12,852 results
Search Results
2. Unsupervised Multimodal Supervoxel Merging Towards Brain Tumor Segmentation
- Author
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Pelluet, Guillaume, Rizkallah, Mira, Acosta, Oscar, Mateus, Diana, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crimi, Alessandro, editor, and Bakas, Spyridon, editor
- Published
- 2022
- Full Text
- View/download PDF
3. Online Prediction of Aggregated Retailer Consumer Behaviour
- Author
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Spenrath, Yorick, Hassani, Marwan, van Dongen, Boudewijn F., van der Aalst, Wil, Series Editor, Mylopoulos, John, Series Editor, Ram, Sudha, Series Editor, Rosemann, Michael, Series Editor, Szyperski, Clemens, Series Editor, Munoz-Gama, Jorge, editor, and Lu, Xixi, editor
- Published
- 2022
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- View/download PDF
4. Automatic Unsupervised Clustering of Videos of the Intracytoplasmic Sperm Injection (ICSI) Procedure
- Author
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Storås, Andrea M., Riegler, Michael A., Haugen, Trine B., Thambawita, Vajira, Hicks, Steven A., Hammer, Hugo L., Kakulavarapu, Radhika, Halvorsen, Pål, Stensen, Mette H., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Zouganeli, Evi, editor, Yazidi, Anis, editor, Mello, Gustavo, editor, and Lind, Pedro, editor
- Published
- 2022
- Full Text
- View/download PDF
5. A Comprehensive Tensor Framework for the Clustering of Hyperspectral Paper Data With an Application to Forensic Document Analysis
- Author
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Jobin Francis, Baburaj Madathil, Sudhish N. George, and Sony George
- Subjects
Forensic document analysis ,hyperspectral imaging (HSI) ,clustering ,self-expressiveness property ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
In forensic document analysis, the authenticity of a document must be properly checked in the context of suspected forgery. Hyperspectral Imaging (HSI) is a non-invasive way of detecting fraudulent papers in a multipage document. The occurrence of a forged paper in a multi-page document may have a substantial difference from rest of the papers in its age, type, color, texture, and so on. Each pixel in an HSI data can be used as the material fingerprint for the spatial point it corresponds to. Hence, hyperspectral data of paper samples made of the same substance have similar characteristics and can be grouped into a single cluster. Similarly, paper samples made of different substances have different spectral properties. This paper relies on this heuristic and proposes a tensor based clustering framework for hyperspectral paper data, with an application to detect the forged papers in multi-page documents. Information embedded in the hyperspectral patches of the papers to be clustered is arranged into individual lateral slices of a third-order tensor in this framework. Further, this work employs the self-expressiveness property of submodules and an objective function is formulated to extract self-expressive representation tensor with low multirank and f-diagonal structure. Objective function of the proposed method incorporates $l_{\frac {1}{2}}$ -induced Tensor Nuclear Norm (TNN) and $l_{\frac {1}{2}}$ regularization to impart better low rankness and f-diagonal structure to the representation tensor. Experimental results of the proposed method were compared to the state-of-the-art subspace clustering approaches. The results demonstrate improved performance of the proposed method over the existing clustering algorithms.
- Published
- 2022
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- View/download PDF
6. Temporal dynamics of research field integration on slow-cited papers and the awakeners
- Author
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Miura, Takahiro, Asatani, Kimitaka, and Sakata, Ichiro
- Subjects
Sleeping beauty ,Awakener ,Slow-cited paper ,Research field formation ,Clustering - Abstract
Understanding the long-term impact of scientific findings requires understanding the dynamic process of new research fields' formation. In new research fields, slow-cited papers (SCP) and the awakeners (AW) are more likely to exist, indicating explorers revisited underrated but significant past papers relocating the findings in the new paradigm. This study acquired SCP-AW pairs located in the integrated point of two different research fields using the inheritance of clusters. We found that research field integration, including SCP-AW pairs, was diverse but followed a similar pattern throughout history, generating an equal mix of SCP and AW fields. The recent trend toward more AW-centric disciplinary combinations supports the belief that field integration will become increasingly technology-driven in the coming years.
- Published
- 2022
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7. Deduction of time-dependent machine tool characteristics by fuzzy-clustering
- Author
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Frieß, Uwe, Kolouch, Martin, Putz, Matthias, inIT - Institut für industrielle Informa, Beyerer, Jürgen, editor, Kühnert, Christian, editor, and Niggemann, Oliver, editor
- Published
- 2019
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- View/download PDF
8. The Thematic Structure of Papers on Depression Treatment in PubMed from 2005 to 2014
- Author
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Seyed Hossein Hosseininasab, Fatemeh Makkizadeh, Ebrahim Zalzadeh, and Afsaneh Hazeri
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Co-words ,Clustering ,PubMed ,Thematic Structure ,Depression ,Computer applications to medicine. Medical informatics ,R858-859.7 - Abstract
Introduction: Due to the high prevalence of depression and the considerable pressure it puts on individuals, society and healthcare system, it is essential to conduct sufficient research to help with decision making in prevention, treatment and control of this condition. To assist with research planning and setting priorities, this paper aimed to identify the thematic structure of papers related to depression treatment. Methods: This was an applied research study which used Scientometrics approach. To obtain data, the keyword “Depression” was searched as a mesh descriptor with the subdivision “Therapy” in PubMed database for the period of 2005 to 2014. The data were analyzed using co-word and clustering methods with the help of Excel and SPSS software packages. Results: The growth of scientific production in this field appeared to have been balanced throughout the ten-year period examined in the study. The most active research areas for the two five year periods (2005-2009 and 2010-2014) were drug therapy, psychology, and medication adherence. Two hierarchical graphs of descriptors for each five-year period were prepared both of which were composed of 12 clusters with 34 common descriptors. Conclusion: The findings based on the inclusion index showed that only 20 percent of topics in the second five-year period of the study were novel. Therefore, it could be concluded that research areas that were related with each other in previous years will probably co-occur with other topics in the future might disappear. In every discipline, some topics are considered to be fundamental and research works are being carried out on them almost every year.
- Published
- 2016
9. (Position paper) Characterizing the Behavior of Small Producers in Smart Grids A Data Sanity Analysis.
- Author
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Stefan, Maria, Gutierrez, Jose, Barlet, Pere, Prieto, Eduardo, Gomis, Oriol, and Olsen, Rasmus L.
