 dataset. Based on evaluations, the proposed approach has demonstrated an impressive accuracy rate of 98.90%, outperforming many state-of-the-art models in performance, response time, and resource utilization. These findings suggest a notable decrease in both false positives and false negatives while maintaining accuracy. The proposed distributed malware analysis framework is scalable, robust, and helpful for the timely detection of malicious activity, especially in geographically distributed environments such as the IIoT.

RatHole: Authentication Algorithm for Controling Access to Mobile Phone File Management System

Millions of Naira are being laundered by criminals through internet fraud, which deceives the victim with a false presentation, or hacking, which involves breaking information system security to steal financial information or data. Cybercrime, more specifically internet fraud, has become a common problem facing the world. In any case, this only occurred if the attacker was able to obtain the victim's account privileges (bank account, credit card information, or login credentials). This work presented an authentication technique that can be inexpensively implemented and used to create a secure architecture that is hard to breach. It can also reduce the amount of overhead that is placed on the authentication server during an attempt at authentication. The possibility of breaking the authentication technique was calculated using probability theory, and the mobile-based authentication algorithm was implemented using Android Studio. The result of this research shows the implementations of RatHole algorithm using android operating system which is file management android app that is being protected with RatHole algorithm and the probability of breaking down the algorithm for 1 to 10 authentication method keeps reducing by averagely 0.12. The implemented Rat-Hole on android devices also consumed 20 to 30 megabytes of data very less than memory consumption of recent research based on block chain solution which consumed 1 gigabyte to 50 gigabyte of memory.