International Journal of Intelligent Computing and Information Sciences最新文献_第4页

A REVIEW OF CLUSTERING ALGORITHMS FOR DETERMINATION OF CANCER SIGNATURES 聚类算法用于确定癌症特征的综述

International Journal of Intelligent Computing and Information Sciences Pub Date : 2022-08-01 DOI: 10.21608/ijicis.2022.146718.1197

H. Ramadan, Khaled A. ElBahnasy

{"title":"A REVIEW OF CLUSTERING ALGORITHMS FOR DETERMINATION OF CANCER SIGNATURES","authors":"H. Ramadan, Khaled A. ElBahnasy","doi":"10.21608/ijicis.2022.146718.1197","DOIUrl":"https://doi.org/10.21608/ijicis.2022.146718.1197","url":null,"abstract":": Important information needed to comprehend the biological processes that happen in a specific organism, and for sure with a relevance to its environment. Gene expression data is responsible to hide that. We can improve our understanding of functional genomics, and this is possible if we understood the underlying trends in gene expression data. The difficulty of understanding and interpreting the resulting deluge of data is exacerbated by the complexity of biological networks. These issues need to be resolved, so clustering algorithms is used as a start for that. Also, they are needed in many files like the data mining. They can find the natural structures. They are able to extract the most effective patterns. It has been demonstrated that clustering gene expression data is effective for discovering the gene expression data’s natural structure, comprehending cellular processes, gene functions, and cell subtypes, mining usable information from comprehending gene regulation, and noisy data. This review examines the various clustering algorithms that could be applied to the gene expression data, this is aiming to identify the signature genes of biological diseases, which is one the most significant applications of clustering techniques.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"331 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133730634","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

Multi-Stage Hybrid Text-to-Image Generation Models. 多阶段混合文本到图像生成模型。

International Journal of Intelligent Computing and Information Sciences Pub Date : 2022-08-01 DOI: 10.21608/ijicis.2022.117124.1157

Razan Bayoumi, Marco Alfonse, Abdel-Badeeh M. Salem

{"title":"Multi-Stage Hybrid Text-to-Image Generation Models.","authors":"Razan Bayoumi, Marco Alfonse, Abdel-Badeeh M. Salem","doi":"10.21608/ijicis.2022.117124.1157","DOIUrl":"https://doi.org/10.21608/ijicis.2022.117124.1157","url":null,"abstract":"Generative Adversarial Networks (GANs) have proven their outstanding potential in creating realistic images that can't differentiate between them and the real images, but text-to-image (conditional generation) still faces some challenges. In this paper, we propose a new model called (AttnDM GAN) stands for Attentional Dynamic Memory Generative Adversarial Memory, which seeks to generate realistic output semantically harmonious with an input text description. AttnDM GAN is a three-stage hybrid model of the Attentional Generative Adversarial Network (AttnGAN) and the Dynamic Memory Generative Adversarial Network (DM-GAN), the 1 st stage is called the Initial Image Generation, in which low resolution 64x64 images are generated conditioned on the encoded input textual description. The 2 nd stage is the Attention Image Generation stage that generates higher-resolution images 128x128, and the last stage is Dynamic Memory Based Image Refinement that refines the images to 256x256 resolution images. We conduct an experiment on our model the AttnDM GAN using the Caltech-UCSD Birds 200 dataset and evaluate it using the Frechet Inception Distance (FID) with a value of 19.78. We also proposed another model called Dynamic Memory Attention Generative Adversarial Networks (DMAttn-GAN) which considered a variation of the AttnDM GAN model, where the second and the third stages are switched together, its FID value is 17.04.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122350399","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

PREDICTING STUDENTS’ PERFORMANCE USING AN ENHANCED AGGREGATION STRATEGY FOR SUPERVISED MULTICLASS CLASSIFICATION 基于监督多类分类的增强聚合策略预测学生表现

International Journal of Intelligent Computing and Information Sciences Pub Date : 2022-08-01 DOI: 10.21608/ijicis.2022.146420.1195

