Cloud Based Predictive Analytics: Text Classification, Recommender Systems and Decision Support

2013 ◽  
Author(s):  
Klavdiya Hammond ◽  
Aparna S. Varde
Keyword(s):  
Decision Support ◽  
Recommender Systems ◽  
Text Classification ◽  
Predictive Analytics

scholarly journals Weighted Deep Neural Network Based Clinical Decision Support System for the Determination of Fetal Health

2019 ◽  
Vol 8 (4) ◽  
pp. 8564-8569
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Deep Neural Network ◽  
Predictive Analytics ◽  
Clinical Decision ◽  
Svm Classifier ◽  
Healthcare Data ◽  
The Individual

Healthcare industry is undergoing changes at a tremendous rate due to healthcare innovations. Predictive analytics is increasingly being used to diagnose the patient’s ailments and provide actionable insights into already existing healthcare data. The paper looks at a decision support system for determining the health status of the foetus from cardiotographic data using deep learning neural networks. The foetal health records are classified as normal, suspect and pathological. As the multiclass cardiotographic datset of the foetus shows a high degree of imbalance a weighted deep neural network is applied. To overcome the accuracy paradox due to the multiclass imbalance, relevant metrics such as the sensitivity, specificity, F1 Score and Gmean are used to measure the performance of the classifier rather than accuracy. The metrics are applied to the individual classes to ensure that the positive cases are identified correctly. The weighted DNN based classifier is able to classify the positive instances with Gmean score of 91% which is better than than the SVM classifier.

Predictive data analytics for contract renewals: a decision support tool for managerial decision-making

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Serhat Simsek ◽  
Abdullah Albizri ◽  
Marina Johnson ◽  
Tyler Custis ◽  
Stephan Weikert
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Organizational Performance ◽  
Predictive Analytics ◽  
Design Science ◽  
Decision Support Tool ◽  
Managerial Decision ◽  
Managerial Decision Making ◽  
Content Type ◽  
Support Tool

PurposePredictive analytics and artificial intelligence are perceived as significant drivers to improve organizational performance and managerial decision-making. Hiring employees and contract renewals are instances of managerial decision-making problems that can incur high financial costs and long-term impacts on organizational performance. The primary goal of this study is to identify the Major League Baseball (MLB) free agents who are likely to receive a contract.Design/methodology/approachThis study used the design science research paradigm and the cognitive analytics management (CAM) theory to develop the research framework. A dataset on MLB's free agents between 2013 and 2017 was collected. A decision support tool was built using artificial neural networks.FindingsThere are clear links between a player's statistical performance and the decision of the player to sign a new offered contract. “Age,” “Wins above Replacement” and “the team on which a player last played” are the most significant factors in determining if a player signs a new contract.Originality/valueThis paper applied analytical modeling to personnel decision-making using the design science paradigm and guided by CAM as the kernel theory. The study employed machine learning techniques, producing a model that predicts the probability of free agents signing a new contract. Also, a web-based tool was developed to help decision-makers in baseball front offices so they can determine which available free agents to offer contracts.

scholarly journals Artificial Intelligence: reshaping the practice of radiological sciences in the 21st century

2020 ◽  
Vol 93 (1106) ◽  
pp. 20190855 ◽  
Author(s):  
Issam El Naqa ◽  
Masoom A Haider ◽  
Maryellen L Giger ◽  
Randall K Ten Haken
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Diagnostic Imaging ◽  
Predictive Analytics ◽  
Current Status ◽  
Cost Burden ◽  
Computer Aided Detection ◽  
Daily Lives ◽  
Computer Aided ◽  
Software Platforms

Advances in computing hardware and software platforms have led to the recent resurgence in artificial intelligence (AI) touching almost every aspect of our daily lives by its capability for automating complex tasks or providing superior predictive analytics. AI applications are currently spanning many diverse fields from economics to entertainment, to manufacturing, as well as medicine. Since modern AI’s inception decades ago, practitioners in radiological sciences have been pioneering its development and implementation in medicine, particularly in areas related to diagnostic imaging and therapy. In this anniversary article, we embark on a journey to reflect on the learned lessons from past AI’s chequered history. We further summarize the current status of AI in radiological sciences, highlighting, with examples, its impressive achievements and effect on re-shaping the practice of medical imaging and radiotherapy in the areas of computer-aided detection, diagnosis, prognosis, and decision support. Moving beyond the commercial hype of AI into reality, we discuss the current challenges to overcome, for AI to achieve its promised hope of providing better precision healthcare for each patient while reducing cost burden on their families and the society at large.

scholarly journals Security of the Use of Cognitive Information Technologies of Decision-Making

2021 ◽  
pp. 112-131
Author(s):  
Nataliia Gennadevna Mironova
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Information Technologies ◽  
Business Processes ◽  
Predictive Analytics ◽  
Unmanned Vehicles ◽  
Cognitive Information ◽  
Customer Profiling ◽  
Smart Technologies ◽  
Automation Tools

The article considers the intelligent automation of decision-making and management procedures that is being implemented in many areas of socio-economic practice, including financial and credit business processes, in trade and e-commerce (customer profiling, marketing micro-targeting), telecommunications, industry (technological control, robotics, neurocontrol, strategic planning and forecasting), intelligent automation also came to business management, to public administration. It is claimed that automation of personnel management is expanding (monitoring compliance with requirements, profiling and assessing KPIs, predicting conflicts and violations), unmanned vehicles and other neural network automation are used in medicine, the transport industry and agriculture; smart technologies come to education (in Moscow, a system of predictive analytics of the digital footprint of students is being tested to optimize and target educational services, help orientate in the future profession). The use of cognitive technologies in the creation of expert, advisory systems, decision support systems provides not only convenience and savings in time and effort, but gives rise to a variety of organizational, economic, ethical, social problems, giving rise to new risks. This study provides an overview of intelligent technologies that are used in social management, threats associated with the practical use of intelligent automation tools and decision support, ways and measures to reduce some of the risks associated with these threats.

A Predictive Analytics-Based Decision Support System for Drug Courts

2019 ◽  
Vol 22 (6) ◽  
pp. 1323-1342
Author(s):  
Hamed M. Zolbanin ◽  
Dursun Delen ◽  
Durand Crosby ◽  
David Wright
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Predictive Analytics ◽  
Drug Courts
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