دانلود و نمایش مقالات مرتبط با Predictive analytics::صفحه 2
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نتیجه جستجو - Predictive analytics

تعداد مقالات یافته شده: 34
ردیف عنوان نوع
11 The impact of entrepreneurship orientation on project performance: A machine learning approach
تأثیر گرایش کارآفرینی بر عملکرد پروژه: یک رویکرد یادگیری ماشین-2020
Recent studies in project management have shown the important role of entrepreneurship orientation of the individuals in project performance. Although identifying the role of entrepreneurship orientation as a critical success factor in project performance has been considered as an important issue, it is also important to develop a measurement system for predicting performance based on the degree of an individual’s entrepreneurial orientation. In this study, we use predictive analytics by proposing a machine learning approach to predict individuals’ project performance based on measures of several aspects of entrepreneurial orientation and entrepreneurial attitude of the individuals. We investigated this relationship using a sample of 185 observations and a range of machine learning algorithms including lasso, ridge, support vector machines, neural networks, and random forest. Our results showed that the best method for predicting project performance is lasso. After identifying the best predictive model, we then used the Bayesian Information Criterion and the Akaike Infor mation Criterion to identify the most significant factors. Our results identify all three aspects of entrepreneurial attitude (social self-efficacy, appearance self-efficacy, and comparativeness) and one aspect of entrepreneurial orientation (proactiveness) as the most important factors. This study contributes to the relationship between entrepreneurship skills and project performance and provides insights into the application of emerging tools in data science and machine learning in operations management and project management research.
Keywords: Project performance | Entrepreneurship orientation | Machine learning | Supervised learning | Predictive analytics
مقاله انگلیسی
12 AI-Assisted Knowledge-Defined Network Orchestration for Energy-Efficient Data Center Networks
ارکستراسیون شبکه تعریف شده توسط دانش به کمک هوش مصنوعی برای شبکه های مرکز داده با مصرف انرژی-2020
In this article, we discuss the design and implementation of a novel DCN system, which utilizes a knowledge-defined NO-M to operate a HOEDCN cost-effectively and energy-efficiently. The motivations behind the proposed HOE-DCN system are the urgent need to address the scalability, energy, and manageability issues in existing DCN systems. To realize the knowledge-defined NO-M, we follow the principle of predictive analytics in the human brain to design three artificial intelligence modules based on deep learning and make them operate collaboratively. The proposed HOE-DCN system is implemented in a network testbed, and we conduct experiments that involve both control and data plane operations to demonstrate its advantages. The experimental results show that the HOE-DCN simultaneously achieves high-performance service provisioning and improved energy efficiency. Furthermore, by analyzing the pros and cons of the HOE-DCN system, we also point out several directions to work on in the future.
مقاله انگلیسی
13 Educational data mining and learning analytics for 21st century higher education: A review and synthesis
داده کاوی آموزشی و تجزیه و تحلیل یادگیری برای آموزش عالی قرن بیست و یکم: بررسی و ترکیب-2019
The potential influence of data mining analytics on the students’ learning processes and outcomes has been realized in higher education. Hence, a comprehensive review of educational data mining (EDM) and learning analytics (LA) in higher education was conducted. This review covered the most relevant studies related to four main dimensions: computer-supported learning analytics (CSLA), computer-supported predictive analytics (CSPA), computer-supported behavioral analytics (CSBA), and computer-supported visualization analytics (CSVA) from 2000 till 2017. The relevant EDM and LA techniques were identified and compared across these dimensions. Based on the results of 402 studies, it was found that specific EDM and LA techniques could offer the best means of solving certain learning problems. Applying EDM and LA in higher education can be useful in developing a student-focused strategy and providing the required tools that institutions will be able to use for the purposes of continuous improvement.
Keywords: Data analytics | Educational data mining | Learning analytics | Higher education
مقاله انگلیسی
14 Machine learning technology in the application of genome analysis: A systematic review
فناوری یادگیری ماشینی در کاربرد آنالیز ژنوم: یک مرور سیستماتیک-2019
Machine learning (ML) is a powerful technique to tackle many problems in data mining and predictive analytics. We believe that ML will be of considerable potentials in the field of bioinformatics since the high-throughput technology is producing ever increasing biological data. In this review, we summarized major ML algorithms and conditions that must be paid attention to when applying these algorithms to genomic problems in details and we provided a list of examples from different perspectives and data analysis challenges at present.