M. Yacoub, Huda Amin, Nivin Atef, S. Soto, Tarek G Gharib

{"title":"PREDICTING STUDENTS’ PERFORMANCE USING AN ENHANCED AGGREGATION STRATEGY FOR SUPERVISED MULTICLASS CLASSIFICATION","authors":"M. Yacoub, Huda Amin, Nivin Atef, S. Soto, Tarek G Gharib","doi":"10.21608/ijicis.2022.146420.1195","DOIUrl":"https://doi.org/10.21608/ijicis.2022.146420.1195","url":null,"abstract":": Predicting students performance efficiently became one of the most interesting research topics. Efficiently mining the educational data is the cornerstone and the first step to make the appropriate intervention to help at-risk students achieve better performance and enhance the educational outcomes. The objective of this paper is to efficiently predict students’ performance by predicting their academic performance level. This is achieved by proposing an enhanced aggregation strategy on a supervised multiclass classification problem to improve the prediction accuracy of students’ performance. Two binary classification techniques: Support Vector Machine (SVM) and Perceptron algorithms, have been experimented to use their output as an input to the proposed aggregation strategy to be compared with a previously used aggregation strategy. The proposed strategy improved the prediction performance and achieved an accuracy, recall, and precision of 75.0%, 76.0%, and 75.48% using Perceptron, respectively. Moreover, the proposed strategy outperformed and achieved an accuracy, recall, and precision of 73.96%, 73.93%, and 75.33% using SVM, respectively.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128311318","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

Comparative Study on Feature Selection Methods for Protein 蛋白质特征选择方法的比较研究

International Journal of Intelligent Computing and Information Sciences Pub Date : 2022-08-01 DOI: 10.21608/ijicis.2022.144051.1190

Walaa Alkady, Khaled A. ElBahnasy, Walaa K. Gad

{"title":"Comparative Study on Feature Selection Methods for Protein","authors":"Walaa Alkady, Khaled A. ElBahnasy, Walaa K. Gad","doi":"10.21608/ijicis.2022.144051.1190","DOIUrl":"https://doi.org/10.21608/ijicis.2022.144051.1190","url":null,"abstract":"Received 2022-06-11; Revised 2022-07-22; Accepted 2022-07-24 Abstract: The automated and high-throughput identification of protein function is one of the main issues in computational biology. Predicting the protein's structure is a crucial step in this procedure. In recent years, a wide range of approaches for predicting protein structure has been put forth. They can be divided into two groups: database-based and sequence-based. The first is to identify the principles behind protein structure and attempts to extract valuable characteristics from amino acid sequences. The second one uses pre-existing public annotation databases for data mining. This study emphasizes the sequence-based method and makes use of the ability of amino acid sequences to predict protein activity. The amino acid composition approach, the amino acid tuple approach, and several optimization algorithms were compared. Different protein sequence data sets were used in our experiments. Five classifiers were tested in this research. The best accuracy is 98% using across 10fold cross-validation. This represents the highest performance in the Human dataset.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117026624","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

The Identification of the Top Positive Influential Users of the Social Networks to Help in the Control of Covid-19 Spread 识别社交网络上最具积极影响力的用户以帮助控制Covid-19的传播

International Journal of Intelligent Computing and Information Sciences Pub Date : 2022-07-28 DOI: 10.21608/ijicis.2022.105691.1139

A. Samir, Tarek G Gharib, S. Rady

{"title":"The Identification of the Top Positive Influential Users of the Social Networks to Help in the Control of Covid-19 Spread","authors":"A. Samir, Tarek G Gharib, S. Rady","doi":"10.21608/ijicis.2022.105691.1139","DOIUrl":"https://doi.org/10.21608/ijicis.2022.105691.1139","url":null,"abstract":": Covid-19 pandemic is considered the most worldwide problem, and causes horrible crises for all human being. Social networks can play a vital role in the prevention of the spread of the Covid-19 pandemic. The top influential users of social networks like Twitter can have positive or negative effect in the broadcast of useful and same time harmful information about how to deal with the virus, and encourage people to follow up the rules announced by World Health Organization (WHO). So the detection of the top positive and negative influential users can help in the control of the spread of the virus. The proposed approach is based on applying influence maximization solutions to identify the top influential users from Twitter social network graph, and to determine if the influence is positive or not. The proposed approach has four main phases, the first phase is collecting Covid-19 pandemic related tweets dataset and extract the related users and their followers. The second phase is creating a social network graph from the collected dataset. The third phase is using LKG influence maximization approach to identify the most effective users from the social network graph. The last phase is based on using hashtags frequency analysis to be able to identify the type of influence of each top influential user.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114686617","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

