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Compressive strength is one of the most important qualities of concrete, and most of the conventional regression models for predicting the concrete strength could not achieve an expected result due to the unstructured factors. Moreover, the utilization of machine learning and statistical approaches playing its vital role in predicting the concrete compressive strength based on mixture proportions accounting to its industrial importance as well. In this manner, this paper attempts to introduce a new deep learning-based prediction model that makes the prediction more accurate, hence Deep Belief Network (DBN) is used. Moreover, to make the prediction more precise, it is planned to have the fine-tuning of activation function and weights of DBN, which makes the model efficient in its performance. For this purpose, an improved optimization concept is introduced called Lion Algorithm with new Rate Evaluation, which is the modified Lion Algorithm (LA). Finally, the performance of the proposed model is evaluated over other state-of-the-art models concerning certain error analysis.

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India is dealing with the problem of high automotive emissions as the number of vehicles on the road is rising. The primary cause of these emissions is the burning of crude oil. India imports crude oil from nations with abundant oil supplies because it lacks the resources to meet all of the country's energy needs for cars. There is a need to look for a carbon-free alternative fuel that is locally accessible in sufficient quantity to suit India's energy needs in order to address the problems related to oil imports and vehicle emissions. Hydrogen is a clean fuel choice among the potential energy sources because its combustion only yields water as a byproduct, and also due to its purity and high energy content, hydrogen energy offers a sustainable alternative to fossil fuels. India has the benefit of producing hydrogen from renewable sources like solar and wind during periods of lower demand because of its continuously expanding renewable energy generation capacity, and Asia is one of the continents with a wealth of these resources. Also, the exponential increase in food waste generation has prompted the scientific community to convert it into value-added resources. The main elements influencing the adoption of hydrogen as a cost-effective energy source in Asian nations are the availability of resources, safety requirements, public acceptance, and appropriate government incentives. The current analysis discusses the need to use hydrogen as an alternative fuel, its production processes, storage concerns, transportation, and the sources that are available. In various Asian nations like Japan, Korea, China, India, and Malaysia, particular focus has been placed on the notion of a renewable hydrogen economy. Perspectives on fuel supply, environmental effects, and social acceptance could help the biohydrogen energy sector evolve in a favorable way.