scholarly journals A Novel Autonomous Perceptron Model for Pattern Classification Applications

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 763 ◽  
Author(s):  
Alaa Sagheer ◽  
Mohammed Zidan ◽  
Mohammed M. Abdelsamea
Keyword(s):  
Pattern Classification ◽  
Data Science ◽  
Science Research ◽  
Efficient Solutions ◽  
Classification Model ◽  
Computational Time ◽  
Quantum Bit ◽  
Training Samples ◽  
Artificial Neural Network Ann ◽  
Definition Of

Pattern classification represents a challenging problem in machine learning and data science research domains, especially when there is a limited availability of training samples. In recent years, artificial neural network (ANN) algorithms have demonstrated astonishing performance when compared to traditional generative and discriminative classification algorithms. However, due to the complexity of classical ANN architectures, ANNs are sometimes incapable of providing efficient solutions when addressing complex distribution problems. Motivated by the mathematical definition of a quantum bit (qubit), we propose a novel autonomous perceptron model (APM) that can solve the problem of the architecture complexity of traditional ANNs. APM is a nonlinear classification model that has a simple and fixed architecture inspired by the computational superposition power of the qubit. The proposed perceptron is able to construct the activation operators autonomously after a limited number of iterations. Several experiments using various datasets are conducted, where all the empirical results show the superiority of the proposed model as a classifier in terms of accuracy and computational time when it is compared with baseline classification models.

scholarly journals Building an integrated enhanced virtual research environment metadata catalogue

2019 ◽  
Vol 37 (6) ◽  
pp. 929-951 ◽  
Author(s):  
Laurent Remy ◽  
Dragan Ivanović ◽  
Maria Theodoridou ◽  
Athina Kritsotaki ◽  
Paul Martin ◽  
...  
Keyword(s):  
Data Model ◽  
Data Science ◽  
Science Research ◽  
Dublin Core ◽  
Content Type ◽  
Knowledge Based ◽  
Novel Approach ◽  
Virtual Research Environment ◽  
Definition Of ◽  
Practical Implications

Purpose The purpose of this paper is to boost multidisciplinary research by the building of an integrated catalogue or research assets metadata. Such an integrated catalogue should enable researchers to solve problems or analyse phenomena that require a view across several scientific domains. Design/methodology/approach There are two main approaches for integrating metadata catalogues provided by different e-science research infrastructures (e-RIs): centralised and distributed. The authors decided to implement a central metadata catalogue that describes, provides access to and records actions on the assets of a number of e-RIs participating in the system. The authors chose the CERIF data model for description of assets available via the integrated catalogue. Analysis of popular metadata formats used in e-RIs has been conducted, and mappings between popular formats and the CERIF data model have been defined using an XML-based tool for description and automatic execution of mappings. Findings An integrated catalogue of research assets metadata has been created. Metadata from e-RIs supporting Dublin Core, ISO 19139, DCAT-AP, EPOS-DCAT-AP, OIL-E and CKAN formats can be integrated into the catalogue. Metadata are stored in CERIF RDF in the integrated catalogue. A web portal for searching this catalogue has been implemented. Research limitations/implications Only five formats are supported at this moment. However, description of mappings between other source formats and the target CERIF format can be defined in the future using the 3M tool, an XML-based tool for describing X3ML mappings that can then be automatically executed on XML metadata records. The approach and best practices described in this paper can thus be applied in future mappings between other metadata formats. Practical implications The integrated catalogue is a part of the eVRE prototype, which is a result of the VRE4EIC H2020 project. Social implications The integrated catalogue should boost the performance of multi-disciplinary research; thus it has the potential to enhance the practice of data science and so contribute to an increasingly knowledge-based society. Originality/value A novel approach for creation of the integrated catalogue has been defined and implemented. The approach includes definition of mappings between various formats. Defined mappings are effective and shareable.

scholarly journals An Outline of the Basic Theory of Accident Science based on the Safety Science Methodology in Risk Process

2020 ◽  
Author(s):  
Tongyuan Luo ◽  
Chao Wu
Keyword(s):  
Hierarchical Classification ◽  
Science Research ◽  
Risk Process ◽  
Classification Model ◽  
Research Results ◽  
Existing Problems ◽  
Safety Science ◽  
Definition Of ◽  
Science Methodology ◽  
Relationship Of

