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.

Apple is one of the most influential and recognisable brands in the world, responsible for the rise of the smartphone with the iPhone. Valued at over $2 trillion in 2021, it is also the most valuable...

The number of Apple iPhone unit sales dramatically increased between 2007 and 2023. Indeed, in 2007, when the iPhone was first introduced, Apple shipped around *** million smartphones. By 2023, this number reached over *** million units. The newest models and iPhone’s lasting popularity Apple has ventured into its 17th smartphone generation with its Phone ** lineup, which, released in September 2023, includes the **, ** Plus, ** Pro and Pro Max. Powered by the A16 bionic chip and running on iOS **, these models present improved displays, cameras, and functionalities. On the one hand, such features come, however, with hefty price tags, namely, an average of ***** U.S. dollars. On the other hand, they contribute to making Apple among the leading smartphone vendors worldwide, along with Samsung and Xiaomi. In the first quarter of 2024, Samsung shipped over ** million smartphones, while Apple recorded shipments of roughly ** million units. Success of Apple’s other products Apart from the iPhone, which is Apple’s most profitable product, Apple is also the inventor of other heavy-weight players in the consumer electronics market. The Mac computer and the iPad, like the iPhone, are both pioneers in their respective markets and have helped popularize the use of PCs and tablets. The iPad is especially successful, having remained as the largest vendor in the tablet market ever since its debut. The hottest new Apple gadget is undoubtedly the Apple Watch, which is a line of smartwatches that has fitness tracking capabilities and can be integrated via iOS with other Apple products and services. The Apple Watch has also been staying ahead of other smart watch vendors since its initial release and secures around ** percent of the market share as of the latest quarter.

The release of the first iPhone was On June 29, 2007 so this dataset provide the historical data of APPl stock

This dataset contains historical data for appl stock. The dataset provides information for each trading day, including the date, price, open, high, low, volume, and percentage change.

Here is a description of the columns in the dataset :

1) Date: (datetime64) This column represents the date of the trading day. It indicates when the data was recorded.

2) Price: (float64) The price column represents the index's closing price on each trading day. It shows the value at which the index concluded its trading session.

3) Open: (float64) This column denotes the opening price of the index on each trading day. It represents the value at which the index began trading at the start of the session.

4) High: (float64) The high column indicates the highest price reached by the index during the trading day. It represents the peak value recorded for the index's price.

5) Low: (float64) The low column represents the lowest price reached by the index during the trading day. It indicates the minimum value recorded for the index's price.

6) Vol.: (object) The volume column denotes the trading volume, usually measured in millions, for each trading day. It represents the total number of shares or contracts traded during the session.

7) Change %: (float64) This column provides the percentage change in the index's price from the previous trading day. It indicates the daily price movement of the index.

The dataset contains 4034 rows, including the header row that describes the columns. The actual data starts from the second row and provides information for each trading day in descending order, with the most recent date appearing first.

Context

This dataset was recorded as part of an investigation into machine learning algorithms for iOS. 20,136 glyphs were drawn by 257 subjects on the touch screen of an iPhone 6.

An iOS app was developed to record the dataset. Firstly, subjects entered their age, sex, nationality and handedness. Each subject was then instructed to draw the digits 0 to 9 on the touchscreen using their index finger and thumb. This was repeated four times for each subject resulting in 80 glyphs drawn per subject, 40 using index finger and 40 using thumb. The sequence of glyph entry was random. Instructions to the user were provided using voice synthesis to avoid suggesting a specific glyph rendering.

The index finger and thumb were both used to account for situations in which the subject may only have one hand free. The aim here was to train a model that could accurately classify the glyph drawn in as many real life scenarios as possible.

Cubic interpolation of touches during gesture input was rendered on the screen to provide visual feedback to the subject and to compute arclengths. The screen was initially blank (white) and the gestures were displayed in black. The subject could use most of screen to draw with small areas at the top and bottom reserved for instructions/interactions/guidance. The subject was permitted to erase and repeat the entry, if desired.

Content

https://raw.githubusercontent.com/PhilipCorr/numeral-gesture-dataset/master/database.png" alt="Database Schema">

The database consists of 4 tables as seen in the schema. The tables are Subject, Glyph, Stroke and Touch. This is a logical structure as each subject draws 80 glyphs, each glyph consists of a number of strokes and each stroke consists of a number of touches. The four tables are presented in csv format and sqlite format.

Note that, in the files below, all columns start with a capital Z. This is automatically prepended to column names by Core Data, apples database framework. Column names which start with Z_ were automatically created by Core Data and hence, do not appear in the schema above.

The tables are connected through the first column in each table (Z_PK). This primary key links to the relevant column name in the next table. For example, the subject that entered any given glyph can be found by taking the value from the ZSUBJECT column in the glyph table and finding the matching Z_PK value in the subject table.

Some questions to get you started...

• What is the best model for classifying glyphs?
• What is the best model for classifying sequences of these glyphs?
• What is the best model to predict what number a glyph is before completion?
• How much of the glyph needs to be completed before a prediction can be made?
• What is the best method for interpolating between the touches in the dataset?
• How can a trained model be integrated into iOS apps?

CITATION REQUEST

Please cite the following paper in any publications reporting on use of this dataset:

Philip J. Corr, Guenole C. Silvestre, Chris J. Bleakley Open Source Dataset and Deep Learning Models for Online Digit Gesture Recognition on Touchscreens Irish Machine Vision and Image Processing Conference (IMVIP) 2017 Maynooth, Ireland, 30 August-1 September 2017 http://arxiv.org/abs/1709.06871

The global smartphone penetration in was forecast to continuously increase between 2024 and 2029 by in total 20.3 percentage points. After the fifteenth consecutive increasing year, the penetration is estimated to reach 74.98 percent and therefore a new peak in 2029. Notably, the smartphone penetration of was continuously increasing over the past years.The penetration rate refers to the share of the total population.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 smartphone penetration in countries like North America and the Americas.

A longitudinal retrospective study was conducted to assess the impact of BMI on impulse oscillometry (IOS) estimates of airway resistance and reactance in children with sickle cell disease (C-SCD). The study encompassed the period from 2015 to 2020. Additionally, African-American children with asthma (C-Asthma) who underwent IOS testing during the same timeframe were included in the study to evaluate the influence of BMI on IOS estimates in this group. The association between BMI and IOS measures was estimated using a generalized linear mixed model (GLMM), which accounted for potential confounding factors. These factors included the diagnosis of asthma and the use of hydroxyurea in C-SCD, as well as gender and concurrent use of ICS +/-LABA for both study cohorts. Furthermore, a comparison was conducted between C-SCD and C-Asthma groups regarding age, BMI, and IOS estimates.

These columns provide measurements and estimates related to airway resistan...

The number of smartphone users in the Philippines was forecast to increase between 2024 and 2029 by in total 5.6 million users (+7.29 percent). This overall increase does not happen continuously, notably not in 2026, 2027, 2028 and 2029. The smartphone user base is estimated to amount to 82.33 million users 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).