Google API Research Articles

Implementation of Geographic Information System Base On Google Maps API to Determine Bidikmisi Scholarship Recipient Distribution in Central Sulawesi Indonesia

The presence of information technology in web-based digital mapping, especially using the Google API, has helped various parties make decisions regarding mapping to find out the distribution of various objects. Bidikmisi scholarship recipients are a group of university students who receive fundings Indonesian government every year. They come from various regions and economic backgrounds. However, the distribution of origin of students receiving the Bidikmisi scholarships has never been investigated accurately. As a result, information related to the geographical distribution of Bidikmisi scholarship recipients, both from an economic point of view and the quality of their education, has not yet been mapped. This study, therefore, developed a geographic information system (GIS) application based on google API to map the distribution of students receiving Bidikmisi scholarship based on region and economic geography. Data were gathered through surveys and interviews with experts and stakeholders from the State Islamic University of Datokarama Palu, Indonesia, to obtain information regarding the GIS design and also the criteria for Bidikmisi scholarship recipients. The results of the study show that 305 students received the scholarship from twelve regencies in Central Sulawesi, Indonesia. The origins of the students were successfully mapped throughout the province. Our study helped universities, especially in the field of student affairs and the Quality Assurance Department, to monitor the distribution of Bidikmisi scholarship recipients. Our study can be used as a reference in making quick and accurate decisions in future scholarship distribution.

A geospatial database management system for the collection of medicinal plants.

Medicinal plants are increasingly used, both for medical applications and personal healthcare. However, existing herbal database systems for plant retrieval offer only basic information and do not support real-time analysis of the spatial aspects of plantations and distribution sites. Moreover, data records are usually static and not publicly available as they rely on costly proprietary software packages. To address these shortcomings, including limiting the time needed for collection and data processing, a novel medicinal plants geospatial database management system is proposed. The system allows localization of plant sites and data presentation on an interactive heat map displaying spatial information of plants selected by the user within a specific radius from the user's location, including automatic presentation of an itinerary giving the optimal route between user location and plant destinations selected. The approach relies on dynamic and role-based data management, an interactive map that includes graphics and integrated geospatial analyses thanks to cross-platform, geographical a JavaScript library and Google API. Both spatial data and attributes are available in real time. The system would support effective collaboration, among herb farmers, government agencies, private investors, healthcare professionals and the general public with regard to various aspects of medicinal plants and their applications.

Application Design for Identification of Car Garbage Prone Points for Pringsewu Regional Government Cleanliness Based on Mobile Web

Pringsewu Regency is one of 15 regencies/cities in Lampung Province with an area of 625 km2. In order to realize Pringsewu's vision and mission to be harmonious, clean and prosperous (SAHAJA). The government and the people work together to make Pringsewu Regency better, one of which is cleanliness. In an effort to achieve cleanliness and prevent diseases originating from waste, monitoring in waste handling is important and must be focused on. Cleaning trash bins , apart from being a shared responsibility of the community and local government, waste management is also the responsibility of the Environment Agency. Management and monitoring of waste handling is currently still manual with a weak monitoring system. For example, if the garbage has piled up in a temporary shelter, it is not immediately picked up because sometimes the car is picking up garbage at another location. There is no special information system for monitoring the Garbage Location. Therefore, through the design and manufacture of the Garbage Cloud R Point Monitoring Application with the combination of Web Mobile which is expected to be able to help display the Location a map of the Waste Hazard Points in Pringsewu Regency, so that the Garbage Officer and the Community know which locations must be cleaned first. Presentation of information in the form of an application that will make it easier for the community to monitor it . The system development methodology used in this research is the system development life cycle (SDLC) method. The map design displayed in the system uses the Google API, the software used in building this application is Android Studio with a combination of PHP (hypertext preprocessor) as a programming language.

