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Titlebook: Networking and Parallel/Distributed Computing Systems; Volume 18 Roger Lee Book 2024 The Editor(s) (if applicable) and The Author(s), under

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Book 2024.The book reports state-of-the-art results in Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing in both printed and electronic form. . .This book presents original papers on both theory and practice that address foundations, state-of-the-art problems and solutions, and crucial challenges..
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https://doi.org/10.1007/978-3-031-53274-0SNPD 2023; Computational Intelligence; Networking; Software Engineering; Artificial Intelligence; Paralle
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A Deep Learning-Based Study for Cyclone Track Forecasting: Comparative Analysis Using Historical Damachine learning like MSE, MAE, RMSE, R., we use another evaluation matrix that is average distance error. Average distance error calculates the distance error of prediction plots and actual plots in kilometers. This matrix helps to accurately demonstrate the models’ efficiency in a different dimens
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Text-Based Data Analysis for Mental Health Using Explainable AI and Deep Learning, employing a range of classification algorithms and text vectorization techniques, we aim to develop machine-learning models that effectively identify addiction, alcoholism, anxiety, depression, and suicidal thoughts within text-based discussions. This research contributes to mental health analysis
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Using the Internet of Things and Machine Learning to Monitor and Detect COVID-19,g them, DTC performed best with an accuracy of 91.03% and F1-score of 89.39% for detecting COVID-19. A website is used to record cough, show the IoT data, and send an email if COVID-19 is detected. The work shows how digital devices can monitor and detect COVID-19.
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Predicting Oil Spills in Real-Time: A Machine Learning and AIS Data-Driven Approach, or minimize damage to the reputation of the company responsible and the country where the spill takes place. The model‘s performance on the MV Wakashio oil spill provides insight into its ability to detect and respond to real world oil spills, highlighting areas for improvement and further research
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Self-supervised Monocular Depth Estimation and Ego-Motion Made Better: A Masking Constraints,d into self-supervised learning constraints, thereby reducing errors in depth estimation and ego-motion. The experiments also show that the method offers better interpretability and insights that can be derived from the learning process. Moreover, our analysis concludes that a reasonable reduction o
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