Pub Date : 2022-06-26DOI: 10.21608/kjis.2022.246154
Abdelmgeid A. Ali, Nahlaa Fathy, Maha Fahmy
Augmented Reality (AR) is a generic term for associating interactive 2D, 3D objects that blends with our physical reality, sometimes through a camera during this case, with an associated Android device camera. By definition, augmented reality (AR) will be alive, whether directly or indirectly, and will be able to distinguish items in the actual world that have been enhanced by computer-generated sensory input like sound, visual images, or GPS data.. The “AR system” mobile application is constructed by taking photos and videos of a specific building among a University (as example) and making a presentation (by scanning all pictures). While a user focuses his/her Android device camera on a specific image of any building inside faculty, the information associated with that specific department is displayed, when “recognizing” that building from the archived photos. This paper aims to developing an Android augmented reality application that will have the aptitude to point out university field connected information like libraries, schools, and courses offered from a specific department. All this information is offered by obtaining sensing element knowledge from your Android device camera and overlaying pictures in real-time. This application will also help University students to induce information concerning events, faculty, department, or explicit department connected courses with only a click on this application. This AR application uses Vuforia as a package platform and C# as a programming language that provides superior vision-based image recognition and offers the widest set of options and capabilities to enhance the University field guide for the scholars to induce to understand their University faster and easier. The appliance has been prototyped of a set of field buildings.
{"title":"Design an augmented reality application for Android smart phones","authors":"Abdelmgeid A. Ali, Nahlaa Fathy, Maha Fahmy","doi":"10.21608/kjis.2022.246154","DOIUrl":"https://doi.org/10.21608/kjis.2022.246154","url":null,"abstract":"Augmented Reality (AR) is a generic term for associating interactive 2D, 3D objects that blends with our physical reality, sometimes through a camera during this case, with an associated Android device camera. By definition, augmented reality (AR) will be alive, whether directly or indirectly, and will be able to distinguish items in the actual world that have been enhanced by computer-generated sensory input like sound, visual images, or GPS data.. The “AR system” mobile application is constructed by taking photos and videos of a specific building among a University (as example) and making a presentation (by scanning all pictures). While a user focuses his/her Android device camera on a specific image of any building inside faculty, the information associated with that specific department is displayed, when “recognizing” that building from the archived photos. This paper aims to developing an Android augmented reality application that will have the aptitude to point out university field connected information like libraries, schools, and courses offered from a specific department. All this information is offered by obtaining sensing element knowledge from your Android device camera and overlaying pictures in real-time. This application will also help University students to induce information concerning events, faculty, department, or explicit department connected courses with only a click on this application. This AR application uses Vuforia as a package platform and C# as a programming language that provides superior vision-based image recognition and offers the widest set of options and capabilities to enhance the University field guide for the scholars to induce to understand their University faster and easier. The appliance has been prototyped of a set of field buildings.","PeriodicalId":115907,"journal":{"name":"Kafrelsheikh Journal of Information Sciences","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128829259","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 : 2022-06-07DOI: 10.21608/kjis.2022.159008.1010
Sara Shehab, A. Keshk
: One of the most top diseases nowadays is breast cancer that causes death for many women over the world. Breast cancer is a heterogeneous disease defined by molecular types and subtypes. Artificial intelligence has an effect role in detecting and classification the breast cancer. In this work 13 classification method are used like Support Vector Machine, AdaBoost, MLP classifier and others. This work is evaluated using three keys accuracy, cross validation score and execution time. The results detect that Linear SVC Support Vector Machine achieved high accuracy (98.25%) and Random Forest and AdaBoost achieved high cross validation score (97.01%) when compared with other classification methods. Whereas Gaussian NB classifier achieved minimum execution time (0.01 seconds). A data set with 31 feature and 570 records are used for testing the algorithms. 20% of data set will be used in testing and 80% for training. The proposed work achieves high accuracy when compared with the previous works.
