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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.

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Showing 1-25 of 63 citing articles:

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System
Prabhakar Sharma, Zafar Said, Anurag Kumar, et al.
Energy & Fuels (2022) Vol. 36, Iss. 13, pp. 6626-6658
Closed Access | Times Cited: 243
The stability and thermophysical properties of Al2O3-graphene oxide hybrid nanofluids for solar energy applications: Application of robust autoregressive modern machine learning technique
Praveen Kumar Kanti, Prabhakar Sharma, Manoor Prakash Maiya, et al.
Solar Energy Materials and Solar Cells (2023) Vol. 253, pp. 112207-112207
Closed Access | Times Cited: 83
Intelligent approaches for sustainable management and valorisation of food waste
Zafar Said, Prabhakar Sharma, Quach Thi Bich Nhuong, et al.
Bioresource Technology (2023) Vol. 377, pp. 128952-128952
Open Access | Times Cited: 83
Improving the combustion and emission performance of a diesel engine powered with mahua biodiesel and TiO2 nanoparticles additive
Chinmoy Jit Sarma, Prabhakar Sharma, Bhaskor Jyoti Bora, et al.
Alexandria Engineering Journal (2023) Vol. 72, pp. 387-398
Open Access | Times Cited: 64
Viscosity and rheological behavior of Al2O3-Fe2O3/water-EG based hybrid nanofluid: A new correlation based on mixture ratio
V. Vicki Wanatasanappan, Praveen Kumar Kanti, Prabhakar Sharma, et al.
Journal of Molecular Liquids (2023) Vol. 375, pp. 121365-121365
Closed Access | Times Cited: 60
Thermophysical profile of graphene oxide and MXene hybrid nanofluids for sustainable energy applications: Model prediction with a Bayesian optimized neural network with K-cross fold validation
Praveen Kumar Kanti, Prabhakar Sharma, Bhargavi Koneru, et al.
FlatChem (2023) Vol. 39, pp. 100501-100501
Closed Access | Times Cited: 50
Performance and emission analysis of biodiesel blends enriched with biohydrogen and biogas in internal combustion engines
Osama Khan, Ibrahim Alsaduni, Azhar Equbal, et al.
Process Safety and Environmental Protection (2024) Vol. 183, pp. 1013-1037
Closed Access | Times Cited: 22
Renewable energy approach towards powering the CI engine with ternary blends of algal biodiesel-diesel-diethyl ether: Bayesian optimized Gaussian process regression for modeling-optimization
Mansoor Alruqi, Prabhakar Sharma, B. Deepanraj, et al.
Fuel (2022) Vol. 334, pp. 126827-126827
Closed Access | Times Cited: 68
A Review of Modern Machine Learning Techniques in the Prediction of Remaining Useful Life of Lithium-Ion Batteries
Prabhakar Sharma, Bhaskor Jyoti Bora
Batteries (2022) Vol. 9, Iss. 1, pp. 13-13
Open Access | Times Cited: 68
Experimental analysis of novel ionic liquid-MXene hybrid nanofluid's energy storage properties: Model-prediction using modern ensemble machine learning methods
Zafar Said, Prabhakar Sharma, Navid Aslfattahi, et al.
Journal of Energy Storage (2022) Vol. 52, pp. 104858-104858
Closed Access | Times Cited: 65
Exploring the specific heat capacity of water-based hybrid nanofluids for solar energy applications: A comparative evaluation of modern ensemble machine learning techniques
Zafar Said, Prabhakar Sharma, Rajvikram Madurai Elavarasan, et al.
Journal of Energy Storage (2022) Vol. 54, pp. 105230-105230
Closed Access | Times Cited: 64
Improving combustion and emission characteristics of a biogas/biodiesel-powered dual-fuel diesel engine through trade-off analysis of operation parameters using response surface methodology
Bhaskor Jyoti Bora, Thanh Dai Tran, Krushna Prasad Shadangi, et al.
Sustainable Energy Technologies and Assessments (2022) Vol. 53, pp. 102455-102455
Closed Access | Times Cited: 60
A novel optimization approach for biohydrogen production using algal biomass
Prabhakar Sharma, M. Sivaramakrishnaiah, B. Deepanraj, et al.
