To predict how a patient's disease will progress, this research focuses at how effectively cloud-based decision trees (DTs) combined with Internet of Things (IoT) data integration work. The capacity to use these d...
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Transformers are knowledge reservoirs trained on vast and heterogeneous data sets with the potential for cross-functional tasks. These large corpus-trained models can be leveraged for cumbersome tasks like Protein fam...
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In contemporary corporate settings, a candidate's personality is as crucial as their skill set. Achieving success, whether in personal or professional realms, heavily relies on one's personality traits. Theref...
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Skin cancer, particularly malignant melanoma, presents major diagnostic challenges due to similarities with benign tumors. Automatic detection systems based on deep learning algorithms provide intriguing answers, but ...
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Wireless charging is one of the most practical procedures to replenish the energy of the resource-constrained nodes in a Wireless Sensor Network (WSN) that contributes mainly towards enhanced network lifetime. After r...
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A common issue in learning decision-making policies in data-rich settings is spurious correlations in the offline dataset, which can be caused by hidden confounders. Instrumental variable (IV) regression, which utilis...
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A common issue in learning decision-making policies in data-rich settings is spurious correlations in the offline dataset, which can be caused by hidden confounders. Instrumental variable (IV) regression, which utilises a key unconfounded variable known as the instrument, is a standard technique for learning causal relationships between confounded action, outcome, and context variables. Most recent IV regression algorithms use a two-stage approach, where a deep neural network (DNN) estimator learnt in the first stage is directly plugged into the second stage, in which another DNN is used to estimate the causal effect. Naively plugging the estimator can cause heavy bias in the second stage, especially when regularisation bias is present in the first stage estimator. We propose DML-IV, a non-linear IV regression method that reduces the bias in two-stage IV regressions and effectively learns high-performing policies. We derive a novel learning objective to reduce bias and design the DML-IV algorithm following the double/debiased machine learning (DML) framework. The learnt DML-IV estimator has strong convergence rate and O(N−1/2) suboptimality guarantees that match those when the dataset is unconfounded. DML-IV outperforms state-of-the-art IV regression methods on IV regression benchmarks and learns high-performing policies in the presence of instruments. Copyright 2024 by the author(s)
Glacial Lake Outburst Floods (GLOFs) pose a severe risk to populations in high-altitude areas, particularly in Pakistan's northern regions, where glacier melt has created 3,044 glacial lakes in Gilgit-Baltistan an...
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Objectives: The objective is to provide a precise segmentation technique based on ACRF, which can handle the variations between major and minor vessels and reduce the interference present in the model due to overfitti...
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Objectives: The objective is to provide a precise segmentation technique based on ACRF, which can handle the variations between major and minor vessels and reduce the interference present in the model due to overfitting and can provide a high-quality reconstructed image. Therefore, a robust method with statistical properties needs to be presented to enhance the performance of the model. Moreover, a statistical framework is required to classify images precisely. Methods: The Adaptive Conditional Random Field (ACRF) model is used to detect DR disease in the early stages. Here, major vessel potential and minor vessel potential features are extracted for precise segmentation of vessel and non-vessel regions. This feature enhances the efficiency of the model. These major vessel and minor vessel potential features rebuild the retinal vasculature parts precisely and help to capture the contextual information present in the ground truth and label images. This method utilizes an ACRF model to reduce interference and computation complexity. Here, two efficient features are extracted to segment fundus images efficiently, such as major vessel potential and minor vessel potential. The proposed ACRF model can provide the design patterns for both input images and labels with the help of major vessel potential, unlike state-of-art-techniques, which provide patterns for only labels and model the contextual information only in labels, which is essential while performing vessel segmentation. Results: The performance results are tested on the DRIVE dataset. Experimental results verify the superiority of the proposed vessel segmentation technique based on the ACRF model in terms of accuracy, sensitivity, specificity, and F1-measure and segmentation quality. Conclusion: A highly efficient vessel segmentation technique is evaluated to describe major and minor vessel regions efficiently based on the ACRF to recognize DR in early stages and to ensure an effective diagnosis using eye Fundus
Deep learning is a robust framework for solving complex prediction and forecasting problems from multiple fields, such as weather sciences. Due to the abundance and higher resolution of data available, cloud-based dee...
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This project tackles fundamental weaknesses of the current versions of the Medical VQA System: namely, low accuracy in the interpretation of complex medical images and ambiguity in query handling. Its developed versio...
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