Keywords: Machine learning | Bioinformatics | Gene | Genomics
مقاله انگلیسی
15 An improved model for gas-liquid flow pattern prediction based on machine learning
یک مدل بهبود یافته برای پیش بینی الگوی جریان گاز مایع بر اساس یادگیری ماشین-2019
The determination of flow patterns is a fundamental problem in two-phase flow analysis, and an accurate model for gas-liquid flow pattern prediction is critical for any multiphase flow characterization as the model is used in many applications in petroleum engineering. We developed a new model based on machine learning techniques via dimensionally analyzing more than 8000 laboratory multi-phase flow tests. As shown in the test results, the flow pattern is affected by fluid properties, in-situ flow rates of liquid and gas, flow conduit geometry and mechanical properties. Applying hydraulic fundamentals and dimensional analysis, three upscaling numbers are developed to reduce the number of freedom dimensions. These dimensionless variables are easy to use for upscaling and have physical meanings. Machine learning techniques on the dimensionless variables significantly improved their predictive accuracy. Until now the best matching on these laboratory data was approximately 80% using the most recently developed semi-analytical models. The quality of the matching is improved to 90% or greater on the experimental data using machine learning techniques.
Keywords: Machine learning | Data analytics | Two-phase flow model | Predictive analytics | Flow pattern | Gas-liquid modeling
مقاله انگلیسی
16 Sensor analytics for interpretation of EKG signals
تجزیه و تحلیل حسگر برای تفسیر سیگنال های EKG-2019
Motivation and Objectives: Smartphones are emerging as personal fitness assistants, collecting data through in-built or external sensors. The next frontier for these devices is to use advanced sensors and machine learning algorithms to offer more personalized and advanced medical assessments. Along these lines, the objective of this paper is to develop a multi-label classification model to detect heart compli- cations through electrocardiograms (EKGs) collected by an FDA-approved single-lead EKG sensor attached to a smartphone. The EKG sensor produces a standard EKG chart, but for such a sensor to be useful to a consumer, an interpretation of the graph is necessary. Materials and Method: We adapt a machine-learning approach to detect multiple heart conditions simul- taneously from the generated EKG graph. Three different multi-label machine learning models (binary relevance, label powerset and multi-perceptron neural network) were built and compared to categorize five different heart states: Normal, Atrial Fibrillation, Atrioventricular Block, Sinus Bradycardia and Sinus Tachycardia. The binary relevance model was selected based on the accuracy. Results and Implications: The model generated rules inductively from the data to interpret nine out of every ten heart conditions correctly. Our model is being adapted for commercial use by a company (as a part of their App) that markets the EKG sensor for smartphones. Our model is usable in a cardiovascular disease alert expert system that will potentially allow users to monitor their heart health continuously and prevent a serious illness by providing this information in the early stages.
Keywords: Multi-label classification | Machine learning | EKG sensor | Smartphone app | Predictive analytics
مقاله انگلیسی
17 Machine learning powered software for accurate prediction of biogas production: A case study on industrial-scale Chinese production data
نرم افزار طراحی شده توسط ماشین یادگیری برای پیش بینی دقیق تولید بیوگاز: یک مطالعه موردی در مورد داده های تولید چینی در مقیاس صنعتی-2019
The search for appropriate models for predictive analytics is currently a high priority to optimize anaerobic fermentation processes in industrial-scale biogas facilities; operational productivity could be enhanced if project operators used the latest tools in machine learning to inform decision-making. The objective of this study is to enhance biogas production in industrial facilities by designing a graphical user interface to machine learning models capable of predicting biogas output given a set of waste inputs. The methodology involved applying predictive algorithms to daily production data from two major Chinese biogas facilities in order to understand the most important inputs affecting biogas production. The machine learning models used included logistic regression, support vector machine, random forest, extreme gradient boosting, and k-nearest neighbors regression. The models were tuned and crossvalidated for optimal accuracy. Our results showed that: (1) the KNN model had the highest model accuracy for the Hainan biogas facility, with an 87% accuracy on the test set; (2) municipal fecal residue, kitchen food waste, percolate, and chicken litter were inputs that maximized biogas production; (3) an online web-tool based on the machine learning models was developed to enhance the analytical capabilities of biogas project operators; (4) an online waste resource mapping tool was also developed for macro-level project location planning. This research has wide implications for biogas project operators seeking to enhance facility performance by incorporating machine learning into the analytical pipeline.
