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Toward a Multimodal Multitask Model for Neurodegenerative Diseases Diagnosis and Progression Prediction 神经退行性疾病诊断与进展预测的多模态多任务模型
IF 2.6
Data Pub Date : 2021-10-10 DOI: 10.5220/0010600003220328
Sofia Lahrichi, M. Rhanoui, M. Mikram, B. E. Asri
{"title":"Toward a Multimodal Multitask Model for Neurodegenerative Diseases Diagnosis and Progression Prediction","authors":"Sofia Lahrichi, M. Rhanoui, M. Mikram, B. E. Asri","doi":"10.5220/0010600003220328","DOIUrl":"https://doi.org/10.5220/0010600003220328","url":null,"abstract":"Recent studies on modelling the progression of Alzheimer's disease use a single modality for their predictions while ignoring the time dimension. However, the nature of patient data is heterogeneous and time dependent which requires models that value these factors in order to achieve a reliable diagnosis, as well as making it possible to track and detect changes in the progression of patients' condition at an early stage. This article overviews various categories of models used for Alzheimer's disease prediction with their respective learning methods, by establishing a comparative study of early prediction and detection Alzheimer's disease progression. Finally, a robust and precise detection model is proposed.","PeriodicalId":36824,"journal":{"name":"Data","volume":"1 1","pages":"322-328"},"PeriodicalIF":2.6,"publicationDate":"2021-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49258222","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
Removing Operational Friction Using Process Mining: Challenges Provided by the Internet of Production (IoP) 使用流程挖掘消除操作摩擦:生产互联网(IoP)带来的挑战
IF 2.6
Data Pub Date : 2021-07-28 DOI: 10.1007/978-3-030-83014-4_1
Wil M.P. van der Aalst, T. Brockhoff, A. F. Ghahfarokhi, M. Pourbafrani, M. S. Uysal, S. V. Zelst
{"title":"Removing Operational Friction Using Process Mining: Challenges Provided by the Internet of Production (IoP)","authors":"Wil M.P. van der Aalst, T. Brockhoff, A. F. Ghahfarokhi, M. Pourbafrani, M. S. Uysal, S. V. Zelst","doi":"10.1007/978-3-030-83014-4_1","DOIUrl":"https://doi.org/10.1007/978-3-030-83014-4_1","url":null,"abstract":"","PeriodicalId":36824,"journal":{"name":"Data","volume":"1 1","pages":"1-31"},"PeriodicalIF":2.6,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47546089","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: 7
Proceedings of the 10th International Conference on Data Science, Technology and Applications, DATA 2021, Online Streaming, July 6-8, 2021 第十届数据科学、技术与应用国际会议论文集，数据2021，在线流媒体，2021年7月6日至8日
IF 2.6
Data Pub Date : 2021-07-06 DOI: 10.5220/0000148700002993
{"title":"Proceedings of the 10th International Conference on Data Science, Technology and Applications, DATA 2021, Online Streaming, July 6-8, 2021","authors":"","doi":"10.5220/0000148700002993","DOIUrl":"https://doi.org/10.5220/0000148700002993","url":null,"abstract":"","PeriodicalId":36824,"journal":{"name":"Data","volume":"1 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46905796","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
Literature review synthesis on predictors of Green IoT irrigation adoption in Morocco: Theoretical construct essay 摩洛哥采用绿色物联网灌溉预测因素的文献综述:理论建构论文
IF 2.6
Data Pub Date : 2021-04-05 DOI: 10.1145/3460620.3460763
Houda Zitan, Chafik Khalid
{"title":"Literature review synthesis on predictors of Green IoT irrigation adoption in Morocco: Theoretical construct essay","authors":"Houda Zitan, Chafik Khalid","doi":"10.1145/3460620.3460763","DOIUrl":"https://doi.org/10.1145/3460620.3460763","url":null,"abstract":"The agricultural sector is unpredictable and complicated for farmers to manage it, especially with all the immoderate challenges that condemn the development of agricultural production. Regarding Morocco, water scarcity remains the major challenge that face the sector and put at risk the irrigation based fields-that represents approximately the half of the agricultural GDP-, the nutritional needs of the Moroccan population as well as the global sustainable growth. A way to handle this issue is adopting green IT that enable farmers to establish one of the latest trends in the global smart irrigation systems which are Green Internet Of Things or G-IoT driven smart irrigation. These technologies are currently highly requested and used in the Moroccan agricultural context contributing to the economic and environmental performance. Consequently, the aim of this study is to inspect the factors that affect the farmer's intention to adopt Green IoT based irrigation systems in the Moroccan case. The study is conducted on a literature review based on studies concerning the same subject matter in an international level, and contribute to the research development by proposing a theoretical model related to farmers’ Green IoT adoption.","PeriodicalId":36824,"journal":{"name":"Data","volume":"8 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2021-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87735759","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
