Pub Date : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581366
Ramzi A. Jaber, Hiba Bazzi, A. Haidar, B. Owaidat, A. Kassem
This work proposes models for a L-trit TMUL (Ternary Multiplier) and THA (Half-Adder) using TMUXs (Ternary Multiplexers) and unary operators. The target of the proposed designs is to minimize energy consumption in nanoscale embedded circuits to improve their battery usage. To achieve that, different techniques are used: 32-nm CNTFET tranisistor, Multiple-Valued Logic (MVL), two voltage supplies $(V_{dd}, V_{dd}/2)$ TMUXs, and unary operators to reduce the transistors' number and PDP (Power Delay Product). Extensive simulations using HSPICE for different Process, Voltage, Temperature (PVT), and noise effects are applied. The obtained results show improvements regarding PDP, robustness of process variations, and noise tolerance with respect to recent similar designs.
{"title":"1-trit Ternary Multiplier and Adder Designs Using Ternary Multiplexers and Unary Operators","authors":"Ramzi A. Jaber, Hiba Bazzi, A. Haidar, B. Owaidat, A. Kassem","doi":"10.1109/3ICT53449.2021.9581366","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581366","url":null,"abstract":"This work proposes models for a L-trit TMUL (Ternary Multiplier) and THA (Half-Adder) using TMUXs (Ternary Multiplexers) and unary operators. The target of the proposed designs is to minimize energy consumption in nanoscale embedded circuits to improve their battery usage. To achieve that, different techniques are used: 32-nm CNTFET tranisistor, Multiple-Valued Logic (MVL), two voltage supplies $(V_{dd}, V_{dd}/2)$ TMUXs, and unary operators to reduce the transistors' number and PDP (Power Delay Product). Extensive simulations using HSPICE for different Process, Voltage, Temperature (PVT), and noise effects are applied. The obtained results show improvements regarding PDP, robustness of process variations, and noise tolerance with respect to recent similar designs.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116993717","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 : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581809
Firuz Kamalov, F. Thabtah, Ikhlaas Gurrib
The autocorrelation function (ACF) is a fundamental concept in time series analysis including financial forecasting. In this note, we investigate the properties of the sample ACF for a time series with linear trend. In particular, we show that the sample ACF of the time series approaches 1 for all lags as the number of time steps increases. The theoretical results are supported by numerical experiments. Our result helps researchers better understand the ACF patterns and make correct ARMA selection.
{"title":"Autocorrelation for time series with linear trend","authors":"Firuz Kamalov, F. Thabtah, Ikhlaas Gurrib","doi":"10.1109/3ICT53449.2021.9581809","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581809","url":null,"abstract":"The autocorrelation function (ACF) is a fundamental concept in time series analysis including financial forecasting. In this note, we investigate the properties of the sample ACF for a time series with linear trend. In particular, we show that the sample ACF of the time series approaches 1 for all lags as the number of time steps increases. The theoretical results are supported by numerical experiments. Our result helps researchers better understand the ACF patterns and make correct ARMA selection.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125349348","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 : 2021-09-29DOI: 10.1109/3ICT53449.2021.9582077
A. Al-Omary, M. Akram, V. Dhamodharan
Social intelligence in robots is a relatively new concept. In many application areas and circumstances where robots must communicate and work with other robots or people, social and interaction capabilities have become more evident. This paper presents the design and implementation of intelligent socialized 3D humanoid robot called “RUBEX”. The designed robot was implemented by integrating different technologies and parts like 3D printing, electronical and mechanical parts and different AI and machine learning algorithms. RUBEX has very engaging, rich, and friendly dialogue and interaction with the appearance that resembles humans. In designing the robot head, 3D printer is used to manufacture a handy human like face. Servo motors and sensors are used to control robot face emotions and interaction. The robot was trained to greet people upon their recognition, interact with them and was also customized to detect the emotions and communicate accordingly with people. The intelligent socializing 3D humanoid robot was implemented successfully, tested, and validated and proved to be a successful product that can be manufactured in a large scale in future.
