Facial expressions is an intuitive reflection of a person’s emotional state, and it is one of the most important forms of interpersonal communication. Due to the complexity and variability of human facial expressions, traditional methods based on handcrafted feature extraction have shown insufficient performances. For this purpose, we proposed a new system of facial expression recognition based on MobileNet model with the addition of skip connections to prevent the degradation in performance in deeper architectures. Moreover, multi-head attention mechanism was applied to concentrate the processing on the most relevant parts of the image. The experiments were conducted on FER2013 database, which is imbalanced and includes ambiguities in some images containing synthetic faces. We applied a pre-processing step of face detection to eliminate wrong images, and we implemented both SMOTE and Near-Miss algorithms to get a balanced dataset and prevent the model to being biased. The experimental results showed the effectiveness of the proposed framework which achieved the recognition rate of 96.02% when applying multi-head attention mechanism
{"title":"Deep learning based mobilenet and multi-head attention model for facial expression recognition","authors":"Aicha Nouisser, Ramzi Zouari, M. Kherallah","doi":"10.34028/iajit/20/3a/6","DOIUrl":"https://doi.org/10.34028/iajit/20/3a/6","url":null,"abstract":"Facial expressions is an intuitive reflection of a person’s emotional state, and it is one of the most important forms of interpersonal communication. Due to the complexity and variability of human facial expressions, traditional methods based on handcrafted feature extraction have shown insufficient performances. For this purpose, we proposed a new system of facial expression recognition based on MobileNet model with the addition of skip connections to prevent the degradation in performance in deeper architectures. Moreover, multi-head attention mechanism was applied to concentrate the processing on the most relevant parts of the image. The experiments were conducted on FER2013 database, which is imbalanced and includes ambiguities in some images containing synthetic faces. We applied a pre-processing step of face detection to eliminate wrong images, and we implemented both SMOTE and Near-Miss algorithms to get a balanced dataset and prevent the model to being biased. The experimental results showed the effectiveness of the proposed framework which achieved the recognition rate of 96.02% when applying multi-head attention mechanism","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73473723","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}
Image segmentation plays a significant role in image processing and scientific research. In this paper, we develop a novel approach, which provides effective and robust performances for image segmentation based on the region-based (block-based) graph instead of pixel-based graph. The modified Discrete Cosine Transform (DCT) is applied to obtain the Square Block Structures (DCT-SBS) of the image in the compressed domain together with the coefficients, due to its low memory requirement and high processing efficiency on extracting the block feature. A novel weight computation approach focusing on multi-feature fusion from the location, texture and RGB-color information is employed to efficiently obtain weights between the DCT-SBS. The energy function is redesigned to meet the region-based requirement and can be easily transformed into the traditional Normalized cuts (Ncuts). The proposed image segmentation algorithm is applied to the salient region detection database and Corel1000 database. The performance results are compared with the state-of-the-art segmentation algorithms. Experimental results clearly show that our method outperforms other algorithms, and demonstrate good segmentation precision and high efficiency.
