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Background Isometric handgrip (IHG) exercise causes changes in heart rate variability (HRV) and hemodynamic responses with individual differences. We test the hypothesis that baseline measurements affect post-exercise outcomes. Methods In this cross-sectional study, 45 males aged 17 to 22 were enrolled from SMS Medical College, Jaipur, India. Hemodynamic and HRV data were collected at baseline, during, and after 5 minutes of IHG exercise. Nonparametric tests compared the absolute and relative responses before, during, and after the exercise. The relationship of baseline measurements with outcomes was evaluated using Pearson's correlation. Results The study showed a significant increase in the absolute values of hemodynamic parameters and LF/HF ratio during and after 5 minutes of IHG exercise compared to the baseline. However, a significant decrease and increase occurred in all the relative hemodynamic parameters and most HRV parameters, respectively, after rather than during the 5 minutes of IHG exercise. Hemodynamic parameters significantly correlated with baseline during and after 5 minutes of IHG exercise. Similarly, baseline affects all the HRV parameters during IHG exercise, but not after 5 minutes of IHG exercise. Conclusion The present study concludes that initial baseline status should be considered while evaluating the post-IHG exercise responses.

Background Type 2 Diabetes Mellitus (DM) is another pandemic of 21 century, and its control is of immense importance. Researchers developed many predictor models using soft computing techniques. The present study developed a prediction model for Type 2 DM using machine learning classifiers. The analysis excludes plasma glucose concentration and insulin concentration as predictors to explore relationships with other predictors. Methods This cross-sectional study enrolled 108 participants aged 25 to 67 years from SMS Medical College, Jaipur (Rajasthan, India), after approval from the ethics committee. The study developed a prediction model using machine learning techniques. The classifiers used in the application include decision trees, support vector machines, K-nearest neighbors, and ensemble learning classifiers. A total of 25 predictors were collected and underwent feature reduction. The response levels include diabetes mellitus, prediabetes, and no diabetes mellitus. The models were run using three predictors and a response variable. The prediction model with the best accuracy and area under the receiver operator characteristic curve was selected. Results The features that vary among the three groups include age, WHR, biceps skinfold thickness, total lipids, phospholipids, triglycerides, total cholesterol, LDL, VLDL, and serum creatinine, and family history of DM. After feature reduction, the age, biceps skinfold thickness, and serum creatinine were run on the Classification learner application to predict the diabetic category. The best model was subspace discriminant with accuracy, sensitivity, specificity, and AUC under the ROC curve was 62.4%, 74%, 94%, and 0.70, respectively. Conclusion The present study concludes that age, biceps skinfold thickness, and serum creatinine combination have higher specificity in predicting type 2 DM. The study emphasized the selection of appropriate predictors along with newer machine learning algorithms.

Palm fractures are due to punching heavy objects (such as a wall or a jaw). A major part of the human body is the palm. Palm provides the hand movement capability. Palm Bone breaks in the human body are fundamental. If the outcome of these fractures is someone's sting in the lips, the skin may be broken. In such instances, wound can be contaminated with bacteria from the other person's mouth and cause illnesses that can permanently impair use of the hand if they are not handled quickly. For analysis of the broken palm, the specialists use the X-ray picture. The manual crack detection is repetitive and the risk of error is high. In order to analyses the broken bone, a robotic system must therefore be developed. In this article, several methods are examined for identification, extraction and characterization. In addition, an inspection is often completed in good conditions and inconvenience. The proposed CNNs are also exploring the expectations of palm bone cracks as positive or negative.

Giant biomedical robots including Lasik machine are used to detect cancer. Picture segmentation is an effective form of image analysis for retinal eye detection. A picture interpretation method for the diagnosis of eye cancer is established in this article. In this scheme, the cancer is divided into various image processing methods and marked on the original image. The pictures are smoothed with two dimensional filters. In order to apply the picture to the original, the backdrop is retracted and results in a better field of interest or region of cancer. The tests have shown that the device indicated is able to detect retina cancer in the eye based on an image threshold. The study suggested comprises of two naming stages, the Eye Detection System and the Smart Retina Cancer Detection System. In the search for eye cancer the results are compared accordingly.

