Machine Learning

Cattle health monitoring is essential in the modern world, because of the high demand for dairy products. Regular monitoring is essential to extend the lifecycle of cattle and maintain the quality of dairy products. Unfortunately, Observing the health of cattle regularly is difficult in large farms where workers do not have enough time to do so. This paper described IoT devices such as skin temperature, heart rate, and motion sensor. Using this device, you can monitor cattle’s heart rate, activity level, heat stress, the surrounding temperature, and sleep tracking.

In this investigation, the researchers have used a commercially available millimeter-wave (MMW) radar to collect data and assess the performance of deep learning algorithms in distinguishing different objects. The research looks at how varied ambiance factors, such as height, distance, and lighting, affect object recognition ability in both static and dynamic stages of the radar.

A new approach addressing the spectrum scarcity challenge in 6G networks by implementing an enhanced licensed shared access (LSA) framework is considered. The proposed mechanism aims to ensure fairness in spectrum allocation to mobile network operators (MNOs) through a novel weighted auction called the fair Vickery-Clarke-Groves (FVCG) mechanism in which the determination of weights is based on the results of the previous auctions.

The recording data include the following anthropometries: age (AG), weight (WE), height (HE), body mass index (BMI), waist circumference (WA), waist/height ratio (WHT), arm circumference (AR), hip circumference (HP), systolic blood pressure (BSY), diastolic blood pressure (DSY), heart rate (HR); the health indicator: glucose (DX); and the following functional fitness parameters: muscle (MM), visceral fat (VF), body fat (BF), and body age (BA). Ageing (AGG) is the ratio AG/BA.