2022 5th International Conference on Computing and Informatics (ICCI)最新文献

{"title":"Predicting Banking Customer Churn based on Artificial Neural Network","authors":"Amany Zaky, Shimaa Ouf, Mohamed Roushdy","doi":"10.1109/icci54321.2022.9756072","DOIUrl":"https://doi.org/10.1109/icci54321.2022.9756072","url":null,"abstract":"Customer churn has become one of the major issues in the banking industry. Because it is difficult to gain new clients, the major focus of customer relationship management is on existing clients. Customer Churn is defined as when customers switch to another provider due to their low prices and better offers. There are many research papers that found solutions to solve the customer churn problem with the help of the techniques of machine learning. In this research paper, we have suggested a framework that introduces a solution to the problem of customer churn in the banking industry. We used the techniques of deep learning namely the artificial neural network to analyze bank customer data and predict the customer churn. The experiment was conducted on a dataset called churn modeling and the results reveal that we were able to attain an accuracy of 87 % for bank customer data by using the ANN algorithm. The proposed framework presented a cost-effective option for maintaining bank customers, which increases bank profits by retaining customers.","PeriodicalId":122550,"journal":{"name":"2022 5th International Conference on Computing and Informatics (ICCI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125374287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Evaluation of Time Series-Based Modeling and Forecasting of Infectious Diseases Progression using Statistical Versus Compartmental Methods","authors":"Noha Gamal El-Din Saad, Samy S. A. Ghoniemy, Hossam M. Faheem, Noha A. Seada","doi":"10.1109/icci54321.2022.9756060","DOIUrl":"https://doi.org/10.1109/icci54321.2022.9756060","url":null,"abstract":"As a case study for our research, COVID-19, that was caused by a unique coronavirus, has substantially affected the globe, not only in terms of healthcare, but also in terms of economics, education, transportation, and politics. Predicting the pandemic's course is critical to combating and tracking its spread. The objective of our study is to evaluate, optimize and fine-tune state of the art prediction models in order to enhance its performance and to automate its function as possible. Therefore, a comparison between statistical versus compartmental methods for time series-based modeling and forecasting of infectious disease progression was conducted. The comparison included several classical univariate time series statistical models, including Exponential Smoothing, Holt, Holt-Winters, and Seasonal Auto Regressive Integrated Moving Average (SARIMA), as opposed to an optimized version of the compartmental multivariate epidemiological model SEIRD, which is referred to in our study, as, Non-Linear L-BFGS-B Fitted SEIRD. The mentioned methods were implemented and fine-tuned to model and forecast COVID-19 outbreak situation represented by confirmed cases, recoveries, and fatalities in (Australia, Canada, Egypt, India, United States of America and United Kingdom). Through the implementing and tuning of both types of models, we have observed that while univariate time series forecasting models such as SARIMA produce highly accurate predictions due to their ease of use and procedure, as well as their ability to deal with seasonality and cycles in time series, multivariate epidemiological models are more powerful and extendible. Despite their complexity, epidemiological models have aided extensively in understanding the spread and severity of infectious disease pandemics such as the COVID-19 global pandemic. Using our optimized SEIRD, we have obtained a Mean Squared Log Error of 10−3 order, demonstrating the forecasts' elevated accuracy and reliability. In addition to forecasting the course of the pandemic for a 3 months season in all countries under investigation, we were able to estimate the transmission potential of COVID-19 represented by its effective reproduction number Rt. With $mathrm{R}_{mathrm{t}}=1$ is considered as the pandemic control threshold, it is evident that all of the countries under investigation are hovering just above the control threshold. This study might be relieving since it can demonstrate that the world is on the right track in terms of putting an end to the pandemic as soon as possible. The whole study shows how powerful is compartmental methods compared to classical statistical methods when used to model and forecast an infectious disease outbreak which encourages our further related research concerning the study of implementing advanced compartmental models considering additional parameters and controls.","PeriodicalId":122550,"journal":{"name":"2022 5th International Conference on Computing and Informatics (ICCI)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129801716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An English Islamic Articles Dataset (EIAD) for developing an IslamBot Question Answering Chatbot","authors":"M. Mohammed, Salsabil Amin, M. Aref","doi":"10.1109/icci54321.2022.9756122","DOIUrl":"https://doi.org/10.1109/icci54321.2022.9756122","url":null,"abstract":"A chatbot is one of the most vastly recommended technologies to be used during these decades, especially through the digitization era. It could save much consumed time for both the users and the customer service employees. Chatbots could provide an answer to the asked questions instantly. IslamBot is an Islamic religion chatbot “i.e.,” responsible for answering any inquiries related to the Islamic religion. The aimed audience is non-Muslims people willing to join Islam or New-Muslims. Building such types of chatbots need to have an enormous amount of trusted data. Accordingly, in this paper The English Islamic Articles dataset (EIAD) is proposed as a benchmark reference for English Islamic question answering. So, this dataset contains about 10000 English Islamic articles. It is scrapped from authenticated and trusted websites like NewMuslims.com [1] IslamReligion.com [2], and