- Subjects
ELECTRIC power distribution grids ,DATA analysis ,ENERGY consumption ,FORECASTING ,CONSUMER profiling ,LOAD forecasting (Electric power systems) - Abstract
Renewable energy production throughout low-voltage grids has gradually increased in electrical distribution systems, therefore introducing small energy producers - prosumers. This paradigm challenges the traditional unidirectional energy distribution flow to include disperse power production from renewables. To understand how energy usage can be optimized in the dynamic electrical grid, it is important to understand the behavior of prosumers and their impact on the grid's operational procedures. The main focus of this study is to investigate how grid operators can obtain an automatic data-driven system for the low-voltage electrical grid management, by analyzing the available grid topology and time-series consumption data from a real-life test area. The aim is to argue for how different consumer profiles, clustering and prediction methods contribute to the grid-related operations. Ultimately, this work is intended for future research directions that can contribute to improving the trade-off between systematic and scalable data models and software computational challenges. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
10. Multi-View Data approaches in Recommender Systems: an Overview: (Invited Paper).
- Author
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Palomares, Iván and Kovalchuk, Sergey V.
- Subjects
RECOMMENDER systems ,DATA science ,DECISION support systems ,LEARNING ,AGGREGATION operators - Abstract
This paper overviews an assortment of recent research work undertaken on recommender system models based on using multiple views of user and item-related data across the recommendation process. A summary of representative literature on multi-view recommender approaches is provided, describing their main characteristics, such as: their potential to overcome most common shortcomings in conventional recommender systems, as well as the use of data science, learning techniques and aggregation processes to combine information stemming from multiple views. A tabular summary is provided to facilitate the comparison of the similarities and differences among the surveyed works, along with commonly identified directions for future research in the topic. [ABSTRACT FROM AUTHOR]
- Published
- 2017
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- View/download PDF
11. Gene expression responses of paper birch (Betula papyrifera) to elevated CO2 and O3 during leaf maturation and senescence
- Author
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Kontunen-Soppela, Sari, Parviainen, Juha, Ruhanen, Hanna, Brosché, Mikael, Keinänen, Markku, Thakur, Ramesh C., Kolehmainen, Mikko, Kangasjärvi, Jaakko, Oksanen, Elina, Karnosky, David F., and Vapaavuori, Elina
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GENE expression ,PAPER birch ,EFFECT of carbon dioxide on plants ,EFFECT of atmospheric carbon dioxide on plants ,ATMOSPHERIC ozone & the environment ,OZONE & the environment ,EFFECT of ozone on plants ,EFFECT of atmospheric ozone on plants - Abstract
Gene expression responses of paper birch (Betula papyrifera) leaves to elevated concentrations of CO
2 and O3 were studied with microarray analyses from three time points during the summer of 2004 at Aspen FACE. Microarray data were analyzed with clustering techniques, self-organizing maps, K-means clustering and Sammon''s mappings, to detect similar gene expression patterns within sampling times and treatments. Most of the alterations in gene expression were caused by O3 , alone or in combination with CO2 . O3 induced defensive reactions to oxidative stress and earlier leaf senescence, seen as decreased expression of photosynthesis- and carbon fixation-related genes, and increased expression of senescence-associated genes. The effects of elevated CO2 reflected surplus of carbon that was directed to synthesis of secondary compounds. The combined CO2 +O3 treatment resulted in differential gene expression than with individual gas treatments or in changes similar to O3 treatment, indicating that CO2 cannot totally alleviate the harmful effects of O3 . [Copyright &y& Elsevier]- Published
- 2010
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12. Establishing the original order of the poems in Harward’s Almanac using paleography, codicology, X-ray fluorescence spectroscopy, and statistical analysis
- Author
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Biolcati, Veronica, Woolley, James, Lévêque, Élodie, Rossi, Andrea, Hoffmann, Anna Grace, Visentin, Andrea, Macháin, Pádraig Ó, and Iacopino, Daniela
- Published
- 2023
- Full Text
- View/download PDF
13. Adaptive weights clustering of research papers
- Author
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Kirill Efimov, Larisa Adamyan, Wolfgang Karl Härdle, and Cathy Yi-Hsuan Chen
- Subjects
JEL system ,Adaptive algorithm ,Point (typography) ,Computer science ,330 Wirtschaft ,05 social sciences ,Nonparametric statistics ,020206 networking & telecommunications ,02 engineering and technology ,computer.software_genre ,Clustering ,Weighting ,0502 economics and business ,ddc:330 ,0202 electrical engineering, electronic engineering, information engineering ,Key (cryptography) ,Economic articles ,Nonparametric ,Data mining ,050207 economics ,Cluster analysis ,computer ,Research center - Abstract
The JEL classification system is a standard way of assigning key topics to economic articles to make them more easily retrievable in the bulk of nowadays massive literature. Usually the JEL (Journal of Economic Literature) is picked by the author(s) bearing the risk of suboptimal assignment. Using the database of the Collaborative Research Center from Humboldt-Universität zu Berlin we employ a new adaptive clustering technique to identify interpretable JEL (sub)clusters. The proposed Adaptive Weights Clustering (AWC) is available on http://www.quantlet.de/ and is based on the idea of locally weighting each point (document, abstract) in terms of cluster membership. Comparison with $$k$$ k -means or CLUTO reveals excellent performance of AWC.
- Published
- 2020
14. Recuperación de información para artículos científicos soportada en el agrupamiento de documentos XML.
- Author
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Magdaleno, Damny, Fuentes, Ivett E., Cabezas, Michel, and García, María M.
