Sign Language Recognition using Deep Learning

Sign Language Recognition is a form of action recognition problem. The purpose of such a system is to automatically translate sign words from one language to another. While much work has been done in the SLR domain, it is a broad area of study and numerous areas still need research attention. The work that we present in this paper aims to investigate the suitability of deep learning approaches in recognizing and classifying words from video frames in different sign languages. We consider three sign languages, namely Indian Sign Language, American Sign Language, and Turkish Sign Language. Our methodology employs five different deep learning models with increasing complexities. They are a shallow four-layer Convolutional Neural Network, a basic VGG16 model, a VGG16 model with Attention Mechanism, a VGG16 model with Transformer Encoder and Gated Recurrent Units-based Decoder, and an Inflated 3D model with the same. We trained and tested the models to recognize and classify words from videos in three different sign language datasets. From our experiment, we found that the performance of the models relates quite closely to the model's complexity with the Inflated 3D model performing the best. Furthermore, we also found that all models find it more difficult to recognize words in the American Sign Language dataset than the others

Abstract Pattern Image Generation using Generative Adversarial Networks

Abstract pattern is very commonly used in the textile and fashion industry. Pattern design is an area where designers need to come up with new and attractive patterns every day. It is very difficult to find employees with a sufficient creative mindset and the necessary skills to come up with new unseen attractive designs. Therefore, it would be ideal to identify a process that would allow for these patterns to be generated on their own with little to no human interaction. This can be achieved using deep learning models and techniques. One of the most recent and promising tools to solve this type of problem is Generative Adversarial Networks (GANs). In this paper, we investigate the suitability of GAN in producing abstract patterns. We achieve this by generating abstract design patterns using the two most popular GANs, namely Deep Convolutional GAN and Wasserstein GAN. By identifying the best-performing model after training using hyperparameter optimization and generating some output patterns we show that Wasserstein GAN is superior to Deep Convolutional GAN

Fatty infiltration of the pancreas: a systematic concept analysis

Fatty infiltration of the pancreas (FIP) has been recognized for nearly a century, yet many aspects of this condition remain unclear. Regular literature reviews on the diagnosis, consequences, and management of FIP are crucial. This review article highlights the various disorders for which FIP has been established as a risk factor, including type 2 diabetes mellitus (T2DM), pancreatitis, pancreatic fistula (PF), metabolic syndrome (MS), polycystic ovary syndrome (PCOS), and pancreatic duct adenocarcinoma (PDAC), as well as the new investigation tools. Given the interdisciplinary nature of FIP research, a broad range of healthcare specialists are involved. This review article covers key aspects of FIP, including nomenclature and definition of pancreatic fat infiltration, history and epidemiology, etiology and pathophysiology, clinical presentation and diagnosis, clinical consequences, and treatment. This review is presented in a detailed narrative format for accessibility to clinicians and medical students

A SIMPLE METHOD FOR DETERMINATION AND CHARACTERIZATION OF IMIDAZOLINONE HERBICIDE (IMAZAPYR/IMAZAPIC) RESIDUES IN CLEARFIELD® RICE SOIL

A study was conducted to evaluate residues of imidazolinone (IMI) in soil. Samples were taken from three Clearfield® rice fields as IMI which have been used for six years. IMI herbicides (imazapic/imazapyr) were widely used in Clearfield® rice soils. To date, few studies are available on the residues of these herbicides, especially in the context of Malaysian soil. Therefore, for this purpose, high performance liquid chromatography (HPLC) with UV detection was performed using a Zorbax stable bond C18 (4.6× 250 mm, 5 µm) column, with two mobile phases. The average percentage recovery for imazapyr and imazapic varied from 76%-107% and 71-77%, with 0.1-5 µg/ml fortification level, respectively. The limit of detection (LOD) and limit of quantification (LOQ) were found to be 1.05 and 4.09 for imazapic and 0.171 and 0.511 µg/ml for imazapyr respectively, in the top 15 cm. In the extracted soil sample, it was 0.19 µg/ml for imazapic and 0.04 µg/ml for imazapyr, respectively. Based on this study, a pre-harvest period of 40-60 day is suggested for rice crops after IMI application

