Stateful serverless systems commonly adopt an architectural paradigm characterized by compute and storage separation within cloud data centers. Nevertheless, guaranteeing prompt response for real-time tasks at the edg...
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In the realm of machine learning, understanding how models arrive at their predictions or decisions, known as mechanistic interpretability, is crucial for trust, transparency, and refinement. This involves unraveling ...
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The prevention of signature forgery on physical documents is a critical concern in sectors like insurance, where the authenticity of claims documentation is pivotal. Handwritten signatures pose a challenge for verific...
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The increasing prevalence of liver disease necessitates efficient identification methods to alleviate the diagnostic burden on healthcare providers. Machine learning offers a promising solution by evaluating vital par...
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Prediction of the nutrient deficiency range and control of it through application of an appropriate amount of fertiliser at all growth stages is critical to achieving a qualitative and quantitative *** fertiliser in op...
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Prediction of the nutrient deficiency range and control of it through application of an appropriate amount of fertiliser at all growth stages is critical to achieving a qualitative and quantitative *** fertiliser in optimum amounts will protect the environment’s condition and human health *** identification also prevents the disease’s occurrence in groundnut crops.A convo-lutional neural network is a computer vision algorithm that can be replaced in the place of human experts and laboratory methods to predict groundnut crop nitro-gen nutrient deficiency through image *** chlorophyll and nitrogen are proportionate to one another,the Smart Nutrient Deficiency Prediction System(SNDP)is proposed to detect and categorise the chlorophyll concentration range via which nitrogen concentration can be *** model’sfirst part is to per-form preprocessing using Groundnut Leaf Image Preprocessing(GLIP).Then,in the second part,feature extraction using a convolution process with Non-negative ReLU(CNNR)is done,and then,in the third part,the extracted features areflat-tened and given to the dense layer(DL)***,the Maximum Margin clas-sifier(MMC)is deployed and takes the input from DL for the classification process tofind *** dataset used in this work has no visible symptoms of a deficiency with three categories:low level(LL),beginning stage of low level(BSLL),and appropriate level(AL).This model could help to predict nitrogen deficiency before perceivable *** performance of the implemented model is analysed and compared with ImageNet pre-trained *** result shows that the CNNR-MMC model obtained the highest training and validation accuracy of 99%and 95%,respectively,compared to existing pre-trained models.
Employee attrition presents a significant challenge for organizations, impacting both finances and operations. Mean-while, the automation of human resource processes, including recruitment and performance monitoring, ...
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computer vision and the Internet of Things have led to significant advancements in student attendance monitoring. In this work, an innovative system has been designed to facilitate real-time, automated student attenda...
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Sequential Recommendation utilizes interaction history to uncover users' dynamic interest changes and recommend the most relevant items for their next interaction. In recent years, multi-behavior modeling and mult...
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Credit card fraud detection remains a critical challenge in financial transactions, particularly due to the prevalence of unbalanced datasets and the limitations of traditional algorithms. This paper suggests a novel ...
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This research paper explores the application of deep learning techniques for the detection of AI-generated images within the context of news and journalism. In response to the escalating risk posed by the pervasive us...
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