{"title":"Design and Implementation of Intelligent Socializing 3D Humanoid Robot","authors":"A. Al-Omary, M. Akram, V. Dhamodharan","doi":"10.1109/3ICT53449.2021.9582077","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9582077","url":null,"abstract":"Social intelligence in robots is a relatively new concept. In many application areas and circumstances where robots must communicate and work with other robots or people, social and interaction capabilities have become more evident. This paper presents the design and implementation of intelligent socialized 3D humanoid robot called “RUBEX”. The designed robot was implemented by integrating different technologies and parts like 3D printing, electronical and mechanical parts and different AI and machine learning algorithms. RUBEX has very engaging, rich, and friendly dialogue and interaction with the appearance that resembles humans. In designing the robot head, 3D printer is used to manufacture a handy human like face. Servo motors and sensors are used to control robot face emotions and interaction. The robot was trained to greet people upon their recognition, interact with them and was also customized to detect the emotions and communicate accordingly with people. The intelligent socializing 3D humanoid robot was implemented successfully, tested, and validated and proved to be a successful product that can be manufactured in a large scale in future.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122950999","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 : 2021-09-29DOI: 10.1109/3ICT53449.2021.9582069
M. Yıldırım, A. Cinar, Emine Cengil
Flowers have an important place in human life. Because flowers can appear at every stage of human life. People want to know these types of flowers that they come across even in daily life. However, due to a large number of flower types, there are difficulties in recognizing these types. We used deep learning methods in this study to overcome these difficulties. Deep learning methods have been widely used in different fields recently. In this study, we used 3 different deep learning methods. In the first stage, we performed the classification process using the pre-trained Efficientnetb0, MobilenetV2 and Alexnet architectures. In the second step, we extracted the feature maps of the images in the dataset using these three pre-trained deep learning models. Then, we optimized these features using the NCA size reduction method to save time and cost. Next, we classified these optimized features in the features Subspace Discriminant classifier. In the final stage, we combined the features we obtained with three pre-trained deep learning architectures. After optimizing these combined features with the NCA method, we classified the features in the Subspace Discriminant classifier. In the first step, the highest accuracy we achieved in the three pre-trained deep learning architectures was 83.67%, while our accuracy rate was 94% in this hybrid method we recommend. This shows that our proposed model is successful.
{"title":"Classification of flower species using CNN models, Subspace Discriminant, and NCA","authors":"M. Yıldırım, A. Cinar, Emine Cengil","doi":"10.1109/3ICT53449.2021.9582069","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9582069","url":null,"abstract":"Flowers have an important place in human life. Because flowers can appear at every stage of human life. People want to know these types of flowers that they come across even in daily life. However, due to a large number of flower types, there are difficulties in recognizing these types. We used deep learning methods in this study to overcome these difficulties. Deep learning methods have been widely used in different fields recently. In this study, we used 3 different deep learning methods. In the first stage, we performed the classification process using the pre-trained Efficientnetb0, MobilenetV2 and Alexnet architectures. In the second step, we extracted the feature maps of the images in the dataset using these three pre-trained deep learning models. Then, we optimized these features using the NCA size reduction method to save time and cost. Next, we classified these optimized features in the features Subspace Discriminant classifier. In the final stage, we combined the features we obtained with three pre-trained deep learning architectures. After optimizing these combined features with the NCA method, we classified the features in the Subspace Discriminant classifier. In the first step, the highest accuracy we achieved in the three pre-trained deep learning architectures was 83.67%, while our accuracy rate was 94% in this hybrid method we recommend. This shows that our proposed model is successful.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123035193","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 : 2021-09-29DOI: 10.1109/3ict53449.2021.9581702
J. Hualde, Rodrigo Lopes de Barros, Rachel Garza, Nicholas M. Blaker
{"title":"Our Keynote Speakers","authors":"J. Hualde, Rodrigo Lopes de Barros, Rachel Garza, Nicholas M. Blaker","doi":"10.1109/3ict53449.2021.9581702","DOIUrl":"https://doi.org/10.1109/3ict53449.2021.9581702","url":null,"abstract":"","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129024087","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 : 2021-09-29DOI: 10.1109/3ICT53449.2021.9582050
Saber Modallal, Mamoon Hassan, Amjad Hawash
With the population explosion and the increasing number of different types of buildings and the variety of their related facilities, the need for rapid proper searching of craftsmen to make new installations and/or make some repairs is also increased. Usually, people seek close, reasonable wages, and professional craftsmen to make some repairs or installations. Searching for such craftsmen is not an easy task with the increase of population in countries as well as the lack of related and accurate information. In this work, we are suggesting a fuzzy logic-based recommendation system embedded within a web-based database application. The system enables clients (customers) to manually search for craftsmen as well as the ability of the system to suggest craftsmen to clients according to the professionalism of craftsmen and their closeness, all ranked in descending order. Since manual searching for craftsmen is also not an easy task, the recommendation system is able to suggest the most suitable craftsmen to clients according to their needs. The experimental tests at the end of the work emphasize the importance of using a recommendation system instead of the manual search of craftsmen by comparing the manual and the fuzzy-based craftsmen searching in terms of time and effort.
