Analogical proportion-based methods for recommendation – First investigations

This chapter concentrates on the instruments of data collection and the methods of data analysis. The first two paragraphs focus on the linkage of the empirical concepts through the instruments of data collection (questionnaires, public reports and bibliometric methods) to the empirical world and to the relevant literature. Following this, the measures taken to guarantee the reliability of the methods of data analysis are discussed. This chapter ends with describing the selection criteria and approach of the study population.

Plant metabolomics is an important research area in plant science. Chemometrics is a useful tool for plant metabolomic data analysis and processing. Among them, high-order chemometrics represented by tensor modeling provides a new and promising technical method for the analysis of complex multi-way plant metabolomics data. This paper systematically reviews different tensor methods widely applied to the analysis of complex plant metabolomic data. The advantages and disadvantages as well as the latest methodological advances of tensor models are reviewed and summarized. At the same time, application of different tensor methods in solving plant science problems are also reviewed and discussed. The reviewed applications of tensor methods in plant metabolomics cover a wide range of important plant science topics including plant gene mutation and phenotype, plant disease and resistance, plant pharmacology and nutrition analysis, and plant products ingredient characterization and quality evaluation. It is evident from the review that tensor methods significantly promote the automated and intelligent process of plant metabolomics analysis and profoundly affect the paradigm of plant science research. To the best of our knowledge, this is the first review to systematically summarize the tensor analysis methods in plant metabolomic data analysis.

We present a general review on methods of correlation function profile analysis in photon correlation spectroscopy. The limited. objectives are: (1) to emphasize the interactive nature of experimental measurements, data analysis and theory, (2) to provide simple basic equations with a minimum of mathematical details for the different methods of data analysis, and (3) to summarize some useful relations between methods of data analysis and model fitting including molecular polydispersity.

The age-old tricks of predicting based on gut intuition have been overruled by mathematics since day one. The study of dispersed data and being able to predict how the data is spread around the median only to show us how other similar data could be spread could also be used to predict the behaviour of data. And such is this research paper's goal to discuss the methods and help as the analysis is carried out to build the models. Many prediction models that exist have common drawbacks like irregular dataset, overfitting and underfitting of data into the model, which is what this research work is trying to avoid by creating a proper standard prediction model. Anyone will be able to build a regression model by the end of this research paper after seeing the process of making an example model. At the end of the paper, one should be able to understand how data can be studied and used to build a predictor of their own, to guess according to the given specific situation. Building data models is essential in data analysis and prediction as its the core building block as to how the information is interlinked. The proposed model will use the IPL Match data's to inspect the data flow and predict the future.

Analogical proportion-based classification methods have been introduced a few years ago. They look in the training set for suitable triples of examples that are in an analogical proportion with the item to be classified, on a maximal set of attributes. This can be viewed as a lazy classification technique since, like k-nn algorithms, there is no static model built from the set of examples. The amazing results (at least in terms of accuracy) that have been obtained from such techniques are not easy to justify from a theoretical viewpoint. In this paper, we show that there exists an alternative method to build analogical proportion-based learners by statically building a set of inference rules during a preliminary training step. This gives birth to a new classification algorithm that deals with pairs rather than with triples of examples. Experiments on classical benchmarks of the UC Irvine repository are reported, showing that we get comparable results.KeywordsDisagreement PatternChange RuleInductive Logic ProgrammingContinuity PrincipleBrute Force MethodThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Since January 2015 the government has begun to abolish some product subsidies, especially for the marketing area of Java Bali and Madura. This opens up opportunities for new players to enter the oil and gas sector given the increasingly promising profits. the emergence of this new competitor adds to fierce competition between outlets, and to maintain cost efficiency a flexibility strategy and innovation strategy are implemented, especially related to the distribution of products from the company to all outlets where transportation equipment is adjusted so that it can transport various types of products to several outlets at a time . This condition is accompanied by a policy that a conveyance may send to various outlets at a time. Initially, business performance improved rapidly but with the increasing number of outlets there were several product distribution accidents, subsidized deposits, customers switching to other outlets including those owned by foreign companies, and backlog transaction constraints. The problem is that the partner's trust has declined including customers to the company as a result of very strict supervision in the midst of environmental changes as a result of changes in government policies and regulations lately. The sample became the object of research is customer stations in Central Java and Yogyakarta as many as 200 customer in the Gas Station Central Java and Yogyakarta. Methods of data analysis used in this study is a quantitative analysis method where quantitative data analysis method is a method of analysis in the form of figures and calculations using statistical methods. In simplify the analysis process, will be used SEM, AMOS.
 Keywords : Product Cohesiveness, Quality Business Strategy, Business Performance, Competitive Advantage

