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A binary whale optimization algorithm with hyperbolic tangent fitness function for feature selection 基于双曲正切适应度函数的特征选择二元鲸优化算法
2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2017-12-01 DOI: 10.1109/INTELCIS.2017.8260031
A. Hussien, E. H. Houssein, A. Hassanien
{"title":"A binary whale optimization algorithm with hyperbolic tangent fitness function for feature selection","authors":"A. Hussien, E. H. Houssein, A. Hassanien","doi":"10.1109/INTELCIS.2017.8260031","DOIUrl":"https://doi.org/10.1109/INTELCIS.2017.8260031","url":null,"abstract":"To overcome the curse of dimensionality problem, a binary variant of the whale optimization algorithm (bWOA) with V-shaped is proposed. A hyperbolic tangent function is employed as a fitness function for mapping the continuous values to binary ones. Feature selection (FS) has attracted much attention in recent years and played a critical role in dealing with high-dimensional problems and can be modeled as an optimization problem. Eleven datasets from UCI repository from various applications are used. During the experiments, the effectiveness of feature selection is tested via a different type of data and size of features in the generic dataset. Furthermore, Wilcoxons rank-sum nonparametric statistical test was carried out at 5% significance level to judge whether the results of the proposed algorithms differ from those of the other compared algorithms in a statistically significant way. The quantitative and qualitative results revealed that the proposed binary algorithm in the FS domain is capable of minimizing the number of selected features as well as maximizing the classification accuracy within an appropriate time.","PeriodicalId":321315,"journal":{"name":"2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":"36 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":"132761070","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: 87
Single depth map super-resolution: A self-structured sparsity representation with non-local total variation technique 单深度图超分辨率:非局部全变分技术的自结构稀疏表示
2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2017-12-01 DOI: 10.1109/INTELCIS.2017.8260025
Doaa A. Altantawy, A. Saleh, S. Kishk
{"title":"Single depth map super-resolution: A self-structured sparsity representation with non-local total variation technique","authors":"Doaa A. Altantawy, A. Saleh, S. Kishk","doi":"10.1109/INTELCIS.2017.8260025","DOIUrl":"https://doi.org/10.1109/INTELCIS.2017.8260025","url":null,"abstract":"Recently, depth maps introduce a very effective representation for solving many fundamental computer vision problems. However, modern 3D scanning devices, such as TOF (Time Of Flight) cameras and Microsoft Kinect sensor, provide a huge unbalance between the resolution of the intensity image and its corresponding depth map. Here, we address the problem of single depth map up-sampling using a new non-local total variation decomposition process with a self-learning structured sparsity model. This technique considers the fact of the decomposed components should be regularized by different constraints, hence better representation for depth maps in sparse domain can be achieved. Using different datasets, experimental results demonstrate superior effectiveness in terms of qualitative and quantitative measures.","PeriodicalId":321315,"journal":{"name":"2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":"1 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":"131456384","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
Electrocardiogram (ECG) heart disease diagnosis using PNN, SVM and Softmax regression classifiers 基于PNN、SVM和Softmax回归分类器的心电图心脏病诊断
2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2017-12-01 DOI: 10.1109/INTELCIS.2017.8260040
Hadeer El-Saadawy, M. Tantawi, Howida A. Shedeed, M. Tolba
{"title":"Electrocardiogram (ECG) heart disease diagnosis using PNN, SVM and Softmax regression classifiers","authors":"Hadeer El-Saadawy, M. Tantawi, Howida A. Shedeed, M. Tolba","doi":"10.1109/INTELCIS.2017.8260040","DOIUrl":"https://doi.org/10.1109/INTELCIS.2017.8260040","url":null,"abstract":"In this paper, an automatic method is proposed to classify heart beats into 15 classes mapped to five main categories. Dynamic segmentation strategy is utilized to keep into consideration the heart rate variation. Discrete Wavelet Transform (DWT) is then applied on the segmented heart beats to extract the features for the description of each segment. The features extracted are then subjected to principle component analysis (PCA) to remove the irrelevant features due to its high dimension. Thereafter, Support Vector Machine (SVM), Softmax regression and Probabilistic Neural Network (PNN) algorithms are applied to the reduced features. Finally, MIT-BIH dataset is utilized as an evaluation dataset with tenfold cross validation strategy to achieve 97.1%, 98.3% and 93.3% overall accuracy and 88.7%, 83.3% and 42.0% average accuracy using SVM, PNN and Softmax regression respectively.","PeriodicalId":321315,"journal":{"name":"2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":"1 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":"130323684","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: 8
Robust and blind watermark to protect 3D mesh models against connectivity attacks 鲁棒和盲水印保护3D网格模型免受连接攻击
2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2017-12-01 DOI: 10.1109/INTELCIS.2017.8260022
M. R. Mouhamed, Mona M. Solima, A. Darwish, A. Hassanien
