scholarly journals Machine Learning in Nutritional Follow-up Research

2017 ◽  
Vol 7 (1) ◽  
pp. 41-45 ◽  
Author(s):  
Rita Reis ◽  
Hugo Peixoto ◽  
José Machado ◽  
António Abelha
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Decision Making ◽  
Daily Basis ◽  
Decision Makers ◽  
Learning Tools ◽  
Healthcare Organizations ◽  
Data Mining Techniques ◽  
Take The Best

Abstract Healthcare is one of the world’s fastest growing industries, having large volumes of data collected on a daily basis. It is generally perceived as being ‘information rich’ yet ‘knowledge poor’. Hidden relationships and valuable knowledge can be discovered in the collected data from the application of data mining techniques. These techniques are being increasingly implemented in healthcare organizations in order to respond to the needs of doctors in their daily decision-making activities. To help the decision-makers to take the best decision it is fundamental to develop a solution able to predict events before their occurrence. The aim of this project was to predict if a patient would need to be followed by a nutrition specialist, by combining a nutritional dataset with data mining classification techniques, using WEKA machine learning tools. The achieved results showed to be very promising, presenting accuracy around 91%, specificity around 97% and precision about 95%.

scholarly journals Latest Tools for Data Mining and Machine Learning

2019 ◽  
Vol 8 (9S) ◽  
pp. 18-23 ◽  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Decision Making ◽  
Feature Selection ◽  
Open Source ◽  
Predictive Analysis ◽  
Learning Tools ◽  
Pros And Cons ◽  
Selection For ◽  
Extract Information

Nowadays, Data Mining is used everywhere for extracting information from the data and in turn, acquires knowledge for decision making. Data Mining analyzes patterns which are used to extract information and knowledge for making decisions. Many open source and licensed tools like Weka, RapidMiner, KNIME, and Orange are available for Data Mining and predictive analysis. This paper discusses about different tools available for Data Mining and Machine Learning, followed by the description, pros and cons of these tools. The article provides details of all the algorithms like classification, regression, characterization, discretization, clustering, visualization and feature selection for Data Mining and Machine Learning tools. It will help people for efficient decision making and suggests which tool is suitable according to their requirement.

scholarly journals USE OF DATA MINING TECHNIQUES IN ADVANCE DECISION MAKING PROCESSES IN A LOCAL FIRM

2015 ◽  
Vol 10 (2) ◽  
Author(s):  
Onur Doğan ◽  
Hakan  Aşan ◽  
Ejder Ayç
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Big Data ◽  
Data Cleaning ◽  
Decision Makers ◽  
Data Sets ◽  
Data Set ◽  
Scientific Methods ◽  
Local Firm ◽  
Data Mining Techniques

In today’s competitive world, organizations need to make the right decisions to prolong their existence. Using non-scientific methods and making emotional decisions gave way to the use of scientific methods in the decision making process in this competitive area. Within this scope, many decision support models are still being developed in order to assist the decision makers and owners of organizations. It is easy to collect massive amount of data for organizations, but generally the problem is using this data to achieve economic advances. There is a critical need for specialization and automation to transform the data into the knowledge in big data sets. Data mining techniques are capable of providing description, estimation, prediction, classification, clustering, and association. Recently, many data mining techniques have been developed in order to find hidden patterns and relations in big data sets. It is important to obtain new correlations, patterns, and trends, which are understandable and useful to the decision makers. There have been many researches and applications focusing on different data mining techniques and methodologies.In this study, we aim to obtain understandable and applicable results from a large volume of record set that belong to a firm, which is active in the meat processing industry, by using data mining techniques. In the application part, firstly, data cleaning and data integration, which are the first steps of data mining process, are performed on the data in the database. With the aid of data cleaning and data integration, the data set was obtained, which is suitable for data mining. Then, various association rule algorithms were applied to this data set. This analysis revealed that finding unexplored patterns in the set of data would be beneficial for the decision makers of the firm. Finally, many association rules are obtained, which are useful for decision makers of the local firm. 