{"title":"Breast Cancer Classification Using Ml Algorithms","authors":"Sara Shehab, A. Keshk","doi":"10.21608/kjis.2022.159008.1010","DOIUrl":"https://doi.org/10.21608/kjis.2022.159008.1010","url":null,"abstract":": One of the most top diseases nowadays is breast cancer that causes death for many women over the world. Breast cancer is a heterogeneous disease defined by molecular types and subtypes. Artificial intelligence has an effect role in detecting and classification the breast cancer. In this work 13 classification method are used like Support Vector Machine, AdaBoost, MLP classifier and others. This work is evaluated using three keys accuracy, cross validation score and execution time. The results detect that Linear SVC Support Vector Machine achieved high accuracy (98.25%) and Random Forest and AdaBoost achieved high cross validation score (97.01%) when compared with other classification methods. Whereas Gaussian NB classifier achieved minimum execution time (0.01 seconds). A data set with 31 feature and 570 records are used for testing the algorithms. 20% of data set will be used in testing and 80% for training. The proposed work achieves high accuracy when compared with the previous works.","PeriodicalId":115907,"journal":{"name":"Kafrelsheikh Journal of Information Sciences","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127254156","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 : 2022-06-01DOI: 10.21608/kjis.2022.246104
Mahmoud Yasin, A. Abohany
: Conflicts, deadlock and rolled-back transactions are being considered as the most recent challenges related to executing the transaction concurrently on different environments of Database Management Systems (DBMS). More precisely, in distributed database systems, to handle and avoid these challenges, there are different techniques and protocols are utilized. In this paper, we highlight some of these techniques which includes Two-Phase Commit (2PC) protocol and Three-Phase Commit (3PC) protocol) as well as and Deadlock-Free Cell lock (DFCL) algorithm. Moreover, the paper surveys all these protocols and demonstrate the pros and cons of each techniques. Afterwards, we proposed the solution of some important problems related to concurrency control techniques in DBMS
{"title":"A Review on Concurrency Control Techniques in Database Management Systems","authors":"Mahmoud Yasin, A. Abohany","doi":"10.21608/kjis.2022.246104","DOIUrl":"https://doi.org/10.21608/kjis.2022.246104","url":null,"abstract":": Conflicts, deadlock and rolled-back transactions are being considered as the most recent challenges related to executing the transaction concurrently on different environments of Database Management Systems (DBMS). More precisely, in distributed database systems, to handle and avoid these challenges, there are different techniques and protocols are utilized. In this paper, we highlight some of these techniques which includes Two-Phase Commit (2PC) protocol and Three-Phase Commit (3PC) protocol) as well as and Deadlock-Free Cell lock (DFCL) algorithm. Moreover, the paper surveys all these protocols and demonstrate the pros and cons of each techniques. Afterwards, we proposed the solution of some important problems related to concurrency control techniques in DBMS","PeriodicalId":115907,"journal":{"name":"Kafrelsheikh Journal of Information Sciences","volume":"216 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128615161","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 : 2022-06-01DOI: 10.21608/kjis.2022.246103
R. Mareay, Manal Ali, Tamer Medhat
: The soft set is used in a variety of disciplines. It is a tool for handling ambiguous, uncertain, and indeterminate data. Numerous academics have introduced and researched the notion of soft sets in several domains, including game theory, operation research, probability, and decision-making. The concepts of soft sets and soft topological spaces are introduced in this study. This article introduces the definitions of the soft topology and discusses its foundations and associated characteristics. Examples from the real-world have been provided to assist explain some of the traits of this field. These methods have shown to be quite beneficial in many applications.