{"title":"Image segmentation with multi-feature fusion in compressed domain based on region-based graph","authors":"Hongchuan Luo, Bo Sun, Hang Zhou, Wenyuan Cao","doi":"10.34028/iajit/20/2/2","DOIUrl":"https://doi.org/10.34028/iajit/20/2/2","url":null,"abstract":"Image segmentation plays a significant role in image processing and scientific research. In this paper, we develop a novel approach, which provides effective and robust performances for image segmentation based on the region-based (block-based) graph instead of pixel-based graph. The modified Discrete Cosine Transform (DCT) is applied to obtain the Square Block Structures (DCT-SBS) of the image in the compressed domain together with the coefficients, due to its low memory requirement and high processing efficiency on extracting the block feature. A novel weight computation approach focusing on multi-feature fusion from the location, texture and RGB-color information is employed to efficiently obtain weights between the DCT-SBS. The energy function is redesigned to meet the region-based requirement and can be easily transformed into the traditional Normalized cuts (Ncuts). The proposed image segmentation algorithm is applied to the salient region detection database and Corel1000 database. The performance results are compared with the state-of-the-art segmentation algorithms. Experimental results clearly show that our method outperforms other algorithms, and demonstrate good segmentation precision and high efficiency.","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81771404","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}
With the increasing proliferation of the Internet of Things (IoT) devices, digital forensics professionals face numerous challenges whilst investigating cybercrimes. The vast number of IoT devices, the heterogeneity of their formats, and the diversity of the data they generate make the identification and collection of relevant evidence a daunting task. In this research paper, we explore the complex landscape of IoT forensics, highlighting the major challenges and emerging solutions. We start by listing the available digital forensics models and frameworks. We then delve into evidence management during different IoT forensic investigation stages such as Identification, Acquisition, Preservation and Protection, Analysis and Correlation, Attack and Deficit Attribution and lastly Presentation. Furthermore, we highlight the current challenges, open issues and major security and privacy concerns related to IoT forensics. Finally, we review the state-of-the-art in IoT forensics, exploring the possible solutions proposed in recent literature. Overall, this paper provides a comprehensive overview of the current IoT forensics ecosystem, the challenges, and proposes the latest possible solutions, which is critical for ensuring the security and integrity of IoT-enabled critical infrastructures and can serves as a valuable resource for researchers and practitioners in the field
{"title":"Navigating the complex landscape of IoT forensics: challenges and emerging solutions","authors":"Nurashidah Musa, N. Mirza, Adnan Ali","doi":"10.34028/iajit/20/3a/7","DOIUrl":"https://doi.org/10.34028/iajit/20/3a/7","url":null,"abstract":"With the increasing proliferation of the Internet of Things (IoT) devices, digital forensics professionals face numerous challenges whilst investigating cybercrimes. The vast number of IoT devices, the heterogeneity of their formats, and the diversity of the data they generate make the identification and collection of relevant evidence a daunting task. In this research paper, we explore the complex landscape of IoT forensics, highlighting the major challenges and emerging solutions. We start by listing the available digital forensics models and frameworks. We then delve into evidence management during different IoT forensic investigation stages such as Identification, Acquisition, Preservation and Protection, Analysis and Correlation, Attack and Deficit Attribution and lastly Presentation. Furthermore, we highlight the current challenges, open issues and major security and privacy concerns related to IoT forensics. Finally, we review the state-of-the-art in IoT forensics, exploring the possible solutions proposed in recent literature. Overall, this paper provides a comprehensive overview of the current IoT forensics ecosystem, the challenges, and proposes the latest possible solutions, which is critical for ensuring the security and integrity of IoT-enabled critical infrastructures and can serves as a valuable resource for researchers and practitioners in the field","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84326649","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}
Cross-domain sentiment classification transfers the knowledge from the source domain to the target domain lacking supervised information for sentiment classification. Existing cross-domain sentiment classification methods establish connections by extracting domain-invariant features manually. However, these methods have poor adaptability to bridge connections across different domains and ignore important sentiment information. Hence, we propose a Topic Lite Bidirectional Encoder Representations from Transformers (T-LBERT) model with domain adaption to improve the adaptability of cross-domain sentiment classification. It combines the learning content of the source domain and the topic information of the target domain to improve the domain adaptability of the model. Due to the unbalanced distribution of information in the combined data, we apply a two-layer attention adaptive mechanism for classification. A shallow attention layer is applied to weigh the important features of the combined data. Inspired by active learning, we propose a deep domain adaption layer, which actively adjusts model parameters to balance the difference and representativeness between domains. Experimental results on Amazon review datasets demonstrate that the T-LBERT model considerably outperforms other state-of-the-art methods. T-LBERT shows stable classification performance on multiple metrics.