IslamQA.com [3]. The dataset is about 275 articles from NewMuslims.com [1], 1550 articles from IslamReligion.com [2], and 8292 articles from IslamQA.com [3]. The EIAD dataset is a structured dataset “i.e.,” labeled and categorized. This dataset contains about 15 different categories. Each category is covering several different topics. This paper focuses on discussing how The English Islamic Articles dataset (EIAD) has been collected.","PeriodicalId":122550,"journal":{"name":"2022 5th International Conference on Computing and Informatics (ICCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130144281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting COVID-19 Total Daily Cases in Indonesia Using LSTM Networks","authors":"Clarissa Angelita Indriyani, Claudia Rachel Wijaya, N. N. Qomariyah","doi":"10.1109/icci54321.2022.9756062","DOIUrl":"https://doi.org/10.1109/icci54321.2022.9756062","url":null,"abstract":"The COVID-19 virus has taken over the course of the world for over two years; governments all over the world have been trying to mitigate its effects in several ways such as instilling most jobs to be done at home instead of working from the office. Thus, it is important to be able to see predictions of COVID-19 cases to better plan the intervention of the virus spreading. With the use of machine learning, our paper aims to propose and evaluate an LSTM (Long Short Term Memory) model that can forecast daily COVID-19 cases in Indonesia. Several tests show that 50 epochs and a batch size of eight are the best parameters to use for our model. Furthermore, after comparison with differing amounts of lookbacks, we have decided that 10 is best for our model as it consistently does better than other numbers of lookbacks. Based on our model, there will still be an increase of COVID-19 cases in the future.","PeriodicalId":122550,"journal":{"name":"2022 5th International Conference on Computing and Informatics (ICCI)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124586484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Content-based Medical Image Retrieval based on Deep Features Expansion","authors":"M. Rashad, Ibrahem Afifi, Mohamed Abdelfatah","doi":"10.1109/icci54321.2022.9756114","DOIUrl":"https://doi.org/10.1109/icci54321.2022.9756114","url":null,"abstract":"The collections of various digital image databases have significantly grown and many users have recognized that finding and recovering important images from large collections is a difficult task. Where the success of any image retrieval system is heavily dependent on the feature extraction capacity of the feature descriptor, therefore successful and effective retrieval method has been developed to provide an effective and rapid search and retrieval process. We present a unique deep learning-based approach for extracting high-level and compact features from medical images in this paper. To capture the discriminative features of medical images, we use Residual Networks (ResNets), a popular multi-layered deep neural network. The query is then broadened by reformulating the query image using the mean values for deep features from each database class's top-ranking images. Two publicly available databases in various forms were used to evaluate the performance of our technique. These studies demonstrated the benefits of our proposed strategy, with retrieval accuracy greatly improved.","PeriodicalId":122550,"journal":{"name":"2022 5th International Conference on Computing and Informatics (ICCI)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121831690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sliding Time Analysis in Traffic Segmentation for Botnet Activity Detection","authors":"Dandy Pramana Hostiadi, T. Ahmad","doi":"10.1109/icci54321.2022.9756077","DOIUrl":"https://doi.org/10.1109/icci54321.2022.9756077","url":null,"abstract":"Botnets are a threat in a dangerous cyber era. Botnets involve malicious software to attack the system based on instructions from the botmaster. Previous research had introduced a botnet activity detection model, such as using activity time analysis through a sliding time-based traffic segmentation process. However, the introduced model has not analyzed the ideal time in the sliding process in the segmentation process. The sliding process is needed to detect the botnet attack activity chain correctly. This paper analyzed the ideal time in the sliding process in traffic data segmentation to detect botnet activity and obtain information about botnet attacks. It aimed to get the optimal time in the sliding process and see its effect on detection accuracy. The test was carried out using a public dataset, namely the CTU-13 dataset, based on the two detection models in previous research. The result showed that the optimal time in the sliding process was 30 minutes in both detection models, with the best scenario detection results of 231 and the best detection accuracy of 97.93%.","PeriodicalId":122550,"journal":{"name":"2022 5th International Conference on Computing and Informatics (ICCI)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130665392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Enhanced Parallel Automation Testing Architecture for Test Case Execution","authors":"Sarah M. Nagy, H. A. Maghawry, N. Badr","doi":"10.1109/icci54321.2022.9756109","DOIUrl":"https://doi.org/10.1109/icci54321.2022.9756109","url":null,"abstract":"Customer requests for incorporating complicated business logic into software applications are common. As a result, as business requirements expand, the demand on the testing team to deliver a high-quality product in a short amount of time grows. Software testing guarantees that customers receive high-quality software. Manual testing becomes difficult since it is time-consuming. Besides, its cost rises as test suite sizes grow. In addition, human mistakes can slip into a system, resulting in losses for the company. Therefore, automation testing is best suited in situations when requirements change frequently, and a large volume of regression testing is required. Automation testing enhances accuracy while also saving the tester's time and the organization's money. The