Forest fires are highly destructive. They not only damage forest ecosystems, but also threaten the lives and property of surrounding residents, causing incalculable losses. Effective forest ...

In the world of Material Informatics (MI), conventional methods involve tremendous laboratory work or extensive simulations that may not yield the expected results. Our objectives are to contribute to ...

As geospatial datasets grow in scale, complexity, and dimensionality, traditional computing methods are increasingly strained in delivering real-time spatial insights. Quantum computing, a field once ...

Mobile edge computing offloads compute-intensive tasks generated on mobile wireless devices (WD) to edge servers (ES), which provides mobile users with low-latency computing services. Opportunistic ...

BIRCH is an alternative to MinibatchKMeans and is designed for large datasets. The algorithm converts data into a tree structure, facilitating efficient clustering. Clustering is the process of ...

Rodents engage in active touch using their facial whiskers: they explore their environment by making rapid back-and-forth movements. The fast nature of whisker movements, during which whiskers often ...

Abstract: Large-scale data clustering is an essential key for big data problem. However, no current existing approach is “optimal” for big data due to high complexity, which remains it a great ...

As mentioned in our previous blog post Advanced AI topic clustering in Customer Service Insights, we’re publishing a series of “AI inside-out” blog posts to cover the key areas on how AI works in ...

Machine learning is a branch of artificial intelligence that includes methods, or algorithms, for automatically creating models from data. Unlike a system that performs a task by following explicit ...

DBSCAN was one of the many clustering algorithms I had learnt in Exploratory Data Analytics taught by Dr. Edward McFowland III during my Fall Semester at Carlson School of Management. I was quite ...