A Survey of Similarity Measures for Time stamped Temporal Datasets 时间戳时间数据集相似性度量的研究
IF 2.6
Data Pub Date : 2021-04-05 DOI: 10.1145/3460620.3460754
Aravind Cheruvu, V. Radhakrishna
{"title":"A Survey of Similarity Measures for Time stamped Temporal Datasets","authors":"Aravind Cheruvu, V. Radhakrishna","doi":"10.1145/3460620.3460754","DOIUrl":"https://doi.org/10.1145/3460620.3460754","url":null,"abstract":"Temporal transactional databases are transactional databases which store data in a temporal aspect. Usage of similarity of measures in temporal data mining tasks have gained significant importance to retrieve information and interesting patterns in data. It is always crucial to understand and decide what similarity measure we should use while performing a data mining task and this is always driven by the actual data and nature of the temporal data sets. The main objective of this research is to perform a detailed survey of the various similarity measures used in the temporal data mining in recent research contributions. This paper also provides insights on how these similarity measures are used in the Temporal association rule mining algorithms based on the works carried out in the literature.","PeriodicalId":36824,"journal":{"name":"Data","volume":"33 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2021-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82344851","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
Graffiti and government in smart cities: a Deep Learning approach applied to Medellín City, Colombia 智慧城市中的涂鸦和政府:应用于Medellín城市的深度学习方法，哥伦比亚
IF 2.6
Data Pub Date : 2021-04-05 DOI: 10.1145/3460620.3460749
Javier Rozo Alzate, Marta S. Tabares-Betancur, Paola Vallejo-Correa
{"title":"Graffiti and government in smart cities: a Deep Learning approach applied to Medellín City, Colombia","authors":"Javier Rozo Alzate, Marta S. Tabares-Betancur, Paola Vallejo-Correa","doi":"10.1145/3460620.3460749","DOIUrl":"https://doi.org/10.1145/3460620.3460749","url":null,"abstract":"Graffiti is an element of graphic expression that manifests different states of the human being. However, for many governments worldwide, it has been an element of discord between them and the communities that express themselves through graffitis. This article proposes identifying graffiti and concentration zones through Computer Vision and object detection and localization to support public policy management in smart cities. ASUM-DM methodology is used to achieve the aim. Initially, the current problems faced by municipal governments in the management of public graffiti policy are identified. Then available datasets of images from Google Street View (GSV) and other acquired datasets are identified for the case study carried out in the city of Medellín (Colombia) and border municipalities. A training dataset of 1,395 images and a production dataset of 71,100 panoramas is placed on strictly using the experimental method of the division of training data, validation, and a production sample, to make a correct estimation of the generalization error. As a result of the training process, we obtained an Average Precision of 69,14%, which presented a high precision Tag of 89.23%, and low precision of 59.13% in Mural. Finally, it is possible to build heat maps of graffiti concentration areas that could guide rulers to create or improve public policies related to graffiti expression.","PeriodicalId":36824,"journal":{"name":"Data","volume":"14 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2021-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88457706","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: 3
MACHINE LEARNING FRAMEWORK FOR COVID-19 DIAGNOSIS COVID-19诊断的机器学习框架
IF 2.6
Data Pub Date : 2021-04-05 DOI: 10.1145/3460620.3460624
Sravan kiran Vangipuram, Rajesh Appusamy
{"title":"MACHINE LEARNING FRAMEWORK FOR COVID-19 DIAGNOSIS","authors":"Sravan kiran Vangipuram, Rajesh Appusamy","doi":"10.1145/3460620.3460624","DOIUrl":"https://doi.org/10.1145/3460620.3460624","url":null,"abstract":"With the alarming global health crisis and pandemic, the entire medical industry and every human in this world are desperately looking for new technologies and solutions to monitor and contain the spread of this COVID-19 virus through early detection of its presence among infected patients. The early diagnosis of COVID-19 is hence critical for prevention and limiting this pandemic before it engulfs the humanity. With early diagnosis, the patient may be suggested for self-isolation (or) quarantine under medical supervision. Early detection of COVID-19 can save the patient and minimize the risk of falling prey to CoviD-19. Machine learning, a subset field of Artificial Intelligence can provide a viable solution for early diagnosis of disease and facilitate continuous monitoring of infected patients. AI based approaches can provide a view of the degree of disease severity. In general, Artificial intelligence (AI) could be a better technique for quantitative evaluation of the disease to obtain fruitful results. This paper throws light on the emerging need for AI powered solutions to foster early diagnosis of COVID-19 and suggest an ML based health monitoring framework for diagnosis of infected patients.","PeriodicalId":36824,"journal":{"name":"Data","volume":"5 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2021-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75018615","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