In order to establish a new discipline specializing in accident science from the perspective of safety science. Under the guidance of the current research theories and methods of safety science, combined with the research paradigm of humanities and social medicine, this paper puts forward new viewpoints, new theories and new models about accident research. First of all, through literature retrieval, this paper analyzes the relevant research results of accidents at home and abroad, and expounds the existing problems and the basic trend of accident science research. Secondly, it puts forward eight kinds of attribute relations of the accident, and makes clear the characteristics and connotation of the accident. In the study of accident types, a hierarchical classification model based on accident cognition is created for the first time. It also points out the logical relevance of five levels of accident science research and the realistic relevance of three levels. At the same time, according to the thought of science of science, this paper puts forward a new definition of safety under the thinking of accident science and other basic concepts related to safety science, and explains the connotation. In addition, it creates and constructs the basic concept of accident science, establishes the conceptual model of accident science, and points out the “3-4-5” model of accident science research and its connotation. Thirdly, draw lessons from the interdisciplinary paradigm to study the relevant theoretical basis and discipline classification relationship of accident science, and construct the tree of accident science. Finally, the research contents of three main aspects of accident science are summarized. The results show that the research results in this paper not only play a fundamental role in the basic construction of accident science, but also further enrich and perfect the discipline system of safety science, which has a certain theoretical significance.

scholarly journals Implementation of deep neural networks (DNN) with batch normalization for batik pattern recognition

2020 ◽  
Vol 10 (2) ◽  
pp. 2045
Author(s):  
Ida Nurhaida ◽  
Vina Ayumi ◽  
Devi Fitrianah ◽  
Remmy A. M. Zen ◽  
Handrie Noprisson ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Time Complexity ◽  
Science Research ◽  
Research Area ◽  
Feature Reduction ◽  
Classification Model ◽  
Computational Time ◽  
Computer Science Research ◽  
New Research ◽  
Improved Accuracy

One of the most famous cultural heritages in Indonesia is batik. Batik is a specially made drawing cloth by writing Malam (wax) on the cloth, then processed in a certain way. The diversity of motifs both in Indonesia and the allied countries raises new research topics in the field of information technology, both for conservation, storage, publication and the creation of new batik motifs. In computer science research area, studies about Batik pattern have been done by researchers and some algorithms have been successfully applied in Batik pattern recognition. This study was focused on Batik motif recognition using texture fusion feature which is Gabor, Log-Gabor, and GLCM; and using PCA feature reduction to improve the classification accuracy and reduce the computational time. To improve the accuracy, we proposed a Deep Neural Network model to recognise batik pattern and used batch normalisation as a regularises to generalise the model and to reduce time complexity. From the experiments, the feature extraction, selection, and reduction gave better accuracy than the raw dataset. The feature selection and reduction also reduce time complexity. The DNN+BN significantly improve the accuracy of the classification model from 65.36% to 83.15%. BN as a regularization has successfully made the model more general, hence improve the accuracy of the model. The parameters tuning also improved accuracy from 83.15% to 85.57%.

Context in Action and How to Study It

2019 ◽  
Keyword(s):  
Social Science Research ◽  
Science Research ◽  
Research Process ◽  
Care Patient ◽  
Organizational Life ◽  
Integration Of Care ◽  
Research Experiences ◽  
Organization And Management ◽  
Care Outcomes ◽  
Definition Of

The concept of context is a cornerstone of a large part of social science research, particularly in organization and management studies, yet it has received little theoretical and methodological attention in lieu of its relevance. This book offers a definition of context as a theoretical construct, a discussion of the methodological implications of this, and a framework for how to reflect upon and operationalize the role of context in the different stages of a research process, from formulating research questions to analyzing and writing about results. The chapters presented here integrate lessons derived from various research experiences across the complex and dynamic field of health care. Contributors share their experiences with theorizing about and empirically studying significant organizational phenomena such as implementation of policy, organizational change, integration of care, patient involvement, human-technology interactions in practice, and the interplay between work environment and care outcomes in eldercare. These contributions exemplify how a nuanced approach to context might unfold in different fields, through different designs, methods, and analytical lenses. Relevant to researchers and practitioners, within both healthcare, organization and management studies, and the social sciences more broadly, this book leaves the reader with a practical framework from which to carry out contextual research and analysis and a gain deeper understanding of the significance of context in organizational life.

scholarly journals Wineinformatics: Using the Full Power of the Computational Wine Wheel to Understand 21st Century Bordeaux Wines from the Reviews

Beverages ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. 3
Author(s):  
Zeqing Dong ◽  
Travis Atkison ◽  
Bernard Chen
Keyword(s):  
21St Century ◽  
Data Science ◽  
Naive Bayes ◽  
Science Research ◽  
Naïve Bayes ◽  
Classification Algorithm ◽  
Wine Quality ◽  
Research Results ◽  
Full Power ◽  
Wine Region

