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crossrefSystems2025-03-11Cited by 6

Estimation of CO2 Emissions in Transportation Systems Using Artificial Neural Networks, Machine Learning, and Deep Learning: A Comprehensive Approach

Seval Ene Yalçın

This study focuses on estimating transportation system-related emissions in CO2 eq., considering several socioeconomic and energy- and transportation-related input variables. The proposed approach incorporates artificial neural networks, machine learning, and deep learning algorithms. The case of Turkey was considered as an example. Model performance was evaluated using a dataset of Turkey, and future projections were made based on scenario analysis compatible with Turkey’s climate change mitigation strategies. This study also adopted a transportation type-based analysis, exploring the role of Turkey’s road, air, marine, and rail transportation systems. The findings of this study indicate that the aforementioned models can be effectively implemented to predict transport emissions, concluding that they have valuable and practical applications in this field.

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crossrefSystems2025-10-06Cited by 2

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This study constructs a neural-network-inspired multi-layer machine learning model (RQLNet) to measure and analyze the effects of tail risk spillover and its associated sensitivities to macroeconomic factors among petroleum supply chain enterprises. On this basis, the study const…

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crossrefSystems2025-07-10Cited by 2

Cross-Project Multiclass Classification of EARS-Based Functional Requirements Utilizing Natural Language Processing, Machine Learning, and Deep Learning

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Software requirements are primarily classified into functional and non-functional requirements. While research has explored automated multiclass classification of non-functional requirements, functional requirements remain largely unexplored. This study addressed that gap by intr…

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crossrefSystems2025-12-04Cited by 1

Are Neural Networks Better than Machine Learning? A Comparative Study for Travel Mode Predictions

Tongkai Zhang, Cheng-Jie Jin, Yuchen Song, Dawei Li

Predicting how people choose their travel modes accurately is important in the transportation field. Machine learning (ML) and neural networks (NNs) have gradually become popular in recent years. However, which is better is seldom discussed in previous studies. Therefore, we coll…

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crossrefSystems2023-06-22Cited by 253

Exploring the Computational Effects of Advanced Deep Neural Networks on Logical and Activity Learning for Enhanced Thinking Skills

Deming Li, Kellyt D. Ortegas, Marvin White

The Logical and Activity Learning for Enhanced Thinking Skills (LAL) method is an educational approach that fosters the development of critical thinking, problem-solving, and decision-making abilities in students using practical, experiential learning activities. Although LAL has…

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crossrefSystems2025-12-31

Investigating User Acceptance of Autonomous Vehicles in Developing Cities Using Machine Learning: Lessons from Alexandria, Egypt

Sherif Shokry, Ahmed Mahmoud Darwish, Hazem Mohamed Darwish, Omar Elsnossy Ibrahim, Maged Zagow, Marwa Elbany, et al.

The willingness to adopt Autonomous Vehicles (AVs) represents a crucial advancement from the sustainable mobility perspective. This is progressively continuing in the developed countries. A comparable shift is expected in developing nations; however, empirical studies remain limi…

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crossrefSystems2024-11-27Cited by 12

Development of a Forecasting Framework Based on Advanced Machine Learning Algorithms for Greenhouse Gas Emissions

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The reduction of greenhouse gas emissions, in order to effectively address the issue of climate change, has critical importance worldwide. To achieve this aim and implement the necessary strategies and policies, the projection of greenhouse gas emissions is essential. This paper…

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