Perancangan Arsitektur Aplikasi Web Menggunakan Data Real Time Dari Google Form Berbasis File CSV

The use of information technology is now so massive, that even reports on community activities have also used information technology. Online forms for reporting are widely used and very easy to create, with complete features. One of the products from Google is Google Forms. Starting from reservation, registration, data collection, reporting, and many activities of sending information and data can be done using this form online. The results of the data have also been presented in full, either in the form of sheets or graphic information, so that the manager or admin does not need to process it again. In the development of information needs, it turns out that the information needs from the google form are not only needed by managers or admins, but some parties need that information in real-time and do not have to log in as managers or admins, so technology is needed to distribute the data and information and processed or presented in a real-time reporting information system. In the google form, several tools have been provided such as Google APIs, Share to Sheet, CSV, etc. to share information with other parties. This study performs modeling in the design of web application architecture using real-time data from google forms based on CSV files and has been tested and produced an application model that can be pasted into other applications so that information from the google form can be conveyed without having to log in to the manager or admin of the google form.

Agro Expert Using Google API

Agriculture is the backbone of Indian economy. Mostly crops are loss due to the erroneous selection of the crop, climate change. The farmers are generally not aware of the requirements of the crops. i.e. The minerals, soil moisture & other soil requirements one more problem that a farmer generally encounters is the pest & diseases that can affect the crops they grow which they are generally unaware of in an early stage. This are the problems of farmer is addressed in our paper and we have tried to solve it with the help of Agro Expert using Google API system by the help of our model. We predict the suitable crop to the farmers and also detect the pest and may affect as suggest the pest control technique. In this paper we have applied collaborative filtering algorithm, aprori algorithm, linear regration prediction technique.

Smart Garbage Tracking System

A new age of computing called the Internet of Things (IoT) is used to connect devices through the internet. It is a huge network which gathers information such as how they are used , where they are operated and their surroundings. The process of collecting the waste is not done in the efficient manner some bins would be left out without collecting the waste and sometimes even though the bin overflows with waste it will be collected only at the scheduled days. This may lead to diseases like malaria ,dengue etc. The existing system used RFID, GIS, GSM technology to pass the information of the garbage bin to the garbage collecting truck driver. Rather than sending messages through text ,viewing the information in google map will be more efficient. Using IoT technology, a smart living concept called smart garbage tracking system is implemented. This system was an integration of technologies such as IOT, Android, GPS and Google API. We can overcome social issues such as overflow of dustbins, control the spread of bacterial and viral infections and also empower SWACHH BHARAT mission.

Air Quality Monitoring and Predicting the People to be Affected using LSTM for Hospitals

Air pollution is one of the major concerns in the modern era , around 4.6 million people die in a year due to air pollution , with over 91% of the world population living in unhealthy surroundings which does not meet the standards of the World health organization(WHO). In India around 1.2 million people die early due to air pollution and 9 out of 10 people in India live in unhealthy environments. Though the world is developing at a rapid pace, solutions for such a problem are still unknown, many systems are available to monitor the air pollution around the world but nothing has been done to the healthcare field. This system not only monitors the air pollution but also can predict the number of people going to get affected in a particular area and inform nearby hospitals through which the hospitals can approximately buy medicine for the patients. The Novel Air Quality Monitoring System-Artificial Intelligence( NAQMS-AI) is proposed, which implements artificial intelligence to predict the number of people going to get affected using Long Short Term Memory also known as LSTM algorithm. The air quality of a particular area is calculated based on the location of the device which is computed with the help of Google API , it also helps in finding the nearby hospitals and filter the sensors based on their location. The data which is got from the sensors are stored in cloud which is Google Drive in the form of excel sheet. Thus the proposed system helps the hospitals to keep track of how many people are going to get affected on monthly basis and can approximately estimate the cost for treating all the patients.

Location-Based Mobile Community Using Ants-Based Cluster Algorithm

A location based service (LBS) is widely used on modern smartphone around the world asits built-in features. Each smartphone can access a google API or map. People can therefore share their location (latitude and longitude) among friends. Many LBS spots can easily form “location based mobile community (LBMC).” Since the nodes are mobile, the community group changes dynamically and is unstructured. Ant-based clustering algorithm is a special kind of optimization technique which is highly suitable for finding the adaptive clustering for volatile networks. This Paper Aims To form a location based mobile community (LBMC) by using Ant-based clustering algorithm. Due to the mobile type community, a vanishing community problem is also stated in this paper. Instead of redo a whole algorithm again, we modify an original algorithm by applying a pheromone concept to handle a change. Our algorithm is named as ABCA & VP which stands for Ant-Based Clustering Algorithm with Vanishing problem. More than 5,000 samples from their latitude and longitude coordinates in Thailand. From an experiment, K-means clustering work well in small data size and low number of clusters. In Small size of data between 50 and 1000, our algorithm runs battery when a number of clusters reach 15 clusters. In a big data size (between 1,000 and 5,000 samples), our algorithm outperforms K-means clustering when a number of clusters reach 20 clusters.