- Published
- 2016
15. Cluster-based supplier segmentation: a sustainable data-driven approach
- Author
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Rahiminia, Mohammad, Razmi, Jafar, Shahrabi Farahani, Sareh, and Sabbaghnia, Ali
- Published
- 2023
- Full Text
- View/download PDF
16. (Position paper) Characterizing the behavior of small producers in smart grids: A data sanity analysis
- Author
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Rasmus Løvenstein Olsen, Pere Barlet, Eduardo Prieto, Jose M. Gutierrez, Oriol Gomis, Maria Stefan, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Enginyeria Elèctrica, Universitat Politècnica de Catalunya. CBA - Sistemes de Comunicacions i Arquitectures de Banda Ampla, and Universitat Politècnica de Catalunya. CITCEA - Centre d'Innovació Tecnològica en Convertidors Estàtics i Accionaments
- Subjects
Computer science ,Distributed computing ,02 engineering and technology ,Prediction methods ,Renewable energy sources ,Clustering ,Data modeling ,Energy optimization ,Data sanity ,0202 electrical engineering, electronic engineering, information engineering ,Cluster analysis ,General Environmental Science ,Energies::Energia elèctrica::Automatització i control de l'energia elèctrica [Àrees temàtiques de la UPC] ,business.industry ,Electric power distribution ,020206 networking & telecommunications ,Grid ,Electrical grid ,Renewable energy ,Smart grid ,Scalability ,User modeling and applications ,General Earth and Planetary Sciences ,Energia elèctrica -- Distribució ,020201 artificial intelligence & image processing ,Energies renovables ,business - Abstract
Renewable energy production throughout low-voltage grids has gradually increased in electrical distribution systems, therefore introducing small energy producers - prosumers. This paradigm challenges the traditional unidirectional energy distribution flow to include disperse power production from renewables. To understand how energy usage can be optimized in the dynamic electrical grid, it is important to understand the behavior of prosumers and their impact on the grid’s operational procedures. The main focus of this study is to investigate how grid operators can obtain an automatic data-driven system for the low-voltage electrical grid management, by analyzing the available grid topology and time-series consumption data from a real-life test area. The aim is to argue for how different consumer profiles, clustering and prediction methods contribute to the grid-related operations. Ultimately, this work is intended for future research directions that can contribute to improving the trade-off between systematic and scalable data models and software computational challenges. This work is financially supported by the Danish project RemoteGRID, which is a ForskEL program under Energinet.dk with grant agreement no. 2016-1-12399.
- Published
- 2020
17. A Clustering Expert System using Particle Swarm Optimization and K-means++ for Journal Recommendation to Publish the Papers
- Author
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Mansoureh Maadi, Seyedeh Malihe Khatami, and Rohollah Ramezani
- Subjects
Control and Optimization ,Information retrieval ,Journal recommendation system ,Computer Networks and Communications ,Computer science ,business.industry ,k-means clustering ,Particle swarm optimization ,computer.software_genre ,Fuzzy logic ,Clustering ,Expert system ,Particle Swarm Optimization ,Hardware and Architecture ,Signal Processing ,Electrical and Electronic Engineering ,Cluster analysis ,business ,GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries) ,computer ,Publication ,Fuzzy ,Information Systems - Abstract
In this paper, an android expert system for recommending the suitable journal for publishing the researchers' papers has been presented. In so doing, the expectations of different journals for accepting an article and also the requests of papers' writers for choosing the journals have been examined. Language, quality, waiting time for judgment, waiting time for publication after acceptance, field, length of paper and cost are the system inputs and its output is the degree of suitability of journals for publishing a certain paper. This system includes a database of different journals and their parameters. It uses particle swarm optimization method and K-means++ algorithm for assessing and clustering the journals database and determines an index for every cluster of journals. The process for matching the paper with a cluster's index is done through fuzzy induction system. After choosing the most similar cluster, the paper is compared with all the journals of the cluster in the same way and the result including the most similar journals is presented. This system has been tested for journals and papers in the computer field indexed in Elsevier.
- Published
- 2018
18. Archives of Data Science, Series A. Vol. 1,1: Special Issue: Selected Papers of the 3rd German-Polish Symposium on Data Analysis and Applications
- Author
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Geyer-Schulz, Andreas and Pociecha, Józef
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Data Analysis ,Economics ,statistische Simulation ,Educational Economics ,Bildungsökonomie ,Clustering ,Machine Learning ,Datenanalyse ,Conjoint Analyse ,ddc:330 ,Conjoint Analysis ,Statistical Simulation ,Maschinelles Lernen - Abstract
The first volume of Archives of Data Science, Series A is a special issue of a selection of contributions which have been originally presented at the {\em 3rd Bilateral German-Polish Symposium on Data Analysis and Its Applications} (GPSDAA 2013). All selected papers fit into the emerging field of data science consisting of the mathematical sciences (computer science, mathematics, operations research, and statistics) and an application domain (e.g. marketing, biology, economics, engineering).
- Published
- 2017
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19. The influence of clustering on HR practices and intrapreneurial behavior
- Author
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Canet-Giner, María Teresa, Redondo-Cano, Ana, Balbastre-Benavent, Francisco, Escriba-Carda, Naiara, Revuelto-Taboada, Lorenzo, and Saorin-Iborra, María del Carmen
- Published
- 2022
- Full Text
- View/download PDF
20. Оцінка конкурентоспроможності обробної промисловості лісопромислового комплексу України та країн ЄС
- Author
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Губарєва, І. О. and Ярошенко, І. В.
- Subjects
WOOD products manufacturing ,PAPER products ,MANUFACTURED products ,FURNITURE manufacturing ,ENERGY consumption - Abstract
Copyright of Problems of Economy is the property of Research Centre for Industrial Developmen Problems of Nas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2020
- Full Text
- View/download PDF
21. Investigating critical failure drivers of construction project at planning stage in Saudi Arabia
- Author
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Alsulamy, Saleh
- Published
- 2022
- Full Text
- View/download PDF
22. Adaptive weights clustering of research papers
- Author
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Adamyan, Larisa, Efimov, Kirill, Chen, Cathy Yi-Hsuan, and Härdle, Wolfgang K.
- Subjects
G17 ,JEL system ,Adaptive algorithm ,330 Wirtschaft ,ddc:330 ,C58 ,Economic articles ,Nonparametric ,G11 ,C32 ,C55 ,Clustering - Abstract
The JEL classification system is a standard way of assigning key topics to economic articles in order to make them more easily retrievable in the bulk of nowadays massive literature. Usually the JEL (Journal of Economic Literature) is picked by the author(s) bearing the risk of suboptimal assignment. Using the database of a Collaborative Research Center from Humboldt-Universit¨at zu Berlin and Xiamen University, China we employ a new adaptive clustering technique to identify interpretable JEL (sub)clusters. The proposed Adaptive Weights Clustering (AWC) is available on www.quantlet.de and is based on the idea of locally weighting each point (document, abstract) in terms of cluster membership. Comparison with k-means or CLUTO reveals excellent performance of AWC.
