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Teo Susnjak
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2020 – today
- 2024
- [j30]Teo Susnjak
:
Beyond Predictive Learning Analytics Modelling and onto Explainable Artificial Intelligence with Prescriptive Analytics and ChatGPT. Int. J. Artif. Intell. Educ. 34(2): 452-482 (2024) - [j29]Timothy R. McIntosh
From COBIT to ISO 42001: Evaluating cybersecurity frameworks for opportunities, risks, and regulatory compliance in commercializing large language models. Comput. Secur. 144: 103964 (2024) - [j28]Timothy R. McIntosh
A Culturally Sensitive Test to Evaluate Nuanced GPT Hallucination. IEEE Trans. Artif. Intell. 5(6): 2739-2751 (2024) - [j27]Timothy R. McIntosh
The Inadequacy of Reinforcement Learning From Human Feedback - Radicalizing Large Language Models via Semantic Vulnerabilities. IEEE Trans. Cogn. Dev. Syst. 16(4): 1561-1574 (2024) - [j26]Timothy R. McIntosh
A Reasoning and Value Alignment Test to Assess Advanced GPT Reasoning. ACM Trans. Interact. Intell. Syst. 14(3): 17:1-17:37 (2024) - [i19]Timothy R. McIntosh, Teo Susnjak, Tong Liu, Paul A. Watters, Malka N. Halgamuge:
Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence. CoRR abs/2402.09880 (2024) - [i18]Timothy R. McIntosh, Teo Susnjak, Tong Liu, Paul A. Watters, Raza Nowrozy, Malka N. Halgamuge:
From COBIT to ISO 42001: Evaluating Cybersecurity Frameworks for Opportunities, Risks, and Regulatory Compliance in Commercializing Large Language Models. CoRR abs/2402.15770 (2024) - [i17]Teo Susnjak, Peter Hwang, Napoleon H. Reyes, Andre L. C. Barczak, Timothy R. McIntosh, Surangika Ranathunga:
Automating Research Synthesis with Domain-Specific Large Language Model Fine-Tuning. CoRR abs/2404.08680 (2024) - [i16]Teo Susnjak, Timothy R. McIntosh, Andre L. C. Barczak, Napoleon H. Reyes, Tong Liu, Paul A. Watters, Malka N. Halgamuge:
Over the Edge of Chaos? Excess Complexity as a Roadblock to Artificial General Intelligence. CoRR abs/2407.03652 (2024) - [i15]Grant Wardle, Teo Susnjak:
Image First or Text First? Optimising the Sequencing of Modalities in Large Language Model Prompting and Reasoning Tasks. CoRR abs/2410.03062 (2024) - 2023
- [j25]Paula Maddigan
Chat2VIS: Generating Data Visualizations via Natural Language Using ChatGPT, Codex and GPT-3 Large Language Models. IEEE Access 11: 45181-45193 (2023) - [j24]Martin Brenner
RGB-D and Thermal Sensor Fusion: A Systematic Literature Review. IEEE Access 11: 82410-82442 (2023) - [j23]Vajisha Udayangi Wanniarachchi, Chris Scogings, Teo Susnjak, Anuradha Mathrani:
Hate Speech Patterns in Social Media: A Methodological Framework and Fat Stigma Investigation Incorporating Sentiment Analysis, Topic Modelling and Discourse Analysis. Australas. J. Inf. Syst. 27 (2023) - [j22]Teo Susnjak
Forecasting patient demand at urgent care clinics using explainable machine learning. CAAI Trans. Intell. Technol. 8(3): 712-733 (2023) - [j21]Timothy R. McIntosh
Harnessing GPT-4 for generation of cybersecurity GRC policies: A focus on ransomware attack mitigation. Comput. Secur. 134: 103424 (2023) - [j20]Teo Susnjak
Forecasting patient flows with pandemic induced concept drift using explainable machine learning. EPJ Data Sci. 12(1): 11 (2023) - [j19]Rahila Umer
Current stance on predictive analytics in higher education: opportunities, challenges and future directions. Interact. Learn. Environ. 31(6): 3503-3528 (2023) - [j18]Gomathy Ramaswami
Effectiveness of a Learning Analytics Dashboard for Increasing Student Engagement Levels. J. Learn. Anal. 10(3): 115-134 (2023) - [j17]Gomathy Ramaswami, Teo Susnjak, Anuradha Mathrani, Rahila Umer:
Use of Predictive Analytics within Learning Analytics Dashboards: A Review of Case Studies. Technol. Knowl. Learn. 28(3): 959-980 (2023) - [i14]Paula Maddigan, Teo Susnjak:
