Use Of Mobile Phone

## Overview

Use Of Mobile Phone is a dataset for object detection tasks - it contains Mobile Phone Worker annotations for 371 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total *** billion users (+***** percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach *** billion users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like the Americas and Asia.

Dataset

This dataset was created by Fabina Thasni TK

Released under MIT

Contents

The number of smartphone users in the United States was forecast to continuously increase between 2024 and 2029 by in total 17.4 million users (+5.61 percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach 327.54 million users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Mexico and Canada.

Data is cleaned. All inconsistencies and erroneous records have been removed. These two datasets are used to see how the composition of the contact-book of emergent users differ from those of traditional users in aspects like its size, prevalence use of special symbols, the proportion of dialed contacts through the phone-book, and percentage of unintelligible contact names, etc. Aggregated data for 30 emergent users and 30 traditional users is provided in the form of CSV files to replicate the data analysis results. To reproduce the graphs for usability analysis, R scripts are also provided in the same repository. These scripts contain the required data vectors. These graphs show the efficiency, effectiveness, and satisfaction of emergent users on conventional contact-book interfaces.

IntroductionThis study aimed to investigate the possible associations between problematic smartphone use and brain functions in terms of both static and dynamic functional connectivity patterns.Materials and methodsResting-state functional magnetic resonance imaging data were scanned from 53 young healthy adults, all of whom completed the Short Version of the Smartphone Addiction Scale (SAS-SV) to assess their problematic smartphone use severity. Both static and dynamic functional brain network measures were evaluated for each participant. The brain network measures were correlated the SAS-SV scores, and compared between participants with and without a problematic smartphone use after adjusting for sex, age, education, and head motion.ResultsTwo participants were excluded because of excessive head motion, and 56.9% (29/51) of the final analyzed participants were found to have a problematic smartphone use (SAS-SV scores ≥ 31 for males and ≥ 33 for females, as proposed in prior research). At the global network level, the SAS-SV score was found to be significantly positively correlated with the global efficiency and local efficiency of static brain networks, and negatively correlated with the temporal variability using the dynamic brain network model. Large-scale subnetwork analyses indicated that a higher SAS-SV score was significantly associated with higher strengths of static functional connectivity within the frontoparietal and cinguloopercular subnetworks, as well as a lower temporal variability of dynamic functional connectivity patterns within the attention subnetwork. However, no significant differences were found when directly comparing between the groups of participants with and without a problematic smartphone use.ConclusionOur results suggested that problematic smartphone use is associated with differences in both the static and dynamic brain network organizations in young adults. These findings may help to identify at-risk population for smartphone addiction and guide targeted interventions for further research. Nevertheless, it might be necessary to confirm our findings in a larger sample, and to investigate if a more applicable SAS-SV cutoff point is required for defining problematic smartphone use in young Chinese adults nowadays.

This dataset contains detailed specifications and features of 1,020 smartphone models, offering a comprehensive resource for analyzing and comparing smartphones across various brands and price ranges. It is ideal for use in data analysis, exploratory data visualization, and machine learning applications, such as building recommendation systems or price prediction models.

Why Use this Dataset?

1. Market Analysis: Study trends in smartphone features, pricing, and performance.
2. Recommendation Systems: Build models to suggest smartphones based on user preferences.
3. Customer Insights: Analyze ratings to understand customer satisfaction.
4. Price Prediction: Predict the price of smartphones based on specifications.
5. Missing or Partial Data: Some columns, such as ratings, camera, card support, and OS, contain missing or incomplete values, which provides an opportunity for data cleaning and preprocessing.

Handphone Users Survey - Intention to Change to 3G Smartphone since 2012

Percentage of smartphone users by selected smartphone use habits in a typical day.

The dataset used in the study of smartphone adoption and usage, including bibliometric analysis and content analysis of influential papers.

China is leading the ranking by number of smartphone users, recording ****** million users. Following closely behind is India with ****** million users, while Seychelles is trailing the ranking with **** million users, resulting in a difference of ****** million users to the ranking leader, China. Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

This dataset lists the most popular smartphones of 2023 in India gathered from Flipkart, one of the largest e-commerce platforms in the country.

The dataset can be used to identify which smartphones and price ranges are preferred by users, the impact of discounts, and how ratings vary.

