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Consumer Behaviour in Gamified Environment: A Bibliometric and Systematic Literature Review in Business and Management Area 游戏化环境下的消费者行为:商业与管理领域的文献计量学与系统文献综述
Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Acta Informatica Pragensia
Pub Date : 2023-10-10 DOI: 10.18267/j.aip.221
Deeksha Singh, Sambashiva Rao Kunja
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引用次数: 0
Diagnostic Performance Evaluation of Deep Learning-Based Medical Text Modelling to Predict Pulmonary Diseases from Unstructured Radiology Free-Text Reports 基于深度学习的医学文本建模从非结构化放射学自由文本报告预测肺部疾病的诊断性能评估
Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Acta Informatica Pragensia
Pub Date : 2023-09-05 DOI: 10.18267/j.aip.214
S. Shetty, A. S, Ajit Mahale
{"title":"Diagnostic Performance Evaluation of Deep Learning-Based Medical Text Modelling to Predict Pulmonary Diseases from Unstructured Radiology Free-Text Reports","authors":"S. Shetty, A. S, Ajit Mahale","doi":"10.18267/j.aip.214","DOIUrl":"https://doi.org/10.18267/j.aip.214","url":null,"abstract":"","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49344408","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}
引用次数: 0
Use of Deep Learning and Blockchain Technologies in Healthcare Industry 深度学习和区块链技术在医疗保健行业的应用
Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Acta Informatica Pragensia
Pub Date : 2023-04-19 DOI: 10.18267/j.aip.213
Mazin Abed Mohammed, Seifedine Kadry, O. Geman
This editorial summarises the special issue entitled "Deep Learning Blockchain-enabled Technology for Improved Healthcare Industrial Systems”, which deals with the intersection and use of deep learning and blockchain technologies in the healthcare industry. This special issue consists of eleven scientific articles. © 2023 by the author(s). Licensee Prague University of Economics and Business, Czech Republic.
这篇社论总结了题为“深度学习区块链技术用于改善医疗保健工业系统”的特刊，该特刊涉及深度学习和区块链技术在医疗保健行业的交叉和使用。本特刊由11篇科学文章组成。©2023作者.被许可方捷克共和国布拉格经济与商业大学。
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引用次数: 0
Multi-Class Text Classification on Khmer News Using Ensemble Method in Machine Learning Algorithms 机器学习算法中基于集成方法的高棉新闻多类文本分类
Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Acta Informatica Pragensia
Pub Date : 2023-03-10 DOI: 10.18267/j.aip.210
Raksmey Phann, Chitsutha Soomlek, Pusadee Seresangtakul
{"title":"Multi-Class Text Classification on Khmer News Using Ensemble Method in Machine Learning Algorithms","authors":"Raksmey Phann, Chitsutha Soomlek, Pusadee Seresangtakul","doi":"10.18267/j.aip.210","DOIUrl":"https://doi.org/10.18267/j.aip.210","url":null,"abstract":"","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42387757","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}
引用次数: 0
Multi-Class Skin Cancer Classification Using a Hybrid Dynamic Salp Swarm Algorithm and Weighted Extreme Learning Machines with Transfer Learning 基于混合动态Salp群算法和带迁移学习的加权极值学习机的多类皮肤癌分类
Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Acta Informatica Pragensia
Pub Date : 2023-03-09 DOI: 10.18267/j.aip.211
R. Panneerselvam, Sathiyabhama Balasubramaniam
{"title":"Multi-Class Skin Cancer Classification Using a Hybrid Dynamic Salp Swarm Algorithm and Weighted Extreme Learning Machines with Transfer Learning","authors":"R. Panneerselvam, Sathiyabhama Balasubramaniam","doi":"10.18267/j.aip.211","DOIUrl":"https://doi.org/10.18267/j.aip.211","url":null,"abstract":"","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44449384","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}
引用次数: 0
Deep Learning Techniques for Quantification of Tumour Necrosis in Post-neoadjuvant Chemotherapy Osteosarcoma Resection Specimens for Effective Treatment Planning 深度学习技术量化新辅助化疗后骨肉瘤切除标本中的肿瘤坏死，以制定有效的治疗计划
Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Acta Informatica Pragensia
Pub Date : 2023-03-06 DOI: 10.18267/j.aip.207
T. S. Saleena, P. Ilyas, V. Sajna, A. Haque
{"title":"Deep Learning Techniques for Quantification of Tumour Necrosis in Post-neoadjuvant Chemotherapy Osteosarcoma Resection Specimens for Effective Treatment Planning","authors":"T. S. Saleena, P. Ilyas, V. Sajna, A. Haque","doi":"10.18267/j.aip.207","DOIUrl":"https://doi.org/10.18267/j.aip.207","url":null,"abstract":"","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42814346","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}
引用次数: 0