Chat2VIS: Generating Data Visualisations via Natural Language using ChatGPT, Codex and GPT-3 Large Language Models. CoRR abs/2302.02094 (2023) - [i13]Teo Susnjak:
Applying BERT and ChatGPT for Sentiment Analysis of Lyme Disease in Scientific Literature. CoRR abs/2302.06474 (2023) - [i12]Paula Maddigan, Teo Susnjak:
Chat2VIS: Fine-Tuning Data Visualisations using Multilingual Natural Language Text and Pre-Trained Large Language Models. CoRR abs/2303.14292 (2023) - [i11]Martin Brenner, Napoleon H. Reyes, Teo Susnjak, Andre L. C. Barczak:
RGB-D And Thermal Sensor Fusion: A Systematic Literature Review. CoRR abs/2305.11427 (2023) - [i10]Teo Susnjak:
PRISMA-DFLLM: An Extension of PRISMA for Systematic Literature Reviews using Domain-specific Finetuned Large Language Models. CoRR abs/2306.14905 (2023) - [i9]Teo Susnjak, Elise Griffin:
Towards Clinical Prediction with Transparency: An Explainable AI Approach to Survival Modelling in Residential Aged Care. CoRR abs/2312.00271 (2023) - [i8]Timothy R. McIntosh, Teo Susnjak, Tong Liu, Paul A. Watters, Malka N. Halgamuge:
From Google Gemini to OpenAI Q* (Q-Star): A Survey of Reshaping the Generative Artificial Intelligence (AI) Research Landscape. CoRR abs/2312.10868 (2023) - 2022
- [j16]Gomathy Ramaswami
On Developing Generic Models for Predicting Student Outcomes in Educational Data Mining. Big Data Cogn. Comput. 6(1): 6 (2022) - [j15]Gomathy Ramaswami, Teo Susnjak
Supporting Students' Academic Performance Using Explainable Machine Learning with Automated Prescriptive Analytics. Big Data Cogn. Comput. 6(4): 105 (2022) - [j14]Rahila Umer, Teo Susnjak, Anuradha Mathrani, Suriadi Suriadi:
Data quality challenges in educational process mining: building process-oriented event logs from process-unaware online learning systems. Int. J. Bus. Inf. Syst. 39(4): 569-592 (2022) - [j13]Rory P. Bunker
The Application of Machine Learning Techniques for Predicting Match Results in Team Sport: A Review. J. Artif. Intell. Res. 73: 1285-1322 (2022) - [j12]Nasim Ahmed
Runtime prediction of big data jobs: performance comparison of machine learning algorithms and analytical models. J. Big Data 9(1): 67 (2022) - [i7]Paula Maddigan, Teo Susnjak:
Forecasting Patient Demand at Urgent Care Clinics using Machine Learning. CoRR abs/2205.13067 (2022) - [i6]Teo Susnjak:
A Prescriptive Learning Analytics Framework: Beyond Predictive Modelling and onto Explainable AI with Prescriptive Analytics. CoRR abs/2208.14582 (2022) - [i5]Teo Susnjak, Paula Maddigan:
Forecasting Patient Flows with Pandemic Induced Concept Drift using Explainable Machine Learning. CoRR abs/2211.00739 (2022) - [i4]Yiming Ren, Teo Susnjak:
Predicting Football Match Outcomes with eXplainable Machine Learning and the Kelly Index. CoRR abs/2211.15734 (2022) - [i3]Teo Susnjak
ChatGPT: The End of Online Exam Integrity? CoRR abs/2212.09292 (2022) - 2021
- [j11]Nasim Ahmed
An Enhanced Parallelisation Model for Performance Prediction of Apache Spark on a Multinode Hadoop Cluster. Big Data Cogn. Comput. 5(4): 65 (2021) - [j10]Nasim Ahmed
A parallelization model for performance characterization of Spark Big Data jobs on Hadoop clusters. J. Big Data 8(1): 107 (2021) - 2020
- [j9]Vajisha Udayangi Wanniarachchi, Anuradha Mathrani
A systematic literature review: What is the current stance towards weight stigmatization in social media platforms? Int. J. Hum. Comput. Stud. 135 (2020) - [j8]Nasim Ahmed
A comprehensive performance analysis of Apache Hadoop and Apache Spark for large scale data sets using HiBench. J. Big Data 7(1): 110 (2020)
2010 – 2019
- 2019
- [j7]Timothy R. McIntosh, Julian Jang-Jaccard, Paul A. Watters, Teo Susnjak:
Masquerade Attacks Against Security Software Exclusion Lists. Aust. J. Intell. Inf. Process. Syst. 16(4): 1-8 (2019) - [c17]Vajisha U. Wanniarachchi, Anuradha Mathrani, Teo Susnjak, Chris Scogings:
