Artificial intelligence and banking services

"The research addresses the concept of both artificial intelligence and banking services, and explains how artificial intelligence is employed in banking services, along with the resulting advantages and disadvantages."

Applying machine learning techniques from artificial intelligence in improving banking risk assessment (an analytical study on credit risk)

This study aimed to analyze the application of machine learning techniques from artificial intelligence to improve credit risk assessment in Syrian banks. The study sample included historical financial data and demographic information of bank customers. Machine learning methods were used to analyze the data and develop a model for accurately predicting credit risks. Techniques such as random forests and neural networks were utilized for data analysis and model building. The study found that the use of machine learning techniques, such as random forests and neural networks, can significantly enhance the accuracy of credit risk assessments compared to traditional methods. This improvement helps reduce default rates and increase bank profitability, enhancing financial stability and economic growth. Additionally, the results showed the superiority of machine learning-based models in early detection of potential risks, enabling banks to take effective preventive measures. The results indicated that applying these techniques contributes to improving banking operations' efficiency and reducing costs, allowing banks to offer better services to customers and enhance their competitive ability in the market. The study also highlighted the challenges associated with integrating these technologies with current systems, including the need for continuous employee training and updating technological infrastructure to ensure optimal use of machine learning techniques. In light of the study results, the researcher presented several recommendations including promoting the use of machine learning techniques in Syrian banks, providing technical support and continuous training for employees, and leveraging successful global experiences to develop effective strategies for credit risk assessment. The study also recommended establishing specialized units in banks to manage and develop machine learning and artificial intelligence applications. Keywords: Machine Learning, Artificial Intelligence, Banking Risk, Credit Prediction, Syrian Banks

Academic Integrity in the Era of Generative Artificial Intelligence (ChatGpt)

The objective of this research is to conduct a systematic literature review, analyzing the influence of implementing the ChatGPT tool in the field of education. The data for this study was gathered through a systematic review of studies published since the launch of ChatGPT in November 2022. Three prominent educational databases (Web of Science, Taylor& Francis Online, Eric) were utilized for this purpose. The study incorporated a sample of 18 relevant studies, and a descriptive and quantitative methodology was employed to present the most noteworthy findings. The outcomes indicate that the incorporation of ChatGPT in the educational setting positively impacts the teaching and learning processes. Nevertheless, the results also shed light on topics such as factors that determine students' attitudes toward the application, positive and negative effects, and how to ensure academic integrity when applying AI in education. Despite ChatGPT's potential to enhance the educational experience, its successful integration hinges on educators being well-versed in its functionalities. These insights lay a robust foundation for future research endeavors and informed decision-making concerning the incorporation of ChatGPT in educational contexts.

Factors affecting kindergarten students’ acceptance of using artificial intelligence applications in the teaching and learning processes

The aim of the current research is to identify the factors that affect the acceptance of female kindergarten students and how to improve these factors. The researcher used the descriptive approach, and the research tool was a questionnaire prepared by the researcher and studied its psychometric properties. It was applied to a representative sample of the original community of (30) third-year female students. The fourth is the kindergarten specialty in the Faculty of Education at Tishreen University.

Roads Extraction from Satellite Images using Convolution Neural Network Model (Deeplabv3+) A Case Study in Lattakia city

The purpose of this paper is to extract roads from satellite images, based on developing the performance of the deep convolutional neural network model (Deeplabv3+) for roads segmentation, and to evaluate and test the performance of this model after training on our data.This experimental study was applied at Google Colab cloud platform, by software instructions and advanced libraries in the Python.We conducted data pre -processing to prepare ground truth masks,then we trained the model.The training and validation process required (Epochs=4), by(Patch Size=4images).The Loss function decreased to its minimum value (0.025). Training time was three hours and ten minutes, aided by the advanced Graphics Processing Unit (GPU) and additional RAM.We achieved good results in evaluating the accuracy of the predictions of the trained model (IoU = 0.953). It was tested on two different areas, one of which is residential and the other agricultural in Lattakia city. The results showed that the trained model (DeepLabv3+) in our research can extract the road network accurately and effectively.But its performance is poor in some areas which includes tree shadows on the edges of the road, and where the spectral characteristics are similar to the road, such as the roofs of some buildings, and it is invalid for extracting side and unpaved roads. The research presented several recommendations to improve the performance of the (Deeplabv3+) in extracting roads from high-resolution satellite images, which is useful for updating road maps and urban planning works.

Prediction of Electric Power Consumption Using RNN Networks

The electric power service in the Syrian Arab Republic suffers from many difficulties resulting from the lack of resources (fuel), in addition to the sabotage of many generation centers by terrorist groups, which led to the implementation of rationing programs in the governorates according to the consumption of those governorates and the production centers located in them. (factories, pumping centers, hospitals and the population). Forecasting electric energy consumption also requires knowledge of daily consumption quantities, consumption times and other influencing factors that constitute large amounts of data. Predicting the exact electrical load is still a challenging task due to many problems such as the non-linear nature of the time series or the seasonal patterns it displays, which are very time consuming and affect the accuracy of the prediction performance. The process can be improved by using RNNs.[2] Initially, the optimal and appropriate consumption for the region was determined, compared with production and the possibility of passing the surplus to other backup operations or providing production centers with the surplus that could be obtained through the previous forecasting process. Also, Recurrent Neural Networks (RNN) were used, which are time series based on data sequences according to time indices and their ability to predict future values ​​based on past data. Then the performance of those networks was compared with DNN networks (Dense Neural Network) to obtain an optimal future prediction that can be served by the Ministry of Electricity in the Syrian Arab Republic and to solve the problem of predicting the electrical load compared to previous studies. The time-based successive division method has also been adopted, which has the ability to work more accurately for randomly sampled data. For cases of low regulation of the hourly data for wattage consumption, we can sample a set of data over time and take 20 percent of the data for example as training and test samples. Based on the prediction values ​​resulting from this study, work is being done to distribute electrical energy in the most appropriate manner and in accordance with the importance of higher usage.

