Implementation of Dijkstra Algorithm with React Native to Determine Covid-19 Distribution

Since Covid-19 was declared a global pandemic because it has spread throughout the world, every effort has been made to help prevent and tackle the transmission of Covid-19, including information technology. Information technology developed to determine the shortest distance for Covid-19 cases around us needs to be developed. This research implements Dijkstra's Algorithm written in the React Native programming language to build a Covid-19 tracking application. The system can display the closest distance with a radius of at least one meter, and the test results can map the nearest radius of 41 meters and the most immediate radius of 147 meters. This system is built for the compatibility of Android OS and iOS applications with React Native programming.

An Improved Model for House Price/Land Price Prediction using Deep Learning

House price predictions are a crucial reflection of the economy; sometimes house prices include the land prices and demand of the place and location. The house price and land price are two different things, but both are important for both buyers and sellers. This chapter introduced the combination of ML and DL approaches to predict the house price with the updated regression algorithm. The algorithm named as ‘Mopuri algorithm' reads the 14 attributes like crime rate, population density, rooms, etc. and produces the cost estimation result as a prediction. The proposed model accurately estimates the worth of the house as per the given features. The results of the model tested with the different datasets existing in the Kaggle data source using Python libraries with the Jupyter platform and continuation of the model using the Android OS to develop the smart home web-based application.

CameraEEG: Synchronized Recording of Video with Electroencephalogram data on an Android Application

We present a novel android application named CameraEEG that enables synchronized acquisition of Electroencephalogram(EEG) and camera data using a smartphone. Audio-visual events of interest experienced by the subject were also recorded using a button press on the CameraEEG app. Unlike lab-restricted experiments, which usually constrain the subject's mobility, this wearable solution enables monitoring of the human brain during everyday life activities. The app was built using Android SDK version 28 and Smarting mobi SDK from mbraintrain. It works on all android devices having a minimum Android OS - Lollipop. We successfully recorded thirty minutes of synchronized Video and EEG during eyes closed and walking tasks using the app. Event markers enabled by the subject using the app during walking tasks were also recorded. Timing tests showed that temporal synchronization of video and EEG data was good. We analysed the recorded data and were able to identify the task performed by the subject from the event markers. The power spectrum density of the two tasks showed different power spectrums with a peak in the alpha band for eyes closed task. We also provide android studio codes for download and detailed help documentation for the community to test the developed application.

FG-Droid: Grouping Based Feature Size Reduction for Android Malware Detection through Machine Learning

The number of applications prepared for use on mobile devices has increased rapidly with the widespread use of the Android OS. This has resulted in the undesired installation of Android apks that violate user privacy or malicious. The increasing similarity between Android malware and benign applications makes it difficult to distinguish them from each other and causes a situation of concern for users. In this study, FG-Droid, a machine-learning based classifier with an efficient working system, using the method of grouping the features obtained by static analysis, was proposed. It was created as a result of experiments with Machine learning (ML), DNN, RNN, LSTM and GRU based models using Drebin, Genome and Arslan datasets. Experimental results reveal that FG-Droid has achieved 97.7% AUC score with a vector includes only 11 static features, and ExtraTree algorithm. FG-Droid analyze the applications with using the least number of features compare to previous studies, and required the least processing time for training and prediction. As a result, it has been shown that Android malware can be detected in high accuracy rate with an effective feature set and there is no need to use a large number of features extracted with different techniques (static, dynamic or hybrid).

The calculation of point quantity for lighting based on android OS using ionic framework and rule based expert system

At this time, the Covid-19 pandemic has not been finished so all activities and expenditure of funds must be economical. On the each building will be required electricity resources as a means for lighting the room. Reduced activities carried out outside the room make people carry out activities indoors, this results in a significant increase in electricity consumption for lighting. PT. PLN as the provider in Indonesia of electricity services for the community experiences a shortage of electricity resources and most of the electricity resources are used for lighting purposes. Indonesia as a developing country has a very large of population growth, so it requires many buildings as a place to live or a place of business and other activities. This paper will be discussion how a smartphone with an Android operating system can be used to determining the lighting requirements using the Ionic Framework and Rule Based Expert Systems. Using this application system, so electricity to lighting can be optimal and not too wasteful, which in turn can save on the use of electrical energy resources and can save of the cost.

Designing A Virtual Jakarta Tourism Application On Android Os And Virtual Reality Tools

The background of this research is to make a prototype of a Jakarta virtual tourism application system using Android OS and using virtual reality tools. The method used by this panel is to use a literature review and create an application prototype that will be applied to the making of a virtual Jakarta tourism program. The results of this study are the application of virtual reality tools on the Jakarta virtual tourism application which aims to help the tourism sector affected by the COVID-19 pandemic and the manufacture of application prototypes that use the Android OS. The limitation of this research lies in making an application prototype using the Android OS which is not perfect and has not produced a real application. This research is very useful for the development of the tourism sector during the COVID-19 pandemic and in the future, so that tourists can choose to travel online or offline.

Development and Usability Validation of a Social Robot Platform for Physical and Cognitive Stimulation in Elder Care Facilities

This article shows our work for developing an elder care platform for social interaction and physical and cognitive stimulation using the Pepper robot and Android OS as clients, based on the knowledge acquired on our long-term social robotics research experience. The first results of the user’s acceptance of the solution are presented in this article. The platform is able to provide different services to the user, such as information, news, games, exercises or music. The games, which have a bi-modal way of interacting (speech and a touch screen interface), have been designed for cognitive stimulation based on the items of the mini-mental state examination. The results of the user’s performance are stored in a cloud database and can be reviewed by therapists through a web interface that also allows them to establish customized therapy plans for each user. The platform has been tested and validated, first using adult people and then deployed to an elder care facility where the robot has been interacting with users for a long period of time. The results and feedback received have shown that the robot can help to keep the users physically and mentally active as well as establish an emotional link between the user and the robot.