{"title":"Fuzzy Logic based Recommendation System: Crafts to Clients Suggestion","authors":"Saber Modallal, Mamoon Hassan, Amjad Hawash","doi":"10.1109/3ICT53449.2021.9582050","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9582050","url":null,"abstract":"With the population explosion and the increasing number of different types of buildings and the variety of their related facilities, the need for rapid proper searching of craftsmen to make new installations and/or make some repairs is also increased. Usually, people seek close, reasonable wages, and professional craftsmen to make some repairs or installations. Searching for such craftsmen is not an easy task with the increase of population in countries as well as the lack of related and accurate information. In this work, we are suggesting a fuzzy logic-based recommendation system embedded within a web-based database application. The system enables clients (customers) to manually search for craftsmen as well as the ability of the system to suggest craftsmen to clients according to the professionalism of craftsmen and their closeness, all ranked in descending order. Since manual searching for craftsmen is also not an easy task, the recommendation system is able to suggest the most suitable craftsmen to clients according to their needs. The experimental tests at the end of the work emphasize the importance of using a recommendation system instead of the manual search of craftsmen by comparing the manual and the fuzzy-based craftsmen searching in terms of time and effort.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124679869","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 : 2021-09-29DOI: 10.1109/3ICT53449.2021.9582001
Z. Salman, M. Hammad, A. Al-Omary
Homomorphic Encryption (HE) comes as a sophisticated and powerful cryptography system that can preserve the privacy of data in all cases when the data is at rest or even when data is in processing and computing. All the computations needed by the user or the provider can be done on the encrypted data without any need to decrypt it. However, HE has overheads such as big key sizes and long ciphertexts and as a result long execution time. This paper proposes a novel solution for big data analytic based on clustering and the Elliptical Curve Cryptography (ECC). The Extremely Distributed Clustering technique (EDC) has been used to divide big data into several subsets of cloud computing nodes. Different clustering techniques had been investigated, and it was found that using hybrid techniques can improve the performance and efficiency of big data analytic while at the same time data is protected and privacy is preserved using ECC.
{"title":"A Homomorphic Cloud Framework for Big Data Analytics Based on Elliptic Curve Cryptography","authors":"Z. Salman, M. Hammad, A. Al-Omary","doi":"10.1109/3ICT53449.2021.9582001","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9582001","url":null,"abstract":"Homomorphic Encryption (HE) comes as a sophisticated and powerful cryptography system that can preserve the privacy of data in all cases when the data is at rest or even when data is in processing and computing. All the computations needed by the user or the provider can be done on the encrypted data without any need to decrypt it. However, HE has overheads such as big key sizes and long ciphertexts and as a result long execution time. This paper proposes a novel solution for big data analytic based on clustering and the Elliptical Curve Cryptography (ECC). The Extremely Distributed Clustering technique (EDC) has been used to divide big data into several subsets of cloud computing nodes. Different clustering techniques had been investigated, and it was found that using hybrid techniques can improve the performance and efficiency of big data analytic while at the same time data is protected and privacy is preserved using ECC.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126796135","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 : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581469
Mahmood A. Bazel, Fathey Mohammed, Mogeeb Alsabaiy, H. Abualrejal
The spread of new coronavirus pandemic (COVID-19) has led to a major crisis in the economic and health sector, which required prompt response by medical personnel, health organizations, scientists, as well as the government sector. Globally, health care institutions have been affected greatly and unexpectedly by this COVID-19 pandemic put the current systems of healthcare under tremendous pressures, and at their maximum capabilities and resources in order to provide medical services to those infected. In this global health emergency situation and given the current limited healthcare resources, the necessity of finding quick and innovative solutions has been required. As a result, using new technologies to struggle COVID-19 and meeting the pandemic's specified requirements, such as detecting, monitoring, diagnosing, screening, surveillance, tracking, and raising awareness, has become unavoidable. The focus of this research is to understand how the healthcare system use these new technologies to fight against the pandemic. This paper provides a guideline to practitioners on the benefits and application areas of Artificial Intelligence, Internet of things, Blockchain, and Big data technologies in the healthcare industry to face the crisis caused by this pandemic. A detailed analysis of strengths, weaknesses, opportunities, and threats for the thorough implementation of these technologies has been conducted. Also, the paper addresses the obstacles to adopt these technologies in the healthcare systems and make some recommendations for future studies. The paper assists researchers, experts, and readers in recognizing how the use of technology is aiding in the management of the coronavirus infection in a synergistic manner, as well as encourage the need for these techniques in existing and potential times of emergency
