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Titlebook: Data Management, Analytics and Innovation; Proceedings of ICDMA Neha Sharma,Amol C. Goje,Alfred M. Bruckstein Conference proceedings 2024 T

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發(fā)表于 2025-3-30 12:13:11 | 只看該作者
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發(fā)表于 2025-3-30 15:00:43 | 只看該作者
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發(fā)表于 2025-3-30 16:38:16 | 只看該作者
Deep Learning for MRI-Based Brain Tumour Identification and Classification,cal image processing software helps find brain tumours in MRI data. This study segmented and detected brain tumours using MRI sequence images. This process is complicated by the similarity of normal tissues and the wide range of tumour tissues in different patients. Brain tumour detection is the mai
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發(fā)表于 2025-3-30 20:49:49 | 只看該作者
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發(fā)表于 2025-3-31 03:10:03 | 只看該作者
Analysis of Regular Machine Learning and Ensemble Learning Approaches for Term Insurance Prediction spans various domains, including business models, scientific research, banking, medical applications, and industrial applications. To address these multifaceted challenges, a plethora of predictive mechanisms have been developed and explored. This paper undertakes a comprehensive comparison and ana
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發(fā)表于 2025-3-31 05:34:31 | 只看該作者
Platform Independent Satellite Image Processing Using GPGPU,pret and use information efficiently. Traditional methods for processing such data need a lot of time and resources. The proposed system seeks to identify significant changes in environmental parameters using various image processing techniques while allowing hardware and operating system independen
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發(fā)表于 2025-3-31 10:21:19 | 只看該作者
58#
發(fā)表于 2025-3-31 13:59:16 | 只看該作者
An Enhanced Deep Learning Method to Generate Synthetic Images with Features That are Comparable to rial perspective, using these pre-trained models to attain such results mostly falls short. The main reasons are the data-hungry architecture and the limited amount of available data that is used for retraining these models. In this study, we discuss a deep learning method that generates synthetic d
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發(fā)表于 2025-3-31 20:30:45 | 只看該作者
60#
發(fā)表于 2025-3-31 21:56:20 | 只看該作者
Comparative Analysis of Deep Learning Models for Car Part Image Segmentation,diagnostics and maintenance to insurance claim assessments. In this study, we present a quantitative approach to car part segmentation, by evaluating and comparing the power of YOLOv8, the Detectron2 Mask R-CNN with Resnet 101 and Mask R-CNN with ResNeXt 101 32×8d configuration+FPN Backbone architec
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