#iOS 18.6

[itek1]

iOS 18.6 Beta 1 e iPadOS 18.6 Beta 1 - Novità, Funzionalità e Come Installarle

iOS 18.6 Beta 1 e iPadOS 18.6 Beta 3: Novità, Funzionalità e Come Installarle. Apple ha rilasciato iOS 18.6 Beta 1 (build 22G5054d) e iPadOS 18.6 Beta 1 il 16 giugno 2025, destinati agli sviluppatori e agli utenti iscritti al programma beta. Questi aggiornamenti si concentrano su miglioramenti delle prestazioni, correzioni di bug e preparazione per le versioni stabili. In questo articolo,…

[zalopro]

Apple released the first beta for iOS 18.6, macOS 15.6 for developers

Just a week after introducing major software versions such as iOS 26, Apple suddenly released the first beta for iOS 18.6, MacOS 15.6 and other platforms, showing that the current software cycle has not ended. Last week, Apple attracted all the attention when launching large software versions will be released in the fall, including iOS 26, iPados 26, … seemed to be all the resources of software…

[buzzleaktv]

iOS 18.6 Beta Coming Soon Alongside iOS 26 Beta

Unlock the Secrets of Ethical Hacking! Ready to dive into the world of offensive security? This course gives you the Black Hat hacker’s perspective, teaching you attack techniques to defend against malicious activity. Learn to hack Android and Windows systems, create undetectable malware and ransomware, and even master spoofing techniques. Start your first hack in just one hour! Enroll now and…

[iphone-page]

Préparez-vous ! iOS 18.6 sortira officiellement fin juillet !

[idared-serwis]

Co nowego w iOS 18.6: data premiery i najważniejsze funkcje

Minęły już trzy tygodnie od momentu wydania przez Apple aktualizacji iOS 18.5, a fani systemu wciąż wyczekują na pojawienie się pierwszej wersji beta iOS 18.6. Informacje pochodzące z wewnętrznych logów wskazują, że testy nowej wersji rozpoczęły się już pod koniec marca, co sugeruje, że premiera iOS 18.6 może nastąpić niebawem. Trudno jednak jednoznacznie określić dokładną datę udostępnienia…

[dealkhuyenmai]

iOS 18.5 Beta Sắp Ra Mắt, Nhưng Apple Đã Bắt Đầu Phát Triển iOS 18.6?!

iOS 18.5 Beta Sắp Ra Mắt, Nhưng Apple Đã Bắt Đầu Phát Triển iOS 18.6?! #iOS18 #Apple #CậpNhậtMới #iOS185 #iOS186 #TinTứcCôngNghệ Với sự ra mắt chính thức của iOS 18.4, chu kỳ phát triển cập nhật đã kết thúc, và Apple dường như đã sẵn sàng cho bước tiếp theo. Bản beta đầu tiên của iOS 18.5 dự kiến sẽ sớm được tung ra cho các nhà phát triển. Tuy nhiên, điều bất ngờ là Apple dường như đang song song…

[ciotechviews]

Apple rebounded into the top five smartphone vendors in China in the third quarter of 2024, capitalizing on the tailwinds of its latest iPhone 16. According to IDC, Apple currently accounted for 15.6% of the Chinese smartphone market, placing it second by market share; that came at the cost of a small year-over-year decline, though, down to 16.1% in the same quarter last year. Apple’s year-over-year shipment growth did not budge during the period.

Huawei has also taken a very close lead over Apple in this competitive arena and stood at the helm with 15.3% market share. In fact, technological giant has witnessed an impressive revamp from what all it has envisaged, and its smart phone shipment ramped up by 42% on a year-over-year basis reflecting tremendous recovery in the world’s largest smartphone market. Recovery for this MNC mainly arisen after the success of Mate 60 smartphone, and advanced chip technology paved way for the rejuvenation of its re-entry in the Chinese market.

With the Mate 60, which it released last year, the competition from Apple and Huawei has hit an all-time high. Although Huawei is regressing, primarily due to U.S. sanctions that have disqualified it from using high-end semiconductors and even access to the latest software, Huawei still does not stop trying, revealing this overly ambitious tri-foldable Mate XT.

