Division Charts as Granules and Their Merging Algorithm for Rule Generation in Nondeterministic Data

We have been proposing a framework rough Nondeterministic information analysis, which considers granular computing concepts in tables with incomplete and nondeterministic information, as well as rule generation. We have recently defined an expression named division chart with respect to an implication and a subset of objects. Each division chart takes the role of the minimum granule for rule generation, and it takes the role of contingency table in statistics. In this paper, we at first define a division chart in deterministic information systems (DISs) and clarify the relation between a division chart and a corresponding implication. We also consider a merging algorithm for two division charts and extend the relation in DISs to nondeterministic information systems. The relation gives us the foundations of rule generation in tables with nondeterministic information

Harnessing Data-Driven Insights: Predictive Modeling for Diamond Price Forecasting using Regression and Classification Techniques

In the multi-faceted world of gemology, understanding diamond valuations plays a pivotal role for traders, customers, and researchers alike. This study delves deep into predicting diamond prices in terms of exact monetary values and broader price categories. The purpose was to harness advanced machine learning techniques to achieve precise estimations and categorisations, thereby assisting stakeholders in informed decision-making. The research methodology adopted comprised a rigorous data preprocessing phase, ensuring the data's readiness for model training. A range of sophisticated machine learning models were employed, from traditional linear regression to more advanced   ensemble methods like Random Forest and Gradient Boosting. The dataset was also transformed to facilitate classification into predefined price tiers, exploring the viability of models like Logistic Regression and Support Vector Machines in this context. The conceptual model encompasses a systematic flow, beginning with data acquisition, transitioning through preprocessing, regression, and classification analyses, and culminating in a comparative study of the performance metrics. This structured approach underscores the originality and value of our research, offering a holistic view of diamond price prediction from both regression and classification lenses. Findings from the analysis highlighted the superior performance of the Random Forest regressor in predicting exact prices with an R2 value of approximately 0.975. In contrast, for classification into price tiers, both Logistic Regression and Support Vector Machines emerged as frontrunners with an accuracy exceeding 95%. These results provide invaluable insights for stakeholders in the diamond industry, emphasising the potential of machine learning in refining valuation processes

Index to NASA tech briefs, 1971

The entries are listed by category, subject, author, originating source, source number/Tech Brief number, and Tech Brief number/source number. There are 528 entries

A decision support system for ground improvement method selection

Abstract unavailable please refer to PD

A Survey on Concept Drift Adaptation

Concept drift primarily refers to an online supervised learning scenario when the relation between the in- put data and the target variable changes over time. Assuming a general knowledge of supervised learning in this paper we characterize adaptive learning process, categorize existing strategies for handling concept drift, discuss the most representative, distinct and popular techniques and algorithms, discuss evaluation methodology of adaptive algorithms, and present a set of illustrative applications. This introduction to the concept drift adaptation presents the state of the art techniques and a collection of benchmarks for re- searchers, industry analysts and practitioners. The survey aims at covering the different facets of concept drift in an integrated way to reflect on the existing scattered state-of-the-art

Self-Evaluation Applied Mathematics 2003-2008 University of Twente

This report contains the self-study for the research assessment of the Department of Applied Mathematics (AM) of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente (UT). The report provides the information for the Research Assessment Committee for Applied Mathematics, dealing with mathematical sciences at the three universities of technology in the Netherlands. It describes the state of affairs pertaining to the period 1 January 2003 to 31 December 2008

Ground Improvement by Deep Vibratory Methods

Vibro compaction and vibro stone columns are the two dynamic methods of soil improvement most commonly used worldwide. These methods have been developed over almost eighty years and are now of unrivalled importance as modern foundation measures. Vibro compaction works on granular soils by densification, and vibro stone columns are used to displace and reinforce fine-grained and cohesive soils by introducing inert material. This second edition includes also a chapter on vibro concrete columns constructed with almost identical depth vibrators. These small diameter concrete piles are increasingly used as ground improvement methods for moderately loaded large spread foundations, although the original soil characteristics are only marginally improved. This practical guide for professional geotechnical engineers and graduate students systematically covers the theoretical basis and design principles behind the methods, the equipment used during their execution, and state of the art procedures for quality assurance and data acquisition. All the chapters are updated in line with recent developments and improvements in the methods and equipment. Fresh case studies from around the world illustrate the wide range of possible applications. The book concludes with variations to methods, evaluates the economic and environmental benefits of the methods, and gives contractual guidance