The mediating effect of media usage on the relationship between anxiety/fear and physician–patient trust during the COVID-19 pandemic

Objective Our study explored whether and how media usage can mediate the path from anxiety and fear to physician–patient trust. Design Study 1 was a population-based, longitudinal study using nationally representative data from 29 provinces in mainland China. The baseline sample (N = 3233) was obtained from February 1 to 9, 2020. Follow-up (N = 1380) took place during March 17 to 24, 2020. Study 2 was a machine learning-based sentiment analysis in which data were captured from Sina Weibo, a Chinese microblogging website, among the most popular official, unofficial, and health-related media accounts. The screened blogs from November to December 2019 and February to March 2020 were scored by Google APIs for positivity and magnitude. Main Outcome Measures Physician-patient trust. Results Study 1 showed fear and anxiety affected changes in physician–patient trust through media usage, the indirect effect of which was 0.14 (0.03) and the 95% CI was [0.08, 0.19]. Study 2 indicated a more positive image of physicians after the outbreak compared to before [F (2, 3537) = 3.646, p = 0.026, partial η2 =0.002]. Conclusion The negative impact of anxiety and fear on physician–patient trust was mediated by media use, which can be explained by the more positive media image during the pandemic.

Artificial intelligence translation under the influence of multimedia teaching to study English learning mode

With the gradual advancement of education reform, English learning has become more and more important, and efficient and fast English learning has become a concern of people. In this study, an online translation platform based on artificial intelligence was selected for exploration. Use SQLite software database server as text data source. When the user performs English translation, the query word will be input into the translation platform, and at the same time, the artificial intelligence Google API translation technology will translate the text. Based on the collected actual user service usage records, build edge computing solutions and analyze user data input records. Use word2vec to construct the feature vector of the word, and use LSTM to construct the word ranking of the text. The word ranking method is used to predict the user's service usage, select the corresponding edge server, and combine the relevant probability model to preload the service. Combined with the edge algorithm compression data processing technology of the Internet of Things, the data is synchronized and tracked. After the data is compressed, the translated text is displayed on the application interface in the form of voice and words. The research results show that the use of edge computing of the Internet of Things increases the preservation rate of historical query records of intelligent translation by 30%, and the intelligent translation platform predicts that the matching degree of users' translation queries is 90%. The use of edge algorithm compression technology of the Internet of Things can greatly save the network traffic and bandwidth of the server. The optimized query algorithm can improve query efficiency, save query time, and increase users' enthusiasm for learning English.

Advanced Safe Home Systems using Face-Recognition with Unique Passcode Systems

In this paper, we present a model for advanced security measure for safe home security system using face-recognition system and a unique pass-code for each user. Both face as well as unique passcode must be verified for the user to gain access to the building. For face-recognition system, we use HAAR cascade object detection algorithm by using HAAR classifiers. For unique passcode, we use google api for voice input submission and verification.

Aplicación móvil en Android para la adquisición de Gas Licuado de Petróleo (GLP) doméstico

At present, access to Liquefied Petroleum Gas (LPG) for domestic use is carried out in a traditional way, by means of gas transport vehicles that circulate through cities. The problem with this form of distribution is the dependence on schedules and routes of mobile gas distributors, being an ineffective way to commercialize gas for domestic use and bringing consequences like vehicular traffic, noise pollution and environmental pollution. For this reason, a mobile application was developed that allows the optimization of the gas distribution process using software development tools such as Android Studio, Firebase and Google APIs.