This is a tremendous competitive pressure on Apple because the company had slipped out of the top five smartphone rankings in China earlier this year. Analysts point out that Huawei has shown steady growth during each of the last four quarters, and its recent launch of a tri-foldable phone has huge potential for catapulting the foldable smartphone market further.

Given this competitive trend, Apple is relying on its iPhone 16 series to put the shine back in China. Also, it plans to launch a new set of AI functionalities called Apple Intelligence, which will roll into the US market by fall. A release date for China hasn’t been announced because regulatory conditions are complex there.

The country leader in the Q3 in volume share was Vivo with 18.6% and a year-on-year shipment growth of 21.5%. Xiaomi and Honor ranked fourth and fifth, respectively. “In China, the iOS shipments declined by 6 per cent YoY, continuing a decline that started earlier this year, said Canalys, underlining further how the company is facing trouble as the competition firm and optimistic.

[unwrapping]

Behold iOS version 18.6 of Tumblr: Home of Fandom

[iibdae]

ساعة شاومي باند Xiaomi Mi Band 6 اللون الاسود سوارمخصص لتتبع اللياقة البدنية والتعرف على التمارين رياضية تلقائيا، لذلك لديه القدرة على التعرف على العديد من التمارين أبرزها الجري، المشي، جهاز المشي، ركوب الدراجات، التجديف، الوضع الإهليلجي.كما يعمل سوار XiaomiMi Band6 على قيام بوظائف صحية متل تتبع النوم وتقديم نصائح وإرشادات ومراقبة الإجهاد وتتبع صحة النساء خصوصا الدورة الشهرية.سوار مقاوم للماء بعمق 50 مترا لذلك يمكن استخدامه في السباحة بدون مشاكل ويدعم شحن المغناطيسي خلال 2 ساعتين ما يصل الى 14 يوما من الاستعمال، والبطارية بسعة 125 ميلي أمبير.المواصفات· المنتج سوار XiaomiMi Band6· حزام المادة: سيليكون· الطول: 155-219 ملم أبعاد 47.4×18.6��12.7 ملم· عرض شاشة AMOLED مقاس 1.56 بوصة 152×486 بكسل، 326 بكسل في البوصة· سطوع يصل إلى 450 شمعة· زجاج محمي بطلاء مضاد لبصمات الأصابع· مجسمات· مستشعر SP02· مستشعر معدل ضربات القلب PPG· 3 محاور التسارع 3 محاور جيرسكوب· وظائف اللياقة البدنية· واجهة قابلة لتخصيص· دعم 30 وضعية للياقة البدنية· 6 أوضاع للكشف التلقائي: الجري، المشي، جهاز المشي، ركوب الدراجات، التجديف، الوضع الإهليلجي· تمرين التنفس· وظائف الصحة· تتبع النوم (جودة التنفس أثناء النوم، وحركة العين السريعة، والقيلولة)· مراقبة الإجهاد· تتبع صحة النساء· PAI (ذكاء النشاط الشخصي)· تنبيهات الخمول· الحماية مقاوم للماء بعمق 50 متر· الاتصال والتوافق· تطبيق Mi Wear· تطبيق Mi Fit· متوافق مع تطبيق Strava· بلوتوث 5.0· أندرويد 5.0 وما فوق· iOS 10 وما فوق· تحكم في كاميرا الموبايل عن بعد· البطارية تشحن· الشحن المغناطيسي· وقت الشحن: 2 ساعة· 14 يومًا من الاستخدام العادي· بطارية بسعة 125 مللي أمبيرضمان سنتيللطلب من المتجر 👇🏼متجر ابداع وجهتك لعام التقنيةخدمة العملاءمتجر ابداع

