Pub Date : 2022-01-18DOI: 10.21608/ijicis.2022.97513.1121
R. Tolba, T. Elarif, Zaki Taha
With the raise of COVID-19 pandemic in 2020, the traditional teaching-learning process became inefficient. Technology-Enhanced Learning (TEL) research has increasingly focused on emergent technologies such as Augmented Reality (AR). It became one of the technologies that has received great attention and interest in the last decade. In this paper, we conducted a systematic review that describes the current state of using AR as a learning tool. Taking into consideration the needs of all students including those with a disability, in different levels of education. It is done through the analysis of the following factors: AR in learning system, AR in levels of education and categories of educational AR applications. A total of 103 studies between 2011 and 2021 were analyzed through searching in four interdisciplinary databases: Springer, IEEE Xplore, ResearchGate, and Google Scholar. This analysis helped to see in which direction AR systems for education are heading and how it will be designed to fit the students’ needs and improve their learning. Further research and development will make AR a more promising learning tool.
{"title":"AUGMENTED REALITY IN TECHNOLOGY-ENHANCED LEARNING: SYSTEMATIC REVIEW 2011-2021","authors":"R. Tolba, T. Elarif, Zaki Taha","doi":"10.21608/ijicis.2022.97513.1121","DOIUrl":"https://doi.org/10.21608/ijicis.2022.97513.1121","url":null,"abstract":"With the raise of COVID-19 pandemic in 2020, the traditional teaching-learning process became inefficient. Technology-Enhanced Learning (TEL) research has increasingly focused on emergent technologies such as Augmented Reality (AR). It became one of the technologies that has received great attention and interest in the last decade. In this paper, we conducted a systematic review that describes the current state of using AR as a learning tool. Taking into consideration the needs of all students including those with a disability, in different levels of education. It is done through the analysis of the following factors: AR in learning system, AR in levels of education and categories of educational AR applications. A total of 103 studies between 2011 and 2021 were analyzed through searching in four interdisciplinary databases: Springer, IEEE Xplore, ResearchGate, and Google Scholar. This analysis helped to see in which direction AR systems for education are heading and how it will be designed to fit the students’ needs and improve their learning. Further research and development will make AR a more promising learning tool.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127387647","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 : 2022-01-18DOI: 10.21608/ijicis.2022.105794.1140
Hewayda M. Lotfy
Receiving the most relevant images from image databases is a challenging and critical issue in many applications. Texture is a substantial feature of an image which depicts the spatial behavior of gray-levels in any given neighborhood. Color features uses a variety of color systems and are meaningful to differentiate image segments. Presently, many of the favorable methods for image content description use local descriptors as their starting point with several conducts. The content in an image may appear in some feature descriptor's components more accurately than other components. This paper presents an innovative idea for local image retrieval using a new methodology for feature extraction welding named Blend of Extended Features’ Components (BoEFC). The paper shows that an image's content may be described individually by the feature descriptor's components or collectively through the Extended Feature Components (EFC). Retrieval options are attempted using a selection method of Feature Components then the relevant results are collected and ordered according to newly adapted feature similarity measures. The experiments were performed using a general-purpose image database which itself represent a challenge and the INRIA Holiday image database. The experiments was performed by varying the EFCs to compute recall, precision and draw the Precision-Recall (PR) curves which showed increased recall and precision with some components. In addition, calculating mAP and mAR showed increased performance due to the BoEFC blending process.
