Comparative Analysis of Forecasting Methods using Neural Networks for Improved Accuracy

Wein Liu^*
*Correspondence: Wein Liu, Department of Clinical Virology, University of Dhaka, Dhaka, Bangladesh, Email:

Description

Nowadays, medical imaging and radiotherapy unquestionably play a significant role in disorder diagnosis and treatment. Systems like standard radiography, mammography, CT scans, accelerators, etc., for these images and treatments, make use of numerous technologies. The X-ray, one of the rays utilized in such treatments, has enabled clinicians to demonstrate the anatomical conditions of patients' bodies since its discovery by Roentgen in 1895. Noninvasive nuclear medicine imaging provides functional information at the molecular and cellular level that aids in determining health status by monitoring the uptake and turnover of target-specific radiotracers in tissue. Protein-protein interactions, cell-cell interactions, neurotransmitter activity, expression of cell receptors in healthy and unhealthy cells, cell-cell trafficking, tissue invasion, and programmed cell death are among these functional activities. By providing information on these processes, nuclear medicine imaging provides a wide range of methods for examining healthy and disease-related states of tissue function and response to therapy.

There are three Lely Astronaut milking machines in the robotic facilities, each of which can milk up to 180 cows per day. Cows are identified and their information, activity, and production data are recorded by wearing a transponder neck collar. Cows that voluntarily approached the facilities for milking were directed to the crush for video recording either before or after milking to avoid bias and stress caused by the milking effect. Data were collected on July 14–15 and August 4–5, 2021, from 9 a.m. to 4 p.m. Using a FLIR DUO PRO, which can simultaneously capture infrared thermal videos (IRTV) and visible red, green, and blue (RGB) videos, each cow was recorded for one minute each day. Following the most recent advances in artificial intelligence utilizing VisV to evaluate fam creature biometrics, this paper proposed progressed demonstrating procedures in view of ML involving biometrics as contributions to target complex information like SCC, creature weight, rumination, and feed consumption and utilizing highlight extraction (utilizing profound learning) from creature faces as contributions to target cow age as an objective utilizing grouping ML displaying techniques. This paper's findings may make it easier to automate RDF for evaluating milk productivity, quality, animal welfare, and the early detection of diseases like mastitis. The robotic dairy facilities at Dookie College served as the setting for the study. All protocols were approved by The University of Melbourne's Animal Ethics Committee.

A comprehensive review of these technologies for farm animals like cattle, pigs, sheep, and dairy cows was recently published by our research group. In particular, fruitful uses of computerized devices to evaluate creature biometrics have been made to survey the early identification of respiratory illnesses in pigs and biometrics for sheep, dairy cows, and steers. With these advancements, direct contact sensors can produce monitoring parameters like heart rate (HR), respiration rate (RR), and skin/eye temperature readings automatically and more effectively without putting animals under additional stress. However, for welfare evaluation or illness detection based on more invasive tools like handling animals and blood work, they still rely on the interpretation of professional veterinarians. Somatic cell count (SCC), animal weight, ruminance, and feed intake are some important well-being parameters to keep an eye on in the dairy cows analyzed in this paper.The SCC is a mastitis-related infection of the udder and a sign of milk quality. In contrast, rumination is the process of regurgitating feed, followed by mastication to break down the particles so they can be swallowed and pass through the reticulo-omasal orifice; however, animal weight is an important indicator of health, welfare, and milk production. This makes it possible to improve the digestion of fiber. Feed intake is the amount of feed the cow consumed from the robotic milker's total supply in this study. This could be influenced by a number of things, like stress; As a result, the robot can measure it and change it. In the past, machine learning (ML) models aimed at indirect milk production and quality traits were developed using artificial intelligence (AI) techniques based on automated computer vision algorithms for animal recognition and feature extraction.

Additionally, radiotherapy employs this radiation. Once this radiation is produced, it must pass through devices known as beam lines to increase its strength and radiation quality before it can reach the sample, which is frequently the human body in medical applications. Building a synchrotron and utilizing its radiation for medicinal reasons in Iran is necessary given the challenges in the medical profession that have been mentioned, such as poor image clarity and excessive doses given to patients during diagnosis and treatment. Furthermore, it is crucial for Iranian scholars to investigate and research the many components of this system given Iran’s involvement in the Sesame project as well as the plans to construct Iran’s national synchrotron accelerator [1-5].

Acknowledgement

We thank the anonymous reviewers for their constructive criticisms of the manuscript. The support from ROMA (Research Optimization and recovery in the Manufacturing industry), of the Research Council of Norway is highly appreciated by the authors.

Conflict of Interest

The Author declares there is no conflict of interest associated with this manuscript.

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