Among the aspects of social analytics, motion detection and facial recognition particularly
stand out. Motion detection can be used to assess the public interest in fashion shows where it
observes input from a person like their mouth and eye position to calculate their interest and
focus for a subject. This can open new opportunities for producing tailored video clips.
Quick Challenge
List at least five algorithms that can help in social analytics.
Security
IoT and data analytics can join hands to provide video analytics for security. Video surveillance
is hampered by issues because sometimes the criminal masterminds are able to trick the
authorities through deception techniques. By using analytics, businesses are trying to look
for “anomalies” in the video footages. Whenever any abnormal data is collected by the IoT
devices, they instantly notify the authorities. Moreover, it is possible to increase or decrease
the level of observed patterns in such video analytics by modifying it according to security
management.
Use of Machine Learning in IoT with Real Life
Machine Learning is a subset of AI which makes computer applications more precise with their
predictions. What makes it unique is that it does not require explicit programming to run.
MLborrows elements from predictive analytics and data mining. Similar to them, it goes through
data for the identification of patterns and modifies itself. The example of Machine Learning can
be easily observed in daily internet use. Have you ever noted how you get relevant items for your
online shopping? Similarly, whenever you are on Netflix or another movie streamingwebsite,
you get recommendations according to your history. This filtering and personalization run
through recommendation engines, an application of ML.
ML algorithms fall into the category of supervised and unsupervised learning. The former
category needs an experienced data engineer or analyst to generate the input and the required
output. Data analysts think about the variables or features that require analysis by the model
for the process of calculating predictions. On the other hand, unsupervised learning is done
without the help of a professional. Clustering in machine learning or K-means algorithm is an
example of the unsupervised algorithm. It makes use of deep learning (iterative approach) to
process data and generates conclusions.
Let’s look over some real-life examples of ML.
Smart Home
The ML and IoT revolution will start from your homes! From smartphones, we have gone over
to smart homes. The dierence between a standard home and smart home is simple: the latter
has appliances and devices with internet. Alarm clocks, refrigerators, TVs, and dishwashers
everything would be connected to the internet. Add ML in the mix, and your entire home would
learn from your habits and adapt accordingly. For instance, an alarm clock can program itself to
continuously ring daily to wake you up for oce while it remains disabled on the weekends as
ML-based algorithms learn the concept of holidays.
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Power Plug
Weather
Sensor
Vibration
Sensor
Smart
Light
Door
Sensor
Smoke
Detector
Inlet Switch
Smart
Gateway
Motion
Sensor
Smart Ring
Smart Shirt
with Heart & Respiration
Sensors Incide
Smart
Finger
Smart
Glasses
Smart
Bracelet
Smart
Watch
Bluetooth
Key Tracker
Smart
Belt
Smart
Pants
Smart
Shoes
Smart
Socks
Wearables
Over the last few years, wearables are in-demand. With IT giants like Samsung and Google
investing in wearables, why are they so important? Wearables are devices with IoT-based
equipment like sensors. They gather data about users and process it to provide useful insights.
Theyare used for entertainment, fitness, and health purposes. With machine learning, wear-
ables are saving lives. While monitoring the heart rate and other essential bodily functions,
252 Internet of Things
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