[amtechhive]

iOS 18.6 Beta Code Includes Reference to Apple's Rumored Home Hub

Apple is rumored to be working on an all-new smart home hub, and an alleged reference to the device has been discovered in the iOS 18.6 beta. 9to5Mac today reported that iOS 18.6’s code includes a new “apple-logo-1088@2x~home.png” image asset. According to the report, the “~home” suffix indicates that the image asset is intended to be limited to Apple’s rumored homeOS operating system for the…

[buzzleaktv]

iOS 18.6 Apple Intelligence Launch in China Delayed by U.S.-China Trade Tensions

Unlock the Secrets of Ethical Hacking! Ready to dive into the world of offensive security? This course gives you the Black Hat hacker’s perspective, teaching you attack techniques to defend against malicious activity. Learn to hack Android and Windows systems, create undetectable malware and ransomware, and even master spoofing techniques. Start your first hack in just one hour! Enroll now and…

[amitstuff]

Classifiaction and Descision trees

Decision tree analysis was performed to test nonlinear relationships among a series of explanatory variables and a binary, categorical response variable. All possible separations (categorical) or cut points (quantitative) are tested. For the present analyses, the entropy “goodness of split” criterion was used to grow the tree and a cost complexity algorithm was used for pruning the full tree into a final subtree.

The following explanatory variables were included as possible contributors to a classification tree model evaluating smoking experimentation (my response variable), age, gender, (race/ethnicity) Hispanic, White, Black, Native American and Asian. Alcohol use, marijuana use, cocaine use, inhalant use, availability of cigarettes in the home, whether or not either parent was on public assistance, any experience with being expelled from school. alcohol problems, deviance, violence, depression, self-esteem, parental presence, parental activities, family connectedness, school connectedness and grade point average.

The deviance score was the first variable to separate the sample into two subgroups. Adolescents with a deviance score greater than 0.112 (range 0 to 2.8 –M=0.13, SD=0.209) were more likely to have experimented with smoking compared to adolescents not meeting this cutoff (18.6% vs. 11.2%).

Of the adolescents with deviance scores less than or equal to 0.112, a further subdivision was made with the dichotomous variable of alcohol use without supervision. Adolescents who reported having used alcohol without supervision were more likely to have experimented with smoking. Adolescents with a deviance score less than or equal to 0.112 who had never drank alcohol were less likely to have experimented with smoking. The total model classified 63% of the sample correctly, 52% of experimenters (sensitivity) and 65% of nonsmokers (specificity).

Code to run descision tree in python

from pandas import Series, DataFrame import pandas as pd import numpy as np import os import matplotlib.pylab as plt from sklearn.cross_validation import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import classification_report import sklearn.metrics

os.chdir("C:\TREES")

""" Data Engineering and Analysis """ #Load the dataset

AH_data = pd.read_csv("tree_addhealth.csv")

data_clean = AH_data.dropna()

data_clean.dtypes data_clean.describe()

""" Modeling and Prediction """ #Split into training and testing sets

predictors = data_clean[['BIO_SEX','HISPANIC','WHITE','BLACK','NAMERICAN','ASIAN', 'age','ALCEVR1','ALCPROBS1','marever1','cocever1','inhever1','cigavail','DEP1', 'ESTEEM1','VIOL1','PASSIST','DEVIANT1','SCHCONN1','GPA1','EXPEL1','FAMCONCT','PARACTV', 'PARPRES']]

targets = data_clean.TREG1

pred_train, pred_test, tar_train, tar_test  =   train_test_split(predictors, targets, test_size=.4)

pred_train.shape pred_test.shape tar_train.shape tar_test.shape

#Build model on training data classifier=DecisionTreeClassifier() classifier=classifier.fit(pred_train,tar_train)

predictions=classifier.predict(pred_test)

sklearn.metrics.confusion_matrix(tar_test,predictions) sklearn.metrics.accuracy_score(tar_test, predictions)

#Displaying the decision tree from sklearn import tree #from StringIO import StringIO from io import StringIO #from StringIO import StringIO from IPython.display import Image out = StringIO() tree.export_graphviz(classifier, out_file=out) import pydotplus graph=pydotplus.graph_from_dot_data(out.getvalue()) Image(graph.create_png())

[jayateerth]

ML for Data Analysis - Week 1 Assignment

Age, Picture - Decision Tree for Regular Smoking (Run 2)

* Run 1 decision is not included because that was done for all the 24 explanatory variables and the split was not able to explain a specific non linear relationship among variables.

