Pub Date : 2022-08-01DOI: 10.21608/ijicis.2022.146718.1197
H. Ramadan, Khaled A. ElBahnasy
: 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.
{"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}
Pub Date : 2022-08-01DOI: 10.21608/ijicis.2022.117124.1157
Razan Bayoumi, Marco Alfonse, Abdel-Badeeh M. Salem
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.
{"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}
Pub Date : 2022-08-01DOI: 10.21608/ijicis.2022.146420.1195
M. Yacoub, Huda Amin, Nivin Atef, S. Soto, Tarek G Gharib
: 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.
{"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}
Pub Date : 2022-08-01DOI: 10.21608/ijicis.2022.144051.1190
Walaa Alkady, Khaled A. ElBahnasy, Walaa K. Gad
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.
{"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}
Pub Date : 2022-07-28DOI: 10.21608/ijicis.2022.105691.1139
A. Samir, Tarek G Gharib, S. Rady
: 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.
{"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}
Pub Date : 2022-07-24DOI: 10.21608/ijicis.2022.117646.1158
Mona A. Sadik, Sherin M. Moussa, Ahmed El-Sayed, Z. Fayed
: 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.
{"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}
Pub Date : 2022-07-20DOI: 10.21608/ijicis.2022.136628.1179
نجوي عبدالعال
{"title":"An Intelligent Educational System for Breast Cancer Management\"","authors":"نجوي عبدالعال","doi":"10.21608/ijicis.2022.136628.1179","DOIUrl":"https://doi.org/10.21608/ijicis.2022.136628.1179","url":null,"abstract":"","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125068331","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}
Pub Date : 2022-06-21DOI: 10.21608/ijicis.2022.130393.1174
Mahmoud Mohamed, S. Ghoniemy, N. Ghali
: Due to the observed growth in recent years of digital image communication, computer technologies, and image processing techniques image security has been an essential demand due to the different image attacks. Image security approaches are classified into cryptography and data hiding techniques, including digital watermarking and steganography. This study paper reviews existing picture data hiding techniques, their benefits and drawbacks, and future research directions. In addition to the survey, we included a brief explanation of several geometric and image processing attacks that impair picture transmission. General multimedia security ideas, primary requirements, and recent applications We addressed various approaches and their characteristics, types, requirements, and working mechanisms. We classify the techniques based on different domains. General concepts of data hiding approaches, their characteristics, recent applications used in, also recent research work for proposed techniques is discussed in the following sections, finally, a comparison between different methodologies has been presented in a table.
{"title":"A Survey on Image Data Hiding Techniques**","authors":"Mahmoud Mohamed, S. Ghoniemy, N. Ghali","doi":"10.21608/ijicis.2022.130393.1174","DOIUrl":"https://doi.org/10.21608/ijicis.2022.130393.1174","url":null,"abstract":": Due to the observed growth in recent years of digital image communication, computer technologies, and image processing techniques image security has been an essential demand due to the different image attacks. Image security approaches are classified into cryptography and data hiding techniques, including digital watermarking and steganography. This study paper reviews existing picture data hiding techniques, their benefits and drawbacks, and future research directions. In addition to the survey, we included a brief explanation of several geometric and image processing attacks that impair picture transmission. General multimedia security ideas, primary requirements, and recent applications We addressed various approaches and their characteristics, types, requirements, and working mechanisms. We classify the techniques based on different domains. General concepts of data hiding approaches, their characteristics, recent applications used in, also recent research work for proposed techniques is discussed in the following sections, finally, a comparison between different methodologies has been presented in a table.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121477475","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}
Pub Date : 2022-05-23DOI: 10.21608/ijicis.2022.99424.1126
R. Hossieny, M. Tantawi, H. Shedeed, Mohamed Tolba
: Recently, a significant increase appears in the number of patients with severe motor disabilities even though the cognitive parts of their brains are intact. These disabilities prevent them from being able to move all their limbs except for the movement of their eyes. This creates great difficulty in carrying out the simplest daily activities, as well as difficulty in communicating with their surrounding environment. With the advent of Human Computer Interfaces (HCI), a new method of communication has been found based on determining the direction of eye movement. The eye movement is recorded by Electro-oculogram (EOG) using a set of electrodes placed around the eye horizontally and vertically. In this work, The horizontal and vertical EOG signals are filtered and analyzed to determine six eye movement directions (Right, left, up, down, center, and double blinking). The deep learning models namely Residual network and ResNet-50 network have been examined. The experimental results show that the ResNet-50 network gives the best average accuracy 95.8%.
{"title":"Developing a Method for Classifying Electro-Oculography (EOG) Signals Using Deep Learning","authors":"R. Hossieny, M. Tantawi, H. Shedeed, Mohamed Tolba","doi":"10.21608/ijicis.2022.99424.1126","DOIUrl":"https://doi.org/10.21608/ijicis.2022.99424.1126","url":null,"abstract":": Recently, a significant increase appears in the number of patients with severe motor disabilities even though the cognitive parts of their brains are intact. These disabilities prevent them from being able to move all their limbs except for the movement of their eyes. This creates great difficulty in carrying out the simplest daily activities, as well as difficulty in communicating with their surrounding environment. With the advent of Human Computer Interfaces (HCI), a new method of communication has been found based on determining the direction of eye movement. The eye movement is recorded by Electro-oculogram (EOG) using a set of electrodes placed around the eye horizontally and vertically. In this work, The horizontal and vertical EOG signals are filtered and analyzed to determine six eye movement directions (Right, left, up, down, center, and double blinking). The deep learning models namely Residual network and ResNet-50 network have been examined. The experimental results show that the ResNet-50 network gives the best average accuracy 95.8%.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131866185","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}
Pub Date : 2022-05-19DOI: 10.21608/ijicis.2022.98138.1124
Amira Samir, Huda Amin, N. Badr
: 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.
{"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}
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