In recent years, text mining has become a branch of Natural Language Processing. This chapter focuses on the analysis of textual data, illustrated by examples treated with the software R, which will be used to manipulate texts and strings, to transform them by word embedding methods and to analyse them by supervised and unsupervised statistical and machine learning methods. It discusses methods of topic modeling and sentiment analysis. There are two types of methods in textual data analysis: descriptive methods and predictive methods. The chapter describes the various lexical analyses that are often performed to categorize documents or to detect the topics contained in a set of documents. The analysis of textual data is most often done on vector representations of the data, which are stored in matrices containing the documents in rows and the terms in columns. A recent application of natural language processing is sentiment analysis.

Purpose The purpose of this paper is to focus on measuring the contribution generated by social media when used for business purposes, distinguishing between metrics and methods for data collection and data analysis. Organizations worldwide have widely endorsed social media, but available studies on the contribution generated by these technologies for organizations are fragmented. A performance measurement system (PMS) framework to monitor social media is theoretically derived, highlighting the methods for data collection and data analysis and metrics to quantify social media impacts in terms of financials, network structure, interactions, conversations and users’ opinion. Design/methodology/approach This is a qualitative research based on a literature review of papers in management, information technology, marketing and public relations. Findings A PMS framework to quantify the contribution of social media is theoretically derived, distinguishing between metrics and methods. PMS metrics support the measurement of the financial and relational impact of social media, as well as the impact of social media conversations and users’ opinions. PMS methods comprise different approaches for data collection and data analysis that range from manual to automated data collection and from content to sentiment analysis techniques. Originality/value The PMS framework contributes to the academic literature by integrating a unique model of the available approaches for social media measurement that can serve as a basis for future research directions. The framework also supports practitioners that face necessity to quantify financial and relational contributions of social media as well as the contribution of social media conversation and users’ opinion.

Purpose: The research objective of the article is to assess the effectiveness and practical application of various data analysis methods in internal audit processes. The main research problem involves the identification of the most efficient data analysis methods, their specific applications during audits, and the analysis of benefits derived from their use in building added value and increasing the efficiency of audit processes. This publication is not a typical scientific article. Due to the subject matter and limitations in the availability of literature in this area, the main goal of the authors was to present methods identified and tested in practice, which can be immediately applied in daily work. Design/methodology/approach: Empirical research supported by interviews with audit teams regarding the use of data analysis methods in selected organizations and implementation of data analysis methods for the internal audit unit of a real organization, preceded by an analysis of available literature and checking the possibility of using selected IT tools for this purpose. Findings: As a result of the research, the authors indicate the approach to implementation and use of data analysis methods by internal audit units. They discuss a selection of appropriate tools and present benefits, exemplary scenarios and procedures related to the use of these methods in the practice of internal auditing. Research limitations/implications: The publication and the proposed approach to the implementation of data analysis methods refer to the experience of the authors and research conducted by them in selected organizations, therefore, individual specificity of the organization and technical and process-related conditions of each company should be considered during implementation of such methods. In addition, the data analysis tools and scenarios indicated in the publication are selected examples and do not fully exhaust the technological possibilities existing on the market and do not relieve the need to adapt audit procedures to the individual needs and risks characteristic of respective organizations. Originality/value: Currently, there are not enough sources and scientific materials regarding analysis methods given to audit units relating to both organizational aspects and practical implementation possibilities. The methods presented in the article in a structured way indicate specific solutions and benefits related to data analysis in audit, combining such elements as approach to the implementation of this type of methods, tools possible for usage, as well as benefits and scenarios for using data analysis methods during specific types of audits. The publication provides value to audit units that have not yet used data analysis methods or used them on a limited basis. Structured implementation of these types of methods can significantly improve an organization’s internal control environment.

