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Nathaniel D. Bastian
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2020 – today
- 2025
- [j29]Pahavalan Rajkumardheivanayahi, Ryan Berry
, Nicola Costagliola, Lance Fiondella
Explainability of Network Intrusion Detection Using Transformers: A Packet-Level Approach. IEEE Access 13: 5154-5174 (2025) - [j28]Quoc H. Nguyen
FedNIDS: A Federated Learning Framework for Packet-Based Network Intrusion Detection System. Digit. Threat. Res. Pract. 6(1): 1-23 (2025) - [j27]David A. Bierbrauer
Data-efficient Federated Learning for Edge Network Intrusion Detection. Eng. Appl. Artif. Intell. 150: 110685 (2025) - [j26]Yasir Ali Farrukh
XG-NID: Dual-modality network intrusion detection using a heterogeneous graph neural network and large language model. Expert Syst. Appl. 287: 128089 (2025) - [j25]Jalal Ghadermazi
Towards Real-Time Network Intrusion Detection With Image-Based Sequential Packets Representation. IEEE Trans. Big Data 11(1): 157-173 (2025) - [j24]Jalal Ghadermazi
GTAE-IDS: Graph Transformer-Based Autoencoder Framework for Real-Time Network Intrusion Detection. IEEE Trans. Inf. Forensics Secur. 20: 4026-4041 (2025) - [j23]Soumyadeep Hore
Deep PackGen: A Deep Reinforcement Learning Framework for Adversarial Network Packet Generation. ACM Trans. Priv. Secur. 28(2): 15:1-15:33 (2025) - [j22]Brian Matejek, Ashish Gehani, Nathaniel D. Bastian, Daniel J. Clouse, Bradford J. Kline, Susmit Jha:
SAFE-NID: Self-Attention with Normalizing-Flow Encodings for Network Intrusion Detection. Trans. Mach. Learn. Res. 2025 (2025) - [c42]Sanggeon Yun, Ryozo Masukawa, William Youngwoo Chung, Minhyoung Na, Nathaniel D. Bastian, Mohsen Imani:
Continuous GNN-Based Anomaly Detection on Edge Using Efficient Adaptive Knowledge Graph Learning. DATE 2025: 1-7 - [c41]Anirban Roy, Adam D. Cobb, Ramneet Kaur, Sumit Jha, Nathaniel D. Bastian, Alexander M. Berenbeim, Robert H. Thomson, Iain Cruickshank, Alvaro Velasquez, Susmit Jha:
Zero-Shot Detection of Out-of-Context Objects Using Foundation Models. WACV 2025: 9186-9195 - [c40]Hanning Chen, Yang Ni, Wenjun Huang, Yezi Liu, Sungheon Jeong, Fei Wen, Nathaniel D. Bastian, Hugo Latapie, Mohsen Imani:
VLTP: Vision-Language Guided Token Pruning for Task-Oriented Segmentation. WACV 2025: 9353-9363 - [i39]Yerin Kim, Alexander Benvenuti, Bo Chen, Mustafa O. Karabag, Abhishek Ninad Kulkarni, Nathaniel D. Bastian, Ufuk Topcu, Matthew Hale:
Deceptive Sequential Decision-Making via Regularized Policy Optimization. CoRR abs/2501.18803 (2025) - [i38]Paulo Shakarian, Gerardo I. Simari, Nathaniel D. Bastian:
Probabilistic Foundations for Metacognition via Hybrid-AI. CoRR abs/2502.05398 (2025) - [i37]Noel Ngu, Aditya Taparia, Gerardo I. Simari, Mario A. Leiva, Jack Corcoran, Ransalu Senanayake, Paulo Shakarian, Nathaniel D. Bastian:
Multiple Distribution Shift - Aerial (MDS-A): A Dataset for Test-Time Error Detection and Model Adaptation. CoRR abs/2502.13289 (2025) - [i36]Ryozo Masukawa, Sanggeon Yun, Sungheon Jeong, Wenjun Huang, Yang Ni, Ian Bryant, Nathaniel D. Bastian, Mohsen Imani:
