The future of predictive models in radiation oncology: from extensive data mining to reliable modeling of the results

2013 ◽  
Vol 9 (3) ◽  
pp. 311-313 ◽  
Author(s):  
Vincenzo Valentini ◽  
Nicola Dinapoli ◽  
Andrea Damiani
Keyword(s):  
Data Mining ◽  
Predictive Models ◽  
Radiation Oncology ◽  
Extensive Data ◽  
The Future

IMPLEMENTASI ALGORITMA REGRESI LINEAR SEDERHANA DALAM MEMPREDIKSI BESARAN PENDAPATAN DAERAH (STUDI KASUS: DINAS PENDAPATAN KAB. DELI SERDANG)

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Fransiskus Ginting ◽  
Efori Buulolo ◽  
Edward Robinson Siagian
Keyword(s):  
Data Mining ◽  
Linear Regression ◽  
Visual Basic ◽  
Large Contribution ◽  
Simple Linear Regression ◽  
Information Discovery ◽  
Regional Income ◽  
Local Revenue ◽  
Local Taxes ◽  
The Future

Data Mining is an information discovery by extracting information patterns that contain trend searches in a very large amount of data and assist the process of storing data in making a decision in the future. In determining the pattern classification techniques do to collect records (Training set). Regional income is generally derived from local taxes and levies, local taxes are one source of funding for the region on the national average has not been able to make a large contribution to the formation of local revenue. By utilizing Regional Revenue data, it can produce forecasting and predictions of Regional Revenue income in the future to match the reality / reality so that the planned RAPBD can run smoothly. Simple Linear Regression or often abbreviated as SLR (Simple Linear Regression) is one of the statistical methods used in production to make predictions or predictions about the characteristics of quality and quantity to describe the processes associated with data processing for the acquisition of regional income. So that in the testing phase with visual basic net can help in processing valid Regional Revenue Amount data. Keywords: Data Mining, Local Revenue, Simple Linear Regression Algorithm, Visual Basic net 2008

Research on Persona-Based User Requirement in the Future Exploratory Products Design

2014 ◽  
Vol 912-914 ◽  
pp. 1710-1713
Author(s):  
Qing Zhang ◽  
Sui Huai Yu ◽  
Ming Jiu Yu
Keyword(s):  
Data Mining ◽  
User Modeling ◽  
Modeling Method ◽  
Mobile Internet ◽  
User Requirement ◽  
Operating Rules ◽  
The Future ◽  
Key Factor

During the design processing of the future exploratory products, requirements from users seems to be a key factor for products availability achievement. As a practical user modeling method, Persona may accomplish the potential needs data mining effectively based on the analyzing of users. This review mainly focused on how to apply the persona in the exploratory products investigation to acquire useful information from the products design. The method to establish persona and the operating rules were also discussed in this article. The concept of the mobile internet device in future was used as an case to demonstrate the persona mentioned above.

In the future, disruptive innovation in radiation oncology technology will be initiated mostly by entrepreneurs

2019 ◽  
Vol 46 (5) ◽  
pp. 1949-1952
Author(s):  
Cedric X. Yu ◽  
Thomas Bortfeld ◽  
Jing Cai
Keyword(s):  
Radiation Oncology ◽  
Disruptive Innovation ◽  
The Future

Proposal of Analytical Model for Business Problems Solving in Big Data Environment

Web Services ◽  
2019 ◽  
pp. 618-638
Author(s):  
Goran Klepac ◽  
Kristi L. Berg
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Predictive Models ◽  
Analytical Approach ◽  
Fraud Detection ◽  
Analytical Techniques ◽  
Data Sources ◽  
Business Decisions ◽  
Mining Projects ◽  
Structured Approach

This chapter proposes a new analytical approach that consolidates the traditional analytical approach for solving problems such as churn detection, fraud detection, building predictive models, segmentation modeling with data sources, and analytical techniques from the big data area. Presented are solutions offering a structured approach for the integration of different concepts into one, which helps analysts as well as managers to use potentials from different areas in a systematic way. By using this concept, companies have the opportunity to introduce big data potential in everyday data mining projects. As is visible from the chapter, neglecting big data potentials results often with incomplete analytical results, which imply incomplete information for business decisions and can imply bad business decisions. The chapter also provides suggestions on how to recognize useful data sources from the big data area and how to analyze them along with traditional data sources for achieving more qualitative information for business decisions.

