Pub Date : 2023-01-01Epub Date: 2022-11-20DOI: 10.1007/s00354-022-00195-x
Ankit Kumar Dubey, Krishna Kumar Mohbey
In the past few years, most of the work has been done around the classification of covid-19 using different images like CT-scan, X-ray, and ultrasound. But none of that is capable enough to deal with each of these image types on a single common platform and can identify the possibility that a person is suffering from COVID or not. Thus, we realized there should be a platform to identify COVID-19 in CT-scan and X-ray images on the fly. So, to fulfill this need, we proposed an AI model to identify CT-scan and X-ray images from each other and then use this inference to classify them of COVID positive or negative. The proposed model uses the inception architecture under the hood and trains on the open-source extended covid-19 dataset. The dataset consists of plenty of images for both image types and is of size 4 GB. We achieved an accuracy of 100%, average macro-Precision of 100%, average macro-Recall of 100%, average macro f1-score of 100%, and AUC score of 99.6%. Furthermore, in this work, cloud-based architecture is proposed to massively scale and load balance as the Number of user requests rises. As a result, it will deliver a service with minimal latency to all users.
{"title":"Combined Cloud-Based Inference System for the Classification of COVID-19 in CT-Scan and X-Ray Images.","authors":"Ankit Kumar Dubey, Krishna Kumar Mohbey","doi":"10.1007/s00354-022-00195-x","DOIUrl":"10.1007/s00354-022-00195-x","url":null,"abstract":"<p><p>In the past few years, most of the work has been done around the classification of covid-19 using different images like CT-scan, X-ray, and ultrasound. But none of that is capable enough to deal with each of these image types on a single common platform and can identify the possibility that a person is suffering from COVID or not. Thus, we realized there should be a platform to identify COVID-19 in CT-scan and X-ray images on the fly. So, to fulfill this need, we proposed an AI model to identify CT-scan and X-ray images from each other and then use this inference to classify them of COVID positive or negative. The proposed model uses the inception architecture under the hood and trains on the open-source extended covid-19 dataset. The dataset consists of plenty of images for both image types and is of size 4 GB. We achieved an accuracy of 100%, average macro-Precision of 100%, average macro-Recall of 100%, average macro f1-score of 100%, and AUC score of 99.6%. Furthermore, in this work, cloud-based architecture is proposed to massively scale and load balance as the Number of user requests rises. As a result, it will deliver a service with minimal latency to all users.</p>","PeriodicalId":54726,"journal":{"name":"New Generation Computing","volume":"41 1","pages":"61-84"},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9676871/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9333906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01Epub Date: 2023-02-20DOI: 10.1007/s00354-023-00206-5
Sven Groppe
{"title":"Renewal of the Major Fields of New-Generation Computing.","authors":"Sven Groppe","doi":"10.1007/s00354-023-00206-5","DOIUrl":"10.1007/s00354-023-00206-5","url":null,"abstract":"","PeriodicalId":54726,"journal":{"name":"New Generation Computing","volume":"41 1","pages":"3-4"},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9940667/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9328875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-27DOI: 10.1007/s00354-022-00198-8
Koichi Nagatsuka, Clifford Broni-Bediako, M. Atsumi
{"title":"Length-Based Curriculum Learning for Efficient Pre-training of Language Models","authors":"Koichi Nagatsuka, Clifford Broni-Bediako, M. Atsumi","doi":"10.1007/s00354-022-00198-8","DOIUrl":"https://doi.org/10.1007/s00354-022-00198-8","url":null,"abstract":"","PeriodicalId":54726,"journal":{"name":"New Generation Computing","volume":"41 1","pages":"109-134"},"PeriodicalIF":2.6,"publicationDate":"2022-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45363029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-06DOI: 10.1007/s00354-022-00188-w
Fatemeh Hosseini, F. S. Gharehchopogh, Mohammad Masdari
