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Aim: This research aims to determine the presence of breast cancer using Machine learning techniques and improving the accuracy of breast cancer prediction. Materials and Methods: This study is done on the data obtained from the UCI Machine Learning Repository and is used to acquire the data sets for the research of Innovative breast cancer prediction using machine learning algorithms. Naive Bayes (N=20) and Support vector machine (N=20) with sample size in accordance to total sample size calculated using clincalc.com by keeping alpha error-threshold at 0.05, confidence interval at 95%, enrollment ratio as 0:1, and power at 80%. Results: The Naive Bayes algorithm results in an accuracy of 92.25% with P=0.001,p

Background: The present study aims to assess the uric acid levels in first trimester to predict the gestational diabetic mellitus. Materials And Methods: The quantitative approach with prospective cohort study was conducted in the host institution of Saveetha Medical College And Hospitals. A total of 60 study participants were recruited using purposive sampling technique based on the inclusion criteria, the demographic and clinical data was collected by using a self-structured questionnaire and the levels of serum uric acid was estimated during 0-12 weeks and the study participants were followed till 24- 28 weeks of gestation, and the levels of Oral Glucose Tolerance Test (Oral GTT) were estimated during 24- 28 weeks of gestation. Results: The outcome results identified that, among our study participants during 0-12 weeks of gestation, 10% had uric acid level less than normal,73.3% had normal uric acid level, 16.7% had uric acid level more than normal and after a follow up till 24- 28 weeks Oral Glucose Tolerance Test (Oral GTT) and the results identified that, among our study participants 86.67% were not at the risk of developing GDM during pregnancy and 13.33% had the risk of developing GDM during pregnancy. Conclusion: This study demonstrates a positive association between increased first trimester uric acid and GDM.

Background: The present study aims to compare and assess the blood sugar level among post COVID patients with diabetes patient and non diabetic clients in Saveetha Medical College and Hospital. Material and Methods: The quantitative approach with comparative study was conducted in host institution of Saveetha Medical College and Hospital. A total of 30 participants were recruited using Non-probability convenient sampling technique based on the inclusion criteria, the demographic and clinical data was collected by using structured questionnaire and the HbA1c test was used to assess the blood sugar level among the patients during post COVID period of time. Result: The outcome results identified that the mean score of HbA1c among post COVID diabetic patients was 9.82±3.22 and the mean score of HbA1c among post COVID non-diabetic patients was 5.83±3.32. The mean difference score was 3.99. The calculated Student Independent ‘t’ test value of t = 3.345 was found to be statistically significant t p

Aim: Currently kidney disease is a major problem. Because there are so many people with this disease. Kidney disease is very dangerous if not immediately treated on time, and may be fatal. The main objective of this study aims to find the best-suited algorithm that will give us the most ideal prediction. The Novel Decision Tree is compared to Logistic regression to find out which of these can give us the best accuracy. Material and Methods: The study used 220 samples with Novel Decision Tree and Logistic regression is executed with varying training and testing splits for predicting the accuracy for kidney disease prediction with the G-power value of 80% and the kidney datasets were collected from various web sources with recent study findings and threshold 0.05%, confidence interval 95% mean and standard deviation. The performance of the classifiers are evaluated based on their accuracy rate using the chronic kidney disease dataset. Results: The accuracy of predicting kidney disease in Novel Decision Tree (96.66%) and Logistic regression (85.25%) is obtained. There is a statistical 2-tailed significant difference in accuracy for two algorithms is 0.000 (p

Aim: This work provides an effective approach to detect liver disorder using customised SVM algorithm in comparison with random forest algorithm to measure accuracy. Materials & Methods: For identification of effective approaches to detect liver disorder, the customised SVM algorithm is used comparatively with the random forest algorithm which is an existing algorithm. The number of samples used in each group is 20. Pretest analysis was done from clinical.cal.com by keeping G-power at 80%, threshold at 0.05% & confidential interval 95%. Result: This is an innovative liver disorder detection method and it is observed that the customised SVM algorithm is much better than random forest algorithm. Each sample gives different accuracies where customised SVM have better mean accuracy 93.80% which is greater than random forest algorithm 91.25%. The statistical results are also provided where SVM and random forest algorithms have statistical insignificance different values p=0.176 i.e., p>0.05 (Independent sample T-test). Conclusion: This study concludes that the random forest algorithm is having less accuracy value when compared to customised SVM algorithm 93.80%. By this study the effective approach for detection of liver disorder was determined.

