Hands on MAHOUT—Machine Learning Tool

Dulhare, Uma N.; Gouse, Sheikh

doi:10.1002/9781119654834.ch14

Cited by 9 publications

(1 citation statement)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mahin et al have extended a paper [29] that is a case study to discuss the classification techniques of machine learning language and its applicability to justify the results on different testbeds for various classifiers concerning multiple performance metrics. Uma Dulhare et al in [30] have proposed a framework of distributed Linear Algebra in Apache Software Foundation to demonstrate an installation,…”

Section: Literature Surveymentioning

confidence: 99%

Automatic Real-Time Medical Mask Detection Using Deep Learning to Fight COVID-19

Rahmani¹,

Taranum²,

Nikhat³

et al. 2022

Computer Systems Science and Engineering

View full text Add to dashboard Cite

The COVID-19 pandemic is a virus that has disastrous effects on human lives globally; still spreading like wildfire causing huge losses to humanity and economies. There is a need to follow few constraints like social distancing norms, personal hygiene, and masking up to effectively control the virus spread. The proposal is to detect the face frame and confirm the faces are properly covered with masks. By applying the concepts of Deep learning, the results obtained for mask detection are found to be effective. The system is trained using 4500 images to accurately judge and justify its accuracy. The aim is to develop an algorithm to automatically detect a mask, but the approach does not facilitate the percentage of improper usage. Accuracy levels are as low as 50% if the mask is improperly covered and an alert is raised for improper placement. It can be used at traffic places and social gatherings for the prevention of virus transmission. It works by first locating the region of interest by creating a frame boundary, then facial points are picked up to detect and concentrate on specific features. The training on the input images is performed using different epochs until the artificial face mask detection dataset is created. The system is implemented using Tensor-Flow with OpenCV and Python using a Jupyter Notebook simulation environment. The training dataset used is collected from a set of diverse open-source datasets with filtered images available at Kaggle Medical Mask Dataset by Mikolaj Witkowski, Kera, and Prajna Bhandary. To simulate MobilNetV2 classifier is used to load and pre-process the image dataset for building a fully connected head. The objective is to assess the accuracy of the identification, measuring the efficiency and effectiveness of algorithms for precision, recall, and F1 score.

show abstract

Section: Literature Surveymentioning

confidence: 99%