International Journal of Intelligent Computing and Information Sciences最新文献_第5页

UNCOVERING THE EFFECTS OF DATA VARIATION ON PROTEIN SEQUENCE CLASSIFICATION USING DEEP LEARNING 利用深度学习揭示数据变化对蛋白质序列分类的影响

International Journal of Intelligent Computing and Information Sciences Pub Date : 2022-05-18 DOI: 10.21608/ijicis.2022.123177.1168

F. Mostafa, Y. Afify, R. Ismail, N. Badr

{"title":"UNCOVERING THE EFFECTS OF DATA VARIATION ON PROTEIN SEQUENCE CLASSIFICATION USING DEEP LEARNING","authors":"F. Mostafa, Y. Afify, R. Ismail, N. Badr","doi":"10.21608/ijicis.2022.123177.1168","DOIUrl":"https://doi.org/10.21608/ijicis.2022.123177.1168","url":null,"abstract":": Bioinformaticians face an issue in analyzing and studying protein similarity as the number of proteins grows. Protein sequence analysis helps in the prediction of protein functions. It is critical for the analysis process to be able to appropriately categorize proteins based on their sequences. The extraction of features from protein sequences is done using a variety of methods. The goal of this study is to investigate the different variations of data on the classification performance of a deep learning model employing 3D data. First, few research questions were formulated regarding the impact of the following criteria: dataset size, IMF importance, feature size, and preprocessing on the proposed deep learning classification process. Second, comprehensive experiments were conducted to answer the research questions. Six feature extraction methods were utilized to create 3D features with two sizes (7x7x7 and 9x9x9), which were then fed into a convolutional neural network. Three datasets different in their sorts, sizes, and balance state were used. Accuracy, precision, recall and F1-score are the standard assessment metrics used. Experimental results draw significant conclusions. First, the 7x7x7 feature matrix has a positive correlation between its dimensions, which improved the results. Second, using the sum of the first three IMF components had better impact than using the first IMF component. Third, the classification process did not benefit from the normalization of features for small datasets unlike the large dataset. Finally, the dataset size had a significant impact on training the CNN model, with a training accuracy reaching 84.03%.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126144887","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

An efficient Hybrid approach for diagnosis High dimensional data for Alzheimer's diseases Using Machine Learning algorithms 基于机器学习算法的阿尔茨海默病高维数据诊断的高效混合方法

International Journal of Intelligent Computing and Information Sciences Pub Date : 2022-05-18 DOI: 10.21608/ijicis.2022.116420.1153

Nour ElZawawi, H. Saber, M. Hashem, Tarek G Gharib

引用次数: 0

ONTOLOGY-BASED DATA ACCESS TO HETEROGENEOUS DATA SOURCES: STATE OF THE ART APPROACHES AND APPLICATIONS 对异构数据源的基于本体的数据访问:最新的方法和应用程序

International Journal of Intelligent Computing and Information Sciences Pub Date : 2022-04-28 DOI: 10.21608/ijicis.2022.110450.1144

Naglaa Fathy, Walaa K. Gad, N. Badr, M. Hashem

引用次数: 0

Smart Support System for Evaluating Clustering as a Service: Behaviour Segmentation Case Study 评价聚类服务的智能支持系统:行为分割案例研究

International Journal of Intelligent Computing and Information Sciences Pub Date : 2022-04-27 DOI: 10.21608/ijicis.2021.69041.1074

M. Galal, Tamer Salah, M. Aref, Esam Elgohary

{"title":"Smart Support System for Evaluating Clustering as a Service: Behaviour Segmentation Case Study","authors":"M. Galal, Tamer Salah, M. Aref, Esam Elgohary","doi":"10.21608/ijicis.2021.69041.1074","DOIUrl":"https://doi.org/10.21608/ijicis.2021.69041.1074","url":null,"abstract":": Modern surveys reveal diminishing of socio-demographic segment descriptors, and evolution of dramatic increase of online services and customers. These conditions attract both researchers and decision makers to enhance market segmentation to gain customer loyalty and prevent customer attrition. This research contributes in developing a minor expert system to automate the evaluation of clustering process to enhance the Clustering as a Service (CaaS) through customer behavior segmentation case study. It comes as a part of the software development process to develop Customer Loyalty Intelligent Personalization (CLIP) system. The proposed expert system has been successfully implemented and tested over four months in two different dataset to proof the flexibility of implementation . The used data is a real customer data, it consists of 1659 customers, 146 products, and 5685 orders. The other datset consists of 668 transactions of real data in restaurant. The clustering is applied using the hierarchical clustering and it reached a good results with high efficiency. The proposed solution aims to be integrated with a plug and play product as it will be configured in different domains.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127622396","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

