23 citations · 49 across the 12 of their papers we have counts for
13 papers
SPINEX_ Symbolic Regression: Similarity-based Symbolic Regression with Explainable Neighbors Exploration
MZ Naser, Ahmed Z Naser
This article introduces a new symbolic regression algorithm based on the SPINEX (Similarity-based Predictions with Explainable Neighbors Exploration) family. This new algorithm (SP…
SPINEX-Optimization: Similarity-based Predictions with Explainable Neighbors Exploration for Single, Multiple, and Many Objectives Optimization
MZ Naser, Ahmed Z Naser
This article introduces an expansion within SPINEX (Similarity-based Predictions with Explainable Neighbors Exploration) suite, now extended to single, multiple, and many objective…
SPINEX-Clustering: Similarity-based Predictions with Explainable Neighbors Exploration for Clustering Problems
MZ Naser, Ahmed Naser
This paper presents a novel clustering algorithm from the SPINEX (Similarity-based Predictions with Explainable Neighbors Exploration) algorithmic family. The newly proposed cluste…
SPINEX: Similarity-based Predictions with Explainable Neighbors Exploration for Anomaly and Outlier Detection
MZ Naser, Ahmed Z Naser
This paper presents a novel anomaly and outlier detection algorithm from the SPINEX (Similarity-based Predictions with Explainable Neighbors Exploration) family. This algorithm lev…
A Review of 315 Benchmark and Test Functions for Machine Learning Optimization Algorithms and Metaheuristics with Mathematical and Visual Descriptions
M. Z. Naser, Mohammad Khaled al-Bashiti, Arash Teymori Gharah Tapeh +7
In the rapidly evolving optimization and metaheuristics domains, the efficacy of algorithms is crucially determined by the benchmark (test) functions. While several functions have…
The Firefighter Algorithm: A Hybrid Metaheuristic for Optimization Problems
M. Z. Naser, A. Z. Naser
This paper presents the Firefighter Optimization (FFO) algorithm as a new hybrid metaheuristic for optimization problems. This algorithm stems inspiration from the collaborative st…
Beyond development: Challenges in deploying machine learning models for structural engineering applications
Mohsen Zaker Esteghamati, Brennan Bean, Henry V. Burton +1
Machine learning (ML)-based solutions are rapidly changing the landscape of many fields, including structural engineering. Despite their promising performance, these approaches are…
Large Language Models in Fire Engineering: An Examination of Technical Questions Against Domain Knowledge
Haley Hostetter, M. Z. Naser, Xinyan Huang +1
This communication presents preliminary findings from comparing two recent chatbots, OpenAI's ChatGPT and Google's Bard, in the context of fire engineering by evaluating their resp…
Causal Diagrams for Structural Engineers
M. Z. Naser
Causal diagrams are logic and graphical tools that depict assumptions about presumed causal relations. Such diagrams have proven effective in tackling a variety of problems in soci…
SPINEX: Similarity-based Predictions and Explainable Neighbors Exploration for Regression and Classification Tasks in Machine Learning
M. Z. Naser, M. K. albashiti, A. Z. Naser
The field of machine learning (ML) has witnessed significant advancements in recent years. However, many existing algorithms lack interpretability and struggle with high-dimensiona…
Simplifying Causality: A Brief Review of Philosophical Views and Definitions with Examples from Economics, Education, Medicine, Policy, Physics and Engineering
M. Z. Naser
This short paper compiles the big ideas behind some philosophical views, definitions, and examples of causality. This collection spans the realms of the four commonly adopted appro…
Causal Discovery and Causal Learning for Fire Resistance Evaluation: Incorporating Domain Knowledge
M. Z. Naser, Aybike Ozyuksel Ciftcioglu
Experiments remain the gold standard to establish an understanding of fire-related phenomena. A primary goal in designing tests is to uncover the data generating process (i.e., the…
Causality, Causal Discovery, and Causal Inference in Structural Engineering
M. Z. Naser
Much of our experiments are designed to uncover the cause(s) and effect(s) behind a data generating mechanism (i.e., phenomenon) we happen to be interested in. Uncovering such rela…