This study examines the use of experimental designs, specifically full and fractional factorial designs, for predicting Alzheimer’s disease with fewer variables. The full factorial design systematically investigates ...
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The map matching of cellular data reconstructs real trajectories of users by exploiting the sequential connections between mobile devices and cell towers. The difficulty in obtaining paired cellular-GPS data and the c...
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Background: The population of Fontan patients, patients born with a single functioningventricle, is growing. There is a growing need to develop algorithms for this population that can predicthealth outcomes. Artiffcia...
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Background: The population of Fontan patients, patients born with a single functioningventricle, is growing. There is a growing need to develop algorithms for this population that can predicthealth outcomes. Artiffcial intelligence models predicting short-term and long-term health outcomes forpatients with the Fontan circulation are needed. Generative adversarial networks (GANs) provide a solutionfor generating realistic and useful synthetic data that can be used to train such models. Methods: Despitetheir promise, GANs have not been widely adopted in the congenital heart disease research communitydue, in some part, to a lack of knowledge on how to employ them. In this research study, a GAN was usedto generate synthetic data from the Pediatric Heart Network Fontan I dataset. A subset of data consistingof the echocardiographic and BNP measures collected from Fontan patients was used to train the *** sets of synthetic data were created to understand the effect of data missingness on synthetic datageneration. Synthetic data was created from real data in which the missing values were imputed usingMultiple Imputation by Chained Equations (MICE) (referred to as synthetic from imputed real samples). Inaddition, synthetic data was created from real data in which the missing values were dropped (referred to assynthetic from dropped real samples). Both synthetic datasets were evaluated for ffdelity by using visualmethods which involved comparing histograms and principal component analysis (PCA) plots. Fidelitywas measured quantitatively by (1) comparing synthetic and real data using the Kolmogorov-Smirnovtest to evaluate the similarity between two distributions and (2) training a neural network to distinguishbetween real and synthetic samples. Both synthetic datasets were evaluated for utility by training aneural network with synthetic data and testing the neural network on its ability to classify patients thathave ventricular dysfunction using echocardiograph measures an
Power load forecasting is essential for optimizing power generation and distribution efficiency. This paper proposes a novel method for daily average load forecasting, referred to as LARSI-TPE-XGB, which integrates th...
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The increasing emergence of IoT in healthcare, industrial automation, manufacturing, infrastructure, business and the home undoubtedly provides more conveniences in different aspects of human life. Any IoT security an...
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During its growth stage,the plant is exposed to various *** and early detection of crop diseases is amajor challenge in the horticulture *** infections can harmtotal crop yield and reduce farmers’income if not identi...
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During its growth stage,the plant is exposed to various *** and early detection of crop diseases is amajor challenge in the horticulture *** infections can harmtotal crop yield and reduce farmers’income if not identified ***’s approved method involves a professional plant pathologist to diagnose the disease by visual inspection of the afflicted plant *** is an excellent use case for Community Assessment and Treatment Services(CATS)due to the lengthy manual disease diagnosis process and the accuracy of identification is directly proportional to the skills of *** alternative to conventional Machine Learning(ML)methods,which require manual identification of parameters for exact results,is to develop a prototype that can be classified without *** automatically diagnose tomato leaf disease,this research proposes a hybrid model using the Convolutional Auto-Encoders(CAE)network and the CNN-based deep learning architecture of *** date,none of the modern systems described in this paper have a combined model based on DenseNet,CAE,and ConvolutionalNeuralNetwork(CNN)todiagnose the ailments of tomato leaves *** trained on a dataset obtained from the Plant Village *** dataset consisted of 9920 tomato leaves,and the model-tomodel accuracy ratio was 98.35%.Unlike other approaches discussed in this paper,this hybrid strategy requires fewer training ***,the training time to classify plant diseases with the trained algorithm,as well as the training time to automatically detect the ailments of tomato leaves,is significantly reduced.
Cloud computing is an on-demand service resource that includes applications to data centres on a pay-per-use basis. While allocating resources, the node failure causes the cloud service failures. This reduces the qual...
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Tensor train (TT) decomposition represents an N-order tensor using O(N) matrices (i.e., factors) of small dimensions, achieved through products among these factors. Due to its compact representation, TT decomposition ...
