Showing results for Volunteer Vector
StackOverflow
https://stackoverflow.com/questions/65813555/using-for-loops-in-r-to-apply-same-function-to-several-variables-in-a-data-frame
Using For loops in R to apply same function to several variables in a data frame
Tags: r, loops, dplyr, apply
StackOverflow
https://stackoverflow.com/questions/14946665/reading-large-formatted-text-file-with-numpy
Reading large formatted text file with NumPy
Tags: numpy, python-2.7
StackOverflow
https://stackoverflow.com/questions/37264706/names-of-a-dataset-returns-null-r-version-3-2-4-revised-ubuntu-14-04-lts
names of a dataset returns NULL- R version 3.2.4 Revised-Ubuntu 14.04 LTS
Tags: r, dataframe
StackOverflow
https://stackoverflow.com/questions/64118813/local-html-files-with-simple-openlayers-maps-will-not-open-in-ios-but-will-open
Local HTML files with simple OpenLayers maps will not open in IOS but will open on all other platforms
Tags: javascript, html, ios, openlayers
GitHub Repo
https://github.com/delip10/Blood-Donation-Analysis
delip10/Blood-Donation-Analysis
One of the interesting aspects about blood is that it is not a typical commodity. First, there is the perishable nature of blood. Grocery stores face the dilemma of perishable products such as milk, which can be challenging to predict accurately so as to not lose sales due to expiration. Blood has a shelf life of approximately 42 days according to the American Red Cross (Darwiche, Feuilloy et al. 2010). However, what makes this problem more challenging than milk is the stochastic behavior of blood supply to the system as compared to the more deterministic nature of milk supply. Whole blood is often split into platelets, red blood cells, and plasma, each having their own storage requirements and shelf life. For example, platelets must be stored around 22 degrees Celsius, while red blood cells 4 degree Celsius, and plasma at -25 degrees Celsius. Moreover, platelets can often be stored for at most 5 days, red blood cells up to 42 days, and plasma up to a calendar year. Amazingly, only around 5% of the eligible donor population actually donate (Linden, Gregorio et al. 1988, Katsaliaki 2008). This low percentage highlights the risk humans are faced with today as blood and blood products are forecasted to increase year-on-year. This is likely why so many researchers continue to try to understand the social and behavioral drivers for why people donate to begin with. The primary way to satisfy demand is to have regularly occurring donations from healthy volunteers. Aim Of Project: To build a model which can identify who is likely to donate blood again. Models implemented: Logistic Regression, Suport Vector Machine, Random Forest, Decision Tree, MLP Classifier.
StackOverflow
https://stackoverflow.com/questions/56765791/how-to-access-store-data-in-mounted-function-in-vuex
How to access store data in mounted function in vuex
Tags: javascript, vue.js, components, vuex
StackOverflow
https://stackoverflow.com/questions/63511294/measuring-and-splitting-up-coastline
Measuring and splitting up coastline
Tags: openlayers, openstreetmap
GitHub Repo
https://github.com/Ishikashah2510/cleaning-and-getting-data
Ishikashah2510/cleaning-and-getting-data
Data Set Information: The experiments have been carried out with a group of 30 volunteers within an age bracket of 19-48 years. Each person performed six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist. Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. The obtained dataset has been randomly partitioned into two sets, where 70% of the volunteers was selected for generating the training data and 30% the test data. The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used. From each window, a vector of features was obtained by calculating variables from the time and frequency domain. Check the README.txt file for further details about this dataset. A video of the experiment including an example of the 6 recorded activities with one of the participants can be seen in the following link: [Web Link] An updated version of this dataset can be found at [Web Link]. It includes labels of postural transitions between activities and also the full raw inertial signals instead of the ones pre-processed into windows.
GitHub Repo
https://github.com/Machinfy/Human-Activity-Recognition-with-Smartphones
Machinfy/Human-Activity-Recognition-with-Smartphones
The Human Activity Recognition database was built from the recordings of 30 study participants performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors. The objective is to classify activities into one of the six activities performed. Description of experiment The experiments have been carried out with a group of 30 volunteers within an age bracket of 19-48 years. Each person performed six activities (WALKING, WALKINGUPSTAIRS, WALKINGDOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist. Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. The obtained dataset has been randomly partitioned into two sets, where 70% of the volunteers was selected for generating the training data and 30% the test data. The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used. From each window, a vector of features was obtained by calculating variables from the time and frequency domain. Attribute information For each record in the dataset the following is provided: Triaxial acceleration from the accelerometer (total acceleration) and the estimated body acceleration. Triaxial Angular velocity from the gyroscope. A 561-feature vector with time and frequency domain variables. Its activity label. An identifier of the subject who carried out the experiment. Relevant papers Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. Human Activity Recognition on Smartphones using a Multiclass Hardware-Friendly Support Vector Machine. International Workshop of Ambient Assisted Living (IWAAL 2012). Vitoria-Gasteiz, Spain. Dec 2012 Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, Jorge L. Reyes-Ortiz. Energy Efficient Smartphone-Based Activity Recognition using Fixed-Point Arithmetic. Journal of Universal Computer Science. Special Issue in Ambient Assisted Living: Home Care. Volume 19, Issue 9. May 2013 Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. Human Activity Recognition on Smartphones using a Multiclass Hardware-Friendly Support Vector Machine. 4th International Workshop of Ambient Assited Living, IWAAL 2012, Vitoria-Gasteiz, Spain, December 3-5, 2012. Proceedings. Lecture Notes in Computer Science 2012, pp 216-223. Jorge Luis Reyes-Ortiz, Alessandro Ghio, Xavier Parra-Llanas, Davide Anguita, Joan Cabestany, Andreu Català. Human Activity and Motion Disorder Recognition: Towards Smarter Interactive Cognitive Environments. 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013. Bruges, Belgium 24-26 April 2013. Citation Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. A Public Domain Dataset for Human Activity Recognition Using Smartphones. 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013. Bruges, Belgium 24-26 April 2013.
