Effat University Institutional Repository
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The Effect of Diversification On Firms Performance Case Of Saudi ArabiaThe study examines The Effect of Diversification on Firm Performance Case of Saudi Arabia. The aim of the study investigate how diversification impacts the financial performance of Saudi Arabia Firms Explore the dependent and independent variables that has direct or indirect relationship with performance and diversification. Moreover, the data will be imported from TASI & Bloomberg for five industry Listed in Tadawul which are Food and beverage, Diversified Financials, Food & Staples, Retailing, Energy, Capital Goods, Consumer Durables & Apparel, Materials, Bank in each industry we select five companies total firms forty. During period of time 2012 -2022. The model that will be used Regression model, to minimize the variance between the observed responses and the predicted one. This paper found diversification has a insignificant relationship on firm performance, the diversified firms have lower performance than non-diversified firms, lastly Diversified firms profitable more than undiversified firms.
Financial Performance Analysis of the Hospitality Industry in Saudi ArabiaThis study examines the performance of the hospitality industry in Saudi Arabia by traditional financial ratios over the past five years by using the financial statements from the Saudi Exchange Tadawul website. The study aims to analyze the financial performance of hospitality companies to explore the development ratio of the hospitality field in Saudi Arabia, included in the Tadawul during the 2018-2022 period. It will examine the financial performance of the hospitality industry by using ROE and ROA affected by the leverage, Liquidity, Size, and GROW. From the results, it was seen that Hospitality companies in Saudi Arabia should focus on improving their net margin and asset turnover to improve their financial performance. Consequently, the hospitality industry's financial performance has been adversely affected by COVID-19.
Applications of Artificial Intelligence in Medical ImagingArtificial intelligence (AI) plays an important role in the field of medical image analysis, including computer-aided diagnosis, image-guided therapy, image registration, image segmentation, image annotation, image fusion, and retrieval of image databases. With advances in medical imaging, new imaging methods and techniques are needed in the field of medical imaging, such as cone-beam/multi-slice CT, MRI, positron emission tomography (PET)/CT, 3D ultrasound imaging, diffuse optical tomography, and electrical impedance tomography, as well as new AI algorithms/applications. To provide adequate results, single-sample evidence given by the patient’s imaging data is often not appropriate. It is usually difficult to derive analytical solutions or simple equations to describe objects such as lesions and anatomy in medical images, due to wide variations and complexity. Tasks in medical image analysis therefore require learning from examples for correct image recognition (IR) and prior knowledge. This book offers advanced or up-to-date medical image analysis methods through the use of algorithms/techniques for AI, machine learning (ML), and IR. A picture or image is worth a thousand words, indicating that, for example, IR may play a critical role in medical imaging and diagnostics. Data/information can be learned through AI, IR, and ML in the form of an image, that is, a collection of pixels, as it is impossible to recruit experts for big data.
A novel automated tower graph based ECG signal classification method with hexadecimal local adaptive binary pattern and deep learningElectrocardiography (ECG) signal recognition is one of the popular research topics for machine learning. In this paper, a novel transformation called tower graph transformation is proposed to classify ECG signals with high accuracy rates. It employs a tower graph, which uses minimum, maximum and average pooling methods altogether to generate novel signals for the feature extraction. In order to extract meaningful features, we presented a novel one-dimensional hexadecimal pattern. To select distinctive and informative features, an iterative ReliefF and Neighborhood Component Analysis (NCA) based feature selection is utilized. By using these methods, a novel ECG signal classification approach is presented. In the preprocessing phase, tower graph-based pooling transformation is applied to each signal. The proposed one-dimensional hexadecimal adaptive pattern extracts 1536 features from each node of the tower graph. The extracted features are fused and 15,360 features are obtained and the most discriminative 142 features are selected by the ReliefF and iterative NCA (RFINCA) feature selection approach. These selected features are used as an input to the artificial neural network and deep neural network and 95.70% and 97.10% classification accuracy was obtained respectively. These results demonstrated the success of the proposed tower graph-based method.