artificial intelligence as a tool to enhance decision-making in personalized medicine and drug screening

Objective This research aimed to describe several areas in which AI could play a role in the development of Personalized Medicine and Drug Screening, and the transformations it has created in the field of biology and therapy. It also addressed the limitations faced by the application of artificial intelligence techniques and make suggestions for further research. Methods We have conducted a comprehensive review of research and papers related to the role of AI in personalized medicine and drug screening, and filtered the list of works for those relevant to this review. Results Artificial Intelligence can play an important role in the development of personalized medicines and drug screening at all clinical phases related to development and implementation of new customized health products, starting with finding the appropriate medicines to testing their usefulness. In addition, expertise in the use of artificial intelligence techniques can play a special role in this regard. Discussion The capacity of AI to enhance decision-making in personalized medicine and drug screening will largely depend on the accuracy of the relevant tests and the ways in which the data produced is stored, aggregated, accessed, and ultimately integrated. Conclusion The review of the relevant literature has revealed that AI techniques can enhance the decision-making process in the field of personalized medicine and drug screening by improving the ways in which produced data is aggregated, accessed, and ultimately integrated. One of the major obstacles in this field is that most hospitals and healthcare centers do not employ AI solutions, due to healthcare professionals lacking the expertise to build successful models using AI techniques and integrating them with clinical workflows.

A Bag of Tricks for Dialogue Summarization

Dialogue summarization comes with its own peculiar challenges as opposed to news or scientific articles summarization. In this work, we explore four different challenges of the task: handling and differentiating parts of the dialogue belonging to multiple speakers, negation understanding, reasoning about the situation, and informal language understanding. Using a pretrained sequence-to-sequence language model, we explore speaker name substitution, negation scope highlighting, multi-task learning with relevant tasks, and pretraining on in-domain data. Our experiments show that our proposed techniques indeed improve summarization performance, outperforming strong baselines.

مخاطر الذكاء الصنعي على الأمن ومستقبل العمل

يستكشفة هذا المنظور التحليلي أثار انتشار الذكاء الاصطناعي في مجالين رئيسين يتعلقان بالسياسات هما الأمن والتوظيف. وقد ركزنا ههنا نقاط الضعف وعدم الانصاف التي قد يفرضها استخدام الذكاء الاصطناعي على هذين البعدين للمجتمع. حدد فريق من الزملاء في مؤسسة رند ذوي خبرات وتجارب متنوعة هذين المجالين من بين سواهما باعتبارهما يستحقان اهتماما دقيقا في عصر الذكاء الاصطناعي. ومن المجالات التي تمت الاشارة إليها أيضا نذكر: تأثير الذكاء الاصطناعي على الصحة وصنع القرارات وتسوية النزاعات في المنازعات والأمن الالكتروني. وتوضح الطبيعة متعدد التخصصات التي تتسم بها المشاكل التي خلصنا إليها الحاجة إلى مواصلة إشراك الباحثين والمحللين الذي يتمتعون بمجموعة متنوعة من الخبرات والتجارب من أجل اطلاع صناع القرارات المتعلقة بالسياسات على المواقف والخطوات الواجب القيام بها في ما يتعلق بالأدوات الاصطناعية والذكاء الصنعي على اوسع نطاق. يبين هذا البحث المحارو الشاملة لأثار الذكاء الاصطناعي: ١- الأدوات الاصطناعية هي في الواقع مضاعفات للانتباه قادرة على ان تحدث اثارا نظامية غير متوقعة وخطيرة. ٢- يزيد الاعتماد على الادوات الاصطناعية خطر تقلص المرونة. ٣- للذكاء الاصطناعي القدرة على التسبب بفوضى اقتصادية اجتماعية سريعة غير مسبوقة. ٤- تعد تفضيلات هجرة وتوظيف ذوي المواهب في مجال بحث وتطوير الذكاء الاصطناعي حول العالم من المخاوف الجغرافية السياسية المهمة.

The Role of Artificial Intelligence in Raising the Efficiency of Administrative Systems for Human Resources Management at the University of Tabuk

The role of artificial intelligence in raising the efficiency of administrative systems for human resources management at the University of Tabuk. To conduct the study, the researcher was used the descriptive and analytical approach. In order to achieve the objectives of the study, the study tool (questionnaire) was developed as a tool to collect data from the individuals of the study sample that were chosen randomly of data collection from the human resources administrators at the University of Tabuk, who numbered (70 (male and female employees, after ensuring their validity and reliability. The study tool consisted of (36) items to measure the role of artificial intelligence in raising the efficiency of administrative systems for human resources management at the University of Tabuk. The results of the study showed that there were no statistically significant differences (α = 0.05) in the study tool due to the study variables (gender, educational level, number of years of experience) at the level of significance (0.05). In light of these results of the study, the researcher recommended several recommendations, including the necessity of conducting more studies on artificial intelligence programs and their relationship to the efficiency of administrative systems for managing human resources to include larger samples from universities in the Kingdom.