Android Mobile Devices Research Articles

A Bed-Fall Notification System Using Pressure and Ultrasonic Sensors

Falls are a major cause of unintentional injury and mortality in the senior population. Therefore, early fall notification can prevent any undesirable damage. This paper aims to present a mobile electronic device-based bed-fall notification using the combination of six thin-film pressure sensors and six ultrasonic sensors. The proposed system monitors movements, detects a bed-fall, and sends an alarm to caregivers. The Finite State Machine (FSM) is used to classify 3 states of movement patterns: the lying, sitting, and falling states. The Internet of Things (IoT) system is adopted to transmit the movement status to the application on an android mobile device. The proposed bed-fall classification system yielded promising results for accuracy, sensitivity, and specificity: 86.67%, 73.33%, and 100%, respectively. In future work, the proposed system can be improved by increasing the number of thin-film pressure sensors, modifying circuit, and applying machine learning models. Accreditation of the system is expected to benefit young and elderly patients in nursing homes, hospitals, and residential areas.

SISTEM PAKAR IDENTIFIKASI PANEN BUAH NANAS MENGGUNAKAN METODE BACKWARD CHAINING BERBASIS ANDROID

This study aims to create an expert system based on android mobile device platform that can analyze the harvest period of pineapple plants based on the source of expert knowledge. This study uses 4 main variables namely Seed Origin, Planting Age, Etherel (Carbit) and Percentage of Maturity. In addition this identification system provides features for sources of knowledge in the cultivation of pineapple plants including ways of cultivation and obstacles that can interfere with the growth of these plants. The identification system uses the Backward Chaining inference method that determines the main conclusions in the rules and then determines supporting hypotheses that refer to these conclusions. System functional testing uses the Equivalence Partitioning method in the functional testing scenario. Testing accuracy using 10 random test cases then validated by an expert. From the results of testing the accuracy of using 10 test-cases obtained 80% accuracy results so that the results of the identification of the system are categorized well in determining the harvest period of pineapple.

Design and implementation of C-MEX S-functions in an Android-based networked control system laboratory

The Android-based networked control system laboratory (NCSLab) is a remote control laboratory that adopts an extensible architecture, mainly including Android mobile devices, MATLAB servers, controllers and test rigs. In order to conduct various simulations and experiments more effectively in NCSLab, the first key issue that needs to be solved is to enable users to design their own control algorithms or functional blocks on the Android client, rather than just using the basic block libraries provided by the system. So, this paper proposes and implements a scheme for Android-based compilation of C-MEX S-functions. With this new feature, users can design personalized algorithm according to their requirements in the form of S-functions, which can be called and executed after being compiled by MATLAB server. Finally, through the experiment validation of the three-degree-of-freedom air bearing spacecraft platform, it is proved that the method of Android-based C-MEX S-functions is reliable and efficient, and this scheme well enhances the functionality and mobility of Android-based NCSLab.

An Android-Based Image Steganography System for Concealing Data and Information Transmission Using Adaptive Image Steganography

The growth in the volume of information transmission and transaction over the internet has necessitated the development and enhancement of measures and techniques required to ensure the safety of critical information during transmission. with developments in mobile technologies and the adoption and diffusion of android mobile devices, research must therefore continue to explore more secured media in which information can be hidden. One of such medium is the use of image carriers referred to as steganography. In tis paper, we provide insughts into the development of an android-based Image Steganography System for concealing data and information using adaptive image steganography. The Least Significant Bit (LSB) algorithm was employed as the algorithmic sequence for the development. Preliminary results from implementation and testing showed promising results Keywords: Android, image steganography, data, information, transmission, images, security