Although wine has been produced for several thousands of years, the ancient beverage has remained popular and even more affordable in modern times. Among all wine making regions, Bordeaux, France is probably one of the most prestigious wine areas in history. Since hundreds of wines are produced from Bordeaux each year, humans are not likely to be able to examine all wines across multiple vintages to define the characteristics of outstanding 21st century Bordeaux wines. Wineinformatics is a newly proposed data science research with an application domain in wine to process a large amount of wine data through the computer. The goal of this paper is to build a high-quality computational model on wine reviews processed by the full power of the Computational Wine Wheel to understand 21st century Bordeaux wines. On top of 985 binary-attributes generated from the Computational Wine Wheel in our previous research, we try to add additional attributes by utilizing a CATEGORY and SUBCATEGORY for an additional 14 and 34 continuous-attributes to be included in the All Bordeaux (14,349 wine) and the 1855 Bordeaux datasets (1359 wines). We believe successfully merging the original binary-attributes and the new continuous-attributes can provide more insights for Naïve Bayes and Supported Vector Machine (SVM) to build the model for a wine grade category prediction. The experimental results suggest that, for the All Bordeaux dataset, with the additional 14 attributes retrieved from CATEGORY, the Naïve Bayes classification algorithm was able to outperform the existing research results by increasing accuracy by 2.15%, precision by 8.72%, and the F-score by 1.48%. For the 1855 Bordeaux dataset, with the additional attributes retrieved from the CATEGORY and SUBCATEGORY, the SVM classification algorithm was able to outperform the existing research results by increasing accuracy by 5%, precision by 2.85%, recall by 5.56%, and the F-score by 4.07%. The improvements demonstrated in the research show that attributes retrieved from the CATEGORY and SUBCATEGORY has the power to provide more information to classifiers for superior model generation. The model build in this research can better distinguish outstanding and class 21st century Bordeaux wines. This paper provides new directions in Wineinformatics for technical research in data science, such as regression, multi-target, classification and domain specific research, including wine region terroir analysis, wine quality prediction, and weather impact examination.

scholarly journals Has the Credibility of the Social Sciences Been Credibly Destroyed? Reanalyzing the “Many Analysts, One Data Set” Project

2021 ◽  
Vol 7 ◽  
pp. 237802312110244
Author(s):  
Katrin Auspurg ◽  
Josef Brüderl
Keyword(s):  
Social Sciences ◽  
Causal Reasoning ◽  
Research Question ◽  
Social Science Research ◽  
Science Research ◽  
Skin Tone ◽  
Data Set ◽  
The Social ◽  
The Many ◽  
Definition Of

In 2018, Silberzahn, Uhlmann, Nosek, and colleagues published an article in which 29 teams analyzed the same research question with the same data: Are soccer referees more likely to give red cards to players with dark skin tone than light skin tone? The results obtained by the teams differed extensively. Many concluded from this widely noted exercise that the social sciences are not rigorous enough to provide definitive answers. In this article, we investigate why results diverged so much. We argue that the main reason was an unclear research question: Teams differed in their interpretation of the research question and therefore used diverse research designs and model specifications. We show by reanalyzing the data that with a clear research question, a precise definition of the parameter of interest, and theory-guided causal reasoning, results vary only within a narrow range. The broad conclusion of our reanalysis is that social science research needs to be more precise in its “estimands” to become credible.

scholarly journals A New Epigenetic Model to Stratify Glioma Patients According to Their Immunosuppressive State

Cells ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 576
Author(s):  
Maurizio Polano ◽  
Emanuele Fabbiani ◽  
Eva Adreuzzi ◽  
Federica Di Cintio ◽  
Luca Bedon ◽  
...  
Keyword(s):  
The Cancer Genome Atlas ◽  
Classification Model ◽  
Low Grade ◽  
Machine Learning Classification ◽  
Immune State ◽  
Out Of Sample ◽  
Cancer Genome Atlas ◽  
The Central Nervous System ◽  
Neural Network Classifiers ◽  
Definition Of

Gliomas are the most common primary neoplasm of the central nervous system. A promising frontier in the definition of glioma prognosis and treatment is represented by epigenetics. Furthermore, in this study, we developed a machine learning classification model based on epigenetic data (CpG probes) to separate patients according to their state of immunosuppression. We considered 573 cases of low-grade glioma (LGG) and glioblastoma (GBM) from The Cancer Genome Atlas (TCGA). First, from gene expression data, we derived a novel binary indicator to flag patients with a favorable immune state. Then, based on previous studies, we selected the genes related to the immune state of tumor microenvironment. After, we improved the selection with a data-driven procedure, based on Boruta. Finally, we tuned, trained, and evaluated both random forest and neural network classifiers on the resulting dataset. We found that a multi-layer perceptron network fed by the 338 probes selected by applying both expert choice and Boruta results in the best performance, achieving an out-of-sample accuracy of 82.8%, a Matthews correlation coefficient of 0.657, and an area under the ROC curve of 0.9. Based on the proposed model, we provided a method to stratify glioma patients according to their epigenomic state.

