Pub Date : 2022-04-01DOI: 10.33166/aetic.2022.02.003
M. Rahim, Jungpil Shin, K. Yun
Human hand gestures are becoming one of the most important, intuitive, and essential means of recognizing sign language. Sign language is used to convey different meanings through visual-manual methods. Hand gestures help the hearing impaired to communicate. Nevertheless, it is very difficult to achieve a high recognition rate of hand gestures due to the environment and physical anatomy of human beings such as light condition, hand size, position, and uncontrolled environment. Moreover, the recognition of appropriate gestures is currently considered a major challenge. In this context, this paper proposes a probabilistic soft voting-based ensemble model to recognize Bengali sign gestures. We have divided this study into pre-processing, data augmentation and ensemble model-based voting process, and classification for gesture recognition. The purpose of pre-processing is to remove noise from input images, resize it, and segment hand gestures. Data augmentation is applied to create a larger database for in-depth model training. Finally, the ensemble model consists of a support vector machine (SVM), random forest (RF), and convolution neural network (CNN) is used to train and classify gestures. Whereas, the ReLu activation function is used in CNN to solve neuron death problems and to accelerate RF classification through principal component analysis (PCA). A Bengali Sign Number Dataset named “BSN-Dataset” is proposed for model performance. The proposed technique enhances sign gesture recognition capabilities by utilizing segmentation, augmentation, and soft-voting classifiers which have obtained an average of 99.50% greater performance than CNN, RF, and SVM individually, as well as significantly more accuracy than existing systems.
{"title":"Soft Voting-based Ensemble Model for Bengali Sign Gesture Recognition","authors":"M. Rahim, Jungpil Shin, K. Yun","doi":"10.33166/aetic.2022.02.003","DOIUrl":"https://doi.org/10.33166/aetic.2022.02.003","url":null,"abstract":"Human hand gestures are becoming one of the most important, intuitive, and essential means of recognizing sign language. Sign language is used to convey different meanings through visual-manual methods. Hand gestures help the hearing impaired to communicate. Nevertheless, it is very difficult to achieve a high recognition rate of hand gestures due to the environment and physical anatomy of human beings such as light condition, hand size, position, and uncontrolled environment. Moreover, the recognition of appropriate gestures is currently considered a major challenge. In this context, this paper proposes a probabilistic soft voting-based ensemble model to recognize Bengali sign gestures. We have divided this study into pre-processing, data augmentation and ensemble model-based voting process, and classification for gesture recognition. The purpose of pre-processing is to remove noise from input images, resize it, and segment hand gestures. Data augmentation is applied to create a larger database for in-depth model training. Finally, the ensemble model consists of a support vector machine (SVM), random forest (RF), and convolution neural network (CNN) is used to train and classify gestures. Whereas, the ReLu activation function is used in CNN to solve neuron death problems and to accelerate RF classification through principal component analysis (PCA). A Bengali Sign Number Dataset named “BSN-Dataset” is proposed for model performance. The proposed technique enhances sign gesture recognition capabilities by utilizing segmentation, augmentation, and soft-voting classifiers which have obtained an average of 99.50% greater performance than CNN, RF, and SVM individually, as well as significantly more accuracy than existing systems.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47165405","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 : 2022-04-01DOI: 10.33166/aetic.2022.02.001
A. Ali, Loay E. George
This study presented a model for improving audio files quality using fractal coding specifically when a high compression ratio is required. The proposed high synthetic audio compression model which can be called (HSACM) is based on conventional fractal coding and lifting wavelet transform. Various lifting wavelet transform families and levels are used and their effects on the reconstructed audio files are discussed as well. Audio files from GTZAN dataset and standard measurements for data compression are used in the evaluation of the proposed model. The results reveal that using block length 50 samples which is the worst case, PSNR is increased, on average, from 34.1 to 44.8 dB and from 34.1 to 40.5 dB using lifting wavelet transform with 3 and 2 levels, respectively. Thus, the PSNR is improved by 10 and 5 dB with slightly reducing the compression ratio by 6.2 and 12.5%, respectively. Moreover, it can be noticed that adopting lifting wavelet transform with basis Haar, db1, db4, db5, cdf1.1 and cdf2.2 provide higher audio quality while db6, db8, sym7 and sym8 give the worst audio quality. Furthermore, the performance of HSACM is compared with that of existing work to highlight its performance.