A Study of XML Models for Data Mining

Data Mining ◽  
2013 ◽  
pp. 1-27
Author(s):  
Sangeetha Kutty ◽  
Richi Nayak ◽  
Tien Tran
Keyword(s):  
Data Mining ◽  
Data Model ◽  
Data Representation ◽  
Data Models ◽  
Data Mining Techniques ◽  
Xml Documents ◽  
The Future ◽  
Future Data

With the increasing number of XML documents in varied domains, it has become essential to identify ways of finding interesting information from these documents. Data mining techniques can be used to derive this interesting information. However, mining of XML documents is impacted by the data model used in data representation due to the semi-structured nature of these documents. In this chapter, we present an overview of the various models of XML documents representations, how these models are used for mining, and some of the issues and challenges inherent in these models. In addition, this chapter also provides some insights into the future data models of XML documents for effectively capturing its two important features, structure and content, for mining.

Data Mining for Combining Forecasts in Inventory Management

2008 ◽  
pp. 2792-2797
Author(s):  
Chi Kin Chan
Keyword(s):  
Data Mining ◽  
Inventory Management ◽  
Traditional Approach ◽  
The Past ◽  
Aggregate Information ◽  
Forecasting Methods ◽  
Combining Forecasts ◽  
Forecasting Method ◽  
The Future ◽  
Single Method

The traditional approach to forecasting involves choosing the forecasting method judged most appropriate of the available methods and applying it to some specific situations. The choice of a method depends upon the characteristics of the series and the type of application. The rationale behind such an approach is the notion that a “best” method exists and can be identified. Further that the “best” method for the past will continue to be the best for the future. An alternative to the traditional approach is to aggregate information from different forecasting methods by aggregating forecasts. This eliminates the problem of having to select a single method and rely exclusively on its forecasts.

scholarly journals The evolution of predictive models and tourism

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Najmeh Gharibi
Keyword(s):  
Technology Acceptance ◽  
Predictive Models ◽  
Future Research ◽  
Content Type ◽  
Technology Acceptance Models ◽  
New Directions ◽  
The Future ◽  
Future Outcomes ◽  
Tourism Services

Purpose This study aims to investigate the predictive technology acceptance models and their evolution in the tourism context. These predictive models make a knowledgeable decision about the possibility of future outcomes by analysing data. As futurists are interested in making a prediction about the likelihood of different behaviours over time, researchers of these predictive models have focussed on behaviour and predicting the intentions of users. This study proposes to demonstrate the revolution of these models and how are changed overtime. It also indicates the role of them in future studies. Design/methodology/approach By reviewing the predictive models and literature, this study looks in-depth in the process of alteration of these models. Findings This study explores the reasons of the evolution of predictive models and how they are changed. It shed light on the role of predictive models in future research and will suggest new directions for forthcoming studies. Research limitations/implications One of the main limitations of this study is that as the world is currently struggling with COVID-19 and predictability of these models will be changed. As the future is disruptive, it cannot be concluded that how these models will be altered in future. Practical implications Role of predictive behavioural models of tourists is fundamentally crucial in assessing the performance of planners and marketers of tourism services in the future. It will also vastly helps the successful development of tourism sectors, and it has practical value for all tourism stakeholders. Originality/value Few studies have focussed on the evaluation of these models and their role in future research.

scholarly journals Combining chronicle mining and semantics for predictive maintenance in manufacturing processes

Semantic Web ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 927-948 ◽  
Author(s):  
Qiushi Cao ◽  
Ahmed Samet ◽  
Cecilia Zanni-Merk ◽  
François de Bertrand de Beuvron ◽  
Christoph Reich
Keyword(s):  
Data Mining ◽  
Hybrid Approach ◽  
Failure Prediction ◽  
Operating Conditions ◽  
Predictive Maintenance ◽  
Semantic Gap ◽  
Manufacturing Processes ◽  
Temporal Constraints ◽  
Industrial Data ◽  
The Future