{"title":"IMAGE RETRIEVAL USING BLENDING OF EXTENDED FEATURE COMPONENTS","authors":"Hewayda M. Lotfy","doi":"10.21608/ijicis.2022.105794.1140","DOIUrl":"https://doi.org/10.21608/ijicis.2022.105794.1140","url":null,"abstract":"Receiving the most relevant images from image databases is a challenging and critical issue in many applications. Texture is a substantial feature of an image which depicts the spatial behavior of gray-levels in any given neighborhood. Color features uses a variety of color systems and are meaningful to differentiate image segments. Presently, many of the favorable methods for image content description use local descriptors as their starting point with several conducts. The content in an image may appear in some feature descriptor's components more accurately than other components. This paper presents an innovative idea for local image retrieval using a new methodology for feature extraction welding named Blend of Extended Features’ Components (BoEFC). The paper shows that an image's content may be described individually by the feature descriptor's components or collectively through the Extended Feature Components (EFC). Retrieval options are attempted using a selection method of Feature Components then the relevant results are collected and ordered according to newly adapted feature similarity measures. The experiments were performed using a general-purpose image database which itself represent a challenge and the INRIA Holiday image database. The experiments was performed by varying the EFCs to compute recall, precision and draw the Precision-Recall (PR) curves which showed increased recall and precision with some components. In addition, calculating mAP and mAR showed increased performance due to the BoEFC blending process.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124977499","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 : 2021-11-24DOI: 10.21608/ijicis.2021.82144.1107
Dalia Rizk, H. Hosny, Sayed ElHorbety, Abdel-Badeeh M. Salem
Nowadays, Internet of Things (IoT) is invading almost all sectors of life since it is based on connecting living or non-living things together through computer technology. It is responsible for connecting physical objects together through the internet. Healthcare and hospitals are one of the most important sectors that require a lot of attention to transfer their old form of documentation into SMART management systems. It is essential to analyze health data in order to increase the quality of patient’s care. Egypt being a development country is starting to substitute its old governmental systems into electronic SMART technology. IoT devices produce different types of data and transfer them to the cloud computing for storage and analysis. The benefits of using IoT in collecting, transferring, and analyzing patients’ data for the hospitals are attracting a lot of researchers. Therefore, the arrangement of smarter and more money saving healthcare services are becoming highly required. Security and privacy, device communication, and data collection and management are some of the challenges that face the IoT technology especially when used with hospital’s data. Accordingly, a proposed reference model for making SMART hospital management system is under construction in order to achieve the best performance. The model is taking into consideration both the functional and non-functional requirements of the different participants involved in the hospital management system. International Journal of Intelligent Computing and Information Sciences https://ijicis.journals.ekb.eg/ 2 D. k. A. A. Rizk et al.
如今，物联网(IoT)几乎侵入了生活的所有领域，因为它是基于通过计算机技术将生物或非生物连接在一起。它负责通过互联网将物理对象连接在一起。医疗保健和医院是最重要的部门之一，需要大量关注将其旧形式的文档转移到SMART管理系统中。为了提高病人的护理质量，对健康数据进行分析是必不可少的。作为一个发展中国家，埃及正开始用电子智能技术取代旧的政府系统。物联网设备产生不同类型的数据，并将其传输到云计算进行存储和分析。使用物联网为医院收集、传输和分析患者数据的好处吸引了许多研究人员。因此，对更智能、更省钱的医疗保健服务的要求越来越高。安全和隐私、设备通信以及数据收集和管理是物联网技术面临的一些挑战，特别是在与医院数据一起使用时。在此基础上，提出了智能医院管理系统建设的参考模型，以期达到最佳绩效。该模型同时考虑了医院管理系统中不同参与者的功能性和非功能性需求。国际智能计算与信息科学学报https://ijicis.journals.ekb.eg/ 2 D. k. A. A. Rizk等。
{"title":"SMART Hospital Management Systems Based on Internet of Things: Challenges, Intelligent Solutions and Functional Requirements","authors":"Dalia Rizk, H. Hosny, Sayed ElHorbety, Abdel-Badeeh M. Salem","doi":"10.21608/ijicis.2021.82144.1107","DOIUrl":"https://doi.org/10.21608/ijicis.2021.82144.1107","url":null,"abstract":"Nowadays, Internet of Things (IoT) is invading almost all sectors of life since it is based on connecting living or non-living things together through computer technology. It is responsible for connecting physical objects together through the internet. Healthcare and hospitals are one of the most important sectors that require a lot of attention to transfer their old form of documentation into SMART management systems. It is essential to analyze health data in order to increase the quality of patient’s care. Egypt being a development country is starting to substitute its old governmental systems into electronic SMART technology. IoT devices produce different types of data and transfer them to the cloud computing for storage and analysis. The benefits of using IoT in collecting, transferring, and analyzing patients’ data for the hospitals are attracting a lot of researchers. Therefore, the arrangement of smarter and more money saving healthcare services are becoming highly required. Security and privacy, device communication, and data collection and management are some of the challenges that face the IoT technology especially when used with hospital’s data. Accordingly, a proposed reference model for making SMART hospital management system is under construction in order to achieve the best performance. The model is taking into consideration both the functional and non-functional requirements of the different participants involved in the hospital management system. International Journal of Intelligent Computing and Information Sciences https://ijicis.journals.ekb.eg/ 2 D. k. A. A. Rizk et al.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126396004","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 : 2021-11-17DOI: 10.21608/ijicis.2021.79289.1100
Randa ELGawish, M. Hashem, R. Elgohary, Mohamed Abu-Rizka
Ransomware attacks have led many healthcare hospitals to migrate back to their traditional methods of monitoring patients using pen and paper instead of using implantable medical devices remotely. Studying the behaviour of payload ransomware on an approved actual healthcare dataset obtained from ICU and correctly clustering them into normal and malicious records after manifestation is the primary focus of this study. The features decided were upon the possibility of being captured remotely and their frequency of occurrences. Data transformation was included, to handle the encrypted values and perform data normalization, prior to the clustering process. Unsupervised machine learning gained a lot of attention in the cybersecurity domain for its efficiency and capability of clustering tuples into malicious and benign categories. However, on the internet of medical things (IoMT), due to the constraints of the interconnected nodes, clustering of malicious activities became highly challenging and demanded to secure the infrastructure. This work used unsupervised machine learning techniques of k-means, DBscan, and mean shift compared to a threshold-based method which outperformed them with a precision of 100%. The performance metrics used in this work are; precision, recall and f1 score.
