Computers have been used for numerous applications involving the automatic or semiautomatic recognition of patterns in image. Advanced manufacturing system requires automated inspection and test method to increase production and yield best quality of product. Methods are available today is machine vision. Machine vision systems are widely used today in the manufacturing industry for inspection and sorting application. The objective of this paper is to apply machine vision technology for measuring geometric dimension of an automotive part. Vision system usually requires reprogramming or parameterization of software when it has to be configured for a part or product. A web camera used to capture an image of an automotive part that has been ch

Shadow removal is crucial for robot and machine vision as the accuracy of object detection is greatly influenced by the uncertainty and ambiguity of the visual scene. In this paper, we introduce a new algorithm for shadow detection and removal based on different shapes, orientations, and spatial extents of Gaussian equations. Here, the contrast information of the visual scene is utilized for shadow detection and removal through five consecutive processing stages. In the first stage, contrast filtering is performed to obtain the contrast information of the image. The second stage involves a normalization process that suppresses noise and generates a balanced intensity at a specific position compared to the neighboring intensit

Cryptocurrency became an important participant on the financial market as it attracts large investments and interests. With this vibrant setting, the proposed cryptocurrency price prediction tool stands as a pivotal element providing direction to both enthusiasts and investors in a market that presents itself grounded on numerous complexities of digital currency. Employing feature selection enchantment and dynamic trio of ARIMA, LSTM, Linear Regression techniques the tool creates a mosaic for users to analyze data using artificial intelligence towards forecasts in real-time crypto universe. While users navigate the algorithmic labyrinth, they are offered a vast and glittering selection of high-quality cryptocurrencies to select. The

Statistical learning theory serves as the foundational bedrock of Machine learning (ML), which in turn represents the backbone of artificial intelligence, ushering in innovative solutions for real-world challenges. Its origins can be linked to the point where statistics and the field of computing meet, evolving into a distinct scientific discipline. Machine learning can be distinguished by its fundamental branches, encompassing supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Within this tapestry, supervised learning takes center stage, divided in two fundamental forms: classification and regression. Regression is tailored for continuous outcomes, while classification specializes in c

Many consumers of electric power have excesses in their electric power consumptions that exceed the permissible limit by the electrical power distribution stations, and then we proposed a validation approach that works intelligently by applying machine learning (ML) technology to teach electrical consumers how to properly consume without wasting energy expended. The validation approach is one of a large combination of intelligent processes related to energy consumption which is called the efficient energy consumption management (EECM) approaches, and it connected with the internet of things (IoT) technology to be linked to Google Firebase Cloud where a utility center used to check whether the consumption of the efficient energy is s

Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att

The study aims at explaining the extent to which the principles of educational management reform contribute to Ibn Ashour in achieving educational management reform and the extent to which the pillars of the Kingdom's Vision 2030 in the field of Education in achieving the educational management reform. The study also aims to provide a future vision of what the educational administrative reform and its results should be in the Kingdom during the next ten years. To achieve the goals of the study, the researcher followed two approaches: on the theoretical side, he relied on applying the content analysis method. As for the applied side, the researcher adopted the Delphi method by two questionnaires to ask (36) participants from the experts a

     Smishing is a cybercriminal attack targeting mobile Short Message Service (SMS) devices that contains a malicious link, phone number, or email. The attacker intends to use this message to steal the victim's sensitive information, such as passwords, bank account details, and credit cards. One method of combating smishing is to raise awareness and educate users about the various tactics used by SMS phishers. But even so, this method has been criticized for becoming inefficient because smishing tactics are continually evolving. A more promising anti-smishing method is to use machine learning. This paper introduces a number of machine learning algorithms that can be used for detecting smishing. Furthermore, the differences and simil

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