Spare Parts Inventory Management Literature and Direction Towards the Use of Data Mining Technique

2018 ◽  
pp. 534-558 ◽  
Author(s):  
S. P. Sarmah ◽  
Umesh Chandra Moharana
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Inventory Management ◽  
Enterprise Resource Planning ◽  
Resource Planning ◽  
Daily Basis ◽  
Data Mining Technique ◽  
Data Mining Techniques ◽  
Related Data ◽  
Mining Technique

In the current age of information technology, most of the industries have implemented integrated information systems or enterprise resource planning applications to automate their business process, reduce lead time, improve productivity and reduce cost. These industries generate large amount of inventory and maintenance related data on daily basis, which are stored in a central database. Data mining techniques are most suitable to discover valuable information from this large amount of data. The valuable information can be in the form of patterns, associations, classifications, changes etc. which can be helpful for maintenance and inventory managers for better decision making. This chapter reviews application of data mining technique in inventory management through a survey of literature and classified the articles. Also the chapter suggests other inventory management areas where data mining techniques can be applied for better decision making. Keywords and abstracts were used to identify 107 articles concerning management of inventory and application of data mining techniques.

scholarly journals Data mining techniques with machine learning algorithm to predict patients of heart disease

2021 ◽  
Vol 1088 (1) ◽  
pp. 012035
Author(s):  
Mulyawan ◽  
Agus Bahtiar ◽  
Githera Dwilestari ◽  
Fadhil Muhammad Basysyar ◽  
Nana Suarna
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Heart Disease ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Data Mining Techniques

Data Mining-based Financial Statement Fraud Detection: Systematic Literature Review and Meta-analysis to Estimate Data Sample Mapping of Fraudulent Companies Against Non-fraudulent Companies

2021 ◽  
pp. 097215092098485
Author(s):  
Sonika Gupta ◽  
Sushil Kumar Mehta
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Classification Accuracy ◽  
Meta Analysis ◽  
Financial Statement ◽  
Research Articles ◽  
Financial Statement Fraud ◽  
Data Mining Techniques

Data mining techniques have proven quite effective not only in detecting financial statement frauds but also in discovering other financial crimes, such as credit card frauds, loan and security frauds, corporate frauds, bank and insurance frauds, etc. Classification of data mining techniques, in recent years, has been accepted as one of the most credible methodologies for the detection of symptoms of financial statement frauds through scanning the published financial statements of companies. The retrieved literature that has used data mining classification techniques can be broadly categorized on the basis of the type of technique applied, as statistical techniques and machine learning techniques. The biggest challenge in executing the classification process using data mining techniques lies in collecting the data sample of fraudulent companies and mapping the sample of fraudulent companies against non-fraudulent companies. In this article, a systematic literature review (SLR) of studies from the area of financial statement fraud detection has been conducted. The review has considered research articles published between 1995 and 2020. Further, a meta-analysis has been performed to establish the effect of data sample mapping of fraudulent companies against non-fraudulent companies on the classification methods through comparing the overall classification accuracy reported in the literature. The retrieved literature indicates that a fraudulent sample can either be equally paired with non-fraudulent sample (1:1 data mapping) or be unequally mapped using 1:many ratio to increase the sample size proportionally. Based on the meta-analysis of the research articles, it can be concluded that machine learning approaches, in comparison to statistical approaches, can achieve better classification accuracy, particularly when the availability of sample data is low. High classification accuracy can be obtained with even a 1:1 mapping data set using machine learning classification approaches.

