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293 papers and 42 presentations; 320 titles.

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Conference × Year × Title × Authors Extras Pages
PHUSE US 2026 AI-Augmented Bayesian Methods for Late-Onset Toxicity in Early-Phase Trials Rachana Lele,
Arfan Afzal		 source
presentation source		 8
PHUSE US 2026 AI-Driven Quality Risk Management for Contract Manufacturing Oversight: An ML Approach Kiran Kumar Gande source
presentation source		 7
PHUSE APAC 2026 Bridging Statistical Expertise Through Intuitive Design: A Modern Web Interface for Automated SAS Procedure Selection in Cardiovascular Clinical Trials Likith Gopikumar source
presentation source		 10
PHUSE APAC 2026 Machine Learning Algorithms to Explore the Relationship Between Primary Obsessive-Compulsive Disorder (OCD) and Co-morbid Depression Symptoms Ajay Kumar Singh,
Sabyasachi Mukherjee		 source
presentation source		 12
SESUG 2025 Short-term forecasting with a computationally efficient nonparametric transfer function model Jun Liu 31
PHUSE US 2025 Enhancing Clinical Trial Data Queries with LLMs and Neo4j: A Flexible Framework for ADaM Dataset Management Jaime Yan source
presentation source		 28
PHUSE US 2025 LLMs in Drug Development - The Future is Closer Than We Think Chris McSpiritt source
presentation source		 9
PHUSE US 2025 Synthetic Data Will Save the Day: Fact or Fiction Sherrine Eid source
presentation source		 7
PHUSE US 2025 Transforming Clinical Trials: Leveraging Bayesian Statistics in Innovative Trial Design, Analysis, Applications and Navigating Challenges Jaskaran Singh Saini source
presentation source		 11
MWSUG 2024 Charting Your Organization's Machine Learning Roadmap Ryan Paul Lafler source 7
MWSUG 2024 Developing Artificial and Convolutional Neural Networks with Python's Keras API for TensorFlow Ryan Paul Lafler,
Anna T. K. Wade		 source 18
MWSUG 2024 Introduction to Machine Learning in SAS [presentation] Melodie Rush source 71
MWSUG 2024 Predicting the Impact of Storms on Utility Customers [presentation] David J. Corliss source 23
MWSUG 2024 Teaching Your Computer to See: Using Computer Vision to Detect Defects [presentation] Scott Koval source 23
PHUSE EU 2024 Artificial Intelligence Potential Use Cases in Clinical Data Engineering Deepak Pakalapati,
Mounika Pillamarapu		 source
presentation source		 8
PHUSE EU 2024 Hi. I Am a Chatbot. How Can I Help You? Enhancing the Reliability and Traceability of Large Language Models Vicky Poulsen,
Anja Vrecer		 source
presentation source		 9
PHUSE EU 2024 Leveraging 'Ensemble' Machine Learning Models to Drive Automation of Specification Generation and Data Mapping in Clinical Trial Data Submission Venu Mallarapu source
presentation source		 10
SESUG 2024 Charting Your Organization's Machine Learning Roadmap Ryan Paul Lafler source 7
SESUG 2024 Developing Artificial and Convolutional Neural Networks with Python's Keras API for TensorFlow Ryan Paul Lafler,
Anna T. K. Wade		 source 18
WUSS 2024 Airbnb Consumer Behavior Analysis Evelyn Larson,
Anabeli Chandra		 source 14
WUSS 2024 Charting Your Organization's Machine Learning Roadmap Ryan Paul Lafler source 6
WUSS 2024 Developing Artificial and Convolutional Neural Networks with Python's Keras API for TensorFlow Ryan Paul Lafler,
Anna T. K. Wade		 source 18
WUSS 2024 Flight Delay Analysis and Prediction Eric C Braga,
Miguel Ángel Bravo Martínez del Valle,
Riley Rutan,
Fernanda Carrillo		 source 19
WUSS 2024 X-Ray Image Classification with Neural Networks Riley Rutan,
Eric C Braga,
Xinyu Du		 source 10
PharmaSUG 2024 Unraveling the Layers within Neural Networks: Designing Artificial and Convolutional Neural Networks for Classification and Regression Using Python-s Keras & TensorFlow Ryan Paul Lafler,
Anna T. K. Wade		 source 18
PHUSE US 2024 Applying Machine Learning for Predicting Potential Adverse Events from Real-World Data Samiul Haque source
presentation source		 6
PHUSE US 2024 Large Language Models to Support RBM and Clinical Trial Development Sebastiaan Höppner,
Laura Trotta		 source
presentation source		 5
PHUSE US 2024 Risk-Based Monitoring Using AI/ML Detection of Systematic Bias in Clinical Data Kostiantyn Drach,
Sergey Glushakov		 source
presentation source		 12
PHUSE EU 2023 GPT (Generative Pretrained Transformers) and LLM (Large Language Models) have generated a lot of attention since their inception in early 2023 with the advent of OpenAI GPT3.5, then GPT4.0 Jerome Vetillard,
Mark Lambrecht		 source
presentation source		 16
PHUSE EU 2023 Using Machine learning for building a demonstrative model to counter the under-diagnosis of dementia Ashapriya Sebastian source
presentation source		 12
WUSS 2023 Introduction to Machine Learning - Descriptions and Best Practices Jim Box source 13
WUSS 2023 Unraveling the Layers within Neural Networks: Designing Artificial and Convolutional Neural Networks for Classification and Regression Tasks using TensorFlow in Python Ryan Paul Lafler,
Anna T. K. Wade		 source 24
PharmaSUG 2023 Deep Learning to Classify Adverse Events from Patient Narratives Tom Sabo,
