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Titlebook: Soft Computing and Its Engineering Applications; 5th International Co Kanubhai K. Patel,KC Santosh,Ashish Ghosh Conference proceedings 2024

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樓主: Malinger
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發(fā)表于 2025-3-30 10:49:34 | 只看該作者
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發(fā)表于 2025-3-30 13:22:22 | 只看該作者
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發(fā)表于 2025-3-30 20:14:52 | 只看該作者
Conference proceedings 2024, icSoftComp 2023, held in Changa, Anand, India, in December 2023.?.The 42 full papers and 2 short papers included in this book were carefully reviewed and selected from 351 submissions. They are organized in topical sections as follows:?.Volume number 2020: Theory and Methods; Systems and Applicati
54#
發(fā)表于 2025-3-30 22:23:58 | 只看該作者
Metagenomic Gene Prediction Using Bidirectional LSTMg or non-coding classes. The proposed model is compared with other DL methods, such as convolutional neural networks (CNN) and LSTM models. It achieved an area under the curve (AUC) value of 99%, Accuracy of 95.3%, Precision of 96.53%, Recall of 94.57% and F1-score of 95.22%.
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發(fā)表于 2025-3-31 04:31:58 | 只看該作者
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發(fā)表于 2025-3-31 06:41:03 | 只看該作者
Enhancing IDC Histopathology Image Classification: A Comparative Study of?Fine-Tuned and?Pre-trainedlearning networks, Xception, DenseNet169, ResNet101 and MobileNetV2. The dataset used is a publicly available IDC dataset containing 168 whole slide images. The evaluation results show that the fine-tuned models give better classification results than feature extractor models for IDC histopathology image classification.
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發(fā)表于 2025-3-31 12:31:59 | 只看該作者
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發(fā)表于 2025-3-31 14:43:37 | 只看該作者
Metagenomic Gene Prediction Using Bidirectional LSTM a large amount of genomes to public archives today. Annotation tools are essential to understanding these microorganisms. The metagenomic sequences are fragmented, which makes accurate gene prediction challenging. Most computational gene predictor models use machine learning (ML) and deep learning
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發(fā)表于 2025-3-31 21:06:07 | 只看該作者
Energy-Efficient Task Scheduling in Fog Environment Using TOPSISate high data traffic and reduce latency, fog emerged as a paradigm that brings cloud services closer to users through accessible networks. By doing so, fog computing alleviates traffic congestion and delays. Moreover, fog devices are constrained in terms of power supply, processing capabilities, an
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發(fā)表于 2025-4-1 01:01:06 | 只看該作者
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