Machine Learning in Neuro-Oncology: Can Data Analysis From 5346 Patients Change Decision-Making Paradigms?

The dynamics of higher education are changing and emphasize the need to adapt quickly. Higher education is under the supervision of accreditation agencies, governments and other stakeholders to seek new ways to improve and monitor student success and other institutional policies. Many agencies fail to make efficient use of the large amounts of available data. With the use of big data analytics in higher education, it can be obtained more insight into students, academics, and the process in higher education so that it supports predictive analysis and improves decision making. The purpose of this research is to implement big data analytical to increase the decision making of the competent party. This research begins with the identification of process data based on analytical learning, academic and process in the campus environment. The data used in this study is a public dataset from UCI machine learning, from the 33 available varibales, 4 varibales are used to measure student performance. Big data analysis in this study uses spark apace as a library to operate pyspark so that python can process big data analysis. The data already in the master slave is grouped using k-mean clustering to get the best performing student group. The results of this study succeeded in grouping students into 5 clusters, cluster 1 including the best student performance and cluster 5 including the lowest student performance

Download Full-text

Biased Data

Advances in Computer and Electrical Engineering - Handbook of Research on Engineering Innovations and Technology Management in Organizations ◽

10.4018/978-1-7998-2772-6.ch006 ◽

2020 ◽

pp. 106-122

Author(s):

Abigail Christina Fernandez

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Decision Making ◽

Data Analysis ◽

Data Collection ◽

Data Science ◽

Analysis Data ◽

Relevant Field

Data is just data if it is not put to proper comprehensive usage. Information is Knowledge and Knowledge gets upgraded to wisdom pertaining to insight in the relevant field of analysis. Data Science has become the key that unravels many pitches of interest in diversified fields of quest. It is of optimal stipulation that the solutions that the Artificial Intelligence Algorithms provide should do justice to the intent for which what it was built. But at times, inadvertently the word bias is declaimed, which has become an implicit or explicit inclusion in the Algorithms and the data collection methodologies incorporated. IT companies manoeuvring this technology need to treat this hushed underplay in prediction and decision making with top-notch priority to epitomise this imminent episode of Machine Learning in Data Analysis.

Download Full-text

Census Data Analysis and Visualization Using R Tool

Advances in Data Mining and Database Management - Managerial Perspectives on Intelligent Big Data Analytics ◽

10.4018/978-1-5225-7277-0.ch008 ◽

2019 ◽

pp. 137-161

Author(s):

Veena Gadad ◽

Sowmyarani C. N.

Keyword(s):

Machine Learning ◽

Decision Making ◽

Data Analysis ◽

Data Visualization ◽

Census Data ◽

University Of California ◽

Visual Images ◽

Data Set ◽

Resultant Data ◽

Major Decision

As a result of increased usage of internet, a huge amount of data is collected from variety of sources like surveys, census, and sensors in internet of things. This resultant data is coined as big data and analysis of this leads to major decision making. Since the collected data is in raw form, it is difficult to understand inherent properties and it becomes just a liability if not analyzed, summarized, and visualized. Although text can be used to articulate the relation between facts and to explain the findings, presenting it in the form of tables and graphs conveys information effectively. Presentation of data using tools to create visual images in order to gain more insights into data is called as data visualization. Data analysis is processing and interpretation of data to discover useful information and to deduce certain inferences based on the values. This chapter concerns usage of R tool and understanding its effectiveness for data analysis and intelligent data visualization by experimenting on data set obtained from University of California Irvine Machine Learning Repository.

Download Full-text

Automated Decision-Making and Administrative Law

The Oxford Handbook of Comparative Administrative Law ◽

10.1093/oxfordhb/9780198799986.013.39 ◽

2020 ◽

pp. 655-676

Author(s):

Michèle Finck

Keyword(s):

Machine Learning ◽

Decision Making ◽

Deep Learning ◽

Data Analysis ◽

Computational Intelligence ◽

Sustainable Use ◽

Administrative Law ◽

Guiding Principles ◽

Automated Decision Making

This chapter examines the uses of automated decision-making (ADM) systems in administrative settings. First, it introduces the current enthusiasm surrounding computational intelligence before a cursory overview of machine learning and deep learning is provided. The chapter thereafter examines the potential of these forms of data analysis in administrative processes. In addition, this chapter underlines that, depending on how they are used; these tools risk impacting pejoratively on established concepts of administrative law. This is illustrated through the example of the principle of transparency. To conclude, a number of guiding principles designed to ensure the sustainable use of these tools are outlined and topics for further research are suggested.

Download Full-text

Research on Enterprise Human Resource Decision-Making Technology Based on Machine Learning Big Data Analysis Model

Advances in Data Science and Adaptive Analysis ◽

10.1142/s2424922x2142002x ◽

2021 ◽

Author(s):

Xiaolong Zheng ◽

Li Pan ◽

Bin Lu ◽

Hanqing Hu ◽

Adnan Khurshid

Keyword(s):

Machine Learning ◽

Decision Making ◽

Big Data ◽

Data Analysis ◽

Human Resource ◽

Big Data Analysis ◽

Analysis Model ◽

Resource Decision

Download Full-text

Medical Diagnosis from a Clinician’s Point of View

Methods of Information in Medicine ◽

10.1055/s-0038-1636673 ◽

1978 ◽

Vol 17 (01) ◽

pp. 28-35

Author(s):

F. T. De Dombal

Keyword(s):

Decision Making ◽

Data Analysis ◽

Computer Science ◽

Medical Diagnosis ◽

Information Science ◽

Point Of View ◽

Joint Research ◽

The World

This paper discusses medical diagnosis from the clinicians point of view. The aim of the paper is to identify areas where computer science and information science may be of help to the practising clinician. Collection of data, analysis, and decision-making are discussed in turn. Finally, some specific recommendations are made for further joint research on the basis of experience around the world to date.

Download Full-text