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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Comparative study of machine learning techniques to predict fuel consumption of a marine diesel engine
Onur Yüksel, Murat Bayraktar, Mustafa Sokukcu
Ocean Engineering (2023) Vol. 286, pp. 115505-115505
Closed Access | Times Cited: 25

Showing 25 citing articles:

Data-driven optimization of turbulent kinetic energy and tumble-y in combustion engines: A comparative study of machine learning models
Amirali Shateri, Zhiyin Yang, Yun Liu, et al.
Fuel (2025) Vol. 389, pp. 134590-134590
Open Access | Times Cited: 2
Harnessing AI for Sustainable Shipping and Green Ports: Challenges and Opportunities
Irmina Durlik, Tymoteusz Miller, Ewelina Kostecka, et al.
Applied Sciences (2024) Vol. 14, Iss. 14, pp. 5994-5994
Open Access | Times Cited: 14
Utilizing Artificial intelligence to identify an Optimal Machine learning model for predicting fuel consumption in Diesel engines
Amirali Shateri, Zhiyin Yang, Jianfei Xie
Energy and AI (2024) Vol. 16, pp. 100360-100360
Open Access | Times Cited: 9
Ship energy consumption prediction: Multi-model fusion methods and multi-dimensional performance evaluation
Zhihui Hu, Ailong Fan, Wengang Mao, et al.
Ocean Engineering (2025) Vol. 322, pp. 120538-120538
Closed Access | Times Cited: 1
Investigation of ship energy consumption based on neural network
Yaqing Shu, Benshuang yu, Wei Liu, et al.
Ocean & Coastal Management (2024) Vol. 254, pp. 107167-107167
Closed Access | Times Cited: 7
Real-time prediction of fuel consumption and emissions based on deep autoencoding support vector regression for cylinder pressure-based feedback control of marine diesel engines
Jie Gu, Yingyuan Wang, Jiancun Hu, et al.
Energy (2024) Vol. 300, pp. 131570-131570
Closed Access | Times Cited: 5
Ship fuel consumption prediction based on transfer learning: Models and applications
Xi Luo, Mingyang Zhang, Yi Han, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 141, pp. 109769-109769
Closed Access | Times Cited: 5
Improving Ship Fuel Consumption and Carbon Intensity Prediction Accuracy Based on a Long Short-Term Memory Model with Self-Attention Mechanism
Zhihuan Wang, Tianye Lu, Yi Han, et al.
Applied Sciences (2024) Vol. 14, Iss. 18, pp. 8526-8526
Open Access | Times Cited: 4
Comparative analysis of machine learning methods for the prediction of brake power and rate of revolution for bulk carriers
Marko Valčić, Ivana Martić, Nastia Degiuli, et al.
Ocean Engineering (2025) Vol. 322, pp. 120517-120517
Closed Access
Evaluation of just in time strategy regarding carbon intensity indicator
Çağlar Karatuğ
Journal of Marine Engineering & Technology (2025), pp. 1-11
Closed Access
Maritime decarbonization through machine learning: A critical systematic review of fuel and power prediction models
Son Nguyen, Matthieu Gadel, K. Wang, et al.
Cleaner Logistics and Supply Chain (2025), pp. 100210-100210
Open Access
Analysis of combustion characteristics of a diesel engine run on ternary blends using machine learning algorithms
Jakeer Hussain Shaik, Naseem Khayum, Krishna Kumar Pandey
Environmental Progress & Sustainable Energy (2025)
Closed Access
Predictive Analysis of Engine Power Limitations for Fuel Reduction in a Tanker Ship Using a Rule-Based Machine Learning Technique
Alper Eylem Ersoy, Uğur Buğra Çelebi, Onur Yüksel, et al.
Journal of Cleaner Production (2025), pp. 145535-145535
Closed Access
Parametric machine learning integrated approach for assessing environmental and engine variables on fuel consumption and carbon intensity
Onur Yüksel, Murat Bayraktar, Olgun Konur
Journal of Marine Engineering & Technology (2025), pp. 1-21
Open Access
Fuel injection prediction for a heavy-duty diesel engine based on deep self-encoder Gaussian process regression
Mingzhang Pan, Yunlong Qiu, Sheng Cao, et al.
Fuel (2025) Vol. 399, pp. 135566-135566
Closed Access
Comparison of machine learning algorithms on a low heat rejection diesel engine running on ternary blends
Krishna Kumar Pandey, Naseem Khayum, Jakeer Hussain Shaik
Journal of Renewable and Sustainable Energy (2024) Vol. 16, Iss. 5
Closed Access | Times Cited: 3
Research on Carbon Intensity Prediction Method for Ships Based on Sensors and Meteorological Data
Chunchang Zhang, Tianye Lu, Zhihuan Wang, et al.
Journal of Marine Science and Engineering (2023) Vol. 11, Iss. 12, pp. 2249-2249
Open Access | Times Cited: 8
Discovering supply chain operation towards sustainability using machine learning and DES techniques: a case study in Vietnam seafood
Luan Thanh Le, Trang Xuan-Thi-Thu
Maritime Business Review (2024) Vol. 9, Iss. 3, pp. 243-262
Open Access | Times Cited: 2
Bubble collapse patterns recognition and flow field prediction based on machine learning
Hao Chen, Shaofei Ren, Shi-Min Li, et al.
Physics of Fluids (2024) Vol. 36, Iss. 8
Closed Access | Times Cited: 2
Investigation of Seasonal Effects on Two-Stroke Marine Diesel Engine Performance Parameters and Emissions
Bulut Ozan Ceylan
Journal of Marine Science and Application (2023) Vol. 22, Iss. 4, pp. 795-808
Closed Access | Times Cited: 5
Machine Learning Approach for the Investigation of Metal Ion Concentration on Distillate Marine Diesel Fuels through Feed Forward Neural Networks
Ambrosios-Antonios Savvides, Leonidas Papadopoulos, George Intzirtzis, et al.
Lubricants (2024) Vol. 12, Iss. 4, pp. 127-127
Open Access | Times Cited: 1
Multi-Dimensional Global Temporal Predictive Model for Multi-State Prediction of Marine Diesel Engines
Liyong Ma, Siqi Chen, Shuli Jia, et al.
Journal of Marine Science and Engineering (2024) Vol. 12, Iss. 8, pp. 1370-1370
Open Access | Times Cited: 1
A Novel Approach to Enhancing the Accuracy of Prediction in Ship Fuel Consumption
Tianrui Zhou, Jinggai Wang, Qinyou Hu, et al.
Journal of Marine Science and Engineering (2024) Vol. 12, Iss. 11, pp. 1954-1954
Open Access | Times Cited: 1
Expanding the range of ship fuel consumption prediction: A multi-algorithm feature selection approach
Shengyu Piao, Min-Ho Park, Siljung Yeo, et al.
Ocean Engineering (2024) Vol. 316, pp. 119944-119944
Closed Access | Times Cited: 1
Investigation of Ship Energy Consumption Based on Neural Network
Yaqing Shu, Benshuang yu, Wei Liu, et al.
(2024)
Closed Access
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