{"title":"Soft Sets and Soft Topological Spaces via its Applications","authors":"R. Mareay, Manal Ali, Tamer Medhat","doi":"10.21608/kjis.2022.246103","DOIUrl":"https://doi.org/10.21608/kjis.2022.246103","url":null,"abstract":": The soft set is used in a variety of disciplines. It is a tool for handling ambiguous, uncertain, and indeterminate data. Numerous academics have introduced and researched the notion of soft sets in several domains, including game theory, operation research, probability, and decision-making. The concepts of soft sets and soft topological spaces are introduced in this study. This article introduces the definitions of the soft topology and discusses its foundations and associated characteristics. Examples from the real-world have been provided to assist explain some of the traits of this field. These methods have shown to be quite beneficial in many applications.","PeriodicalId":115907,"journal":{"name":"Kafrelsheikh Journal of Information Sciences","volume":"313 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132726872","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 : 2021-10-07DOI: 10.21608/kjis.2021.198376
Abdelmgeid A. Ali, Usama Mohammed, Rehab Nour
The goal of this research is to compare between the performance of the traditional machine learning classification algorithm using Bag of Visual Words (BoVW) method and off-the-shelf deep features extracted by VGG-19, and Inception-V3 models and trained SVMs using the extracted features. By comparing the AUC, sensitivity, and specificity of SVM with VGG19 and Inception-V3, we can conclude that off-the-shelf deep features has an important impact on food grains image
{"title":"Product Based Classification of Bulk Food Grains using Bag of Visual Words and Deep Features","authors":"Abdelmgeid A. Ali, Usama Mohammed, Rehab Nour","doi":"10.21608/kjis.2021.198376","DOIUrl":"https://doi.org/10.21608/kjis.2021.198376","url":null,"abstract":"The goal of this research is to compare between the performance of the traditional machine learning classification algorithm using Bag of Visual Words (BoVW) method and off-the-shelf deep features extracted by VGG-19, and Inception-V3 models and trained SVMs using the extracted features. By comparing the AUC, sensitivity, and specificity of SVM with VGG19 and Inception-V3, we can conclude that off-the-shelf deep features has an important impact on food grains image","PeriodicalId":115907,"journal":{"name":"Kafrelsheikh Journal of Information Sciences","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115178621","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 : 2021-10-01DOI: 10.21608/kjis.2021.187822
M. Sakr
Studying of gas deflagration is important for a safety purpose in gas industry. A modelling approach based on large eddy simulation (LES) technique for modelling turbulent flow combined with the species mass fraction equations for modelling combustion is used. Different flame acceleration mechanisms, hydrodynamic & thermo-diffusive instabilities, turbulence, and their interaction in addition to flame quenching model are used to model chemical reaction rate. An algebraic model for flame-generated turbulence is incorporated. The model is tested against large scale open atmosphere hydrogen-air experiment. The flame propagation radius and the overpressures are qualitatively compared well with experiment and the state-of-the-art simulations.
{"title":"Modelling and computation of large-scale open atmosphere hydrogenair deflagration","authors":"M. Sakr","doi":"10.21608/kjis.2021.187822","DOIUrl":"https://doi.org/10.21608/kjis.2021.187822","url":null,"abstract":"Studying of gas deflagration is important for a safety purpose in gas industry. A modelling approach based on large eddy simulation (LES) technique for modelling turbulent flow combined with the species mass fraction equations for modelling combustion is used. Different flame acceleration mechanisms, hydrodynamic & thermo-diffusive instabilities, turbulence, and their interaction in addition to flame quenching model are used to model chemical reaction rate. An algebraic model for flame-generated turbulence is incorporated. The model is tested against large scale open atmosphere hydrogen-air experiment. The flame propagation radius and the overpressures are qualitatively compared well with experiment and the state-of-the-art simulations.","PeriodicalId":115907,"journal":{"name":"Kafrelsheikh Journal of Information Sciences","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123491631","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 : 2021-09-01DOI: 10.21608/kjis.2021.192207
Abeer Saber, Mohamed Sakr, Osama Abou-Seida, A. Keshk
Breast cancer (BC) is a leading cause of cancer death among women in which breast cells develop out of control is by encouraging patients to receive timely care, early detection of BC increases the likelihood of survival. In this context, a new deep learning (DL) model is presented for automatic detection and classification of the suspected area of the breast based on the transfer learning (TL) technique. A pre-trained visual geometry group (VGG)-19, VGG16, and InceptionV3 networks are used in the presented model to transfer their learning parameters for improving the performance of breast tumor classification. The main goals of this project are to use segmentation to automatically determine the affected breast tumor region, reduce training time, and improve classification performance. In the presented model, the Mammographic Image Analysis Society (MIAS) dataset is used for extracting the breast tumor features. We have chosen four evaluation metrics for evaluating the performance of the presented model accuracy, sensitivity, specificity, and area under the ROC curve (AUC). The experiments showed that transferring parameters from the model of VGG16 is a powerful for BC classification than VGG19 and Inception V3 with overall specificity, accuracy, sensitivity, and AUC 98%,96.8%, 96%, and 0.99, respectively. Keywords—breast cancer, deep-learning, segmentation, transfer-learning, image processing