International Journal of Hydrogen Energy (2022) Vol. 52, pp. 94-103
Closed Access | Times Cited: 52
Sonication impact on thermal conductivity of f-MWCNT nanofluids using XGBoost and Gaussian process regression
Zafar Said, Prabhakar Sharma, Bhaskor Jyoti Bora, et al.
Journal of the Taiwan Institute of Chemical Engineers (2023) Vol. 145, pp. 104818-104818
Closed Access | Times Cited: 38
Exploration of the dual fuel combustion mode on a direct injection diesel engine powered with hydrogen as gaseous fuel in port injection and diesel-diethyl ether blend as liquid fuel
Debabrata Barik, Bhaskor Jyoti Bora, Prabhakar Sharma, et al.
International Journal of Hydrogen Energy (2023) Vol. 52, pp. 827-840
Closed Access | Times Cited: 37
Prediction of combustion, performance, and emission parameters of ethanol powered spark ignition engine using ensemble Least Squares boosting machine learning algorithms
D. Jesu Godwin, Edwin Geo Varuvel, Leenus Jesu Martin
Journal of Cleaner Production (2023) Vol. 421, pp. 138401-138401
Closed Access | Times Cited: 31
Precise Prediction of Biochar Yield and Proximate Analysis by Modern Machine Learning and SHapley Additive exPlanations
Lê Anh Tuấn, Ashok Pandey, Ranjan Sirohi, et al.
Energy & Fuels (2023) Vol. 37, Iss. 22, pp. 17310-17327
Closed Access | Times Cited: 31
A Machine Learning Approach for Carbon di oxide and Other Emissions Characteristics Prediction in a Low Carbon Biofuel-Hydrogen Dual Fuel Engine
Femilda Josephin Joseph Shobana Bai
Fuel (2023) Vol. 341, pp. 127578-127578
Closed Access | Times Cited: 29
Experimental investigation of performance and emissions of a CI engine operating with HVO and farnesane in dual-fuel mode with natural gas and biogas
G.M. Pinto, Roberto Berlini Rodrigues da Costa, Túlio Augusto Zucareli de Souza, et al.
Energy (2023) Vol. 277, pp. 127648-127648
Closed Access | Times Cited: 26
Artificial neural networks-based performance and emission characteristics prediction of compression ignition engines powered by blends of biodiesel derived from waste cooking oil
Somya Patnaik, Narendra Khatri, Eldon R. Rene
Fuel (2024) Vol. 370, pp. 131806-131806
Open Access | Times Cited: 12
Interpretable knowledge-guided framework for modeling minimum miscible pressure of CO2-oil system in CO2-EOR projects
Bin Shen, Shenglai Yang, Xinyuan Gao, et al.
Engineering Applications of Artificial Intelligence (2022) Vol. 118, pp. 105687-105687
Closed Access | Times Cited: 30
Model-prediction and optimization of the performance of a biodiesel – Producer gas powered dual-fuel engine
Prabhakar Sharma, Avdhesh Kr. Sharma, B. Deepanraj, et al.
Fuel (2023) Vol. 348, pp. 128405-128405
Closed Access | Times Cited: 19
Comparative diesel engine performance and emission forecasting using extreme learning and quadratic regression techniques burning waste cooking biodiesel
M.S. Gad, Ahmed Alenany
International Journal of Hydrogen Energy (2024) Vol. 60, pp. 94-106
Closed Access | Times Cited: 7
Multi-step ahead forecasting of electrical conductivity in rivers by using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model enhanced by Boruta-XGBoost feature selection algorithm
Masoud Karbasi, Mumtaz Ali, Sayed M. Bateni, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 7
Developing a novel hybrid Auto Encoder Decoder Bidirectional Gated Recurrent Unit model enhanced with empirical wavelet transform and Boruta-Catboost to forecast significant wave height
Masoud Karbasi, Mehdi Jamei, Mumtaz Ali, et al.
Journal of Cleaner Production (2022) Vol. 379, pp. 134820-134820
Closed Access | Times Cited: 26
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