{"title":"A Threshold-based Technique to Cluster Ransomware Infected Medical Records on the Internet of Medical Things","authors":"Randa ELGawish, M. Hashem, R. Elgohary, Mohamed Abu-Rizka","doi":"10.21608/ijicis.2021.79289.1100","DOIUrl":"https://doi.org/10.21608/ijicis.2021.79289.1100","url":null,"abstract":"Ransomware attacks have led many healthcare hospitals to migrate back to their traditional methods of monitoring patients using pen and paper instead of using implantable medical devices remotely. Studying the behaviour of payload ransomware on an approved actual healthcare dataset obtained from ICU and correctly clustering them into normal and malicious records after manifestation is the primary focus of this study. The features decided were upon the possibility of being captured remotely and their frequency of occurrences. Data transformation was included, to handle the encrypted values and perform data normalization, prior to the clustering process. Unsupervised machine learning gained a lot of attention in the cybersecurity domain for its efficiency and capability of clustering tuples into malicious and benign categories. However, on the internet of medical things (IoMT), due to the constraints of the interconnected nodes, clustering of malicious activities became highly challenging and demanded to secure the infrastructure. This work used unsupervised machine learning techniques of k-means, DBscan, and mean shift compared to a threshold-based method which outperformed them with a precision of 100%. The performance metrics used in this work are; precision, recall and f1 score.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133433989","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 : 2021-11-01DOI: 10.21608/ijicis.2021.92207.1118
Mennatullah Hesham, H. Khaled, H. Faheem
{"title":"Image colorization using Scaled-YOLOv4 detector","authors":"Mennatullah Hesham, H. Khaled, H. Faheem","doi":"10.21608/ijicis.2021.92207.1118","DOIUrl":"https://doi.org/10.21608/ijicis.2021.92207.1118","url":null,"abstract":"","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116908948","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 : 2021-11-01DOI: 10.21608/ijicis.2021.84159.1111
aml Ismaiel, Walaa K. Gad, T. Mostafa, N. Badr
The rapid use of social media made location prediction the key to research studies based on-location services like; advertising, recommendations, climatological forecast, and security system. Locations are the center of information for these applications. According to millions of users who post tweets every day, Twitter is known as one of the most important and familiar social media blogs. Depending on the importance of catching the location of the users and the rapid usage of Twitter, Location prediction on Twitter has been a point of research in many studies. This survey provides a comprehensive overview picture of the prediction of the user's location on Twitter. that focuses on the home location prediction and tweet location prediction. This occurs by; first, defining these two kinds of research and the inputs of these research views that are content, network, and context. Then, reviewing existing location-prediction techniques and the latent challenges. Finally, the conclusion of the survey and a list of the future research directions.
