Fuzzy Integrated Delphi-ISM-MICMAC Hybrid Multi-Criteria Approach to Optimize the Artificial Intelligence (AI) Factors Influencing Cost Management in Civil Engineering

Information, Год журнала: 2024, Номер 15(5), С. 280 - 280

Опубликована: Май 14, 2024

This research paper presents a comprehensive study on optimizing the critical artificial intelligence (AI) factors influencing cost management in civil engineering projects using multi-criteria decision-making (MCDM) approach. The problem addressed revolves around need to effectively manage costs endeavors amidst growing complexity of and increasing integration AI technologies. methodology employed involves utilization three MCDM tools, specifically Delphi, interpretive structural modeling (ISM), Cross-Impact Matrix Multiplication Applied Classification (MICMAC). A total 17 factors, categorized into eight broad groups, were identified analyzed. Through application different techniques, relative importance interrelationships among these determined. key findings reveal role certain such as risk mitigation components, processes. Moreover, hierarchical structure generated through ISM influential via MICMAC provide insights for prioritizing strategic interventions. implications this extend informing decision-makers domain about effective strategies leveraging their practices. By adopting systematic approach, stakeholders can enhance project outcomes while resource allocation mitigating financial risks.

Язык: Английский

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Spectral intelligent detection for aflatoxin B1 via contrastive learning based on Siamese network

Food Chemistry, Год журнала: 2024, Номер 449, С. 139171 - 139171

Опубликована: Апрель 6, 2024

Язык: Английский

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Sustainable Groundwater Management Using Machine Learning-Based DRASTIC Model in Rurbanizing Riverine Region: A Case Study of Kerman Province, Iran

Water, Год журнала: 2024, Номер 16(19), С. 2748 - 2748

Опубликована: Сен. 27, 2024

Groundwater salinization poses a critical threat to sustainable development in arid and semi-arid rurbanizing regions, exemplified by Kerman Province, Iran. This region experiences groundwater ecosystem degradation as result of the rapid conversion rural agricultural land urban areas under chronic drought conditions. study aims enhance Pollution Risk (GwPR) mapping integrating DRASTIC index with machine learning (ML) models, including Random Forest (RF), Boosted Regression Trees (BRT), Generalized Linear Model (GLM), Support Vector Machine (SVM), Multivariate Adaptive Splines (MARS), alongside hydrogeochemical investigations, promote water management Province. The RF model achieved highest accuracy an Area Under Curve (AUC) 0.995 predicting GwPR, outperforming BRT (0.988), SVM (0.977), MARS (0.951), GLM (0.887). RF-based map identified new high-vulnerability zones northeast northwest showed expanded moderate vulnerability zone, covering 48.46% area. Analysis revealed exceedances WHO standards for total hardness (TH), sodium, sulfates, chlorides, electrical conductivity (EC) these areas, indicating contamination from mineralized aquifers unsustainable practices. findings underscore model’s effectiveness prediction highlight need stricter monitoring management, regulating extraction improving use efficiency riverine aquifers.

Язык: Английский

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Artificial Neural Network-Driven Techno-Economic Predictions for Micro Gas Turbines (MGT) Based Energy Applications

Energy and AI, Год журнала: 2025, Номер unknown, С. 100483 - 100483

Опубликована: Фев. 1, 2025

Язык: Английский

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Application of Multiple-Criteria Decision-Making Technology in Emergency Decision-Making: Uncertainty, Heterogeneity, Dynamicity, and Interaction

Mathematics, Год журнала: 2025, Номер 13(5), С. 731 - 731

Опубликована: Фев. 24, 2025

With the increasing frequency of natural and man-made disasters, emergency management has become a key research field aimed at saving lives reducing environmental economic losses. As core link in responding to sudden crisis events, decision-making is directly related stability society, safety citizens, robustness infrastructure. scientific method, multiple-criteria (MCDM) technology gradually an important tool for solving complex problems management. It can handle uncertainty, heterogeneity, dynamicity, interaction emergencies select best alternative or rank all options multiple reference attributes limited number solve problems. This paper comprehensively reviews existing relevant literature, analyzes current status challenges MCDM its application process management, proposes gaps development directions this field.

Язык: Английский

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The value of expert judgments in Decision Support Systems

Applied Soft Computing, Год журнала: 2025, Номер unknown, С. 112806 - 112806

Опубликована: Янв. 1, 2025

Язык: Английский

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CFI-LFENet: Infusing cross-domain fusion image and lightweight feature enhanced network for fault diagnosis

Information Fusion, Год журнала: 2023, Номер 104, С. 102162 - 102162

Опубликована: Ноя. 30, 2023

Язык: Английский

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Optimization of milling conditions for AISI 4140 steel using an integrated machine learning-multi objective optimization-multi criteria decision making framework

Measurement, Год журнала: 2024, Номер unknown, С. 115837 - 115837

Опубликована: Сен. 1, 2024

Язык: Английский

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IFNN: Enhanced interpretability and optimization in FNN via Adam algorithm

Information Sciences, Год журнала: 2024, Номер 678, С. 121002 - 121002

Опубликована: Июнь 13, 2024

Язык: Английский

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Evaluating airline service quality through a comprehensive text-mining and multi-criteria decision-making analysis

Journal of Air Transport Management, Год журнала: 2024, Номер 120, С. 102655 - 102655

Опубликована: Авг. 1, 2024

Язык: Английский

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