Ingratiate 发表于 2025-3-30 10:49:34

http://reply.papertrans.cn/88/8705/870448/870448_51.png

Toxoid-Vaccines 发表于 2025-3-30 13:22:22

http://reply.papertrans.cn/88/8705/870448/870448_52.png

垫子 发表于 2025-3-30 20:14:52

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

Ophthalmologist 发表于 2025-3-30 22:23:58

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%.

ARC 发表于 2025-3-31 04:31:58

http://reply.papertrans.cn/88/8705/870448/870448_55.png

和平主义 发表于 2025-3-31 06:41:03

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.

genesis 发表于 2025-3-31 12:31:59

http://reply.papertrans.cn/88/8705/870448/870448_57.png

acclimate 发表于 2025-3-31 14:43:37

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

脱水 发表于 2025-3-31 21:06:07

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

浪费时间 发表于 2025-4-1 01:01:06

http://reply.papertrans.cn/88/8705/870448/870448_60.png
页: 1 2 3 4 5 [6] 7
查看完整版本: Titlebook: Soft Computing and Its Engineering Applications; 5th International Co Kanubhai K. Patel,KC Santosh,Ashish Ghosh Conference proceedings 2024