Titlebook: Data Science and Communication; Proceedings of ICTDs João Manuel R. S. Tavares,Joel J. P. C. Rodrigues, Conference proceedings 2024 The Edi

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Somatoform and Factitious Disordershat improves the accuracy of FD computation. The proposed method is tested and compared with the traditional DBC using simulated images, Brodatz texture images, and medical images from the Mini-MIAS database. The results show that the proposed method produces good results with less computational error in terms of accuracy.

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Classification of Alzheimer Disease Using Feature Segmentation and 3D CNN extracted image features. The experiments were carried on ADNI1 dataset. The obtained result shows the accuracy of 96% for AD classification. Moreover, the sensitivity of the proposed method reveals that the model is less error prone in normal conditions.

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A Comparative Study of Disease Detection in Potato Plants Using Machine Learning and Deep Learning Mich comes under deep learning methods. We tried both machine learning (SVM) and deep learning model (ResNet, UNet). We found best result from ResNet algorithm over other models with an accuracy of 99.6%.

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2730-6437 India.Serves as a reference resource for researchers and pra.The book presents selected papers from the International Conference on Data Science and Communication (ICTDsC 2023) organized by the Department of Electronics and Communication Engineering and Department of Engineering Science and Humaniti

HAIL

Claudia Bender,Axel Kramer,Matthias B. Stope of the issue (indirect wave incidence and plasma anisotropy) compensate one another. In this situation, the scattered waves‘ angular spectrum does not broaden or peak. The results of the numerical analysis of this spectrum‘s evaluation, which thickens the plasma layer, are consistent with those of the geometrical optics approximation.

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