In smart grid, more data will be collected at finer granularity. It will promote the development of power system control and innovation technology. With the advantages of cloud computing technology are becoming increasingly prominent, it is gradually entering into power industry research. This paper focuses on cloud-based data processing and analysis techniques of user side in smart grid. To prove that the integration of cloud computing, smart grid, data analysis technique is practical, we introduced the cloud platform architecture designed for data processing and its key technologies first. Then, main sections of cloud-based data processing, including data collection, storage, analysis and visualization are discussed. Especially, based on smart grid scenes, data analysis methods in data analysis section are described in detail.

Determining canopy cover (CC) temporal variation is critical for sustainable management of natural resources and environmental protection efforts. Data analysis and interpretation methods for remote sensing are important for understanding these changes and adapting to natural systems. In this study used the Parcel Identification System (LPIS) database physical blocks as field ground data. In the study area, agricultural areas were determined from LPIS data, including classes A0, A1, A3, A4, S1, T0, and T1, and a total of 8424 physical blocks and an area of 14651.9 hectares were evaluated. CC estimates were made using 3-m spatial resolution Planet Scope multispectral satellite images of July and August 2023, and it was determined that there were significant differences in parcel-based distinctions, especially in parcels A0, A1, T0, and T1 (P<0.05). According to the study results, it was determined that using the estimated CC data, the A0 (69.27%) and T0 (30.43%) land cover types could be successfully used to determine the changes in the phenological period caused by environmental impact assessment such as climate change. At the same time, this study contributes to the rapid monitoring of agricultural production areas caused by climate change by using physical blocks of agricultural land classes within the LPIS data, the rapid determination of agricultural land management, and support payments with remote sensing data. In this regard, the use of modern technologies and data analysis methods will contribute to increasing agricultural sustainability.

Data Analysis Methods in Physical Oceanography fills a real need for a single‐volume summary of such methods suitable for both graduate teaching and research use. It is very successful in meeting its primary objective—to be a practical guide to oceanographic data collection and analysis that provides the reader with a solid understanding of both logistical difficulties and data analysis theory. The writing is clear and succinct. Furthermore, the book's coverage is even broader than the title suggests. The first 158 pages summarize physical oceanographic data collection methods from the obvious to the obscure. Because of its broad scope, it is sometimes less complete than one might wish with regard to new developments. Even when this is true, however, references to a fuller treatment are usually provided.

The aim of this study was to test the hypothesis whether it is easily possible to transfer and apply the methods of data extraction and analysis of a performed study to a data pool of a different medical centre using the same type of Anaesthesia Information Management System (AIMS). For this purpose the objective of a study in cardiac anaesthesia, investigated at the University Hospital Giessen, was applied to the data pool of the Heart Centre Siegburg. The Giessen study evaluated factors related to the use of positive inotropic drugs (PIDs) in adults undergoing elective cardiac surgery with cardiopulmonary bypass (CPB). The same objective and methods were applied to data of 1672 patients of the Heart Centre Siegburg. In both centres anaesthetic procedures were recorded with the AIMS NarkoData. Existing database queries were adapted according to the Siegburg database configuration for detection of patients having received PIDs during or after weaning from CPB. It was revealed that data from the Siegburg database using the same data model and configuration, were identical to the Giessen database except for a few items only. Thus database queries of the Giessen study could be applied to the new data pool requiring no considerable additional input. We could demonstrate that multicentre data analysis in anaesthesia using AIM systems can be carried out successfully. Once the methods of data extraction and analysis are established they can be transferred to data pools of different centres without requiring additional time, personnel and material resources.

The measurement and data analysis used to evaluate highway roughness

Collaborative filtering (CF) technique in recommender systems (RS) is a well-known and popular technique that exploits relationships between users or items to make product recommendations to an active user. The effectiveness of existing memory based algorithms depend on the similarity measure that is used to identify nearest neighbours. However, similarity measures utilize only the ratings of co-rated items while computing the similarity between a pair of users or items. In most of the e-commerce applications, the rating matrix is too sparse since even active users of an online system tend to rate only a few items of the entire set of items. Therefore, co-rated items among users are even sparser. Moreover, the ratings a user gives an individual item tells us nothing about his comprehensive interest without which the generated recommendations may not be satisfactory to a user. In order to be able to address these issues, a comprehensive study is made of the various existing measures of similarity in a collaborative filtering recommender system (CFRS) and a hierarchical categorization of products has been proposed to understand the interest of a user in a wider scope so as to provide better recommendations as well as to alleviate data sparsity.