{"title":"T-LBERT with Domain Adaptation for Cross-Domain Sentiment Classification","authors":"Hongye Cao, Qianru Wei, Jiangbin Zheng","doi":"10.34028/iajit/20/1/15","DOIUrl":"https://doi.org/10.34028/iajit/20/1/15","url":null,"abstract":"Cross-domain sentiment classification transfers the knowledge from the source domain to the target domain lacking supervised information for sentiment classification. Existing cross-domain sentiment classification methods establish connections by extracting domain-invariant features manually. However, these methods have poor adaptability to bridge connections across different domains and ignore important sentiment information. Hence, we propose a Topic Lite Bidirectional Encoder Representations from Transformers (T-LBERT) model with domain adaption to improve the adaptability of cross-domain sentiment classification. It combines the learning content of the source domain and the topic information of the target domain to improve the domain adaptability of the model. Due to the unbalanced distribution of information in the combined data, we apply a two-layer attention adaptive mechanism for classification. A shallow attention layer is applied to weigh the important features of the combined data. Inspired by active learning, we propose a deep domain adaption layer, which actively adjusts model parameters to balance the difference and representativeness between domains. Experimental results on Amazon review datasets demonstrate that the T-LBERT model considerably outperforms other state-of-the-art methods. T-LBERT shows stable classification performance on multiple metrics.","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87195718","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}
Z. Ashraf, Adnan Sohail, Sohaib Latif, Abdul Hameed Pitafi, Muhammad Yousaf Malik
The rapid proliferation of the Internet has exhausted Internet Protocol version 4 (IPv4) addresses offered by Internet Assigned Number Authority (IANA). The new version of the IP i.e. IPv6 was launched by Internet Engineering Task Force (IETF) with new features, such as a simpler packet header, larger address space, new anycast addressing type, integrated security, efficient segment routing, and better Quality of Services (QoS). Virtualized network architectures such as Network Function Virtualization (NFV) and Software Defined Network (SDN) have been introduced. These new paradigms have entirely changed the way of internetworking and provide a lot of benefits in multiple domains of applications that have used SDN and NFV. ISPs are trying to move from existing IPv4 physical networks to virtualized next-generation IPv6 networks gradually. The transition from physical IPv4 to software-based IPv6 is very slow due to the usage of IPv4 addresses by billions of devices around the globe. IPv4 and IPv6 protocols are different in format and behaviour. Therefore, direct communication between IPv4 and IPv6 is not possible. Both protocols will co-exist for a long time during transition despite the incompatibility issues. The core issues between IPv4 and IPv6 protocols are compatibility, interoperability, and security. The transition creates many challenges for ISPs during shifting the network toward a software-based IPv6 network. Packet traversing, routing scalability, the guarantee of performance, and security are the main challenges faced by ISPs. In this research, we focused on a qualitative and comprehensive survey. We summarize the challenges during the transition process, recommended appropriate solutions, and an in-depth analysis of their mitigations during moving towards the next-generation virtual IPv6 network
{"title":"Challenges and Mitigation Strategies for Transition from IPv4 Network to Virtualized Next-Generation IPv6 Network","authors":"Z. Ashraf, Adnan Sohail, Sohaib Latif, Abdul Hameed Pitafi, Muhammad Yousaf Malik","doi":"10.34028/iajit/20/1/9","DOIUrl":"https://doi.org/10.34028/iajit/20/1/9","url":null,"abstract":"The rapid proliferation of the Internet has exhausted Internet Protocol version 4 (IPv4) addresses offered by Internet Assigned Number Authority (IANA). The new version of the IP i.e. IPv6 was launched by Internet Engineering Task Force (IETF) with new features, such as a simpler packet header, larger address space, new anycast addressing type, integrated security, efficient segment routing, and better Quality of Services (QoS). Virtualized network architectures such as Network Function Virtualization (NFV) and Software Defined Network (SDN) have been introduced. These new paradigms have entirely changed the way of internetworking and provide a lot of benefits in multiple domains of applications that have used SDN and NFV. ISPs are trying to move from existing IPv4 physical networks to virtualized next-generation IPv6 networks gradually. The transition from physical IPv4 to software-based IPv6 is very slow due to the usage of IPv4 addresses by billions of devices around the globe. IPv4 and IPv6 protocols are different in format and behaviour. Therefore, direct communication between IPv4 and IPv6 is not possible. Both protocols will co-exist for a long time during transition despite the incompatibility issues. The core issues between IPv4 and IPv6 protocols are compatibility, interoperability, and security. The transition creates many challenges for ISPs during shifting the network toward a software-based IPv6 network. Packet traversing, routing scalability, the guarantee of performance, and security are the main challenges faced by ISPs. In this research, we focused on a qualitative and comprehensive survey. We summarize the challenges during the transition process, recommended appropriate solutions, and an in-depth analysis of their mitigations during moving towards the next-generation virtual IPv6 network","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88586792","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}