Blockchain Based Detection of Android Malware using Ranked Permissions

Android mobile devices are a prime target for a huge number of cyber-criminals as they aim to create malware for disrupting and damaging the servers, clients, or networks. Android malware are in the form of malicious apps, that get downloaded on mobile devices via the Play Store or third-party app markets. Such malicious apps pose serious threats like system damage, information leakage, financial loss to user, etc. Thus, predicting which apps contain malicious behavior will help in preventing malware attacks on mobile devices. Identifying Android malware has become a major challenge because of the ever-increasing number of permissions that applications ask for, to enhance the experience of the users. And most of the times, permissions and other features defined in normal and malicious apps are generally the same. In this paper, we aim to detect Android malware using machine learning, deep learning, and natural language processing techniques. To delve into the problem, we use the Android manifest files which provide us with features like permissions which become the basis for detecting Android malware. We have used the concept of information value for ranking permissions. Further, we have proposed a consensus-based blockchain framework for making more concrete predictions as blockchain have high reliability and low cost. The experimental results demonstrate that the proposed model gives the detection accuracy of 95.44% with the Random Forest classifier. This accuracy is achieved with top 45 permissions ranked according to Information Value.

Object Tracking Devices using Android Application

Millions of smart devices are mushrooming in this digital environment to meet the difficulties posed by innovations and technological advancements in ubiquitous computing. All of these smart gadgets are exposed to everyday objects that are linked to the Internet. Web of Things is a term that defines how various objects are connected to the Internet and communicate with one another using web standards. The emergence of omnipresent systems makes human beings' jobs easier. People were able to live lavish existence in the most comfortable environment because of these inventions. Finally, everyone began to keep their valuables in accordance with their activities inside the house or at work, and they began to hide them under misplaced or lost belongings. Every house has a history of searching for keys, wallets, pen drives, and hand purses, which may be a time-consuming task, especially during times of high demand. A device model is presented to reduce this effort and save time by allowing lost objects to be tracked and traced out. The suggested work serves as a foundational notion for describing how to track items in a practical manner. This is a basic device model that describes how to track an object using an Android mobile device and a Wi-Fi connection with the Blynk app and GPS controllers.

Automatic Mobile Warning System against People with Elevated Body Temperature

The paper proposes a system that allows for the automatic detection of people with elevated body temperature and estimates distance from them using a smartphone-type device and a single mobile thermal camera. The algorithm automatically finds and selects humans with the highest temperature, and tracks changes in their position in an image sequence. On the basis of the change in the position of the human head in the image, in subsequent frames, the algorithm estimates the distance between camera and human. Owing to the use of fast machine-learning methods, the proposed system can immediately alert the user about the presence of a people with an elevated temperature at a distance of 1–3 m as soon as it appears in the field of view of the camera. The effectiveness of the algorithm was assessed as the ratio of correct distance classifications in the test image set to the total number of test images. Values ranging from 73% to 100% were obtained for over 4000 images of humans at different distances. The proposed method allows for the quick and completely automatic warning aboutt people with elevated temperature, and can be used in popular Android mobile devices.

Dc Motor Control System Through Android Application Using Arduino Nano

Abstract The study aims for the development of a control system for D.C motors through an application made for Android mobile devices. The D.C motors are represented by a robot model car and the control application communicates with the system via Bluetooth technology. The Android mobile device user must install an application on his mobile device, then, the user must turn on the Bluetooth communication on the mobile device. The user can use various commands to control the robot model car such as front, back, stop, left, right. These commands are sent from the Android mobile device to the Bluetooth receiver, which is interfaced with the control system. The control system has a Bluetooth HC-06 receiver that receives commands from the control application and sends them to the Arduino Nano microcontroller to control the motors through two L298N drivers.

Application for Introduction of Augmented Reality-Based Transportation Tools for Children

The introduction of a means of transportation to children can stimulate the brain to imagine and train creativity. With the development of information technology, means of introducing means of transportation can be done using devices such as computers, laptops or smartphones. The development of Augmented Reality technology on smartphones will make it easier for children to get to know several types of transportation that they often encounter. The purpose of writing this article is to create an application for Introduction to Transportation Tools using Augmented Reality technology for children.
 The method used in making this application of Introduction to Transportation Tools is observation and interviews. The production begins with collecting data on what types of transportation will be made as a model using the Blender software. Augmented Reality application design uses Unity software by designing markers made from the Vuforia platform. The features in this application are several types of transportation that are displayed in 3D with sound and information using Augmented Reality technology.
 The results of testing the Transportation Tool Introduction application can run on various Android mobile devices. It has been tested on several different smartphone brands and specifications - the results of the Transportation Tool Introduction can run smoothly except under Android version 4.3 (Jellybean). Based on the test results contained in the Application of Introduction to Transportation Tools using Augmented Reality, it is suitable for introducing more interesting and innovative types of transportation to early childhood. There are still many deficiencies that can be developed, for example, increasing the number of 3D objects in the transportation tool and making animations even better, so that the application is more interesting, interactive, and easy to understand.

