Titlebook: Evolutionary Artificial Intelligence; Proceedings of ICEAI David Asirvatham,Francisco M. Gonzalez-Longatt,R. Conference proceedings 2024 T

內(nèi)向者

Dilated Long Short-Term Memory Network Augmentation for Precise Fake News Classification, to capture long-term relationships in the text because of the dilated convolution design, which is critical for properly recognizing the fake news. The experimental findings show that the proposed strategy is capable of achieving a high degree of accuracy when instances of fake news are identified.

諂媚于性

CRDP: Chronic Renal Disease Prediction and Evaluation with Reduced Prominent Features,on and prediction of CRD. The CRDP algorithm is implemented, and the results are predominantly used in logistic regression and K-nearest neighbor classification techniques to enhance and improve their prediction accuracy on CRD.

DEVIL

Foreshadow

Conference proceedings 2024imization, evolutionary neural networks, evolutionary reinforcement learning, genetic algorithms, memetic algorithms, novel bio-inspired algorithms, evolving multi-agent systems, agent-based evolutionary approaches, and evolutionary game theory..

微不足道

Mark H. Karwan,Jaap Spronk,Jyrki Walleniusest-suited algorithm for diabetes risk prediction. From the obtained results, it is evident that the random forest with extra trees classifier has delivered the best accuracy for predicting diabetes with an accuracy rate of 99.04%. With an accuracy value of 97.12%, the gradient boosting and bagging classifier models produced the next-best result.

transdermal

Colin C. Graham,O. Carruth McGehee achieved higher accuracy in predicting the outcomes when compared to other machine learning methods, with an accuracy of 92 and 94%, respectively. This study also highlights the importance of feature selection and prediction boosting in order to optimize the credit default prediction rates.

companion

milligram

Model Accuracy Test for Early Stage of Diabetes Risk Prediction with Data Science Approach,est-suited algorithm for diabetes risk prediction. From the obtained results, it is evident that the random forest with extra trees classifier has delivered the best accuracy for predicting diabetes with an accuracy rate of 99.04%. With an accuracy value of 97.12%, the gradient boosting and bagging classifier models produced the next-best result.

先驅(qū)

,A Comparative Study of?Machine Learning Algorithms for?Enhanced Credit Default Prediction, achieved higher accuracy in predicting the outcomes when compared to other machine learning methods, with an accuracy of 92 and 94%, respectively. This study also highlights the importance of feature selection and prediction boosting in order to optimize the credit default prediction rates.

組成

,CigaretteCNN: A Convolutional Neural Network for?Detecting Cigarette Smoking Activity,s demonstrate the effectiveness of the proposed CNN model for detecting smoking activity. The model achieved 89.51% accuracy on the test set which measures the model’s ability to accurately distinguish between smoking and non-smoking activities.