Gendered objectification of weight stigma in social media: a mixed method analysis. ACIS 2019: 39 - [c16]Timothy R. McIntosh
The Inadequacy of Entropy-Based Ransomware Detection. ICONIP (5) 2019: 181-189 - [i2]Andre L. C. Barczak
Assessment of the Local Tchebichef Moments Method for Texture Classification by Fine Tuning Extraction Parameters. CoRR abs/1910.09758 (2019) - [i1]Rory P. Bunker
The Application of Machine Learning Techniques for Predicting Results in Team Sport: A Review. CoRR abs/1912.11762 (2019) - 2016
- [j6]Suriadi Suriadi, Teo Susnjak
Using Data-Driven and Process Mining Techniques for Identifying and Characterizing Problem Gamblers in New Zealand. Complex Syst. Informatics Model. Q. 9: 44-66 (2016) - [j5]David Parsons
Design from detail: Analyzing data from a global day of coderetreat. Inf. Softw. Technol. 75: 39-55 (2016) - [c15]Suriadi Suriadi, Teo Susnjak, Agate M. Ponder-Sutton, Paul A. Watters, Christoph Schumacher:
Characterizing Problem Gamblers in New Zealand: A Novel Expression of Process Cubes. CAiSE Forum 2016: 185-192 - 2015
- [c14]David Parsons
The Software Developer Cycle: Career demographics and the market clock: or, is SQL the new COBOL? ASWEC (2) 2015: 86-90 - [c13]Teo Susnjak
Wisdom of Crowds: An Empirical Study of Ensemble-Based Feature Selection Strategies. Australasian Conference on Artificial Intelligence 2015: 526-538 - [c12]Safar Alqahtani, Andre L. C. Barczak
Automatic alignment and comparison on images of petri dishes containing cell colonies. IVCNZ 2015: 1-6 - [c11]Napoleon H. Reyes
Fast and Smooth Replanning for Navigation in Partially Unknown Terrain: The Hybrid Fuzzy-D*lite Algorithm. RiTA 2015: 31-41 - 2014
- [j4]David Parsons
Coderetreats: Reflective Practice and the Game of Life. IEEE Softw. 31(4): 58-64 (2014) - [j3]David Parsons
Influences on regression testing strategies in agile software development environments. Softw. Qual. J. 22(4): 717-739 (2014) - [c10]Andre L. C. Barczak
Characterisation of the Discriminative Properties of the Radial Tchebichef Moments for Hand-written Digits. IVCNZ 2014: 154 - [c9]W. Wang, Napoleon H. Reyes, Andre L. C. Barczak
Multi-Behaviour Robot Control using Genetic Network Programming with Fuzzy Reinforcement Learning. RiTA 2014: 151-158 - 2013
- [j2]Teo Susnjak
Coarse-to-fine multiclass learning and classification for time-critical domains. Pattern Recognit. Lett. 34(8): 884-894 (2013) - [c8]Andre L. C. Barczak
Colour segmentation for multiple low dynamic range images using boosted cascaded classifiers. IVCNZ 2013: 136-141 - 2012
- [j1]Teo Susnjak
Adaptive cascade of boosted ensembles for face detection in concept drift. Neural Comput. Appl. 21(4): 671-682 (2012) - [c7]Napoleon H. Reyes, Andre L. C. Barczak
Tuning Fuzzy-Based Hybrid Navigation Systems Using Calibration Maps. RiTA 2012: 713-722 - [c6]Teo Susnjak, Andre L. C. Barczak
Multiclass Cascades for Ensemble-based Boosting Algorithms. STAIRS 2012: 330-335 - 2011
- [c5]Teo Susnjak
A New Ensemble-Based Cascaded Framework for Multiclass Training with Simple Weak Learners. CAIP (1) 2011: 563-570 - [c4]Andre L. C. Barczak, Napoleon H. Reyes, Teo Susnjak, Martin J. Johnson:
Real-time computation of moment invariants combined with contrast stretching. EUSIPCO 2011: 544-548 - 2010
- [c3]Teo Susnjak
Adaptive Ensemble Based Learning in Non-stationary Environments with Variable Concept Drift. ICONIP (1) 2010: 438-445 - [c2]Teo Susnjak
A Modular Approach to Training Cascades of Boosted Ensembles. SSPR/SPR 2010: 640-649
2000 – 2009
- 2008
- [c1]Teo Susnjak
Accelerated Classifier Training Using the PSL Cascading Structure. ICONIP (1) 2008: 945-952
Coauthor Index
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last updated on 2024-11-08 20:30 CET by the dblp team
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