2021 12th International Conference on Information and Knowledge Technology (IKT)最新文献

Paths-oriented Test Data Generation using Genetic Algorithm 基于遗传算法的面向路径的测试数据生成

2021 12th International Conference on Information and Knowledge Technology (IKT) Pub Date : 2021-12-14 DOI: 10.1109/IKT54664.2021.9685262

Mohammad Reza Hassanpour Charmchi, B. R. Cami

引用次数: 0

Fast Online Character Recognition Using a Novel Local-Global Feature Extraction Method 基于局部-全局特征提取方法的快速在线字符识别

2021 12th International Conference on Information and Knowledge Technology (IKT) Pub Date : 2021-12-14 DOI: 10.1109/IKT54664.2021.9685875

Ayoub Parvizi, M. Kazemifard, Ziba Imani

{"title":"Fast Online Character Recognition Using a Novel Local-Global Feature Extraction Method","authors":"Ayoub Parvizi, M. Kazemifard, Ziba Imani","doi":"10.1109/IKT54664.2021.9685875","DOIUrl":"https://doi.org/10.1109/IKT54664.2021.9685875","url":null,"abstract":"Online handwriting recognition is one of the most active subjects of research in the field of pattern recognition. Feature extraction is one of the main steps in handwritten recognition which has a significant impact on accuracy and speed of recognition. Low time complexity of feature extraction method and short feature vector length reduce the computational cost and memory consumption. In this paper, a new feature extraction method is presented for fast online Persian/Arabic character recognition. In this method, in order to recognize the character pattern, the character signal sequence is first segmented into several equal parts in terms of number of points. Then the angle between horizon line and transient line of first and last points of each segment is considered as local feature. The global features are the angles between horizon line and transient line of first point of the character and the end point of segments. This method has been tested on two standard datasets including numbers and characters of Persian/Arabic languages. The experimental results show that the proposed method, in addition to high recognition accuracy, has less computational complexity and shorter vector length compared to other new methods of feature extraction and handwriting recognition.","PeriodicalId":274571,"journal":{"name":"2021 12th International Conference on Information and Knowledge Technology (IKT)","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116585561","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

Experimental analysis of automated negotiation agents in modeling Gaussian bidders 自动谈判代理建模高斯投标人的实验分析

2021 12th International Conference on Information and Knowledge Technology (IKT) Pub Date : 2021-12-14 DOI: 10.1109/IKT54664.2021.9685464

Fatemeh Hassanvand, Faria Nassiri-Mofakham

引用次数: 1

Extending Interaction Flow Modeling Language as a Profile for Form-making Systems 扩展交互流建模语言作为表单制作系统的概要文件

2021 12th International Conference on Information and Knowledge Technology (IKT) Pub Date : 2021-12-14 DOI: 10.1109/IKT54664.2021.9685877

Ghazaleh Shahin, B. Zamani

引用次数: 0

ISPREC: Integrated Scientific Paper Recommendation using heterogeneous information network ISPREC:基于异构信息网络的综合科学论文推荐

2021 12th International Conference on Information and Knowledge Technology (IKT) Pub Date : 2021-12-14 DOI: 10.1109/IKT54664.2021.9686013

Elaheh Jafari, Bita Shams, Saman Haratizadeh

引用次数: 0

NFV-Based Distributed Service Function Chaining with Imperfect Information 基于nfv的不完全信息分布式业务功能链

2021 12th International Conference on Information and Knowledge Technology (IKT) Pub Date : 2021-12-14 DOI: 10.1109/IKT54664.2021.9685058

