This paper explores the transformative potential of Explainable Artificial Intelligence (XAI) in the context of coffee quality assessment, an area traditionally governed by subjective evaluation. By applying machine l...
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ISBN:
(数字)9798350381764
ISBN:
(纸本)9798350381771
This paper explores the transformative potential of Explainable Artificial Intelligence (XAI) in the context of coffee quality assessment, an area traditionally governed by subjective evaluation. By applying machine learning models, specifically a Random Forest Classifier enhanced by SHAP (SHapley Additive exPlanations) values, we identified crucial determinants of coffee quality, such as Category Two defects and high-altitude growth conditions. Our study demonstrates that machine learning can not only match but potentially exceed the accuracy of human experts in predicting coffee quality. More importantly, XAI has provided these models with a layer of transparency, making their complex predictions accessible and actionable for stakeholders in the coffee industry. This integration of AI into coffee quality assessment promises to standardize and optimize the evaluation process, offering a reliable guide for improving practices across the production chain. The findings underscore the broader impact of AI in agriculture, suggesting that such technology could be a harbinger of increased efficiency, sustainability, and trust in food production systems worldwide.
Every patient has health record, it was written as statement of patient's conditions, treatments, and medications, and nowadays it is become digitalized, it can be copied and shared easily, but the nature of EHR i...
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In this paper, we propose a switching scheme of GCR Block Ack and GCR Unsolicited Retry, standardized in IEEE 802.11aa, according to network conditions for video and audio groupcast over wireless LANs. We utilize thre...
In this paper, we propose a switching scheme of GCR Block Ack and GCR Unsolicited Retry, standardized in IEEE 802.11aa, according to network conditions for video and audio groupcast over wireless LANs. We utilize three transmission modes in the proposed method: GCR Block Ack with four retries, GCR Block Ack with two retries, and GCR Unsolicited Retry with twice transmission. The proposed method is compared with the three individual methods by computer simulation under various network conditions to evaluate application-level QoS. We then assess QoE by a subjective experiment. We show that the proposed method can choose an appropriate mode and achieve better QoE than the individual methods.
This paper explored the use of Region-Convolutional Neural Network (CNN) algorithm. Classified through extracted region proposals from the picture a mere 2000 sections fed into CNN. This study aims to automate the cla...
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ISBN:
(纸本)9781450390439
This paper explored the use of Region-Convolutional Neural Network (CNN) algorithm. Classified through extracted region proposals from the picture a mere 2000 sections fed into CNN. This study aims to automate the classification of chicken eggs according to their grade (Grade A, B, C, Inedible). It highlights the application of image processing for which image classification technique is used to identify the attributes of the subject through images and overpassing challenges through viewpoints, image deformation, and lighting conditions. With a reading of 93.3% accuracy, this paper concludes that the image processing method used in identifying classifications has been a successful development in terms of image detection and recognition by computer vision for it recognize chicken eggs classification grading's based on the images being captured. Classifications of chicken eggs through computer vision have been achieved.
Simulation is a valuable tool for designing and evaluating the performance of x-ray imaging systems. In previous work,1, 2 a hybrid computed tomography(CT) and x-ray diffraction(XRD) imaging system was developed for i...
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We employed several algorithms with high efficacy to analyze the public transcriptomic data,aiming to identify key transcription factors(TFs)that regulate regeneration in Arabidopsis ***,we utilized CollaborativeNet,a...
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We employed several algorithms with high efficacy to analyze the public transcriptomic data,aiming to identify key transcription factors(TFs)that regulate regeneration in Arabidopsis ***,we utilized CollaborativeNet,also known as TF-Cluster,to construct a collaborative network of all TFs,which was subsequently decomposed into many subnetworks using the Triple-Link and Compound Spring Embedder(CoSE)*** analysis of these subnetworks led to the identification of nine subnetworks closely associated with *** further applied principal component analysis and gene ontology(GO)enrichment analysis to reduce the subnetworks from nine to three,namely subnetworks 1,12,and *** for TF-binding sites in the promoters of the co-expressed and co-regulated(CCGs)genes of all TFs in these three subnetworks and Triple-Gene Mutual Interaction analysis of TFs in these three subnetworks with the CCGs involved in regeneration enabled us to rank the TFs in each ***,six potential candidate TFs-WOx9A,LEC2,PGA37,WIP5,PEI1,and AIL1 from subnetwork 1-were identified,and their roles in somatic embryogenesis(GO:0010262)and regeneration(GO:0031099)were discussed,so were the TFs in Subnetwork 12 and 17 associated with *** TFs identified were also assessed using the CIS-BP database and Expression *** analyses suggest some novel TFs that may have regulatory roles in regeneration and embryogenesis and provide valuable data and insights into the regulatory mechanisms related to *** tools and the procedures used here are instrumental for analyzing high-throughput transcriptomic data and advancing our understanding of the regulation of various biological processes of interest.
We present Reusable Motion prior (ReMP), an effective motion prior that can accurately track the temporal evolution of motion in various downstream tasks. Inspired by the success of foundation models, we argue that a ...
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ISBN:
(数字)9798331510831
ISBN:
(纸本)9798331510848
We present Reusable Motion prior (ReMP), an effective motion prior that can accurately track the temporal evolution of motion in various downstream tasks. Inspired by the success of foundation models, we argue that a robust spatio-temporal motion prior can encapsulate underlying 3D dynamics applicable to various sensor modalities. We learn the rich motion prior from a sequence of complete parametric models of posed human body shape. Our prior can easily estimate poses in missing frames or noisy measurements despite significant occlusion by employing a temporal attention mechanism. More interestingly, our prior can guide the system with incomplete and challenging input measurements to quickly extract critical information to estimate the sequence of poses, significantly improving the training efficiency for mesh sequence recovery. ReMP consistently outperforms the baseline method on diverse and practical 3D motion data, including depth point clouds, LiDAR scans, and IMU sensor data. Project page is available in https://***/ReMP.
The purpose of this study is to find out what makes Generation Z students accept and use Canva as a tool for making presentation materials. The conceptual framework of this study is the combination of "Technology...
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As data shift or new data become available, updating clinical machine learning models may be necessary to maintain or improve performance over time. However, updating a model can introduce compatibility issues when th...
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This study focuses on the economic comparison between 12 MWp PV floating and PV on-ground solar farms in an industrial estate in Thailand. The objective is to evaluate the economic feasibility and electricity-produced...
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