{"title":"High Synthetic Audio Compression Model Based on Fractal Audio Coding and Error-Compensation","authors":"A. Ali, Loay E. George","doi":"10.33166/aetic.2022.02.001","DOIUrl":"https://doi.org/10.33166/aetic.2022.02.001","url":null,"abstract":"This study presented a model for improving audio files quality using fractal coding specifically when a high compression ratio is required. The proposed high synthetic audio compression model which can be called (HSACM) is based on conventional fractal coding and lifting wavelet transform. Various lifting wavelet transform families and levels are used and their effects on the reconstructed audio files are discussed as well. Audio files from GTZAN dataset and standard measurements for data compression are used in the evaluation of the proposed model. The results reveal that using block length 50 samples which is the worst case, PSNR is increased, on average, from 34.1 to 44.8 dB and from 34.1 to 40.5 dB using lifting wavelet transform with 3 and 2 levels, respectively. Thus, the PSNR is improved by 10 and 5 dB with slightly reducing the compression ratio by 6.2 and 12.5%, respectively. Moreover, it can be noticed that adopting lifting wavelet transform with basis Haar, db1, db4, db5, cdf1.1 and cdf2.2 provide higher audio quality while db6, db8, sym7 and sym8 give the worst audio quality. Furthermore, the performance of HSACM is compared with that of existing work to highlight its performance.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42264195","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 : 2022-04-01DOI: 10.33166/aetic.2022.02.004
Hatem M. Bahig
The security of many public-key cryptosystems and protocols relies on the difficulty of factoring a large positive integer n into prime factors. The Fermat factoring method is a core of some modern and important factorization methods, such as the quadratic sieve and number field sieve methods. It factors a composite integer n=pq in polynomial time if the difference between the prime factors is equal to ∆=p-q≤n^(0.25) , where p>q. The execution time of the Fermat factoring method increases rapidly as ∆ increases. One of the improvements to the Fermat factoring method is based on studying the possible values of (n mod 20). In this paper, we introduce an efficient algorithm to factorize a large integer based on the possible values of (n mod 20) and a precomputation strategy. The experimental results, on different sizes of n and ∆, demonstrate that our proposed algorithm is faster than the previous improvements of the Fermat factoring method by at least 48%.
许多公钥密码系统和协议的安全性依赖于将大正整数n分解为素数因子的难度。费马分解法是二次型筛法、数域筛法等现代重要的分解方法的核心。如果质因数之差等于∆=p-q≤n^(0.25)，则在多项式时间内分解复合整数n=pq，其中p>q。费马分解法的执行时间随着∆的增大而迅速增加。对费马分解法的改进之一是基于对(n mod 20)的可能值的研究。本文介绍了一种基于(n mod 20)可能值的大整数的高效因式分解算法和一种预计算策略。在不同大小的n和∆上的实验结果表明，我们提出的算法比之前改进的费马分解方法至少快48%。
{"title":"Speeding Up Fermat’s Factoring Method using Precomputation","authors":"Hatem M. Bahig","doi":"10.33166/aetic.2022.02.004","DOIUrl":"https://doi.org/10.33166/aetic.2022.02.004","url":null,"abstract":"The security of many public-key cryptosystems and protocols relies on the difficulty of factoring a large positive integer n into prime factors. The Fermat factoring method is a core of some modern and important factorization methods, such as the quadratic sieve and number field sieve methods. It factors a composite integer n=pq in polynomial time if the difference between the prime factors is equal to ∆=p-q≤n^(0.25) , where p>q. The execution time of the Fermat factoring method increases rapidly as ∆ increases. One of the improvements to the Fermat factoring method is based on studying the possible values of (n mod 20). In this paper, we introduce an efficient algorithm to factorize a large integer based on the possible values of (n mod 20) and a precomputation strategy. The experimental results, on different sizes of n and ∆, demonstrate that our proposed algorithm is faster than the previous improvements of the Fermat factoring method by at least 48%.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44991834","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 : 2022-01-01DOI: 10.33166/aetic.2022.01.002
Z. Alansari, M. Siddique, M. Ashour
Wireless sensor networks (WSNs) are set of sensor nodes to monitor and detect transmitted data to the sink. WSNs face significant challenges in terms of node energy availability, which may impact network sustainability. As a result, developing protocols and algorithms that make the best use of limited resources, particularly energy resources, is critical issues for designing WSNs. Routing algorithms, for example, are unique algorithms as they have a direct and effective relationship with lifetime of network and energy. The available routing protocols employ single-hop data transmission to the sink and clustering per round. In this paper, a Fuzzy Clustering and Energy Efficient Routing Protocol (FCERP) that lower the WSNs energy consuming and increase the lifetime of network is proposed. FCERP introduces a new cluster-based fuzzy routing protocol capable of utilizing clustering and multiple hop routing features concurrently using a threshold limit. A novel aspect of this research is that it avoids clustering per round while considering using fixed threshold and adapts multi-hop routing by predicting the best intermediary node for clustering and the sink. Some Fuzzy factors such as residual energy, neighbors amount, and distance to sink considered when deciding which intermediary node to use.
