International Journal of Intelligent Computing and Information Sciences最新文献_第8页

Comparative Study and Analysis of Recent Computer Aided Diagnosis Systems for Masses Detection in Mammograms 乳腺肿块检测计算机辅助诊断系统的比较研究与分析

International Journal of Intelligent Computing and Information Sciences Pub Date : 2021-02-01 DOI: 10.21608/IJICIS.2021.56425.1050

Ghada Hamed, M. Marey, S. Amin, M. Tolba

{"title":"Comparative Study and Analysis of Recent Computer Aided Diagnosis Systems for Masses Detection in Mammograms","authors":"Ghada Hamed, M. Marey, S. Amin, M. Tolba","doi":"10.21608/IJICIS.2021.56425.1050","DOIUrl":"https://doi.org/10.21608/IJICIS.2021.56425.1050","url":null,"abstract":"Nowadays, breast cancer is considered one of the most threatening and common cancers for women due to the high rate of deaths that occurred yearly that reaches about 25% in all cancers. One of the most keys to decrease the mortal rate caused by breast cancer is its early detection. So, the research on developing computer-aided diagnosis systems (CADs) has been widely increased to improve the accuracy of breast cancer localization and classification. Generally, a proposed CAD is developed through four stages: data preparation and preprocessing, cancer detection, followed by its pathology classification. In this paper, the most recent proposed approaches to detect lesions in the breast mammograms and classify them are discussed with a comparative analysis to list the advantages and the disadvantages of most approaches. The main objective of this paper is to group the CADs with a performance evaluation and detailed analysis in order to furtherly develop others by avoiding the main weak points in the existing systems and to achieve high detection accuracy and classification performance at the same time.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128311227","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}

引用次数: 7

Fast Detection of Distributed Denial of Service Attacks in VoIP Networks Using Convolutional Neural Networks 基于卷积神经网络的VoIP网络分布式拒绝服务攻击快速检测

International Journal of Intelligent Computing and Information Sciences Pub Date : 2020-12-31 DOI: 10.21608/IJICIS.2021.51555.1046

Waleed Nazih, Yasser Hifny, Wail S. Elkilani, T. Mostafa

{"title":"Fast Detection of Distributed Denial of Service Attacks in VoIP Networks Using Convolutional Neural Networks","authors":"Waleed Nazih, Yasser Hifny, Wail S. Elkilani, T. Mostafa","doi":"10.21608/IJICIS.2021.51555.1046","DOIUrl":"https://doi.org/10.21608/IJICIS.2021.51555.1046","url":null,"abstract":"Voice over Internet Protocol (VoIP) is a recent technology used to transfer media and voice over Internet Protocol (IP). Many organizations moved to VoIP services instead of the traditional telephone systems because of its low cost and variety of introduced services. The Session Initiation Protocol (SIP) is the most used protocol for signaling functions in VoIP networks. It has simple implantation but suffers from less protection against attacks. The Distributed Denial of Service (DDoS) attack is a dangerous attack that preventing legitimate users from using VoIP services and draining their resources. In this paper, we proposed an approach that utilizes deep learning to detect DDoS attacks. The proposed approach uses token embedding to improve the extracted features of SIP messages. Then, Convolutional Neural Network (CNN) was used to detect DDoS attacks with different intensities. Furthermore, a real VoIP dataset that contains different scenarios of attacks was used to evaluate the proposed approach. Our experiments find that the CNN model achieved a high F1 score (99-100%) as another deep learning approach that utilizes Recurrent Neural Network (RNN) but with less detection time. Also, it outperforms another system that depends on classical machine learning in case of low-rate DDoS attacks. https://ijicis.journals.ekb.eg/","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"61 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130715014","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}

引用次数: 3

Early Diagnosis of Alzheimer’s Disease using Unsupervised Clustering 使用无监督聚类进行阿尔茨海默病的早期诊断

International Journal of Intelligent Computing and Information Sciences Pub Date : 2020-12-31 DOI: 10.21608/IJICIS.2021.51180.1044

Yasmeen Farouk, S. Rady

引用次数: 9

A Face Recognition System Based on Deep Learning (FRDLS) to Support the Entry and Supervision Procedures on Electronic Exams 基于深度学习(FRDLS)的人脸识别系统支持电子考试的报名和监督程序

International Journal of Intelligent Computing and Information Sciences Pub Date : 2020-06-01 DOI: 10.21608/ijicis.2020.23149.1015

A. Amin

{"title":"A Face Recognition System Based on Deep Learning (FRDLS) to Support the Entry and Supervision Procedures on Electronic Exams","authors":"A. Amin","doi":"10.21608/ijicis.2020.23149.1015","DOIUrl":"https://doi.org/10.21608/ijicis.2020.23149.1015","url":null,"abstract":"The novelty of this paper is represented in using some artificial intelligence techniques in the entry control to the electronic exams (E-exam) in addition to monitoring students and distinguish the situation they are during the E-exam. Therefore, the proposed system divides into two main parts, the first part to support Eexams to handle some of the weaknesses points such as validation from students' entry by using deep learning. The Self-Organized Maps (SOM) neural network was used to recognition on students' faces. SOM is characterized by its efficient for faces' image data management, as well as it's the closest technique to match inputted untrained faces' images with a database of trained faces' images accurately. On the other part, the Bag of Words model (BoWM) is used to discriminate the status of students during the exam process. The BoWM is based on Speeded-Up Robust Features (SURF) that building on the strengths of the leading existing detectors and descriptors by using a Hessian matrix. Then extracts a report showing the status of the student such as confusion, concentration, cheating ... etc. From the experimental results, the proposed system was verified images of students' faces with high accuracy and execution time have a significant indication. Determining the status of the student during the exam by adopting the technique of retrieving documents known as the bag of word model, which proved the accuracy of determining the status of the student arrived in some cases to 100%.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133021131","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}

