With the availability of data and computational technologies in modern world, machine learning (ML) has emerged as a preferred methodology for analysis prediction. While ML holds great promise, results from such models are not fully unreliable due to challenges introduced by uncertainty. An model generates an optimal solution based on its training data. However, if uncertainty parameters considered, solutions have high risk failure actual world deployment. This paper surveys different...

Farming is a predominantly manual process. The incorporation of any form automation through the means machine learning algorithms still in incipient stage. This paper aims to introduce fundamental approach inaugurate use systems farming A comparative study between had been carried out order determine which algorithm most accurate predicting best crop for particular land. Here, signifies increase terms yield per unit area compared previous years. will ensure proper allocation throughout...

Space weather phenomena like geomagnetic disturbances (GMDs) and geomagnetically induced currents (GICs) pose significant risks to critical technological infrastructure. While traditional predictive models, grounded in simulation, hold theoretical robustness, they grapple with challenges, notably the assimilation of imprecise data extensive computational complexities. In recent years, Tiny Machine Learning (TinyML) has been adopted develop (ML)-enabled magnetometer systems for predicting...

Functional Near-Infrared Spectroscopy (fNIRS) is a non-invasive form of Brain-Computer Interface (BCI). It used for the imaging brain hemodynamics and has gained popularity due to certain pros it poses over other similar technologies. The overall functionalities encompass capture, processing classification signals. Since hemodynamic responses are contaminated by physiological noises, several methods have been implemented in past literature classify focus from unwanted ones. However, methods,...

Microcontroller-based smart devices and sensing systems have exploded in popularity recent years, owing to the growing adoption of Internet Things (IoT) platforms. Due widespread deployment environmental technology digitization, data creation has surged never-before-seen levels. However, it is computationally expensive transport all these cloud for decision making specially battery operated sensor nodes. As a result, edge AI arisen as viable alternative traditional AI, capable simple...

Geomagnetically Induced Currents are one of the most hazardous effects caused by geomagnetic storms. In past literature, variations in ground magnetic fields over time, dB / dt were used as a proxy value for GIC. Machine Learning (ML) techniques have emerged preferred methodology to predict . However, space weather data highly dynamic nature, and distribution is subject change time due environmental variability. The ML models developed prone uncertainty input therefore suffer from high...

Geomagnetically Induced Currents (GICs) are one of the most hazardous effects space weather. The rate change in ground horizontal magnetic component <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$dB_{H}/dt$</tex> is used as a proxy measure for GIC. In order to monitor and predict , ground-based fluxgate magnetometers used. However, base-line correction crucial before such magnetometer data can be utilized. this paper, low-cost Machine Learning...

Magnetometers play a vital role in geophysics and space weather prediction applications by collecting terrestrial magnetic field data. They monitor solar-induced geomagnetic disturbances, providing essential insights into predicting effects on technologies such as satellites, power grids, communication networks. However, this data often contains inherent background noise, necessitating accurate baseline correction methods. Traditional techniques are robust but computationally demanding...

Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are essential in managing type 2 diabetes mellitus, promoting glucose regulation, weight reduction, and cardiovascular protection. Here, we report a unique case of semaglutide-induced pancreatitis complicated by distributive shock death after four years use. A 74-year-old male with diabetes, atrial fibrillation, coronary artery disease, obesity (BMI 31.7) presented severe epigastric pain was diagnosed pancreatitis. He reported no recent...

Introduction: Pulmonary artery pressure monitoring with an implantable device (PAPM) improves heart failure hospitalizations (HFH) in patients symptomatic failure. We sought to explore whether cardiac index (CI) predicted need for initiation of inotrope within 1 year PAPM. Purpose: To examine the association between CI at PAPM implantation and inotropes yr. Methods: retrospectively analyzed 277 consecutive HF who underwent from 1/1/19 through 10/31/22 least yr post-implant follow-up....

Background: Pulmonary artery pressure monitoring with an implantable device (PAPM) improves heart failure hospitalizations (HFH) in patients symptomatic failure. This has been previously validated clinical trials among participants a mean age of 69 years and maximum 78 but not the octogenarian-plus group (≥80). Methods: We retrospectively evaluated 277 (HF) who had PAPM placed between 1/1/19 10/31/22, at least 1 yr follow up compared outcomes aged &lt;80yr (n=218) vs ≥80yr (n=59). Continuous...

Space weather phenomena like geomagnetic disturbances (GMDs) and geomagnetically induced currents (GICs) pose significant risks to critical technological infrastructure. While traditional predictive models, grounded in simulation, hold theoretical robustness, they grapple with challenges, notably the assimilation of imprecise data extensive computational complexities. In recent years, Tiny Machine Learning (TinyML) has been adopted develop (ML)-enabled magnetometer systems for predicting...

Geomagnetically Induced Currents (GICs) are one of the most hazardous effects space weather. The rate change in ground horizontal magnetic component dBH/dt is used as a proxy measure for GIC. In order to monitor and predict dBH/dt, ground-based fluxgate magnetometers used. However, baseline correction crucial before such magnetometer data can be utilized. this paper, low-cost Machine Learning (ML) enabled system has been implemented perform realtime data. predicted geomagnetic components...

Functional Near-Infrared Spectroscopy (fNIRS) is a non-invasive form of Brain-Computer Interface (BCI). It used for the imaging brain hemodynamics and has gained popularity due to certain pros it poses over other similar technologies. The overall functionalities encompass capture, processing classification signals. Since hemodynamic responses are contaminated by physiological noises, several methods have been implemented in past literature classify focus from unwanted ones. However, methods,...