Surbhi Kapoor, Akashdeep Sharma, Aman Verma, Vishal Dhull, Chahat Goyal
Unmanned Aerial Vehicle )UAV( finds its significant application in video surveillance due to its low cost, high portability and fast-mobility. In this paper, the proposed approach focuses on recognizing the human activity in aerial video sequences through various keypoints detected on the human body via OpenPose. The detected keypoints are passed onto machine learning and deep learning classifiers for classifying the human actions. Experimental results demonstrate that multilayer perceptron and SVM outperformed all the other classifiers by reporting an accuracy of 87.80% and 87.77% respectively whereas LSTM did not produce very good results as compared to other classifiers. Stacked Long Short-Term Memory networks (LSTM( produced an accuracy of 71.30% and Bidirectional LSTM yielded an accuracy of 76.04%. The results also indicate that machine learning models performed better than deep learning models. The major reason for this finding is the lesser availability of data and the deep learning models being data hungry models require a large amount of data to work upon. The paper also analyses the failure cases of OpenPose by testing the system on aerial videos captured by a drone flying at a higher altitude. This work provides a baseline for validating machine learning classifiers and deep learning classifiers against recognition of human action from aerial videos.
{"title":"A comparative study on deep learning and machine learning models for human action recognition in aerial videos","authors":"Surbhi Kapoor, Akashdeep Sharma, Aman Verma, Vishal Dhull, Chahat Goyal","doi":"10.34028/iajit/20/4/2","DOIUrl":"https://doi.org/10.34028/iajit/20/4/2","url":null,"abstract":"Unmanned Aerial Vehicle )UAV( finds its significant application in video surveillance due to its low cost, high portability and fast-mobility. In this paper, the proposed approach focuses on recognizing the human activity in aerial video sequences through various keypoints detected on the human body via OpenPose. The detected keypoints are passed onto machine learning and deep learning classifiers for classifying the human actions. Experimental results demonstrate that multilayer perceptron and SVM outperformed all the other classifiers by reporting an accuracy of 87.80% and 87.77% respectively whereas LSTM did not produce very good results as compared to other classifiers. Stacked Long Short-Term Memory networks (LSTM( produced an accuracy of 71.30% and Bidirectional LSTM yielded an accuracy of 76.04%. The results also indicate that machine learning models performed better than deep learning models. The major reason for this finding is the lesser availability of data and the deep learning models being data hungry models require a large amount of data to work upon. The paper also analyses the failure cases of OpenPose by testing the system on aerial videos captured by a drone flying at a higher altitude. This work provides a baseline for validating machine learning classifiers and deep learning classifiers against recognition of human action from aerial videos.","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81242970","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}
Shimin Xiong, Bin Li, Shiao Zhu, Dongfei Cui, Xiaonan Song
Traffic sign detection is a key part of intelligent assisted driving, but also a challenging task due to the small size and different scales of objects in foreground and closed range. In this paper, we propose a new traffic sign detection scheme: Spatial Pyramid Pooling and Adaptively Spatial Feature Fusion based Yolov3 (SPP and ASFF-Yolov3). In order to integrate the target detail features and environment context features in the feature extraction stage of Yolov3 network, the Spatial Pyramid Pooling module is introduced into the pyramid network of Yolov3. Additionally, Adaptively Spatial Feature Fusion module is added to the target detection phase of the pyramid network of Yolov3 to avoid the interference of different scale features with the process of gradient calculation. Experimental results show the effectiveness of the proposed SPP and ASFF-Yolov3 network, which achieves better detection results than the original Yolov3 network. It can archive real-time inference speed despite inferior to the original Yolov3 network. The proposed scheme will add an option to the solutions of traffic sign detection with real-time inference speed and effective detection results.