{"title":"A Botnet Detection in IoT Using a Hybrid Multi-objective Optimization Algorithm","authors":"Fatemeh Hosseini, F. S. Gharehchopogh, Mohammad Masdari","doi":"10.1007/s00354-022-00188-w","DOIUrl":"https://doi.org/10.1007/s00354-022-00188-w","url":null,"abstract":"","PeriodicalId":54726,"journal":{"name":"New Generation Computing","volume":"40 1","pages":"809 - 843"},"PeriodicalIF":2.6,"publicationDate":"2022-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46400972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-03DOI: 10.1007/s00354-022-00186-y
Yuichi Komorida, Shin-ya Katsumata, Nick Hu, Bartek Klin, Samuel Humeau, Clovis Eberhart, Ichiro Hasuo
Bisimilarity as an equivalence notion of systems has been central to process theory. Due to the recent rise of interest in quantitative systems (probabilistic, weighted, hybrid, etc.), bisimilarity has been extended in various ways, such as bisimulation metric between probabilistic systems. An important feature of bisimilarity is its game-theoretic characterization, where Spoiler and Duplicator play against each other; extension of bisimilarity games to quantitative settings has been actively pursued too. In this paper, we present a general framework that uniformly describes game characterizations of bisimilarity-like notions. Our framework is formalized categorically using fibrations and coalgebras. In particular, our characterization of bisimilarity in terms of fibrational predicate transformers allows us to derive what we call codensity bisimilarity games: a general categorical game characterization of bisimilarity. Our framework covers known bisimilarity-like notions (such as bisimulation metric and bisimulation seminorm) as well as new ones (including what we call bisimulation topology).
{"title":"Codensity Games for Bisimilarity","authors":"Yuichi Komorida, Shin-ya Katsumata, Nick Hu, Bartek Klin, Samuel Humeau, Clovis Eberhart, Ichiro Hasuo","doi":"10.1007/s00354-022-00186-y","DOIUrl":"https://doi.org/10.1007/s00354-022-00186-y","url":null,"abstract":"<p>Bisimilarity as an equivalence notion of systems has been central to process theory. Due to the recent rise of interest in quantitative systems (probabilistic, weighted, hybrid, etc.), bisimilarity has been extended in various ways, such as bisimulation metric between probabilistic systems. An important feature of bisimilarity is its game-theoretic characterization, where Spoiler and Duplicator play against each other; extension of bisimilarity games to quantitative settings has been actively pursued too. In this paper, we present a general framework that uniformly describes game characterizations of bisimilarity-like notions. Our framework is formalized categorically using fibrations and coalgebras. In particular, our characterization of bisimilarity in terms of fibrational predicate transformers allows us to derive what we call codensity bisimilarity games: a general categorical game characterization of bisimilarity. Our framework covers known bisimilarity-like notions (such as bisimulation metric and bisimulation seminorm) as well as new ones (including what we call bisimulation topology).</p>","PeriodicalId":54726,"journal":{"name":"New Generation Computing","volume":"9 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2022-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138496800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-30DOI: 10.1007/s00354-022-00187-x
Nassira Achich, F. Ghorbel, Sonda Ammar Bouhamed, F. Hamdi, Elisabeth Métais, F. Gargouri, Haithem Kharfia, Bilel Gargouri
{"title":"An Evidence Theory-Based Approach to Handling Conflicting Temporal Data in OWL 2","authors":"Nassira Achich, F. Ghorbel, Sonda Ammar Bouhamed, F. Hamdi, Elisabeth Métais, F. Gargouri, Haithem Kharfia, Bilel Gargouri","doi":"10.1007/s00354-022-00187-x","DOIUrl":"https://doi.org/10.1007/s00354-022-00187-x","url":null,"abstract":"","PeriodicalId":54726,"journal":{"name":"New Generation Computing","volume":"40 1","pages":"845 - 870"},"PeriodicalIF":2.6,"publicationDate":"2022-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48482405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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