Sundaresh Sankaran,
Qais Hatim		 source 11
PharmaSUG 2023 What is Machine Learning, anyway? Jim Box source 11
PHUSE US 2023 Introduction to Machine Learning Models, How to Train Them and What to Do if They Become Sassy [presentation] Thomas Kalfas source 34
WUSS 2022 What's your model really doing? Understanding human biases in Machine Learning. Jim Box source 5
PharmaSUG 2022 Generating Real World Evidence On The Likelihood Of Metastatic Cancer In Patients Through Machine Learning In Observational Research: Insights For Prevention Sherrine Eid,
Samiul Haque,
S. Robert Collins		 source 8
PharmaSUG 2022 Potential features analysis affect Covid-19 spread Yida Bao,
Philippe Gaillard		 source 11
PharmaSUG 2022 What-s your model really doing? Human bias in Machine Learning Jim Box source 4
PHUSE US 2022 Using the Geometric Properties of Datasets to Analyze the Accuracy of COVID-19 Forecasting Models [presentation] Irina Kotenko,
Kostiantyn Drach		 source 30
PHUSE EU 2021 Deep Active Learning for Clinical Entity Recognition [presentation] Nadia Abraham source
video		 33
SESUG 2021 Use SAS Enterprise Miner Workstation 15.1 to Do Predictive Analysis for Mobile Strategy Games Industry Chun Du source 11
PHUSE US 2021 Automate Scientific Document such as CSR Generation using ML/NLP [presentation] Farha Feroze source
video		 13
PHUSE US 2021 SDTM Mapping and Conversion Using NLP [presentation] Eric Weising source
video		 16
PHUSE US 2021 Using Computer Vision to Identify and Extract Tables from Various File Formats [presentation] Kanishk Mair,
Shashank Pagidimarri		 source
video		 22
PHUSE US 2021 Using Natural Language Processing to Create Subject Safety Narratives [presentation] Frank Meloni,
Tim Willittes		 source
video		 8
PharmaSUG 2021 How I became a Machine Learning Engineer from a Statistical Programmer Kevin Lee source 4
PharmaSUG 2021 Machine Learning Programming Workshop - Natural Language Processing (NLP) Kevin Lee source 10
SGF 2021 Developing a SAS Deep Learning MedDRA encoder (MedDRA-DeepCoder) for Patient Narratives Qais Hatim source 11
SGF 2021 Investing in Education Intelligently Sam Edison,
Rushya Puttam,
Akhil Emani,
Nithisha Katta		 source
presentation		 7
SGF 2021 Let's enjoy the computer vision using SAS Viya! Shinjo Yada,
Ryuji Uozumi		 source 19
SGF 2021 Next-Generation Data Mining and Machine Learning: Python and SAS Together at Last George S. Habek,
Suman Kumar Das		 source
presentation		 24
PHUSE EU 2020 SDTM Mapping and Conversion Using ML/NLP [presentation] Eric Weising,
Farha Feroze		 source
video		 15
SGF 2020 Achieving Net-Zero: Forecasting Power Usage to Improve Sustainability on Clemson University's Campus David L. White source 10
SGF 2020 Apply the Key Ideas from Andrew Ng's Machine Learning Certification (Coursera) in SAS Viya Petri Roine source 9
SGF 2020 Automation in SAS Visual Data Mining and Machine Learning Wendy Czika source 26
SGF 2020 Bringing Computer Vision to the Edge: An Overview of Real-Time Image Analysis with SAS Maggie Du source 15
SGF 2020 Deploying Computer Vision by Combining Deep Learning Action Sets with Open-Source Technology Jonny McElhinney,
Duncan Bain,
Haidar Altaie		 source 20
SGF 2020 Exploring Online Drug Reviews using Text Analytics, Sentiment Analysis, and Data Mining Models Thu Dinh,
Goutam Chakraborty		 source 23
SGF 2020 How SAS Viya Provides a Way to Deliver Analytics to Everyone: Image Classification Examples Pavel Rogatch source 5
SGF 2020 How to Explain Your Black-Box Models in SAS Viya Funda Güneş source 26
SGF 2020 Modelling Imbalanced Classes Humphrey Brydon source 6
SGF 2020 Multilingual Sentiment Analysis: An RNN-Based Framework for Limited Data Ethem Can,
Aysu Ezen-Can		 source 7
SGF 2020 Neural-Network Based Forecasting Strategies in SAS Viya Steven C. Mills source 14
SGF 2020 NLP with BERT: Sentiment Analysis Using SAS Deep Learning and DLPy Doug Cairns,
Xiangxiang Meng		 source 11
SGF 2020 Pricing Life Insurance Contracts with Early Exercise Features Using Neural Networks Helgard Raubenheimer source 9
SGF 2020 The Seven Most Popular Machine Learning Algorithms for Online Fraud Detection and Their Use in SAS Patrick Maher source 6
SGF 2020 Transfer Learning for Mining Digital Phenotype by SAS Viya Satoki Fujita source 18
SGF 2020 Using Analytics to Predict Tax Recovery and Prioritize Audits and Investigations in Canada Jason A. Oliver source 19
SGF 2020 Using Machine Learning and Demand Sensing to Enhance Short-Term Forecasting for CPGs Kedar Prabhudesai source 10
PharmaSUG 2020 Detecting Side Effects and Evaluating the Effectiveness of Drugs from Customers' Online Reviews using Text Analytics, Sentiment Analysis and Machine Learning Models Thu Dinh,
Goutam Chakraborty		 source 22
PharmaSUG 2020 Gradient Descent: Using SAS for Gradient Boosting Karen Walker source 11
PHUSE US 2020 Accelerating Translational Research by Leveraging AI and Machine Learning Leveraging AI source
presentation source		 9
PHUSE US 2020 Data Review of RECIST Criteria using ML/AI A Proof of Concept Example Priya Gopal source
presentation source		 7
