Artificial intelligence-based technologies are rapidly advancing and significantly influencing the engineering sector, particularly in the automotive industry, through AI-driven neural network tools and Sankey diagrams. Meanwhile, the depletion of fossil fuels and rising emissions have pushed global efforts towards renewable and clean fuel solutions. Hydrogen, as a key clean fuel, has garnered considerable research interest. Combining hydrogen with biomass-derived fuels has gained attention due to its dual benefits of addressing biomass waste disposal and alleviating hydrogen storage and safety concerns. This study focuses on the production of aquatic plant oil (duckweed bio-oil) and its combination with hydrogen gas, evaluating their effects on the performance of a Reactivity Controlled Compression Ignition (RCCI) engine. The results revealed that the H40 blend demonstrated a 1% higher brake thermal efficiency (BTE) than diesel, along with emission reductions of 40% for HC, 6% for NOx, 27% for CO, and 14% for smoke. The results were further validated using an Artificial Neural Network (ANN) and a Sankey diagram. The ANN achieved low RMSE values (0.9965–0.9996) and MPAE values within 4%, while the Sankey diagram effectively illustrated energy distribution with minimal loss. These findings highlight the potential of hydrogen-enriched fuels for future internal combustion engines.

Analysing dataset representing ten years (1999-2008) of clinical care at 130 US hospitals and integrated delivery networks which includes over 50 features representing patient and hospital outcomes, we have implemented various machine learning and deep learning models to achieve higher accuracy in classifying diabetic patients in order to predict the early re-admission cases. To overcome the sever challenge we have faced with class imbalance in our datasets, we have introduced SMOTE sampling technique. Having resampled our data and implemented our deep learning architecture, we have obtained an accuracy of 98%. The relatively higher performances which were obtained upon using both support vector machine and deep learning models are suggestive of implementing these algorithms in order to overcome challenges related to hospital diabetic re-admission cases in both managing the patents and preventing this devastating disease.

In this work we have studied the criteria for assessing the quality of educational games and their impact on student learning outcomes by designing two experiments that were conducted in a duration of total of 6 years. The first experiment involves students in their final year of high school studies and the second on first year students in higher education. Our main results have shown that in comparison with traditional methods in education, teaching with the use of educational games increases the degree of mastering of educational material. It is also observed to increase the involvement and motivation of students to perform educational tasks by significantly reducing the time that will be required for reinforcement of the passed material. Additionally, it tends to cause less fatigue and holds their concentration much longer. Based on the results of our research, we have also identified the main criteria for evaluating the effectiveness of educational games. Our results suggest that a robust and comprehensive integration of educational games into the learning process will significantly improve the quality of education at all levels.

The growing shift toward electrifying the transportation sector is driving a significant increase in electricity demand, with large‐scale electric vehicle (EV) charging emerging as a major contributor. This surge is creating new challenges for power distribution systems. One of the key problems is the limited understanding of how EVs impact grid energy consumption, making it difficult to manage and plan for large‐scale charging needs effectively. Traditional methods for electric vehicle charging prediction are difficult to adapt to the dynamic charging behaviours, high volatility of data, and high prediction accuracy requirements, and therefore, more intelligent prediction models based on machine learning and deep learning are needed. It suggests that to meet the projected long‐term energy requirements for EV charging, the future grid must be adequately equipped to handle such a large load. In this research, we solve this difficulty by constructing analytics‐driven models generated from EV charging trends by applying deep learning (DL) as effectively as the machine learning (ML) techniques. Our objective is to create information that can assist in estimating energy utilisation trends and lessen the possible stress on the grid. First, we examined how extensive EV charging might affect the grid system. Following this research, we proposed an optimisation target that maximises the entire value of EVs charging on the electrical grid while obeying crucial restrictions like voltage reliability and power boundaries. Next, we employed a graphical illustrating approach to evaluate how EV energy utilisation is spread over duration, utilising real‐world information from real electric charging stations. Ultimately, we created forecasting models utilising four distinct deep learning as well as machine learning regression methods: fine decision trees, support vector machine (SVM), linear regression, as well as neural networks. In order to analyse the predictive model, we utilised a 5‐fold cross‐validating approach during training to verify the algorithms could be extended effectively and prevent overfitting. We next examined model efficacy using typical regression measurement techniques. The suggested ML/DL models demonstrated good predictive power with nearly identical R ² values and negligible prediction error rates, proving their effectiveness for dependable EV charging demand forecasting strategies. The findings of our research demonstrated that the algorithms we developed achieved more accuracy in forecasting in comparison with similar studies in the field, suggesting the effectiveness of our technique. The forecasting results are utilised more to increase optimisation through supporting forecasting decision‐making, the best possible utilisation of assets, and improved system performance under diverse operational conditions.