Utilizing Artificial intelligence to identify an Optimal Machine learning model for predicting fuel consumption in Diesel engines

This research investigates the heat transfer characteristics in the squeezing flow of a non-Newtonian fluid, specifically focusing on the Casson fluid between two parallel circular plates. The research includes the development of nonlinear ordinary differential equations (ODEs), the utilization of conservation laws and similarity transformations, and the subsequent analytical resolution through the application of the Differential Transform Method (DTM) and Akbari-Ganji’s Method (AGM). The investigation explores the influence of various factors on flow characteristics, presenting the results visually and engaging in detailed discussions. The analysis encompasses a comprehensive examination across a range of parameters, including the squeeze number, Casson fluid parameter, Prandtl number, and Eckert number. Notably, the results indicate an acceleration in the rate of motion with respect to both the squeeze number and the Casson fluid parameter. In addition, the skin friction coefficient shows an increasing trend with respect to S, while the influence of [Formula: see text] has the opposite effect. Meanwhile, the Nusselt number decreases as both S and [Formula: see text] increase but exhibits an upward trend with increasing values of PR, Ec, and [Formula: see text]. The findings of this study contribute to the broader understanding of heat transfer in non-Newtonian fluid flows and provide valuable insights for practical applications.

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Contract Farming Optimization Through Leveraging Artificial Intelligence to Combat Side Marketing Practices

Mukucha,

Jaravaza,

Chingwaru

et al. 2025

Advances in Marketing, Customer Relationship Management, and E-Services

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Contract farming has become the main financing strategy for most cash crops grown in developing countries in general and Africa in particular. The serendipitous emergence of artificial intelligence brings forth a wide array of possibilities for minimising the incidences of side marketing. A qualitative study was conducted through interviewing a purposive sample of 30 agricultural extension officers directly involved with contract farming of cash crops such as cotton and tobacco, and the data was thematically analysed. The results indicated that artificial intelligence can be deployed to minimize side marketing practices through analysing historical delivery patterns for detecting anomalies, using predictive analytics leveraging past delivery patterns to identify the farmers with a propensity to indulge in side marketing behaviours, and predicting the expected harvest through AI powered drone technology. The study suggested that AI algorithm developers incorporate the above suggested applications in developing tailor made modules for combating side marketing practices.

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Utilizing Artificial intelligence to identify an Optimal Machine learning model for predicting fuel consumption in Diesel engines

Cited by 3 publications

References 31 publications

Gas exchange optimization in aircraft engines using sustainable aviation fuel: A design of experiment and genetic algorithm approach

Gas exchange optimization in aircraft engines using sustainable aviation fuel: A design of experiment and genetic algorithm approach

Heat transfer characteristics in the squeezing flow of casson fluid between circular plates: A comprehensive study

Contract Farming Optimization Through Leveraging Artificial Intelligence to Combat Side Marketing Practices

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