引用次数: 3

E-PAYMENT SYSTEMS RISKS, OPPORTUNITIES, AND CHALLENGES FOR IMPROVED RESULTS IN E-BUSINESS 电子支付系统对改善电子商务结果的风险、机遇和挑战

International Journal of Intelligent Computing and Information Sciences Pub Date : 2020-06-01 DOI: 10.21608/ijicis.2020.31514.1018

Mohamed Hassan Nasr, M. Farrag, Mona Nasr

引用次数: 8

A survey on sentiment analysis in tourism 旅游情感分析的调查研究

International Journal of Intelligent Computing and Information Sciences Pub Date : 2020-06-01 DOI: 10.21608/ijicis.2020.22851.1014

Sarah Anis, S. Saad, M. Aref

引用次数: 2

Architecture Optimization Model for the Deep Neural Network 深度神经网络的体系结构优化模型

International Journal of Intelligent Computing and Information Sciences Pub Date : 2019-10-01 DOI: 10.21608/ijicis.2019.96101

K. Ukaoha, E. C. Igodan

{"title":"Architecture Optimization Model for the Deep Neural Network","authors":"K. Ukaoha, E. C. Igodan","doi":"10.21608/ijicis.2019.96101","DOIUrl":"https://doi.org/10.21608/ijicis.2019.96101","url":null,"abstract":"The daunting and challenging tasks of specifying the optimal network architecture and its parameters are still a major area of research in the field of Machine Learning (ML) till date. These tasks though determine the success of building and training an effective and accurate model, are yet to be considered on a deep network having three hidden layers with varying optimized parameters to the best of our knowledge. This is due to expert’s opinion that it is practically difficult to determine a good Multilayer Perceptron (MLP) topology with more than two or three hidden layers without considering the number of samples and complexity of the classification to be learnt. In this study, a novel approach that combines an evolutionary genetic algorithm and an optimization algorithm and a supervised deep neural network (Deep-NN) using alternative activation functions with the view of modeling the prediction for the admission of prospective university students. The genetic algorithm is used to select optimal network parameters for the Deep-NN. Thus, this study presents a novel methodology that is effective, automatic and less human-dependent in finding optimal solution to diverse binary classification benchmarks. The model is trained, validated and tested using various performance metrics to measure the generalization ability and its performance.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129762739","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}

引用次数: 7

Application of K-MTh Algorithm for Accurate Lunge Cancer Detection k - m - th算法在肺癌精确检测中的应用

International Journal of Intelligent Computing and Information Sciences Pub Date : 2019-10-01 DOI: 10.21608/ijicis.2020.18561.1010

M. Aouf

引用次数: 4

GIS Utilization for Strategic Planning Service Delivery Based on Service: Location, Time, And Type; A Case Study: 1st Primary Schools’ Strategic Planning in Mansoura City. 基于服务的地理信息系统在战略规划服务交付中的应用:地点、时间和类型以曼苏拉市第一小学战略规划为例

International Journal of Intelligent Computing and Information Sciences Pub Date : 2019-10-01 DOI: 10.21608/ijicis.2019.11736.1006

B. Shabana

{"title":"GIS Utilization for Strategic Planning Service Delivery Based on Service: Location, Time, And Type; A Case Study: 1st Primary Schools’ Strategic Planning in Mansoura City.","authors":"B. Shabana","doi":"10.21608/ijicis.2019.11736.1006","DOIUrl":"https://doi.org/10.21608/ijicis.2019.11736.1006","url":null,"abstract":"Smart cities are new and evolving concept based on innovative information and communication technologies. Thinks and Internet of Thinks (IoT) must be core technologies in smart cities. Thinks’ services and maintenance must be managed through digital planes. Location based service (LBS) are service targeted to a wide range of users. The increasing number of LBS will have a bad impact on transitions. One of the objectives of building smart cities is to reduce the traffic congestion and transportation demand. This paper utilizes Geographic Information System (GIS) and spatial analysis techniques for decision support of LBS. the objective is the advance determination of the best service location for covering services requirement based on three factors: location, time and type of service. Education is the greatest service required for humanity. This paper focuses on considering service factors and client requirements by applying a developed GIS platform to inform service provider by the assets required to cover customer needs and meets smart cities goals. While 1st stage of primary schools accepts students in age of 6 years, the suggested platform determines schools’ plane for 5 years in advance, according to: student location, date of birth, and education type requirement. The suggested platform was applied on Mansoura city, Egypt. That platform was designed by: using geoprocessing tools, and Python programming language of GIS (arcPy) for implementing functions and procedures (“split by attribute”, “periodic service plane”). Geocoding processing is used for converting students’ addresses to spatial location, which is necessary for geo-relational analysis.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128607981","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}

引用次数: 1

SCLUSTREAM: AN EFFICIENT ALGORITHM FOR TRACKING CLUSTERS OVER SLIDING WINDOW IN BIG DATA STREAMING scclustream:一种高效的算法，用于在大数据流中通过滑动窗口跟踪集群

International Journal of Intelligent Computing and Information Sciences Pub Date : 2019-10-01 DOI: 10.21608/ijicis.2019.62592

D. Sayed, S. Rady, M. Aref

引用次数: 2