Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a

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        Artificial Neural networks (ANN) are powerful and effective tools in time-series applications. The first aim of this paper is to diagnose better and more efficient ANN models (Back Propagation, Radial Basis Function Neural networks (RBF), and Recurrent neural networks) in solving the linear and nonlinear time-series behavior. The second aim is dealing with finding accurate estimators as the convergence sometimes is stack in the local minima. It is one of the problems that can bias the test of the robustness of the ANN in time series forecasting. To determine the best or the optimal ANN models, forecast Skill (SS) employed to measure the efficiency of the performance of ANN models. The mean square error and

The research aims at clarifying the role of green human resource management practices in achieving sustainable development. The research problem is that the health sector is less concerned with environmental aspects, specifically green human resource management practices, Which are reflected on sustainable development with their economic, environmental and social dimensions as well as reducing costs, waste minimization and recycling, And the research started from two main hypotheses to explore the correlation and influence between the variables of the research by analyzing the answers of the research sample, which included (136) employees of the Al-Imamein Al-kadhemein medical city, Data and information were collected using quest

Copper is a cheaper alternative to various noble metals with a range of potential applications in the field of nanoscience and nanotechnology. However, copper nanoparticles have major limitations, which include rapid oxidation on exposure to air. Therefore, alternative pathways have been developed to synthesize metal nanoparticles in the presence of polymers and surfactants as stabilizers, and to form coatings on the surface of nanoparticles. These surfactants and polymeric ligands are made from petrochemicals which are non- renewable. As fossil resources are limited, finding renewable and biodegradable alternative is promising.The study aimed at preparing, characterizing and evaluating the antibacterial properties of copper nanoparticle

The study aims to measure life management (in its various approaches) among   university academics and to identify the differences among life management strategies (in its various approaches) in regard of gender (male, female), field of education (scientific, humanitarian) and title (assistant lecturer, lecturer, assistant professor, and professor). The study sample consisted of (246) male and female academics from four colleges (Arts, Science, Science education, and Humanitarian Science Education). To achieve the goals of the study, the researchers used the Freund & Baltes (2002) life-management strategies scale after translating it and determining its parameters of reliability and validity. The statistical analysis of the

Abstract:

The study focused on the application of smart tourism as one of the applications of electronic commerce in the form of (B2C) to attract tourists to Saudi Arabia, where the tourism sector is one of the important sectors on which the Kingdom depends on the diversity of its economy.

The purpose of the research: With the issuance of tourist visas for the first time the study noted a deterioration in the case of Saudi tourist sites over the Internet, which do not live up to this great interest by the Saudi government for this sector, which became vital to them. The study tried to identify the reality of the Saudi tourist sites through the Internet in order to identify the sui

Advanced strategies for production forecasting, operational optimization, and decision-making enhancement have been employed through reservoir management and machine learning (ML) techniques. A hybrid model is established to predict future gas output in a gas reservoir through historical production data, including reservoir pressure, cumulative gas production, and cumulative water production for 67 months. The procedure starts with data preprocessing and applies seasonal exponential smoothing (SES) to capture seasonality and trends in production data, while an Artificial Neural Network (ANN) captures complicated spatiotemporal connections. The history replication in the models is quantified for accuracy through metric keys such as m

This paper presents a brief study undertaken for improving the performance of information and communication management of construction projects through investing in information and communication technologies (ICT). The work aims at first to investigate and diagnose the problems, challenges, weaknesses, and inefficiencies related to information and communication management in projects in the construction industry of Iraq. Studying the diagnosed matters and the different solutions of ICT to improve project management performance is following the investigation process. The research presents a technological system suggested to process a lot of the diagnosed problems, challenges, weakness, and inefficiencies of the construction projects and t