Column Description

1. title - the name of the smartphone + color of the model + memory + RAM
2. price - price of the smartphone after discount
3. prod_rating - rating of the smartphone
4. rating_count - the number of people
5. discount - the discount offered in percentage
6. seller_rating - rating of the seller of that particular smartphone as rated by their buyer on the whole seller experience

Project Ideas

1) Extract information from the title like brand name, model, color, memory, and RAM. Use different strategies and see which works the best.

2) Correlation analysis - the price of the smartphone could be influenced by rating, number of ratings, discount, and seller rating.

3) Regression - build a regression model to predict the price of a smartphone, by using variables such as "prod_rating," "rating_count," "discount," and "seller_rating" as independent.

4) Visualizations - Get creative with visualizations, create an interactive dashboard, and create forecast charts.

Check out my other dataset on top-rated TV shows: https://www.kaggle.com/datasets/titassaha/top-rated-tv-shows

I write articles on data analysis and analytics, techniques, and document my learning process on my blog - https://emptyjar.in

Thanks.

Handphone Users Survey - Read E-Books Trend Through Smartphone since 2012

Phone Call Usage

## Overview

Phone Call Usage is a dataset for object detection tasks - it contains Cell Phone annotations for 3,115 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

Handphone Users Survey - Use of Smartphones for Phone Calls since 2012

Dataset

This dataset was created by Consuelo Evans

Released under Apache 2.0

Contents

Dataset

This dataset was created by Daniel Gill 27

Contents

This data was collected using the custom made application "Tymer", which recorded smartphone use data and requested participants to fill in micro-surveys throughout the day, and surveys that participants filled out at either a briefing or debriefing session. The following describes the data collected and used for a study conducted by Beryl Noë, Liam D. Turner, David E. J. Linden, Stuart M. Allen, Gregory R. Maio and Roger M. Whitaker. This project seeks to determine the feasibility of using everyday human interaction with the smartphone to detect mood states for mental health monitoring. Smartphones are an increasingly useful proxy for human behaviour, accompanying their owners for a significant proportion of each day and mediating access to the web, diverse services and communication. Our hypothesis is that from focusing on daily patterns of human smartphone usage we can develop an accurate approach to mental health monitoring that is effective and unobtrusive. Seventy-six participants were recruited through posters and online advertisement at Cardiff University, UK. Participants were selected on two aspects: they needed to own a smartphone running Android 4.4 or higher, and they had to have no history of mental illness. The study was approved by the ethics committee of the School of Psychology, Cardiff University and all participants provided written, informed consent. Thirtynine participants were male, 36 female, and 1 participant chose not to disclose their gender. The participants were all between 19 and 46 years old (M = 24.94, SD = 5.69). Data was collected from participants through 3 different ways: (1) surveys administered in a lab setting at a briefing session and at a debriefing session that were at least 8 weeks apart (2) raw usage data obtained from a custom-made app that participants had installed on their phone for 8 weeks, starting after the briefing session (3) micro-surveys consistent of 1 to 3 questions administered through the notifications sent by the app everyday for 8 weeks

Mobile phone log data used to mine contextual behavioral rules of individual mobile phone users

Smartphone usage characteristics are useful for identification of the risk factors for smartphone addiction. Risk rating scores can be developed based on smartphone usage characteristics. This study aimed to investigate the smartphone addiction risk rating (SARR) score using smartphone usage characteristics. We evaluated 593 smartphone users using online surveys conducted between January 2 and January 31, 2019. We identified 102 smartphone users who were addicted to smartphones and 491 normal users based on the Korean Smartphone Addiction Proneness Scale for Adults. A multivariate logistic regression analysis was used to identify significant risk factors for smartphone addiction. The SARR score was calculated using a nomogram based on the significant risk factors. Weekend average usage time, habitual smartphone behavior, addictive smartphone behavior, social usage, and process usage were the significant risk factors associated with smartphone addiction. Furthermore, we developed the SARR score based on these factors. The SARR score ranged between 0 and 221 points, with the cut-off being 116.5 points. We developed a smartphone addiction management application using the SARR score. The SARR score provided insights for the development of monitoring, prevention, and prompt intervention services for smartphone addiction.