Acquiring vulnerability indicators to geo-hydrological hazards: An example of mobile phone-based data collection

Geo-hydrological risk reduction is a key issue for local governments in Italy. In this context, a collaboration was undertaken between multiple actors in the La Spezia municipality aimed at: (i) monitoring building characteristics, using specific and valuable indicators, and (ii) increasing the knowledge of geo-hydrological hazards across residents and local land planners (iii) implementing local emergency civil protection plan. An extensive mobile data collection was carried out through apps specifically developed for Android and IOS mobile devices. The digital forms were differentiated on the basis of the potential hazard: one of 46 fields and one of 125 fields designed for buildings respectively located in flood prone areas and in medium to very high landslide susceptibility areas. The digital version of the forms was designed using the Open Data Kit (ODK) and GISCloud client-server approach. All the collected data, including geospatial locations and images, were automatically sent to a central server, stored and organized in a database. Geospatial data-analysis and maps resulted useful in evaluating possible impacts to exposed buildings to potential geo-hydrological processes. The proposed public participation method for data-gathering increased the knowledge across residents providing a better understanding of the urban systems, of the buildings condition and their relation respect to the geo-hydrological risk. The method can be considered as part of the decision support systems for civil protection purposes to better planning geo-hydrological mitigation measures. The application of mobile technology for data collection can be effectively used when local government resources are limited.

YAICD: Yet Another IMSI Catcher Detector in GSM

In GSM, the network is not authenticated which allows for man-in-the-middle (MITM) attacks. Attackers can track traffic and trace users of cellular networks by creating a rogue base transceiver station (BTS). Such a defect in addition to the need for backward compatibility of mobile networks makes all GSM, UMTS, and LTE networks susceptible to MITMs. These attacks are conducted using IMSI-Catchers (ICs). Most of the solutions proposed for detecting ICs in the literature are based on using specific mobile devices with root access. Also, they cannot identify ICs to which users are not connected. In this paper, we propose an approach called YAICD for detecting ICs in the GSM network. YAICD consists of a sensor that can be installed on Android mobile devices. It detects ICs by extracting 15 parameters from signals received from BTSs. We also established a lab-scale testbed to evaluate YAICD for various detection parameters and for comparing it against existing solutions in the literature. The experimental results show that YAICD not only successfully detects ICs using the parameters but also identifies ICs to which users are not yet connected to the network.

Deep learning feature exploration for Android malware detection

Android mobile devices and applications are widely deployed and used in industry and smart city. Malware detection is one of the most powerful and effective approaches to guarantee security of Android systems, especially for industrial platform and smart city. Recently, researches using machine learning-based techniques for Android malware detection increased rapidly. Nevertheless, most of the appeared approaches have to perform feature analysis and selection, so-called feature engineering, which is time-consuming and relies on artificial experience. To solve the inefficiency problem of feature engineering, we propose TC-Droid, an automatic framework for Android malware detection based on text classification method. The core idea of TC-Droid is derived from the field of text classification. TC-Droid feeds on the text sequence of APPs analysis reports generated by AndroPyTool, applies a convolutional neural network (CNN) to explore significant information (or knowledge) under original report text, instead of manual feature engineering. In an evaluation with different number of real-world samples, TC-Droid outperforms state-of-the-art model (Drebin) and several classic models (NB, LR, KNN, RF) as well. With multiple experimental settings and corresponding comparisons, TC-Droid achieves effective and flexible performance in Android malware detection task.

