Nowadays, There are many risks related to bank loans, for the bank and for those who get the loans. The analysis of risk in bank loans need understanding what is the meaning of risk. In addition, the number of transactions in banking sector is rapidly growing and huge data volumes are available which represent the customers behavior and the risks around loan are increased. Data Mining is one of the most motivating and vital area of research with the aim of extracting information from tremendous amount of accumulated data sets. In this paper a new model for classifying loan risk in banking sector by using data mining. The model has been built using data form banking sector to predict the status of loans. Three algorithms have been used to build the proposed model: j48, bayesNet and naiveBayes. By using Weka application, the model has been implemented and tested. The results has been discussed and a full comparison between algorithms was conducted. J48 was selected as best algorithm based on accuracy.
The advancement in mobile technology and wireless network increase the using of mobile device in database driven application, these application require high reliability and availability due to nature inheritance of mobile environment, transaction is the center component in database systems, In this paper we present useful work done in mobile transaction, we show the mobile database environment and overview a lot of proposed model of mobile transaction and show many techniques used to enhance transaction execution.
In developed and developing countries, breast cancer is one of the leading forms of cancer affecting women alike. As a consequence of growing life expectancy, increasing urbanization and embracing Western lifestyles, the high prevalence of this cancer is noted in the developed world. This paper aims to develop a novel model that diagnoses Breast Cancer by using heterogeneous datasets. The model can work as a strong decision support system to help doctors to make the right decision in diagnosing breast cancer patients. The proposed model is based on three datasets to develop three sub-models. Each sub-model works independently. The final diagnosis decision is taken by the three sub-models independently. The power of the model comes from the diversity checks of patients and this reduces the risk of wrong diagnosing. The model has been developed by conducting intensive experiments. Several classification algorithms were used to select the best one in each sub-model. As the final results, the sub-model accuracies were 72%, 74% and 97%.
Background: CVA is the loss of brain function due to a disturbance in the blood supply of the brain. This disturbance is due to either ischemia or hemorrhage. Aim: To study the major risk factors & presentation among patients with stroke in Atbara teaching hospital. Methodology: Cross-sectional descriptive hospital based study was conducted in Atbara teaching Hospital from February 2021 to April 2022. The data was collected by interviewing the patients through a closed-ended questionnaire and analyzed by using the statistical computerized program SPSS. Results: In our study, we found that 86.2%of study group their age group was more than 51years and most of them are males, 73.1% of them had a hemiplegic weakness, 60.8% of them had a transient ischemic stroke,90% had an ischemic stroke, 66.9% with one attack of stroke,77.7% of them have a chronic illness,53.1%have diabetes,63.8% have hypertension,28.5% of them have ischemic heart disease,23.8%have atrial fibrillation,11.5% have the valvular disease, 35.4 % of them are smokers and 6.9% are drinking alcohol. Conclusion: The study concluded that stroke increased with age above 51 years old and most commonly in males. The most common risk factor is HTN, DM, and heart disease. The common risk bad habit in strokes patients are smoking.
Abstract-Data-warehouse is an emerging technology with great potential. Nowadays, businesses are co mpeting fiercely to dominate the market where profitability is promising using every availab le means to reach their goal. Performance and storage are big challenges in build ing data-warehouse focusing by researchers recent years. In this paper a new model for developing an efficient data warehouse by using mobile agent technology has been proposed. The main idea behind this model is to use the mobile agent to extract and analy ze operational data in their location. So, instead of using ETL, the mob ile agent will be used. After the mobile agent comp leting its journey among operational databases, all tasks of ETL will be performed. By this way no need h igh storage med ia to extract the data from the operational database. As cost of time, the model proves less consuming of t ime. The model has been implemented using .Net Framework and C# and the results have been presented and discussed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.