PHUSE US 2020 Disruption is Personal: RWE and AI Changing the Game for Personalized Medicine Arnab Goswami source
presentation source		 7
PHUSE US 2020 R for Deep Learning with Tensorflow with Applications in Cancer Immunotherapy Philip Bowsher source 14
PHUSE EU 2019 AI-assisted Code Reviews Igor Khorlo source
presentation source		 7
PHUSE EU 2019 Preparing Big Data for Analysis Thomas Ellebæk source
poster source		 4
PHUSE EU 2019 SMART RPA - powered by Machine Learning Charan Kumar Kuyyamudira Janardhana source
presentation source		 10
PHUSE EU 2019 The Impact of AI, RWE, Precision Medicine and Mobile Health Technology on Streamlined Clinical Trials Stijn Rogiers,
Olivier Bouchard		 source
presentation source		 11
PHUSE EU 2019 Using Image recognition in production support Thomas Dekelver source
presentation source		 8
PHUSE EU 2019 Using R to build artificial neural networks in medical data Thomas Wollseifen source
presentation source		 17
SESUG 2019 Face Recognition Using SAS Viya: Guess Who the Person Is! Pratyush Dash source 6
SESUG 2019 Glass Box Neural Networks Ross Bettinger source 14
SESUG 2019 Hybrid Rare Event Sampling Technique Ross Bettinger source 15
SESUG 2019 Macros for Neural Network Modeling Ross Bettinger source 23
SESUG 2019 Summarizing some conventional methods to classify a binary target Yida Bao,
Philippe Gaillard		 source 10
SCSUG 2019 Building a Neural Network Scoring Model with JMP and Microsoft SQL Server to predict Aggregate Motor Vehicle Driver Alcohol Impairment David Whitchurch source 10
SCSUG 2019 Uplift Modeling in Retail Marketing using SAS Aashish Arjan Bugalia,
Agastya Komarraju,
Miriam McGaugh		 source 15
MWSUG 2019 Classifying Risk in Life Insurance using Predictive Analytics Sai Gopi Krishna Govindarajula 6
MWSUG 2019 Deploying SAS Viya Docker images to the cloud - a step by step guide Alan Zablocki 25
MWSUG 2019 Detecting Side Effects and Evaluating Effectiveness of Drugs from Customers' Online Reviews using Text Analytics and Data Mining Models Thu Dinh 23
WUSS 2019 Telecom Industry: Customer Churn Prediction Aakash Dwivedi source 7
PharmaSUG 2019 An Introduction to the Process of Improving a Neural Network YuTing Tian source 22
PharmaSUG 2019 Artificial Intelligence and Real World Evidence - it takes two to tango Charan Kumar Kuyyamudira Janardhana source 9
PharmaSUG 2019 Hands-on Training for Machine Learning Programming Kevin Lee source 12
PharmaSUG 2019 Machine Learning Approaches to Identify Rare Diseases Xuan Sun,
Ruohan Wang		 source 5
PharmaSUG 2019 Using Real-World Evidence to Affect the Opioid Crisis Sherrine Eid,
Andrea Coombs		 source 10
SGF 2019 An Introduction to the Process of Improving a Neural Network YuTing Tian source 22
SGF 2019 Asking the Right Questions: A study in Multiclass Imbalanced Classification for H1-B Visas Matthew Bunker,
Owen Kelly		 source 7
SGF 2019 Exploring Computer Vision in Deep Learning: Object Detection and Semantic Segmentation Xindian Long,
Maggie Du,
Xiangqian Hu		 source 20
SGF 2019 Factors Affecting Mental Health in Employees and Their Relation to Suicide Rates at a Workplace Shambhavi Pandey source 11
SGF 2019 Factors Influencing Academic Performance Onkar Mayekar,
Miriam McGaugh		 source 9
SGF 2019 How to Use Deep Learning with Your Internet of Things (IoT) Digital Twin Bradley W. Klenz source 9
SGF 2019 Latin Hypersquare Sampling and Genetic Algorithm in SAS/IML for Hyperparameter Optimization Andrea Magatti source 16
SGF 2019 Make SAS Viya Realize Strategic Data Science - Data Governance, AI Platform and AI SAS Programmer Ryo Kiguchi,
Katsunari Hirano,
Yoshitake Kitanishi		 source 8
SGF 2019 Medical Image Analytics in SAS Viya for the Treatment of Colorectal Cancer Spread to the Liver Fijoy Vadakkumpadan,
Joost Huiskens		 source 11
SGF 2019 Modeling with Deep Recurrent Architectures: A Case Study of Using SAS and Python for Deep Learning Linh H. Le,
Ying Xie		 source 12
SGF 2019 Predicting the success of a startup company Vrushank Shah,
Miriam McGaugh		 source 11
SGF 2019 Real-World Evidence and Population Health Analytics: Intersection and Application David Olaleye source 16
SGF 2019 The Aftermath What Happens after you Deploy Your Models and Decisions David R. Duling source 18
PHUSE US 2019 Artificial Intelligence and Healthcare Aman Bahl,
Hrideep Antony		 source
presentation source		 10
PHUSE US 2019 Metadata-driven Automation in Drug Research Hanming Tu,
Zhongping Lin		 source
presentation source		 13
PHUSE US 2019 Smart Transformation of Clinical & Nonclinical Data for Insights Isaac Mativo,
Raja Ramesh,
Phaneendra Bonda		 source
presentation source		 1
PHUSE US 2019 Voice-based Clinical Monitoring Assistant Anoop Ambika source
presentation source		 7
SCSUG 2018 Machine Learning in Litigation Prediction - Workers' Compensation Litigation Propensity Predictive Model Mei Najim source 17
PHUSE EU 2018 Introduction to Machine Learning Nicolas Dupuis,
Kevin Lee		 source
presentation source		 9
PHUSE EU 2018 Serious Adverse Events Identification: A Machine Learning Approach [presentation] Jingshu Liu,
Laura Katz		 source 37
