M. Z. Naser

Orcid: 0000-0003-1350-3654

According to our database1, M. Z. Naser authored at least 34 papers between 2019 and 2025.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2025
SPINEX-anomaly: similarity-based predictions with explainable neighbors exploration for anomaly and outlier detection.
J. Big Data, December, 2025

SPINEX-clustering: similarity-based predictions with explainable neighbors exploration for clustering problems.
Clust. Comput., October, 2025

The Engineer's Dilemma: A Review of Establishing a Legal Framework for Integrating Machine Learning in Construction by Navigating Precedents and Industry Expectations.
CoRR, July, 2025

The firefighter algorithm for optimization problems.
Neural Comput. Appl., June, 2025

SPINEX-symbolic regression: similarity-based symbolic regression with explainable neighbors exploration.
J. Supercomput., May, 2025

A Look into How Machine Learning is Reshaping Engineering Models: the Rise of Analysis Paralysis, Optimal yet Infeasible Solutions, and the Inevitable Rashomon Paradox.
CoRR, January, 2025

A Guide to Machine Learning Epistemic Ignorance, Hidden Paradoxes, and Other Tensions.
WIREs Data. Mining. Knowl. Discov., 2025

From failure to fusion: A survey on learning from bad machine learning models.
Inf. Fusion, 2025

SPINEX-TimeSeries: Similarity-based predictions with explainable neighbors exploration for time series and forecasting problems.
Comput. Ind. Eng., 2025

2024
A review on machine learning and deep learning image-based plant disease classification for industrial farming systems.
J. Ind. Inf. Integr., March, 2024

Causality and causal inference for engineers: Beyond correlation, regression, prediction and artificial intelligence.
WIREs Data. Mining. Knowl. Discov., 2024

A deep learning approach to detect diseases in pomegranate fruits via hybrid optimal attention capsule network.
Ecol. Informatics, 2024

SPINEX_ Symbolic Regression: Similarity-based Symbolic Regression with Explainable Neighbors Exploration.
CoRR, 2024

SPINEX-TimeSeries: Similarity-based Predictions with Explainable Neighbors Exploration for Time Series and Forecasting Problems.
CoRR, 2024

SPINEX-Clustering: Similarity-based Predictions with Explainable Neighbors Exploration for Clustering Problems.
CoRR, 2024

SPINEX: Similarity-based Predictions with Explainable Neighbors Exploration for Anomaly and Outlier Detection.
CoRR, 2024

A Review of 315 Benchmark and Test Functions for Machine Learning Optimization Algorithms and Metaheuristics with Mathematical and Visual Descriptions.
CoRR, 2024

The Firefighter Algorithm: A Hybrid Metaheuristic for Optimization Problems.
CoRR, 2024

Beyond development: Challenges in deploying machine learning models for structural engineering applications.
CoRR, 2024

Large Language Models in Fire Engineering: An Examination of Technical Questions Against Domain Knowledge.
CoRR, 2024

SPINEX: Similarity-based predictions with explainable neighbors exploration for regression and classification.
Appl. Soft Comput., 2024

2023
Machine learning and model driven bayesian uncertainty quantification in suspended nonstructural systems.
Reliab. Eng. Syst. Saf., September, 2023

SPINEX: Similarity-based Predictions and Explainable Neighbors Exploration for Regression and Classification Tasks in Machine Learning.
CoRR, 2023

Can AI Chatbots Pass the Fundamentals of Engineering (FE) and Principles and Practice of Engineering (PE) Structural Exams?
CoRR, 2023

2022
Simplifying Causality: A Brief Review of Philosophical Views and Definitions with Examples from Economics, Education, Medicine, Policy, Physics and Engineering.
CoRR, 2022

Causal Discovery and Causal Learning for Fire Resistance Evaluation: Incorporating Domain Knowledge.
CoRR, 2022

Causality, Causal Discovery, and Causal Inference in Structural Engineering.
CoRR, 2022

2021
Can past failures help identify vulnerable bridges to extreme events? A biomimetical machine learning approach.
Eng. Comput., 2021

Demystifying Ten Big Ideas and Rules Every Fire Scientist & Engineer Should Know About Blackbox, Whitebox & Causal Artificial Intelligence.
CoRR, 2021

Explainable Machine Learning using Real, Synthetic and Augmented Fire Tests to Predict Fire Resistance and Spalling of RC Columns.
CoRR, 2021

RAI: Rapid, Autonomous and Intelligent machine learning approach to identify fire-vulnerable bridges.
Appl. Soft Comput., 2021

2020
Concrete under fire: an assessment through intelligent pattern recognition.
Eng. Comput., 2020

Insights into Performance Fitness and Error Metrics for Machine Learning.
CoRR, 2020

2019
AI-based cognitive framework for evaluating response of concrete structures in extreme conditions.
Eng. Appl. Artif. Intell., 2019


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