Aim: The study is about to compare the antibiofilm properties of Novel Fluorescein derivatives to those of commercial antibiotics, using docking to see how the ligand, protein, and antibiotic interact molecularly. Materials and methods: The 3D structures of protein and ligands were procured from the PubChem, Drug bank, and PDB databases. The sample size was calculated using G power with pretest power at 80% and alpha value of 0.05. The sample size per group is 10 and total sample size is 30. Docking studies of protein and ligands were performed using Auto dock software. Statistics of the interactions were analyzed using IBM SPSS software. Result: Protein ligand interactions revealed that fluorescein shows more binding affinity towards the protein compared to commercial antibiotics cephalexin and cephalexin monohydrate. The binding affinity values were compared using SPSS software, which reveals that the statistical insignificance observed between the fluorescein and antibiotics cephalexin and cephalexin monohydrate is 0.519 and 0.400 respectively. Conclusion: The fluorescein ligand appears to be more specific and selectively works against the target protein when compared to the antibiotics.

Aim: To perform Predicting heart disease using the Forest algorithm and comparing its feature extraction precision with the K-nearest neighbors algorithm for working on the precision of the forecast. Materials and Methods: In the proposed work, Predicting heart disease was carried out using machine learning algorithms such as K-nearest neighbors algorithm (n=10) and Forest Algorithm (n=10). Here the pretest power examination was done with gpower 80% and the sample size for the two gatherings was 20. Results: From The implemented experiment, the Forest algorithm accuracy is significantly better and it is 90.0% than the K-nearest neighbors algorithm 83.00%. There is a measurable 2-tailed significant distinction in exactness for two calculations is 0.001 (p

Background: Biodiversity is one of the proud domains of any country in the world. Wildlife as a whole is one of the greatest contributors to the environment worldwide in the human race. It is known as one of the vital assets of all countries of the world. Protecting wildlife is also synonymously used as protecting the future of the earth and humanity by researchers worldwide. It becomes essential in the changing environment to give more attention to the conservation of wildlife. There seem many inherent problems in protecting wildlife, and it is very difficult to deal with these problems. One of the needs is to address the problems of audit and audit ability of wildlife in general and snakes, particularly in India. We need data and facts for auditing. Can this be done with ‘counting snakes? (Interesting / chilling /challenging?) The management and governance problems even with the controlled environment like reserved forests from audit perspective for the bodies like forest department of the country and government agencies like “Zoological Survey of India (ZSI)/Botanical Survey of India (BSI),” etc. How difficult is it to count animals? Especially snakes? Almost impossible. Needs innovative approaches to solving these problems.

Aim: To improve the accuracy in Heart Disease Prediction using Novel Logistic Regression and Decision tree Materials and Methods: This study contains 2 groups i.e Novel Logistic Regression and Decision tree Each group consists of a sample size of 10 and the study parameters include alpha value 0.01, beta value 0.2, and the Gpower value 0.8. Results: The Novel Logistic Regression (91.60) achieved improved accuracy than the Decision Tree (89.42) in Heart Disease Prediction. The statistical significance difference is 0.01 (p

Aim: The main aim of this research is to detect heart plaque using the Decision Tree algorithm with improved accuracy and comparing it with Least Squares Support Vector Machine. Materials and Methods: Decision tree and Least squares Support Vector Machine algorithms are two groups compared in this study. Each group has 20 samples and calculations utilized pretest power of 0.08 with 95% confidence interval. The G power is estimated for samples using clincalc, which has two groups: alpha, power, and enrollment ratio. These samples are split into two groups: training dataset (n = 489 [70%]) and test dataset (n = 277 [30%]). Results: The accuracy obtained for Decision Tree was 68.13 % and 67.3 % for the Least Squares Support Vector Machine technique. Since p (2-tailed) < 0.05, in SPSS statistical analysis, a significant difference exists between the two groups. Conclusion: It is found that the Decision Tree algorithm is significantly better than the Least Squares Support Vector Machine algorithm in Heart plaque disease detection for the dataset considered.