{"title":"Robust and blind watermark to protect 3D mesh models against connectivity attacks","authors":"M. R. Mouhamed, Mona M. Solima, A. Darwish, A. Hassanien","doi":"10.1109/INTELCIS.2017.8260022","DOIUrl":"https://doi.org/10.1109/INTELCIS.2017.8260022","url":null,"abstract":"Due to the significant development of the internet and communications, a huge amount of data is sent and received every day through this network, the evil face of this evolution is the security of these data. The 3D presentation is a type of these data that needs to be secured, where these data used in a confidential issue like GIS maps. Also, it costs a lot of money and time to produce the 3D presentation like the data which generated by CAD. This paper presents a blind 3D watermarking technique that prevents the 3D data from illegal piracy and guarantees the authentication of these data, and the technique used the spherical coordinates of the vertices of the model to embed the watermark using a random table as a key to makes it more secure. Experimental results demonstrate that the proposed approach is giving pretty immeasurable imper-ceptibility compared with other techniques likewise is robust against different non-noxious attacks, for example, Similarity transformation, cropping, subdivision, and simplification.","PeriodicalId":321315,"journal":{"name":"2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":"161 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":"127747979","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
Evaluation of cloud service ontologies 云服务本体的评估
2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2017-12-01 DOI: 10.1109/INTELCIS.2017.8260045
Y. Afify, N. Badr, I. Moawad, M. Tolba
{"title":"Evaluation of cloud service ontologies","authors":"Y. Afify, N. Badr, I. Moawad, M. Tolba","doi":"10.1109/INTELCIS.2017.8260045","DOIUrl":"https://doi.org/10.1109/INTELCIS.2017.8260045","url":null,"abstract":"It is the cloud computing era. Substantial number of Cloud Services (CS) have emerged. The automation of the cloud services life cycle can be enhanced using domain ontologies in the cloud environment. Ontologies allow more efficient CS publication, discovery, selection, composition and recommendations. However, no broad cloud ontology for this purpose has dominated yet. This paper presents an in-depth analysis of existing cloud taxonomies and service ontologies. We present a comparison of the cloud service ontologies implementation features. Moreover, a semiotic metrics suite of ontology quality is used for the assessment process.","PeriodicalId":321315,"journal":{"name":"2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":"47 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":"116627416","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
Patterned fabric defect detection system using near infrared imaging 采用近红外成像技术的花纹织物疵点检测系统
2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2017-12-01 DOI: 10.1109/INTELCIS.2017.8260041
A. A. Hamdi, M. Fouad, M. Sayed, M. Hadhoud
{"title":"Patterned fabric defect detection system using near infrared imaging","authors":"A. A. Hamdi, M. Fouad, M. Sayed, M. Hadhoud","doi":"10.1109/INTELCIS.2017.8260041","DOIUrl":"https://doi.org/10.1109/INTELCIS.2017.8260041","url":null,"abstract":"Patterned fabric defect detection based on inspection with visual light source suffers from two main problems; the fabric pattern itself, which complicates the defect detection process and the undesirable effect of surrounding illumination. These problems lead to reducing the detection success rates due to underdetection and misdetection. In this paper, a computer vision system that can detect fabric defects in patterned fabrics is proposed. The proposed system utilizes near-infrared imaging to overcome visual light source imaging drawbacks. It employs the non-extensive standard deviation filtering and minimum error thresholding method to detect defects. In addition to the simplicity of the proposed algorithm, it produces high accuracy rate that reaches 97%. The proposed algorithm can also be extended to defect detection on plain fabrics.","PeriodicalId":321315,"journal":{"name":"2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":"255 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":"115784306","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
A comparative study on opinion mining algorithms of social media statuses 社交媒体状态意见挖掘算法的比较研究
2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2017-12-01 DOI: 10.1109/INTELCIS.2017.8260067
Donia Gamal, Marco Alfonse, El-Sayed M. El-Horbaty, A. M. Salem
{"title":"A comparative study on opinion mining algorithms of social media statuses","authors":"Donia Gamal, Marco Alfonse, El-Sayed M. El-Horbaty, A. M. Salem","doi":"10.1109/INTELCIS.2017.8260067","DOIUrl":"https://doi.org/10.1109/INTELCIS.2017.8260067","url":null,"abstract":"The Social Media (SM) is affecting clients' preferences by modeling their thoughts, attitudes, opinions, views and public mood. Observing the SM activities is a decent approach to measure clients' loyalty, keeping a track on their opinion towards products preferences or social event. Opinion Mining (OM) is the most rising research field of text mining using Machine Learning (ML) algorithms and Natural Language Processing (NLP). Several algorithms such as Support Vector Machines (SVM), Naïve Bayes (NB) and Maximum Entropy (ME), were utilized to extract information that differentiates the user's opinion whether it's positive, negative or neutral. User's opinions and reviews are very beneficial information for individuals, businesses, and governments. In this paper, we compare the intelligent algorithms, which are utilized for OM in SM data over the last five years. The results show that using SVM with Part Of Speech (POS) or POS, Unigram and Bigram with J48 accomplish Sentiment Classification (SC) accuracy 92%.","PeriodicalId":321315,"journal":{"name":"2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":"67 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":"114774504","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