Decision tree analysis was performed to test nonlinear relationships among a series of explanatory variables and a binary, categorical response variable. 

All possible separations (categorical) or cut points (quantitative) are tested. For the present analyses, the entropy “goodness of split” criterion was used to grow the tree and a cost complexity algorithm was used for pruning the full tree into a final subtree.

For Run -1, all the 24 explanatory variables were included as possible contributors to a classification tree model evaluating smoking (response variable), age, gender, (race/ethnicity) Hispanic, White, Black, Native American and Asian. Alcohol use, marijuana use, cocaine use, inhalant use, availability of cigarettes in the home, whether or not either parent was on public assistance, any experience with being expelled from school. alcohol problems, deviance, violence, depression, self-esteem, parental presence, parental activities, family connectedness, school connectedness and grade point average.

The deviance score was the first variable to separate the sample into two subgroups. Adolescents with a deviance score greater than 0.112 (range 0 to 2.8 –M=0.13, SD=0.209) were more likely to have experimented with smoking compared to adolescents not meeting this cutoff (18.6% vs. 11.2%).

Of the adolescents with deviance scores less than or equal to 0.112, a further subdivision was made with the dichotomous variable of alcohol use without supervision. Adolescents who reported having used alcohol without supervision were more likely to have experimented with smoking. Adolescents with a deviance score less than or equal to 0.112 who had never drank alcohol were less likely to have experimented with smoking. 

The total model of run 1 classified 79% of the sample correctly,below is the confusion matrix.

[988, 154], [116, 115]]

For Run -1, following variables were used - Age,  Alcohol use, marijuana use, cocaine use ,  availability of cigarettes in the home,  and grade point average.

The total model of run 2 classified 78% of the sample correctly,below is the confusion matrix.

[[1308,  211],  [ 186,  125]]

The code is included below.

# -*- coding: utf-8 -*- """ Created on Tue Mar 30, 17:15:34 2020 @author: Jayateerth Kulkarni

Python code for Classification using Decision Trees Week 1 Assignment - ML for Data Analysis """ """ OBJECTIVE - Decision tree analysis to test nonlinear relationships among a list of explanatory variables (Categorical & Quantitative) to classify categorical response variable. """

from pandas import Series, DataFrame import pandas as pd import numpy as np import os import matplotlib.pylab as plt

# the previous command where sklearn.cross-validation was used #is no longer applicable here hence changed to model selection

from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import classification_report import sklearn.metrics

os.chdir("D:\ANALYTICS (DS, DAS ETC.)\R_Python\Python")

#Load the dataset

raw_data = pd.read_csv("tree_addhealth.csv") """ the dataframe.dropna() drops missing values for data frame""" data_clean = raw_data.dropna() """ the df.dtypes() shows the data type of the dataset""" data_clean.dtypes """ the df.describe() shows the summary of dataset""" data_clean.describe()

""" RUN-1 - with all variables""" #Split into training and testing sets

features = data_clean[['BIO_SEX','HISPANIC','WHITE','BLACK','NAMERICAN','ASIAN', 'age','ALCEVR1','ALCPROBS1','marever1','cocever1','inhever1','cigavail','DEP1', 'ESTEEM1','VIOL1','PASSIST','DEVIANT1','SCHCONN1','GPA1','EXPEL1','FAMCONCT','PARACTV', 'PARPRES']]

response = data_clean.TREG1

pred_train, pred_test, tar_train, tar_test  =   train_test_split(features, response, test_size=.3)

pred_train.shape pred_test.shape tar_train.shape tar_test.shape

#Build model on training data classifier=DecisionTreeClassifier() classifier=classifier.fit(pred_train,tar_train)

predictions=classifier.predict(pred_test)

sklearn.metrics.confusion_matrix(tar_test,predictions) sklearn.metrics.accuracy_score(tar_test, predictions)

