scholarly journals Calabi-Yau Spaces in the String Landscape

2020 ◽  
Author(s):  
Yang-Hui He
Keyword(s):  
Machine Learning ◽  
Computer Science ◽  
Data Science ◽  
Theoretical Physics ◽  
Superstring Theory ◽  
Data Set ◽  
Pure Mathematics ◽  
String Landscape ◽  
Natural Solution ◽  
Vacuum Solutions

Calabi-Yau spaces, or Kähler spaces admitting zero Ricci curvature, have played a pivotal role in theoretical physics and pure mathematics for the last half century. In physics, they constituted the first and natural solution to compactification of superstring theory to our 4-dimensional universe, primarily due to one of their equivalent definitions being the admittance of covariantly constant spinors. Since the mid-1980s, physicists and mathematicians have joined forces in creating explicit examples of Calabi-Yau spaces, compiling databases of formidable size, including the complete intersecion (CICY) data set, the weighted hypersurfaces data set, the elliptic-fibration data set, the Kreuzer-Skarke toric hypersurface data set, generalized CICYs, etc., totaling at least on the order of 1010 manifolds. These all contribute to the vast string landscape, the multitude of possible vacuum solutions to string compactification. More recently, this collaboration has been enriched by computer science and data science, the former in bench-marking the complexity of the algorithms in computing geometric quantities, and the latter in applying techniques such as machine learning in extracting unexpected information. These endeavours, inspired by the physics of the string landscape, have rendered the investigation of Calabi-Yau spaces one of the most exciting and interdisciplinary fields.

Comparative Analysis of Machine Learning Techniques Using Predictive Modeling

2020 ◽  
Vol 13 ◽  
Author(s):  
Ritu Khandelwal ◽  
Hemlata Goyal ◽  
Rajveer Singh Shekhawat
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Data Science ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Future Trends ◽  
Data Set ◽  
Learning Stage ◽  
Learning Techniques ◽  
Different Types

Introduction: Machine learning is an intelligent technology that works as a bridge between businesses and data science. With the involvement of data science, the business goal focuses on findings to get valuable insights on available data. The large part of Indian Cinema is Bollywood which is a multi-million dollar industry. This paper attempts to predict whether the upcoming Bollywood Movie would be Blockbuster, Superhit, Hit, Average or Flop. For this Machine Learning techniques (classification and prediction) will be applied. To make classifier or prediction model first step is the learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations. Methods: All the techniques related to classification and Prediction such as Support Vector Machine(SVM), Random Forest, Decision Tree, Naïve Bayes, Logistic Regression, Adaboost, and KNN will be applied and try to find out efficient and effective results. All these functionalities can be applied with GUI Based workflows available with various categories such as data, Visualize, Model, and Evaluate. Result: To make classifier or prediction model first step is learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations Conclusion: This paper focuses on Comparative Analysis that would be performed based on different parameters such as Accuracy, Confusion Matrix to identify the best possible model for predicting the movie Success. By using Advertisement Propaganda, they can plan for the best time to release the movie according to the predicted success rate to gain higher benefits. Discussion: Data Mining is the process of discovering different patterns from large data sets and from that various relationships are also discovered to solve various problems that come in business and helps to predict the forthcoming trends. This Prediction can help Production Houses for Advertisement Propaganda and also they can plan their costs and by assuring these factors they can make the movie more profitable.

scholarly journals Advancing data science in drug development through an innovative computational framework for data sharing and statistical analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ann-Marie Mallon ◽  
Dieter A. Häring ◽  
Frank Dahlke ◽  
Piet Aarden ◽  
Soroosh Afyouni ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Trial ◽  
Drug Development ◽  
Phase Ii ◽  
Data Science ◽  
Clinical Trial Data ◽  
Trial Data ◽  
Computational Framework ◽  
Data Set ◽  
Collaborative Development

