Using AI and BES/MFC to decrease the prediction time of BOD measurement.

Biochemical oxygen demand (BOD) is one of the most important water/wastewater quality parameters. BOD is the amount of oxygen consumed in 5 days by microorganisms that oxidize biodegradable organic materials in an aerobic biochemical manner. The primary objective of this research is to apply microbial fuel cells (MFCs) to reduce the time requirement of BOD measurements.

Passive Microwave Radiometry and microRNA Detection for Breast Cancer Diagnostics.

Breast cancer prevention is an important health issue for women worldwide. In this study, we compared the conventional breast cancer screening exams of mammography and ultrasound with the novel approaches of passive microwave radiometry (MWR) and microRNA (miRNA) analysis. While mammography screening dynamics could be completed in 3-6 months, MWR provided a prediction in a matter of weeks or even days.

Dynamic Weight Agnostic Neural Networks and Medical Microwave Radiometry (MWR) for Breast Cancer Diagnostics.

Background And Objective: Medical microwave radiometry (MWR) is used to capture the thermal properties of internal tissues and has usages in breast cancer detection. Our goal in this paper is to improve classification performance and investigate automated neural architecture search methods.

Methods: We investigated extending the weight agnostic neural network by optimizing the weights using the bi-population covariance matrix adaptation evolution strategy (BIPOP-CMA-ES) once the topology was found.

Some Methods for Substantiating Diagnostic Decisions Made Using Machine Learning Algorithms.

Various classification algorithms used in the diagnosis of breast cancer based on microwave radiometry data are considered. In particular, their principles of operation and the possibility of substantiating diagnoses using numerical data are discussed. A substantiation algorithm based on decision trees and a naive Bayesian classifier is presented.

Using AI and passive medical radiometry for diagnostics (MWR) of venous diseases.

We studied the possibility of using artificial intelligence (AI) passive microwave radiometry (MWR) for the diagnostics of venous diseases. MWR measures non-invasive microwave emission (internal temperature) from human body 4 cm deep. The method has been used for early diagnostics in cancer, back pain, brain, COVID-19 pneumonia, and other diseases.