Sign Language Recognition System

We witness many people who face disabilities like being deaf, dumb, blind etc. They face a lot of challenges and difficulties trying to interact and communicate with others. This paper presents a new technique by providing a virtual solution without making use of any sensors. Histogram Oriented Gradient (HOG) along with Artificial Neural Network (ANN) have been implemented. The user makes use of web camera, which takes input from the user and processes the image of different gestures. The algorithm recognizes the image and identifies the pending voice input. This paper explains two way means of communication between impaired and normal people which implies that the proposed ideology can convert sign language to text and voice.

Sign Language Recognition System

We witness many people who face disabilities like being deaf, dumb, blind etc. They face a lot of challenges and difficulties trying to interact and communicate with others. This paper presents a new technique by providing a virtual solution without making use of any sensors. Histogram Oriented Gradient (HOG) along with Artificial Neural Network (ANN) have been implemented. The user makes use of web camera, which takes input from the user and processes the image of different gestures. The algorithm recognizes the image and identifies the pending voice input. This paper explains two way means of communication between impaired and normal people which implies that the proposed ideology can convert sign language to text and voice.

Mechanism of Depression through Brain Function Imaging of Depression Patients and Normal People

In recent years, functional magnetic resonance technology has discovered that abnormal connections in different brain regions of the brain may serve as the pathophysiological mechanism of mental illness. Exploring the mechanism of information flow and integration between different brain regions is of great significance for understanding the pathophysiological mechanism of mental illness. This article aims to analyze the mechanism of depression by comparing human brain images of normal people and patients with depression and conduct research. Fluoxetine, a selective 5-HT reuptake inhibitor (SSRI) widely used in clinical practice, can selectively inhibit 5-HT transporter and block the reuptake of 5-HT by the presynaptic membrane. The effect of 5-HT is prolonged and increased, thereby producing antidepressant effects. It has low affinity for adrenergic, histaminergic, and cholinergic receptors and has a weaker effect, resulting in fewer adverse reactions. This paper uses the comparative experiment method and the Welch method and uses the average shortest path length L to describe the average value of the shortest path length between two nodes in the network. Attention refers to the ability of a person’s mental activity to point and to concentrate on something. Sustained attention means that attention is kept on a certain cognitive object or activity for a certain period of time, which is also called the stability of attention. The research on attention of depression patients generally focuses on continuous attention, and the results obtained show inconsistencies. Most studies have shown that the sustained attention of the depression group is significantly worse than that of the healthy control group. An overview of magnetic resonance imaging technology and an analysis of depression based on resting state were carried out. The key brain areas of the sample core network were scanned, and the ALFF results were analyzed. The data showed that the severity of depression in the depression group was negatively correlated with the ReHo value in the posterior left cerebellum ( P = 0.010 ). The sense of despair was negatively correlated with the ReHo value in the posterior right cerebellum ( P = 0.013 ). The diurnal variation was negatively correlated with the ReHo value of the left ring ( P = 0.014 ). It was positively correlated with the ReHo value of the left ventricle ( P = 0.048 ). This experiment has better completed the research on the mechanism of depression by analyzing the functional images of patients with depression and normal human brain.

COVID – 19 OUTBREAK ANALYSIS ON THE BASIS OF MACHINE LEARNING

Hand gesture is language through which normal people can communicate with deaf and dumb people. Hand gesture recognition detects the hand pose and converts it to the corresponding alphabet or sentence. In past years it received great attention from society because of its application. It uses machine learning algorithms. Hand gesture recognition is a great application of human computer interaction. An emerging research field that is based on human centered computing aims to understand human gestures and integrate users and their social context with computer systems. One of the unique and challenging applications in this framework is to collect information about human dynamic gestures. Keywords: Covid-19, SIRD model, Linear Regression, XGBoost, Random Forest Regression, SVR, LightGBM, Machine learning, Intervention.