- Published
- 2017
23. Innovation within networks – patent strategies for blockchain technology
- Author
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Dehghani, Milad, Mashatan, Atefeh, and Kennedy, Ryan William
- Published
- 2021
- Full Text
- View/download PDF
24. Reverse logistics risk management: identification, clustering and risk mitigation strategies
- Author
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Panjehfouladgaran, Hamidreza and Lim, Stanley Frederick W.T.
- Published
- 2020
- Full Text
- View/download PDF
25. A methodology for classification and validation of customer datasets
- Author
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Nie, Dongyun, Cappellari, Paolo, and Roantree, Mark
- Published
- 2021
- Full Text
- View/download PDF
26. An anomaly detection method to improve the intelligent level of smart articles based on multiple group correlation probability models
- Author
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Lu, Xudong, Wang, Shipeng, Kang, Fengjian, Liu, Shijun, Li, Hui, Xu, Xiangzhen, and Cui, Lizhen
- Published
- 2019
- Full Text
- View/download PDF
27. The role of pension knowledge in voluntary pension and banking savings in Chile
- Author
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Diaz, David, Ruiz, José L., and Tapia, Pablo
- Published
- 2021
- Full Text
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28. MINIMUM POWER CONSUMPTION ROUTING USING HIERARCHICAL FUZZY LOGIC CLUSTERING FOR INTERNET OF THINGS.
- Author
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PRADEEPA, K. and PRAVEEN, M.
- Subjects
ROUTING algorithms ,HIERARCHICAL clustering (Cluster analysis) ,FUZZY logic ,ENERGY consumption ,INTERNET of things ,MULTICASTING (Computer networks) - Abstract
The Internet of Things (IoT) is a very famous network because of its many applications. IoT network has an integration of large-scale IoT devices that generate data. These IoT devices are very low power computing devices due to which they have a low level of communication. These devices construct data and transmit the data to the base station via intermediate IoT devices. The base station gathers and integrates the data and sends it to the administrator for further processing. The data attains the base station using various routing algorithms with the goal of low power consumption. When discussing low power IoT devices, power efficiency is an important performance measurement when creating a routing algorithm. This paper proposes a Minimum Power Consumption Routing (MPCR) algorithm using Hierarchical Fuzzy Logic Clustering (HFLC) algorithm for IoT networks. The MPCR with HFLC algorithm is an energy-efficient algorithm because of its lower power consumption for the cluster by aggregating the data within the cluster head and decreasing the number of data transmissions to the base station. In this paper, the cluster formation and cluster-head selection are explained, and a simulation has been conducted. In addition, the proposed algorithm is compared with the existing algorithms based on different metrics such as throughput, packet delivery ratio, and energy consumption of the network. The experimental results show that the proposed MPCR with the HFLC algorithm provides high throughput and packet delivery ratio and reduces energy consumption more efficiently than other existing algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2024
29. Mining efficient training patterns of non-professional cyclists (Discussion Paper)
- Author
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Cintia P., Pappalardo L., and Pedreschi D.
- Subjects
Sport mining ,Science of success ,Clustering - Abstract
The recent emergence of the so called online social fitness open up new scenarios for fascinating challenges in the field of data sci- ence. Through these platforms, users can collect, monitor and share with friends their sport performance, with interesting details about heartrate, watt consumption and calories burned. The availability of this data, col- lected among a large number of users, gives us the possibility to explore new data mining applications. In the current work, we present the results of a study conducted on a sample of 29; 284 cyclists downloaded via APIs from the social fitness platform Strava.com. We defined two basic metrics: A measure of train- ing effort, that is how much a cyclist struggled during the workout; and a measure of training performance indicating the results achieved during the training. Although the average effort is weakly correlated with the average performance, by deeply investigating workouts time evolution and cyclists' training characteristics interesting findings came out. We found that athletes that better improve their performance follow precise training patterns usually referred as overcompensation theory, with alter- nation of stress peaks and rest periods. Studies and experiments related to such theory, up to now, have always been conducted by sports doctors on a few dozen professionals athletes. To the best of our knowledge, our study is the first corroboration on large scale of this theory.
- Published
- 2014
30. Innovative propensity with a fuzzy multicriteria approach : Analysis of the Spanish industrial sector with data mining techniques
- Author
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Cobo, Angel, Rocha, Eliana Rocio, and Villamizar, Marco Antonio
- Published
- 2019
- Full Text
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31. A STATISTICAL STUDY ANALYSIS ON EXPLORING CONSUMPTION PATTERNS REGARDING FOOD LOSS AND WASTE.
- Author
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NIJLOVEANU, Daniel, TIȚA, Victor, BOLD, Nicolae, SMEDESCU, Dragoș, FÎNTÎNERU, Alexandru, TUDOR, Valentina, SMEDESCU, Cosmina, and JERCA, Emanuela
- Subjects
DIETARY patterns ,FOOD waste ,FOOD consumption ,MULTIVARIATE analysis ,STATISTICS ,UNIVARIATE analysis - Abstract
Food loss ans waste, referred next as FLW, has a great extent on the economic and behavioural patterns of consumption. In this paper, we aim to present statistical univariate and multivariate analyses based on a statistical study run during a period of several months in the Romanian territory. In this matter, the main purpose of the analysis developed in this paper is to determine several patterns of FLW phenomenon. This is made based on a methodology which comprises two main sections: the univariate analysis, consisting in statistical determinations of indicators and direct observation, and the multivariate analysis, related to the clustering analysis. The clustering analysis is based on the usage of hierarchical method, which has as final result the determination of clusters describing patterns of consumption related to food loss and waste. After the analysis was run, five main clusters related to food waste behaviour patterns were determined, with slight differences between food consumption habits, but with various combinations between the parameters taken into consideration. The obtained clusters offer important information for policymakers and stakeholders aiming to customize interventions and programs to target specific demographic groups or segments of the population. [ABSTRACT FROM AUTHOR]
- Published
- 2024
32. What do we want to know about MOOCs? Results from a machine learning approach to a systematic literature mapping review.