#Displaying the decision tree from sklearn import tree #from StringIO import StringIO from io import StringIO #from StringIO import StringIO from IPython.display import Image out = StringIO() tree.export_graphviz(classifier, out_file=out) """ as pydotplus module not found we need to use the following conda install -c conda-forge pydotplus""" import pydotplus

graph=pydotplus.graph_from_dot_data(out.getvalue()) Image(graph.create_png()) graph.write_pdf("graph.pdf")

""" RUN-2 - with limited variables""" #Split into training and testing sets

features2 = data_clean[['age','ALCEVR1','marever1','cocever1', 'cigavail','GPA1']]

response2 = data_clean.TREG1

pred_train, pred_test, tar_train, tar_test  =   train_test_split(features2, response2, test_size=.4)

pred_train.shape pred_test.shape tar_train.shape tar_test.shape

#Build model on training data classifier=DecisionTreeClassifier() classifier=classifier.fit(pred_train,tar_train)

predictions=classifier.predict(pred_test)

sklearn.metrics.confusion_matrix(tar_test,predictions) sklearn.metrics.accuracy_score(tar_test, predictions)

#Displaying the decision tree from sklearn import tree #from StringIO import StringIO from io import StringIO #from StringIO import StringIO from IPython.display import Image out2 = StringIO() tree.export_graphviz(classifier, out_file=out2) """ as pydotplus module not found we need to use the following conda install -c conda-forge pydotplus""" import pydotplus

graph=pydotplus.graph_from_dot_data(out2.getvalue()) Image(graph.create_png()) graph.write_pdf("graph.pdf")

[spinningblueball]

Astronomers discovered 10 new moons of Jupiter!

Jupiter is often described as a solar system in miniature. The gas giant is made out of the same basic ingredients as the sun — hydrogen and helium — and is surrounded by an array of geologically diverse moons.

Today, the Jupiter system grows even more impressive. The International Astronomical Union has confirmed the discovery of 10 new moons, bringing the grand total to 79. This discovery includes one very odd moon that helps explain why there are so many others.

Where have these moons been all this time? How could 10 moons just go unnoticed, you ask?

The short answer: These moons are relatively tiny, just a couple miles across or smaller. And even with high-powered telescopes, it can be very hard to spot small, dim objects next to something as massive and luminous as Jupiter.

“As our technology gets better and better, we’re able to look fainter and fainter, so we’re discovering smaller and smaller moons,” said Scott S. Sheppard, the Carnegie Institution for Science astronomer who led the discoveries.

These new moons were discovered using a 520-megapixel camera attached to the huge Victor M. Blanco Telescope in Chile. (For reference: The latest iPhone has a 12-megapixel camera.) The camera not only has a dense resolution, it’s also specially calibrated to find faint objects.

Also, Jupiter’s gravity is so strong, it can keep objects in orbit up to 18.6 million miles away. (Recall that our moon is just 239,000 miles away.) This means there’s a lot of space around Jupiter for astronomers to examine that could possibly contain moons.

Sheppard and his team weren’t actually looking for the new moons when they discovered them — they were looking for other far-flung and hard-to-spot objects out past Pluto.

When their telescope scans the sky, it takes in objects at all distances. It can spot stars millions of light-years away. It can spot Kuiper belt objects near the edge of the solar system. And it can see objects closer to home, like the eight planets. All of those objects could be in the same image.

It’s the motion of these objects over a period of time that tells astronomers what and where they are. “It’s like when you’re driving in your car,” Sheppard explains. “When you look out the side of the road, the street signs are flying by really fast as you are driving, and the mountains in the background are moving very slow.” Slower objects are farther away. And if an object is moving at the same speed as Jupiter, it’s likely in the same location.

The team first noticed the moons in 2017, but they needed a year of follow-up observations to chart the shape of their orbits and to confirm they weren’t actually asteroids or comets orbiting the sun.

