Providing diagnosis on diabetes using cloud computing environment to the people living in rural areas of India

Rajput, Dharmendra Singh; Basha, Syed Muzamil; Xin, Qin; Gadekallu, Thippa Reddy; Kaluri, Rajesh; Lakshmanna, Kuruva; Maddikunta, Praveen Kumar Reddy

doi:10.1007/s12652-021-03154-4

Cited by 50 publications

(42 citation statements)

References 47 publications

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“…Deep learning methods are currently used to solve many problems [27][28][29][30]. Unlike the priorbased method, the learning-based method adopts a data-driven approach and try to directly estimate the intermediate transmission map, the atmospheric light map and the final dehazing result.…”

Section: The Learning-based Methodsmentioning

confidence: 99%

Unsupervised water scene dehazing network using multiple scattering model

Huang

Wang

et al. 2021

PLoS ONE

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In water scenes, where hazy images are subject to multiple scattering and where ideal data sets are difficult to collect, many dehazing methods are not as effective as they could be. Therefore, an unsupervised water scene dehazing network using atmospheric multiple scattering model is proposed. Unlike previous image dehazing methods, our method uses the unsupervised neural network and the atmospheric multiple scattering model and solves the problem of difficult acquisition of ideal datasets and the effect of multiple scattering on the image. In our method, in order to embed the atmospheric multiple scattering model into the unsupervised dehazing network, the unsupervised dehazing network uses four branches to estimate the scene radiation layer, transmission map layer, blur kernel layer and atmospheric light layer, the hazy image is then synthesized from the four output layers, minimizing the input hazy image and the output hazy image, where the output scene radiation layer is the final dehazing image. In addition, we constructed unsupervised loss functions which applicable to image dehazing by prior knowledge, i.e., color attenuation energy loss and dark channel loss. The method has a wide range of applications, with haze being thick and variable in marine, river and lake scenes, the method can be used to assist ship vision for target detection or forward road recognition in hazy conditions. Through extensive experiments on synthetic and real-world images, the proposed method is able to recover the details, structure and texture of the water image better than five advanced dehazing methods.

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Section: The Learning-based Methodsmentioning

confidence: 99%

Unsupervised water scene dehazing network using multiple scattering model

Huang

Wang

et al. 2021

PLoS ONE

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“…All data were analysed with R3.6.3, using the caret package to build ML models [27]. Commonly used classification ML methods were first tested on all three data sets (i.e., Swedish, Swiss and Swedish+Swiss) [28, 29]. Table 3 presents a list of common ML methods considered and implemented with R packages in this study with binary outcome.…”

Section: Methodsmentioning

confidence: 99%

Machine learning algorithms can predict tail biting outbreaks in pigs using feeding behaviour records

Ollagnier

Kasper

Wallenbeck

et al. 2021

Preprint

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Tail biting is a detrimental behaviour that impacts the welfare and health of pigs. Early detection of tail biting precursor signs allows for preventive measures to be taken, thus avoiding the occurrence of the tail biting event. This study aimed to build a machine-learning algorithm for real time detection of upcoming tail biting outbreaks, using feeding behaviour data recorded by an electronic feeder. Prediction capacities of seven machine learning algorithms (e.g., random forest, neural networks) were evaluated from daily feeding data collected from 65 pens originating from 2 herds of grower-finisher pigs (25-100kg), in which 27 tail biting events occurred. Data were divided into training and testing data, either by randomly splitting data into 75% (training set) and 25% (testing set), or by randomly selecting pens to constitute the testing set. The random forest algorithm was able to predict 70% of the upcoming events with an accuracy of 94%, when predicting events in pens for which it had previous data. The detection of events for unknown pens was less sensitive, and the neural network model was able to detect 14% of the upcoming events with an accuracy of 63%. A machine-learning algorithm based on ongoing data collection should be considered for implementation into automatic feeder systems for real time prediction of tail biting events.

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“…The input layer X can be considered the first layer in the network, Z [0] . Since the interclass separation occurs between layers Z [1] and y, the hinge loss in Eq 2 can be re-written in terms of the neurons and parameters in said layers. Since y = A [2] (w [2] z [1] + b [2] ) and z [1] = A [1] (w [1] x + b [1] ), the hinge-loss with respect to this network is:…”

Section: Plos Onementioning

confidence: 99%

“…In order to easily visualize this concept, we illustrate it using the special case of a 2-neuron first hidden layer, and point out that the idea applies for any number of neurons k [1] . We consider the first hidden layer because it is optimally-separated due to Eq 5.…”

Section: Plos Onementioning

confidence: 99%

“…In disciplines such as disease diagnosis and prevention, much of the current research focus is on maximizing prediction accuracy of outcome labels. For example, the authors of [1] compared and contrasted the performances across a large arsenal of predictive models (ex. Logistic Regression, Support Vector Machines, Random Forests, Naives Bayes k-Nearest Neighbors) for the task of diabetes-2 diagnosis.…”

Section: Introductionmentioning

confidence: 99%

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Identifying indicator species in ecological habitats using Deep Optimal Feature Learning

2021

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Much of the current research on supervised modelling is focused on maximizing outcome prediction accuracy. However, in engineering disciplines, an arguably more important goal is that of feature extraction, the identification of relevant features associated with the various outcomes. For instance, in microbial communities, the identification of keystone species can often lead to improved prediction of future behavioral shifts. This paper proposes a novel feature extractor based on Deep Learning, which is largely agnostic to underlying assumptions regarding the training data. Starting from a collection of microbial species abundance counts, the Deep Learning model first trains itself to classify the selected distinct habitats. It then identifies indicator species associated with the habitats. The results are then compared and contrasted with those obtained by traditional statistical techniques. The indicator species are similar when compared at top taxonomic levels such as Domain and Phylum, despite visible differences in lower levels such as Class and Order. More importantly, when our estimated indicators are used to predict final habitat labels using simpler models (such as Support Vector Machines and traditional Artificial Neural Networks), the prediction accuracy is improved. Overall, this study serves as a preliminary step that bridges modern, black-box Machine Learning models with traditional, domain expertise-rich techniques.

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Providing diagnosis on diabetes using cloud computing environment to the people living in rural areas of India

Cited by 50 publications

References 47 publications

Unsupervised water scene dehazing network using multiple scattering model

Unsupervised water scene dehazing network using multiple scattering model

Machine learning algorithms can predict tail biting outbreaks in pigs using feeding behaviour records

Identifying indicator species in ecological habitats using Deep Optimal Feature Learning

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