PacketCLIP: Multi-Modal Embedding of Network Traffic and Language for Cybersecurity Reasoning. CoRR abs/2503.03747 (2025) - [i35]Chung-En Yu, Hsuan-Chih Chen, Brian Jalaian, Nathaniel D. Bastian:
Hydra: An Agentic Reasoning Approach for Enhancing Adversarial Robustness and Mitigating Hallucinations in Vision-Language Models. CoRR abs/2504.14395 (2025) - [i34]Trilok Padhi, Ramneet Kaur, Adam D. Cobb, Manoj Acharya, Anirban Roy, Colin Samplawski, Brian Matejek, Alexander M. Berenbeim, Nathaniel D. Bastian, Susmit Jha:
Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding. CoRR abs/2505.03788 (2025) - [i33]Sanggeon Yun, Ryozo Masukawa, Hyunwoo Oh, Nathaniel D. Bastian, Mohsen Imani:
A Few Large Shifts: Layer-Inconsistency Based Minimal Overhead Adversarial Example Detection. CoRR abs/2505.12586 (2025) - [i32]Aditya Taparia, Noel Ngu, Mario A. Leiva, Joshua Shay Kricheli, John Corcoran, Nathaniel D. Bastian, Gerardo I. Simari, Paulo Shakarian, Ransalu Senanayake:
VLC Fusion: Vision-Language Conditioned Sensor Fusion for Robust Object Detection. CoRR abs/2505.12715 (2025) - [i31]Alexandre Broggi, Nathaniel D. Bastian, Lance Fiondella, Gökhan Kul:
Adaptive Pruning of Deep Neural Networks for Resource-Aware Embedded Intrusion Detection on the Edge. CoRR abs/2505.14592 (2025) - [i30]Mario A. Leiva, Noel Ngu, Joshua Shay Kricheli, Aditya Taparia, Ransalu Senanayake, Paulo Shakarian, Nathaniel D. Bastian, Jack Corcoran, Gerardo I. Simari:
Consistency-based Abductive Reasoning over Perceptual Errors of Multiple Pre-trained Models in Novel Environments. CoRR abs/2505.19361 (2025) - [i29]Huynh T. T. Tran, Jacob Sander, Achraf Cohen, Brian Jalaian, Nathaniel D. Bastian:
Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning. CoRR abs/2506.04454 (2025) - [i28]Emilia Rivas, Sabrina Saika, Ahtesham Bakht, Aritran Piplai, Nathaniel D. Bastian, Ankit Shah:
Adapting Under Fire: Multi-Agent Reinforcement Learning for Adversarial Drift in Network Security. CoRR abs/2506.06565 (2025) - [i27]Ayush Gupta, Anirban Roy, Rama Chellappa, Nathaniel D. Bastian, Alvaro Velasquez, Susmit Jha:
TOGA: Temporally Grounded Open-Ended Video QA with Weak Supervision. CoRR abs/2506.09445 (2025) - [i26]Yasir Ali Farrukh, Syed Wali, Irfan Khan, Nathaniel D. Bastian:
Prmpt2Adpt: Prompt-Based Zero-Shot Domain Adaptation for Resource-Constrained Environments. CoRR abs/2506.16994 (2025) - 2024
- [j21]Andrew Strelzoff
Human Intuition and Algorithmic Efficiency Must Be Balanced to Enhance Data Mesh Resilience. Commun. ACM 67(5): 48-51 (2024) - [j20]Soumyadeep Hore
A sequential deep learning framework for a robust and resilient network intrusion detection system. Comput. Secur. 144: 103928 (2024) - [j19]Galamo Monkam, Michael J. De Lucia, Nathaniel D. Bastian
A topological data analysis approach for detecting data poisoning attacks against machine learning based network intrusion detection systems. Comput. Secur. 144: 103929 (2024) - [j18]Yasir Ali Farrukh
