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Anahita Khojandi
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2020 – today
- 2024
- [j24]Mahboubeh Madadi
, Shahrbanoo Rezaei, Anahita Khojandi
Dynamic joint sensor selection and maintenance optimization in partially observable deteriorating systems. Comput. Ind. Eng. 187: 109853 (2024) - [j23]Curtis Rookard
RRIoT: Recurrent reinforcement learning for cyber threat detection on IoT devices. Comput. Secur. 140: 103786 (2024) - [j22]Benjamin Smith
Bias in Reinforcement Learning: A Review in Healthcare Applications. ACM Comput. Surv. 56(2): 52:1-52:17 (2024) - [j21]Akash Mishra, Vikram Bajaj, Toni Fitzpatrick, Jeremy Watts
Differential Responses to Low- and High-Frequency Subthalamic Nucleus Deep Brain Stimulation on Sensor-Measured Components of Bradykinesia in Parkinson's Disease. Sensors 24(13): 4296 (2024) - 2023
- [j20]Kalina Scarbrough, Padmini Persaud, Isidora Fletcher, Aaron Alexander Akin
Real-time sensor-based prediction of soil moisture in green infrastructure: A case study. Environ. Model. Softw. 162: 105638 (2023) - [j19]Matt Baucum
Optimizing Patient-Specific Medication Regimen Policies Using Wearable Sensors in Parkinson's Disease. Manag. Sci. 69(10): 5964-5982 (2023) - [j18]Matt Baucum
Optimizing Substance Use Treatment Selection Using Reinforcement Learning. ACM Trans. Manag. Inf. Syst. 14(2): 13:1-13:30 (2023) - [c16]Curtis Rookard, Anahita Khojandi:
Applying Deep Reinforcement Learning for Detection of Internet-of- Things Cyber Attacks. CCWC 2023: 389-395 - [c15]Jeremy Watts, Elexis Allen, Ahmad Mitoubsi, Anahita Khojandi, James Eales, Theodore Papamarkou:
Towards Faster Gene Expression Prediction via Dimensionality Reduction and Feature Selection. EMBC 2023: 1-4 - [c14]Shahrbanoo Rezaei, Jeremiah Gbadegoye, Neda Masoud
A Deep Learning-Based Approach for Vehicle Motion Prediction in Autonomous Driving. ICCAD 2023: 1-6 - [c13]Rupam Kumar Dey, Anahita Khojandi, Kalyan Perumalla:
Determining the Most Significant Metadata Features to Indicate Defective Software Commits. SERA 2023: 258-265 - 2022
- [j17]Zefeng Lyu
Q-learning and traditional methods on solving the pocket Rubik's cube. Comput. Ind. Eng. 171: 108452 (2022) - [j16]Zeyu Liu
A Machine Learning-Enabled Partially Observable Markov Decision Process Framework for Early Sepsis Prediction. INFORMS J. Comput. 34(4): 2039-2057 (2022) - [j15]Jeremy Watts
A Dynamic Deep Reinforcement Learning-Bayesian Framework for Anomaly Detection. IEEE Trans. Intell. Transp. Syst. 23(12): 22884-22894 (2022) - [c12]Jeremy Watts, Elexis Allen, Ahmad Mitoubsi, Anahita Khojandi, James Eales, Farideh Jalali-najafabadi
Adapting Random Forests to Predict Obesity-Associated Gene Expression. EMBC 2022: 4407-4410 - [c11]Curtis Rookard, Austin Crabtree, Joseph Dan Trujillo, Anahita Khojandi:
Secure Analytics for Embedded Systems through Machine Learning Applications using TrustZones. UEMCON 2022: 531-537 - 2021
- [j14]Zeyu Liu
HeMA: A hierarchically enriched machine learning approach for managing false alarms in real time: A sepsis prediction case study. Comput. Biol. Medicine 131: 104255 (2021) - [j13]Jeremy Watts
Improving Medication Regimen Recommendation for Parkinson's Disease Using Sensor Technology. Sensors 21(10): 3553 (2021) - [j12]Matthew Baucum
Improving Deep Reinforcement Learning With Transitional Variational Autoencoders: A Healthcare Application. IEEE J. Biomed. Health Informatics 25(6): 2273-2280 (2021) - [j11]Yiyang Wang
Real-Time Sensor Anomaly Detection and Recovery in Connected Automated Vehicle Sensors. IEEE Trans. Intell. Transp. Syst. 22(3): 1411-1421 (2021) - [c10]Matt Baucum, Anahita Khojandi, Theodore Papamarkou:
Hidden Markov models as recurrent neural networks: An application to Alzheimer's disease. BIBE 2021: 1-6 - [c9]Aradhana Soni, Benjamin Amrhein, Matthew Baucum, Eun Jin Paek