DEEP LEARNING APPROACH BASED ON TRANSFER LEARNING WITH DIFFERENT CLASSIFIERS FOR ECG DIAGNOSIS 基于不同分类器迁移学习的心电诊断深度学习方法

International Journal of Intelligent Computing and Information Sciences Pub Date : 2022-04-27 DOI: 10.21608/ijicis.2022.105574.1137

M. Bassiouni, I. Hegazy, N. Rizk, E. El-Dahshan, A. Salem

{"title":"DEEP LEARNING APPROACH BASED ON TRANSFER LEARNING WITH DIFFERENT CLASSIFIERS FOR ECG DIAGNOSIS","authors":"M. Bassiouni, I. Hegazy, N. Rizk, E. El-Dahshan, A. Salem","doi":"10.21608/ijicis.2022.105574.1137","DOIUrl":"https://doi.org/10.21608/ijicis.2022.105574.1137","url":null,"abstract":": Heart diseases are one of the main reasons that cause human death. The early-stage detection of heart diseases can prevent irreversible heart muscle damage or heart failure. Electrocardiogram (ECG) is one of the main heart signals that can be useful in early diagnosis because of its obvious peaks and segments. This paper focuses on using a methodology depending on deep learning for the diagnosis of the electrocardiogram records into normal (N), Supraventricular arrhythmia (SV), ST-segment changes (ST), and myocardial infarction (MYC) conditions. The continuous wavelet transform (CWT) converts the ECG signals to the time-frequency domain to compute the scalogram of the ECG signals and for the conversion of ECG signal from one dimension signal to a two-dimension image. In addition to this, a pertained model using transfer learning is applied based on Resnet50. Moreover, three main classifiers are verified to estimate the accuracy of the proposed system which are based on the Softmax, Random Forest (RF), and XGBoost classifier. An experiment is applied for the diagnosis of four main kinds of ECG records. Finally, the results based on the class-oriented schema achieved an accuracy of 98.3% based on Resnet50 with the XGBoost classifier. The comparison with the related previous work presented the excellent performance of the proposed methodology as it can be applied as a clinical application.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133638169","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: 4

RepConv: A novel architecture for image scene classification on Intel scenes dataset RepConv:一种基于Intel场景数据集的图像场景分类新架构

International Journal of Intelligent Computing and Information Sciences Pub Date : 2022-04-27 DOI: 10.21608/ijicis.2022.118834.1163

Mohamed Soudy, Y. Afify, N. Badr

{"title":"RepConv: A novel architecture for image scene classification on Intel scenes dataset","authors":"Mohamed Soudy, Y. Afify, N. Badr","doi":"10.21608/ijicis.2022.118834.1163","DOIUrl":"https://doi.org/10.21608/ijicis.2022.118834.1163","url":null,"abstract":"Image understanding and scene classification are keystone tasks in computer vision. The advancement of technology and the abundance of available datasets in the field of image classification and recognition study provide plenty of attempts for advancement. In the scene classification problem, transfer learning is commonly utilized as a branch of machine learning. Despite existing machine learning models' superior performance in image interpretation and scene classification, there are still challenges to overcome. The weights and current models aren't suitable in most circumstances. Instead of using the weights of data-dependent models, in this work, a novel machine learning model for the scene classification task is provided that converges rapidly. The proposed model has been tested on the Intel scenes dataset for a comprehensive evaluation of our model. The proposed model RepConv over-performed four existing benchmark models in a low number of epochs and training parameters, and it achieved 93.55 ± 0.11, 75.54 ± 0.14 accuracies for training and validation data respectively. Furthermore, re-categorization of the data set is performed for a new classification problem that is not previously reported in the literature (natural scenes; real scenes). The accuracy of the proposed model on the binary model was 98.08 ± 0.05 on training data and 92.70 ± 0.08 on validation data which is not reported previously in any other publication.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114864313","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: 4