Abstract Background Novartis and the University of Oxford’s Big Data Institute (BDI) have established a research alliance with the aim to improve health care and drug development by making it more efficient and targeted. Using a combination of the latest statistical machine learning technology with an innovative IT platform developed to manage large volumes of anonymised data from numerous data sources and types we plan to identify novel patterns with clinical relevance which cannot be detected by humans alone to identify phenotypes and early predictors of patient disease activity and progression. Method The collaboration focuses on highly complex autoimmune diseases and develops a computational framework to assemble a research-ready dataset across numerous modalities. For the Multiple Sclerosis (MS) project, the collaboration has anonymised and integrated phase II to phase IV clinical and imaging trial data from ≈35,000 patients across all clinical phenotypes and collected in more than 2200 centres worldwide. For the “IL-17” project, the collaboration has anonymised and integrated clinical and imaging data from over 30 phase II and III Cosentyx clinical trials including more than 15,000 patients, suffering from four autoimmune disorders (Psoriasis, Axial Spondyloarthritis, Psoriatic arthritis (PsA) and Rheumatoid arthritis (RA)). Results A fundamental component of successful data analysis and the collaborative development of novel machine learning methods on these rich data sets has been the construction of a research informatics framework that can capture the data at regular intervals where images could be anonymised and integrated with the de-identified clinical data, quality controlled and compiled into a research-ready relational database which would then be available to multi-disciplinary analysts. The collaborative development from a group of software developers, data wranglers, statisticians, clinicians, and domain scientists across both organisations has been key. This framework is innovative, as it facilitates collaborative data management and makes a complicated clinical trial data set from a pharmaceutical company available to academic researchers who become associated with the project. Conclusions An informatics framework has been developed to capture clinical trial data into a pipeline of anonymisation, quality control, data exploration, and subsequent integration into a database. Establishing this framework has been integral to the development of analytical tools.

scholarly journals TOPIC SEGMENTATION METHODS COMPARISON ON COMPUTER SCIENCE TEXTS

2021 ◽  
pp. 59-66
Author(s):  
Volodymyr Sokol ◽  
Vitalii Krykun ◽  
Mariia Bilova ◽  
Ivan Perepelytsya ◽  
Volodymyr Pustovarov ◽  
...  
Keyword(s):  
Information Systems ◽  
Computer Science ◽  
Management System ◽  
Data Science ◽  
Continuous Training ◽  
Data Set ◽  
Topic Segmentation ◽  
Science Texts ◽  
Segmentation Methods ◽  
Segmentation Problem

The demand for the creation of information systems that simplifies and accelerates work has greatly increased in the context of the rapidinformatization of society and all its branches. It provokes the emergence of more and more companies involved in the development of softwareproducts and information systems in general. In order to ensure the systematization, processing and use of this knowledge, knowledge managementsystems are used. One of the main tasks of IT companies is continuous training of personnel. This requires export of the content from the company'sknowledge management system to the learning management system. The main goal of the research is to choose an algorithm that allows solving theproblem of marking up the text of articles close to those used in knowledge management systems of IT companies. To achieve this goal, it is necessaryto compare various topic segmentation methods on a dataset with a computer science texts. Inspec is one such dataset used for keyword extraction andin this research it has been adapted to the structure of the datasets used for the topic segmentation problem. The TextTiling and TextSeg methods wereused for comparison on some well-known data science metrics and specific metrics that relate to the topic segmentation problem. A new generalizedmetric was also introduced to compare the results for the topic segmentation problem. All software implementations of the algorithms were written inPython programming language and represent a set of interrelated functions. Results were obtained showing the advantages of the Text Seg method incomparison with TextTiling when compared using classical data science metrics and special metrics developed for the topic segmentation task. Fromall the metrics, including the introduced one it can be concluded that the TextSeg algorithm performs better than the TextTiling algorithm on theadapted Inspec test data set.

Application of Machine Learning Techniques As a Means of Mooring Integrity Monitoring

2019 ◽  
Author(s):  
Jonathan M. Gumley ◽  
Hayden Marcollo ◽  
Stuart Wales ◽  
Andrew E. Potts ◽  
Christopher J. Carra
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Single Point ◽  
Original System ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Mooring Line ◽  
Artificial Noise ◽  
Data Set ◽  
Learning Techniques

Abstract There is growing importance in the offshore floating production sector to develop reliable and robust means of continuously monitoring the integrity of mooring systems for FPSOs and FPUs, particularly in light of the upcoming introduction of API-RP-2MIM. Here, the limitations of the current range of monitoring techniques are discussed, including well established technologies such as load cells, sonar, or visual inspection, within the context of the growing mainstream acceptance of data science and machine learning. Due to the large fleet of floating production platforms currently in service, there is a need for a readily deployable solution that can be retrofitted to existing platforms to passively monitor the performance of floating assets on their moorings, for which machine learning based systems have particular advantages. An earlier investigation conducted in 2016 on a shallow water, single point moored FPSO employed host facility data from in-service field measurements before and after a single mooring line failure event. This paper presents how the same machine learning techniques were applied to a deep water, semi taut, spread moored system where there was no host facility data available, therefore requiring a calibrated hydrodynamic numerical model to be used as the basis for the training data set. The machine learning techniques applied to both real and synthetically generated data were successful in replicating the response of the original system, even with the latter subjected to different variations of artificial noise. Furthermore, utilizing a probability-based approach, it was demonstrated that replicating the response of the underlying system was a powerful technique for predicting changes in the mooring system.

