This study aims to enhance biodiesel yield prediction's precision by applying the advanced Extreme Learning Machine (ELM) model. The optimisation algorithms include Cuckoo Search, Dragonfly Optimization, Firefly Optimization, and Fruitfly Optimization to improve the predictive efficiency of the ELM model. Two datasets are investigated: biodiesel from Ceiba pentandra oil and the bio-oil yield using a Box-Behnken method for the experiment design. Optimisation approaches find the optimal hidden size of the ELM model. The results obtained from the first dataset reveal that Dragonfly Optimization was found to be the most effective algorithm, exhibiting the lowest Root Mean Square Error (RMSE) of 0.098 for Dataset I and 0.000306 for Dataset II and a strong relationship between predicted and actual values with a high coefficient of determination (R-squared) of 0.99 in both datasets. Firefly Optimization achieved exceptional accuracy for the second dataset with a hidden size of 28. High coefficient of determination values, low RMSE, and Mean Square Error (MSE) values highlight the predictive precision of Firefly Optimization. The comparative analysis underscores the potential of optimisation methods to significantly enhance biodiesel yield estimates, with Firefly Optimization and Dragonfly Optimization emerging as standout performers for their respective datasets. This study contributes valuable insights into biodiesel production by showcasing the efficiency of optimised ELM models in predicting biodiesel and bio-oil outputs.
Smart buoys have emerged as a promising technology for accurate and real-time wave height measurement in coastal regions and maritime environments. These buoys utilize advanced sensors and communication systems to collect and transmit data on wave heights, enabling improved safety, planning, and decision-making. This paper provides an overview of the concept of smart buoys for wave height measurement, discussing the technological advancements, data accuracy, and potential applications. It also highlights the integration of smart buoys into data networks and the future developments in this field. The findings emphasize the significance of smart buoys in enhancing our understanding of wave behavior and their impact on various sectors and industries
An auction can be used in various domains, such as cloud computing, data trading, energy trading, and spectrum allocation, which can help to identify the true value of selling goods. The introduction of the Internet has already paved the way for electronic auction (e-auction). Electronic auctions have several benefits over traditional physical auctions; however, in e-auctions, privacy becomes a major issue. Many works are reported in the literature to address this issue. Collusion of various entities involved in the auction, such as bidder, seller, auctioneer, etc. However, most works related to privacy-preserving auctions lack the consideration of collusion of involved entities. In this paper, we have discussed various important works related to privacy-preserving auctions and their associated issues. Problems created due to the collusion of entities in the works are also discussed. We also summarized works in allied disciplines where privacy-preserving auctions are applied. We have also discussed the future research directions in brief.