Meteorological forecasting based on big data analysis 基于大数据分析的气象预报
IF 2.6
Data Pub Date : 2021-04-05 DOI: 10.1145/3460620.3460622
Shadi A. Aljawarneh, J. A. L. Torralbo
{"title":"Meteorological forecasting based on big data analysis","authors":"Shadi A. Aljawarneh, J. A. L. Torralbo","doi":"10.1145/3460620.3460622","DOIUrl":"https://doi.org/10.1145/3460620.3460622","url":null,"abstract":"In this paper, we present the main ideas behind the development of a system that can be used to deal with meteorological big data. In particular, the system captures data online and downloads it locally onto a MongoDB database. After that, the user can create a particular database and corresponding minable views for analysis. The results provided by the systems are predictive models with the ability to predict some weather-related variables, such as temperature and rainfall. The system has been validated from a triple perspective (usability, experts’ validation, and performance assessment), obtaining satisfactory results. This paper aims to be a brief guide for authors who intend to developed similar systems either in the meteorological field or other domains generating big amounts of data.","PeriodicalId":36824,"journal":{"name":"Data","volume":"22 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2021-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77487687","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: 12
A SURVEY ON SIMILARITY MEASURES AND MACHINE LEARNING ALGORITHMS FOR CLASSIFICATION AND PREDICTION 分类和预测的相似性度量和机器学习算法的综述
IF 2.6
Data Pub Date : 2021-04-05 DOI: 10.1145/3460620.3460755
Sravan kiran Vangipuram, Rajesh Appusamy
{"title":"A SURVEY ON SIMILARITY MEASURES AND MACHINE LEARNING ALGORITHMS FOR CLASSIFICATION AND PREDICTION","authors":"Sravan kiran Vangipuram, Rajesh Appusamy","doi":"10.1145/3460620.3460755","DOIUrl":"https://doi.org/10.1145/3460620.3460755","url":null,"abstract":"An important observation which figures out when we look into several applications which are the result of applying data science, machine learning, and deep learning techniques is that most of these techniques are based on the concept of measuring similarity between any two vectors. These vectors may act as representatives for objects being considered. Similarity measurement thus gains a great importance in the design of machine learning or deep learning algorithms and techniques. In similar lines, when we are required to carry a supervised or unsupervised learning task, an algorithm is required to carry the task efficiently. Thus, in this paper, our objective is to outline various similarity measures that have been considered for carrying supervised or unsupervised learning tasks and also to throw light on different machine learning algorithms employed for supervised and unsupervised learning tasks from disease classification and prediction point of view and also interdisciplinary domains such as time series analysis, temporal data mining, medical data mining, and anomaly or intrusion detection.","PeriodicalId":36824,"journal":{"name":"Data","volume":"4 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2021-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87266536","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: 4
Ontology-Based Extraction of Kazakh Language Word Combinations in Natural Language Processing 自然语言处理中基于本体的哈萨克语词组合提取
IF 2.6
Data Pub Date : 2021-04-05 DOI: 10.1145/3460620.3460631
Gaziza Yelibayeva, A. Sharipbay, G. Bekmanova, A. Omarbekova
{"title":"Ontology-Based Extraction of Kazakh Language Word Combinations in Natural Language Processing","authors":"Gaziza Yelibayeva, A. Sharipbay, G. Bekmanova, A. Omarbekova","doi":"10.1145/3460620.3460631","DOIUrl":"https://doi.org/10.1145/3460620.3460631","url":null,"abstract":"This article provides an ontological model of nominative word combinations in the Kazakh language. It is necessary for creation of the automated templates for search of nominative word combinations of the Kazakh language in text corpora. The presented model expands the theory of applied linguistics in the field of extracting information from the text during corpus studies. The results will be used in semantic searches, Q&A systems and in the development of software applications for obtaining knowledge, as well as for training and evaluation of knowledge on the syntax of the Kazakh language in the system of e-learning.","PeriodicalId":36824,"journal":{"name":"Data","volume":"9 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2021-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89373942","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
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