This research involves simulating remote sensing conditions using previously collected hyperspectral imagery (HSI) data. The Reed–Xiaoli (RX) anomaly detector is well-known for its unsupervised ability to detect anomalies in hyperspectral images. However, the RX detector assumes uncorrelated and homogeneous data, both of which are not inherent in HSI data. To address this difficulty, we propose...

The coronavirus (COVID19), also known as the novel coronavirus, first appeared in December 2019 Wuhan, China. After that, it quickly spread throughout world and became a disease. It has significantly impacted our everyday lives, national international economies, public health. However, early diagnosis is critical for prompt treatment reducing trauma healthcare system. Clinical radiologists prim...

Near-future electric distribution grids operation will have to rely on demand-side flexibility, both by implementation of demand response strategies and by taking advantage of the intelligent management of increasingly common small-scale energy storage. Home energy management systems (HEMS) will play a crucial role on the flexibility provision to both system operators and market players like ag...

Most of the traditional machine learning algorithms are based on the vector, but in tensor space, Tensor learning is helpful to overcome the over-fitting problem than vector learning. In the meanwhile, these algorithms based on tensor require a smaller set of decision variables as compared to those approaches based on vector. Support tensor machine (STM) is a prevalent machine learning approach...

Machine learning (ML) algorithms are nowadays widely adopted in different contexts to perform autonomous decisions and predictions. Due the high volume of data shared recent years, ML more accurate reliable since training testing phases precise. An important concept analyze when defining concerns adversarial machine attacks. These attacks aim create manipulated datasets mislead algorithm decisi...

Anomaly detection is a challenging problem in machine learning, and made even more so when dealing with instances that are captured low-level, raw data representations without well-known well-behaved set of engineered features. Images or streams from sensors good examples such representations. The Radial Basis Function Data Descriptor (RBFDD) network an effective solution for anomaly detection,...

Subcellular localization is one of the key functional characteristics of proteins. An automatic and efficient prediction method for the protein subcellular localization is highly required owing to the need for large-scale genome analysis. From a machine learning point of view, a dataset of protein localization has several characteristics: the dataset has too many classes (there are more than 10...