A newly emerged human Covid-19 virus from Wuhan, China caused by SARS-Cov2 having 96.1% similar genomic sequence as that of a bat has resulted in global pandemic and adversely affected the healthcare sector in terms of management, manpower, planning. The findings can be used to help the healthcare sector with better pandemic management and identify the root causes and the factors affecting the transmission rate of covid-19 cases. The study focuses on: i) Forecasting covid-19 cases, ii) to study relationship of temperature, density, lockdown, and seasonal factor on the covid-19 transmission rate, iii) how forecasting will help in terms of managerial aspects. The method used to forecast covid-19 cases was data visualization of secondary data accessed and gathered from Kaggle by using Tableau as a tool. It was done by establishing multiple connections; comparing temperature, density, lockdown and seasonal factor individually with covid-19 cases and its effect on the viral transmission rate. Density is found to be independent of transmission rate, which is affected by temperature, seasonal factor & lockdown. The rate was less at higher temperatures and under a complete lockdown imposed on the State. Healthcare workers will have a better hold in the management henceforth with creative patient consideration approaches like customized medical coverage, clinical choice supportive networks and better resource planning with the forecasted data of Covid-19 cases.

Aim: The objective of this study is to use machine learning algorithms to detect the presence of breast cancer tumors and compare accuracy, sensitivity, and precision between the Linear Discriminant Analysis (LDA) and Support vector machine algorithm (SVM). Materials and Methods: The research uses two sets of data from the Wisconsin Breast Cancer dataset, which is obtained from the UCI Machine Learning Repository. Linear Discriminant Analysis (N=20) and Support vector machine (N=20) with sample size in accordance to total sample size calculated using clincalc.com by keeping alpha error-threshold at 0.05, confidence interval at 95%, enrollment ratio as 0:1, and power at 80%. The accuracy, sensitivity, and precision are calculated using MATLAB software. Results: Comparison of accuracy (%), sensitivity (%), and precision (%) are done using SPSS software using independent sample t-test. Linear Discriminant Analysis algorithm results in an accuracy of 88.25% (p

Background: Diabetes is a chronic condition in that Type II diabetes (Formerly called as Non-Insulin Dependent) is the most prevalent, generally affecting adults, diabetes frequently reduces HDL (good) cholesterol levels while raising triglycerides and LDL (bad) cholesterol levels. Both of these factors raise the likelihood of heart disease and stroke. The Indian system of traditional medicine, Ayurveda, also uses curry leaf as a common treatment. Curry leaves can prevent several diseases, including type 2 diabetes and heart disease, because they are rich in antioxidants like beta-carotene,also rich in fibre and vitamin C. Objectives: To determine the effectiveness of curry leaves powder on reducing blood glucose and lipid profile among the clients with Type II diabetes with mild elevated lipid level in the experimental group. To compare the post-test level of blood glucose and lipid profile between the experimental and control group. Methodology: Research approach: quantitative approach, quasi experimental research design was used for 60 samples by Nonprobability purposive sampling technique. Result: on comparing the pre and post-test of blood glucose and lipid profile in relation to administering of curry leaves powder among Type II Diabetic Patients in experimental group, the percentage of reduction for fasting blood glucose is 5.72%, For Postprandial blood sugar is 6.39%, for total Cholesterol is 3.68%, for HDL the increased rate is 7.13%, for LDL the decreased percentage is 6.36%, and for Triglyceride the decrease percentage is 9.04%.

Aim: Aim of this research is to analyze and compare ventricular Cardiac Arrhythmia (CA) classification using Sodium Channel (Na+) parameters with Artificial Neural Network (ANN) and K-Nearest Neighbour (KNN) classifiers. Materials and Methods: Ten Tusscher Human Ventricular Cell Model (THVCM) (data) is used for arrhythmias classification. THVCM has well defined sodium (Na+) channel dynamics. Sample size was calculated by keeping threshold 0.05, G Power 80%, confidence interval 95% and enrolment ratio as 1. Number of samples considered is 20 for each analysis and will be imported to the classifier, K-Nearest Neighbour (KNN) and Artificial Neural Network (ANN) classifier to find better accuracy. Finally, the results (accuracy) will be validated by using Statistical Package for the Social Science (SPSS) software. Result: Ventricular normal, tachycardia and bradycardia data are fed into novel ANN and KNN classifiers. The results obtained from classifiers for 20 samples are fed to SPSS. In that ANN shows accuracy of 35.6% with standard deviation (3.17822) and Standard error mean (0.71067). Similarly KNN produces an accuracy value of 18.05% with standard deviation (1.19593) and Standard error mean (0.26739). Conclusion: As per the results, it clearly shows that the novel ANN has better accuracy for classification than KNN.