aim of this work is to propose an improved parallel automation testing architecture to significantly decrease testing time. The main problem with parallel testing is the existence of idle nodes that causes an increase in execution time. The proposed architecture solved this problem by running test cases in parallel using Selenium, Docker containers and implementing a dispatcher to ease and faster the process of distributing test cases between the network's nodes. As a result, the proposed architecture decreases testing process time, and no idle nodes exists anymore.","PeriodicalId":122550,"journal":{"name":"2022 5th International Conference on Computing and Informatics (ICCI)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132000150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A PLC-SCADA Pipeline for Managing Oil Refineries","authors":"Ossama Rashad, Omneya Attallah, I. Morsi","doi":"10.1109/icci54321.2022.9756108","DOIUrl":"https://doi.org/10.1109/icci54321.2022.9756108","url":null,"abstract":"This paper proposes a PLC (Programmable Logic Controller)/HMI (Human Machine Interface) system for tracking oil products refineries. The proposed pipeline includes AOI (Add on Instruction) programming, and PLC to automatically display petroleum products terminal. It provides an AOI in programming to obtain the most utilization of processor capabilities. Besides, it makes use of AOI for programming in cooperation with a ladder logic program. This results in simplifying the ladder program, decreasing scan time, and making troubleshooting easier. The proposed system is constructed in two stages. First is the PLC controller programming stage. In the second stage, the HMI graphic presentations are drawn and connected to the PLC tags. The proposed system outcomes confirmed that the number of logic ladders, maximum program size, and maximum scan time has decreased. The outcomes imply that the AOI can assist in tracing the program more without difficulty in faults situations. Besides, it adds extra program commands in less processor memory, lowering system creation, and upgrade costs.","PeriodicalId":122550,"journal":{"name":"2022 5th International Conference on Computing and Informatics (ICCI)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132208020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Machine Learning Techniques to Explore the Possibilities of Reducing the Spread of Corona Virus and its New Variants","authors":"Hossam Meshref","doi":"10.1109/icci54321.2022.9756105","DOIUrl":"https://doi.org/10.1109/icci54321.2022.9756105","url":null,"abstract":"The Corona pandemic has been around for a while, and its threat to the world is growing. We believe that climate parameters and health prevention measures could be related to the number of reported Corona daily cases. In the literature there were different views on the nature of these relations using several datasets recorded from various parts of the world. In our research, data collected from zones with concentrated Corona cases: China, Europe and the United States were analyzed to understand the relation with climate as well as data at the global level to understand the relation with health prevention measures. Feature importance analysis revealed that temperature is the most important contributing attribute to the Corona cases' prediction models, followed by relative humidity. As well, the percentage of mask use and percentage of fully vaccinated individuals were found to have a great influence on the number of new Corona daily cases. The designed machine learning ensemble techniques had a maximum predication accuracy of 89.08%, and the produced possible interpretations for the designed models agreed with the performed feature importance analyses. We believe that the analysis approach followed in this research as well as the achieved findings could be very useful to other researchers who are interested in conducting more research investigation in the same research area on the new Corona variants. We also believe that policy makers could consider the findings of our research as they effectively plan their future health precautions measures to avoid further spread of the virus.","PeriodicalId":122550,"journal":{"name":"2022 5th International Conference on Computing and Informatics (ICCI)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116102938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A deep learning framework for predicting the student's performance in the virtual learning environment","authors":"Soha Ahmed, Y. Helmy, Shimaa Ouf","doi":"10.1109/icci54321.2022.9756058","DOIUrl":"https://doi.org/10.1109/icci54321.2022.9756058","url":null,"abstract":"Nowadays predicting the student's performance in the virtual learning environment is considered a critical point as it includes some of the student learning activities such as the course registration, tasks submissions, exams, as well as all the virtual interactions that happen so all of these are considered as a fertile field for research. In addition, Deep learning which is under the umbrella of artificial intelligence played an important role in the prediction's domain. Consequently, the study focused to discuss the role of artificial intelligence in the e-learning system in general and specifically the role of deep learning in predicting the student's performance, and it found that most of the studies focused only on the dropout prediction and neglect the other performance features as well as they didn't focus on improving the quality of the dataset. Consequently, the study proposed a deep learning framework to predict the student's academic performance in the virtual learning environment taking into consideration the quality of the dataset in the preprocessing layer, based on the deep neural networks the proposed model achieved a high accuracy of about 91.29% and low loss value about 0.18 compared to the other studies which utilized the same dataset.","PeriodicalId":122550,"journal":{"name":"2022 5th International Conference on Computing and Informatics (ICCI)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117089609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}