- Author
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Despujol, Ignacio, Castañeda, Linda, Marín, Victoria I., and Turró, Carlos
- Subjects
MASSIVE open online courses ,MACHINE learning ,EDUCATIONAL resources ,LARGE-scale brain networks ,CONTENT analysis ,EDUCATORS - Abstract
By the end of 2020, over 16,300 Massive Open Online Courses (MOOCs) from 950 universities worldwide had enrolled over 180 million students. Interest in MOOCs has been matched by significant research on the topic, including a considerable number of reviews. This study uses Machine Learning techniques and human expert supervision to generate a comprehensive systematic literature mapping review that overcomes some limitations of the traditional ones and provides a broader overview of the content and main topics studied in the specialized literature devoted to MOOCs. The sample consisted of 6320 publications automatically classified within six research topics, denominated by human experts: institutional approach, pedagogical approach, evaluation, analytics, participation, and educational resources. The content analysis of the topics identified was conducted using visual network analysis, which supported the identification of different thematic sub-clusters and endorsed the classification. Results from the review show that the lowest production of MOOC papers is within the topics of the pedagogical approach and educational resources. In contrast, participation and evaluation are the most frequent ones. In addition, the most cited papers are on the topics of analytics and resources, being the pedagogical approach and the institutional approach the less cited. This highlights the need for more MOOC research from a pedagogical perspective and calls upon the presence of educators. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
33. A comparative data analytic approach to construct a risk trade-off for cardiac patients’ re-admissions
- Author
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Nasir, Murtaza, South-Winter, Carole, Ragothaman, Srini, and Dag, Ali
- Published
- 2019
- Full Text
- View/download PDF
34. Context-aware knowledge selection and reliable model recommendation with ACCORDION.
- Author
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Ahmed, Yasmine, Telmer, Cheryl A., Gaoxiang Zhou, and Miskov-Zivanov, Natasa
- Subjects
EXPERT systems ,LITERARY sources ,GRAPH algorithms ,BIOLOGICAL systems ,NATURAL language processing - Abstract
New discoveries and knowledge are summarized in thousands of published papers per year per scientific domain, making it incomprehensible for scientists to account for all available knowledge relevant for their studies. In this paper, we present ACCORDION (ACCelerating and Optimizing model RecommenDatIONs), a novel methodology and an expert system that retrieves and selects relevant knowledge from literature and databases to recommend models with correct structure and accurate behavior, enabling mechanistic explanations and predictions, and advancing understanding. ACCORDION introduces an approach that integrates knowledge retrieval, graph algorithms, clustering, simulation, and formal analysis. Here, we focus on biological systems, although the proposed methodology is applicable in other domains. We used ACCORDION in nine benchmark case studies and compared its performance with other previously published tools. We show that ACCORDION is: comprehensive, retrieving relevant knowledge from a range of literature sources through machine reading engines; very effective, reducing the error of the initial baseline model by more than 80%, recommending models that closely recapitulate desired behavior, and outperforming previously published tools; selective, recommending only the most relevant, contextspecific, and useful subset (15%-20%) of candidate knowledge in literature; diverse, accounting for several distinct criteria to recommend more than one solution, thus enabling alternative explanations or intervention directions. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
35. Echo State Network-Based Content Prediction for Mobile Edge Caching Networks.
- Author
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Cai, Zengyu, Chen, Xi, Zhang, Jianwei, Zhu, Liang, and Hu, Xinhua
- Subjects
CACHE memory ,INTELLIGENT computer terminals ,DOCUMENT clustering ,INTERNET ,COMPUTER networks - Abstract
With the rapid development of internet communication and the wide application of intelligent terminal, moving the cache to the edge of the network is an effective solution to shorten the delay of users accessing content. However, the existing cache work lacks the comprehensive consideration of users and content, resulting in low cache hit ratio and low accuracy of the whole system. In this paper, the authors propose a collaborative caching model that considers both user request content and content prediction, so as to improve the caching performance of the whole network. Firstly, the model uses the clustering algorithm based on Akike information criterion to cluster users. Then, combined with the clustering results, echo state network is used as the machine learning framework to predict the content. Finally, the cache contents are selected according to the prediction results and cached in the cache unit of the small base station. Simulation results show that compared with the existing cache algorithms, the proposed method has obvious improvement in cache hit ratio, accuracy, and recall rate. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
36. ON WHICH SOCIOECONOMIC GROUPS DO REVERSE MORTGAGES HAVE THE GREATEST IMPACT? EVIDENCE FROM SPAIN.
- Author
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BOJ, Eva, CLARAMUNT, M. Mercè, and VAREA, Xavier
- Subjects
REVERSE mortgage loans ,HOUSEHOLD surveys ,STOCHASTIC models ,HOUSEHOLDS ,MARKOV processes - Abstract
Reverse mortgage is one of the products (perhaps the main one) that is good to obtain additional income by using the habitual residence as collateral. The main objective of this paper is to analyse the effects that reverse mortgage contracting has on household finances over the lifetime of a family according to the socioeconomic group to which it belongs in Spain. Four indicators are employed to measure the immediate and long-term effects. We use a stochastic model with a double source of randomness, survival and entry into dependency, and apply it to the three socioeconomic groups obtained with cluster methodology from the 2017 Spanish Household Financial Survey data. We conclude that the effects are very different depending on the group: regarding only the effects of hiring a reverse mortgage on the income of the family, widowed women aged between 81 and 85 years, with low income and expenses as well as little net wealth, and a habitual residence that represents half of her net wealth (Cluster 1) are the most benefited; considering that the highest impact indicators are on the probability of illiquidity and on the value of lack of liquidity, the use of reverse mortgages benefits more the families in Cluster 3 (high income and expenses and really high net wealth, head of household aged between 76 and 80 years) and less the families in Cluster 2 (medium income, net wealth and expenses, head of household aged between 65 and 75 years). [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
