Click-Through Rate Prediction Using Decision Tree

Kolan, Anusha; Moukthika, Dasika; Sreevani, K.; Jayasree, H.

doi:10.1007/978-981-15-1480-7_3

Cited by 2 publications

(3 citation statements)

References 4 publications

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“…This paper [6] proposes a meta-heuristic approach to parking vehicles. The developed method for determining parking efficiency considers two parameters: the time spent searching for parking spaces and the efficiency of parking.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A Feed Forward Back Propagation Neural Network (NN) approach is used to improve parking efficiency and reduce parking space search time. A parking space search mechanism is proposed in this paper [6] that uses the Firefly algorithm to reduce parking space search time while minimizing the factors that result in problems. Briefly, technological advancements increase the utilization of vehicles in cities.…”

Section: Literature Reviewmentioning

confidence: 99%

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Parking recommendation with meta-heuristic algorithms

Wu,

Yang

2024

TNS

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Due to the exponential growth of cars in urban areas, parking problems have become a significant concern. Addressing this issue requires efficient methods for locating available parking spaces, enhancing the overall experience for drivers. This paper introduces a parking lot recommendation model leveraging meta-heuristic algorithms to generate a list of potential parking locations based on the users travel destinations. The primary objectives of these algorithms include minimizing travel distance, reducing total parking fees, and selecting parking lots with ample available spaces. The proposed model incorporates bio-inspired algorithms, including simulated annealing, genetic algorithms, and their adaptive variants. Our evaluation compares the performance of these algorithms, highlighting the adaptive simulated annealings superior quality of solutions and robustness against local minima. However, it is important to note that this approach comes with a trade-off, requiring longer execution times. In summary, this research contributes a novel parking lot recommendation model that effectively addresses the challenges posed by urban parking. The performance evaluation underscores the efficacy of the adaptive simulated annealing approach, showcasing its potential for practical implementation despite its relatively longer execution time.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Parking recommendation with meta-heuristic algorithms

Wu,

Yang

2024

TNS

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“…Since the random forest model contains multiple parameters, it is hard to compute each combination and show them in a figure like what this paper did for the K-nearest neighbors' algorithms [8][9][10]. Thus, this paper chooses to implement the GridsearchCV to indentify the optimal parameters for the random forest model.…”

Section: Random Forestmentioning

confidence: 99%

Comparison of machine learning algorithms over prediction of Titanic database

2023

ACE

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With the popularize of artificial intelligent, algorithms such as machine learning algorithm takes an important part in many fields. In terms of data prediction, it is important to know which machine learning algorithm fits the specific dataset the most. Comparison between machine learning algorithms helps us figure out the advantage and disadvantage of them and thus save time, and money during the working process. In this paper, this paper will be comparing K-nearest neighbours algorithm, Random Forest algorithm, and Support vector machine algorithm mostly focusing on their performance over the survival prediction of Titanic dataset. It could be seen that algorithm with more adjustable hyper parameters could produce more accurate result. However, finding the suitable hyper parameters is time consuming. In the future, this paper may try to explore ensembled algorithms for better result.

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Click-Through Rate Prediction Using Decision Tree

Cited by 2 publications

References 4 publications

Parking recommendation with meta-heuristic algorithms

Parking recommendation with meta-heuristic algorithms

Comparison of machine learning algorithms over prediction of Titanic database

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