4 research outputs found
Trends of Manufacturing Systems with Distributed Computing
No full textThe industry developed dramatically in the second half of the 20th century, And with it developed and manufacturing systems ranging from manual to fully computerized systems employing information and communication technology (ICT). This fact has made the manufacturing systems to be totally dependent on ICT and therefore these systems have to keep pace with the advancement in ICT. Distributed computing has totally changed the computing paradigm in recent times resulting in rapid employment of these technologies in the manufacturing sector. An important variable in the equation determining the trend of manufacturing technologies is the purchaser choice and preference which has become active recently. To address these heterogeneous user demands, the Autonomous Decentralized System (ADS) concept was introduced five decades ago. The ADS has been a significant development incorporated in modern manufacturing systems and have been standardised as the de-facto standard for factory automation. These systems hold the assure for on-line system maintenance, timeliness and assurance, ensuring greater productivity and cost benefit emerging as the system of choice in automated manufacturing systems. This paper reviews the ADS, its application to a manufacturing system, assesses the state of the art and discusses the future trends.</jats:p
Facial features extraction using active shape model and constrained local model: a comprehensive analysis study
No full textHuman facial feature extraction plays a critical role in various applications, including biorobotics, polygraph testing, and driver fatigue monitoring. However, many existing algorithms rely on end-to-end models that construct complex classifiers directly from face images, leading to poor interpretability. Additionally, these models often fail to capture dynamic information effectively due to insufficient consideration of respondents' personal characteristics. To address these limitations, this paper evaluates two prominent approaches: the constrained local model (CLM), which accurately extracts facial features depending on patch experts, and the active shape model (ASM), designed to simultaneously extract the appearance and shape of an object. We assess the performance of these models on the MORPH dataset using point to point error as evaluation metrics. Our experimental results demonstrate that the CLM achieves higher accuracy, while the ASM exhibits better efficiency. These findings provide valuable insights for selecting the appropriate model based on specific application requirements
A comprehensive analysis of eye diseases and medical data classification
No full textVision loss is a critical health issue that presents substantial challenges to both individuals and communities. For those affected, it can lead to difficulties in performing daily activities, hinder educational and employment opportunities, and significantly impact mental health and overall quality of life. The inability to see can also lead to increased dependence on others, creating emotional and financial strains on families and caregivers. This paper highlights the benefit of machine learning (ML) in exploring conditions that significantly affect vision loss. The goals that will be achieved in this paper are to determine the best classifier capable of dealing with medical datasets and to determine the best strategy for dealing with medical data. Determine which feature selection is most applicable to use for examining medical data. Two medical datasets, 4 strategies, 19 classifiers, and 2 feature selections were used. As for the best classifier, the stochastic gradient descent (SGD) model was the best in dataset 1 and 2. The function strategy showed the best performance, followed by the rules strategy. CorrelationAttributeEval was shown to be the best feature selection, while ClassifierAttributeEval was the second-best feature selection