Keywords: Biogas | Machine learning | China | Graphical user interface
مقاله انگلیسی
18 Using Machine Learning Applied to Real-World Healthcare Data for Predictive Analytics: An Applied Example in Bariatric Surgery
استفاده از یادگیری ماشینی کاربردی در داده های بهداشت و درمان در دنیای واقعی برای تجزیه و تحلیل پیش بینی کننده: یک نمونه کاربردی در جراحی چاقی-2019
Objectives: Laparoscopic metabolic surgery (MxS) can lead to remission of type 2 diabetes (T2D); however, treatment response to MxS can be heterogeneous. Here, we demonstrate an open-source predictive analytics platform that applies machine-learning techniques to a common data model; we develop and validate a predictive model of antihyperglycemic medication cessation (validated proxy for A1c control) in patients with treated T2D who underwent MxS. Methods: We selected patients meeting the following criteria in 2 large US healthcare claims databases (Truven Health MarketScan Commercial [CCAE]; Optum Clinformatics [Optum]): underwent MxS between January 1, 2007, to October 1, 2013 (first = index); aged $18 years; continuous enrollment 180 days pre-index (baseline) to 730 days postindex; baseline T2D diagnosis and treatment. The outcome was no antihyperglycemic medication treatment from 365 to 730 days after MxS. A regularized logistic regression model was trained using the following candidate predictor categories measured at baseline: demographics, conditions, medications, measurements, and procedures. A 75% to 25% split of the CCAE group was used for model training and testing; the Optum group was used for external validation. Results: 13 050 (CCAE) and 3477 (Optum) patients met the study inclusion criteria. Antihyperglycemic medication cessation rates were 72.9% (CCAE) and 70.8% (Optum). The model possessed good internal discriminative accuracy (area under the curve [AUC] = 0.778 [95% CI = 0.761-0.795] in CCAE test set N = 3527) and transportability (external AUC = 0.759 [95% CI = 0.741-0.777] in Optum N = 3477). Conclusion: The application of machine learning techniques to real-world healthcare data can yield useful predictive models to assist patient selection. In future practice, establishment of prerequisite technological infrastructure will be needed to implement such models for real-world decision support.
Keywords: prediction | machine learning | type 2 diabetes | metabolic surgery | antihyperglycemic medication
مقاله انگلیسی
19 Utilizing predictive modeling to enhance policy and practice through improved identification of at-risk clients: Predicting permanency for foster children
استفاده از مدلسازی پیشگویانه برای ارتقای سیاست و روش ازطریق بهبود شناسایی مشتریان پرخطر: پیش بینی پایداری برای پرورش بچه ها-2018
Child welfare agencies are increasingly required to leverage their limited resources to meet nearly limitless demands. As a result, agencies are searching for new opportunities to efficiently improve policy and practice, and advances in data availability and technology have brought increased attention to the utility of predictive modeling. While the literature has often highlighted the considerable potential of predictive models leveraging “big data”, discussions of the methodology and the associated best practices remain critically absent. To address this gap, this paper provides an illustrative case involving the development and testing of models used to predict the probability of whether U.S. foster children would achieve legal permanency. The models were trained and tested using a national administrative dataset of 233,633 foster care children that discharged from state child welfare systems in 2013. The optimal model, a boosted tree, predicted whether children would achieve permanency with 97.66% accuracy. The paper concludes with a discussion of best practices detailing how agencies can utilize predictive modeling to enhance policy and practice.
keywords: Predictive modeling |Permanency |High-risk clients |Predictive analytics |Big data |Best practices
مقاله انگلیسی
20 Concurrence of big data analytics and healthcare: A systematic review
انطباق با تجزیه و تحلیل داده های بزرگ و مراقبت های بهداشتی: یک مرور سیستماتیک-2018
Background: The application of Big Data analytics in healthcare has immense potential for improving the quality of care, reducing waste and error, and reducing the cost of care. Purpose: This systematic review of literature aims to determine the scope of Big Data analytics in healthcare including its applications and challenges in its adoption in healthcare. It also intends to identify the strategies to overcome the challenges. Data sources: A systematic search of the articles was carried out on five major scientific databases: ScienceDirect, PubMed, Emerald, IEEE Xplore and Taylor & Francis. The articles on Big Data analytics in healthcare published in English language literature from January 2013 to January 2018 were considered. Study selection: Descriptive articles and usability studies of Big Data analytics in healthcare and medicine were selected. Data extraction: Two reviewers independently extracted information on definitions of Big Data analytics; sources and applications of Big Data analytics in healthcare; challenges and strategies to overcome the challenges in healthcare. Results: A total of 58 articles were selected as per the inclusion criteria and analyzed. The analyses of these articles found that: (1) researchers lack consensus about the operational definition of Big Data in healthcare; (2) Big Data in healthcare comes from the internal sources within the hospitals or clinics as well external sources including government, laboratories, pharma companies, data aggregators, medical journals etc.; (3) natural language processing (NLP) is most widely used Big Data analytical technique for healthcare and most of the processing tools used for analytics are based on Hadoop; (4) Big Data analytics finds its application for clinical decision support; optimization of clinical operations and reduction of cost of care (5) major challenge in adoption of Big Data analytics is non-availability of evidence of its practical benefits in healthcare. Conclusion: This review study unveils that there is a paucity of information on evidence of real-world use of Big Data analytics in healthcare. This is because, the usability studies have considered only qualitative approach which describes potential benefits but does not take into account the quantitative study. Also, majority of the studies were from developed countries which brings out the need for promotion of research on Healthcare Big Data analytics in developing countries.
Keywords: Big data , Analytics , Healthcare , Predictive analytics , Evidence-based medicine
مقاله انگلیسی
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