AIS-NIDS: An intelligent and self-sustaining network intrusion detection system. Comput. Secur. 144: 103982 (2024) - [j17]Marc Chalé
Constrained optimization based adversarial example generation for transfer attacks in network intrusion detection systems. Optim. Lett. 18(9): 2169-2188 (2024) - [j16]Lei Zhang
Multi-Memristor Based Distributed Decision Tree Circuit for Cybersecurity Applications. IEEE Trans. Circuits Syst. I Regul. Pap. 71(8): 3526-3537 (2024) - [j15]Zong-Zhi Lin
A Hypergraph-Based Machine Learning Ensemble Network Intrusion Detection System. IEEE Trans. Syst. Man Cybern. Syst. 54(11): 6911-6923 (2024) - [c39]Amirhossein Ravari, Guangyu Jiang, Zuyuan Zhang, Mahdi Imani, Robert H. Thomson, Aryn A. Pyke, Nathaniel D. Bastian, Tian Lan:
Adversarial Inverse Learning of Defense Policies Conditioned on Human Factor Models. IEEECONF 2024: 188-195 - [c38]Alice Bizzarri
Neuro-Symbolic Integration for Open Set Recognition in Network Intrusion Detection. AI*IA 2024: 50-63 - [c37]Alice Bizzarri
A Neuro-Symbolic Artificial Intelligence Network Intrusion Detection System. ICCCN 2024: 1-9 - [c36]Priscila Silva, Gaspard Baye, Alexandre Broggi, Nathaniel D. Bastian, Gökhan Kul, Lance Fiondella:
Predicting F1-Scores of Classifiers in Network Intrusion Detection Systems. ICCCN 2024: 1-6 - [c35]Alexander M. Berenbeim, Ramneet Kaur, Adam D. Cobb, Anirban Roy, Susmit Jha, Nathaniel D. Bastian:
Post-hoc Uncertainty Quantification for Neurosymbolic Artificial Intelligence. MILCOM 2024: 1-6 - [c34]Yerin Kim, Alexander Benvenuti, Bo Chen, Mustafa O. Karabag, Abhishek Ninad Kulkarni, Nathaniel D. Bastian, Ufuk Topcu, Matthew Hale:
Defining and Measuring Deception in Sequential Decision Systems: Application to Network Defense. MILCOM 2024: 1-6 - [c33]Emily A. Nack
ACI-IoT-2023: A Robust Dataset for Internet of Things Network Security Analysis. MILCOM 2024: 1-6 - [c32]Jacob Sander, Chung-En Johnny Yu, Brian Jalaian, Nathaniel D. Bastian:
Uncertainty-Quantified Neurosymbolic AI for Open Set Recognition in Network Intrusion Detection. MILCOM 2024: 13-18 - [c31]Joseph Riem, Lei Zhang, Jingdi Chen, Henry Mackay, Tian Lan, Nathaniel D. Bastian, Gina C. Adam:
Co-Design of Decision Trees for Network Intrusion Detection at the Edge on Digital vs. Analog Hardware. MILCOM 2024: 39-44 - [c30]Galamo F. Monkam, Nathaniel D. Bastian:
Model Poisoning Detection via Forensic Analysis. MILCOM 2024: 209-214 - [c29]Gaspard Baye, Priscila Silva, Alexandre Broggi, Nathaniel D. Bastian, Lance Fiondella, Gökhan Kul:
varMax: Towards Confidence-Based Zero-Day Attack Recognition. MILCOM 2024: 863-868 - [c28]Jingdi Chen, Hanhan Zhou, Yongsheng Mei, Carlee Joe-Wong, Gina C. Adam, Nathaniel D. Bastian, Tian Lan:
RGMDT: Return-Gap-Minimizing Decision Tree Extraction in Non-Euclidean Metric Space. NeurIPS 2024 - [c27]Iain J. Cruickshank
Seeing the Whole Elephant - A Comprehensive Framework for Data Education. SIGCSE (1) 2024: 248-254 - [c26]Alexander Wei, David A. Bierbrauer, Emily A. Nack
Offline Reinforcement Learning for Autonomous Cyber Defense Agents. WSC 2024: 1978-1989 - [d2]Nathaniel D. Bastian