{"title":"Spatial pyramid pooling and adaptively feature fusion based yolov3 for traffic sign detection","authors":"Shimin Xiong, Bin Li, Shiao Zhu, Dongfei Cui, Xiaonan Song","doi":"10.34028/iajit/20/4/5","DOIUrl":"https://doi.org/10.34028/iajit/20/4/5","url":null,"abstract":"Traffic sign detection is a key part of intelligent assisted driving, but also a challenging task due to the small size and different scales of objects in foreground and closed range. In this paper, we propose a new traffic sign detection scheme: Spatial Pyramid Pooling and Adaptively Spatial Feature Fusion based Yolov3 (SPP and ASFF-Yolov3). In order to integrate the target detail features and environment context features in the feature extraction stage of Yolov3 network, the Spatial Pyramid Pooling module is introduced into the pyramid network of Yolov3. Additionally, Adaptively Spatial Feature Fusion module is added to the target detection phase of the pyramid network of Yolov3 to avoid the interference of different scale features with the process of gradient calculation. Experimental results show the effectiveness of the proposed SPP and ASFF-Yolov3 network, which achieves better detection results than the original Yolov3 network. It can archive real-time inference speed despite inferior to the original Yolov3 network. The proposed scheme will add an option to the solutions of traffic sign detection with real-time inference speed and effective detection results.","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76717774","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}
Heart disease is a rapidly increasing disease that causes death worldwide. Therefore, scientists around the globe start studying this issue from a different perspective to assure early prediction of diagnosis to save patients' life from bad consequences that cause death. In this regard, Internet of Medical Things (IoMT) applications and algorithms should be utilized effectively to overcome this problem. Hoeffding Tree Algorithm (HTA) is a standard decision tree algorithm to handle large sizes of data sets. In this paper, an Adaptive Hoeffding Tree (AHT) algorithm is suggested to carry out classifications of data sets for early diagnosis of heart disease-related factors, and the obtained results by this algorithm are compared with other suggested Machine Learning (ML) algorithms in the literature. Therefore, a total of 3000 records of data sets are used in the classification, 33% of the data are utilized for female patient information, and the rest of the data are utilized for male patient information. In the original data set, each patient record includes 76 attributes, however only the most important 16 patient attributes are used for the classification. Data are retrieved from the University of California Irvine (UCI) Machine Learning Repository, which is collected from the Hungarian Institute of Cardiology, University Hospital at Zurich, University Hospital at Basel, and V.A. Medical Center. The obtained results from this study and the provided comparative results show the effectiveness of the AHT algorithm over other ML algorithms. Compared to other ML algorithms, AHT outperforms other algorithms with 95.67% accuracy for early estimation of diagnosis of heart disease.
{"title":"Heart Disease Classification for Early Diagnosis based on Adaptive Hoeffding Tree Algorithm in IoMT Data","authors":"E. Elbasi, A. Zreikat","doi":"10.34028/iajit/20/1/5","DOIUrl":"https://doi.org/10.34028/iajit/20/1/5","url":null,"abstract":"Heart disease is a rapidly increasing disease that causes death worldwide. Therefore, scientists around the globe start studying this issue from a different perspective to assure early prediction of diagnosis to save patients' life from bad consequences that cause death. In this regard, Internet of Medical Things (IoMT) applications and algorithms should be utilized effectively to overcome this problem. Hoeffding Tree Algorithm (HTA) is a standard decision tree algorithm to handle large sizes of data sets. In this paper, an Adaptive Hoeffding Tree (AHT) algorithm is suggested to carry out classifications of data sets for early diagnosis of heart disease-related factors, and the obtained results by this algorithm are compared with other suggested Machine Learning (ML) algorithms in the literature. Therefore, a total of 3000 records of data sets are used in the classification, 33% of the data are utilized for female patient information, and the rest of the data are utilized for male patient information. In the original data set, each patient record includes 76 attributes, however only the most important 16 patient attributes are used for the classification. Data are retrieved from the University of California Irvine (UCI) Machine Learning Repository, which is collected from the Hungarian Institute of Cardiology, University Hospital at Zurich, University Hospital at Basel, and V.A. Medical Center. The obtained results from this study and the provided comparative results show the effectiveness of the AHT algorithm over other ML algorithms. Compared to other ML algorithms, AHT outperforms other algorithms with 95.67% accuracy for early estimation of diagnosis of heart disease.","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77084595","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}
Mohammed Alghaili, Zhiyong Li, Ahmed Jawad A. AlBdairi, Malasy Katiyalath
Although significant advances have been made recently in the field of ethnics recognition through face recognition, there is still a lack of studies of ethnics recognition through facial recognition. This study is concerned with ethnics recognition through facial representation using a few images used as samples for any selected group of ethnics using a deep neural network with a Variational Feature Learning (VFL) loss function that has been used to increase the performance accuracy during the evaluation process. The output of a deep neural network is an embedding of 128 bytes for each face image in each group of ethnics. After that, all embeddings of every face in each group of ethnics pass to a machine learning classification method like a Support Vector Machine (SVM). We achieved state-of-the-art ethnic recognition. The system achieved a classification accuracy of 97.3% on a collected group of image dataset collected from three different countries.
