Cross-Validation in AI and Machine Learning for Healthcare

Every time we create a machine learning model, we feed it with data to train it. Then we give the model some unlabeled data (test data) to check how well it performs and generalizes to new data. This model is stable if it works well on anonymous data, is consistent, and can forecast with high accuracy on a wide range of input data.

But, this isn't always the case! Machine learning models are not always stable; thus, we must assess their stability. Cross-Validation enters the scene at this point.

How AI-Assisted Medical Diagnosis Increases Assistance and Efficiency

In the medical sector, artificial intelligence (AI) has become synonymous with assistance and efficiency. From a technology that was looked at with mistrust as promises pushed it as a replacement for medical professionals, AI has grown into the second set of eyes that never need to sleep.

Artificial intelligence, AI in medical diagnosis, and healthcare gives dependable support to overworked medical practitioners and institutions, reducing workload pressure and increasing practitioner efficiency.

Effective Computing: How Does It Work Along With Some Use Cases

What did you feel while seeing the price tag of a product in a supermarket? You may not realize it, but machines can read these incredibly nuanced subtilities of human expressions, and retailers can use it to their advantage. A device loaded with a specific software program can predict whether a person is smiling out of frustration or joy. Human-computer interaction has gone beyond the realms of sci-fi fantasies and turned into reality. The rise of emotionally aware machines has blurred the human-machine divide, and it is now redefining the way people experience technology.

What Is Affective Computing? 

Emotion AI, also known as Affective Computing, is all about how AI can decode the emotional status of a human being by analyzing their facial expressions, such as head motion, facial distortions, movement of jaws, and speech pattern, etc. It detects, recognizes, and emulates human emotions through a programmed AI neural network. Without a doubt, humans can analyze and interpret complex emotional signals better. However, the gap is narrowing faster than you can imagine, thanks to advancements in big data capability and powerful algorithms.