Here’s what the telescope actually captured. Most of the white dots in the photo are stars. But you can see a moon, marked with orange lines on either side, moving ever so slightly from one frame to the next. This was the big clue it was a moon of Jupiter. It was moving against a background of static stars.

Humans have been discovering moons of Jupiter since Galileo spotted the first four large ones — Callisto, Io, Europa, and Ganymede — in 1610.

It’s not exactly a shock that we’re still discovering them, considering how our telescopes are getting better and better at making out the faintest of objects. Two moons were announced just last year. And Sheppard suspects there are still more to find.

Only one of the moons has a name for now. Introducing Valetudo.

Nine of the moons remain nameless (for now). But one special one has been named.

It’s called Valetudo, named after the Roman goddess of health and hygiene. New Jupiter moons are named after Roman gods related to Jupiter. Valetudo is Jupiter’s great-granddaughter. And adorably, Sheppard chose Valetudo as a nod to his girlfriend, whom he describes as a “very cleanly person.”

Moons close to Jupiter tend to orbit in a “prograde” motion, meaning in the same direction as Jupiter’s rotation. Those farther away rotate in a retrograde motion. But Valetudo is an odd duck. It’s orbiting in a prograde motion in the retrograde region.

“It’s like driving down the highway the wrong way,” Sheppard says. “It’s going around Jupiter in one direction, and there’s, like, 40-something objects going around Jupiter in the other direction.” This means it’s “very likely to have some sort of head-on collision over time,” he says.

And that’s actually an important clue to why there are so many moons around Jupiter. Sheppard explains that a long time ago, there were probably fewer, larger moons orbiting Jupiter in this retrograde region. But over time, they were broken into pieces.

It’s possible that Valetudo — or a larger previous version of it — was the destructive force behind the collision. Imagine the chaos that would ensue if a tractor-trailer was driving against traffic on a highway; that’s Valetudo. And that possibly “gives us this whole swarm of objects we see today,” Sheppard says.

There’s still a lot that’s unknown about these moons, like what they exactly look like or what they’re made of. NASA’s Juno spacecraft, which is currently orbiting Jupiter, is not in a position to image them. It will take a future mission to

Jupiter to get a clearer view of the moons. The only things we know about them are their approximate sizes and the shape of their orbits.

But if we do investigate them further, they might also reveal clues about the origin of our solar system. The outer planets — Jupiter, Saturn, Uranus, Neptune — were formed by vacuuming up smaller objects in their paths. And the objects that weren’t consumed were captured in their orbits.

“These outer moons,” Sheppard says, “are like the last remnants of the planetesimals, the first objects that formed our solar system.”

[severepeanutperfection]

Dashboard Camera Market Share and Growth Factors Impact Analysis 2032

The Global Dashboard Camera market was valued at US$ 2 Bn in 2021 and is expected to reach US$ 13 Bn by 2032 finds Future Market Insights (FMI) in a recent market survey. As per the findings, revenue through the basic Dashboard Camera Market grew at a CAGR of 19.8% during 2017-2021. Basic Dashboard Cameras are cheap and easy to install. Basic dash cams are notable for continuously recording video and looping over older material to make room for current footage. Basic dashboard cameras include removable storage, allowing more control over the video captured.

Dashboard camera market in the U.K. to grow at 16.8% CAGR during 2022 – 2032

By 2032, the dashboard camera market in the U.K. will be worth US$ 522.1 Mn. From 2017 to 2021, revenue from dashboard cameras increased by 18.6% CAGR. Because insurers are recognizing footage as proof, more number of individuals are installing dashboard cameras to prevent road accidents. It is estimated that a quarter of the UK's 32.7 Mn drivers have a dashcam placed in their cars, an 850% rise since 2015. The absolute dollar opportunity growth in the Dashboard Camera market in the United Kingdom will be US$ 411.3 Mn.

“The dashboard camera market is expected to be driven by an increase in road safety awareness, greater use of fleet vehicles, and rapid technological advancements in dashboard cameras.” comments an analyst at Future Market Insights.

