2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS)最新文献

{"title":"Developing an efficient spectral clustering algorithm on large scale graphs in spark","authors":"A. Taloba, Marwan R. Riad, T. H. Soliman","doi":"10.1109/INTELCIS.2017.8260077","DOIUrl":"https://doi.org/10.1109/INTELCIS.2017.8260077","url":null,"abstract":"Recently, most of the data can be represented by graph structures, such as social media, Protein-Protein Interaction, transportation system, systems biology,…, etc. Many researches have been achieved to cluster very large graphs but more efficient algorithms are required since such a process takes a long time and requires more memory. In this paper, we propose an Efficient Spectral Clustering Algorithm on Large Scale Graphs in Spark (ESCALG), using map reduce function and shuffling phases in Dijkstra's algorithm. In addition, ESCALG depends mainly on a sparse matrix as a data structure, which less time in execution. Then, GraphX is applied to deal with graph data processing and in GraphX used Pregel in computing shortest path. To test the performance of ESCALG, it is compared with Large-Scale Spectral Clustering on Graphs and Standard Spectral Clustering Algorithms using seven datasets, where ESCALG proved high efciency in terms of memory and time performance.","PeriodicalId":321315,"journal":{"name":"2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131614812","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}

{"title":"Test pattern generator optimization for digital testing of analogue circuits","authors":"Aiman M. Mousa, M. El-Mahlawy","doi":"10.1109/INTELCIS.2017.8260042","DOIUrl":"https://doi.org/10.1109/INTELCIS.2017.8260042","url":null,"abstract":"In this paper, the proposed design for digital testing of analogue circuits (DTAC) is presented. The proper selection of the analogue test pattern generator (ATPG) for stimulating and detecting faults is the target issue. Component tolerance of the analogue circuit under test produces signature boundaries in the analogue test response compactor. Signature boundary difference (SBD) is determined for different ATPGs. The minimization of the SBD increases the differentiation between the faulty and golden cases. Each part of the DTAC is modeled and evaluated to select the proper ATPG such that the SBD is minimized. Based on the experimental results of some analogue benchmark circuits, the best ATPG for detecting faults is selected based on the minimal SBD. Different ATPGs may change from a circuit to another one. These results are contrary to the previous published work that selected the pulse waveform as the best ATPG because of its superiority in terms of power spectral density.","PeriodicalId":321315,"journal":{"name":"2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117292969","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}

{"title":"Privacy preserving recommender system based on improved MASK and query restriction","authors":"Reham Kamal, Wedad Hussein, R. Ismail","doi":"10.1109/INTELCIS.2017.8260071","DOIUrl":"https://doi.org/10.1109/INTELCIS.2017.8260071","url":null,"abstract":"In the last few decades, recommendation systems have received an iconic representation in the field of information technology. With the noticed rapid advancement of data mining, the issue of privacy has become an inevitable necessity. Hence, the main challenge that accompanies data mining is developing a cutting-edge strategy to protect private information. In this paper, we suggest a framework of recommendation for privacy protection based on an improved version of mining association with secrecy Konstraints'(MASK) using data perturbation and query restriction. Experimental results showed that our proposed system performance is high and can protect data privacy without decreasing the recommendations accuracy.","PeriodicalId":321315,"journal":{"name":"2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131151908","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}

{"title":"Long-term wind speed prediction based on optimized support vector regression","authors":"S. Osama, A. Darwish, E. H. Houssein, A. Hassanien, A. Fahmy, A. Mahrous","doi":"10.1109/INTELCIS.2017.8260035","DOIUrl":"https://doi.org/10.1109/INTELCIS.2017.8260035","url":null,"abstract":"Wind energy is considered as one of the most remarkably renewable energy origins that reduce the expenditure of electricity production. In the last decade, there are several forecasting speed of wind algorithms that have been to improve prediction reliability. Support Vector Regression (SVR) parameters such as kernel parameter, penalty factor (C) have a great effect on the complexity and reliability of forecasting algorithm. This paper proposed a hybrid approach based on Whale Optimization Algorithm (WOA) and SVR namely WOA-SVR for fixing issues which traditional methods cannot handle effectively and have shown high performance in many respects. The performance of proposed algorithm (WOA-SVR) is evaluated using several different aspects as well as the daily average wind speed data from Space Weather Monitoring Center (SWMC) in Egypt as a case study is used. For verification, the results of the proposed algorithm are compared with Particle Swarm Optimization (PSO) and the original SVR without parameters optimization. The experimental results showed that the proposed WOA-SVR algorithm is capable of finding the optimal values of SVR parameters, avoid local optima problem, and it is competitive for forecasting speed of the wind.","PeriodicalId":321315,"journal":{"name":"2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117100780","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}

