scholarly journals Disease Prediction and Drug Recommendation Android Application using Data Mining (Virtual Doctor)

2019 ◽  
Vol 8 (3) ◽  
pp. 6996-7001
Keyword(s):  
Data Mining ◽  
Research Work ◽  
Application Programming Interface ◽  
Machine Learning Algorithms ◽  
Useful Knowledge ◽  
Medical Databases ◽  
Application Programming ◽  
Mining Procedure ◽  
Using Data ◽  
Programming Interface

Data Mining is a method that requires analyzing and exploring large blocks of data to glean meaningful trends and patterns. In today’s period, every person on earth relies on allopathic treatments and medicines. Data mining techniques can be applied to medical databases that have a vast scope of opportunity for textual as well as visual data. In medical services, there are myriad obscure data that needs to be scrutinized and data mining is the key to gain useful knowledge from these data. This paper provides an application programming interface to recommend drugs to users suffering from a particular disease which would also be diagnosed by the framework through analyzing the user's symptoms by the means of machine learning algorithms. We utilize some insightful information here related to mining procedure to figure out most precise sickness that can be related with symptoms. The patient can without much of a stretch recognize the diseases. The patients can undoubtedly recognize the disease by simply ascribing their issues and the application interface produces what malady the user might be tainted with. The framework will demonstrate complaisant in critical situations where the patient can't achieve a doctor's facility or when there are situations, when professional are accessible in the territory. Predictive analysis would be performed on the disease that would result in recommending drugs to the user by taking into account various features in the database. The experimental results can also be used in further research work and for Healthcare tools.

scholarly journals COVID-19: A Relook at Healthcare Systems and Aged Populations

2020 ◽  
Vol 12 (10) ◽  
pp. 4200 ◽  
Author(s):  
Thanh-Long Giang ◽  
Dinh-Tri Vo ◽  
Quan-Hoang Vuong
Keyword(s):  
Application Programming Interface ◽  
Healthcare Systems ◽  
Finite Sample ◽  
Death Rates ◽  
Development Indicators ◽  
The Public ◽  
Macroeconomic Indicators ◽  
Application Programming ◽  
Using Data ◽  
Programming Interface

Using data from the WHO’s Situation Report on the COVID-19 pandemic from 21 January 2020 to 30 March 2020 along with other health, demographic, and macroeconomic indicators from the WHO’s Application Programming Interface and the World Bank’s Development Indicators, this paper explores the death rates of infected persons and their possible associated factors. Through the panel analysis, we found consistent results that healthcare system conditions, particularly the number of hospital beds and medical staff, have played extremely important roles in reducing death rates of COVID-19 infected persons. In addition, both the mortality rates due to different non-communicable diseases (NCDs) and rate of people aged 65 and over were significantly related to the death rates. We also found that controlling international and domestic travelling by air along with increasingly popular anti-COVID-19 actions (i.e., quarantine and social distancing) would help reduce the death rates in all countries. We conducted tests for robustness and found that the Driscoll and Kraay (1998) method was the most suitable estimator with a finite sample, which helped confirm the robustness of our estimations. Based on the findings, we suggest that preparedness of healthcare systems for aged populations need more attentions from the public and politicians, regardless of income level, when facing COVID-19-like pandemics.

scholarly journals Utilising Building Component Data from BIM for Formwork Planning

2017 ◽  
Vol 17 (4) ◽  
pp. 20-36 ◽  
Author(s):  
Manav Mahan Singh ◽  
Anil Sawhney ◽  
Vaishnavi Sharma
Keyword(s):  
Research Work ◽  
Application Programming Interface ◽  
Resource Planning ◽  
Concrete Wall ◽  
Building Information Modelling ◽  
3 Dimensional ◽  
Building Information ◽  
Application Programming ◽  
Building Component ◽  
Programming Interface

Advancements in the computing realm have assisted the Architecture, Engineering, and Construction (AEC) industry to progress significantly by automating several design tasks and activities. Building Information Modelling (BIM) authoring tools have played a significant role in automating design tasks and reducing the efforts required by the designer in redundant, repetitive or production-oriented activities. This paper explores one such approach that, with the help of BIM authoring tool and its Application Programming Interface (API), reduces the efforts expended on formwork design for concrete structures. The paper utilises the concept of using BIM data as input to compute the quantity of formwork, and generate visualisations and schedule of formwork. The developed approach first takes data input from semantic BIM to the API environment for computation and design of formwork systems, which is then placed within the BIM model, to generate visualisation and prepare schedules. The research work utilises a structural concrete wall as an example to demonstrate the presented approach. The approach will be influential in streamlining the formwork design process in the BIM environment and reducing efforts required by the designer and the planning engineer. Since the formwork elements are generated as 3-Dimensional (3D) solids and smart BIM elements, the generated model of formwork can be used for resolving clashes, scheduling, and resource planning.

scholarly journals Mining and YouTube Data Analysis using Hadoop

2020 ◽  
Vol 9 (3) ◽  
pp. 1461-1465
Keyword(s):  
Real Time ◽  
Application Programming Interface ◽  
Data Set ◽  
Apache Hadoop ◽  
Informed Decisions ◽  
Hadoop Distributed File System ◽  
Application Programming ◽  
Using Data ◽  
Programming Interface ◽  
Social Media Tool