37. A Review of Predictive Analytics Models in the Oil and Gas Industries.
- Author
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R Azmi, Putri Azmira, Yusoff, Marina, and Mohd Sallehud-din, Mohamad Taufik
- Subjects
PREDICTION models ,GAS industry ,PETROLEUM industry ,MACHINE learning - Abstract
Enhancing the management and monitoring of oil and gas processes demands the development of precise predictive analytic techniques. Over the past two years, oil and its prediction have advanced significantly using conventional and modern machine learning techniques. Several review articles detail the developments in predictive maintenance and the technical and non-technical aspects of influencing the uptake of big data. The absence of references for machine learning techniques impacts the effective optimization of predictive analytics in the oil and gas sectors. This review paper offers readers thorough information on the latest machine learning methods utilized in this industry's predictive analytical modeling. This review covers different forms of machine learning techniques used in predictive analytical modeling from 2021 to 2023 (91 articles). It provides an overview of the details of the papers that were reviewed, describing the model's categories, the data's temporality, field, and name, the dataset's type, predictive analytics (classification, clustering, or prediction), the models' input and output parameters, the performance metrics, the optimal model, and the model's benefits and drawbacks. In addition, suggestions for future research directions to provide insights into the potential applications of the associated knowledge. This review can serve as a guide to enhance the effectiveness of predictive analytics models in the oil and gas industries. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
38. Dominant Gray Level-based Genetic K-means Clustering Algorithm for MRI Image Segmentation.
- Author
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Khaleel, Maha Ibrahim, Mohammed, Musab Ahmed, and Qays, Maryam
- Subjects
K-means clustering ,MAGNETIC resonance imaging ,IMAGE segmentation ,GENETIC algorithms ,IMAGE processing ,DIAGNOSTIC imaging - Abstract
In this paper, a method and fresh results associated with medical image segmentation of brain Magnetic Resonance Imaging (MRI) scans are presented. Gray-converted segmentation and Genetic Algorithm (GA) are utilized along with unsupervised k-means classification. The image segmentation employed indicates the tissue type or the anatomical structure of each pixel. The cluster centroid initialization is performed by GA. GA offers efficient search processes (selection, crossover, and mutation), suited to determine global optima regarding centroid problems. As a result, this research offers more accurate, reliable, and efficient image segmentation for MRI, by improving the k-means algorithm with GA. The results indicate that the accuracy obtained from the proposed method is at least 3.5% higher than the PSO algorithm in this matter. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
39. VisualRPI: Visualizing Research Productivity and Impact.
- Author
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Hung, Chihli and Lin, Wei-Chao
- Abstract
Research productivity and impact (RPI) is commonly measured through citation analysis, such as the h-index. Despite the popularity and objectivity of this type of method, it is still difficult to effectively compare a number of related researchers in terms of various citation-related statistics at the same time, such as average cites per year/paper, the number of papers/citations, h-index, etc. In this work, we develop a method that employs information visualization technology, and examine its applicability for the assessment of researchers' RPI. Specifically, our prototype, a visualizing research productivity and impact (VisualRPI) system, is introduced, which is composed of clustering and visualization components. The clustering component hierarchically clusters similar research statistics into the same groups, and the visualization component is used to display the RPI in a clear manner. A case example using information for 85 information systems researchers is used to demonstrate the usefulness of VisualRPI. The results show that this method easily measures the RPI for various performance indicators, such as cites/paper and h-index. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
40. Applying Hybrid Clustering with Evaluation by AUC Classification Metrics.
- Author
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Dakhil, Ali Fattah, Ali, Waffaa M., and Hasan, Mustafa Asaad
- Subjects
MACHINE learning ,RESEARCH questions ,FALSE alarms ,STATISTICAL learning ,DISTANCE education - Abstract
Traditional metrics may not adequately assess performance in certain situations, whereas the Area Under Curve (AUC) offers a comprehensive perspective by considering both sensitivity and specificity. This method enhances interpretability, addresses limitations, and promotes the development of robust clustering algorithms. In unsupervised learning, utilizing AUC is a significant method for improving the precision and accuracy of machine learning models. Our work is inspired by several recent related works that implement approaches to manage the challenges of developing new metrics that can effectively assess and evaluate the performance of clustering algorithms. The research question relies on the concept of using an optimal metric for model evaluation of classification and clustering. Therefore, the paper investigates the use of the classification metric AUC for clustering validation purposes. The methodology we adopt is a hybrid clustering model because such a technique offers a robust model by combining the strengths of each model. The linkage approach directly impacts the clustering results, so we give significant attention to this feature in our implementation. Among the various linkage methods, we utilized single and average linkages. The Manhattan and Euclidean metrics are the distance measures used in this work. Thus, our contribution is to explore the benefit of using linkages and distance measurement in clustering with the help of the AUC metric. In addition, the entire proposed work and the contributions of this paper are evaluated and applied to the NSL-KDD dataset. Based on the proposed approach of using AUC with clustering, the Detection Rate (DR), False Alarm Rate (FAR), and other criteria are chosen to examine the model's results and capabilities. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
41. APPLICATION OF FUZZY METRICS IN CLUSTERING PROBLEMS OF AGRICULTURAL CROP VARIETIES.
- Author
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Stamenković, Andrijana, Milosavljević, Nataša, and Ralević, Nebojša M.
- Subjects
CULTIVARS ,CROPS ,ARTIFICIAL intelligence ,METAHEURISTIC algorithms ,MACHINE learning - Abstract
The problem of image-based detection of the variety of beans, using artificial intelligence, is currently dealt with by scientists of various profiles. The idea of this paper is to show the possibility of applying different types of distances, primarily those that are fuzzy metrics, in clustering models in order to improve existing models and obtain more accurate results. The paper presents the method of variable neighborhood search, which uses both standard and fuzzy t-metrics and dual fuzzy s-metrics characterized by appropriate parameters. By varying those parameters of the fuzzy metric as well as the parameters of the metaheuristic used, we have shown how it is possible to improve the clustering results. The obtained results were compared with existing ones from the literature. The criterion function used in clustering is a fuzzy metric, which is proven in the paper. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
42. Superpixel segmentation based on image density.
- Author
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Dong-Fang Qiu, Hua Yang, Xue-Feng Deng, and Yan-Hong Liu
- Subjects
PIXELS ,IMAGE segmentation ,IMAGE processing ,DENSITY - Abstract
Superpixel segmentation can get the middle features in image processing, effectively reduce the dimensionality of the image, and is widely used in image processing fields. To get the regular and compact superpixels in real-time, a superpixel segmentation algorithm based on image density is proposed in this paper. Firstly, the image is uniformly divided according to the number of superpixels to be obtained. Secondly, to get the clustering ability of the pixels, the density image is produced. Thirdly, the seed is chosen in each sub-region block according to the density and then the superpixels are obtained by clustering. During the clustering process, the pixel around the seed should be added into the superpixel if it meets the conditions, and the small supeipixels are merged into the big superpixels around them. Finally, the result shows that the proposed algorithm has the best segmentation effect, and a good balance in accuracy, regularity, and time cost. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