In Bioinformatics field Protein Structure Classification is the hugest undertaking. The realized proteins have been requested subject to their level, feature, work, amino destructive and family and superfamily. Protein structure segregated into four sorts: all ? protein structure, all ? protein structure, ?+? protein structure, and ?/? protein structure. The use of a standard way to deal with perform plan is a very inconvenient and dreary task. The quantity of cutting edge Machine Intelligence enrolling strategies such Support Vector Machine, Random Forest, Artificial Neural Network, Decision Tree and Naïve Bayes Classifier had been proposed in the composition. Our objective right currently is to develop a system that performs better than anything past markers for protein structure gathering by thinking about the separation among the distinctive Amino Acid buildups. To take a gander at the display of proposed work particular datasets are used.

However, eye cancer may be the rare disease that matches malignancy; it is the most prevalent form of cancer. It is curable in many of the circumstances, equivalent to the alternative types of cancer, if correctly diagnosed, but the diagnosis approach is very complex and the most troubling difficulty for eye cancer care. This document introduces an automated technique for the identification of the skin of the eye, using a neural convolution network (CNN) with a grey victimisation conversion to top picture resolution. 200 samples pre-diagnosed square measurement based on the traditional data, resized and median filtered for low resolution batter image and ultimately supplied to the Convolution Neural Network specification. Although the planned technology requires a broad calculation, a accurate high rate of 92.5% is getting to exceed careful victimisation Convolution Neural Network classifier for classification of features and the extraction of the neural network can be used to extract options from the image..

The ultrasound region of interest is a challenge since the textures and noises are diverse. The most common technique is the ultrasound scans to check for abnormalities in the kidney, in particular the presence of stones. Automatic ultrasonic object detection burns research fields and the study effort now under way is along the same line. Application that allows the physician to detect the stone area in the ultrasound picture has been designed. The practitioner needs to pick the location that is evaluated by the suggested stone presence system. The extraction feature is used to areas that might contain stone. Different characteristics, such contrast, second angular moment, entropy and correlation, are employed. The KNN classification is used for training picture dataset categorization. Classification system total accuracy is about 91%. The confusion matrix will also assess the complexity and exactness of the system being suggested.

The new human Corona affliction (COVID-19) is a lungs ailment accomplished by incredible outrageous respiratory issue crown 2 (SARS-CoV-2). Given the impacts of COVID-19 in pneumonic sensitive tissue, chest radiography imaging acknowledges an immense part in the screening, early region, and checking of the conjectured people. It affected the general economy besides cruelly. In the event that positive cases can be perceived early, this pandemic infection spread can be condensed. Guess of COVID-19 infection is incredible to perceive patients in danger for sicknesses. This paper proposes an exchange learning model utilizing Convolution Neural Network (CNN) for COVID-19 solicitation from chest X-shaft pictures. For picture approach, utilized proposed Fine-tuned CNN plan (FT-CNN). The strongly assembled pictures by our model show the presence of COVID-19. The outcomes got in COVID measure utilizing FT-CNN with an arranging exactness of 90.70% and testing precision of 90.54% feature the use of Transfer Learning models in disease assumption.

In the software engineering cycle for User Interface (UI) designing, there is heavy back-and-forth of prototypes between the UI concept creater, UI designer and UI programmer before finalizing a design. This process is very time-consuming and requires extensive human effort. With the recent developments in Artificial Intelligence (AI) and its capability to automate repetitive tasks which require little to no creativity, the UI design cycle is an ideal candidate for its application. Many algorithms are being developed to shorten this cycle, allowing rapid prototyping, and ultimately reducing cost. Various algorithms are developed to work for different levels of fidelity in wireframes. In this paper we discuss the need and compare such intelligent techniques that have been developed fast-track this designing process.

One of the principles and best measures to contain the ongoing viral episode is the support of the alleged social distancing (SD). To agree to this limitation, governments are receiving limitations over the base between close to home separation between individuals. Given this real situation, it is critical to enormously gauge the consistence to such physical requirement in our life, so as to make sense of the purposes behind the potential breaks of such separation impediments and comprehend if this suggests a likely danger. To this end, the proposed research work presents the Video Social Distancing issue, characterized as the programmed assessment of the between close to home good ways from a picture, and the portrayal of related individuals’ conglomerations. Video Social Distancing is significant for a non-obtrusive investigation of whether individuals follow the Social Distancing limitation, and to give insights about the degree of security of explicit territories at whatever point this imperative is abused. It has been first viewed that, estimating Video Social Distancing isn’t just a mathematical issue, however it additionally infers a more profound comprehension of the social conduct in the scene. The point is to genuinely identify possibly risky circumstances while keeping away from bogus alerts (e.g., a family with youngsters or family members, a senior with their guardians), the entirety of this by following current security strategies. At that point, the proposed research work will discuss about how video social distancing is related with past writing in social signal processing and show a way to investigate new computer vision techniques that can give an answer for such issue. This paper is concluded with future moves that are identified with the viability of video social distancing frameworks, moral ramifications and future application situations.