PHUSE EU 2018 Using SAS Advanced Analytic to Gain Insight into the Opioid Epidemic [presentation] Andy Bayliss source 17
PHUSE EU 2018 Want to Get Started with Deep Learning? Prototyping a Deep Learning Image Classifier Thomas Ellebaek source
presentation source		 9
SESUG 2018 Building Neural Network Model in Base SAS (From Scratch) Soujanya Mandalapu,
Yan Wang,
Xuelei (Sherry) Ni		 source 20
SESUG 2018 Fight Human Trafficking with Text Analytics Stuti Mehrotra,
Miriam McGaugh		 source 10
MWSUG 2018 An Introduction to the Process of Improving a Neural Network YuTing Tian 22
MWSUG 2018 Pan-Cancer Epigenetic Biomarker Selection from Blood Sample Using SAS Xi Chen,
Jin Xie,
Qingcong Yuan		 9
WUSS 2018 Deep neural nets in SAS Enterprise Miner Russell M. Lavery source 49
WUSS 2018 Machine Learning - Why we should know it and how it works Kevin Lee source 8
PHUSE US 2018 Mine the Gap: How the Role of Data Scientist Fills a Need in the Pharmaceutical Industry Michael S Rimler,
Jorine Putter		 source
presentation source		 8
PHUSE US 2018 Modeling and Text Analysis to Empower FAERS Adverse Event Mitigation Qais Hatim,
Emily McRae		 source
presentation source		 24
PharmaSUG 2018 Machine Learning - Why we should know and How it works Kevin Lee source 8
PharmaSUG 2018 Visualizing and Modeling Clinical Trial Data Alex Ford,
Andrea Coombs		 source 7
SGF 2018 A Study of Modeling Approaches for Predicting Dropout in a Business College Helmut Schneider,
Xuan Wang		 source 8
SGF 2018 Accelerate your End-to-End Enterprise Decisions with SAS Viya David R. Duling,
Steve Sparano		 source 12
SGF 2018 Claim Analytics: A Litigation Prediction Case Study Mei Najim source 18
SGF 2018 Demystify Income Models with Advanced Machine Learning Techniques for Ultimate Accuracy Vickey Chang source 18
SGF 2018 Image Classification Using SAS Enterprise Miner 14.1 Akshay Arora source 5
SGF 2018 Image Processing: Seeing the World through the Eyes of SAS Viya Ivan Gomez source 11
SGF 2018 Interpreting Black-Box Machine Learning Models Using Partial Dependence and Individual Conditional Expectation Plots Ray Wright source 19
SGF 2018 Make SAS Enterprise Miner Play Nicely with SAS Viya Beth Ebersole source 31
SGF 2018 Mitigating the Effects of Class Imbalance Using SMOTE and Tomek Link Undersampling in SAS Kyle Biron source 20
SGF 2018 Performing Machine Learning Techniques in a Contextual Marketing Scenario Francisco Capetillo,
Alvaro Velasquez		 source 11
SGF 2018 Real-Time Image Processing and Analytics Using SAS Event Stream Processing Frederic Combaneyre source 10
SGF 2018 Risk Pathways: Using Machine Learning Techniques of SAS Viya to Understand Customer Risk Drivers Leigh Ann Herhold source 14
SGF 2018 Shorter Waiting Time, Better Emergency Healthcare: Forecasting Stockholms' Emergency Room Visits Martin Nordberg,
Oskar Eriksson		 source 24
SGF 2018 The Ensemble of Neural Network and Gradient Boosting for the Prediction of Customer Profitability Sunny Lam source 15
SGF 2018 Top 10 Tips for SAS Enterprise Miner Based on 20 Years' Experience Melodie Rush source 21
SGF 2018 Using Predictive Analytics to Improve a Student Advising System Thanuja Sakruti,
Youyou Zheng		 source 13
SESUG 2017 Churn the Data Around - A Machine Learning Approach to Understanding Why Customers Leave Sean Ankenbruck source 9
SESUG 2017 Finding the Gold in Your Data: An Overview of Data Mining David A. Dickey source 25
SCSUG 2017 The True Price for Your House Mettilda Kaimathuruth,
Goutam Chakraborty		 source 15
MWSUG 2017 Claim Analytics Mei Najim 18
WUSS 2017 An Overview of SAS Visual Data Mining and Machine Learning on SAS Viya Radhikha Myneni source 23
WUSS 2017 Nonlinear Modeling with Big Data in SAS and JMP Chong Ho,
Hyun Seo Lee,
Siyan Gan,
Emily Brown		 source 12
SGF 2017 A Data Mining Approach to Predict Students at Risk Youyou Zheng,
Thanuja Sakruti		 source 13
SGF 2017 A Man with One Watch Always Knows What Time It Is Tim Fairchild source 9
SGF 2017 An Overview of SAS Visual Data Mining and Machine Learning on SAS Viya Jonathan Wexler,
Susan Haller		 source 23
SGF 2017 Interactive Modeling in SAS Visual Analytics Donald R. Chapman,
Jonathan Wexler		 source 16
SGF 2017 Modeling the Merchandise Return Behavior of Anonymous and Non-Anonymous Online Apparel Retail Shoppers Sunny Lam source 20
SGF 2017 Predictive Accuracy: A Misleading Performance Measure for Highly Imbalanced Data Josephine Akosa source 12
SGF 2017 Property and Casualty Insurance Predictive Analytics in SAS Mei Najim source 17
SGF 2017 Random Forests with Approximate Bayesian Model Averaging Tiny Du Toit,
Andre De Waal		 source 11
SGF 2017 The Rise of the Machines, Analytics, and Humans: Artificial Intelligence in the Age of the IIoT Kathy Ball,
Robert Silverman		 source 10
SCSUG 2016 Analysis of degradation patterns in the clinical efficacies of anti-microbial drug usage Shweta Arya,
Bhuvaneswari Yallabandi		 source 12
SESUG 2016 A Data Mining Approach to Predict Student-at-risk Youyou Zheng,
Thanuja Sakruti,
Abhishek Uppalapati,
Sivakumar Jaganathan		 source 12