{"title":"A Novel Transfer-Learning Model for Automatic Detection and Classification of Breast Cancer Based Deep CNN","authors":"Abeer Saber, Mohamed Sakr, Osama Abou-Seida, A. Keshk","doi":"10.21608/kjis.2021.192207","DOIUrl":"https://doi.org/10.21608/kjis.2021.192207","url":null,"abstract":"Breast cancer (BC) is a leading cause of cancer death among women in which breast cells develop out of control is by encouraging patients to receive timely care, early detection of BC increases the likelihood of survival. In this context, a new deep learning (DL) model is presented for automatic detection and classification of the suspected area of the breast based on the transfer learning (TL) technique. A pre-trained visual geometry group (VGG)-19, VGG16, and InceptionV3 networks are used in the presented model to transfer their learning parameters for improving the performance of breast tumor classification. The main goals of this project are to use segmentation to automatically determine the affected breast tumor region, reduce training time, and improve classification performance. In the presented model, the Mammographic Image Analysis Society (MIAS) dataset is used for extracting the breast tumor features. We have chosen four evaluation metrics for evaluating the performance of the presented model accuracy, sensitivity, specificity, and area under the ROC curve (AUC). The experiments showed that transferring parameters from the model of VGG16 is a powerful for BC classification than VGG19 and Inception V3 with overall specificity, accuracy, sensitivity, and AUC 98%,96.8%, 96%, and 0.99, respectively. Keywords—breast cancer, deep-learning, segmentation, transfer-learning, image processing","PeriodicalId":115907,"journal":{"name":"Kafrelsheikh Journal of Information Sciences","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132834157","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 : 2021-08-01DOI: 10.21608/kjis.2021.22087.1007
Kjis
Software and systems improvement requests to merge various interpretations from several improvement models and techniques. A particular challenge is the multitude of models for requirements and quality, which can get time consuming and error prone to trace, change, and verify. Lately, Ontologies have been used across several domains and for numerous purposes to be applied for many applications. Besides, recent work in Artificial Intelligence is discovering the use of formal ontologies as a way of identifying content-specific agreements for the sharing and reuse of knowledge among software entities. Therefore, this paper describes how ontology engineering is used to construct an Ontological structure of the proposed SPI-CMMI framework –which based on using Six sigma approach integrated with CMMI-Dev model and Quality Function Deployment (QFD) technique- with its progressive phases, related activities, recommended tools and the CMMI-Dev 1.3 representation. The SPI-CMMI Ontology provides a shared improvement terminology, defines precise and unambiguous semantics for the software enterprises and enables reuse of improvement phase’s knowledge; in addition it makes domain assumptions explicit and separate domain knowledge from the operational knowledge.
{"title":"An OWL-Based Ontology Structure for representing Multimodel Process Improvement Framework","authors":"Kjis","doi":"10.21608/kjis.2021.22087.1007","DOIUrl":"https://doi.org/10.21608/kjis.2021.22087.1007","url":null,"abstract":"Software and systems improvement requests to merge various interpretations from several improvement models and techniques. A particular challenge is the multitude of models for requirements and quality, which can get time consuming and error prone to trace, change, and verify. Lately, Ontologies have been used across several domains and for numerous purposes to be applied for many applications. Besides, recent work in Artificial Intelligence is discovering the use of formal ontologies as a way of identifying content-specific agreements for the sharing and reuse of knowledge among software entities. Therefore, this paper describes how ontology engineering is used to construct an Ontological structure of the proposed SPI-CMMI framework –which based on using Six sigma approach integrated with CMMI-Dev model and Quality Function Deployment (QFD) technique- with its progressive phases, related activities, recommended tools and the CMMI-Dev 1.3 representation. The SPI-CMMI Ontology provides a shared improvement terminology, defines precise and unambiguous semantics for the software enterprises and enables reuse of improvement phase’s knowledge; in addition it makes domain assumptions explicit and separate domain knowledge from the operational knowledge.","PeriodicalId":115907,"journal":{"name":"Kafrelsheikh Journal of Information Sciences","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130978907","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 : 2021-08-01DOI: 10.21608/kjis.2021.42695.1009
Osama M. Abu Zaid, adham mohamed, Kamel El-Sehly, Mahmoud Ossman, Mostafa Kamal, M. Aly
This paper proposed a smart wearable system for heart disease detection using machine learning and embedded systems. A smart wearable system that able to monitor the heart beat rate condition of patient. The heart beat rate is detected using photoplethysmogram (PPG). The signal is processed using ATmega32 Microcontroller to determine heart beat rate per minute. Then, it sends the heart rate represented as BPM to Android App Via Bluetooth Communication, Android app sends SMS alert to the mobile phone of medical experts or patient's family member, or their relatives via SMS contains user's current location, Android app calculates daily steps count. Android/Desktop app allow user to check nearest hospitals, cardiac centers, nearest Health centers (GYM) and also user's current location. Android/Desktop app allow user to know if he suffers from heart disease or not by one click which run a machine/Deep learning module that analyze user ‘s data to detect heart disease.
