Absolutely, technology offers numerous tools and resources to customize and optimize dietary choices based on individual preferences, health goals, and nutritional needs. Leveraging various digital platforms, apps, and services, individuals can access personalized nutrition plans, track dietary intake, receive tailored recommendations, and even connect with professionals for specialized guidance. Here's a comprehensive look at how technology allows for customized diets: Personalized Nutrition Apps and Platforms: 1. Nutrition Assessment and Goal Setting: Several apps and platforms offer personalized nutrition assessments based on factors like age, gender, weight, activity level, health conditions, and dietary preferences. Users can set specific health goals , such as weight loss, muscle gain, or improved nutrition. 2. Tailored Meal Plans and Recipes: Personalized nutrition apps generate custom meal plans and recipes aligned with dietary goals, allergies, restrictions, or s
Artificial aptitude is a field of computer science where a computer system can mimic human intelligence. On the other hand, Machine Learning is a subset of AI which allows a machine to automatically learn from past data to predict the future. ML uses algorithmic models to train from past data and make predictions. For example, an ML model can detect whether a person is wearing a mask if we train it first by showing it pictures of people wearing a mask. With sufficient data, we can train ML to detect a variety of things like gender analysis, weapon detection, facial recognition, amongst many others, which plays an integral part in video surveillance. techwadia
Table of Contents
• How does
it work?
• AI &
ML Applications in Video Surveillance
• Some of
the key functionalities video analytics provides in video surveillance are
briefly explained below:
How does it work?
To understand how machine learning works let us take an
example of an ATM where we want to identify a person who enters the ATM
premises while wearing a helmet.
Data collection: The very first step in Machine learning is
to collect the data which we are going to habit towards train our model. What this
means is we need to find hundreds or even thousands of images of people wearing
helmets. It is important that the data we are collecting is of good quality
because this is going to determine our model’s accuracy down the line. The
greater number of pictures of people in helmets we can collect, the better it
is for our model. Having pictures from different angles and distances are
things that will help our model learn better and identify more accurately.
Data Preparation: Consists of cleaning of data that may be
required. For example, removing duplicate images, converting all images into
the same data type like jpeg, etc. Then we need to Randomize our data, which
essentially removes the effect of order in which data was collected. Finally,
we need to split the data into a training set and evaluation set. Training set
is the set of images we use to train our computer and evaluation set is the set
of images that we use to check the efficiency of our model.
Also Read: Exploring the Benefits of
Integrating PMS in Hotel Technology
Training model: Here our goal is to make the right
predictions as often as possible using our algorithm. For this, the most widely
used software’s are Tensorflow and Keras. Both of these are open-source
software which have simplified the way we train our models, and what used to
take over 1000 lines of hard coding can now be done in 5 lines of code. Here
our goal is to perform as many iterations as possible and in each iteration
improve the accuracy of our systems.
Evaluating the model: Here we measure the performance of the
model by testing it against previously unseen images. This gives U's an idea of
how the model is going to perform in the real world.
If the model’s accuracy is acceptable, it can then be
deployed for commercial use. Even after deployment, the model can learn and
improve itself as more and more data are fed to it.