SESUG 2016 Know your Interest Rate Anirban Chakraborty,
Goutam Chakraborty		 source 16
MWSUG 2016 An Animated Guide: Deep Neural Networks in SAS Enterprise Miner Russell M. Lavery 47
MWSUG 2016 Identifying the factors responsible for loan defaults and classification of customers using SAS Enterprise Miner Juhi Bhargava,
Prashanth Reddy Musuku		 13
MWSUG 2016 Property and Casualty Insurance Predictive Analytics in SAS Mei Najim 17
SGF 2016 A Property and Casualty Insurance Predictive Modeling Process in SAS Mei Najim source 14
SGF 2016 Application of Data Mining Techniques in Improving Breast Cancer Diagnosis Josephine Akosa source
presentation		 10
SGF 2016 Application of Gradient Boosting through SAS Enterprise Miner to Classify Human Activities Minh Pham,
Mary Ruppert-Stroescu,
Mostakim Tanjil		 source
presentation		 8
SGF 2016 Ensemble Modeling: Recent Advances and Applications Wendy Czika,
Ye Liu		 source 20
SGF 2016 Forecasting Short Time Series Using SAS Forecast Server Cobey Abramowski,
Christian Haxholdt,
Chris Houck,
Benjamin Perryman		 source 5
SGF 2016 Lyric Complexity and Song Popularity: Analysis of Lyric Composition and Relation among Billboard Top 100 Songs John Harden,
Yang Gao,
Vojtech Hrdinka,
Chris Linn		 source 15
SGF 2016 Mass-Scale, Automated Machine Learning and Model Deployment Using SAS Factory Miner and SAS Decision Manager Jonathan Wexler,
Steve Sparano		 source 19
SGF 2016 Predicting Human Activity Sensor Data Using an Auto Neural Model with Stepwise Logistic Regression Inputs Venkata Vennelakanti source
presentation		 10
SGF 2016 Predicting If Mental Health Facilities Will Offer Free Treatment to Patients Who Cannot Afford It Ram Poudel,
Lynn Xiang		 source 11
SGF 2016 Predicting the Current Market Value of a Housing Unit across the Four Census Regions of the United States Using SAS Enterprise Miner 12.3 Mostakim Tanjil,
Goutam Chakraborty		 source
presentation		 12
MWSUG 2015 A Property & Casualty Insurance Predictive Modeling Process in SAS Mei Najim 14
MWSUG 2015 Predictive Modeling Using Artificial Neural Networks in SAS Enterprise Miner Kechen Zhao 4
SGF 2015 A Case Study: Improve Classification of Rare Events with SAS Enterprise Miner Ruizhe Wang,
Novik Lee,
Yun Wei		 source
zip		 12
SGF 2015 Applied Analytics in Festival Tourism: A Case Study of Intention-to-Revisit Prediction in an Annual Local Food Festival in Thailand Jongsawas Chongwatpol,
Thanathorn Vajirakachorn		 source 13
SGF 2015 Determining the Key Success Factors for Hit Songs in the Billboard Music Charts Piboon Banpotsakun,
Jongsawas Chongwatpol		 source 12
SGF 2015 SAS Does Data Science: How to Succeed in a Data Science Competition Patrick Hall source
zip		 11
SGF 2015 Unconventional Data-Driven Methodologies Forecast Performance in Unconventional Oil and Gas Reservoirs Keith R. Holdaway,
Louis Fabbi,
Dan Lozie		 source 20
SESUG 2014 Text Analytics using High Performance SAS Text Miner Edward R. Jones source 7
PHUSE EU 2014 Exploring Safety and Diagnostic Data using Self-Organising Maps Colin de Klerk source
presentation source		 9
MWSUG 2014 Text Analytics in High Performance SAS and SAS Enterprise Miner Edward R. Jones 16
PharmaSUG CN 2014 From Coal Mining to Data Mining: Advancing Programming Management for Clinical Projects with Text Analytics Zhouming Sun 10
WUSS 2014 Decision Trees as a Predictive Modeling Method Gerry Hobbs source 8
SGF 2014 An Overview of Machine Learning with SAS Enterprise Miner Patrick Hall,
Jared Dean,
Ilknur Kaynar Kabul,
Jorge Silva		 source
zip		 24
SGF 2014 How to Interpret SVD Units in Predictive Models? Murali Krishna Pagolu,
Goutam Chakraborty		 source 10
SGF 2014 Improving the Thermal Efficiency of Coal-Fired Power Plants: A Data Mining Approach Thanrawee Phurithititanapong,
Jongsawas Chongwatpol		 source 12
SGF 2014 Internet Gambling Behavioral Markers: Using the Power of SAS Enterprise Miner 12.1 to Predict High-Risk Internet Gamblers Sai Vijay Kishore Movva,
Vandana Reddy,
Goutam Chakraborty		 source 11
SGF 2014 Text Mining Reveals the Secret of Success: Identification of Sales Determinants Hidden in Customers' Opinions Rafal Wojdan source 18
SGF 2014 The Role of Customer Response Models in Customer Solicitation Center's Direct Marketing Campaign Arun Mandapaka,
Amit Kushwah,
Goutam Chakraborty		 source 12
SGF 2014 The Soccer Oracle: Predicting Soccer Game Outcomes Using SAS Enterprise Miner [presentation] Vandana Reddy,
Sai Vijay Kishore Movva		 source 3
SGF 2014 Using SAS Enterprise Miner to Predict the Injury Risk Involved in Car Accidents Prateek Khare,
Vandana Reddy,
Goutam Chakraborty		 source 7
WUSS 2013 Decision Trees as a Predictive Modeling Method Gerry Hobbs source 8
SESUG 2013 Finding the Gold in Your Data: An Overview of Data Mining David A. Dickey source 16
SESUG 2013 What Do Your Consumer Habits Say About Your Health? Using Third-Party Data to Predict Individual Health Risk and Costs Satish Garla source 15
MWSUG 2013 Finding the Gold in Your Data: An Overview of Data Mining David A. Dickey 16
NESUG 2013 An Animated Guide: An Introduction to Neural Nets: Russell M. Lavery 12
