Sensitivity of SnO2 nanoparticles/reduced graphene oxide hybrid to NO2 gas: A DFT study
Publication Date
Mon Jul 01 2019
Journal Name
International Journal Of Heat And Mass Transfer
(311)
(319)
Publication Date
Mon Jun 01 2026
Journal Name
Statistics, Optimization & Information Computing
Predicting Public Budget Surplus and Deficit Using a Hybrid 1D-CNN–LSTM Model
The fiscal position of governments in rentier economies depends heavily on oil revenues. The relationship between oil prices and the budget surplus or deficit is often nonlinear and characterized by complex temporal dependencies, which may limit the predictive capability of conventional econometric models. Accordingly, this study aims to forecast the Iraqi budget surplus and deficit and compare the predictive performance of the ARDL, NARDL, LSTM, 1D-CNN, and hybrid 1D-CNN-LSTM models using oil prices as the primary predictive variable. The hybrid model integrates the feature-extraction capability of One-Dimensional Convolutional Neural Networks (1D-CNN) with the ability of Long Short-Term Memory (LSTM) networks to capture long-term
... Show More
Publication Date
Wed Aug 27 2025
Journal Name
2025 International Conference On Electrical, Communication And Computer Engineering (icecce)
A Hybrid Deep Learning Approach for Fault Classification in Electric Vehicle Drive Motors
Convolutional Neural Network
Electric Vehicle
Fault Classification
Long Short-Term Memory
Random Forest Classifier
A new and hybrid deep learning-based approach for diagnosing faults in electric vehicle (EV) drive motors is proposed in this article. This article presents a new and hybrid deep learning-based method of diagnosing faults in the drive motors of electric vehicles (EV). In contrast to standard CNNLSTM approaches that depend on SoftMax classification, the introduced framework combines a Random Forest (RF) classifier to enhance the generalization, interpretability, and robustness of fault prediction. Furthermore meant for use on edge computing equipment with IoT integration, the design allows for real-time monitoring in resource-limited settings. The introduced algorithm utilizes a Random Forest (RF) classifier for accurate fault classification
... Show More
(1)
(1)
Publication Date
Tue Nov 09 2021
Journal Name
Abu Dhabi International Petroleum Exhibition & Conference
Numerical Simulation of Gas Lift Optimization Using Artificial Intelligence for a Middle Eastern Oil Field
Abstract<p>Artificial lift techniques are a highly effective solution to aid the deterioration of the production especially for mature oil fields, gas lift is one of the oldest and most applied artificial lift methods especially for large oil fields, the gas that is required for injection is quite scarce and expensive resource, optimally allocating the injection rate in each well is a high importance task and not easily applicable. Conventional methods faced some major problems in solving this problem in a network with large number of wells, multi-constrains, multi-objectives, and limited amount of gas. This paper focuses on utilizing the Genetic Algorithm (GA) as a gas lift optimization algorit</p>
... Show More
evolutionary algorithm
gas lift
artificial intelligence
upstream oil & gas
machine learning
optimization problem
correlation
presented
allocation
reservoir pressure
(8)
(5)
Publication Date
Mon Jun 12 2017
Journal Name
Day 3 Wed, June 14, 2017
A New Practical Method for Predicting Equivalent Drainage Area of Well in Tight Gas Reservoirs
Abstract<p>The tight gas is one of the main types of the unconventional gas. Typically the tight gas reservoirs consist of highly heterogeneous low permeability reservoir. The economic evaluation for the production from tight gas production is very challenging task because of prevailing uncertainties associated with key reservoir properties, such as porosity, permeability as well as drainage boundary. However one of the important parameters requiring in this economic evaluation is the equivalent drainage area of the well, which relates the actual volume of fluids (e.g gas) produced or withdrawn from the reservoir at a certain moment that changes with time. It is difficult to predict this equival</p>
... Show More
(12)
(8)
Publication Date
Tue Mar 03 2026
Journal Name
International Journal Of Engineering Pedagogy (ijep)
Design of a Hybrid AI-Driven Engineering Model for Energy-Efficient and Sustainable Educational Systems
hybrid AI
energy efficiency
sustainable educational buildings
building energy simulation
predictive control
optimization.
A substantial percentage of the world’s energy consumption (almost 40%) and carbon dioxide (CO2) emissions (around 37%) come from the construction industry, especially schools. This work presents a new hybrid artificial intelligence (AI) engineering model that aims to maximize energy performance on campuses in a holistic way. Modules for data-driven forecasting, metaheuristic optimization, and real-time adaptive control are all part of the concept. A thorough energy simulation of a university campus building is used in conjunction with the AI model to assess its performance through a co-simulation framework. Findings show that yearly peak electricity demand may be reduced by 18.7% and total site energy consumption by 22.4% when co
... Show More
Publication Date
Tue Dec 22 2020
Journal Name
Collaboration And Integration In Construction, Engineering, Management And Technology
(3)
Publication Date
Sun Jun 01 2025
Journal Name
Chemical Engineering And Processing - Process Intensification
(11)
(8)
Publication Date
Sun Jun 01 2025
Journal Name
Chemical Engineering And Processing - Process Intensification
(11)
(8)
Publication Date
Sun Jun 01 2025
Journal Name
Chemical Engineering And Processing - Process Intensification
(11)
(8)