Titlebook: Proceedings of International Conference on Data, Electronics and Computing; ICDEC 2023, Volume 2 Nibaran Das,Ajoy Kumar Khan,Debotosh Bhatt

濕潤(rùn)

BLUE-Net: BLUmberg Function-Based Ensemble Network for Liver and Tumor Segmentation from CT Scans,rning models, particularly U-Net-like architectures, have obtained notable success in medical image segmentation. Ensemble learning is a powerful approach that helps leverage the performance of an overall model by incorporating the decisions of multiple models. In this paper, we propose an ensemble

藥物

Identification of Lung Cancer Affected CT-Scan Images Using a Light-Weight Deep Learning Architecture,ply various measures for reducing the adversity of this disease among the patients. It usually requires the understanding of Computed Tomography Scan (CT-Scan) images captured from the lung for inferring if the patient is affected by lung cancer or not. This is essentially a classification task that

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Anonymous

A Testable and Fault-Tolerant Synthesis for Paper-Based Digital Microfluidic Biochips Using Swarm Optimization, biochemical and biomedical assays. On a P-DMFB chip, patterned electrode array and control lines are printed by CNT ink on a paper with dielectric parylene-C film and hydrophobic teflon film. Here the droplets are controlled by electrowetting technology. Manufacturing of P-DMFBs is efficient and le

troponins

小官

A CNN Accelerator on RISC-V-Based SoC for Latency Constrained Edge Networks, deep learning techniques used in different domains like computer vision, audio and video processing, etc. Hence various smaller scale deep learning models have already been proposed for execution on edge devices. Either the accuracy or latency of these lightweight models is not sufficient for using

商店街

智力高

Federated Learning-Based Malware Detection for IoT Platforms,lace. This study presents a comprehensive exploration of the potential of federated learning to address IoT malware concerns while delving into the security intricacies inherent in this novel learning paradigm. We introduce a novel framework that leverages federated learning to detect malware threat

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