Dashboard Camera Market: Competition Insights

Dashboard Camera manufacturers are largely aiming at setting up manufacturing facilities, winning orders, and showcasing technology at exhibitions. The key companies operating in the Dashboard Camera market include ABEO Technology CO., Pittasoft Co. Ltd. , Garmin Ltd, Falcon Zero LLC, Cobra Electronics Co , Safe Cams, THINKWARE, Harman, Amcrest Technologies LLC, HP Development Company LP, DOD TechDJI, Panasonic Corporation, DOD Tech, Waykens Unc., Papago Inc., LG Innotek, Koninklijke Philips N.V, and Honeywell International Inc.

Some of the recent developments by key providers of Dashboard Camera are as follows:

In May 2022, THINKWARE launched Q1000 dashboard cameras. It enables users to view real-time footage in detailed recording mode and even while parking. It boasts of Super Night Vision 3.0 with HDR and is able to record brighter videos in darkness. It is built with the aim to assist fleet management and commercial vehicles to track vehicles and enhance efficiency.

In February 2022, THINKWARE launched F790 Dash Camin, its F-series line meant for commercial and personal use. Its additional features involve de-warping technology, 1GHz Quad-Core CPU for optimized video quality, wifi connectivity for android and iOS devices, and parking mode.

More Insights Available

Future Market Insights, in its new offering, presents an unbiased analysis of the Automotive Backing Plate Market, presenting historical market data (2017-2021) and forecast statistics for the period of 2022-2032.

The study reveals extensive growth in the Automotive Dashboard Camera Market in terms of Technology (Basic, Advanced, Smart), By Product Type (1-Channel, 2-Channel, Rear View), By Video Quality (SD and HD, Full HD and 4K), by Application (Commercial Vehicle, Personal Vehicle), across five regions (North America, Latin America, Europe, Asia Pacific and Middle East & Africa).

Speak to our Research Expert: https://www.futuremarketinsights.com/ask-question/rep-gb-15674

About Future Market Insights (FMI)

Future Market Insights, Inc. is an ESOMAR-certified business consulting & market research firm, a member of the Greater New York Chamber of Commerce and is headquartered in Delaware, USA. A recipient of Clutch Leaders Award 2022 on account of high client score (4.9/5), we have been collaborating with global enterprises in their business transformation journey and helping them deliver on their business ambitions. 80% of the largest Forbes 1000 enterprises are our clients. We serve global clients across all leading & niche market segments across all major industries.

[wemresearch]

Fitness Apps Market Analysis, Key Company Profiles, Types, Applications and Forecast to 2028

The Global Fitness App Market size was evaluated at USD 1.1 Billion in 2021 and will expand with a CAGR of 17.6% from 2022 to 2030, reaching an estimated revenue of $4.8 Billion. The pandemic led to nationwide lockdowns followed by social distancing norms, which hence, aided the transition from traditional studios and gyms to virtual fitness.

One of the major sectors of the IT business, which has had tremendous growth over the past ten years in the United States, is fitness. This can be ascribed to a number of things, such as people spending more time online, rising public awareness of the advantages of wearable technology, and rising middle-class household spending power.

The fitness app market consists of the sales of fitness apps by businesses, sole proprietors, and partnerships. These apps are intended to train users on how to perform exercises, follow dietary plans, engage in physical activity, or learn about fitness-related issues. An application that can be downloaded on any mobile device and used as a platform to encourage healthy behaviour change by providing individualised exercises, fitness guidance, and nutrition programmes is referred to as a fitness app. Wearable technology and fitness applications may be used to synchronise users' health data to other devices for easy access.

Get Free Sample:- https://wemarketresearch.com/sample-request/fitness-apps-market/8/

Global Fitness Apps Market Segmentation

Segmentation on the basis of Type    

Diet and Nutrition

Activity Tracking

Exercise and Weight Loss

 The exercise and weight loss segment was the largest revenue share holder. Exercise and weight loss apps motivate the users to exercise with the help of scheduled notifications. These workout apps are installed with audio cues, video demos, and fitness tracking tools, which can help in maintaining an exercise routine.