{"title":"Ubiquitous smart learning system for smart cities","authors":"Hisham Elhoseny, M. Elhoseny, Samir Abdelrazek, A. Riad, A. Hassanien","doi":"10.1109/INTELCIS.2017.8260058","DOIUrl":"https://doi.org/10.1109/INTELCIS.2017.8260058","url":null,"abstract":"This paper proposes a customized Ubiquitous smart Teaching and smart-Learning system that use Internet of Things (IoT), huge information, supercomputing, and profound figuring out how to give improved advancement, administration, and conveyance of instructing and learning in smart society settings. A proof of an idea is that framework has been created in light of the structure. A detailed, gritty outline, execution, and assessment of the smart learning framework, including its five parts, are given utilizing 11 utilized datasets.","PeriodicalId":321315,"journal":{"name":"2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122828623","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}

{"title":"FPSS: Fingerprint-based semantic similarity detection in big data environment","authors":"M. Elhoseny, M. Zaher, A. Shehab, A. Hassanien","doi":"10.1109/INTELCIS.2017.8260066","DOIUrl":"https://doi.org/10.1109/INTELCIS.2017.8260066","url":null,"abstract":"Although the problem of plagiarism is an ancient problem that exists before the start of internet revolution, the accessibility of free and easy accessed electronic paper on the Internet complicated and increased the problem. However, there are many systems for detecting plagiarism in natural language documents. Contrary to Latin documents, the same Arabic letter can be written into three various ways based on its position in the word. The complex nature of writing Arabic documents makes such system is a big challenge. Accordingly, this paper presents a Fingerprint-Based Semantic Similarity detection system, called (FPSS) to detect plagiarism in Arabic documents. It generates a digital fingerprint (df) for each sentence and compares all the df values. Moreover, it analyzes corresponding detection schemes to detect Semantic Similarity effectively. FPSS improves the effectiveness regarding the matched similarity ratio, the precision ratio, the recall ratio, the F-measure ratio, the plagdet ratio, and the granularity ratio.","PeriodicalId":321315,"journal":{"name":"2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125090775","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}

{"title":"Occlusion resolving inside public crowded scenes based on social deep learning model","authors":"A. S. Elons, Magdy Abol-Ela","doi":"10.1109/INTELCIS.2017.8260050","DOIUrl":"https://doi.org/10.1109/INTELCIS.2017.8260050","url":null,"abstract":"Past decade, the field of video analytics has been rapidly developing specially for crowd scenes. The advances in computational resources inspired researchers to build reliable video analytics systems that works real. The main root for any video analytics system is threat activity localization inside video streams. One major issue toward achieving that objective is Occlusion due to crowd intensity. In this paper, a hybrid deep learning model that exploits Convolution Neural Network (CNN) and Social Long Short-Term Memory (LSTM) for real-time video streaming analytics. The experiments were conducted on public available dataset UCY which contains 2 main scenes with 786 persons and 55 actions. The results concluded the superiority of Social LSTM over conventional LSTM and Mean Square Error (MSE) does not exceed 0.25.","PeriodicalId":321315,"journal":{"name":"2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127039635","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}