GitHub Repo
https://github.com/EgitiGuruVenkataKrishna/smart-resource-allocator
EgitiGuruVenkataKrishna/smart-resource-allocator
Google Solution Challenge 2026: A smart triage system that digitizes analog field reports with Gemini Vision and uses semantic vector matching to instantly route volunteers to crisis zones.
GitHub Repo
https://github.com/Key0412/Getting-and-Cleaning-Data-Course-Project
Key0412/Getting-and-Cleaning-Data-Course-Project
This project's objective was to "tidy" the original data tables using knowledge acquired during the Getting and Cleaning Data MOOC, by the Johns Hopkins University, available on the Coursera platform (<https://www.coursera.org/learn/data-cleaning/home/welcome>). The data used in this project was generated in a study entitled "Human Activity Recognition on Smartphones using a Multiclass Hardware-Friendly Support Vector Machine" (IWAAL 2012). The study involved the measurement and process of 3-axial linear accelerations and 3-axial angular velocities obtained through the accelerometer and gyroscope of a smartphone. The device was attached to the volunteers' waist, which were asked to perform 6 activities while being recorded - walking, walking upstairs, walking downstairs, sitting, standing and laying. For more information on the study: <http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones>.
GitHub Repo
https://github.com/aparnakulkarni/Support-vector-regression-and-random-forest
aparnakulkarni/Support-vector-regression-and-random-forest
In this project, the Volunteered Geographic Information (VGI) dataset was collected from “Natuurkalender”, a Dutch phenological monitoring network, used to identify the influence of climate change on phenological events. Along with VGI data, Meteorological data was used which offered the information of weather for daily average temperature, sum of daily precipitation and the sum of daily evapotranspiration. The phenological model was implemented for 'Speenkruid' species using python. In this model, SVR and RF methods were used and RMSE was calculated for two methods for comapring the two methods.
GitHub Repo
https://github.com/0xLighted/Wumbness
0xLighted/Wumbness
Progressive Web App (PWA) that connects youth patients with certified volunteer counselors through AI triage, vector-based counselor matching, and real-time chat.
StackOverflow
https://stackoverflow.com/questions/42880474/character-vector-using-tidyr
Character vector using tidyr
Tags: r, tidyr
GitHub Repo
https://github.com/shashi9323/Human-Activity-Recognition-using-LSTM
shashi9323/Human-Activity-Recognition-using-LSTM
The Human Activity Recognition database was built from the recordings of 30 study participants performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors. The objective is to classify activities into one of the six activities performed. Description of experiment The experiments have been carried out with a group of 30 volunteers within an age bracket of 19-48 years. Each person performed six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist. Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. The obtained dataset has been randomly partitioned into two sets, where 70% of the volunteers was selected for generating the training data and 30% the test data. The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used. From each window, a vector of features was obtained by calculating variables from the time and frequency domain.
StackOverflow
https://stackoverflow.com/questions/36915024/performance-issue-in-r-code
Performance issue in R Code
Tags: r, shiny
StackOverflow
https://stackoverflow.com/questions/75340301/converting-a-dataset-from-wide-to-long-using-pivot-longer-but-an-error-is-retur
Converting a dataset from wide to long using pivot_longer, but an error is returned saying x is not a vector
Tags: r, dataframe, vector, null, pivot
GitHub Repo
https://github.com/YuvalShahar64/MedicalWarehouseManager
YuvalShahar64/MedicalWarehouseManager
A C++ project simulating a medical supply distribution system, featuring object-oriented design, efficient resource management using the Rule of Five, and dynamic data structures. The program handles beneficiaries, volunteers, and supply requests in real time, leveraging queues, vectors, and enums for streamlined simulation and operations.
StackOverflow
https://stackoverflow.com/questions/65409340/getting-weather-data-for-multiple-stations-conditional-for-specific-dates-in-r
Getting weather data for multiple stations conditional for specific dates in R
Tags: r, package, weatherdata
GitHub Repo
https://github.com/AnikaBelodedova/Getting-and-Cleaning-Data