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ARTICLE

Sentiment analysis uses a variety of machine-learning achieving significant cost savings. According to a study
methods, such as decision trees, logistic regression, and conducted by Research and Markets, the emotional AI
support vector machines (SVM). Using labeled datasets-text market is expected to demonstrate a compound annual
or speech samples annotated with the associated emotions- growth rate (CAGR) of 12.13%, forecasting an increase from
these algorithms are trained. These algorithms can US$27.397 billion in 2021 to US$61.054 billion by 2028.
efficiently evaluate and classify newly acquired; unlabeled
data based on the patterns they have learned after being
Video
trained. Games
Diagnostic
Retailers Software
D. Neurons and deep learning
A branch of machine learning called deep learning models
solves complex problems with artificial neural networks. Call center
These neural networks consist of linked layers of neurons, Intelligent Education
routing
or nodes, that are designed to mimic the structure and
operations of the human brain. EMOTIONAL
AI uses
Workplace
Deep learning has proven to be highly effective in the domain Recruiting
of emotional artificial intelligence (AI) by learning and Security
extracting features from unprocessed input (text, audio, or
photos) on its own without the need for human feature Self- Patient
engineering. This ability allows deep learning models to driving car care
grasp intricate patterns and relationships between data and Automobile
Safety
emotions.

As a popular deep-learning framework for facial expression
recognition, convolutional neural networks (CNNs)
demonstrate the capacity to automatically extract and Here are few of the practical uses of
recognize facial characteristics and expressions from visual Emotional AI.
input. Similarly, emotion recognition from audio and text
Video games: By using computer vision, the game console
makes substantial use of recurrent neural networks (RNNs)
and their variants, like Long Short-Term Memory (LSTM) or video game recognizes and adjusts to player emotions
through their facial expressions.
networks. This is attributed to their proficiency in modeling
temporal dependencies and capturing linguistic context.
Diagnostic software: By utilizing facial and speech
recognition, software can assist physicians in diagnosing
Through the integration of these methodologies and the
conditions like depression and dementia.
ongoing refinement of their models, AI researchers and
engineers are crafting progressively advanced emotional AI Education: Prototypes of learning software that can adjust
systems. These systems exhibit an enhanced capability to
to children's emotions have been created. When a job seems
comprehend and react to human emotions with heightened
too easy or too hard for the youngster to complete, the
accuracy and nuance.
program adjusts the difficulty level to suit the child's needs.
Autistic children are assisted in identifying the emotions of
General uses of Emotional AI: others by an additional learning system.
In recent years, Emotional AI vendors have extended their
presence into new sectors and industries, assisting Workplace security: Emotion artificial intelligence (AI) can
organizations in improving customer experiences and be used to assess the stress and anxiety levels of workers

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