43. Optimization of Linear Quantization for General and Effective Low Bit-Width Network Compression.
- Author
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Yang, Wenxin, Zhi, Xiaoli, and Tong, Weiqin
- Subjects
PARTICLE swarm optimization ,K-means clustering ,ENERGY consumption - Abstract
Current edge devices for neural networks such as FPGA, CPLD, and ASIC can support low bit-width computing to improve the execution latency and energy efficiency, but traditional linear quantization can only maintain the inference accuracy of neural networks at a bit-width above 6 bits. Different from previous studies that address this problem by clipping the outliers, this paper proposes a two-stage quantization method. Before converting the weights into fixed-point numbers, this paper first prunes the network by unstructured pruning and then uses the K-means algorithm to cluster the weights in advance to protect the distribution of the weights. To solve the instability problem of the K-means results, the PSO (particle swarm optimization) algorithm is exploited to obtain the initial cluster centroids. The experimental results on baseline deep networks such as ResNet-50, Inception-v3, and DenseNet-121 show the proposed optimized quantization method can generate a 5-bit network with an accuracy loss of less than 5% and a 4-bit network with only 10% accuracy loss as compared to 8-bit quantization. By quantization and pruning, this method reduces the model bit-width from 32 to 4 and the number of neurons by 80%. Additionally, it can be easily integrated into frameworks such as TensorRt and TensorFlow-Lite for low bit-width network quantization. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
44. A Weighting Model of Cybersecurity Parameters Used for Service Placement.
- Author
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Gashi, Luan, Luma, Artan, Apostolova, Marika, and Januzaj, Ylber
- Subjects
INTERNET security ,PYTHON programming language - Abstract
Most cybersecurity frameworks are based on three major components such as confidentiality, integrity, and availability. All these components have their parameters that are used to secure network nodes. But finding the most cyber secure node in a network needs a measurement method. The aim of the paper is to offer a model that can be used to find the most secure network nodes considering these cybersecurity components and their parameters. This is achieved by modelling numeric values of respective weights for parameters of confidentiality, integrity, and availability. The model is applied to a simulated environment where random values standing for cybersecurity parameters are given to 30 wireless network nodes that are used as an example. Then the weighted values are processed with Python programming language by giving the most secure nodes according to needed cybersecurity components. This model can be used to recommend the right network node that can be used to deploy services securely while avoiding potential vulnerabilities and cyber-attacks. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
45. MEASURING THE EFFECT OF ENTRY INTO THE EUROZONE ON ECONOMIC GROWTH -- DATA STORYTELLING USING CLUSTERING AND ANFIS.
- Author
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BOSNA, Jurica, BRLEČIĆ VALČIĆ, Sonja, and PEŠA, Anita
- Subjects
RATINGS & rankings of public debts ,EUROZONE ,ECONOMIC expansion ,MONETARY unions ,ECONOMIC forecasting ,INTERNATIONAL economic integration - Abstract
The aim of this paper is to examine the impact of a country's entry into the monetary union on its economic variables growth rate of real GDP as well as on GDP per capita growth for the period from 2010 to 2020. The clustering method and the ANFIS method were used in the data analysis. A total of two cluster analyses were performed. The first cluster includes countries that joined the EU in 2004 and became EZ members by 2010. The second cluster refers to those countries that joined the EU in 2004 but are not yet members of the EZ. For the first individual cluster analysis two models were analysed and for the second individual cluster three models were analysed using the ANFIS method. As expected, the results showed that GDP growth is connected with trade, inflation and gross investments in fixed capital in the observed countries, while GDP per capita is connected with unemployment, interest rates and public debt. With regard to GDP growth, the difference between countries that are in the eurozone and those that are not is not significant, which is in line with other studies. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
46. Automatic Group Decision-Making for Algal Bloom Management Based on Information Self-Learning.
- Author
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Bai, Yuting, Niu, Yijie, Zhao, Zhiyao, Jin, Xuebo, and Wang, Xiaoyi
- Subjects
GROUP decision making ,ALGAL blooms ,SUSTAINABILITY ,AUTODIDACTICISM ,WATER quality monitoring ,POTAMOGETON ,MICROCYSTIS - Abstract
The phenomenon of algal bloom seriously affects the function of the aquatic ecosystems, damages the landscape of urban river and lakes, and threatens the safety of water use. The introduction of a multi-attribute decision-making method avoids the shortcomings of traditional algal bloom management that relies on manual experience. However, the weight-calculation part of this method still receives the artificial influence of human factors, which reduces the accuracy and scientific rigor of the decision. This paper presents a group decision-making method based on information self-learning which makes decision weights automatically clustered and assigned. A general framework of decision-making management is constructed for the algal bloom management process. In the decision-making process, an improved density-based clustering algorithm is used to automatically cluster and rank the decision data in the form of the three-parameter interval number, and ultimately obtain the optimal management method that meets the management objectives. Finally, the method was applied at the monitoring station of Sanjiadian Reservoir in Beijing, China. Based on the treatment objectives and water quality monitoring data of the station, relevant experts were invited to evaluate the management solutions, and the information self-organizing algorithm of this paper was used to automatically rank the decision-making methods, and finally obtain the most suitable management method for the station. Comparison with the water quality data and treatment inputs after the previous man-made selection of treatment options, and discussion among experts, show that the decision-making method is feasible and effective, and contributes to the sustainable treatment of algal blooms. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