A Review of Routing Protocols for Mobile Social Networks 移动社交网络路由协议综述

International Journal of Intelligent Computing and Information Sciences Pub Date : 2022-03-25 DOI: 10.21608/ijicis.2022.92114.1120

Hadeer Shahin

引用次数: 0

International Journal of Intelligent Computing and Information Sciences Pub Date : 2022-03-09 DOI: 10.21608/ijicis.2021.83197.1109

A. Amin

{"title":"An Optimal Similarity Neutrosophic Model Based on Distance Measuring to Improving Content-based Image Retrieval","authors":"A. Amin","doi":"10.21608/ijicis.2021.83197.1109","DOIUrl":"https://doi.org/10.21608/ijicis.2021.83197.1109","url":null,"abstract":"A.E. Amin* Department of Computer Science, Mansoura University, Mansoura 35516, Egypt ahmedel_sayed@mans.edu.eg Received 202106-29; Revised 2021-09-30; Accepted 2021-10-12 Abstract: This paper deals with images using the theory of neutrosophic, which the idea of working, on set about the degree of truth, indeterminacy, and falsity. Which helped to discover the hidden features of the images that were segmented by using neutrosophic image processing into objects and then extracting the features into the three truth, indeterminacy, and falsity levels of the image and combining these features to extract the original image features. The proposed similarity model namely weighted Hamming distance measure that based on the single-value neutrosophic set was used to retrieve images from the database, by matching with the query image that extracted its feature in the same way. The results showed that the proposed system is highly efficient in retrieving images compared to different distance measures such as Euclidian, Manhattan, and Minkowski. Finally, A novel similarity model used to match the neutrosophic image features for CBIRs. In the proposed system, an image is segmented into objects, edges, and backgrounds by using neutrosophic image processing.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126339764","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

Experimental Comparative Study on Autoencoder Performance for Aided Melanoma Skin Disease Recognition 自编码器辅助黑色素瘤皮肤病识别性能的实验比较研究

International Journal of Intelligent Computing and Information Sciences Pub Date : 2022-02-01 DOI: 10.21608/ijicis.2022.104799.1136

Zahraa E. Diame, Maryam ElBery, M. Salem, Mohamed Roushdy

引用次数: 2

Protein Key Generation for Secure CT-Chest Images Encryption 用于安全ct胸部图像加密的蛋白质密钥生成

International Journal of Intelligent Computing and Information Sciences Pub Date : 2022-01-28 DOI: 10.21608/ijicis.2021.82820.1108

Sara Shehab

{"title":"Protein Key Generation for Secure CT-Chest Images Encryption","authors":"Sara Shehab","doi":"10.21608/ijicis.2021.82820.1108","DOIUrl":"https://doi.org/10.21608/ijicis.2021.82820.1108","url":null,"abstract":": Nowadays, one of the most complex problems in telemedicine and E-health is the preservation of patient data due to the integration between the development of technology and the medical sector. To protect patient privacy, the transmission of the secured medical image requires adequate techniques. This study aims at encrypting COVID-19 images of Computed Tomography (CT) chest scan into secured and sensitive cipher images for the infected patient. To achieve a high degree of security in the encryption process, protein key generation for the encryption process has been proposed. This study aims to encrypt images using 2 round AES plus Protein key. The histogram has been used to estimate the degree of security for the proposed method. Four criteria have been selected to evaluate the degree of security for the proposed method Number of Pixel Change Rate, Correlation coefficient, Entropy, and Unified Average Changing Intensity. The result indicated that the proposed method has 99.5% and above NPCR, Correlation coefficient close to zero, UACI above 30%, and Entropy near to 8. The results confirm that the proposed method achieves a high level of security and sensitivity when compared with previous work. Therefore, the proposed method can be considered as a successfully applied algorithm to satisfy the security requirements of transmitting CT images for COVID-19 patients .","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123217831","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