SGF 2013 Finding the Gold in Your Data: An Overview of Data Mining David A. Dickey source 16
SGF 2013 Predictive Modeling in Sports Leagues: An Application in the Indian Premier League Goutam Chakraborty,
Pankush Kalgotra,
Ramesh Sharda		 source 10
SGF 2013 Using the Boosting Technique to Improve the Predictive Power of a Credit Risk Model Alejandro Correa Bahnsen,
Andres F. Gonzalez,
Darwin A. Amezquita		 source 7
SGF 2013 What Do Your Consumer Habits Say About Your Health? Using Third-Party Data to Predict Individual Health Risk and Costs Albert Hopping,
Rick Monaco,
Sarah Rittman,
Satish Garla		 source 15
MWSUG 2012 An Analysis of Diabetes Risk Factors Using Data Mining Approach Akkarapol Sa-ngasoongsong,
Jongsawas Chongwatpol		 11
SGF 2012 An Experimental Comparison of Classification Techniques for Imbalanced Credit Scoring Data Sets Using SAS Enterprise Miner Iain L. Brown source 14
SGF 2012 Constructing a Credit Risk Scorecard Using Predictive Clusters Andres F. Gonzalez,
Alejandro Correa Bahnsen,
Ana C. Nieto,
Darwin A. Amezquita		 source 13
SGF 2012 Developing a Predictive Model for Customer Trip Purpose to Be Integrated into Enterprise Strategy and Analytics Jonathan Levine source 16
SGF 2012 Parallel Computing in SAS: Genetic Algorithms Application Alejandro Correa Bahnsen,
Darwin A. Amezquita,
Andres F. Gonzalez		 source 5
SGF 2012 Using SAS Enterprise BI and SAS Enterprise Miner to Reduce Student Attrition Matt T. Bogard,
Chris James,
Tuesdi Helbig,
Gina Huff		 source 10
PHUSE EU 2011 Learning from critical values - adverse event identification and classification Milena Balcerzak source
poster source		 10
MWSUG 2011 Using JMP Visualizations to Build a Statistical Model George J. Hurley 11
NESUG 2011 An Optimal Search Process of "Eigen Knots" for Spline Logistic Regression John Gao,
Cheryl Caswell		 7
PharmaSUG 2011 Using SAS Predictive Modeling to Investigate the Asthma's Patient Future Hospitalization Risk Yehia H. Khalil source 7
SGF 2011 BASE SAS Implementation of Information Theoretic Feature Selection for Neural Networks Martin B. Jetton source 13
SGF 2011 Genetic Algorithm Optimization for Selecting the Best Architecture of a Multi-Layer Perceptron Neural Network: A Credit Scoring Case Alejandro Correa,
Andres F. Gonzalez,
Camilo Ladino		 source 8
SGF 2011 Using JMP Visualizations to Build a Statistical Model George J. Hurley source 11
MWSUG 2010 Gastrointestinal Diseases: Diagnoses, Misdiagnoses, and Comorbidities Pedro Ramos 4
MWSUG 2009 Predictive Modeling in Enterprise Miner Versus Regression Patricia B. Cerrito 27
SGF 2009 Optimizing Product Formulation with JMP and SAS Enterprise Miner John Leveille,
Wayne J. Levin		 source 6
SGF 2009 The Problem of Regression Assumptions and the Use of Predictive Modeling Patricia B. Cerrito source 20
MWSUG 2008 The Difference Between Predictive Modeling and Regression Patricia B. Cerrito 19
SGF 2008 In Search for a Golden Algorithm Tamara S. Slipchenko,
Candice Bowman,
Catherine Sugar,
Allen L. Gifford		 source 9
SGF 2008 Pricing of Options Using Hybrid Artificial Neural Networks Anant Saxena source 25
SGF 2008 Using SAS Enterprise Miner to Prescribe a Pre-Screen Mailing Rex Pruitt source 6
NESUG 2007 A SAS Constellation Diagram Has Many Faces Shi-Tao Yeh pdf 17
MWSUG 2007 Identifying and Overcoming Common Data Mining Mistakes Doug Wielenga 20
WUSS 2007 Case Study: Development of an HIV casefinding algorithm with SAS Enterprise Miner Tamara Slipchenko,
Candice Bowman,
Yuexin (Cindy) Chen,
Catherine Sugar,
Allen L. Gifford		 source 9
WUSS 2007 Data Mining Using SAS Enterprise Miner Randall Matignon source 10
PHUSE EU 2007 Data mining in the clinical research environment David Smith source 7
PharmaSUG 2007 Clinical Adverse Events Data Analysis and Visualization Shi-Tao Yeh 12
SGF 2007 Find the Best Prospects for a New Product by Using a Data Mining Model Ting Millette source 8
SGF 2007 Identifying and Overcoming Common Data Mining Mistakes Doug Wielenga source 20
SGF 2007 SAS Constellation Diagram Has Many Faces Shi-Tao Yeh source 16
SCSUG 2006 Basics of Modeling in a Data Mining Context Keith Cranford 8
SCSUG 2006 Data Mining at the Texas State Auditor's Office Thomas J. Winn Jr. 11
SESUG 2006 Comparing Time series, Generalized Linear Models and Artificial Neural Network Models for Transactional Data Analysis Joseph Twagilimana source 9
WUSS 2006 Find the Best Prospects for a New Product by Using a Data Mining Model Ting Wu source 8
SUGI 2006 Fraud Detection - A Primer for SAS Programmers Thomas J. Winn Jr. source 6
SUGI 2006 Interactive Graphs from the SAS System Shi-Tao Yeh source 10
SCSUG 2005 Techniques And Methods To Implement Neural Networks Using SAS and .NET Shenglin Zheng,
Johnny Dupree,
Uttam Shah,
Marc Torres		 10
MWSUG 2005 Comparison Of Enterprise Miner And SAS/STAT For Data Mining Patricia B. Cerrito 12
PharmaSUG 2005 Data Mining Medication Prescriptions for a Representative National Sample Patricia B. Cerrito,
Antonio Badia,
John C. Cerrito		 13
SUGI 2005 Structured Neural Network Techniques for Modeling Loyalty and Profitability Carl Lee,