Blood Pressure Estimation Using Emotion-Based Optimization Clustering Model 基于情绪优化聚类模型的血压估计
Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Acta Informatica Pragensia
Pub Date : 2023-03-01 DOI: 10.18267/j.aip.209
Vaishali Rajput, Preeti Mulay, Sharnil Pandya, Chandrashekhar Mahajan, Rupali Deshpande
{"title":"Blood Pressure Estimation Using Emotion-Based Optimization Clustering Model","authors":"Vaishali Rajput, Preeti Mulay, Sharnil Pandya, Chandrashekhar Mahajan, Rupali Deshpande","doi":"10.18267/j.aip.209","DOIUrl":"https://doi.org/10.18267/j.aip.209","url":null,"abstract":"","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45252595","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}
引用次数: 0
Longitudinal Investigation of Work Stressors Using Human Voice Features 利用人声特征对工作压力源的纵向调查
Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Acta Informatica Pragensia
Pub Date : 2023-03-01 DOI: 10.18267/j.aip.208
Indhumathi Natarajan, M. Shanmugam, S. Dhanalakshmi, Santhosh Easwaramoorthy, Sethuraja Kuppusamy, S. Balu
{"title":"Longitudinal Investigation of Work Stressors Using Human Voice Features","authors":"Indhumathi Natarajan, M. Shanmugam, S. Dhanalakshmi, Santhosh Easwaramoorthy, Sethuraja Kuppusamy, S. Balu","doi":"10.18267/j.aip.208","DOIUrl":"https://doi.org/10.18267/j.aip.208","url":null,"abstract":"","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42387742","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}
引用次数: 5
Emotion-Based Sentiment Analysis Using Conv-BiLSTM With Frog Leap Algorithms 使用Conv BiLSTM和Frog Leap算法进行基于情绪的情绪分析
Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Acta Informatica Pragensia
Pub Date : 2023-01-17 DOI: 10.18267/j.aip.206
S. Yelisetti, Nellore Geethanjali
{"title":"Emotion-Based Sentiment Analysis Using Conv-BiLSTM With Frog Leap Algorithms","authors":"S. Yelisetti, Nellore Geethanjali","doi":"10.18267/j.aip.206","DOIUrl":"https://doi.org/10.18267/j.aip.206","url":null,"abstract":"","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47967406","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}
引用次数: 0
Deep Learning Convolutional Neural Network for SARS-CoV-2 Detection Using Chest X-Ray Images 基于胸部x线图像的深度学习卷积神经网络检测新冠肺炎
Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Acta Informatica Pragensia
Pub Date : 2023-01-17 DOI: 10.18267/j.aip.205
A. Ahmed, Inteasar Yaseen Khudhair, Salam Abdulkhaleq Noaman
The COVID-19 coronavirus illness is caused by a newly discovered species of coronavirus known as SARS-CoV-2. Since COVID-19 has now expanded across many nations, the World Health Organization (WHO) has designated it a pandemic. Reverse transcription-polymerase chain reaction (RT-PCR) is often used to screen samples of patients showing signs of COVID-19;however, this method is more expensive and takes at least 24 hours to get a positive or negative response. Thus, an immediate and precise method of diagnosis is needed. In this paper, chest X-rays will be utilized through a deep neural network (DNN), based on a convolutional neural network (CNN), to detect COVID-19 infection. Based on their X-rays, those with COVID-19 indications may be categorized as clean, infected with COVID-19 or suffering from pneumonia, according to the suggested CNN network. Sample pieces from every group are used in experiments, and categorization is performed by a CNN. While experimenting, the CNN-derived features were able to generate the maximum training accuracy of 94.82% and validation accuracy of 94.87%. The F1-scores were 97%, 90% and 96%, in clearly categorizing patients afflicted by COVID-19, normal and having pneumonia, respectively. Meanwhile, the recalls are 95%, 91% and 96% for COVID-19, normal and pneumonia, respectively. © 2023 by the author(s). Licensee Prague University of Economics and Business, Czech Republic.
COVID-19冠状病毒疾病是由新发现的冠状病毒SARS-CoV-2引起的。由于COVID-19现已在许多国家蔓延，世界卫生组织(世卫组织)已将其指定为大流行。逆转录聚合酶链反应(RT-PCR)通常用于筛选显示COVID-19症状的患者样本，然而，这种方法更昂贵，并且至少需要24小时才能获得阳性或阴性反应。因此，需要一种即时而精确的诊断方法。在本次研究中，将以卷积神经网络(CNN)为基础，通过深度神经网络(DNN)利用胸部x光片检测COVID-19感染。根据美国有线电视新闻网的建议，根据他们的x光片，有COVID-19适应症的人可能被分类为清洁，感染COVID-19或患有肺炎。实验中使用每组的样本，并通过CNN进行分类。在实验中，cnn衍生的特征能够产生最大的训练准确率为94.82%，验证准确率为94.87%。在明确区分新冠肺炎患者、正常患者和肺炎患者时，f1得分分别为97%、90%和96%。与此同时，新冠肺炎、正常肺炎和肺炎的召回率分别为95%、91%和96%。©由作者(s)。被许可方:捷克共和国布拉格经济与商业大学。
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引用次数: 0
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