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

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Managing Operations in Chaotic Environments with Evolutionary Software Agents,dynamics, handling chemical and technological processes, generating signals in radio engineering, and managing assets in capital markets. The challenge lies in the limited predictability of deterministic chaos models, which results in additional uncertainty arising from random observations, as expla

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,Enhanced Brain Tumor Classification with Inception V3 and Xception Dual-Channel CNN,According to the scientific community, a brain tumor is an unusual growth in brain cells, some of which may develop into cancer. Manually diagnosing the severity of brain tumors is not only a challenge but also error-prone. To mitigate the issues with manual diagnosis, deep learning can be proposed

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Segmentation of Multiple Sclerosis Using Autoencoder and Classifier,cal dysfunction. Accurately identifying the degree of damage to the brain matter is critical for prognosis. Progression of MS must be closely monitored requiring regular assessment of symptoms through diagnostic methods like magnetic resonance imaging (MRI). This process is quite taxing and time-con

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A More Effective Ensemble ML Method for Detecting Breast Cancer,come the world’s most common trigger of death by 2020, having been identified among 7.8 million women over the 5 years preceding that. ML algorithms are being used extensively in the medical field to forecast certain diseases sooner. A breast cancer dataset obtained from Kaggle is used in this study

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Identification of Plant Leaf Disease Using Synthetic Data Augmentation ProGAN to Improve the Perfor of several years. Existing approaches still face challenges in achieving high accuracy and generalization. One key limitation lies in the requisite for a significant amount of labeled data to properly train deep learning models. Moreover, imbalanced and limited datasets often lead to poor predictio

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,A Comparative Study of Machine Learning Algorithms for Enhanced Credit Default Prediction,This research study presents a comparative analysis of various machine learning algorithms, which are used for forecasting the likelihood of credit default. Six diverse algorithms—Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), Logistic Regression, Decision Tree (DT), Gaussian Naive Bayes,