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Tensor train (TT) decomposition represents an N-order tensor using O(N) matrices (i.e., factors) of small dimensions, achieved through products among these factors. Due to its compact representation, TT decomposition has found wide applications, including various tensor recovery problems in signal processing and quantum information. In this paper, we study the problem of reconstructing a TT format tensor from measurements that are contaminated by outliers with arbitrary values. Given the vulnerability of smooth formulations to corruptions, we use an l1 loss function to enhance robustness against outliers. We first establish the l1/l2-restricted isometry property (RIP) for Gaussian measurement operators, demonstrating that the information in the TT format tensor can be preserved using a number of measurements that grows linearly with N. We also prove the sharpness property for the l1 loss function optimized over TT format tensors. Building on the l1/l2-RIP and sharpness property, we then propose two complementary methods to recover the TT format tensor from the corrupted measurements: the projected subgradient method (PSubGM), which optimizes over the entire tensor, and the factorized Riemannian subgradient method (FRSubGM), which optimizes directly over the factors. Compared to PSubGM, the factorized approach FRSubGM significantly reduces the memory cost at the expense of a slightly slower convergence rate. Nevertheless, we show that both methods, with diminishing step sizes, converge linearly to the ground-truth tensor given an appropriate initialization, which can be obtained by a truncated spectral method. To the best of our knowledge, this is the first work to provide a theoretical analysis of the robust TT recovery problem and to demonstrate that TT-format tensors can be robustly recovered even when a certain fraction of measurements are arbitrarily corrupted. We conduct various numerical experiments to demonstrate the effectiveness of the two methods in robust
Detection and segmentation of defocus blur is a challenging task in digital imaging applications as the blurry images comprise of blur and sharp regions that wrap significant information and require effective methods ...
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Detection and segmentation of defocus blur is a challenging task in digital imaging applications as the blurry images comprise of blur and sharp regions that wrap significant information and require effective methods for information *** defocus blur detection and segmentation methods have several limitations i.e.,discriminating sharp smooth and blurred smooth regions,low recognition rate in noisy images,and high computational cost without having any prior knowledge of images i.e.,blur degree and camera ***,there exists a dire need to develop an effective method for defocus blur detection,and segmentation robust to the above-mentioned *** paper presents a novel features descriptor local directional mean patterns(LDMP)for defocus blur detection and employ KNN matting over the detected LDMP-Trimap for the robust segmentation of sharp and blur *** argue/hypothesize that most of the image fields located in blurry regions have significantly less specific local patterns than those in the sharp regions,therefore,proposed LDMP features descriptor should reliably detect the defocus blurred *** fusion of LDMP features with KNN matting provides superior performance in terms of obtaining high-quality segmented regions in the ***,the proposed LDMP features descriptor is robust to noise and successfully detects defocus blur in high-dense noisy *** results on Shi and Zhao datasets demonstrate the effectiveness of the proposed method in terms of defocus blur *** and comparative analysis signify that our method achieves superior segmentation performance and low computational cost of 15 seconds.
The offering strategy of energy storage in energy and frequency response(FR) markets needs to account for country-specific market regulations around FR products as well as FR utilization factors, which are highly unce...
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The offering strategy of energy storage in energy and frequency response(FR) markets needs to account for country-specific market regulations around FR products as well as FR utilization factors, which are highly uncertain. To this end, a novel optimal offering model is proposed for stand-alone price-taking storage participants, which accounts for recent FR market design developments in the UK, namely the trade of FR products in time blocks, and the mutual exclusivity among the multiple FR products. The model consists of a day-ahead stage, devising optimal offers under uncertainty, and a real-time stage, representing the storage operation after uncertainty is materialized. Furthermore, a concrete methodological framework is developed for comparing different approaches around the anticipation of uncertain FR utilization factors(deterministic one based on expected values, deterministic one based on worst-case values, stochastic one, and robust one), by providing four alternative formulations for the real-time stage of the proposed offering model, and carrying out an out-of-sample validation of the four model instances. Finally, case studies employing real data from UK energy and FR markets compare these four instances against achieved profits, FR delivery violations, and computational scalability.
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