ACI IoT Network Traffic Dataset 2023. IEEE DataPort, 2024 - [d1]Irfan Khan
Unified Multimodal Network Intrusion Detection Systems Dataset. IEEE DataPort, 2024 - [i25]Yuzhou Nie, Yanting Wang, Jinyuan Jia, Michael J. De Lucia, Nathaniel D. Bastian, Wenbo Guo, Dawn Song:
TrojFM: Resource-efficient Backdoor Attacks against Very Large Foundation Models. CoRR abs/2405.16783 (2024) - [i24]Alice Bizzarri
A Synergistic Approach In Network Intrusion Detection By Neurosymbolic AI. CoRR abs/2406.00938 (2024) - [i23]Yasir Ali Farrukh, Syed Wali, Irfan Khan, Nathaniel D. Bastian:
XG-NID: Dual-Modality Network Intrusion Detection using a Heterogeneous Graph Neural Network and Large Language Model. CoRR abs/2408.16021 (2024) - [i22]Hanning Chen, Yang Ni, Wenjun Huang, Yezi Liu, Sungheon Jeong, Fei Wen, Nathaniel D. Bastian, Hugo Latapie, Mohsen Imani:
VLTP: Vision-Language Guided Token Pruning for Task-Oriented Segmentation. CoRR abs/2409.08464 (2024) - [i21]Jingdi Chen, Hanhan Zhou, Yongsheng Mei, Carlee Joe-Wong, Gina C. Adam, Nathaniel D. Bastian, Tian Lan:
RGMDT: Return-Gap-Minimizing Decision Tree Extraction in Non-Euclidean Metric Space. CoRR abs/2410.16517 (2024) - [i20]Ramneet Kaur, Colin Samplawski, Adam D. Cobb, Anirban Roy, Brian Matejek, Manoj Acharya, Daniel Elenius, Alexander M. Berenbeim, John A. Pavlik, Nathaniel D. Bastian, Susmit Jha:
Addressing Uncertainty in LLMs to Enhance Reliability in Generative AI. CoRR abs/2411.02381 (2024) - [i19]Sanggeon Yun, Ryozo Masukawa, William Youngwoo Chung, Minhyoung Na, Nathaniel D. Bastian, Mohsen Imani:
Continuous GNN-based Anomaly Detection on Edge using Efficient Adaptive Knowledge Graph Learning. CoRR abs/2411.09072 (2024) - 2023
- [j14]Yasir Ali Farrukh
SeNet-I: An approach for detecting network intrusions through serialized network traffic images. Eng. Appl. Artif. Intell. 126(Part D): 107169 (2023) - [j13]David A. Bierbrauer
Transfer learning for raw network traffic detection. Expert Syst. Appl. 211: 118641 (2023) - [j12]Soumyadeep Hore
Deep VULMAN: A deep reinforcement learning-enabled cyber vulnerability management framework. Expert Syst. Appl. 221: 119734 (2023) - [c25]Jingdi Chen, Lei Zhang, Joseph Riem, Gina C. Adam, Nathaniel D. Bastian, Tian Lan:
RIDE: Real-time Intrusion Detection via Explainable Machine Learning Implemented in a Memristor Hardware Architecture. DSC 2023: 1-8 - [c24]Soumyadeep Hore, Quoc H. Nguyen, Yulun Xu, Ankit Shah, Nathaniel D. Bastian, Trung Q. Le:
Empirical Evaluation of Autoencoder Models for Anomaly Detection in Packet-based NIDS. DSC 2023: 1-8 - [c23]Kelson J. McCollum, Nathaniel D. Bastian, Johannes O. Royset:
Towards Robust Learning using Diametrical Risk Minimization for Network Intrusion Detection. DSC 2023: 1-8 - [c22]Galamo Monkam, Michael J. De Lucia, Nathaniel D. Bastian:
Preprocessing Network Traffic using Topological Data Analysis for Data Poisoning Detection. DSC 2023: 1-8 - [c21]Taylor Bradley, Elie Alhajjar