Using Verb Fluency, Natural Language Processing, and Machine Learning to Detect Alzheimer's Disease. EMBC 2021: 2282-2285 - [c8]Marjory Day, Rupam Kumar Dey, Matthew Baucum, Eun Jin Paek
Predicting Severity in People with Aphasia: A Natural Language Processing and Machine Learning Approach. EMBC 2021: 2299-2302 - [c7]Ahmad Mitoubsi, Zeyu Liu, Danny Banks, Anahita Khojandi, Michael Oliver
Evaluating the Fitness-to-Drive Using Evoked Visual Responses in Alzheimer's Disease. EMBC 2021: 2382-2385 - 2020
- [j10]Zeyu Liu, Xueping Li
On the k-Strong Roman Domination Problem. Discret. Appl. Math. 285: 227-241 (2020) - [j9]Franco van Wyk, Yiyang Wang
Real-Time Sensor Anomaly Detection and Identification in Automated Vehicles. IEEE Trans. Intell. Transp. Syst. 21(3): 1264-1276 (2020) - [c6]Jeremy Watts, Anahita Khojandi, Rama K. Vasudevan, Ritesh A. Ramdhani:
Optimizing Individualized Treatment Planning for Parkinson's Disease Using Deep Reinforcement Learning. EMBC 2020: 5406-5409 - [c5]Ryan Blanchette, Anahita Khojandi, Daniel Cox, Michael Oliver
Predicting Alzheimer's Disease Using Driving Simulator Data. EMBC 2020: 5432-5435 - [i2]Yiyang Wang, Neda Masoud
Anomaly Detection in Connected and Automated Vehicles using an Augmented State Formulation. CoRR abs/2004.09496 (2020) - [i1]Matt Baucum, Anahita Khojandi, Theodore Papamarkou:
Hidden Markov models are recurrent neural networks: A disease progression modeling application. CoRR abs/2006.03151 (2020)
2010 – 2019
- 2019
- [j8]Anahita Khojandi, Oleg Shylo
Automatic EEG classification: a path to smart and connected sleep interventions. Ann. Oper. Res. 276(1-2): 169-190 (2019) - [j7]Franco van Wyk, Anahita Khojandi, Akram Mohammed
A minimal set of physiomarkers in continuous high frequency data streams predict adult sepsis onset earlier. Int. J. Medical Informatics 122: 55-62 (2019) - [j6]Franco van Wyk, Anahita Khojandi
A Cost-Benefit Analysis of Automated Physiological Data Acquisition Systems Using Data-Driven Modeling. J. Heal. Informatics Res. 3(2): 245-263 (2019) - [j5]Franco van Wyk, Anahita Khojandi
Correction to: A Cost-Benefit Analysis of Automated Physiological Data Acquisition Systems Using Data-Driven Modeling. J. Heal. Informatics Res. 3(2): 264-265 (2019) - [j4]Franco van Wyk, Anahita Khojandi
Improving Prediction Performance Using Hierarchical Analysis of Real-Time Data: A Sepsis Case Study. IEEE J. Biomed. Health Informatics 23(3): 978-986 (2019) - [c4]Franco van Wyk, Anahita Khojandi, Neda Masoud
A Path Towards Understanding Factors Affecting Crash Severity in Autonomous Vehicles Using Current Naturalistic Driving Data. IntelliSys (2) 2019: 106-120 - [c3]Zeyu Liu, Xueping Li, Anahita Khojandi, Sanja Lazarova-Molnar
On the Extension of Schelling's Segregation Model. WSC 2019: 285-296 - [c2]Mohammad Ramshani, Xueping Li, Anahita Khojandi, Lorna Treffert:
An Optimization via Agent-based Simulation Framework to Integrate Stochastic Programming with Human Introduced Uncertainty. WSC 2019: 809-818 - 2018
- [j3]Ritesh A. Ramdhani, Anahita Khojandi, Oleg Shylo, Brian H. Kopell:
Optimizing Clinical Assessments in Parkinson's Disease Through the Use of Wearable Sensors and Data Driven Modeling. Frontiers Comput. Neurosci. 12: 72 (2018) - [j2]Anahita Khojandi, Lisa M. Maillart
Dynamic Abandon/Extract Decisions for Failed Cardiac Leads. Manag. Sci. 64(2): 633-651 (2018) - 2017
- [c1]Xueping Li
An agent based model for joint placement of PV panels and green roofs. WSC 2017: 1133-1144 - 2014
- [j1]Anahita Khojandi, Lisa M. Maillart
Optimal Implantable Cardioverter Defibrillator (ICD) Generator Replacement. INFORMS J. Comput. 26(3): 599-615 (2014)
Coauthor Index
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last updated on 2024-08-23 19:27 CEST by the dblp team
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