Prognostic Kalman Filter Based Bayesian Learning Model for Data Accuracy Prediction

Karthik, S.; Bhadoria, Robin Singh; Lee, Jeong Gon; Sivaraman, Arun Kumar; Samanta, Sovan; Balasundaram, A.; Chaurasia, Brijesh Kumar; Ashokkumar, S. R.

doi:10.32604/cmc.2022.023864

Cited by 16 publications

(9 citation statements)

References 47 publications

(36 reference statements)

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“…The construction of an estimation algorithm for the datasets that contain interval censoring is also needed in the future. While models considered in the study are linear static models, dynamic prediction for survival data is an important issue [ 40 ]. In addition, recent development for big data such as machine learning techniques would be incorporated [ 41 ].…”

Section: Discussionmentioning

confidence: 99%

An Accelerated Failure Time Cure Model with Shifted Gamma Frailty and Its Application to Epidemiological Research

Aida

Hayashi²,

Takeuchi³

et al. 2022

Healthcare

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Survival analysis is a set of methods for statistical inference concerning the time until the occurrence of an event. One of the main objectives of survival analysis is to evaluate the effects of different covariates on event time. Although the proportional hazards model is widely used in survival analysis, it assumes that the ratio of the hazard functions is constant over time. This assumption is likely to be violated in practice, leading to erroneous inferences and inappropriate conclusions. The accelerated failure time model is an alternative to the proportional hazards model that does not require such a strong assumption. Moreover, it is sometimes plausible to consider the existence of cured patients or long-term survivors. The survival regression models in such contexts are referred to as cure models. In this study, we consider the accelerated failure time cure model with frailty for uncured patients. Frailty is a latent random variable representing patients’ characteristics that cannot be described by observed covariates. This enables us to flexibly account for individual heterogeneities. Our proposed model assumes a shifted gamma distribution for frailty to represent uncured patients’ heterogeneities. We construct an estimation algorithm for the proposed model, and evaluate its performance via numerical simulations. Furthermore, as an application of the proposed model, we use a real dataset, Specific Health Checkups, concerning the onset of hypertension. Results from a model comparison suggest that the proposed model is superior to existing alternatives.

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Section: Discussionmentioning

confidence: 99%

An Accelerated Failure Time Cure Model with Shifted Gamma Frailty and Its Application to Epidemiological Research

Aida

Hayashi²,

Takeuchi³

et al. 2022

Healthcare

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“…This input image can be any image with people present in it, or it can be the image frames in the video stream. Detection of faces in the image is performed by implementing Single Shot Multi-box Detector with Resnet-10 as the backbone using OpenCV Deep Neural Network [16], which is a Caffe model that performs object detection. Once the face is detected, the image is cropped in such a way that only the face is present.…”

Section: Proposed Methodologymentioning

confidence: 99%

Deep Learning Based Face Detection and Identification of Criminal Suspects

Sandhya¹,

Balasundaram²,

Shaik³

2023

Computers, Materials &Amp; Continua

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Occurrence of crimes has been on the constant rise despite the emerging discoveries and advancements in the technological field in the past decade. One of the most tedious tasks is to track a suspect once a crime is committed. As most of the crimes are committed by individuals who have a history of felonies, it is essential for a monitoring system that does not just detect the person's face who has committed the crime, but also their identity. Hence, a smart criminal detection and identification system that makes use of the OpenCV Deep Neural Network (DNN) model which employs a Single Shot Multibox Detector for detection of face and an auto-encoder model in which the encoder part is used for matching the captured facial images with the criminals has been proposed. After detection and extraction of the face in the image by face cropping, the captured face is then compared with the images in the Criminal Database. The comparison is performed by calculating the similarity value between each pair of images that are obtained by using the Cosine Similarity metric. After plotting the values in a graph to find the threshold value, we conclude that the confidence rate of the encoder model is 0.75 and above.

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“…The competency of the classifier is measured by considering the neighborhood of each sample augmenting with selection criteria (with a threshold option) for a classifier corresponding to the minority class in this neighborhood. The authors in [46], proposed the Bayesian learning probabilistic model to improve the performance of Bayesian classification using the combination of a Kalman filter and K-means. The method is applied to a small dataset just for establishing the fact that the proposed algorithm can reduce the time for computing the clusters from the data.…”

Section: Related Workmentioning

confidence: 99%

Generation of Controlled Synthetic Samples and Impact of Hyper-Tuning Parameters to Effectively Classify the Complex Structure of Overlapping Region

Mahmood

Butt

Rehman³

et al. 2022

Applied Sciences

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The classification of imbalanced and overlapping data has provided customary insight over the last decade, as most real-world applications comprise multiple classes with an imbalanced distribution of samples. Samples from different classes overlap near class boundaries, creating a complex structure for the underlying classifier. Due to the imbalanced distribution of samples, the underlying classifier favors samples from the majority class and ignores samples representing the least minority class. The imbalanced nature of the data—resulting in overlapping regions—greatly affects the learning of various machine learning classifiers, as most machine learning classifiers are designed to handle balanced datasets and perform poorly when applied to imbalanced data. To improve learning on multi-class problems, more expertise is required in both traditional classifiers and problem domain datasets. Some experimentation and knowledge of hyper-tuning the parameters and parameters of the classifier under consideration are required. Several techniques for learning from multi-class problems have been reported in the literature, such as sampling techniques, algorithm adaptation methods, transformation methods, hybrid methods, and ensemble techniques. In the current research work, we first analyzed the learning behavior of state-of-the-art ensemble and non-ensemble classifiers on imbalanced and overlapping multi-class data. After analysis, we used grid search techniques to optimize key parameters (by hyper-tuning) of ensemble and non-ensemble classifiers to determine the optimal set of parameters to enhance the learning from a multi-class imbalanced classification problem, performed on 15 public datasets. After hyper-tuning, 20% of the dataset samples are synthetically generated to add to the majority class of each respective dataset to make it more overlapped (complex structure). After the synthetic sample’s addition, the hyper-tuned ensemble and non-ensemble classifiers are tested over that complex structure. This paper also includes a brief description of tuned parameters and their effects on imbalanced data, followed by a detailed comparison of ensemble and non-ensemble classifiers with the default and tuned parameters for both original and synthetically overlapped datasets. We believe that the underlying paper is the first kind of effort in this domain, which will furnish various research aspects to with a greater focus on the parameters of the classifier in the field of learning from imbalanced data problems using machine-learning algorithms.

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Prognostic Kalman Filter Based Bayesian Learning Model for Data Accuracy Prediction

Cited by 16 publications

References 47 publications

An Accelerated Failure Time Cure Model with Shifted Gamma Frailty and Its Application to Epidemiological Research

An Accelerated Failure Time Cure Model with Shifted Gamma Frailty and Its Application to Epidemiological Research

Deep Learning Based Face Detection and Identification of Criminal Suspects

Generation of Controlled Synthetic Samples and Impact of Hyper-Tuning Parameters to Effectively Classify the Complex Structure of Overlapping Region

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