Vehicles Detection and Tracking in Advanced & Automated Driving Systems: Limitations and Challenges 先进自动驾驶系统中的车辆检测与跟踪:限制与挑战

International Journal of Intelligent Computing and Information Sciences Pub Date : 2022-07-24 DOI: 10.21608/ijicis.2022.117646.1158

Mona A. Sadik, Sherin M. Moussa, Ahmed El-Sayed, Z. Fayed

{"title":"Vehicles Detection and Tracking in Advanced & Automated Driving Systems: Limitations and Challenges","authors":"Mona A. Sadik, Sherin M. Moussa, Ahmed El-Sayed, Z. Fayed","doi":"10.21608/ijicis.2022.117646.1158","DOIUrl":"https://doi.org/10.21608/ijicis.2022.117646.1158","url":null,"abstract":": Automated Driving Systems (ADS) and Advanced Driving Assistance Systems (ADAS) are widely investigated for developing safe and intelligent transportation systems. A common module in both systems is road objects monitoring, in which the semantic segmentation for road scene understanding has encountered lots of challenges. Due to the rapid evolution in technologies applied in vision-based systems in many fields, diverse techniques and algorithms have emerged to tackle such limitations, as invariant-illumination conditions, shadows, false positives, misdetections, weather conditions, real time processing and occlusions. A comparative study is conducted in this paper for vehicle detection and tracking methods applied on images and streams produced from monocular cameras and sensors in ADAS and ADS in terms of the aforementioned problems, the used dataset, along with the extracted features and the associated evaluation criteria. The study deduces the limitations of the current state-of-art techniques in such particular systems and highlights the main directions that can be ado ted for future research and investigations.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131538207","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 Intelligent Educational System for Breast Cancer Management" “乳腺癌管理智能教育系统”

International Journal of Intelligent Computing and Information Sciences Pub Date : 2022-07-20 DOI: 10.21608/ijicis.2022.136628.1179

نجوي عبدالعال

引用次数: 0

A Survey on Image Data Hiding Techniques** 图像数据隐藏技术综述**

International Journal of Intelligent Computing and Information Sciences Pub Date : 2022-06-21 DOI: 10.21608/ijicis.2022.130393.1174

Mahmoud Mohamed, S. Ghoniemy, N. Ghali

引用次数: 0

Developing a Method for Classifying Electro-Oculography (EOG) Signals Using Deep Learning 基于深度学习的眼电信号分类方法研究

International Journal of Intelligent Computing and Information Sciences Pub Date : 2022-05-23 DOI: 10.21608/ijicis.2022.99424.1126

R. Hossieny, M. Tantawi, H. Shedeed, Mohamed Tolba

引用次数: 0

A SURVEY ON AUTOMATED USER INTERFACE TESTING FOR MOBILE APPLICATIONS 关于移动应用程序自动化用户界面测试的调查

International Journal of Intelligent Computing and Information Sciences Pub Date : 2022-05-19 DOI: 10.21608/ijicis.2022.98138.1124

Amira Samir, Huda Amin, N. Badr

{"title":"A SURVEY ON AUTOMATED USER INTERFACE TESTING FOR MOBILE APPLICATIONS","authors":"Amira Samir, Huda Amin, N. Badr","doi":"10.21608/ijicis.2022.98138.1124","DOIUrl":"https://doi.org/10.21608/ijicis.2022.98138.1124","url":null,"abstract":": Nowadays, smartphones play a remarkable role in our lives. Testing mobile applications is significant to guarantee their quality. Automated testing is applied to minimize the cost and the interval of time instead of manual testing. There are different testing levels which are unit testing, integration testing, system testing and acceptance testing. Automated mobile application testing type methodologies are categorized into white-box testing, black-box testing and grey-box testing. Besides, there are several testing types such as functional testing and non-functional testing. Most of the existing studies focus on user interface testing which is type of functional testing. In this paper, testing approaches for user interface testing through different existing studies from 2013 to 2021 have been surveyed. Those approaches are classified into model-based testing, model learning testing, search-based testing, random-based testing, and record & replay testing. Several essential issues related to those approach such as the optimization and redundancy for generation of test suites have been mentioned. Finally, challenges in automated mobile applications user interface testing have been discussed.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123392918","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