Within manufacturing processes, faults and failures may cause severe economic loss. With the vision of Industry 4.0, artificial intelligence techniques such as data mining play a crucial role in automatic fault and failure prediction. However, due to the heterogeneous nature of industrial data, data mining results normally lack both machine and human-understandable representation and interpretation of knowledge. This may cause the semantic gap issue, which stands for the incoherence between the knowledge extracted from industrial data and the interpretation of the knowledge from a user. To address this issue, ontology-based approaches have been used to bridge the semantic gap between data mining results and users. However, only a few existing ontology-based approaches provide satisfactory knowledge modeling and representation for all the essential concepts in predictive maintenance. Moreover, most of the existing research works merely focus on the classification of operating conditions of machines, while lacking the extraction of specific temporal information of failure occurrence. This brings obstacles for users to perform maintenance actions with the consideration of temporal constraints. To tackle these challenges, in this paper we introduce a novel hybrid approach to facilitate predictive maintenance tasks in manufacturing processes. The proposed approach is a combination of data mining and semantics, within which chronicle mining is used to predict the future failures of the monitored industrial machinery, and a Manufacturing Predictive Maintenance Ontology (MPMO) with its rule-based extension is used to predict temporal constraints of failures and to represent the predictive results formally. As a result, Semantic Web Rule Language (SWRL) rules are constructed for predicting the occurrence time of machinery failures in the future. The proposed rules provide explicit knowledge representation and semantic enrichment of failure prediction results, thus easing the understanding of the inferred knowledge. A case study on a semi-conductor manufacturing process is used to demonstrate our approach in detail. The evaluation of results shows that the MPMO ontology is free of bad practices in the structural, functional, and usability-profiling dimensions. The constructed SWRL rules posses more than 80% of True Positive Rate, Precision, and F-measure, which shows promising performance in failure prediction.

scholarly journals The Emergence of Artificial Intelligence within Radiation Oncology Treatment Planning

Oncology ◽  
2020 ◽  
pp. 1-11
Author(s):  
Tucker J. Netherton ◽  
Carlos E. Cardenas ◽  
Dong Joo Rhee ◽  
Laurence E. Court ◽  
Beth M. Beadle
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Treatment Planning ◽  
Radiation Oncology ◽  
Radiation Treatment ◽  
Medical Physics ◽  
External Beam Radiotherapy ◽  
Radiation Treatment Planning ◽  
Planning Team ◽  
The Future

<b><i>Background:</i></b> The future of artificial intelligence (AI) heralds unprecedented change for the field of radiation oncology. Commercial vendors and academic institutions have created AI tools for radiation oncology, but such tools have not yet been widely adopted into clinical practice. In addition, numerous discussions have prompted careful thoughts about AI’s impact upon the future landscape of radiation oncology: How can we preserve innovation, creativity, and patient safety? When will AI-based tools be widely adopted into the clinic? Will the need for clinical staff be reduced? How will these devices and tools be developed and regulated? <b><i>Summary:</i></b> In this work, we examine how deep learning, a rapidly emerging subset of AI, fits into the broader historical context of advancements made in radiation oncology and medical physics. In addition, we examine a representative set of deep learning-based tools that are being made available for use in external beam radiotherapy treatment planning and how these deep learning-based tools and other AI-based tools will impact members of the radiation treatment planning team. <b><i>Key Messages:</i></b> Compared to past transformative innovations explored in this article, such as the Monte Carlo method or intensity-modulated radiotherapy, the development and adoption of deep learning-based tools is occurring at faster rates and promises to transform practices of the radiation treatment planning team. However, accessibility to these tools will be determined by each clinic’s access to the internet, web-based solutions, or high-performance computing hardware. As seen by the trends exhibited by many technologies, high dependence on new technology can result in harm should the product fail in an unexpected manner, be misused by the operator, or if the mitigation to an expected failure is not adequate. Thus, the need for developers and researchers to rigorously validate deep learning-based tools, for users to understand how to operate tools appropriately, and for professional bodies to develop guidelines for their use and maintenance is essential. Given that members of the radiation treatment planning team perform many tasks that are automatable, the use of deep learning-based tools, in combination with other automated treatment planning tools, may refocus tasks performed by the treatment planning team and may potentially reduce resource-related burdens for clinics with limited resources.

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