Alam, Golam RabiulShahir, Rafiad SadatHumayun, ZayedTamim, Mashrufa AkterSaha, Shouri2024-05-152024-05-15©20232023-09ID 20101580ID 20141030ID 20101586ID 20101349http://hdl.handle.net/10361/22835Cataloged from PDF version of thesis.Includes bibliographical references (pages 17-18).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.Despite artificial neural networks being inspired by the functionalities of biological neural networks, unlike biological neural networks, conventional artificial neural networks are often structured hierarchically, which can impede the flow of information between neurons as the neurons in the same layer have no connections between them. Hence, we propose a more robust model of artificial neural networks where the hidden neurons, residing in the same hidden layer, are interconnected that leads to rapid convergence. With the experimental study of our proposed model as fully connected layers in deep networks, we demonstrate that the model results in a noticeable increase in convergence rate compared to the conventional feed-forward neural network.29 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Artificial neural networkRapid convergenceConnected hidden neuronsNeural networks (Computer science)Thesis