47. Path-Based Clustering Algorithm with High Scalability Using the Combined Behavior of Evolutionary Algorithms.
- Author
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Safari-Monjeghtapeh, Leila and Esmaeilpour, Mansour
- Subjects
COMPUTER algorithms ,CLUSTER analysis (Statistics) ,GLOBAL optimization ,SPANNING trees ,CHEBYSHEV approximation ,METAHEURISTIC algorithms ,GENETIC algorithms ,PARTICLE swarm optimization - Abstract
Path-based clustering algorithms typically generate clusters by optimizing a benchmark function. Most optimization methods in clustering algorithms often offer solutions close to the general optimal value. This study achieves the global optimum value for the criterion function in a shorter time using the minimax distance, Maximum Spanning Tree "MST", and meta-heuristic algorithms, including Genetic Algorithm "GA" and Particle Swarm Optimization "PSO". The Fast Path-based Clustering "FPC" algorithm proposed in this paper can find cluster centers correctly in most datasets and quickly perform clustering operations. The FPC does this operation using MST, the minimax distance, and a new hybrid meta-heuristic algorithm in a few rounds of algorithm iterations. This algorithm can achieve the global optimal value, and the main clustering process of the algorithm has a computational complexity of O (k² × n). However, due to the complexity of the minimum distance algorithm, the total computational complexity is O (n²). Experimental results of FPC on synthetic datasets with arbitrary shapes demonstrate that the algorithm is resistant to noise and outliers and can correctly identify clusters of varying sizes and numbers. In addition, the FPC requires the number of clusters as the only parameter to perform the clustering process. A comparative analysis of FPC and other clustering algorithms in this domain indicates that FPC exhibits superior speed, stability, and performance. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
48. Applying Density-Based Clustering for the Analysis of Emission Events in Real Driving Emissions Calibration.
- Author
-
Krysmon, Sascha, Pischinger, Stefan, Claßen, Johannes, Trendafilov, Georgi, Düzgün, Marc, Dorscheidt, Frank, Nijs, Martin, and Görgen, Michael
- Subjects
GREENHOUSE gases ,AIR pollutants ,TECHNOLOGICAL innovations ,DATA analysis ,CALIBRATION - Abstract
Further reducing greenhouse gas and pollutant emissions from road vehicles is a major task for the automotive industry. Stricter regulations regarding emissions and fleet fuel consumption require the continuous development of new powertrains and methods. In particular, the combination of hybrid powertrains on the technical side and the focus on real driving emissions (RDE) on the legislative side pose significant challenges to the vehicle calibration process. Against this background, new test methods and environments are being investigated to counteract the high number of interactions between hybrid drive systems and quasi-infinite test conditions due to RDE. Complementary to new test environments, innovative methods for data analysis are needed that allow the exploitation of the complete potential of measurement data. The application of such a method in the field of emission calibration is presented in this paper. For this purpose, a clustering method (HDBSCAN) is applied to critical sequences from emission tests. Within this presentation, the clustering process is based on a single signal only. This paper shows how signals of various characteristics can be processed with dynamic time warping and generically structured with the clustering method used. Here, 959 single events are automatically categorized into 24 clusters. This provides a new basis for system evaluation, enabling the automatic identification, categorization, and prioritization of calibration weaknesses. Using twelve signals of different characteristics, the generic usability of the clustering method is demonstrated. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
49. A Deep Diagnostic Framework Using Explainable Artificial Intelligence and Clustering.
- Author
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Thunold, Håvard Horgen, Riegler, Michael A., Yazidi, Anis, and Hammer, Hugo L.
- Subjects
ARTIFICIAL intelligence ,FEATURE extraction ,MACHINE learning ,DEEP learning ,IMAGE recognition (Computer vision) - Abstract
An important part of diagnostics is to gain insight into properties that characterize a disease. Machine learning has been used for this purpose, for instance, to identify biomarkers in genomics. However, when patient data are presented as images, identifying properties that characterize a disease becomes far more challenging. A common strategy involves extracting features from the images and analyzing their occurrence in healthy versus pathological images. A limitation of this approach is that the ability to gain new insights into the disease from the data is constrained by the information in the extracted features. Typically, these features are manually extracted by humans, which further limits the potential for new insights. To overcome these limitations, in this paper, we propose a novel framework that provides insights into diseases without relying on handcrafted features or human intervention. Our framework is based on deep learning (DL), explainable artificial intelligence (XAI), and clustering. DL is employed to learn deep patterns, enabling efficient differentiation between healthy and pathological images. Explainable artificial intelligence (XAI) visualizes these patterns, and a novel "explanation-weighted" clustering technique is introduced to gain an overview of these patterns across multiple patients. We applied the method to images from the gastrointestinal tract. In addition to real healthy images and real images of polyps, some of the images had synthetic shapes added to represent other types of pathologies than polyps. The results show that our proposed method was capable of organizing the images based on the reasons they were diagnosed as pathological, achieving high cluster quality and a rand index close to or equal to one. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
50. A New Type of Bipartite Random Graph as a Transform of Seismogram and Its Potential for Organizing Seismic Databases.
- Author
-
Babić, Ranko and Babić, Lidija
- Subjects
BIPARTITE graphs ,RANDOM graphs ,SEISMOGRAMS ,GRAPH algorithms ,DATABASES - Abstract
This paper proposes a method to reduce seismogram variability as a determining factor in its interpretation, processing, and clustering. By introducing the concept of single fluctuations (SFs), the seismogram can be parsed into a sequence of random impulses with subsequent ordering. This rearrangement of SFs, if they are assigned by positive integers, represents the formal mapping of a regular string of integers into a random one, which can be represented with a bipartite random graph (bigraph). Due to its specific randomness, such a bigraph is considered a new type of random balanced bigraph. The R-envelope and R
Q -envelope, its equidistant version, are defined by tracing the peak envelope over ordered SFs. The equivalence and complementariness of the RQ and bigraph are considered and discussed, forming a combined characteristic of the seismogram. The R/RQ provided a considerable reduction in seismogram variability, which was confirmed by creating and analyzing an ensemble of RQ from several seismograms. In the RQ domain, distance is defined as a possible basis for metrics and clustering, but the ensemble variability was quite narrow and not as suitable for this purpose. Otherwise, the ensemble shows high redundancy hidden in the seismogram population. As for the bigraph, the mesh of its edges is structuralized in bundles, forming a skeleton, which reflects the internal structural content of the seismogram. The distance over the domain of bigraphs is proposed to show the possibility of clustering. This means that only a combined RQ and bigraph provides a suitable frame for seismogram representation with reduced variability and, thus, the potential for more effectively organizing seismic databases and a deeper interpretation of seismograms; therefore, RQ s and bigraphs can be considered as a transform of a seismogram. Many aspects of these concepts are thoroughly discussed. The similarity between concepts of SF and wavelets is briefly considered. This very complex theme is new and promises broad further research. All issues considered in the paper are abundantly illustrated. [ABSTRACT FROM AUTHOR]- Published
- 2023
- Full Text
- View/download PDF
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