2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM)最新文献_第4页

Intelligence and Electronic Warfare: Challenges and Future Trends 情报和电子战:挑战和未来趋势

2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM) Pub Date : 2021-08-25 DOI: 10.1109/ICCITM53167.2021.9677877

Ali Al-Khawaja, S. Sadkhan

{"title":"Intelligence and Electronic Warfare: Challenges and Future Trends","authors":"Ali Al-Khawaja, S. Sadkhan","doi":"10.1109/ICCITM53167.2021.9677877","DOIUrl":"https://doi.org/10.1109/ICCITM53167.2021.9677877","url":null,"abstract":"the subjective evaluation of electronic intelligence and warfare has been adopted as quantitive analysis by many organizational bodies to assess defense systems capabilities. One prominent case is the reliance of United States Army on subjective evaluations to drive its systems capabilities based on particular battlefield requirements due to the nonexistence of analytical instruments to meet such goals. Therefore, there is a pressing need to thoroughly review a comprehensive and effective approach for empirically evaluating the impact of intelligence and electronic warfare systems on combat effectiveness. In this direction, we discuss the main important capabilities of electronic intelligence and warfare systems that are necessary for ensuring fielded force mix and greater chance to success on the battlefield. However, the difficulty of the current battle is to process comprehensive electronic warfare (EW) information in a short period of time. Therefore, in this paper, the information system is proposed through the development of Electronic Warfare Intelligent Information System (EWIIS) which contracts electronic warfare processing, communications, radar, maps, warfare missions…etc. thus, aimed This system is at achieving the best performance in spite of the existence of antagonistic influences. Moreover, in this paper, we inclination discuss the design and implementation of a system case study.","PeriodicalId":406104,"journal":{"name":"2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117136110","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: 0

Robust Image Encryption Based on Double Hyper Chaotic Rabinovich System 基于双超混沌Rabinovich系统的鲁棒图像加密

2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM) Pub Date : 2021-08-25 DOI: 10.1109/ICCITM53167.2021.9677722

Huda H. Alsaabri, Saad S. Hreshee

引用次数: 0

Solving Abel's Integral Equation Using (Sadiq-Emad-Eman) Transform 利用(Sadiq-Emad-Eman)变换求解Abel积分方程

2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM) Pub Date : 2021-08-25 DOI: 10.1109/ICCITM53167.2021.9677810

Eman A. Mansour, Emad A. Kuffi, Sadiq A. Mehdi

引用次数: 0

Deep Radial Basis Function Networks 深度径向基函数网络

2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM) Pub Date : 2021-08-25 DOI: 10.1109/ICCITM53167.2021.9677825

Mohie M. Alqezweeni, V. Gorbachenko, D. A. Stenkin

{"title":"Deep Radial Basis Function Networks","authors":"Mohie M. Alqezweeni, V. Gorbachenko, D. A. Stenkin","doi":"10.1109/ICCITM53167.2021.9677825","DOIUrl":"https://doi.org/10.1109/ICCITM53167.2021.9677825","url":null,"abstract":"The use of radial basis functions networks as physics-informed neural networks for solving direct and inverse boundary value problems is demonstrated. On the Levenberg-Marquardt basis optimization method, algorithms have been developed for solving partial differential equations. Comparison of the gradient descent method and the Levenberg-Marquardt method for solving the Poisson equation is given. To solve a direct boundary value problem describing processes in a piecewise homogeneous environment, an algorithm is proposed based on solving individual problems for each region with different properties of the environment associated with the conjugation conditions. It removes restrictions on the radial basis functions used. To solve the coefficient inverse problem of recovering the properties of the piecewise inhomogeneous medium, an algorithm based on parametric optimization is proposed. An algorithm uses two networks of radial basis functions. The first network approximates the solution to the direct problem. And another network approximates a function which describes the properties of the environment. Network learning is performed using an algorithm developed by the authors based on the Levenberg-Marquardt method. Expressions are obtained for the analytical calculation of the Jacobi matrix elements in the Levenberg-Marquardt method and the residual gradient vector elements. The application of the developed algorithms is demonstrated by the example of model direct boundary value problems and inverse coefficient boundary value problems for piecewise homogeneous media.","PeriodicalId":406104,"journal":{"name":"2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126391509","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: 0

Face Direction Estimation based on Mediapipe Landmarks 基于Mediapipe地标的人脸方向估计

2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM) Pub Date : 2021-08-25 DOI: 10.1109/ICCITM53167.2021.9677878