Data Science: A New Paradigm in the Age of Big-Data Science and Analytics

2017 ◽  
Vol 13 (02) ◽  
pp. 119-143 ◽  
Author(s):  
Claude E. Concolato ◽  
Li M. Chen
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Big Data ◽  
Computer Science ◽  
Data Science ◽  
Big Data Analytics ◽  
Modern World ◽  
Growth Data ◽  
New Paradigm ◽  
Great Opportunity

As an emergent field of inquiry, Data Science serves both the information technology world and the applied sciences. Data Science is a known term that tends to be synonymous with the term Big-Data; however, Data Science is the application of solutions found through mathematical and computational research while Big-Data Science describes problems concerning the analysis of data with respect to volume, variation, and velocity (3V). Even though there is not much developed in theory from a scientific perspective for Data Science, there is still great opportunity for tremendous growth. Data Science is proving to be of paramount importance to the IT industry due to the increased need for understanding the insurmountable amount of data being produced and in need of analysis. In short, data is everywhere with various formats. Scientists are currently using statistical and AI analysis techniques like machine learning methods to understand massive sets of data, and naturally, they attempt to find relationships among datasets. In the past 10 years, the development of software systems within the cloud computing paradigm using tools like Hadoop and Apache Spark have aided in making tremendous advances to Data Science as a discipline [Z. Sun, L. Sun and K. Strang, Big data analytics services for enhancing business intelligence, Journal of Computer Information Systems (2016), doi: 10.1080/08874417.2016.1220239]. These advances enabled both scientists and IT professionals to use cloud computing infrastructure to process petabytes of data on daily basis. This is especially true for large private companies such as Walmart, Nvidia, and Google. This paper seeks to address pragmatic ways of looking at how Data Science — with respect to Big-Data Science — is practiced in the modern world. We also examine how mathematics and computer science help shape Big-Data Science’s terrain. We will highlight how mathematics and computer science have significantly impacted the development of Data Science approaches, tools, and how those approaches pose new questions that can drive new research areas within these core disciplines involving data analysis, machine learning, and visualization.

scholarly journals Exploring the Efficiency of Various Supervised Machine Learning Techniques to Predict the Heart Disease using Risk Factors

2019 ◽  
Vol 9 (1S) ◽  
pp. 309-312
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Heart Disease ◽  
Major Part ◽  
Data Science ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Data Set

Data Science in healthcare is a innovative and capable for industry implementing the data science applications. Data analytics is recent science in to discover the medical data set to explore and discover the disease. It’s a beginning attempt to identify the disease with the help of large amount of medical dataset. Using this data science methodology, it makes the user to find their disease without the help of health care centres. Healthcare and data science are often linked through finances as the industry attempts to reduce its expenses with the help of large amounts of data. Data science and medicine are rapidly developing, and it is important that they advance together. Health care information is very effective in the society. In a human life day to day heart disease had increased. Based on the heart disease to monitor different factors in human body to analyse and prevent the heart disease. To classify the factors using the machine learning algorithms and to predict the disease is major part. Major part of involves machine level based supervised learning algorithm such as SVM, Naviebayes, Decision Trees and Random forest.

scholarly journals Earthquake Prediction using Machine Learning Algorithm

2020 ◽  
Vol 8 (6) ◽  
pp. 4684-4688
Keyword(s):  
Machine Learning ◽  
Structural Damage ◽  
Data Science ◽  
Learning Algorithm ◽  
Economic Loss ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Support Vector ◽  
Science Data ◽  
Data Set