 Segmentation on the basis of Platform 

iOS

Android

 The iOS segment accounts for the largest revenue share of over 50.0% . High acceptance of iOS devices has been increasing the segment growth over the years. For instance, active Apple device users increased from 1.4 Billion in the first fiscal quarter.

 The Android segment is also expected to grow at the fastest rate of 18.6% over the predicted period. Android smartphones have become popular in tracking health and fitness activity.

 Segmentation on the basis of Devices 

Smartphones

Tablets

Wearable devices

Smartphones form the largest revenue share of over 65.0%. An increase in the penetration of smartphones globally is a key factor for the growth of the fitness app market. With the changing technology in the fitness industry, majority of people choose to switch to their smartphones instead of going to gyms for their regular routine workout.

 Based on Product 

Care management

Vital management

Health and Wellness apps

Women Health apps

Medication Management Apps

Consultancy Apps

Health and wellness apps are expected to dominate the market as the hectic lifestyle and the emergence of COVID-19 has led people to take care of their health more which will drive the segment growth.

 Based on Application 

Training

Tracking

Fitness Games

Others

The tracking segment is expected to dominate the market because regular reminder boosts motivation levels and with a fitness tracker, one can keep track of its physical activity daily.

Based on Therapeutic Zone

Dermatology and Skin

Cardiovascular

Cancer

Ophthalmology

Diabetes

Respiratory

Audiology

Sleep Disorders

Nutrition

Others

Nutrition is expected to dominate the market due to the rise in awareness regarding eating healthy and the importance of consumption of right nutrition.

Based on Mode of Purchase

Subscription

Non-subscription

Subscription based segment is expected to dominate the market as it provides customized plans, workout routines, diet charts among others. Such advantages are increasing the growth potential of the subscription-based segment.

Based on Distribution Channel 

Third Party

Direct Tender

 The third party distributor segment is expected to dominate the global fitness app market because of overall control over sales.

Based on End-user 

Home Healthcare

Providers

Clinics Healthcare

Others

The providers segment is expected to dominate the market due to an increase in the number of healthcare centers worldwide.

Top Key Players:-

Aaptiv Inc.

Adidas

Appster

AllTrails, LLC

Applico Inc.

ASICS DIGITAL, INC.

AZUMIO

Azumio, Inc.

Fitbit LLC.

FITNESS22 LTD

FitnessKeeper

FITNOTES

Google

Oneplus

Headspace Inc.

Jefit, Inc.

komoot, Freeletics GmbH

Leap Fitness group

Lifesum AB

MyFitnessPal Inc.

Nike, Inc.

Noom Inc.

Strava, Inc

STRONG FITNESS PTE LTD.

Under Armour, Inc.

YAZIO

Interested in purchasing this Report? Click here:- https://wemarketresearch.com/purchase/fitness-apps-market/8/?license=single

Frequently Asked Questions (FAQ):

What is the current market valuation of Global Fitness Apps market?

What are the future prospects of the global fitness apps market?

What was the historic CAGR of Fitness Apps Market?

What are the factors that drives the growth of Fitness Apps Market?

What are some of the Key Players in the Fitness Apps Market?

What is a major driving revenue segment for the Fitness Apps market?

Which country is the driving force behind the growth of Fitness Apps market?

About We Market Research

WE MARKET RESEARCH is an established market analytics and research firm with a domain experience sprawling across different industries. We have been working on multi-county market studies right from our inception. Over the time, from our existence, we have gained laurels for our deep rooted market studies and insightful analysis of different markets.

Our strategic market analysis and capability to comprehend deep cultural, conceptual and social aspects of various tangled markets has helped us make a mark for ourselves in the industry. WE MARKET RESEARCH is a frontrunner in helping numerous companies; both regional and international to successfully achieve their business goals based on our in-depth market analysis. Moreover, we are also capable of devising market strategies that ensure guaranteed customer bases for our clients.

Contact Us:

We Market Research

Phone: +1(929)-450-2887

Email: sales@wemarketresearch.com  

Web: https://wemarketresearch.com/