Currently, nearly two million patients die of gastrointestinal diseases worldwide. Video endoscopy is one of the latest technologies in the medical imaging field for the diagnosis of gastrointestinal diseases, such as stomach ulcers, bleeding, and polyps. Medical video endoscopy generates many images, so doctors need considerable time to follow up all the images. This creates a challenge for manual diagnosis and has encouraged investigations into computer-aided techniques to diagnose all the generated images in a short period and with high accuracy. The novelty of the proposed methodology lies in developing a system for diagnosis of gastrointestinal diseases. This paper introduces three networks, GoogleNet, ResNet-50, and AlexNet, which are based on deep learning and evaluates them for their potential in diagnosing a dataset of lower gastrointestinal diseases. All images are enhanced, and the noise is removed before they are inputted into the deep learning networks. The Kvasir dataset contains 5,000 images divided equally into five types of lower gastrointestinal diseases (dyed-lifted polyps, normal cecum, normal pylorus, polyps, and ulcerative colitis). In the classification stage, pretrained convolutional neural network (CNN) models are tuned by transferring learning to perform new tasks. The softmax activation function receives the deep feature vector and classifies the input images into five classes. All CNN models achieved superior results. AlexNet achieved an accuracy of 97%, sensitivity of 96.8%, specificity of 99.20%, and AUC of 99.98%.
The housing market is a crucial economic indicator to which the government must pay special attention because of its impact on the lives of freshly minted city inhabitants. As a guide for government regulation, individual property purchases, third-party evaluation, and understanding how housing prices are distributed geographically may be of great practical use. Therefore, much research has been conducted on how to arrive at a more accurate and efficient way of calculating housing prices in the current market. The goal of this study was to use the artificial neural network (ANN) technique to correctly identify real estate prices. The novelty of the proposed research is to build a prediction model based on ANN for predicting future house prices in Saudi Arabia. The dataset was collected from Aqar in four main Saudi Arabian cities: Riyadh, Jeddah, Dammam, and Al-Khobar. The results showed that the experimental and predicted values were very close. The results of the proposed system were compared with different existing prediction systems, and the developed model achieved high performance. This forecasting system can also help increase investment in the real estate sector. The ANN model could appropriately estimate the housing prices currently available on the market, according to the findings of the assessments of the model. Thus, this study provides a suitable decision support or adaptive suggestion approach for estimating the ideal sales prices of residential properties. This solution is urgently required by both investors and the general population as a whole.
This study examined the influence of lecturers’ creativity on the entrepreneurial intention of technical college students in Saudi Arabia, applying the theory of planned behaviour (TPB). The study employed a questionnaire, which was completed by 99 technical college students in the study area. Partial Least Squares Structural Equation Modelling (PLS-SEM) was used to perform the data analysis and to test the hypotheses. The study’s findings revealed that attitude towards behaviour and perceived behavioural control predicted entrepreneurial intention. They also partially mediated the relationship between lecturers’ creativity and entrepreneurial intention. Finally, entrepreneurial intention was not influenced by subjective norms. This study contributes to the literature by highlighting the significance of lecturers’ creativity in developing entrepreneurial intention by applying the TPB among technical college students. The results could assist stakeholders, such as technical colleges, universities, policymakers and other parties, in establishing a more creative entrepreneurial environment. The study findings further emphasize the need to support creative lecturers capable of improving students’ essential entrepreneurial skills, knowledge, values and competencies. The study was limited to only one technical college in Saudi Arabia with a limited sample size, making it challenging to generalize the results. Future research could investigate individuals’ exact skills and knowledge, supporting and attracting them to enrol in technical college and pursue entrepreneurial business activities.
Financial literacy has gained much attention amongst scholars, policymakers and other stakeholders due to its role in backing up investment decisions, improving personal financial management and increasing financial wellbeing. This study examines the influence of financial literacy on investment decisions with the moderating effect of the overconfidence behavioural bias. Data were collected from 180 respondents in Saudi Arabia using a questionnaire, and a convenience sampling technique was applied. The study’s findings were analysed using the partial least squares structural equation modelling (PLS-SEM) technique. It was found that financial literacy positively and significantly influenced investment decisions. Moreover, the results show that overconfidence positively affected investment decisions and that the relationship between financial literacy and investment decisions was positively and significantly moderated by overconfidence.
Women’s entrepreneurship is critical to an economy’s growth and development, yet it faces a variety of difficulties. This study aims to conduct a theoretical assessment of women’s entrepreneurship in Yemen and examine the problems it faces in its development. The findings show that women entrepreneurs in Yemen face numerous hurdles, including social, cultural, and institutional barriers; financial constraints; a lack of entrepreneurial education and knowledge; and a deficiency in training and incubation support. Consequently, it is suggested that a complete ecosystem for women’s entrepreneurship be developed, involving various stakeholders and comprising different types of facilities capable of assisting women entrepreneurs and ensuring their optimum advantage.
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