Validation of android-based mobile application for retrieving network signal level

<p><span>In recent years, the evolvement of mobile devices which perform sophisticated functions have been on the rise. Mobile applications which solved engineering challenges are now available due to the high computational capabilities, large random access memory and storage location of the mobile devices. An Android application called signal detect, which measures network signal strength value from 2G-4G received on an android mobile device has been developed using android app development environment called Android studio. Validation becomes necessary because different readings were obtained on smartphones with different specifications. Two validation techniques were used to validate the data obtained. To know the efficiency of the application; a field strength meter was used to compare the readings received on the mobile device with the meter. It was observed that there is a time lag on the received values of the mobile device to the field strength meter. Therefore, a moving average technique was used to correlate the two data which increased the correlation coefficient to about 0.85.</span></p>

A Framework for Multi-Hop Ad-Hoc Networking over Wi-Fi Direct with Android Smart Devices

The wide diffusion of mobile devices that natively support ad hoc communication technologies has led to several protocols for enabling and optimizing Mobile Ad Hoc Networks (MANETs). Nevertheless, the actual utilization of MANETs in real life seems limited due to the lack of protocols for the automatic creation and evolution of ad hoc networks. Recently, a novel P2P protocol named Wi-Fi Direct has been proposed and standardized by the Wi-Fi Alliance to facilitate nearby devices’ interconnection. Wi-Fi Direct provides high-performance direct communication among devices, includes different energy management mechanisms, and is now available in most Android mobile devices. However, the current implementation of Wi-Fi Direct on Android has several limitations, making the Wi-Fi Direct network only be a one-hop ad-hoc network. This paper aims to develop a new framework for multi-hop ad hoc networking using Wi-Fi Direct in Android smart devices. The framework includes a connection establishment protocol and a group management protocol. Simulations validate the proposed framework on the OMNeT++ simulator. We analyzed the framework by varying transmission range, number of hops, and buffer size. The results indicate that the framework provides an eventual 100% packet delivery for different transmission ranges and hop count values. The buffer size has enough space for all packets. However, as buffer size decreases, the packet delivery decreases proportionally.

MODERN TOOLS FOR SECURITY TESTING FROM OWASP

With the development of information technology, humanity is increasingly delving into the world of gadgets, cloud technology, virtual reality, and artificial intelligence. Through web applications, we receive and distribute information, including confidential. During the pandemic, most people switched to online work and study. As a result, most of the data stored on personal computers, company servers, and cloud storage needs protection from cyberattacks. The problem of cybersecurity at the moment is incredibly relevant due to the hacking of cryptocurrencies, websites of ministries, bitcoin wallets or social network accounts. It is necessary to conduct high-quality testing of developed applications to detect cyber threats, to ensure reliable protection of different information. The article states that when testing applications, it checks for vulnerabilities that could arise as a result of incorrect system setup or due to shortcomings in software products. The use of innovation is necessary to improve quality. Modern realities have become a challenge for the development of cybersecurity products. Improvement of technology requires modern companies to update their IT systems and conduct regular security audits. The research is devoted to the analysis of modern OWASP testing tools that contribute to data security, with a view to their further use. The Open Web Application Security Project is an open security project. The research revealed a list of the most dangerous vectors of attacks on Web-applications, in particular, OWASP ZAP performs analyzes the sent and received data system security scanning at the primary level, MSTG performs security testing of mobile applications iOS and Android mobile devices. The practical result of the work is to test a specially developed web-application and identify vulnerabilities of different levels of criticality.

Design, Development and Implementation of Mobile Applications to Support the Educational Process in the Greek Secondary Education System

The purpose of this paper is the design, development and presentation of two applications that are created in order to be used for supporting the students’ learning in the Greek secondary education environment. More specifically, we present the structure and the functions of two applications under the names “Aepp_App” and “CS_App”. The applications are intended to be used by teachers and students during a lesson as well as outside the classroom settings, with the use of android mobile devices. We present the tools that have been used to develop the mobile applications and the applications’ use in the educational process. Additionally, we present the feedback received by teachers, to whom the applications were presented, who seem to like the idea of using game applications for mobile devices in order to enhance their students’ interest.