scholarly journals Energy Minimization Scheme for Split Potential Systems Using Exponential Variational Integrators

2021 ◽  
Vol 2 (3) ◽  
pp. 431-441
Author(s):  
Odysseas Kosmas
Keyword(s):  
Numerical Solutions ◽  
Oscillatory Behavior ◽  
Lagrangian Function ◽  
Computational Time ◽  
Variational Integrators ◽  
Weighted Sum ◽  
Lagrange Equations ◽  
Exponential Functions ◽  
Intermediate Time ◽  
Definition Of

In previous works we developed a methodology of deriving variational integrators to provide numerical solutions of systems having oscillatory behavior. These schemes use exponential functions to approximate the intermediate configurations and velocities, which are then placed into the discrete Lagrangian function characterizing the physical system. We afterwards proved that, higher order schemes can be obtained through the corresponding discrete Euler–Lagrange equations and the definition of a weighted sum of “continuous intermediate Lagrangians” each of them evaluated at an intermediate time node. In the present article, we extend these methods so as to include Lagrangians of split potential systems, namely, to address cases when the potential function can be decomposed into several components. Rather than using many intermediate points for the complete Lagrangian, in this work we introduce different numbers of intermediate points, resulting within the context of various reliable quadrature rules, for the various potentials. Finally, we assess the accuracy, convergence and computational time of the proposed technique by testing and comparing them with well known standards.

scholarly journals Clustering in Wineinformatics with Attribute Selection to Increase Uniqueness of Clusters

Fermentation ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. 27
Author(s):  
Jared McCune ◽  
Alex Riley ◽  
Bernard Chen
Keyword(s):  
Unsupervised Learning ◽  
Data Science ◽  
Science Research ◽  
Research Area ◽  
Attribute Selection ◽  
Short List ◽  
Wine Quality ◽  
Filtration Process ◽  
Target Region ◽  
Related Data

Wineinformatics is a new data science research area that focuses on large amounts of wine-related data. Most of the current Wineinformatics researches are focused on supervised learning to predict the wine quality, price, region and weather. In this research, unsupervised learning using K-means clustering with optimal K search and filtration process is studied on a Bordeaux-region specific dataset to form clusters and find representative wines in each cluster. 14,349 wines representing the 21st century Bordeaux dataset are clustered into 43 and 13 clusters with detailed analysis on the number of wines, dominant wine characteristics, average wine grades, and representative wines in each cluster. Similar research results are also generated and presented on 435 elite wines (wines that scored 95 points and above on a 100 points scale). The information generated from this research can be beneficial to wine vendors to make a selection given the limited number of wines they can realistically offer, to connoisseurs to study wines in a target region/vintage/price with a representative short list, and to wine consumers to get recommendations. Many possible researches can adopt the same process to analyze and find representative wines in different wine making regions/countries, vintages, or pivot points. This paper opens up a new door for Wineinformatics in unsupervised learning researches.

Data-Driven Method for the Prediction of Estimated Time of Arrival

2021 ◽  
pp. 036119812110332
Author(s):  
Xuhao Gui ◽  
Junfeng Zhang ◽  
Zihan Peng ◽  
Chunwei Yang
Keyword(s):  
Pattern Classification ◽  
Trajectory Optimization ◽  
Time Estimation ◽  
Flight Time ◽  
Classification Model ◽  
Time Of Arrival ◽  
Trajectory Clustering ◽  
Clustering Method ◽  
Arrival Pattern ◽  
Multiple Stages

Predicting the estimated time of arrival (ETA) plays an essential role in decision support (conflict detection, arrival sequencing, or trajectory optimization) for air traffic controllers. In this paper, a new multiple stages strategy for ETA prediction is proposed based on radar trajectories, including arrival pattern identification, arrival pattern classification, and flight time estimation. First, an intention-oriented trajectory clustering method is developed based on a new trajectory representation technique. Such a proposed trajectory clustering method can group trajectories into different arrival patterns in an efficient way. Second, an arrival pattern classification model is constructed based on random forest and XGBoost algorithms. Then, a flight time regression model is trained for each arrival pattern by using the XGBoost algorithm. Information on current states, historical states, and traffic situations is considered to build the feature set during these processes. Finally, the arrival operation toward Guangzhou International Airport is chosen as a case study. The results illustrate that the proposed method and feature engineering approach could improve the performance of ETA prediction. The proposed multiple stages strategy is superior to the single-model-based ETA prediction.

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