Novelty Detection in Network Traffic: Using Survival Analysis for Feature Identification. ICAA 2023: 11-18 - [c20]Susmit Jha, Sumit Kumar Jha, Patrick Lincoln, Nathaniel D. Bastian, Alvaro Velasquez, Sandeep Neema
Dehallucinating Large Language Models Using Formal Methods Guided Iterative Prompting. ICAA 2023: 149-152 - [c19]Yasir Ali Farrukh, Syed Wali, Irfan Khan, Nathaniel D. Bastian:
Detecting Unknown Attacks in IoT Environments: An Open Set Classifier for Enhanced Network Intrusion Detection. MILCOM 2023: 121-126 - [c18]Susmit Jha, Anirban Roy, Adam D. Cobb, Alexander M. Berenbeim, Nathaniel D. Bastian:
Challenges and Opportunities in Neuro-Symbolic Composition of Foundation Models. MILCOM 2023: 156-161 - [c17]Brian Jalaian, Nathaniel D. Bastian:
Neurosymbolic AI in Cybersecurity: Bridging Pattern Recognition and Symbolic Reasoning. MILCOM 2023: 268-273 - [c16]Sumit Kumar Jha, Susmit Jha, Patrick Lincoln, Nathaniel D. Bastian, Alvaro Velasquez, Rickard Ewetz, Sandeep Neema:
Counterexample Guided Inductive Synthesis Using Large Language Models and Satisfiability Solving. MILCOM 2023: 944-949 - [c15]Gaspard Baye, Priscila Silva, Alexandre Broggi, Lance Fiondella, Nathaniel D. Bastian, Gökhan Kul:
Performance Analysis of Deep-Learning Based Open Set Recognition Algorithms for Network Intrusion Detection Systems. NOMS 2023: 1-6 - [c14]Joshua A. Wong, Alexander M. Berenbeim, David A. Bierbrauer, Nathaniel D. Bastian:
Uncertainty-Quantified, Robust Deep Learning for Network Intrusion Detection. WSC 2023: 2470-2481 - [i18]Taylor Bradley, Elie Alhajjar
Novelty Detection in Network Traffic: Using Survival Analysis for Feature Identification. CoRR abs/2301.06229 (2023) - [i17]Yash Chandak, Shiv Shankar, Nathaniel D. Bastian, Bruno Castro da Silva, Emma Brunskill, Philip S. Thomas:
Off-Policy Evaluation for Action-Dependent Non-Stationary Environments. CoRR abs/2301.10330 (2023) - [i16]Alexander M. Berenbeim, Iain J. Cruickshank, Susmit Jha, Robert H. Thomson, Nathaniel D. Bastian:
Measuring Classification Decision Certainty and Doubt. CoRR abs/2303.14568 (2023) - [i15]Soumyadeep Hore, Jalal Ghadermazi, Diwas Paudel, Ankit Shah, Tapas K. Das, Nathaniel D. Bastian:
Deep PackGen: A Deep Reinforcement Learning Framework for Adversarial Network Packet Generation. CoRR abs/2305.11039 (2023) - [i14]Iain J. Cruickshank, Jessica Zhu, Nathaniel D. Bastian:
Analysis of Media Writing Style Bias through Text-Embedding Networks. CoRR abs/2305.13098 (2023) - [i13]Yasir Ali Farrukh, Syed Wali, Irfan Khan, Nathaniel D. Bastian:
Detecting Unknown Attacks in IoT Environments: An Open Set Classifier for Enhanced Network Intrusion Detection. CoRR abs/2309.07461 (2023) - [i12]Sumit Kumar Jha, Susmit Jha, Patrick Lincoln, Nathaniel D. Bastian, Alvaro Velasquez, Rickard Ewetz, Sandeep Neema
Neuro Symbolic Reasoning for Planning: Counterexample Guided Inductive Synthesis using Large Language Models and Satisfiability Solving. CoRR abs/2309.16436 (2023) - [i11]Jingdi Chen, Lei Zhang, Joseph Riem, Gina C. Adam, Nathaniel D. Bastian, Tian Lan:
RIDE: Real-time Intrusion Detection via Explainable Machine Learning Implemented in a Memristor Hardware Architecture. CoRR abs/2311.16018 (2023) - [i10]Jingdi Chen, Hanhan Zhou, Yongsheng Mei, Gina C. Adam, Nathaniel D. Bastian, Tian Lan:
Real-time Network Intrusion Detection via Decision Transformers. CoRR abs/2312.07696 (2023) - 2022
- [j11]Marc Chalé
Generating realistic cyber data for training and evaluating machine learning classifiers for network intrusion detection systems. Expert Syst. Appl. 207: 117936 (2022) - [j10]John H. Smith, Nathaniel D. Bastian:
A ranked solution for social media fact checking using epidemic spread modeling. Inf. Sci. 589: 550-563 (2022) - [c13]Yasir Ali Farrukh
Payload-Byte: A Tool for Extracting and Labeling Packet Capture Files of Modern Network Intrusion Detection Datasets. BDCAT 2022: 58-67 - [c12]Tarek F. Abdelzaher, Nathaniel D. Bastian, Susmit Jha, Lance M. Kaplan, Mani B. Srivastava, Venugopal V. Veeravalli:
Context-aware Collaborative Neuro-Symbolic Inference in IoBTs. MILCOM 2022: 1053-1058 - [c11]Yash Chandak, Shiv Shankar, Nathaniel D. Bastian, Bruno C. da Silva, Emma Brunskill, Philip S. Thomas:
Off-Policy Evaluation for Action-Dependent Non-stationary Environments. NeurIPS 2022 - [i9]Soumyadeep Hore, Ankit Shah, Nathaniel D. Bastian:
Deep VULMAN: A Deep Reinforcement Learning-Enabled Cyber Vulnerability Management Framework. CoRR abs/2208.02369 (2022) - [i8]Zong-Zhi Lin, Thomas D. Pike, Mark M. Bailey, Nathaniel D. Bastian:
A Hypergraph-Based Machine Learning Ensemble Network Intrusion Detection System. CoRR abs/2211.03933 (2022) - 2021
- [j9]Elie Alhajjar
Adversarial machine learning in Network Intrusion Detection Systems. Expert Syst. Appl. 186: 115782 (2021) - [c10]Madeleine Schneider, David Aspinall, Nathaniel D. Bastian:
Evaluating Model Robustness to Adversarial Samples in Network Intrusion Detection. IEEE BigData 2021: 3343-3352 - [c9]Sean M. Devine, Nathaniel D. Bastian:
An Adversarial Training Based Machine Learning Approach to Malware Classification under Adversarial Conditions. HICSS 2021: 1-10 - [c8]Kevin Talty, John Stockdale, Nathaniel D. Bastian:
A Sensitivity Analysis of Poisoning and Evasion Attacks in Network Intrusion Detection System Machine Learning Models. MILCOM 2021: 1011-1016 - [c7]Marc Chalé, Nathaniel D. Bastian:
CHALLENGES AND OPPORTUNITIES FOR GENERATIVE METHODS IN THE CYBER DOMAIN. WSC 2021: 1-12 - [c6]Adam D. Cobb, Brian Jalaian, Nathaniel D. Bastian, Stephen Russell:
Robust Decision-Making in the Internet of Battlefield Things Using Bayesian Neural Networks. WSC 2021: 1-12 - [i7]David A. Bierbrauer, Alexander Chang, Will Kritzer, Nathaniel D. Bastian:
Anomaly Detection in Cybersecurity: Unsupervised, Graph-Based and Supervised Learning Methods in Adversarial Environments. CoRR abs/2105.06742 (2021) - 2020
- [j8]Timothy J. Kiely, Nathaniel D. Bastian
The spatially conscious machine learning model. Stat. Anal. Data Min. 13(1): 31-49 (2020) - [c5]Marc Chalé, Nathaniel D. Bastian, Jeffery Weir:
Algorithm selection framework for cyber attack detection. WiseML@WiSec 2020: 37-42 - [i6]Kathleen Kerwin, Nathaniel D. Bastian:
Stacked Generalizations in Imbalanced Fraud Data Sets using Resampling Methods. CoRR abs/2004.01764 (2020) - [i5]Elie Alhajjar, Paul Maxwell, Nathaniel D. Bastian:
Adversarial Machine Learning in Network Intrusion Detection Systems. CoRR abs/2004.11898 (2020) - [i4]Marc Chalé, Nathaniel D. Bastian, Jeffery Weir:
Algorithm Selection Framework for Cyber Attack Detection. CoRR abs/2005.14230 (2020) - [i3]Tyler J. Shipp, Daniel J. Clouse, Michael J. De Lucia, Metin B. Ahiskali, Kai Steverson, Jonathan M. Mullin, Nathaniel D. Bastian:
Advancing the Research and Development of Assured Artificial Intelligence and Machine Learning Capabilities. CoRR abs/2009.13250 (2020)
2010 – 2019
- 2019
- [c4]Paul Maxwell, Elie Alhajjar
Intelligent Feature Engineering for Cybersecurity. IEEE BigData 2019: 5005-5011 - [c3]Sachin Shetty
Simulation for Cyber Risk Management - Where are we, and Where do we Want to Go? WSC 2019: 726-737 - [c2]Nathaniel D. Bastian, Christopher B. Fisher, Andrew O. Hall
Solving The Army's Cyber Workforce Planning Problem Using Stochastic Optimization and Discrete-Event Simulation Modeling. WSC 2019: 738-749 - [i2]Timothy J. Kiely, Nathaniel D. Bastian:
The Spatially-Conscious Machine Learning Model. CoRR abs/1902.00562 (2019) - [i1]Sean M. Devine, Nathaniel D. Bastian:
Intelligent Systems Design for Malware Classification Under Adversarial Conditions. CoRR abs/1907.03149 (2019) - 2017
- [j7]Tulasi K. Paradarami, Nathaniel D. Bastian, Jennifer L. Wightman:
A hybrid recommender system using artificial neural networks. Expert Syst. Appl. 83: 300-313 (2017) - [j6]Sharan Srinivas
Optimizing Student Team and Job Assignments for the Holy Family Academy. Interfaces 47(2): 163-174 (2017) - [c1]Joseph Klobusický, Alexander Murph, Alexander C. Robinson, Nathaniel D. Bastian, Paul M. Griffin, Shravan Kethireddy, Nathan Ryan:
Machine Learning and Statistical Techniques to Predict Sepsis: Unifying Previous Work. CRI 2017 - 2016
- [j5]Eric R. Swenson
Data analytics in health promotion: Health market segmentation and classification of total joint replacement surgery patients. Expert Syst. Appl. 60: 118-129 (2016) - [j4]Nathaniel D. Bastian, Paul M. Griffin, Eric Spero, Lawrence V. Fulton
Multi-criteria logistics modeling for military humanitarian assistance and disaster relief aerial delivery operations. Optim. Lett. 10(5): 921-953 (2016) - 2015
- [j3]Nathaniel D. Bastian, Pat McMurry, Lawrence V. Fulton
The AMEDD Uses Goal Programming to Optimize Workforce Planning Decisions. Interfaces 45(4): 305-324 (2015) - [j2]Benjamin C. Grannan, Nathaniel D. Bastian, Laura A. McLay
A maximum expected covering problem for locating and dispatching two classes of military medical evacuation air assets. Optim. Lett. 9(8): 1511-1531 (2015) - 2013
- [j1]Lawrence V. Fulton
Rainwater harvesting system using a non-parametric stochastic rainfall generator. Simul. 89(6): 693-702 (2013)
Coauthor Index
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