Analysis of structured and consistent data has seen remarkable success in past decades. Whereas, the analysis of unstructured data in the form of multimedia format remains a challenging task. YouTube is one of the most popular and used social media tool. It reveals the community feedback through comments for published videos, number of likes, dislikes, number of subscribers for a particular channel. The main objective of this work is to demonstrate by using Hadoop concepts, how data generated from YouTube can be mined and utilized to make targeted, real time and informed decisions. In our paper, we analyze the data to identify the top categories in which the most number of videos are uploaded. This YouTube data is publicly available and the YouTube data set is described below under the heading Data Set Description. The dataset will be fetched from the Google using the YouTube API (Application Programming Interface) and going to be stored in Hadoop Distributed File System (HDFS). Using MapReduce we are going to analyze the dataset to identify the video categories in which most number of videos are uploaded. The objective of this paper is to demonstrate Apache Hadoop framework concepts and how to make targeted, real-time and informed decisions using data gathered from YouTube.

scholarly journals Cross-Method-Based Analysis and Classification of Malicious Behavior by API Calls Extraction

2019 ◽  
Vol 9 (2) ◽  
pp. 239 ◽  
Author(s):  
Bruce Ndibanje ◽  
Ki Kim ◽  
Young Kang ◽  
Hyun Kim ◽  
Tae Kim ◽  
...  
Keyword(s):  
Application Programming Interface ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Public Security ◽  
Detection Accuracy ◽  
Malicious Behavior ◽  
Application Programming ◽  
Programming Interface ◽  
Method Show

Data-driven public security networking and computer systems are always under threat from malicious codes known as malware; therefore, a large amount of research and development is taking place to find effective countermeasures. These countermeasures are mainly based on dynamic and statistical analysis. Because of the obfuscation techniques used by the malware authors, security researchers and the anti-virus industry are facing a colossal issue regarding the extraction of hidden payloads within packed executable extraction. Based on this understanding, we first propose a method to de-obfuscate and unpack the malware samples. Additional, cross-method-based big data analysis to dynamically and statistically extract features from malware has been proposed. The Application Programming Interface (API) call sequences that reflect the malware behavior of its code have been used to detect behavior such as network traffic, modifying a file, writing to stderr or stdout, modifying a registry value, creating a process. Furthermore, we include a similarity analysis and machine learning algorithms to profile and classify malware behaviors. The experimental results of the proposed method show that malware detection accuracy is very useful to discover potential threats and can help the decision-maker to deploy appropriate countermeasures.

scholarly journals Automation of Data Consumption by Pluggable Module Software

2021 ◽  
Vol 23 (06) ◽  
pp. 1672-1681
Author(s):  
Vinay Balamurali ◽  
◽  
Prof. Venkatesh S ◽  
Keyword(s):  
Software Design ◽  
Data Science ◽  
Source Code ◽  
Application Programming Interface ◽  
Related Information ◽  
Server Systems ◽  
Data Consumption ◽  
Application Programming ◽  
Using Data ◽  
Programming Interface

Servers are required to monitor the health of the various I/O cards connected to it to alert the required personnel to service these cards. The Data Collection Unit (DCU) is responsible for detecting the I/O cards, sending their inventory as well as monitoring their health. Currently, the keys required to detect these I/O cards are manually coded into the source code. Such a task is highly laborious and time-consuming. To eliminate this manual work, a Software Pluggable Module was devised which would read the I/O card-related information from the I/O component list. This software design aims at using Data Science and OOPS concepts to automate certain tasks on server systems. The proposed methodology is implemented on a Linux system. The software design is modular in nature and extensible to accommodate future requirements. Such an automation framework can be used to track information maintained in Excel Spreadsheets and access them using an Application Programming Interface (API).

scholarly journals Application Programming Interface for a Customer Experience Analysis Tool

2021 ◽  
Author(s):  
George Kopsiaftis ◽  
Ioannis Georgoulas ◽  
Ioannis Rallis ◽  
Ioannis Markoulidakis ◽  
Kostis Tzanettis ◽  
...  
Keyword(s):  
Graphical Representation ◽  
Application Programming Interface ◽  
Customer Experience ◽  
Machine Learning Algorithms ◽  
Analysis Tool ◽  
Net Promoter Score ◽  
Application Programming ◽  
Net Promoter ◽  
User Friendly ◽  
Programming Interface

This paper analyzes the architecture of an application programming interface (API) developed for a novel customer experience tool. The CX tool aims to monitor the customer satisfaction, based on several experience attributes and metrics, such as the Net Promoter Score. The API aims to create an efficient and user-friendly environment, which allow users to utilize all the available features of the customer experience system, including the exploitation of state-of-the-art machine learning algorithms, the analysis of the data and the graphical representation of the results.

Sistem Pengawasan Aktifitas Penggunaan Smartphone Android

2018 ◽  
Vol 9 (1) ◽  
pp. 24-31
Author(s):  
Rudianto Rudianto ◽  
Eko Budi Setiawan
Keyword(s):  
Application Programming Interface ◽  
Third Party ◽  
Image Content ◽  
Image File ◽  
Application Programming ◽  
Index Terms ◽  
Programming Interface

Availability the Application Programming Interface (API) for third-party applications on Android devices provides an opportunity to monitor Android devices with each other. This is used to create an application that can facilitate parents in child supervision through Android devices owned. In this study, some features added to the classification of image content on Android devices related to negative content. In this case, researchers using Clarifai API. The result of this research is to produce a system which has feature, give a report of image file contained in target smartphone and can do deletion on the image file, receive browser history report and can directly visit in the application, receive a report of child location and can be directly contacted via this application. This application works well on the Android Lollipop (API Level 22). Index Terms— Application Programming Interface(API), Monitoring, Negative Content, Children, Parent.

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