Per the statistics received from BBC, data varies for every earthquake occurred till date. Approximately, up to thousands are dead, about 50,000 are injured, around 1-3 Million are dislocated, while a significant amount go missing and homeless. Almost 100% structural damage is experienced. It also affects the economic loss, varying from 10 to 16 million dollars. A magnitude corresponding to 5 and above is classified as deadliest. The most life-threatening earthquake occurred till date took place in Indonesia where about 3 million were dead, 1-2 million were injured and the structural damage accounted to 100%. Hence, the consequences of earthquake are devastating and are not limited to loss and damage of living as well as nonliving, but it also causes significant amount of change-from surrounding and lifestyle to economic. Every such parameter desiderates into forecasting earthquake. A couple of minutes’ notice and individuals can act to shield themselves from damage and demise; can decrease harm and monetary misfortunes, and property, characteristic assets can be secured. In current scenario, an accurate forecaster is designed and developed, a system that will forecast the catastrophe. It focuses on detecting early signs of earthquake by using machine learning algorithms. System is entitled to basic steps of developing learning systems along with life cycle of data science. Data-sets for Indian sub-continental along with rest of the World are collected from government sources. Pre-processing of data is followed by construction of stacking model that combines Random Forest and Support Vector Machine Algorithms. Algorithms develop this mathematical model reliant on “training data-set”. Model looks for pattern that leads to catastrophe and adapt to it in its building, so as to settle on choices and forecasts without being expressly customized to play out the task. After forecast, we broadcast the message to government officials and across various platforms. The focus of information to obtain is keenly represented by the 3 factors – Time, Locality and Magnitude.

scholarly journals USE OF THE FLIPPED CLASSROOM TO DESIGN CREATIVE AND ACTIVE ACTIVITIES IN THE FIELD OF COMPUTER SCIENCE

2020 ◽  
Vol 13 (1) ◽  
pp. 136-151
Author(s):  
Ricardo Adán Salas Rueda
Keyword(s):  
Machine Learning ◽  
Computer Science ◽  
Quantitative Research ◽  
Flipped Classroom ◽  
Data Science ◽  
Collaborative Work ◽  
Educational Process ◽  
School Activities ◽  
Teaching Learning ◽  
The Impact

The objective of this quantitative research is to analyze the impact of the flipped classroom in the educational process on computer science considering data science and machine learning. This study proposes the consultation of YouTube videos (before class), collaborative work through MySQL software (during class) and individual work through MySQL software (after class) in the database subject. The results of machine learning (linear regression) indicate that school activities before, during and after the class positively influence the assimilation of knowledge and development of skills on the administration of the database. Likewise, data science identifies 6 predictive models on the use of the flipped classroom in the educational process by means of the decision tree technique. Finally, the flipped classroom improves the teaching-learning conditions through the performance of creative and active activities.

Feature Engineering for Various Data Types in Data Science

2021 ◽  
pp. 1-16
Author(s):  
Nilesh Kumar Sahu ◽  
Manorama Patnaik ◽  
Itu Snigdh
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Input Data ◽  
Data Science ◽  
Learning Algorithm ◽  
Feature Engineering ◽  
Machine Learning Algorithm ◽  
Data Types ◽  
Data Set ◽  
Machine Learning Models

The precision of any machine learning algorithm depends on the data set, its suitability, and its volume. Therefore, data and its characteristics have currently become the predominant components of any predictive or precision-based domain like machine learning. Feature engineering refers to the process of changing and preparing this input data so that it is ready for training machine learning models. Several features such as categorical, numerical, mixed, date, and time are to be considered for feature extraction in feature engineering. Datasets containing characteristics such as cardinality, missing data, and rare labels for categorical features, distribution, outliers, and magnitude are currently considered as features. This chapter discusses various data types and their techniques for applying to feature engineering. This chapter also focuses on the implementation of various data techniques for feature extraction.

Exhibiting App and Analysis for Biofeedback-Based Mental Health Analyzer

2020 ◽  
pp. 265-286
Author(s):  
Rohit Rastogi ◽  
Devendra Kumar Chaturvedi ◽  
Mayank Gupta
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Computer Science ◽  
Experimental Research ◽  
Research Work ◽  
Tension Type Headache ◽  
Data Set ◽  
Closed Set ◽  
Skin Response ◽  
Type Headache

Many apps and analyzers based on machine learning have been designed already to help and cure the stress issue, which is increasing rapidly. The project is based on an experimental research work that the authors have performed at Research Labs and Scientific Spirituality Centers of Dev Sanskriti VishwaVidyalaya, Haridwar and Patanjali Research Foundations, Uttarakhand. In their research work, the correctness and accuracy have been studied and compared for two biofeedback devices, electromyography (EMG) and galvanic skin response (GSR), which can operate in three modes—audio, visual, and audio-visual—with the help of data set of tension type headache (TTH) patients. The authors have realized by their research work that these days people have a lot of stress in their lives so they planned to make an effort for reducing the stress level of people by their technical knowledge of computer science. In their project, the authors have a website that contains a closed set of questionnaires, which have some weight associated with each question.

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