PCA and K-means clustering applied to Raman and PL imaging reveal structural defects in silicon wafers, enhancing understanding of optoelectronic performance.

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions or values from labeled historical data, enabling precise signals such as ...

In this video, I explain how computer scientists simulate evolution to train or evolve AI. Explore the fascinating intersection of natural evolution and artificial intelligence. Young voters are ...

Is the title correct? It says: 1 Principal Component explains [63.13%] of the variance. But it seems that this level is reached only after the 2nd Component is added. I may be misinterpreting the plot ...

An artist’s impression of a quantum electrodynamics simulation using 100 qubits of an IBM quantum computer. The spheres and lines denote the qubits and connectivity of the IBM quantum processor; gold ...

LinkedIn support accidentally revealed its algorithm: it tracks "viewer tolerance," reducing visibility for authors whose posts are consistently ignored. To succeed, diversify content types weekly, ...

Cryptography secures communication in banking, messaging, and blockchain. Good algorithms (AES, RSA, ECC, SHA-2/3, ChaCha20) are secure, efficient, and widely trusted. Bad algorithms (DES, MD5, SHA-1, ...

Algorithms, which are just sets of instructions expressed in code, are harder to restrict than physical goods. But governments, including the U.S., have long tried to prevent their export. The ...

PCA total explained variance ratio is ALWAYS equal to ONE for any number of output dimensions. I know PCA is not a good approach to spectral data. I usually go with NMF, evaluated with ...

Abstract: This paper presents a PCA (Principal Component Analysis) data dimensionality reduction algorithm based on OPNs (Ordered Pair of Normalized Real Numbers), referred to as OPNs-PCA. This ...