This paper investigates the comparative ability of Naive Bayes and Decision Tree algorithms in the prediction and forecasting of diabetes, a chronic condition that has an impact on millions of people worldwide. Prompt...
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In this research work image processing is done for direct application of digital images. Image processing is used for the enhancement of process includes with it. Adaptive histogram equalization is an appropriate meth...
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A computer vision system model for assessing the quality of fermenting biomaterial in a biogas plant has been developed. This model employs computer vision technologies to provide a quantitative and repeatable method ...
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Globally, neurodegenerative diseases like dementia and Alzheimer's represent a major danger to our healthcare system. Clinical evaluations, which may or may not be able to identify the modest cognitive changes tha...
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This project will be focused on the effect of private level solar systems on the EDL grid, specifically on the distribution part of the entire EDL network. In order to show the said effects of the integration of solar...
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We propose a comprehensive computer vision framework that integrates multi-scale signal processing with an enhanced ConvNeXt-YOLO architecture for robust object detection. Our framework addresses three critical challe...
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ISBN:
(纸本)9798350377040;9798350377033
We propose a comprehensive computer vision framework that integrates multi-scale signal processing with an enhanced ConvNeXt-YOLO architecture for robust object detection. Our framework addresses three critical challenges in visual recognition: multi-scale feature representation, signal quality enhancement, and model generalization. The framework implements a sophisticated signal processing pipeline for image preprocessing. Initially, we develop an adaptive resolution normalization algorithm that maintains consistent feature quality across varying input dimensions. Subsequently, we design a context-aware Gaussian filtering mechanism that optimizes the signal-to-noise ratio while preserving essential feature characteristics. These preprocessing techniques significantly enhance the framework's capability to extract discriminative features and maintain computational stability. To optimize the learning process, we introduce a systematic data augmentation strategy incorporating both geometric and signal-level transformations. Our approach combines predetermined rotation sampling (90 degrees, 180 degrees, 270 degrees) with continuous-space ROI augmentation during inference. This hybrid strategy enables the framework to achieve rotation invariance and enhanced generalization capabilities, particularly beneficial for complex object detection scenarios. The core innovation lies in our architectural integration of ConvNeXt with YOLO. We redesign the feature extraction backbone using hierarchical ConvNeXt blocks, enabling efficient multi-scale feature learning. The cross-branch information fusion mechanism, coupled with our signal-aware design, substantially improves the model's representational capacity. Experimental results on standard computer vision benchmarks demonstrate superior performance, achieving state-of-the-art accuracy (improvement of X%) and recall rates (improvement of Y%) compared to conventional approaches.
Multi-stage sleep classification is crucial in diagnosing sleep disorders and in evaluating sleep quality, but conventional polysomnography techniques are intrusive and time consuming. This paper examines whether cont...
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The analysis of modern electronic design automation tools is produced, and their key features are presented. The simulation model of the optical isolator matching unit is developed, and the simulation models of some e...
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This research investigates the synergy between computer vision and programmable logic controllers (PLCs) for enhancing material recycling efficiency. Focusing on aluminum can and glass bottle recycling, this study dev...
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ISBN:
(纸本)9798350349795;9798350349788
This research investigates the synergy between computer vision and programmable logic controllers (PLCs) for enhancing material recycling efficiency. Focusing on aluminum can and glass bottle recycling, this study develops and simulates a user-centric vending machine system. The system employs a convolutional neural network (CNN) for accurate object recognition, integrated with a PLC-based control system via the OPC UA communication protocol. By incentivizing recycling through discount offers, the vending machine aims to promote sustainable waste management practices. Successful simulation demonstrates the feasibility of this approach, highlighting the potential for computer vision and PLC integration in the development of advanced recycling solutions.
To address the issue of challenging detection tasks for tiny and medium-sized objects because of backdrop confusion and inadequate feature representation of steel surface flaws, this paper proposes an efficient featur...
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