Multi-class Classifier-Based Adaboost Algorithm

Kim, Tae Hyun; Park, Dong-Chul; Woo, Dong-Min; Jeong, Taikyeong; Min, Soo-Young

doi:10.1007/978-3-642-31919-8_16

Cited by 23 publications

(8 citation statements)

References 12 publications

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“…In the recent past, various researchers and analysts mainly focused on gray-scale face image ( Ojala, Pietikainen, & Maenpaa, 2002 ). While some were completely built on pattern identification models, possessing initial information of the face model while others were using AdaBoost ( Kim, Park, Woo, Jeong, & Min, 2011 ), which was an excellent classiﬁer for training purposes. Then came the Viola-Jones Detector, which provided a breakthrough in face detection technology, and real-time face detection got possible.…”

Section: Related Workmentioning

confidence: 99%

SSDMNV2: A real time DNN-based face mask detection system using single shot multibox detector and MobileNetV2

Nagrath

Jain

Madan

et al. 2021

Sustainable Cities and Society

349

133

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Section: Related Workmentioning

confidence: 99%

SSDMNV2: A real time DNN-based face mask detection system using single shot multibox detector and MobileNetV2

Nagrath

Jain

Madan

et al. 2021

Sustainable Cities and Society

349

133

View full text Add to dashboard Cite

“…AdaBoost improves classification performance by combining multiple weak classifiers into one strong classifier. It works in part by assigning more weight to instances which can only be classified with greater difficulty than to instances which can be easily classified ( Kim et al, 2012 ). The dearth of true interacting protein-pairs has also prompted researchers to use unsupervised or semi-supervised approaches to infer microbe-host PPIs.…”

Section: Classification Of Computational Methods In Microbiome-host Interactionsmentioning

confidence: 99%

Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions

et al. 2021

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The microbiome, by virtue of its interactions with the host, is implicated in various host functions including its influence on nutrition and homeostasis. Many chronic diseases such as diabetes, cancer, inflammatory bowel diseases are characterized by a disruption of microbial communities in at least one biological niche/organ system. Various molecular mechanisms between microbial and host components such as proteins, RNAs, metabolites have recently been identified, thus filling many gaps in our understanding of how the microbiome modulates host processes. Concurrently, high-throughput technologies have enabled the profiling of heterogeneous datasets capturing community level changes in the microbiome as well as the host responses. However, due to limitations in parallel sampling and analytical procedures, big gaps still exist in terms of how the microbiome mechanistically influences host functions at a system and community level. In the past decade, computational biology and machine learning methodologies have been developed with the aim of filling the existing gaps. Due to the agnostic nature of the tools, they have been applied in diverse disease contexts to analyze and infer the interactions between the microbiome and host molecular components. Some of these approaches allow the identification and analysis of affected downstream host processes. Most of the tools statistically or mechanistically integrate different types of -omic and meta -omic datasets followed by functional/biological interpretation. In this review, we provide an overview of the landscape of computational approaches for investigating mechanistic interactions between individual microbes/microbiome and the host and the opportunities for basic and clinical research. These could include but are not limited to the development of activity- and mechanism-based biomarkers, uncovering mechanisms for therapeutic interventions and generating integrated signatures to stratify patients.

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“…Prior, the research in the area has focused on the edge and grey value of face image being based on pattern recognition combined with the knowledge on the face model. Adaboost [4] was a fantastic training classifier. The facial detection technology has made a breakthrough with the iconic Viola Jones Detector [5], which has greatly enhanced the real-time facial detection.…”

Section: Literature Surveymentioning

confidence: 99%

An Efficient Face Mask Detector with PyTorch and Deep Learning

Basha¹,

Pravallika²,

Shankar³

2021

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: The outbreak ofacoronavirus disease in 2019 (COVID-19) has created a global health epidemic that has had a major effect on the way we view our environment and our daily lives. The Covid-19 affected numbers are rising at a tremendous pace. Because of that, many countries face an economic catastrophe, recession, and much more. One thing we should do is to separate ourselves from society, remain at home, and detach ourselves from the outside world. But that's no longer a choice, people need to earn to survive, and nobody can remain indefinitely within their homes. As a precaution, people should wear masks while keeping social distance, but some ignore such things and walk around.OBJECTIVES: To develop aFace Mask Detector with OpenCV, PyTorch, and Deep Learning that helps to detect whether or not a person wears a mask.METHODS: A Neural Network model called ResNet is trained on the dataset. Furthermore, this work makes use of the inbuilt Face Detector after training. Finally, we predict whether or not a person is wearing a mask along with the percentage of the face covered or uncovered.RESULTS: The validation results have been proposed to be 97% accurate when compared to applying different algorithms.CONCLUSION: This Face Mask Detection System was found to be apt for detecting whether or not people wear masks in public places which contribute to their health and also to the health of their contacts in this COVID-19 pandemic.

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Multi-class Classifier-Based Adaboost Algorithm

Cited by 23 publications

References 12 publications

SSDMNV2: A real time DNN-based face mask detection system using single shot multibox detector and MobileNetV2

SSDMNV2: A real time DNN-based face mask detection system using single shot multibox detector and MobileNetV2

Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions

An Efficient Face Mask Detector with PyTorch and Deep Learning

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