Tim Rey,
James W. Mentele,
Michael Garver		 source 13
NESUG 2004 SAS and Neural Networking: Spatial / Functional Maps of Biological Systems Haftan M. Eckholdt 7
SCSUG 2004 Fraud Detection - A Primer for SAS Programmers Thomas J. Winn Jr. 9
SUGI 2004 Data Mining Applications for a Six-Sigma Project Jong-Hag Jeon source 3
SCSUG 2003 Inventory Forecasting With SAS/OR Tom Taylor 6
SESUG 2003 Missing Data Values: Analyzing their Effects on Rainfall Forecasts Using PROC EXPAND and the SAS Time-Series Forecasting System Richard A. March source 4
SEUGI 2003 Data Mining for Predicting Customer Satisfaction [presentation] Francesco Vivarelli source 35
SEUGI 2003 Developing an Optimal Product Offering Strategy and Maximizing Overall Profitablility of Current Clients [presentation] Tamer Keshi source 13
SEUGI 2003 SAS Manufacturing Intelligence Platform [presentation] Alexander Dahne source 28
SUGI 2003 Multistage Cross-Sell Model of Employers in the Financial Industry Kwan Park,
Steve Donohue		 source 5
SUGI 2003 The Use of Geographic Information Systems to Investigate Environmental Pollutants in Relationship to Medical Treatment Patricia B. Cerrito,
Robert Forbes,
George R. Barnes		 source 6
NESUG 2002 Real Neural Networking: Using SAS To Build And Deliver Behavior-Genetic-Nervous Spatial And Functional Maps Of Biological Systems Haftan M. Eckholdt 5
SEUGI 2002 Bankruptcy scoring using the SAS Enterprise Miner [presentation] Michael Wetzel source 18
SEUGI 2002 Buidling Credit Scorecards using SAS Enterprise Miner [presentation] John Lawrence,
Martin Crossley		 source 11
SEUGI 2002 Calculating the return on investment made in Analytical CRM - does it make business sense? [presentation] AJ Kruger source 27
SEUGI 2002 Data Mining In A Zero Latency Enterprise Environment [presentation] Luca Selvatici source 24
SEUGI 2002 Know whom your cusomers listen to - Scoring for marketing channels reception propensity [presentation] Pawel Gasiorowski,
Wojciech Latusek		 source 20
SEUGI 2002 Predictin Customer Default Times using Survival Analysis Methods in SAS [presentation] Bart Baesens source 16
SUGI 2002 Data Mining in Quality Improvement Bing Deng source 4
SUGI 2002 Neural Networks and Genetic Algorithms as Tools for Forecasting Demand in Consumer Durables Jerry Nickelsburg,
Paul D. McNelis		 source 6
SUGI 2002 To Neural or Not to Neural? - This Is the Question Leon L. Fedenczuk source 6
SUGI 2002 Using SAS Enterprise Miner for Data Quality Monitoring in the Veterans Health Administration's External Peer Review Program James H. Forsythe source 5
VIEWS 2001 Data Mining and Statistical Models in Marketing Campaigns of BT Retail Francesco Vivarelli,
Martyn Johnson		 9
SEUGI 2001 A retrospective evaluation of a data mining approach to fetal asphyxia at delivery in a hospital database [presentation] Olavi Kauhanen,
Seppo Heinonen		 source 53
SEUGI 2001 Building a compelling case for Enterprise Miner Within The Bank of Scotland [presentation] Simon Hughes source 33
SEUGI 2001 Data Mining at CDG Bank: an advantage or a requirement? [presentation] Elisabete Silva,
Henrique Nicola		 source 25
SEUGI 2001 Data Mining In a Zero Latency Enterprise [presentation] Philip Bosinoff source 36
SEUGI 2001 Improving Datamining Results Through Data Selection and Preparation [presentation] Hein Lochner,
Nigel Wigram		 source 49
SEUGI 2001 SAS and CFMS Integration - Attacking Fraud in Next Generation Networks [presentation] Augusto Argento source 35
SEUGI 2001 The SAS Text Mining Solution [presentation] Bernd Drewes,
Wayne Thompson		 source 22
SUGI 2001 A Practical Introduction to the Power of Enterprise Miner Michael S. Lajiness source 7
SUGI 2001 Predictive Modeling in the Insurance Industry Using SAS Terry J. Woodfield source 5
SUGI 2001 Quality Design Based on SAS/EM Wenming Chen source 4
SUGI 2001 Using SAS Enterprise Miner for Forecasting Kattamuri S. Sarma source 6
SESUG 2000 Using Data Mining Technology to Identify and Prioritize Emerging Opportunities for Mortgage Lending to Homeownership-Deficient Communities Mortgage Lending source 10
NESUG 2000 Ad hoc Validation of Multivariate Regression Using PROC FACTOR with BY Groups Susan B. Garavaglia,
Asha Sharma,
Stephen Thompson		 10
SEUGI 2000 Analysing the footprints of your customer - A case study by ask|net and SAS [presentation] Klaus-Peter Huber source 40
SEUGI 2000 Business Intelligence in Telefonica I+D (R&D) [presentation] Alfredo Jesus Serrano source 32
SEUGI 2000 Data Mining for Building & Not Digging [presentation] Kathryn Burn-Thornton source 22
SEUGI 2000 Enterprise Mineer vs Underlying SAS Code: A Performance Comparison [presentation] Usama Hasan source 11
SEUGI 2000 Mining in Melodies [presentation] Bulcsu Fajszi,
Laszlo Surjan		 source 19
SEUGI 2000 Retail & E-tailing: From Customer Understanding to Target Marketing [presentation] Bernd Drewes source 49
SEUGI 2000 SAS Data Warehouse for Risk Analysis in Vilniaus Bankas [presentation] Algirdas Laukaitis source 41
SEUGI 2000 Targeted Drug Discovery through Data Mining [presentation] John C. Brocklebank,
Wayne Thompson		 source 28
