2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS)最新文献_第6页

A Proposed Rough Set Based Case Base Partitioning Approach to Enhance Indexing Using Unique Combinations 一种基于粗糙集的案例基分区方法，利用唯一组合增强索引

2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2019-12-01 DOI: 10.1109/ICICIS46948.2019.9014691

A. Abdel-Halim, Mustafa Abdel-Azim Mustafa, Khaled El-Bahnasy

{"title":"A Proposed Rough Set Based Case Base Partitioning Approach to Enhance Indexing Using Unique Combinations","authors":"A. Abdel-Halim, Mustafa Abdel-Azim Mustafa, Khaled El-Bahnasy","doi":"10.1109/ICICIS46948.2019.9014691","DOIUrl":"https://doi.org/10.1109/ICICIS46948.2019.9014691","url":null,"abstract":"In this research, a rough set-based case base partitioning approach is proposed for enhancing the process of discovering all unique feature combinations (UFCs) for each decision in a case base, which were used as an index for the case base with high accuracy. Discovering all UFCs is an NP-hard problem, which requires-in principle-to verify an exponential number of feature combinations for uniqueness on all data values. UFC-discovery techniques depend on entire case base as a single search space (SS), causing poor flexibility and making parallelization and distribution hard. Moreover, high complexities of some decisions may impede the whole process, and could be an obstacle for computing all UFCs for decisions with low complexities. Achieving efficiency and scalability in this context is a tremendous challenge by itself. The proposed approach divides case base into independent, clean and complete SSs; one for each decision. Each decision's SS is free from useless rules; and is used independently to discover all UFCs for the decision. The approach is designed and implemented using MapReduce to be applicable to large case bases. The validity of the proposed approach is proved mathematically. Experimental evaluation showed that SSs were created successfully and that the accuracy and results of UFC-discovery technique were not affected after partitioning. Applying UFC-discovery technique on decisions' SSs sequentially using single machine performed at 80.8% better than before partitioning. It is possible to reach 96.5% reduction in execution time when applying UFC-discovery technique on all decisions' SSs simultaneously and parallely.","PeriodicalId":200604,"journal":{"name":"2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129029749","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

Steady State Analysis of Buffer Contents in a General Communication System 通用通信系统中缓冲内容的稳态分析

2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2019-12-01 DOI: 10.1109/ICICIS46948.2019.9014737

Fayza A. Nada

引用次数: 3

Transformation of UML State Machine To YAWL UML状态机到YAWL的转换

2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2019-12-01 DOI: 10.1109/ICICIS46948.2019.9014793

Meriem Kherbouche, Khawla Bouafia, B. Molnár

引用次数: 2

Wireless ECG Monitoring System for Telemedicine Application 无线心电监护系统在远程医疗中的应用

2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2019-12-01 DOI: 10.1109/ICICIS46948.2019.9014845

Ahmed Aboalseoud, A. Youssry, M. El-Nozahi, A. El-Rafei, Ahmed Elbialy, H. Ragaai, A. Wahba

引用次数: 4

Lung Nodule Detection and Classification using Random Forest: A Review 基于随机森林的肺结节检测与分类综述

2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2019-12-01 DOI: 10.1109/ICICIS46948.2019.9014706

Nada S. El-Askary, M. A. Salem, Mohamed Roushdy

引用次数: 1

Secured Framework for IoT Using Blockchain 使用区块链的物联网安全框架

2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2019-12-01 DOI: 10.1109/ICICIS46948.2019.9014853

Ali H. Ahmed, Nagwa M. Omar, H. Ibrahim

引用次数: 2

Temporal Action Detection with Fused Two-Stream 3D Residual Neural Networks and Bi-Directional LSTM 融合两流三维残差神经网络和双向LSTM的时间动作检测

2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2019-12-01 DOI: 10.1109/ICICIS46948.2019.9014707

