iWorksafe: Towards Healthy Workplaces During COVID-19 With an Intelligent Phealth App for Industrial Settings

The recent outbreak of the novel Coronavirus Disease (COVID-19) has given rise to diverse health issues due to its high transmission rate and limited treatment options. Almost the whole world, at some point of time, was placed in lock-down in an attempt to stop the spread of the virus, with resulting psychological and economic sequela. As countries start to ease lock-down measures and reopen industries, ensuring a healthy workplace for employees has become imperative. Thus, this paper presents a mobile app-based intelligent portable healthcare (pHealth) tool, called i WorkSafe, to assist industries in detecting possible suspects for COVID-19 infection among their employees who may need primary care. Developed mainly for low-end Android devices, the i WorkSafe app hosts a fuzzy neural network model that integrates data of employees’ health status from the industry’s database, proximity and contact tracing data from the mobile devices, and user-reported COVID-19 self-test data. Using the built-in Bluetooth low energy sensing technology and K Nearest Neighbor and K-means techniques, the app is capable of tracking users’ proximity and trace contact with other employees. Additionally, it uses a logistic regression model to calculate the COVID-19 self-test score and a Bayesian Decision Tree model for checking real-time health condition from an intelligent e-health platform for further clinical attention of the employees. Rolled out in an apparel factory on 12 employees as a test case, the pHealth tool generates an alert to maintain social distancing among employees inside the industry. In addition, the app helps employees to estimate risk with possible COVID-19 infection based on the collected data and found that the score is effective in estimating personal health condition of the app user

A robust analysis of FLD and orthogonal FLD on handwritten characters

Feature extraction is the identification of appropriate measures to characterize the component images distinctly. Extracting features is one of the most important steps in any recognition system. Hence, in this paper, we explore the concept of Orthogonalized Fisher Discriminant (OFD) for unconstrained handwritten Kannada character recognition. OFD exhibits higher performance than Fisher Linear Discriminant (FLD) due to the elimination of dependences among discriminant vectors. For subsequent classification purpose, we explore the concept of probabilistic neural network (PNN) architecture. Experiments show that OFD methods are more effective and efficient than standard FLD for handwritten character recognition

Indexing large class handwritten character database

This paper proposes a method of indexing handwritten characters of a large number of classes by the use of Kd-tree. The Ridgelets and Gabor features are used for the purpose of representation. A multi dimensional feature vectors are further projected to a lower dimensional feature space using PCA. The reduced dimensional feature vectors are used to index the character database by Kd-tree. In a large class OCR system, the aim is to identify a character from a large class of characters. Interest behind this work is to have a quick reference to only those potential characters which can have a best match for given unknown character to be recognized without requiring scanning of the entire database. The proposed method can be used as a supplementary tool to speed up the task of identification. The proposed method is tested on handwritten Kannada character database consisting of 2000 images of 200 classes. Experimental results show that the approach yields a good Correct Index Power (CIP) and also depicts the effectiveness of the indexing approach

An accurate and efficient skew estimation technique for South Indian documents: a new boundary growing and nearest neighbor clustering based approach

Skew angle estimation is essential to enhance the accuracy of optical character recognition (OCR) system. In this paper we present a new boundary growing (BG) and nearest neighbor clustering (NNC)to estimate accurate skew angle for the scanned documents. The BG extracts the boundary characters present in each text line of the document and extracts uppermost, lowermost and centroid coordinates of character components of the scanned document image. The NNC helps us in clustering the characters which is presented due to additional modifiers-characters that are usually present in the South Indian scripts. The extracted coordinates are subjected to moments to estimate skew angle of the document image. Several experiments have been conducted on various types of documents such as documents containing South Indian scripts, English documents, journals, textbook, text with picture, text with tables, text with graphs, different languages, noisy images and document with different fonts, documents with different resolutions, to reveal the robustness of the proposed method. The experimental results revealed that the proposed method is accurate compared to the results of well-known existing methods

Multilingual OCR system for South Indian scripts and English documents: An approach based on Fourier transform and principal component analysis

Character recognition lies at the core of the discipline of pattern recognition where the aim is to represent a sequence of characters taken from an alphabet Kasturi, R., Gorman, L.O., Govindaraju, V., 2002. Document image analysis: a primer. Sadhana 27 (Part 1), 3–22. Though many kinds of features have been developed and their test performances on standard database have been reported, there is still room to improve the recognition rate by developing improved features. In this paper, we present a multilingual character recognition system for printed South Indian scripts (Kannada, Telugu, Tamil and Malayalam) and English documents. South Indian languages are most popular languages in India and around the world. The proposed multilingual character recognition is based on Fourier transform and principal component analysis (PCA), which are two commonly used techniques of image processing and recognition. PCA and Fourier transforms are classical feature extraction and data representation techniques widely used in the area of pattern recognition and computer vision. Our experimental results show the good performance over the data sets considered

DeepSkin: A Deep Learning Approach for Skin Cancer Classification

Skin cancer is one of the most rapidly spreading illnesses in the world and because of the limited resources available. Early detection of skin cancer is crucial accurate diagnosis of skin cancer identification for preventive approach in general. Detecting skin cancer at an early stage is challenging for dermatologists, as well in recent years, both supervised and unsupervised learning tasks have made extensive use of deep learning. One of these models, Convolutional Neural Networks (CNN), has surpassed all others in object detection and classification tests. The dataset is screened from MNIST: HAM10000 which consists of seven different types of skin lesions with the sample size of 10015 is used for the experimentation. The data pre-processing techniques like sampling, dull razor and segmentation using autoencoder and decoder is employed. Transfer learning techniques like DenseNet169 and Resnet 50 were used to train the model to obtain the results

One-shot cluster-based approach for the detection of COVID-19 from chest X-ray images

Coronavirus disease (COVID-19) has infected over more than 28.3 million people around the globe and killed 913K people worldwide as on 11 September 2020. With this pandemic, to combat the spreading of COVID-19, effective testing methodologies and immediate medical treatments are much required. Chest X-rays are the widely available modalities for immediate diagnosis of COVID-19. Hence, automation of detection of COVID-19 from chest X-ray images using machine learning approaches is of greater demand. A model for detecting COVID-19 from chest X-ray images is proposed in this paper. A novel concept of cluster-based one-shot learning is introduced in this work. The introduced concept has an advantage of learning from a few samples against learning from many samples in case of deep leaning architectures. The proposed model is a multi-class classification model as it classifies images of four classes, viz., pneumonia bacterial, pneumonia virus, normal, and COVID-19. The proposed model is based on ensemble of Generalized Regression Neural Network (GRNN) and Probabilistic Neural Network (PNN) classifiers at decision level. The effectiveness of the proposed model has been demonstrated through extensive experimentation on a publicly available dataset consisting of 306 images. The proposed cluster-based one-shot learning has been found to be more effective on GRNN and PNN ensembled model to distinguish COVID-19 images from that of the other three classes. It has also been experimentally observed that the model has a superior performance over contemporary deep learning architectures. The concept of one-shot cluster-based learning is being first of its kind in literature, expected to open up several new dimensions in the field of machine learning which require further researching for various applications