{"title":"A Location Prediction Methods: state of art","authors":"aml Ismaiel, Walaa K. Gad, T. Mostafa, N. Badr","doi":"10.21608/ijicis.2021.84159.1111","DOIUrl":"https://doi.org/10.21608/ijicis.2021.84159.1111","url":null,"abstract":"The rapid use of social media made location prediction the key to research studies based on-location services like; advertising, recommendations, climatological forecast, and security system. Locations are the center of information for these applications. According to millions of users who post tweets every day, Twitter is known as one of the most important and familiar social media blogs. Depending on the importance of catching the location of the users and the rapid usage of Twitter, Location prediction on Twitter has been a point of research in many studies. This survey provides a comprehensive overview picture of the prediction of the user's location on Twitter. that focuses on the home location prediction and tweet location prediction. This occurs by; first, defining these two kinds of research and the inputs of these research views that are content, network, and context. Then, reviewing existing location-prediction techniques and the latent challenges. Finally, the conclusion of the survey and a list of the future research directions.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123182706","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 : 2021-11-01DOI: 10.21608/ijicis.2021.91569.1119
Karim Emara, Aya El-Kady, E. Shaaban, M. ElEliemy
Received 20218-24; Revised 2021-9-30; Accepted 2021-10-7 Abstract— Smartphones became ubiquitous and are used by so many people, at least to know the driving directions to their destination. Smartphones come with rich embedded sensors (e.g., GPS, accelerometer, and camera) as well as built-in radios (e.g., Bluetooth, Wi-Fi, and Cellular), which both enable users to gather data and distribute it among people at any time or location. These features have come up with the mobile crowdsensing (MCS) development which can be used in a wide range of applications. In this paper, we introduce a complete mobile crowdsensing framework for road surface condition detection. Various modules have been addressed such as task management, data fusion, reputation scoring, incentive awarding, security, and privacy, as well as discussing popular techniques and algorithms utilized in the proposed MCS framework modules. A prototype of the crowd sensing application is designed which is related to our framework. The proposed framework considers the data quality and trustiness between the users and the server as well.
{"title":"MOBILE CROWDSENSING FRAMEWORK FOR ROAD SURFACE QUALITY DETECTION.","authors":"Karim Emara, Aya El-Kady, E. Shaaban, M. ElEliemy","doi":"10.21608/ijicis.2021.91569.1119","DOIUrl":"https://doi.org/10.21608/ijicis.2021.91569.1119","url":null,"abstract":"Received 20218-24; Revised 2021-9-30; Accepted 2021-10-7 Abstract— Smartphones became ubiquitous and are used by so many people, at least to know the driving directions to their destination. Smartphones come with rich embedded sensors (e.g., GPS, accelerometer, and camera) as well as built-in radios (e.g., Bluetooth, Wi-Fi, and Cellular), which both enable users to gather data and distribute it among people at any time or location. These features have come up with the mobile crowdsensing (MCS) development which can be used in a wide range of applications. In this paper, we introduce a complete mobile crowdsensing framework for road surface condition detection. Various modules have been addressed such as task management, data fusion, reputation scoring, incentive awarding, security, and privacy, as well as discussing popular techniques and algorithms utilized in the proposed MCS framework modules. A prototype of the crowd sensing application is designed which is related to our framework. The proposed framework considers the data quality and trustiness between the users and the server as well.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130156039","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 : 2021-10-08DOI: 10.21608/ijicis.2021.75531.1095
Mohammed Elghoul
The purpose of this article was to review the growth of the use of blockchain technology in the healthcare data. In this article, an analysis of the existing blockchain technology research and findings in the health care domain was conducted. The goal was to find relevant implementation of the blockchain technology in the healthcare domain and highlight the challenges and potential methodologies. This article covers an overarching introduction and Background about blockchain in the healthcare domain. Furthermore, the research methodology, an analysis of the information and the results found. The results show that Blockchain still has many challenges such as scalability and security problem [3], however the usage of blockchain is increasing in different scientific research areas [4-7] in general and in healthcare area in particular is growing exponentially [8]. International Journal of Intelligent Computing and Information Sciences
{"title":"A Review of Leveraging Blockchain based Framework Landscape in Healthcare Systems","authors":"Mohammed Elghoul","doi":"10.21608/ijicis.2021.75531.1095","DOIUrl":"https://doi.org/10.21608/ijicis.2021.75531.1095","url":null,"abstract":"The purpose of this article was to review the growth of the use of blockchain technology in the healthcare data. In this article, an analysis of the existing blockchain technology research and findings in the health care domain was conducted. The goal was to find relevant implementation of the blockchain technology in the healthcare domain and highlight the challenges and potential methodologies. This article covers an overarching introduction and Background about blockchain in the healthcare domain. Furthermore, the research methodology, an analysis of the information and the results found. The results show that Blockchain still has many challenges such as scalability and security problem [3], however the usage of blockchain is increasing in different scientific research areas [4-7] in general and in healthcare area in particular is growing exponentially [8]. International Journal of Intelligent Computing and Information Sciences","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129972472","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 : 2021-10-08DOI: 10.21608/ijicis.2021.83987.1110