Noorhan Khaled, M. Marey, M. Aref

{"title":"Temporal Action Detection with Fused Two-Stream 3D Residual Neural Networks and Bi-Directional LSTM","authors":"Noorhan Khaled, M. Marey, M. Aref","doi":"10.1109/ICICIS46948.2019.9014707","DOIUrl":"https://doi.org/10.1109/ICICIS46948.2019.9014707","url":null,"abstract":"This work presents an architecture for localizing interesting target events within long sequences of untrimmed videos. Mainly, we focus on finding temporal boundaries of target visual actions and bypassing irrelevant events of other actions. Both the appearance and motion information are crucial for discriminating between different actions. Based on this, we propose a trainable fused two-stream 3D Convolution neural network framework, integrated with a bi-directional Long Short-Term Memory sequence model (2-stream 3DCNN+ LSTM) for learning. The two stream CNN enables us to model features from both RGB and optical flow short video-clips of resolution $delta=16$ frames, extracted from the long input video sequence. This framework produces a sequence of class probability scores at each video-clip. Simple low-cost mean, average and max filters are used to localize and classify each relevant action instance and to label the whole video. Such architecture utilized the power of (1) two streams CNN architecture, (2) the spatiotemporal processing of 3D convolution network for capturing spatial and motion patterns, (3) temporal orderings and long-range dependencies of the sequence model for obtaining robust classifications at each time step. We evaluate our framework using THUMOS'15 dataset, attaining 98.9% accuracy and 35.8 % mAP in the video level classification and relevant action detection tasks, respectively.","PeriodicalId":200604,"journal":{"name":"2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":"237 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116587652","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

Artificial Neural Network as Ensemble Technique Fuser for Improving Classification Accuracy 人工神经网络作为集成技术提高分类精度的融合器

2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2019-12-01 DOI: 10.1109/ICICIS46948.2019.9014791

M. Elnahas, M. Hussein, A. Keshk

{"title":"Artificial Neural Network as Ensemble Technique Fuser for Improving Classification Accuracy","authors":"M. Elnahas, M. Hussein, A. Keshk","doi":"10.1109/ICICIS46948.2019.9014791","DOIUrl":"https://doi.org/10.1109/ICICIS46948.2019.9014791","url":null,"abstract":"Ensemble learning is one of the highly accurate and robust learning approaches. In these approaches, different classifiers are used as an ensemble technique, and then the main questions that faces up is how to fuse the results of each individual classifier into a final decision. In this paper, we will propose a fuser based on an Artificial Neural Network to produce the final decision. our proposed approach has been experimentally validated on three public datasets (available in UCI repository). The first dataset is called Wisconsin Prognosis Breast Cancer (WPBC) dataset. This dataset has 35 attributes and 198 instances. The second dataset is called Breast Cancer Wisconsin (Original) DataSet. This dataset has 10 attributes and 699 instances. The third dataset is called Diabetic Retinopathy Debrecen DataSet. This dataset has 20 attributes with and 1151 instances. Our proposed approach gives higher accuracy in these datasets. The accuracy of our approach is 82.7% with the (WPBC) DataSet, 98.5% with the Breast Cancer Wisconsin (Original) DataSet and 75% with Diabetic Retinopathy Debrecen DataSet","PeriodicalId":200604,"journal":{"name":"2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129792190","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

A Robust 3D Mesh Watermarking Approach Based on Genetic Algorithm 一种基于遗传算法的鲁棒三维网格水印方法

2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2019-12-01 DOI: 10.1109/ICICIS46948.2019.9014787

M. R. Mouhamed, Mona M. Soliman, A. Darwish, A. Hassanien

引用次数: 1

Small Objects Detection in Satellite Images Using Deep Learning 基于深度学习的卫星图像小目标检测

2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2019-12-01 DOI: 10.1109/ICICIS46948.2019.9014842

Ahmad Mansour, W. Hussein, Ehab Said

{"title":"Small Objects Detection in Satellite Images Using Deep Learning","authors":"Ahmad Mansour, W. Hussein, Ehab Said","doi":"10.1109/ICICIS46948.2019.9014842","DOIUrl":"https://doi.org/10.1109/ICICIS46948.2019.9014842","url":null,"abstract":"Using the deep Convolution Neural Networks (CNNs) for Object detection in satellite images accomplish promising results, especially for large objects. While Small objects detection in the same spatial resolution images does not attain the same results. For instance, vehicle detection in high-resolution satellite images, the targeted object maybe existed in an area that does not exceed 15 square pixels, which will not make a sufficient effect in the deeper layers. In addition; the interfering with the surrounding background, noise effect, the neighboring object's shadows, and various vehicle colors. In the proposed paper, an analysis study is performed to evaluate the effect of changing the object size on the detection results. A separate resampling algorithm is applied to the input test images to change its size - bear in mind the built-in detection model resampling layer-, which results in changing the object size, and accordingly extends the object impact in deep layers. Through Transfer Learning, the Faster R-CNN pre-trained object detection model with Inception-V2is applied to submeter satellite images and passenger vehicles as the target objects. The Experimental results show the change in detection accuracy with the change of the object size.","PeriodicalId":200604,"journal":{"name":"2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122455062","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