HAND GESTURE RECOGNITION IN REAL-TIME

Hand gesture is language through which normal people can communicate with deaf and dumb people. Hand gesture recognition detects the hand pose and converts it to the corresponding alphabet or sentence. In past years it received great attention from society because of its application. It uses machine learning algorithms. Hand gesture recognition is a great application of human computer interaction. An emerging research field that is based on human centered computing aims to understand human gestures and integrate users and their social context with computer systems. One of the unique and challenging applications in this framework is to collect information about human dynamic gestures. Keywords: Tensor Flow, Machine learning, React js, handmark model, media pipeline

Promotif dan Preventif Bagi Klien Diabetes di Puskesmas Cipto Mulyo Malang terhadap Resiko Kecelakaan Dalam Berkendaraan

DM clients will continue to carry out daily activities like normal people in general, including driving activities. The most trips made by DM patients are to work, fill spare time, recreation and health services. However, there are dangers that can cause accidental diabetic clients, including blood glucose disorders, decreased visual acuity and neuropathy. The risk of accidents for diabetic clients is high. The purpose of this service is that this activity is expected to be able to increase understanding for diabetic clients and prevent the risk of accidents for DM clients. The sampling technique in this service uses purposive sampling. Diabetic clients who participate in this service generally experience problems in driving. The perceived disturbances include frequent fatigue, visual disturbances, decreased response, hyperglycemia and hypoglycemia. It is necessary to have periodic checks in collaboration with the puskesmas regarding this matter. Thus, diabetic clients who are still actively driving are prevented from having accidents.

Pneumoconiosis computer aided diagnosis system based on X-rays and deep learning

Purpose The objective of this study is to construct a computer aided diagnosis system for normal people and pneumoconiosis using X-raysand deep learning algorithms. Materials and methods 1760 anonymous digital X-ray images of real patients between January 2017 and June 2020 were collected for this experiment. In order to concentrate the feature extraction ability of the model more on the lung region and restrain the influence of external background factors, a two-stage pipeline from coarse to fine was established. First, the U-Net model was used to extract the lung regions on each sides of the collection images. Second, the ResNet-34 model with transfer learning strategy was implemented to learn the image features extracted in the lung region to achieve accurate classification of pneumoconiosis patients and normal people. Results Among the 1760 cases collected, the accuracy and the area under curve of the classification model were 92.46% and 89% respectively. Conclusion The successful application of deep learning in the diagnosis of pneumoconiosis further demonstrates the potential of medical artificial intelligence and proves the effectiveness of our proposed algorithm. However, when we further classified pneumoconiosis patients and normal subjects into four categories, we found that the overall accuracy decreased to 70.1%. We will use the CT modality in future studies to provide more details of lung regions.

Study on the Regulation of Qinxitong Combined with Methotrexate Tablets on Multiple Inflammatory Indicators and Mechanism Factors in Rheumatoid Arthritis

Objective: To study the changes of several inflammatory index mechanism factors in the clinical efficacy of tablet and methotrexate in rheumatoid arthritis. Methods: 20 patients with rheumatoid arthritis were randomly divided into observation (n =10) and control (n =10), then normal people as normal (n=10), all three groups were given methotrexate and the observation group were treated with Qin interest pain tablets. Expression of inflammatory index mechanism factors in each group. Results: After treatment, inflammatory index mechanism factors were detected; a significant decrease of IFN-g, IL-1b, IL-10, IL-37, TNF-a and other factors was found before and after combination treatment (P <0); IL-8 and IL-13 expression (P <0); the CRP, ESR score of the study group was lower than the control group (P <0.05); and the inflammatory index mechanism factors were affected by drug combination. Conclusion: Tablets combined with methotrexate in rheumatoid arthritis patients is better than methotrexate. It obviously changed the expression of the patient’s inflammatory index mechanistic factors, which has a regulatory effect on the inflammatory mechanistic factors.