{"title":"The role of Internet of Things, Blockchain, Artificial Intelligence, and Big Data Technologies in Healthcare to Prevent the Spread of the COVID-19","authors":"Mahmood A. Bazel, Fathey Mohammed, Mogeeb Alsabaiy, H. Abualrejal","doi":"10.1109/3ICT53449.2021.9581469","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581469","url":null,"abstract":"The spread of new coronavirus pandemic (COVID-19) has led to a major crisis in the economic and health sector, which required prompt response by medical personnel, health organizations, scientists, as well as the government sector. Globally, health care institutions have been affected greatly and unexpectedly by this COVID-19 pandemic put the current systems of healthcare under tremendous pressures, and at their maximum capabilities and resources in order to provide medical services to those infected. In this global health emergency situation and given the current limited healthcare resources, the necessity of finding quick and innovative solutions has been required. As a result, using new technologies to struggle COVID-19 and meeting the pandemic's specified requirements, such as detecting, monitoring, diagnosing, screening, surveillance, tracking, and raising awareness, has become unavoidable. The focus of this research is to understand how the healthcare system use these new technologies to fight against the pandemic. This paper provides a guideline to practitioners on the benefits and application areas of Artificial Intelligence, Internet of things, Blockchain, and Big data technologies in the healthcare industry to face the crisis caused by this pandemic. A detailed analysis of strengths, weaknesses, opportunities, and threats for the thorough implementation of these technologies has been conducted. Also, the paper addresses the obstacles to adopt these technologies in the healthcare systems and make some recommendations for future studies. The paper assists researchers, experts, and readers in recognizing how the use of technology is aiding in the management of the coronavirus infection in a synergistic manner, as well as encourage the need for these techniques in existing and potential times of emergency","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121332010","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 : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581879
A. Alalawi, A. Al-Omary
Distributed computing is one of the important technologies for processing big data. A distributed computing system is based on the use of many computing devices by linking them together to process data. A distributed computing system can be leveraged using cloud resources. Pricing for booking cloud resources varies and leasing redundant resources are less expensive with some drawbacks. In this paper, distributed processing cost reduction using excess cloud resources verified framework is presented. The framework is based on using redundant resources via cloud services. The framework is verified through simulation. As a result of implementing the framework, it was found that the use of excess cloud resources reduces the cost of implementing a distributed computing system by 67% compared to use on-demand cloud resources.
{"title":"Verified Framework for Distributed Processing Cost Reduction using Excess Cloud Resources","authors":"A. Alalawi, A. Al-Omary","doi":"10.1109/3ICT53449.2021.9581879","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581879","url":null,"abstract":"Distributed computing is one of the important technologies for processing big data. A distributed computing system is based on the use of many computing devices by linking them together to process data. A distributed computing system can be leveraged using cloud resources. Pricing for booking cloud resources varies and leasing redundant resources are less expensive with some drawbacks. In this paper, distributed processing cost reduction using excess cloud resources verified framework is presented. The framework is based on using redundant resources via cloud services. The framework is verified through simulation. As a result of implementing the framework, it was found that the use of excess cloud resources reduces the cost of implementing a distributed computing system by 67% compared to use on-demand cloud resources.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121491865","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 : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581388
Mahmood Abdulhameed Ahmed, Mohsen Ali, Jassim Ahmed Jassim, H. Al-Ammal
Arabic calligraphy is one of the most aesthetic art forms in the world due to its variety and long history. However, generating calligraphic style is mainly done by human expert calligrapher (also known as Khattat) and has not been carried out by machine learning techniques. Generative adversarial networks (GAN) are deep learning tools that achieved outstanding results in the field of style transfer and generation. In this paper, various GAN architectures were investigated such as CycleGAN, Pix2pix, and deep convolutional generative adversarial networks (DCGAN) within Arabic calligraphy in two aspects: generation and style transfer. The results show that CycleGAN can transfer skeleton letters to both Naskh and Thulth styles, Pix2Pix can denoise the calligraphy papers, and DCGAN can generate realistic Arabic calligraphy letters. The proposed approaches are applicable for other calligraphy styles besides Naskh and Thulth. Finally, the models are evaluated qualitatively using a preference judgment technique survey.
{"title":"Generative Adversarial Networks (GAN) for Arabic Calligraphy","authors":"Mahmood Abdulhameed Ahmed, Mohsen Ali, Jassim Ahmed Jassim, H. Al-Ammal","doi":"10.1109/3ICT53449.2021.9581388","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581388","url":null,"abstract":"Arabic calligraphy is one of the most aesthetic art forms in the world due to its variety and long history. However, generating calligraphic style is mainly done by human expert calligrapher (also known as Khattat) and has not been carried out by machine learning techniques. Generative adversarial networks (GAN) are deep learning tools that achieved outstanding results in the field of style transfer and generation. In this paper, various GAN architectures were investigated such as CycleGAN, Pix2pix, and deep convolutional generative adversarial networks (DCGAN) within Arabic calligraphy in two aspects: generation and style transfer. The results show that CycleGAN can transfer skeleton letters to both Naskh and Thulth styles, Pix2Pix can denoise the calligraphy papers, and DCGAN can generate realistic Arabic calligraphy letters. The proposed approaches are applicable for other calligraphy styles besides Naskh and Thulth. Finally, the models are evaluated qualitatively using a preference judgment technique survey.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126298305","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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