Semantic Segmentation and Depth Estimation of Urban Road Scene Images Using Multi-Task Networks

In autonomous driving, environment perception is an important step in understanding the driving scene. Objects in images captured through a vehicle camera can be detected and classified using semantic segmentation and depth estimation methods. Both these tasks are closely related to each other and this association helps in building a multi-task neural network where a single network is used to generate both views from a given monocular image. This approach gives the flexibility to include multiple related tasks in a single network. It helps reduce multiple independent networks and improve the performance of all related tasks. The main aim of our research presented in this paper is to build a multi-task deep learning network for simultaneous semantic segmentation and depth estimation from monocular images. Two decoder-focused U- Net-based multi-task networks that use a pre-trained Resnet-50 and DenseNet-121 which shared encoder and task-specific decoder networks with Attention Mechanisms are considered. We also employed multi-task optimization strategies such as equal weighting and dynamic weight averaging during the training of the models. The corresponding models’ performance is evaluated using mean IoU for semantic segmentation and Root Mean Square Error for depth estimation. From our experiments, we found that the performance of these multi-task networks is on par with the corresponding single-task networks

Brain Tumor Segmentation in Fluid-Attenuated Inversion Recovery Brain MRI using Residual Network Deep Learning Architectures

Early and accurate detection of brain tumors is very important to save the patient's life. Brain tumors are generally diagnosed manually by a radiologist by analyzing the patient’s brain MRI scans which is a time-consuming process. This led to our study of this research area for finding out a solution to automate the diagnosis to increase its speed and accuracy. In this study, we investigate the use of Residual Network deep learning architecture to diagnose and segment brain tumors. We proposed a two-step method involving a tumor detection stage, using ResNet50 architecture, and a tumor area segmentation stage using ResU-Net architecture. We adopt transfer learning on pre-trained models to help get the best performance out of the approach, as well as data augmentation to lessen the effect of data population imbalance and hyperparameter optimization to get the best set of training parameter values. Using a publicly available dataset as a testbed we show that our approach achieves 84.3% performance outperforming the state-of-the-art using U-Net by 2% using the Dice Coefficient metric

Data Augmentation Using Generative Adversarial Networks to Reduce Data Imbalance with Application in Car Damage Detection

Automatic car damage detection and assessment are very useful in alleviating the burden of manual inspection associated with car insurance claims. This will help filter out any frivolous claims that can take up time and money to process. This problem falls into the image classification category and there has been significant progress in this field using deep learning. However, deep learning models require a large number of images for training and oftentimes this is hampered because of the lack of datasets of suitable images. This research investigates data augmentation techniques using Generative Adversarial Networks to increase the size and improve the class balance of a dataset used for training deep learning models for car damage detection and classification. We compare the performance of such an approach with one that uses a conventional data augmentation technique and with another that does not use any data augmentation. Our experiment shows that this approach has a significant improvement compared to another that does not use data augmentation and has a slight improvement compared to one that uses conventional data augmentation

Drinking water is a significant predictor of Blastocystis infection among rural Malaysian primary schoolchildren

Blastocystis infection has a worldwide distribution especially among the disadvantaged population and immunocompromised subjects. This study was carried out to determine the prevalence and the association of Blastocystis infection with the socio-economic characteristics among 300 primary schoolchildren, living in rural communities in Lipis and Raub districts of Pahang state, Malaysia. Stool samples were collected and examined for the presence of Blastocystis using direct smear microscopy after in vitro cultivation in Jones' medium. The overall prevalence of Blastocystis infection was found to be as high as 25·7%. The prevalence was significantly higher among children with gastrointestinal symptoms as compared to asymptomatic children (x2=4·246; P=0·039). Univariate and multivariate analyses showed that absence of a piped water supply (OR=3·13; 95% CI=1·78, 5·46; P<0·001) and low levels of mothers’ education (OR=3·41; 95% CI=1·62, 7·18; P<0·01) were the significant predictors of Blastocystis infection. In conclusion, Blastocystis is prevalent among rural children and the important factors that determine the infection were the sources of drinking water and mothers' educational level. Interventions with provision of clean water supply and health education especially to mothers are required