{"title":"FCERP: A Novel WSNs Fuzzy Clustering and Energy Efficient Routing Protocol","authors":"Z. Alansari, M. Siddique, M. Ashour","doi":"10.33166/aetic.2022.01.002","DOIUrl":"https://doi.org/10.33166/aetic.2022.01.002","url":null,"abstract":"Wireless sensor networks (WSNs) are set of sensor nodes to monitor and detect transmitted data to the sink. WSNs face significant challenges in terms of node energy availability, which may impact network sustainability. As a result, developing protocols and algorithms that make the best use of limited resources, particularly energy resources, is critical issues for designing WSNs. Routing algorithms, for example, are unique algorithms as they have a direct and effective relationship with lifetime of network and energy. The available routing protocols employ single-hop data transmission to the sink and clustering per round. In this paper, a Fuzzy Clustering and Energy Efficient Routing Protocol (FCERP) that lower the WSNs energy consuming and increase the lifetime of network is proposed. FCERP introduces a new cluster-based fuzzy routing protocol capable of utilizing clustering and multiple hop routing features concurrently using a threshold limit. A novel aspect of this research is that it avoids clustering per round while considering using fixed threshold and adapts multi-hop routing by predicting the best intermediary node for clustering and the sink. Some Fuzzy factors such as residual energy, neighbors amount, and distance to sink considered when deciding which intermediary node to use.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47332019","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 : 2022-01-01DOI: 10.33166/aetic.2022.01.005
M. Bashir
The integration of Artificial Intelligence (AI) into the dredging systems and dredging machinery used in "capital" and "maintenance" dredging in Bangladesh can enhance the efficiency of the machines and dredging process, enabling the operators to perform regular and repetitive dredging tasks safely in the rivers, ports, and estuaries all over the country. AI, including Big Data, Machine Learning, Internet of Thing, Blockchain and Sensors and Simulators with their catalytic potentials, can systematically compile and evaluate specific data collected from different sources, develop applications or simulators, connect the stakeholders on a virtual platform, store lakes of information without compromising their intellectual rights, predicting models to harness the challenges, minimise the cost of dredging, identify possible threats and help protect the already dredged areas by giving timely signals for further maintenance. Furthermore, the application of AI modulated dredging devices and machinery can play a significant role when monitoring aspects becomes crucial, keeping environmental impacts mitigated without affecting the quality of the human environment. This study includes the evaluation of the application of AI – its prospect and challenges in the existing dredging systems in Bangladesh against the backdrop of the challenges faced in capital and maintenance dredging in the major rivers – and assess whether such inclusion of AI is likely to minimise the cost of dredging in the rivers of Bangladesh and facilitate the materialisation of the objectives of Bangladesh Delta Plan 2100.This paper studies the organisation's infrastructural requirement for the integration of AI into dredging systems, using benchmarking such as 1- "Understanding AI Ready Approach", 2-"Strategies for Implementing AI", 3-"Data Management", 4-"Creating AI Literate Workforce and Upskilling", and 5-"Identifying Threats" concerning the management and dredging operations of Bangladesh Inland Water Transport Authority (BIWTA), under Bangladesh Ministry of Shipping and Bangladesh Water Development Board (BWDB). The paper also uses several case studies such as channel dredging to show that the use of AI can bring a significant change in the dredging operations both in reducing the cost of dredging and in terms of harnessing the barriers in adaptive management and environmental impacts.
