Graph classification is a problem with practical applications in many different domains. To solve this problem, one usually calculates certain graph statistics (i.e., graph features) that help discriminate between graphs of different classes. When calculating such features, most existing approaches process the entire graph. In a graphlet-based approach, for instance, the entire graph is processed to get the total count of different graphlets or subgraphs. In many real-world applications, however, graphs can be noisy with discriminative patterns confined to certain regions in the graph only.

Graph classification is a problem with practical applications in many different domains. To solve this problem, one usually calculates certain graph statistics (i.e., graph features) that help discriminate between graphs of different classes. When calculating such features, most existing approaches process the entire graph. In a graphlet-based approach, for instance, the entire graph is processed to get the total count of different graphlets or subgraphs. In many real-world applications, however, graphs can be noisy with discriminative patterns confined to certain regions in the graph only.

I've been playing around with LSTM to understand how they work. So, can we use the features as well as ALL labels from the past to predict the label today? For example -- Suppose I have the daily Open, High, Low, Close for an oil company and the oil Price. I collect the data for first 7 days. Shift all the oil prices down (shift all the labels down -- oil price for day 1 goes to day 2).

Most of us are wondering about "what's next for AI in 2019 leading up to 2020?" Some of us might be wondering about "Singularity". Others are still thinking, "AI is all hype and no action" (Although this subset is on the decline). All valid questions to ponder upon. Artificial Intelligence has made substantial progress in 2018.

Children with autism spectrum conditions often have trouble recognizing the emotional states of people around them -- distinguishing a happy face from a fearful face, for instance. To remedy this, some therapists use a kid-friendly robot to demonstrate those emotions and to engage the children in imitating the emotions and responding to them in appropriate ways. This type of therapy works best, however, if the robot can smoothly interpret the child's own behavior -- whether he or she is interested and excited or paying attention -- during the therapy. Researchers at the MIT Media Lab have now developed a type of personalized machine learning that helps robots estimate the engagement and interest of each child during these interactions, using data that are unique to that child. The long-term goal is not to create robots that will replace human therapists, but to augment them with key information that the therapists can use to personalize the therapy content and also make more engaging and naturalistic interactions between the robots and children with autism.

The first programming exercise "Twitter Sentiment Analysis in Python" is both fun and challenging, where you analyze tons of twitter message to find out the sentiments e.g.

They interrupt blood flow around or inside of the brain, depriving it of oxygen, which is why timely treatment is critical. After three or four minutes, brain cells begin to die. Fortunately, artificial intelligence (AI) promises to drive progress on the diagnostic front. In a paper ("An explainable deep-learning algorithm for the detection of acute intracranial hemorrhages from small datasets") published in the journal Nature Biomedical Engineering last month, researchers at Massachusetts General Hospital in Boston describe a deep learning algorithm that can detect acute intracerebral hemorrhages, or ICHs, with a high degree of accuracy. Their work comes about a month after researchers at the University of California, Berkeley demonstrated an AI system that can predict Alzheimer's disease from brain scans up to six years in advance.

The same image shown using different analysis methods. Defects that don't exist are shown in purple, and defects that weren't identified are orange. In mere hours, researchers created a neural network that performed as well as a human expert, demonstrating MENNDL's ability to significantly reduce the time to analyze electron microscopy images. Finding defects in electron microscopy images takes months. It's called MENNDL, the Multinode Evolutionary Neural Networks for Deep Learning.

Ever wondered what happens when you combine Healthcare and Deep Learning? In my previous publication, I described how artificial intelligence is being applied to personal health. In this blog post, I decided to write about on deep learning in public health. Before discussing how deep learning, also known as a neural network, is being applied to healthcare, it is important to understand the variety of algorithms that exist in deep learning. Each neural network slightly differs by it's architecture.

Biometric recognition technology is a method for identification or verification based on human physiological or behavioral characteristics. Biometric features for authentication include iris, fingerprint, palm print, face, ear type, voiceprint and gait. Face recognition is face-based identification technology, which is a main trend of biometric recognition. Compared with other biometric technologies, face recognition technology has unique advantages. It does not require user cooperation and the acquisition process is non-contact. Meanwhile, it has better concealment.