2017 International Conference on Current Research in Computer Science and Information Technology (ICCIT)最新文献

Developed clustering approaches to enhance the data transmissions in WSNs 提出了提高无线传感器网络数据传输性能的聚类方法

2017 International Conference on Current Research in Computer Science and Information Technology (ICCIT) Pub Date : 2017-04-26 DOI: 10.1109/CRCSIT.2017.7965541

S. T. Hasson, Hawraa Abd Al-kadhum

{"title":"Developed clustering approaches to enhance the data transmissions in WSNs","authors":"S. T. Hasson, Hawraa Abd Al-kadhum","doi":"10.1109/CRCSIT.2017.7965541","DOIUrl":"https://doi.org/10.1109/CRCSIT.2017.7965541","url":null,"abstract":"Wireless sensor networks (WSNs) usually build from huge number of randomly deployed sensor nodes in certain area. The sensors are mainly utilized to monitor physical and environmental conditions, gather information, process this data locally and transfer the sensed data back to the Base Station (BS). The main objective of this paper is to simulate, evaluate and observe the behavior of developed clustering approaches and compare their performance metrics. In this study all the cluster nodes must sensing certain data and transmit it to its cluster head (CH). These data will be collected at particular nodes known as cluster heads that is previously assigned for each cluster. The CH aggregates the data and forwards it to the base station or a node sink. In this study two developed clustering approaches are suggested and created using Net Logo (5.2.1 version 2015). These approaches are Extreme node and double Extreme nodes. In addition to these two approaches, the DB-Scan clustering approach is also suggested to be used as a reference to compare its results with these two suggested algorithms. Results show certain improvement in these suggested algorithms. Many performance metrics can be used to Measure the performance of the suggested WSN such as NRL, PDF, End-to-end and throughput.","PeriodicalId":312746,"journal":{"name":"2017 International Conference on Current Research in Computer Science and Information Technology (ICCIT)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125037038","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: 6

Classification and discrimination of focal and non-focal EEG signals based on deep neural network 基于深度神经网络的病灶与非病灶脑电信号分类与识别

2017 International Conference on Current Research in Computer Science and Information Technology (ICCIT) Pub Date : 2017-04-26 DOI: 10.1109/CRCSIT.2017.7965539

A. M. Taqi, Fadwa Al-Azzo, M. Mariofanna, Jassim M. Al-Saadi

{"title":"Classification and discrimination of focal and non-focal EEG signals based on deep neural network","authors":"A. M. Taqi, Fadwa Al-Azzo, M. Mariofanna, Jassim M. Al-Saadi","doi":"10.1109/CRCSIT.2017.7965539","DOIUrl":"https://doi.org/10.1109/CRCSIT.2017.7965539","url":null,"abstract":"In this paper, a new model of focal and non-focal electroencephalography classification is carried out using a deep neural network (DNN). The Convolution Architecture For Feature Extraction (Caffe) framework with three different models (LeNet, AlexNet, and GoogLeNet) are applied, where the DNN is trained with different training epoch values (TEs). The performance of discriminating the focal and non-focal EEG signals using soft-max classifier is investigated. This classification serves medical specialists for taking a surgery decision of focal epilepsy patient. In this work, the EEG signals acquired from EEG database in literature works for five epilepsy patients are used for examining the proposed scheme. The results demonstrate a significant performance in terms of the classification accuracy and the remarkable short running time, via few numbers of the training epochs (TEs). However, the first model (LeNet) displays the best performance. Overall, the proposed classification approach provides a better performance as compared with the existing state-of-the-art techniques. Classification accuracy result is 100% for LeNet model at TE=2, while the accuracy of AlexNet reaches to 100% at TE=5, and finally, GoogLeNet touches an accuracy of 100% at TE=10.","PeriodicalId":312746,"journal":{"name":"2017 International Conference on Current Research in Computer Science and Information Technology (ICCIT)","volume":"220 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126969174","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: 29

Robot path planning based on PSO and D∗ algorithmsin dynamic environment 动态环境下基于粒子群算法和D *算法的机器人路径规划

2017 International Conference on Current Research in Computer Science and Information Technology (ICCIT) Pub Date : 2017-04-26 DOI: 10.1109/CRCSIT.2017.7965550

A. Sadiq, Ali Hadi Hasan

引用次数: 14

Classification of red blood cells disease using fuzzy logic theory 模糊逻辑理论在红细胞疾病分类中的应用

2017 International Conference on Current Research in Computer Science and Information Technology (ICCIT) Pub Date : 2017-04-26 DOI: 10.1109/CRCSIT.2017.7965558

Ziad M. Abood, G. S. Karam, Rafied E. Hluot

引用次数: 6

Arabic text mining based on clustering and coreference resolution 基于聚类和共指解析的阿拉伯语文本挖掘

2017 International Conference on Current Research in Computer Science and Information Technology (ICCIT) Pub Date : 2017-04-26 DOI: 10.1109/CRCSIT.2017.7965549

S. Mahmood, Faiez Musa Lahmood Al-Rufaye

引用次数: 3

Image processing technique for determining cold thyroid nodules 确定甲状腺冷结节的图像处理技术

2017 International Conference on Current Research in Computer Science and Information Technology (ICCIT) Pub Date : 2017-04-26 DOI: 10.1109/CRCSIT.2017.7965547

Amir Torab-Miandoab, Peyman Rezaei-Hachesu, T. Samad, Sogand Habibi-chenaran

引用次数: 4

Human voice extracted biometric features: What can be used for 提取人类声音的生物特征:可用于什么

2017 International Conference on Current Research in Computer Science and Information Technology (ICCIT) Pub Date : 2017-04-26 DOI: 10.1109/CRCSIT.2017.7965543

S. Sadkhan, Baheeja K. Al-Shukur, Ali K. Mattar

引用次数: 5

Dijkstra algorithm applied: Design and implementation of a framework to find nearest hotels and booking systems in Iraqi Dijkstra算法的应用:设计和实现一个框架，在伊拉克找到最近的酒店和预订系统

2017 International Conference on Current Research in Computer Science and Information Technology (ICCIT) Pub Date : 2017-04-26 DOI: 10.1109/CRCSIT.2017.7965546

A. Ahmed, S. H. Ahmed, Omed Hassan Ahmed

引用次数: 11

A comprehensive study of spectrum sensing techniques in cognitive radio networks 认知无线电网络中频谱感知技术的综合研究

2017 International Conference on Current Research in Computer Science and Information Technology (ICCIT) Pub Date : 2017-04-26 DOI: 10.1109/CRCSIT.2017.7965542

Ahmed M. Jasim, Haidar N. Al-Anbagi

引用次数: 9

Enhancing 3D-playfair algorithm to support all the existing characters and increase the resistanceto brute force and frequency analysis attacks 增强3d游戏公平算法，支持所有现有的字符，并增加对暴力破解和频率分析攻击的抵抗力

2017 International Conference on Current Research in Computer Science and Information Technology (ICCIT) Pub Date : 2017-04-26 DOI: 10.1109/CRCSIT.2017.7965538

A. Ahmed, S. H. Ahmed, Omed Hassan Ahmed

引用次数: 4