{"title":"Application of Artificial Intelligence (AI) in Dredging Efficiency in Bangladesh","authors":"M. Bashir","doi":"10.33166/aetic.2022.01.005","DOIUrl":"https://doi.org/10.33166/aetic.2022.01.005","url":null,"abstract":"The integration of Artificial Intelligence (AI) into the dredging systems and dredging machinery used in \"capital\" and \"maintenance\" dredging in Bangladesh can enhance the efficiency of the machines and dredging process, enabling the operators to perform regular and repetitive dredging tasks safely in the rivers, ports, and estuaries all over the country. AI, including Big Data, Machine Learning, Internet of Thing, Blockchain and Sensors and Simulators with their catalytic potentials, can systematically compile and evaluate specific data collected from different sources, develop applications or simulators, connect the stakeholders on a virtual platform, store lakes of information without compromising their intellectual rights, predicting models to harness the challenges, minimise the cost of dredging, identify possible threats and help protect the already dredged areas by giving timely signals for further maintenance. Furthermore, the application of AI modulated dredging devices and machinery can play a significant role when monitoring aspects becomes crucial, keeping environmental impacts mitigated without affecting the quality of the human environment. This study includes the evaluation of the application of AI – its prospect and challenges in the existing dredging systems in Bangladesh against the backdrop of the challenges faced in capital and maintenance dredging in the major rivers – and assess whether such inclusion of AI is likely to minimise the cost of dredging in the rivers of Bangladesh and facilitate the materialisation of the objectives of Bangladesh Delta Plan 2100.This paper studies the organisation's infrastructural requirement for the integration of AI into dredging systems, using benchmarking such as 1- \"Understanding AI Ready Approach\", 2-\"Strategies for Implementing AI\", 3-\"Data Management\", 4-\"Creating AI Literate Workforce and Upskilling\", and 5-\"Identifying Threats\" concerning the management and dredging operations of Bangladesh Inland Water Transport Authority (BIWTA), under Bangladesh Ministry of Shipping and Bangladesh Water Development Board (BWDB). The paper also uses several case studies such as channel dredging to show that the use of AI can bring a significant change in the dredging operations both in reducing the cost of dredging and in terms of harnessing the barriers in adaptive management and environmental impacts.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44833144","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 : 2022-01-01DOI: 10.33166/aetic.2022.01.004
Petrit Imeraj, Maaruf Ali, Gent Imeraj
The Albanian Alps are situated in a mountainous block in the Northern Albania region, in the counties of Shkodër (also known as Shkodra or Gegëria) and Kukës (Kukësi). The nature of the mountainous terrain formation has led to the creation of isolated communities. The need for integrating these scattered communities into a cohesive co-operating community for area sustainability is now possible by using the Internet to link them all onto an online system. To deal with natural catastrophes, disaster management cells will be created which will serve as hubs. These hubs will be located at geographically strategic positions that will enable a predetermined geofenced region for evaluation of different disasters viz. forest fires, landslide, flooding, avalanches, the burial of villages under heavy snowfalls, etc. These cells will connect the particular case with the most appropriate disaster relief, rescue service and EMR (Emergency Medical Responder), first aid services (e.g. Green Crescent/Red Cross) and EMT (Emergency Medical Technician) personnel. The cells shall be managed by locally trained human resources with the necessary equipment to provide the monitoring/analyses and first aid assistance in case of need. The technology needed for the monitoring and geotechnical management of the isolated Alpine communities will be described. The socio-economic impact of the deployment of these technologies aiding in the sustainability of these vulnerable communities will conclude the research.
