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

Conspiracy

https://doi.org/10.1007/978-1-349-59074-2method that outperforms all other applicable techniques with an accuracy of 99.4152%. Another important contribution is hyper-parameter tuning which selects the best parameters for the above algorithms. Finally, the performance of all applying algorithms for this dataset is compared.

somnambulism

Michael J. Lewis,Charles W. Bamforthrate realistic infected leaf images of Grape for effective data augmentation. By combining the original dataset with these created images, VGG16 deep-learning model, and through extensive analysis, it has been demonstrated that the method greatly improves plant disease identification accuracy. With

Uncultured

构想

A Survey on Thyroid Nodule Detection and Classification, an overview of current research on TC detection based on US images using ML algorithms. It also presents advancements in TC detection using DL algorithms. The information present in this paper is taken from published work of researchers in thyroid cancer with references.

Recess

mutineer

Machine Learning-Powered Cloud-Based Text Summarization,engthen text summarization applications by integrating cloud services like cloud-based machine learning platforms and natural language processing APIs. The developer looks at case studies and practical examples that show how cloud computing and machine learning may be used to effectively conduct tex

向外供接触

Managing Operations in Chaotic Environments with Evolutionary Software Agents,nd technical problems, the currency market is purely informational and lacks inertia. As a result, traditional prediction algorithms used in software robots based on reactive control strategies have proven ineffective. This study aims to address this efficiency issue by exploring control strategies

rectocele

Image Classification Using Few Shot Learning,formance. The proposed model is trained episodically on 4000 training tasks randomly generated from the images of source domain. The target domain is split into four sets, for N = 5 (5 classes randomly selected from target domain) and K = 10 (for each of the 5 classes 10 samples are selected), the a

giggle

,Enhanced Brain Tumor Classification with Inception V3 and Xception Dual-Channel CNN,known for its potentiality in extracting features from multiple scales while the Xception model is known for its superior performance in image classification tasks. Combining the representations learned by the two models, the joint training algorithm boosts the learning process and provides better g

MIR