Ayurvedic plants identification is most important task in ayurvedic medicinal industry. Sometimes, it might be difficult to identify ayurvedic plant. This problem can be simply solved by presenting a new recognition approach based on Leaf Features Fusion and Classification algorithms for classifying the different types of plants. All the plants in this world are identifying in terms of its leaves. And, Leaves are different to one another due to its characteristics such as the shape, color, texture and the margin. There are many features of leaf such as Color features, Vein features, texture features, Shape features and invariant features. Hybrid Features are made from all features. Also classification approach presented in this research is multi-level. So anyone can identify plant type for further application. In this research paper OTSU’S and contour edge detection methods are used for segmentation and ANN, RF and SVM is used as classifier for classification.

The technique of blending two images or more than two images which produces outcome as the composite fused image. The obtained fused image is the upgraded version of original images because it has all the salient information. The present applications makes majority usage of this fused image to speed up their processing tasks in their respective fields. Recent real-time applications which require image fusion are remote sensing applications, medical applications, surveillance application, photography applications etc. the broad categorization of image fusion techniques are Non-transform domain or spatial domain and Transform domain or frequency domain. This paper initiates with the introduction of image fusion. In the second section it explains the analysis of multi-focus techniques. The third section explains hybrid image fusion strategy. Further sections elaborates the taxonomy of image fusion techniques and their comparative analysis with results.

Leukemias are classified as either myelogenous (also called myeloid) or lymphocytic depending on which types of white blood cells are affected. Acute leukemias occur when the bone marrow produces immature white cells, and chronic leukemias occur when the marrow produces mature cells. Acute lymphoblastic leukemia (ALL) is a type of cancer in which the bone marrow makes too many immature lymphocytes (a type of white blood cell). Leukemia may affect red blood cells, white blood cells, and platelets. ALL is most common in childhood, with a peak incidence at 2–5 years of age and another peak in old age. Here is an automatic segmentation technique that uses two-color systems and the clustering algorithm K-means. The proposed approach is evaluated on three public image databases with different characteristics and performance measures: accuracy, speci?city, sensitivity and Kappa index. Segmentation and classification of acute lymphoblastic leukemia can be done by using Supervise Learning Approach. In that hybrid model with color and cluster system will be used.

Background: Diabetic retinopathy (DR) is an important cause of visual impairment. It is preventable with proper screening and treatment of diabetes. Therefore, it is necessary to assess awareness of the disease in the population and educate them. Our study aimed to assess the knowledge, attitude and practices regarding diabetes and DR in India.Methods: We conducted a knowledge, attitude, and practice study of 1152 participants belonging to various economic backgrounds across India, using a validated questionnaire from Shri C. H. Nagri Eye Institute, Ahmedabad. We divided the study population into diabetics and non-diabetics. Statistical analysis was done using SPSS version 20.Results: Out of 1152, 121 (10.5%) were diabetics and 1031 (89.5%) were nondiabetics. Mean age of diabetics (55.71) was higher than non-diabetics (37.55) (p

Prologue: Evidence-based medicine is a medical technological advancement which promulgates decision-making for the care of individual patient which is scientific, proven, evidence-based, recent, and rational. This is possible because of the advancement in information generation and dissemination technology. Aim and Objective: The aim of the study was to generate evidence; this learning experience is presented. This research article is a case study and self-reflection of my experiences which has evolved in the stepwise fashion of my learning experience through a system of Evidence-based Education System (EBES). Methodology: Qualitative as well as quantitative assessment of EBES is done by sharing my experience, as a medical student of the past 4 years at Sumandeep Vidyapeeth deemed to be University. A detail examination of the system, the role of the teacher/facilitator, and its components are studied and presented. Results: It was a difficult concept for a novice; however, overtime, I started solving jigsaw puzzle inform of grasping medical concepts better. My concept of textbook and class room learning was metamorphosed in additional learning in the form of problem-based learning and raising a query for better understanding. I started understanding classroom lectures better when certain terms were used by the medical teacher such as “RCT,” “PICO analysis,” “Meta-analysis,” “Level of evidence,” “Class of recommendation,” and “Cochrane review”. Conclusion: EBES is a novel system which encompasses a vista of principles of evidences in the whole teaching–learning process which is interwoven in the curriculum in a manner which has benefited me and also will benefit other medical students.