Who’s making the decisions? How managers can harness artificial intelligence and remain in charge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pooya Tabesh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Digital Transformation ◽  
Decision Processes ◽  
Decision Makers ◽  
Content Type ◽  
Human Decision ◽  
Practical Implications

Purpose While it is evident that the introduction of machine learning and the availability of big data have revolutionized various organizational operations and processes, existing academic and practitioner research within decision process literature has mostly ignored the nuances of these influences on human decision-making. Building on existing research in this area, this paper aims to define these concepts from a decision-making perspective and elaborates on the influences of these emerging technologies on human analytical and intuitive decision-making processes. Design/methodology/approach The authors first provide a holistic understanding of important drivers of digital transformation. The authors then conceptualize the impact that analytics tools built on artificial intelligence (AI) and big data have on intuitive and analytical human decision processes in organizations. Findings The authors discuss similarities and differences between machine learning and two human decision processes, namely, analysis and intuition. While it is difficult to jump to any conclusions about the future of machine learning, human decision-makers seem to continue to monopolize the majority of intuitive decision tasks, which will help them keep the upper hand (vis-à-vis machines), at least in the near future. Research limitations/implications The work contributes to research on rational (analytical) and intuitive processes of decision-making at the individual, group and organization levels by theorizing about the way these processes are influenced by advanced AI algorithms such as machine learning. Practical implications Decisions are building blocks of organizational success. Therefore, a better understanding of the way human decision processes can be impacted by advanced technologies will prepare managers to better use these technologies and make better decisions. By clarifying the boundaries/overlaps among concepts such as AI, machine learning and big data, the authors contribute to their successful adoption by business practitioners. Social implications The work suggests that human decision-makers will not be replaced by machines if they continue to invest in what they do best: critical thinking, intuitive analysis and creative problem-solving. Originality/value The work elaborates on important drivers of digital transformation from a decision-making perspective and discusses their practical implications for managers.

scholarly journals Failure Analysis in University and Computer Science Contexts With Data Mining

2020 ◽  
Author(s):  
Daniela De Souza Gomes ◽  
Marcos Henrique Fonseca Ribeiro ◽  
Giovanni Ventorim Comarela ◽  
Gabriel Philippe Pereira
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Failure Analysis ◽  
Computer Science ◽  
Educational Administration ◽  
Intelligent Systems ◽  
Data Set ◽  
Data Mining Techniques ◽  
Study Case ◽  
Support Students

High failure rates are a worrying and relevant problem in Brazilian universities. From a data set of student transcripts, we performed a study case for both general and Computer Science contexts, in which Data Mining Techniques were used to find patterns concerning failures. The knowledge acquired can be used for better educational administration and also build intelligent systems to support students’ decision making.

Artificial Intelligence for Decision Makers

2019 ◽  
pp. 0000-0000
Author(s):  
Viktor Elliot ◽  
Mari Paananen ◽  
Miroslaw Staron
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Machine Learning Algorithms ◽  
Decision Makers ◽  
Accounting Data ◽  
Ethical Concerns ◽  
Key Concepts ◽  
Basic Understanding ◽  
Visualization Tools

We propose an exercise with the purpose of providing a basic understanding of key concepts within AI and extending the understanding of AI beyond mathematics. The exercise allows participants to carry out analysis based on accounting data using visualization tools as well as to develop their own machine learning algorithms that can mimic their decisions. Finally, we also problematize the use of AI in decision-making, with such aspects as biases in data and/or ethical concerns.

Machine Learning for Business Analytics

2021 ◽  
pp. 232-256
Author(s):  
Kağan Okatan
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Social Media ◽  
Big Data ◽  
Machine Learning Algorithms ◽  
Decision Makers ◽  
Business Analytics ◽  
Business Intelligence Systems ◽  
Long Time ◽  
Rules Of The Game

All these types of analytics have been answering business questions for a long time about the principal methods of investigating data warehouses. Especially data mining and business intelligence systems support decision makers to reach the information they want. Many existing systems are trying to keep up with a phenomenon that has changed the rules of the game in recent years. This is undoubtedly the undeniable attraction of 'big data'. In particular, the issue of evaluating the big data generated especially by social media is among the most up-to-date issues of business analytics, and this issue demonstrates the importance of integrating machine learning into business analytics. This section introduces the prominent machine learning algorithms that are increasingly used for business analytics and emphasizes their application areas.

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