SUGI 2000 Data Warehousing for Manufacturing Yield Improvement Robert A. Rutledge source 10
SUGI 2000 Using Enterprise Miner to Explore and Exploit Drug Discovery Data Michael S. Lajiness source 8
NESUG 1999 Applying Familiar Non-Parametric Tests to Evaluation of Neural Network Inputs Susan B. Garavaglia 10
SEUGI 1999 Credit risk analysis: an empirical approach [presentation] Angela Ancona,
Emiliano Laruccia,
Flavio Addolorato		 source 22
SEUGI 1999 Integrating management reporting with SAS Software: balanced scorecard, data mining and OLAP Analysis for an insurance company [presentation] Patrick Imark source 30
SUGI 1999 Use of Enterprise Miner and Multivariate Statistical Analysis to Build Fraud-Detection Models Shiao-Ping Lu source 5
WUSS 1998 Use of Data Enterprise Miner & Multivariate Statistical Analysis to Build Fraud-Detection Models Shiao-Ping Lu source 7
NESUG 1998 A Smart Guide to Dummy Variables: Four Applications and a Macro Susan B. Garavaglia 10
SEUGI 1998 Data Mining at IMS America: How We Turned a Mountain of Data into a Few Information-rich Molehills Jerry Kagan,
Paul Kallukaran		 source 7
SEUGI 1998 Database marketing applications in the Italian banking sector Alberto Saccardi,
Guido Cuzzocrea		 source 8
SEUGI 1998 Knowledge Discovery and Data Mining on the Example of Clinical Databases Leon Bobrowski,
Magdalena Topczewska		 source 6
SEUGI 1998 SAS data mining and neural networks: Powerful and efficient tools for customer-oriented pricing and target marketing in deregulated insurance markets Stefan Lecher source 8
SEUGI 1998 SAS software within the Ernst & Young audit practice [presentation] Ton Buffing source 36
SUGI 1998 Finding the Solution to Data Mining: A Map of the Features and Components of SAS Enterprise Miner Software Table of Contents John C. Brocklebank,
Mark Brown		 source 24
SUGI 1998 Strategic Use of the SAS System in Banking Laurie Rose source 5
SCSUG 1997 Applications of Intelligent Technologies for Efficient Businesses D. Thomas Taylor,
Ron Salazar		 6
NESUG 1997 A Self-Organizing Median-Estimation Model Susan B. Garavaglia 9
NESUG 1997 Data Mining at IMS America: How We Turned a Mountain of Data into a Few Information-rich Molehills Paul Kallukaran 8
NESUG 1997 Implementation and Comparison of the Back Propagation Neural Network in SAS Software John S. DaPonte 10
NESUG 1997 Mining Your Data for Health Care Quality Improvement Greg Rogers 7
SEUGI 1997 Early experiences with neural networks or if capacity planners had artificial intelligence, they'd be dangerous Neil McMenemy source 11
SEUGI 1997 Neural Networks by "Profit Line" Charles Oppenheim source 16
SUGI 1997 A SAS Based Data Mining Approach to Find Database Solutions in the Banking Industry Rama Jampani source 2
SUGI 1997 Data Mining Mark Brown,
John C. Brocklebank		 source 4
SUGI 1997 How Many Good Fishes Are There in Our Net? Neural Networks as a Data Analysis Tool in CDE-Mondadori's Data Warehouse Guido Cuzzocrea,
Alberto Saccardi,
Giovanni Lux,
Emilia Porta,
Arianna Benatti		 source 6
SUGI 1997 Mining Your Data for Health Care Quality Improvement Greg Rogers,
Ellen Joyner		 source 7
NESUG 1996 Statistical Analysis of Self-Organizing Maps Susan B. Garavaglia,
Asha Sharma		 10
SEUGI 1996 A network to solve your problems Martin Duffy source 7
SEUGI 1996 Building a metric information system to monitor UNIX, Bull and MVS systems [presentation] UNKNOWN source 33
SEUGI 1996 Business geographics and Database Marketing with the SAS System Bengt Bengtsson source 15
SEUGI 1996 Database marketing with the SAS System Joanna Crosse,
Linda Saul		 source 42
SEUGI 1996 Neural networks and/or classical statistics?! Jorg Imhoff,
Michael Wetzel		 source 19
SEUGI 1996 Neural Networks Application: a case study Rui Alexandre Henriques Gonçalves source 7
SEUGI 1996 Neural networks for credit and behaviour scoring at Neckermann Versand AG Hans-Peter Höschel,
Rolf-Jürgen Müller		 source 11
SEUGI 1996 SAS software solutions for direct marketing data analyses Michael Felsmann source 37
SEUGI 1996 Selecting creditworthy customers Stella Cacoullis source 8
SEUGI 1996 The SAS neural network application Gerhard Held source 17
SEUGI 1995 An Overview of New Data Analysis Features in the SAS System Gerhard Held source 6
SEUGI 1995 Uses of Neural Nets in the SAS System Derek Powell,
Martin Duffy		 source 11
SEUGI 1994 Neural Networks and Statistical Models Derek Powell,
Martin Duffy		 source 9
SUGI 1994 Interpreting Complex Nonlinear Models Lawrence C. Marsh,
Maureen A. McGlynn,
Debopam Chakraborty		 source 5
SUGI 1994 Neural Network Implementation in SAS Software Warren S. Sarle source 23
SUGI 1994 Neural Networks and Statistical Models Warren S. Sarle source 13
SUGI 1993 Estimating Neural Network Nonparametric Production Functions Using SAS Software Daric L. Brummet,
Lawrence C. Marsh		 source 5
MWSUG 1991 Consumer Demand Preference Patterns: Using PROC IML to Forecast Future Demand Daric Brummett,
Lawrence C. Marsh		 5
SUGI 1989 Optimization with Simulated Annealing Hugh Smith source 4

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