FireNot – An IoT based Fire Alerting System: Design and Implementation

Internet of Things (IoT) based systems have revolutionised the way real world systems are inter-connected through internet. At present the application of IoT based systems is extend to real time detection and warning system. However, cost has been a major factor for development and implementation of IoT systems. Considering the cost, ease of implementation, this paper proposes a low cost yet efficient IoT system called FireNot for warning and alerting fire incidents. FireNot is a cloud based system that uses sensors (hardware) to detect fire and alert the user through internet and is maintained and monitored using a simple Android app. The FireNot system uses Raspberry Pi programmed through Python language and utilises Google API for location detection. The FireNot system is also intended to provide an expandable platform for additional daily monitoring tasks and more importunately, resiliency against most cyber-attacks and hi-jacking that targets IoT-based system lacked in most of similar IoT-based designs. This paper practically demonstrates the FireNot system through extensive testing on various operations and the FireNot system is proven to be efficient.

Real-Time metadata-driven routing optimization for electric vehicle energy consumption minimization using deep reinforcement learning and Markov chain model

A real-time, data-driven electric vehicle (EVs) routing optimization to achieve energy consumption minimization is proposed in this work. The proposed framework utilizes the concept of Double Deep Q-learning Network (DDQN) in learning the maximum travel policy of the EV as an agent. The policy model is trained to estimate the agent's optimal action per the obtained reward signals and Q-values, representing the feasible routing options. The agent's energy requirement on the road is assessed following Markov Chain Model (MCM), with Markov's unit step represented as the average energy consumption that takes into consideration the different driving patterns, agent's surrounding environment, road conditions, and applicable restrictions. The framework offers a better exploration strategy, continuous learning ability, and the adoption of individual routing preferences. A real-time simulation in the python environment that considered real-life driving data from Google's API platform is performed. Results obtained for two geographically different drives show that the proposed energy consumption minimization framework reduced the energy utilization of the EVs to reach its intended destination by 5.89% and 11.82%, compared with Google's proposed routes originally. Both drives started at 4.30 PM on April 25th, 2019, in Los Angeles, California, and Miami, Florida, to reach EV's charging stations that are located six miles away from both of the starting locations.

Classification and geovisualization process of soil data using a web-based spatial information system

Abstract Because of the increased demand for information, the scientific community have published their results on the Internet over the last 20 years, adapting to society requirements. Due to the development of web-based spatial information soil systems, access to data on various themes and of varying quality has become substantially easier. The focus of our paper is to demonstrate a freely accessible and usable web-based soil database and soil information system (Soil Information and Soil Classifier System), which is suitable for geovisualization uploaded soil data and for determining reference soil groups (RSGs) in accordance with the World Reference Base of Soil Resources (WRB; RSG). In order to achieve this, we algorithmized the diagnostic soil classification process of the WRB, then we created decision trees to correspond input soil data to the WRB system. In order to facilitate geovisualization of the spatial data, the Keyhole Markup Language file format supported by Google API was applied.

Wireless sensor network for AI-based flood disaster detection

In recent decades, floods have led to massive destruction of human life and material. Time is of the essence for evacuation, which in turn is determined by early warning systems. This study proposes a wireless sensor network decision model for the detection of flood disasters by observing changes in weather conditions compared to historical information at a given location. To this end, we collected data such as air pressure, wind speed, water level, temperature and humidity (DH11), and precipitation (0/1) from sensors located at several points in the area under consideration and obtained sea level air pressure and rainfall from the Google API. The collected data was then transmitted via a LoRaWAN network implemented in Raspberry-Pi and Arduino. The developed support vector machine (SVM) model includes a number of coordinators responsible for a number of sectors (locations). The SVM model sends the binary decisions (flood or no flood) with an accuracy of 98% to a cloud server connected to monitoring rooms, where a decision can be made regarding the response to a possible flood disaster.