Aim: To evaluate the suitable Semen extender for a long duration of cryopreservation among Tris citric acid fructose egg yolk glycerol extender and Tris-egg yolk-glucose extender,. Materials and Methods: The sample size has been calculated by the clinCalc with an alpha error-threshold at 0.05, 95 % confidence interval (CI), G power at 80%, and enrolment ratio 1. Our study has been repeated 14 times which is (N=14) and it was measured using statistical Independent Samples Test. The study involved 28 sample sizes with 2 different groups with each group containing 14 samples. The collected semen samples are diluted with the Tris-citric acid-fructose egg yolk glycerol extender and Tris-egg yolk-glucose extender, based novel semen extenders. A small quantity of the diluted semen sample was taken to a counting device known as makler counting chamber used for analyzing the sperm count and motility of the semen samples. Results: The Tris-citric acid-fructose egg yolk glycerol extenders were better than Tris-egg yolk-glucose extender. So the estimated sperm count and sperm motility were found to be P

Increased Blood Pressure is one of the main causes for ischemic heart disease, and effective management of Hypertension as a key to preventing both primary and secondary Ischemic heart disease. In primary prevention, keeping blood pressure at or below 140/90 mmHg is advised; in secondary prevention, levels of 130/85 mmHg were previously advised. In addition to acting alone to increase Ischemic heart disease (IHD) risk, hypertension also multiplies the likelihood of negative outcomes when it coexists with the other major IHD risk factors mentioned above. Numerous research and guideline recommendations had published over the past 20 years with the goals of explaining the pathophysiology of IHD, identifying an ideal blood pressure (BP) threshold at which to start pharmacotherapy, and defining the ideal pharmacologic components of a treatment plan. The goal blood pressure for IHD patients is generally agreed to be less than 140/90 mmHg. The prescribed pharmaceutical class (or classes) will vary depending on the coexisting conditions that each patient has. It is expected that 1.56 billion adults would have hypertension in 2025. In the South-East Asia region, hypertension claims the lives of up to 1500000 people per year; consequently, it is the world’s biggest cause of death (WHO). The study’s objective is to assess people who have ischemic heart disease (IHD awareness of blood pressure control). The pre-experimental research design for the project was authorized. Patients with ischemic heart disease (IHD) and stage I hypertension made up the study’s sample population. This pilot study’s findings demonstrate the effectiveness of lifestyle modification education on blood pressure control among patients with ischemic heart disease (IHD) who have primary hypertension in particular hospitals.

A biometric is in fact a pattern recognition system that uses biological traits like fingerprints, voice recognition, facial geometry, and hand geometry in addition to different patterns like iris and retina. The fact that different data protection codes like authentications and ID cards can be shared, stolen, or copied is what makes biometric so alluring. Physiological characteristics can be applied to improve the system’s security and dependability. This paper provides an overview of the main biometric technologies, the fundamental techniques used, and the disadvantages of each. The paper then demonstrates how ECG operates and discusses various opportunities for ECG.

Hypertension is when blood pressure is too high. Blood pressure is written as two numbers. The first (systolic) number represents the pressure in blood vessels when the heart contracts or beats. The second (diastolic) number represents the pressure in the vessels when the heart rests between beats.(WHO 2019). Global statistics indicate that only 50% of individuals with hypertension are aware that they actually have high BP and only 14% are controlled (about 8% in low- and middle income countries). The epidemiological evolution for hypertension is witnessed in emergingnations, will give clue to an progress in the incidence of blood pressure. Hypertension as a effect of amplified life suspense and the tension of suburbanization, which is developingspeedily in virtually all unindustrializednations. Domestic history, anxiety, fatness, liquor, a food high in saturated fats or sodium, tobacco use, deskbound lifestyle, and elderly are all threat causes for hypertension. Pre Experimental Research design was adopted in this study.Conclusion: This pilot study concludes by proving the effectiveness of health education on attitude of hypertension among rural adult hypertensive patients .