Stack auto-encoders approach for malignant melanoma diagnosis in dermoscopy images 堆栈自编码器方法在皮肤镜图像中诊断恶性黑色素瘤
2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2017-12-01 DOI: 10.1109/INTELCIS.2017.8260079
Munya A. Arasi, El-Sayed M. El-Horbaty, A. M. Salem, E. El-Dahshan
{"title":"Stack auto-encoders approach for malignant melanoma diagnosis in dermoscopy images","authors":"Munya A. Arasi, El-Sayed M. El-Horbaty, A. M. Salem, E. El-Dahshan","doi":"10.1109/INTELCIS.2017.8260079","DOIUrl":"https://doi.org/10.1109/INTELCIS.2017.8260079","url":null,"abstract":"In this paper we present Computer Aided Diagnosis (CAD) system for malignant melanoma diagnostic based on deep learning using Stack Auto-Encoders. The dermoscopy images are taken from Dermatology Information System (DermIS) and DermQuest, the image enhancement is achieved by various pre-processing approaches. The extracted features are based on Discrete Wavelet Transform (DWT) and texture Analysis. These features become the input to Stack Auto-Encoders (SAEs) for training and testing the lesions as malignant or benign. The experimental results show the rate of accuracy for texture analysis and SAEs is 89.3%, while using DWT and SAEs gives a higher rate of accuracy about 94%. The experimental results prove that the proposed approaches are more accurate than other approaches in this field of melanoma diagnosis.","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":"125436611","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
Prediction of liver cancer development risk in genotype 4 hepatitis C patients using knowledge discovery modeling 利用知识发现模型预测基因4型丙型肝炎患者肝癌发生风险
2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2017-12-01 DOI: 10.1109/INTELCIS.2017.8260080
Tasneem A. Gameel, S. Rady, Khaled El-Bahnasy, S. Kamal
{"title":"Prediction of liver cancer development risk in genotype 4 hepatitis C patients using knowledge discovery modeling","authors":"Tasneem A. Gameel, S. Rady, Khaled El-Bahnasy, S. Kamal","doi":"10.1109/INTELCIS.2017.8260080","DOIUrl":"https://doi.org/10.1109/INTELCIS.2017.8260080","url":null,"abstract":"Hepatitis C is a primary reason for the liver cancer, which is a leading cause of death. The objective of this paper is to predict the hepatitis C infection progression into cirrhosis or liver cancer. For the prediction of the disease progression, a knowledge discovery framework is proposed consisting of three phases: preprocessing, data mining and prediction. While the preprocessing phase focuses on the discretization of the training data, the data mining phase focuses on mining patients' records using a rule based classifier built by the proposed algorithm to generate a set of unique rules. Eventually, the predictor uses the rules to predict patients' disease progression. Experimentation on 1908 chronic hepatitis C Egyptian patients with 27 extracted features collected from blood samples were used to train the model, with other 406 patients' cases for testing which showed accuracy 99.5 %.","PeriodicalId":321315,"journal":{"name":"2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":"8 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":"124341638","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
Robust logo watermarking based on wavelet transform modulus maxima 基于小波变换模极大值的鲁棒图像水印
2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2017-12-01 DOI: 10.1109/INTELCIS.2017.8260049
M. Barr, C. Serdean
{"title":"Robust logo watermarking based on wavelet transform modulus maxima","authors":"M. Barr, C. Serdean","doi":"10.1109/INTELCIS.2017.8260049","DOIUrl":"https://doi.org/10.1109/INTELCIS.2017.8260049","url":null,"abstract":"In this paper, a novel blind logo image watermarking technique is presented which can be used to protect the copyright of digital images. Our technique uses two different watermarks. One is a high capacity multi-bit watermark used to embed a logo and the other is a low capacity single-bit watermark used for the detection of geometrical attacks. Both watermarks are embedded into the red, green, and blue components of an RGB image, weighted based on human visual system considerations. Each non-overlapping watermark is embedded using a spread spectrum approach, based on a pseudorandom noise (PN) sequence and a unique secret key. Robustness against geometric attacks is achieved with the help of wavelet transform modulus maxima, which is shift invariant, and by embedding it in different sub-bands of the wavelet transform. The experimental results show that our proposed watermarking scheme is robust to geometric attacks such as rotation, scaling, and translation and compares favourably against existing techniques.","PeriodicalId":321315,"journal":{"name":"2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":"272 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":"122837199","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
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