{"title":"Heart Disease Detection using ML and ES (Smart Wearable Health Monitoring System)","authors":"Osama M. Abu Zaid, adham mohamed, Kamel El-Sehly, Mahmoud Ossman, Mostafa Kamal, M. Aly","doi":"10.21608/kjis.2021.42695.1009","DOIUrl":"https://doi.org/10.21608/kjis.2021.42695.1009","url":null,"abstract":"This paper proposed a smart wearable system for heart disease detection using machine learning and embedded systems. A smart wearable system that able to monitor the heart beat rate condition of patient. The heart beat rate is detected using photoplethysmogram (PPG). The signal is processed using ATmega32 Microcontroller to determine heart beat rate per minute. Then, it sends the heart rate represented as BPM to Android App Via Bluetooth Communication, Android app sends SMS alert to the mobile phone of medical experts or patient's family member, or their relatives via SMS contains user's current location, Android app calculates daily steps count. Android/Desktop app allow user to check nearest hospitals, cardiac centers, nearest Health centers (GYM) and also user's current location. Android/Desktop app allow user to know if he suffers from heart disease or not by one click which run a machine/Deep learning module that analyze user ‘s data to detect heart disease.","PeriodicalId":115907,"journal":{"name":"Kafrelsheikh Journal of Information Sciences","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116209213","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 : 2018-07-04DOI: 10.21608/KJIS.2018.2741.1000
Woroud Alothman
The problem of traffic delay and congestion of services and products is a very serious problem in the world due to the population growth and difficulties in changing the infrastructure. This issue is being studied by researchers and international traffic centers because of the delay in services and products and negative economic effect that traffic problem causes. It is difficult to improve the traffic system performance by using the traditional control methods. Many studies had been conducted using the fuzzy logic system and neural network to control the road intersections. In this article, the artificial intelligence traffic control principles and approaches which applied in the traffic signal control will be reviewed. Some points of view about future research in this area are proposed. The review shows that the traffic performance of the fuzzy controller has better performance than traditional traffic signal controls, specifically during heavy and uneven traffic volume conditions.
{"title":"A Survey of Intelligent Transportation Systems","authors":"Woroud Alothman","doi":"10.21608/KJIS.2018.2741.1000","DOIUrl":"https://doi.org/10.21608/KJIS.2018.2741.1000","url":null,"abstract":"The problem of traffic delay and congestion of services and products is a very serious problem in the world due to the population growth and difficulties in changing the infrastructure. This issue is being studied by researchers and international traffic centers because of the delay in services and products and negative economic effect that traffic problem causes. It is difficult to improve the traffic system performance by using the traditional control methods. Many studies had been conducted using the fuzzy logic system and neural network to control the road intersections. In this article, the artificial intelligence traffic control principles and approaches which applied in the traffic signal control will be reviewed. Some points of view about future research in this area are proposed. The review shows that the traffic performance of the fuzzy controller has better performance than traditional traffic signal controls, specifically during heavy and uneven traffic volume conditions.","PeriodicalId":115907,"journal":{"name":"Kafrelsheikh Journal of Information Sciences","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124319768","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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