{"title":"Geotechnical Management of Isolated Sustainable Alpine Communities","authors":"Petrit Imeraj, Maaruf Ali, Gent Imeraj","doi":"10.33166/aetic.2022.01.004","DOIUrl":"https://doi.org/10.33166/aetic.2022.01.004","url":null,"abstract":"The Albanian Alps are situated in a mountainous block in the Northern Albania region, in the counties of Shkodër (also known as Shkodra or Gegëria) and Kukës (Kukësi). The nature of the mountainous terrain formation has led to the creation of isolated communities. The need for integrating these scattered communities into a cohesive co-operating community for area sustainability is now possible by using the Internet to link them all onto an online system. To deal with natural catastrophes, disaster management cells will be created which will serve as hubs. These hubs will be located at geographically strategic positions that will enable a predetermined geofenced region for evaluation of different disasters viz. forest fires, landslide, flooding, avalanches, the burial of villages under heavy snowfalls, etc. These cells will connect the particular case with the most appropriate disaster relief, rescue service and EMR (Emergency Medical Responder), first aid services (e.g. Green Crescent/Red Cross) and EMT (Emergency Medical Technician) personnel. The cells shall be managed by locally trained human resources with the necessary equipment to provide the monitoring/analyses and first aid assistance in case of need. The technology needed for the monitoring and geotechnical management of the isolated Alpine communities will be described. The socio-economic impact of the deployment of these technologies aiding in the sustainability of these vulnerable communities will conclude the research.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48777002","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 : 2022-01-01DOI: 10.33166/aetic.2022.01.003
Maiass Zaher, S. Molnár
The growing deployment of Software Defined Network (SDN) paradigm in the academic and commercial sectors resulted in many different Network Operating Systems (NOS). As a result, adopting the right NOS requires an analytical study of the available alternatives according to the target use case. This study aims to determine the best NOS according to the requirements of Cloud Data Center (CDC). This paper evaluates the specifications of the most common open-source NOSs. The studied features have been classified into two groups, i.e., non-functional features such as availability, scalability, ease of use, maturity, security and interoperability, and functional features, such as virtualization, fault verification and troubleshooting, packet forwarding techniques and traffic protection solutions. A Decision support system, Analytical Hierarchy Process (AHP) has been applied for assessing specifications of the inspected NOSs, namely, ONOS, Opendaylight (ODL), Floodlight, Ryu, POX and Tungsten. Our investigation revealed that ODL is the most suitable NOS for CDC compared to the rest studied NOSs. However, ODL and ONOS have almost similar scores compared to the rest NOSs.
{"title":"A Comparative and Analytical Study for Choosing the Best Suited SDN Network Operating System for Cloud Data Center","authors":"Maiass Zaher, S. Molnár","doi":"10.33166/aetic.2022.01.003","DOIUrl":"https://doi.org/10.33166/aetic.2022.01.003","url":null,"abstract":"The growing deployment of Software Defined Network (SDN) paradigm in the academic and commercial sectors resulted in many different Network Operating Systems (NOS). As a result, adopting the right NOS requires an analytical study of the available alternatives according to the target use case. This study aims to determine the best NOS according to the requirements of Cloud Data Center (CDC). This paper evaluates the specifications of the most common open-source NOSs. The studied features have been classified into two groups, i.e., non-functional features such as availability, scalability, ease of use, maturity, security and interoperability, and functional features, such as virtualization, fault verification and troubleshooting, packet forwarding techniques and traffic protection solutions. A Decision support system, Analytical Hierarchy Process (AHP) has been applied for assessing specifications of the inspected NOSs, namely, ONOS, Opendaylight (ODL), Floodlight, Ryu, POX and Tungsten. Our investigation revealed that ODL is the most suitable NOS for CDC compared to the rest studied NOSs. However, ODL and ONOS have almost similar scores compared to the rest NOSs.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43380615","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}
{"title":"Theories of Blockchain","authors":"N. Arora","doi":"10.1201/9781003121466-4","DOIUrl":"https://doi.org/10.1201/9781003121466-4","url":null,"abstract":"","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88405921","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}
{"title":"Introduction to Emerging Technologies in Computer Science and Its Applications","authors":"U. Kant, V. Kumar","doi":"10.1201/9781003121466-1","DOIUrl":"https://doi.org/10.1201/9781003121466-1","url":null,"abstract":"","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77479062","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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