Design, Development and Implementation of Mobile Applications to Support the Educational Process in the Greek Secondary Education System

The purpose of this paper is the design, development and presentation of two applications that are created in order to be used for supporting the students’ learning in the Greek secondary education environment. More specifically, we present the structure and the functions of two applications under the names “Aepp_App” and “CS_App”. The applications are intended to be used by teachers and students during a lesson as well as outside the classroom settings, with the use of android mobile devices. We present the tools that have been used to develop the mobile applications and the applications’ use in the educational process. Additionally, we present the feedback received by teachers, to whom the applications were presented, who seem to like the idea of using game applications for mobile devices in order to enhance their students’ interest.

Implementation Face Recognition Attendance Monitoring System for Lab Surveillance with Hash Encryption

Face recognition (FR) is becoming popular to identify people. In fact, using the FR scheme, surveillance tasks can be built by recognizing people from their faces. This paper presents the implementation of face recognition as a biometric method for smart attendance as well as we also proposed the integrated scheme from capturing data from edge devices (CCTVs), streaming data to the dedicated server, then presenting the real-time data through android mobile devices. In this scheme, we proposed to employ deep learning algorithms based on the Convolutional Neural Network (CNN). Through the CCTV data streaming, faces are captured and matched with the database. Therefore, it is considered as their logging attendance. Furthermore, it is marked and stored into the database. This system prototype is developed by big data technology to tackle this complexity of data. The recognized faces can be monitored in real time monitoring. Eventually, real time reports are delivered through the web and android device with API after the data transmission is secured with hash encryption.

Proactive Assistance System for Visually Challenged Persons Using Internet of Things Based on Machine Learning

In this Paper, mainly focusing the visually challenged persons are struggling to move from one place to another place, identification of location, non-visibility of persons and reading newspapers. Due to lack of non visual information in their nearby environment, In order to reduce the burden of the visually challenged persons problems, the proposed system is consist of Wearable device with well equipped earphone and camera, location identification, face recognition, text recognition by use of Android mobile device. The deep learning approach is proposed to predict secure and consistent walk able commands. The Android Mobile Application is used to voice recognition, identifying the location, face recognition and text recognition. The proposed low cost prototype is effective way the visually challenged person to interact with their surrounding environment.

Lung Cancer Diagnosis and Treatment Using AI and Mobile Applications

Cancer has become very common in this evolving world. Technology advancements, increased radiations have made cancer a common syndrome. Various types of cancers like Skin Cancer, Breast Cancer, Prostate Cancer, Blood Cancer, Colorectal cancer, Kidney Cancer and Lung Cancer exits. Among these various types of cancers, the mortality rate is high in lung cancer which is tough to diagnose and can be diagnosed only in advanced stages. Small cell lung cancer and non-small cell lung cancer are the two types in which non-small cell lung cancer (NSCLC) is the most common type which makes up to 80 to 85 percent of all cases [1]. Digital Image Processing and Artificial Intelligence advancements has helped a lot in medical image analysis and Computer Aided Diagnosis(CAD). Numerous research is carried out in this field to improve the detection and prediction of the cancerous tissues. In current methods, traditional image processing techniques is applied for image processing, noise removal and feature extraction. There are few good approaches that applies Artificial Intelligence and produce better results. However, no research has achieved 100% accuracy in nodule detection, early detection of cancerous nodules nor faster processing methods. Application of Artificial Intelligence techniques like Machine Learning, Deep Learning is very minimal and limited. In this paper [Figure 1], we have applied Artificial intelligence techniques to process CT (Computed Tomography) Scan image for data collection and data model training. The DICOM image data is saved as numpy file with all medical information extracted from the files for training. With the trained data we apply deep learning for noise removal and feature extraction. We can process huge volume of medical images for data collection, image processing, detection and prediction of nodules. The patient is made well aware of the disease and enabled with their health tracking using various mobile applications made available in the online stores for iOS and Android mobile devices.