Arqam M. Al-Nuimi, Ghassan J. Mohammed

引用次数: 10

Solving Abel's Integral Equation Using (Sadiq-Emad-Eman) Transform 利用(Sadiq-Emad-Eman)变换求解Abel积分方程

2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM) Pub Date : 2021-08-25 DOI: 10.1109/ICCITM53167.2021.9677685

Eman A. Mansour, Emad A. Kuffi, Sadiq A. Mehdi

引用次数: 0

Parameters Estimation for Modified Weibull Distribution Using Some Methods of Estimators with Simmulation 基于仿真估计器的修正威布尔分布参数估计

2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM) Pub Date : 2021-08-25 DOI: 10.1109/ICCITM53167.2021.9677666

Loay Waadallh Saleem, H. Saieed

引用次数: 0

Generating digital images of skin diseases based on deep learning 基于深度学习生成皮肤疾病的数字图像

2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM) Pub Date : 2021-08-25 DOI: 10.1109/ICCITM53167.2021.9677769

H. M. Ahmed, M. Kashmola

{"title":"Generating digital images of skin diseases based on deep learning","authors":"H. M. Ahmed, M. Kashmola","doi":"10.1109/ICCITM53167.2021.9677769","DOIUrl":"https://doi.org/10.1109/ICCITM53167.2021.9677769","url":null,"abstract":"Data generation systems and architectures seek to create new and valuable data with specific sizes and characteristics which are similar to the original data that fit the mechanism of the application used. GANs (generative adversarial network architectures) are a type of generative modeling that uses deep learning methods like convolutional neural networks to generate data, particularly digital images which are used in scientific applications. The goal of this paper is to design and build three architectures of Generative adversarial network (based on convolutional neural networks) to generate three types of digital images of skin diseases by defining two supervised models, the generator model that is trained to generate new digital images and the discrimination model that classifies digital images as real or fake from the field. An intelligent architecture for training the generative model was created, where the two models are trained together in a zero-sum field. Each digital image that was generated differs in its accuracy, size, and dimensions. After applying all the architectures, obtaining and comparing digital images, it turns out that the fewer dimensions of the resulting digital images, the faster the generation processes and the lower the memory costs, but their accuracy is low. The importance of building architecture lies in increasing the images of skin diseases to be used in the data set to perform classification operations for these diseases, as digital images were generated in different sizes, which are 64×64, 128×128, and 512×512, and the new images were obtained with accuracy commensurate with the requirements of this paper. The generated digital images with dimensions 128×128 gave the best results in the accuracy of the resulted images. At the same time, they do not consume much memory, which leads to their processing speed.","PeriodicalId":406104,"journal":{"name":"2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115979223","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

Real-Time Big Data Analytics Perspective on Applications, Frameworks and Challenges 实时大数据分析的应用、框架和挑战

2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM) Pub Date : 2021-08-25 DOI: 10.1109/ICCITM53167.2021.9677849

D. Abdullah, Rana Abdul-Ghafoor Mohammed

{"title":"Real-Time Big Data Analytics Perspective on Applications, Frameworks and Challenges","authors":"D. Abdullah, Rana Abdul-Ghafoor Mohammed","doi":"10.1109/ICCITM53167.2021.9677849","DOIUrl":"https://doi.org/10.1109/ICCITM53167.2021.9677849","url":null,"abstract":"The rapid and tremendous growth of data and its diverse resources has become a hallmark of our age, many applications in various fields demand to discover patterns and relationships in real-time to gain awareness of the situation, improve workflows and enhance the bottom line to provide the best insights and make the most appropriate decisions. This requires tools and techniques different from the traditional one by its capability for processing and analyzing these data with its all heterogeneity, complexity, randomness in addition to its huge volume with low latency. Dealing with Big data in real-time poses many challenges in terms of collecting, processing, analyzing the data, and choosing the appropriate framework and architecture to implement the application. The paper reviews different fields of application that require real-time analytics for Big Data with its different approaches and the frameworks used as well as the challenges that may pose for implementing these applications. This review provides a guide for researchers in the future to find out the appropriate study in a specific field of application and the Big Data tools used.","PeriodicalId":406104,"journal":{"name":"2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128295534","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: 2

Energy Aware Nature inspired Routing for Wireless Sensor Networks 能量感知自然启发的无线传感器网络路由

2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM) Pub Date : 2021-08-25 DOI: 10.1109/ICCITM53167.2021.9677837

Yasameen Sajid Razooqi, M. Al-Asfoor

引用次数: 0