Exploring the Relationship between Brain Hemisphere States and Frequency Bands through Deep Learning Optimization Techniques

This study investigates classifier performance across EEG frequency bands using various optimizers and evaluates efficient class prediction for the left and right hemispheres. Three neural network architectures - a deep dense network, a shallow three-layer network, and a convolutional neural network (CNN) - are implemented and compared using the TensorFlow and PyTorch frameworks. Results indicate that the Adagrad and RMSprop optimizers consistently perform well across different frequency bands, with Adadelta exhibiting robust performance in cross-model evaluations. Specifically, Adagrad excels in the beta band, while RMSprop achieves superior performance in the gamma band. Conversely, SGD and FTRL exhibit inconsistent performance. Among the models, the CNN demonstrates the second highest accuracy, particularly in capturing spatial features of EEG data. The deep dense network shows competitive performance in learning complex patterns, whereas the shallow three-layer network, sometimes being less accurate, provides computational efficiency. SHAP (Shapley Additive Explanations) plots are employed to identify efficient class prediction, revealing nuanced contributions of EEG frequency bands to model accuracy. Overall, the study highlights the importance of optimizer selection, model architecture, and EEG frequency band analysis in enhancing classifier performance and understanding feature importance in neuroimaging-based classification tasks.

Evolutionary Algorithm for Graph Coloring Problem

The graph coloring problem (GCP) is one of the most studied NP-HARD problems in computer science. Given a graph , the task is to assign a color to all vertices such that no vertices sharing an edge receive the same color and that the number of used colors, is minimal. Different heuristic, meta-heuristic, machine learning and hybrid solution methods have been applied to obtain the solution. To solve this problem we use mutation of evolutionary algorithm. For this purpose we introduce binary encoding for Graph Coloring Problem. This binary encoding help us for mutation, evaluate, immune system and merge color easily and also reduce coloring dynamically. In the traditional evolutionary algorithm (EA) for graph coloring, k-coloring approach is used and the EA is run repeatedly until the lowest possible is reached. In our paper, we start with the theoretical upper bound of chromatic number, that is, maximum out-degree + 1 and in the process of evolution some of the colors are made unused to dynamically reduce the number of color in every generation. We test few standard DIMACS benchmark and compare resent paper. Maximum results are same as expected chromatic color and few data sets are larger than expected chromatic number