A. Amin
This paper presents a suggested approach to treat a major issue in images classification namely uncertainty. Uncertainty in image classification means some pixels within each cluster are more or less likely to actually belong to this cluster. So, techniques have been used in this paper to deal with the pixels that do not belong to specific regions, helping to raise image retrieval performance. This was done by merging one of the artificial intelligence techniques, which is image processing, with one of the statistical techniques for probability, which is evidential probabilistic. In such contexts, it may be advantageous to resort to two branches: hexagonal image processing based on partial down-sampling of the image resolution in both directions by half using weighted average performance then shifting the remaining pixels in alternate rows. The other is an evidential theory which is rich and flexible formalisms for representing and manipulating uncertain information. Both hexagonal image processing and evidential theory are used to obtain high accuracy in images classification. The hierarchical nature of the hexagonal image processing addressing scheme is exploited to extract features from the image efficiently.
{"title":"Integrating Hexagonal Image Processing with Evidential Probabilistic Supervised Classification Technique to Improve Image Retrieval Systems","authors":"A. Amin","doi":"10.21608/ijicis.2021.83987.1110","DOIUrl":"https://doi.org/10.21608/ijicis.2021.83987.1110","url":null,"abstract":"This paper presents a suggested approach to treat a major issue in images classification namely uncertainty. Uncertainty in image classification means some pixels within each cluster are more or less likely to actually belong to this cluster. So, techniques have been used in this paper to deal with the pixels that do not belong to specific regions, helping to raise image retrieval performance. This was done by merging one of the artificial intelligence techniques, which is image processing, with one of the statistical techniques for probability, which is evidential probabilistic. In such contexts, it may be advantageous to resort to two branches: hexagonal image processing based on partial down-sampling of the image resolution in both directions by half using weighted average performance then shifting the remaining pixels in alternate rows. The other is an evidential theory which is rich and flexible formalisms for representing and manipulating uncertain information. Both hexagonal image processing and evidential theory are used to obtain high accuracy in images classification. The hierarchical nature of the hexagonal image processing addressing scheme is exploited to extract features from the image efficiently.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"368 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120868228","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 : 2021-10-08DOI: 10.21608/ijicis.2021.84588.1112
Mohamed Abdelghafour, Maryam ElBery, Zaki Taha
Nowadays, video analysis is an important research area especially from a security point of view. The discovery of unusual activities is important because it is a difficult task for humans especially with increasing number of surveillance cameras in all crowded places. That is because it requires a lot of human effort, and these activities happen rarely. Also the definition of anomaly events is different based on the location of the event. For example running in the park is a normal event but running in a restaurant is an abnormal event. The event is the same but the place was the factor of making it normal or not. The main objective of this paper is to compile what has been achieved in the field of anomaly detection and compare them, and to look at the different datasets used in the recent period. We will show how to detect and identify anomalies in videos, approaches for video anomaly detection and also what are the latest learning frameworks.
{"title":"Comparative Study for Anomaly Detection in Crowded Scenes","authors":"Mohamed Abdelghafour, Maryam ElBery, Zaki Taha","doi":"10.21608/ijicis.2021.84588.1112","DOIUrl":"https://doi.org/10.21608/ijicis.2021.84588.1112","url":null,"abstract":"Nowadays, video analysis is an important research area especially from a security point of view. The discovery of unusual activities is important because it is a difficult task for humans especially with increasing number of surveillance cameras in all crowded places. That is because it requires a lot of human effort, and these activities happen rarely. Also the definition of anomaly events is different based on the location of the event. For example running in the park is a normal event but running in a restaurant is an abnormal event. The event is the same but the place was the factor of making it normal or not. The main objective of this paper is to compile what has been achieved in the field of anomaly detection and compare them, and to look at the different datasets used in the recent period. We will show how to detect and identify anomalies in videos, approaches for video anomaly detection and also what are the latest learning frameworks.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127618703","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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