Interface bonding between asphalt layers has been a topic of international investigation over the last thirty years. In this condition, a number of researchers have made their own techniques and used them to examine the characteristics of pavement interfaces. It is obvious that test findings won't always be comparable to the lack of a globally standard methodology for interface bonding. Also, several kinds of research have shown that factors like temperature, loading conditions, materials, and others have an impact on surface qualities. This study aims to solve this problem by thoroughly investigating interface bond testing that might serve as a basis for a uniform strategy. First, a general explanation of how

Significant advances in the automated glaucoma detection techniques have been made through the employment of the Machine Learning (ML) and Deep Learning (DL) methods, an overview of which will be provided in this paper. What sets the current literature review apart is its exclusive focus on the aforementioned techniques for glaucoma detection using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines for filtering the selected papers. To achieve this, an advanced search was conducted in the Scopus database, specifically looking for research papers published in 2023, with the keywords "glaucoma detection", "machine learning", and "deep learning". Among the multiple found papers, the ones focusing

Machine learning (ML) is a key component within the broader field of artificial intelligence (AI) that employs statistical methods to empower computers with the ability to learn and make decisions autonomously, without the need for explicit programming. It is founded on the concept that computers can acquire knowledge from data, identify patterns, and draw conclusions with minimal human intervention. The main categories of ML include supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning. Supervised learning involves training models using labelled datasets and comprises two primary forms: classification and regression. Regression is used for continuous output, while classification is employed

Social Networking has dominated the whole world by providing a platform of information dissemination. Usually people share information without knowing its truthfulness. Nowadays Social Networks are used for gaining influence in many fields like in elections, advertisements etc. It is not surprising that social media has become a weapon for manipulating sentiments by spreading disinformation.  Propaganda is one of the systematic and deliberate attempts used for influencing people for the political, religious gains. In this research paper, efforts were made to classify Propagandist text from Non-Propagandist text using supervised machine learning algorithms. Data was collected from the news sources from July 2018-August 2018. After annota

Background: Sialosis described as a specific consequence of diabetes. In diabetic sialosis, the increased volume of the glands is due to the infiltration of adipose in the parenchyma. The B-scan ultrasonography is a generally accepted tool for determining parotid gland enlargement. Oral health is, to a greater extent, dependent on quality and quantity of saliva, both of which may be altered in diabetics. This study was established to detect the enlargement of parotid gland in diabetic patient and study the changes in physical properties of saliva and its relation with the salivary gland enlargement. Subjects, Materials and Methods: A cross-sectional study with highly specified criteria with ages ranged (20-65) years, male and female subject

Lipid disorders and cardiovascular disease (CVD) risk are known to be increased in patients with diabetes mellitus. The effects of statins on serum lipid levels are well known; however, previous studies did not compare the effects of statins on serum lipid levels in diabetic patients with non-diabetic patients. To investigate the effects of Atorvastatin on serum lipid profiles in hyperlipidemic patients with type 2 diabetes mellitus in comparison with hyperlipidemic patients without diabetes.This study was conducted on 33 type 2 diabetic patients & 34 non-diabetic patients; their age range was 40-80 years, all of them were hyperlipidemic, who had been administered 10, 20, & 40 mg daily of Atorvastatin and completed a 6-month foll