VIRTUAL PROMOTION KNOWLEDGE MANAGEMENT TECHNOLOGY

The article presents a new concept of “Management of knowledge about virtual promotion” on the Internet. Usually a real produ ct or service is being divided into four components (product, price, promotion and place) in accordance with the theory of marketing. One of the components is a product promotion. But now this element is becoming a fully virtual tool. It is necessary to consider product promotion as an image or a copy of a real product in a virtual space that lives in parallel on the network. Therefore, the objective of the paper is the presentation of a new object of research based on the experience of more than thirty real projects performed in Ukraine, USA, Europe and Canada. We regard the promotion as a software product, which works according to principles of knowledge management and machine learning. It is proposed that virtual promotion is characterized by four views: customer or user, data, technology and marketing. Thus, the structure of virtual promotion business process was presented. It includes four steps: selection of hypertext sources, knowledge representation and extraction, semantic kernel building and quality criterion evaluation to stop the process. Based on the process structure the research tasks were identified. The central task is semantic kernel forming. Then the software architecture was developed. IT solution contains CRM system as accounting tool and Web site as an image of virtual promotion. CRM plays main role as a commander center. Here we form semantic kernel and then send it via marketing channels such as Web site, telegram or viber accounts. Another part of IT solution is Web service such as Bing API or Google API. They help us to build the kernel. Also the paper demonstrates the list of future tasks that should be solved and the example of real project of proposed approach.

Secure vehicle to vehicle voice chat based MQTT and CoAP internet of things protocol

The congestion of road traffic is one of the most problems facing the ambulance transportation to provide fast healthcare service for patient. In this work, ambulance tracking with messages transfer system has been designed and implemented such that a central monitoring and tracking unit can observe ambulance using MQTT IoT protocol. Where each vehicle is occupied with an intelligent embedded system (Raspberry Pi) unit. When an ambulance is being in the road, it will communicate with other vehicle or road traffic by means of CoAP IoT protocol as a direct device to device communication. The proposed system has been designed such that driver use voice chat and the system are completely hand free. The voice message is being transfer into text by using speech recognition based Google API library, and then the received text message is converted again to speech by using text to speech algorithm. An encryption–decryption process-based stream cipher has been used. The message between IoT nodes has been encrypted using One Time Pad (OTP) and DNA computing. Furthermore, the required key sequence was generated using a linear feedback shift register (LFSR) as a pseudo number key generator. This key sequence was combined to generate a unique key for each message.

Determining the Market Uptake of Demand Responsive Transport Enabled Public Transport Service

Demand responsive transport (DRT) alternatives offer improved mobility to travellers through station-to-destination or door-to-transit operations. In particular, door-to-transit DRT service acts as a feeder to major public transport hubs, making public transport more accessible and attractive to travellers. This work aims to study the mode choice behaviour of travellers between their current modes and a new service, which is a combination of DRT and public transport. The study is conducted in the Northern Beaches area of Sydney, Australia where DRT is expected to serve as a feeder to the newly introduced express bus service called B-Line. A stated preference (SP) experiment is designed where multiple-choice scenarios involving two modes, status quo (SQ) and the new service (combined DRT and public transit), are presented to the participants. The survey uses trip specific information obtained from Google API to form the attributes for the new service. The collected data are analysed using a latent class choice model (LCCM), which segments the observed sample into distinct groups where each group has its own taste and preferences towards the new service option. Results from the study reveal that one of the identified user segments shows 96 percent uptake towards the new service option, while the other user segment shows an uptake of 44 percent. Results also show that individuals making work trips are more likely to opt for the new service. Findings from this study can provide information to urban planners regarding the market uptake of DRT services. Furthermore, the findings can also help planners in implementing segment specific policies aimed at further improving uptake towards DRT along with public transport.

Spatial accessibility measures to educational facilities from public transit: a case of Indian cities

PurposeThe purpose of this paper is to investigate accessibility using spatial assessment techniques, wherein it identifies how educational facilities are connected to public transit (PT) in Indian cities. A new accessibility index is formulated for various ranges of distances which is suitable for Indian cities.Design/methodology/approachThis study developed a new method by comparing Euclidean (near) and network distances, uses Google API and other mobile applications for mapping of PT and educational facilities. The study uses built land cover of the city retrieved from Landsat 8 data with a spatial resolution of 30 m.FindingsThe study attempts to compare the reach to educational facilities from PT in five different cities of India using descriptive statistics, regression analysis, connectivity and detour indices. The results help in identifying local barriers and network issues that influence accessibility from public transit to educational facilities.Originality/valueThe study is conducted entirely using spatial techniques, and the data used are collected from various government organizations. The results are useful for improving overall accessibility and developing a framework which can be included in urban development policies.