Titlebook: Intelligent Computing; Proceedings of the 2 Kohei Arai Conference proceedings 2024 The Editor(s) (if applicable) and The Author(s), under e

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Challenges of Deepfakes,mocratic processes. This paper delves into the multifaceted challenges posed by deepfakes, emphasizing their potential to mislead, manipulate, and disrupt various domains, including politics, national security, and public discourse. The study highlights the complexity of detecting deepfakes, which a

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Predicting Suicide Cases Using Deep Neural Network,, it is imperative to implement effective suicide prevention strategies. In this context, deep neural network (DNN) algorithms have gained prominence and are increasingly applied across various healthcare domains. In our research, we examined the efficacy of employing DNNs for predicting suicide att

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,Deep Feature Discriminability as?a?Diagnostic Measure of?Overfitting in?CNN Models,anced Deep Learning architectures. In this study, we present a novel methodology that identifies and analyzes model overfitting by leveraging unsupervised clustering of the features extracted by CNNs. Our research demonstrates that overfitted models exhibit inadequate class discriminability within t

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,A Meta-VAE for?Multi-component Industrial Systems Generation,sign options, providing a cheaper and faster alternative to the trial and failure approaches. Thanks to the flexibility they offer, Deep Generative Models are gaining popularity amongst Generative Design technologies. However, developing and evaluating these models can be challenging. A notable gap

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,Analysis of?the?Computational Complexity of?Backpropagation and?Neuroevolution,based on stochastic gradient descent, where a network of neurons alter their weights based on an error signal passed back from the output. The second algorithm, called neuroevolution, is based on the genetic algorithm. In it, many weight sets are ranked based on how well the network solves the probl

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,Indoor Obstacle Avoidance System Design and?Evaluation Using Deep Learning and?SLAM-Based Approacheates the fusion of 2D LiDAR-based Simultaneous Localization and Mapping (SLAM) with a Rapidly Exploring Random Trees (RRT) algorithm for effective path planning. Furthermore, we propose an innovative and pioneering approach for obstacle avoidance based on deep learning. The deep learning model is tr

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