{"title":"Generating embedding features using deep learning for ethnics recognition","authors":"Mohammed Alghaili, Zhiyong Li, Ahmed Jawad A. AlBdairi, Malasy Katiyalath","doi":"10.34028/iajit/20/4/13","DOIUrl":"https://doi.org/10.34028/iajit/20/4/13","url":null,"abstract":"Although significant advances have been made recently in the field of ethnics recognition through face recognition, there is still a lack of studies of ethnics recognition through facial recognition. This study is concerned with ethnics recognition through facial representation using a few images used as samples for any selected group of ethnics using a deep neural network with a Variational Feature Learning (VFL) loss function that has been used to increase the performance accuracy during the evaluation process. The output of a deep neural network is an embedding of 128 bytes for each face image in each group of ethnics. After that, all embeddings of every face in each group of ethnics pass to a machine learning classification method like a Support Vector Machine (SVM). We achieved state-of-the-art ethnic recognition. The system achieved a classification accuracy of 97.3% on a collected group of image dataset collected from three different countries.","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88247355","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}
With the increasing prevalence of different mobile apps, many applications require users to enable the location service on their devices. For example, the geofence service can be defined as establishing virtual geographical boundaries. Enabling this service triggers entering and exiting the boundary area and notifies the users and trusted third parties. The foremost concern of using geofence is the privacy of location coordinates shared among different applications. In this paper, a framework called ‘TIET-GEO’ is proposed that allows users to define the geofence boundary; in addition, it monitors Global Positioning System (GPS) devices in real-time when they enter/exit a specific area. The proposed framework also proposes a dummy privacy preservation algorithm to generate K-dummy locations around the real trajectories when the user requests the Point Of Interest (POI) from the Location-Based Services (LBS). This article aims to enhance the location privacy preservation in geofence service, by generating a k-dummy location around the user location based on the radius size of the geofence area. The proposed framework uses token keys authentication to authorize the users in the Vehicular Cloud Network (VCN) service by generating secret token keys authentication between the client and services. The results obtained show the effectiveness of the proposed framework was on parameters like flexibility and reliability of responses from different sources, such as smart IoT devices and datasets.
{"title":"Framework of Geofence Service using Dummy Location Privacy Preservation in Vehicular Cloud Network","authors":"Hani Al-Balasmeh, Maninder P. Singh, Raman Singh","doi":"10.34028/iajit/20/1/8","DOIUrl":"https://doi.org/10.34028/iajit/20/1/8","url":null,"abstract":"With the increasing prevalence of different mobile apps, many applications require users to enable the location service on their devices. For example, the geofence service can be defined as establishing virtual geographical boundaries. Enabling this service triggers entering and exiting the boundary area and notifies the users and trusted third parties. The foremost concern of using geofence is the privacy of location coordinates shared among different applications. In this paper, a framework called ‘TIET-GEO’ is proposed that allows users to define the geofence boundary; in addition, it monitors Global Positioning System (GPS) devices in real-time when they enter/exit a specific area. The proposed framework also proposes a dummy privacy preservation algorithm to generate K-dummy locations around the real trajectories when the user requests the Point Of Interest (POI) from the Location-Based Services (LBS). This article aims to enhance the location privacy preservation in geofence service, by generating a k-dummy location around the user location based on the radius size of the geofence area. The proposed framework uses token keys authentication to authorize the users in the Vehicular Cloud Network (VCN) service by generating secret token keys authentication between the client and services. The results obtained show the effectiveness of the proposed framework was on parameters like flexibility and reliability of responses from different sources, such as smart IoT devices and datasets.","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77113920","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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