{"title":"Modeling real-time safety critical systems using hierarchical communicating real-time state machines and c-lang parser","authors":"A. Bakr, M. Fouda, M. Salama, Abdelwahab K. Alsammak, H. Yahia","doi":"10.1109/INTELCIS.2017.8260054","DOIUrl":"https://doi.org/10.1109/INTELCIS.2017.8260054","url":null,"abstract":"Functional safety is one of the most critical aspects of electronic systems. Testing functional safety of a real-time system requires taking time into consideration. In this paper, a new modeling approach, based on hierarchical communicating real-time state machines (H-CRSM) is proposed that models safety critical hazardous scenarios which may occur in a real-time system. The input system is implemented in ANSI-C that follows ISO 26262 standards. Our proposal automatically generates a model that saves important time aspects and characteristics of the real-time system. C-Lang parser is used to generate abstract syntax tree (AST) that is traversed and an H-CRSM model is generated. Two case studies demonstrate how the approach generates the H-CRSM model.","PeriodicalId":321315,"journal":{"name":"2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127750829","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}

{"title":"Towards effective traffic congestion prediction in Egypt","authors":"John F. W. Zaki, Amr M. T. Ali-Eldin","doi":"10.1109/INTELCIS.2017.8260052","DOIUrl":"https://doi.org/10.1109/INTELCIS.2017.8260052","url":null,"abstract":"Traffic congestion in Egypt is an issue where the aspects of the problem seem different from those of western countries. The problem is exaggerated in cities away from the capital where lack of governmental interest in such areas adds another dimension to the problem. The main objective of this paper is to investigate whether it is useful to apply known traffic congestion prediction approaches on an Egyptian highway and to analyse their performance. In order to do so, the following was performed : 1) Create an Egyptian dataset for a short term highways. 2) Apply known traffic congestion techniques such as artificial neural networks and neuro-fuzzy approaches on that dataset and to test their performance. Empirical work showed that traffic congestion could be effectively predicted using the applied methods. It also showed that error obtained using these methods outperforms related work on other datasets. Additionally, tests for assuring the goodness of fit are performed.","PeriodicalId":321315,"journal":{"name":"2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116802004","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}

{"title":"The influence of the analysis technique on myocardial T2 estimation using cardiac magnetic resonance imaging (CMR)","authors":"M. Hammad, A. Khalifa, W. Al-Atabany, El-Sayed H. Ibrahim","doi":"10.1109/BIBE.2017.00-35","DOIUrl":"https://doi.org/10.1109/BIBE.2017.00-35","url":null,"abstract":"Cardiovascular disease is the main cause of death worldwide. Magnetic resonance imaging (MRI) has been considered as a noninvasive technique for characterizing myocardial tissues. Specifically, T2 mapping technique has been demonstrated to be an excellent tool to detect myocardial edema by estimation of the transverse relaxation time constant (T2) of myocardial tissue. However, there are several factors that could influence the analysis technique and affect the estimating T2 value. These factors include the type of exponential fitting model, which either be single exponential or exponential plus constant fitting model, the signal intensity estimation method, which either be Average, Median or Mapping method and signal-to-noise ratio (SNR) level of the T2-weighted images. In this paper, we discuss the effect of these factors on T2 estimation using a numerical phantom, T2 calibrated phantoms with different T2 values, and human subjects. The results of numerical phantom showed that the average percentage error for T2 measurement when using the single exponential fitting model reached 0.6 %, 0.4 %, and 5.9 % for the Average, Median, and Mapping methods, respectively. However, the exponential plus constant fitting model resulted in high average percentage error among the three signal intensity estimation methods (above 26 %). The experiments of calibrated phantom resulted in high correlation (R2 > 0.99) between the estimated and reference T2 values when using the single exponential fitting model compared to low correlation (R2 < 0.73) when using the exponential plus constant fitting model at high SNR levels. Finally, the results of human subjects were in agreement with both numerical and calibrated phantoms. Based on the results of this paper, the single exponential fitting model with the median estimation method is the preferable analysis technique for T2 measurement as it results in a lower error for T2 measurements at low SNR levels and higher correlation values at high SNR levels compared to the other analysis techniques.","PeriodicalId":321315,"journal":{"name":"2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129001123","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}