M. Alikhani, Marzieh Sheikhi, Vesal Hakami

{"title":"NFV-Based Distributed Service Function Chaining with Imperfect Information","authors":"M. Alikhani, Marzieh Sheikhi, Vesal Hakami","doi":"10.1109/IKT54664.2021.9685058","DOIUrl":"https://doi.org/10.1109/IKT54664.2021.9685058","url":null,"abstract":"Software-defined networking (SDN) and network function virtualization (NFV) technologies have emerged as promising paradigms in recent innovations for deploying users' demanded services. In this context, service function chaining (SFC) helps telecommunication operators to provide complex network services and improve their performance. This paper first addresses the service function chain deployment problem as an integer linear programming (ILP) problem under an impractical non-causal assumption about the network information for which we provide a solution in a centralized fashion. However, in real-life networks, distributed schemes are more scalable. Also, some parameters, such as the latency of the links, fluctuate over time because of the sharing nature of cloud datacenters, and their probabilistic distributions are unknown prior to deployment. Therefore, we re-formulate the NFV -based SFC deployment problem as a noisy weighted congestion game and rely only on the actually experienced delay samples on each of the links to configure SFCs in a near-optimal fashion. In particular, we propose a multi-agent learning based algorithm using which each agent decides its VNF -based service chain only based on its own history of adopted actions and realized costs. By changing the network configuration, simulation results show that our proposed algorithm is at most 18% worse than the optimal solution, and in some situations, it behaves exactly the same as optimal results.","PeriodicalId":274571,"journal":{"name":"2021 12th International Conference on Information and Knowledge Technology (IKT)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133410378","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

Smart City Standardized Evaluation: Use Case of Mashhad 智慧城市标准化评估:马什哈德用例

2021 12th International Conference on Information and Knowledge Technology (IKT) Pub Date : 2021-12-14 DOI: 10.1109/IKT54664.2021.9685532

Seyed Mohammad Reza Mirsarraf, A. Yari, Navid Zohdi, Ali Motevalizadeh

引用次数: 0

Face Recognition Based on Local Statistical Features and Artificial Neural Network 基于局部统计特征和人工神经网络的人脸识别

2021 12th International Conference on Information and Knowledge Technology (IKT) Pub Date : 2021-12-14 DOI: 10.1109/IKT54664.2021.9685142

Mehdi Moghimi, H. Grailu

引用次数: 0

An Improved Image Classification Based In Feature Extraction From Convolutional Neural Network: Application To Flower Classification 基于卷积神经网络特征提取的改进图像分类:在花卉分类中的应用

2021 12th International Conference on Information and Knowledge Technology (IKT) Pub Date : 2021-12-14 DOI: 10.1109/IKT54664.2021.9685994

Faeze Sadati, B. Rezaie

引用次数: 4

Similarity Measures in Medical Image Registration A Review Article 医学图像配准中的相似度测度综述

2021 12th International Conference on Information and Knowledge Technology (IKT) Pub Date : 2021-12-14 DOI: 10.1109/IKT54664.2021.9685453

Zohre Mohammadi, M. Keyvanpour

{"title":"Similarity Measures in Medical Image Registration A Review Article","authors":"Zohre Mohammadi, M. Keyvanpour","doi":"10.1109/IKT54664.2021.9685453","DOIUrl":"https://doi.org/10.1109/IKT54664.2021.9685453","url":null,"abstract":"Image registration is one of the most important problems in medical image analysis. It refers to the process of geometric alignment between two images based on correspondence. A crucial step in medical image registration process is to determine a similarity measure. There are various similarity measure techniques in this field that are applied in different registration applications. Selecting an effective similarity measure is a challenging problem, and this choice affects the accuracy of the registration results. According to past research, the similarity measures have extended from traditional to deep learning based methods. Our goal in this paper is to provide a literature review on various similarity measure techniques in medical image registration, classify them, and introduce main challenges. Thus Similarity measure techniques based on various registration approaches have been classified into two main classes and several subclasses namely distance based, correlation based, and information based in traditional methods; and statistical based, learning based, and similarity measure based loss function in learning based methods. Based on this classification, methods are introduced and each category is evaluated based on accuracy, speed, robustness, and complexity. Finally, recognizing and evaluating the different similarity criteria will help to select the appropriate similarity measure according to the intended application.","PeriodicalId":274571,"journal":{"name":"2021 12th International Conference on Information and Knowledge Technology (IKT)","volume":"85 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116414516","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