Objective: To evaluate the durability and stability of Coaxial & 3D (Three-dimensional) -printed bonded lingual mandibular retainer wire (3-3) at the middle third position with the application of forces. The objective is to evaluate the displacement of teeth, stiffness, and stresses on the wire. Methods: A comparison of coaxial wire (six-stranded stainless-steel wire) with 3D-Printed retainer wire (chrome cobalt alloy) was done by conducting a FEM (Finite Element Method) study for which mesh models were prepared onto which forces were applied. A load of 10N was applied to simulate biting forces and protrusive jaw movements during mastication. Results: There was a uniform displacement of teeth in the 3D-printed retainer wire whereas, in the case of coaxial retainer wire more force was noted onto the central incisor due to its low stiffness and thus inability to transfer force to an adjacent unit. High stiffness, von mises, and shear stresses were found to be present in the case of a 3D-printed retainer for which the high modulus of elasticity is responsible. The factor of safety for both materials was high but when compared to each other it was higher in the case of the 3D-printed retainer. Conclusion: 3D-Printed retainer wire showed higher durability and stability when compared to coaxial wire retainer, but requires clinical studies for better appraisal.

Coronary artery disease is the buildup of plaque in the arteries that supply oxygen-rich blood to your heart. Plaque causes a narrowing or blockage that could result in a heart attack. Symptoms include chest pain or discomfort and shortness of breath. Treatment includes lifestyle changes and medication that target your risk factors and or possible surgery. Coronary artery disease is caused by atherosclerosis. Atherosclerosis is the buildup of plaque inside your arteries. Plaque consists of cholesterol fatty substance, waste products, calcium and the clot making substance fibrin. As plaque continues to collect on your artery walls, your arteries narrow and stiffen. Plaque can clog or damage your arteries, which limits or stops blood flow to your muscle. If your heart does not get enough blood, it can’t get the oxygen and nutrients it needs to work properly. This condition is called ischemia. Not getting enough blood supply to your heart muscle can lead to chest discomfort or chest pain called angina. It puts you at risk for a heart attack. The present study aims to assess the role of HDL to triglycerides Vs LDL to triglycerides as an indicator in coronary artery disease. Quantitative experimental research was conducted among 60 coronary artery disease. A Purposive sampling technique was used to select the samples. Self-structured questionnaires were used to collect demographic data. The study results show, LDL to triglyceride had significant role as an indicator in coronary artery disease., p

Aim: The Motive of this research is to analyze, compare ventricular Cardiac Arrhythmia (CA) classification using potassium channel (k+) parameters with Artificial Neural Network (ANN) and K-Nearest Neighbor (KNN) classifiers. Materials and Methods: D Noble Model For Human Ventricular Tissue (DNFHVT) is used for our classification. The DNFHVT is a mathematical model of action potential focusing on major ionic currents like K+,Na+ and Ca+.. Size of the sample was calculated by keeping threshold 0.05, G Power 80%, confidence interval 95% and enrolment ratio as 1. Number of samples considered is 20. These data are imported to KNN and ANN classifiers to find better accuracy among them. The accuracy of novel ANN and KNN classifiers for 20 samples is obtained by alternating the cross fold validation. These results will be imported to Statistical Package for the Social Science (SPSS) software to identify the overall accuracy for each classifier. Results: The results are obtained from SPSS for novel ANN and KNN classifiers. ANN shows accuracy of 13.14% with standard deviation (1.6800) and Standard error mean (0.3757). Similarly KNN produces an accuracy value of 7.19% with standard deviation (1.6902) and Standard error mean (0.377). Conclusion: As of the results, it clearly shows that ANN has better accuracy for classification than KNN.

The county of origin research has studied the influence of a product’s origination on a buyer’s impression. According to the literature, the country-of-origin effect would lead Indian consumers to choose domestic cosmetics and skincare goods over imported cosmetics and skincare products. Age and gender are taken into account to see how they affect the COO and other factors in the research. The study’s goal was to get 266 active surveys. SPSS 21.0 was used to analyze the data. To assess consideration, factor analysis was carried out using a t-test and a one-way ANOVA. The findings revealed a flimsy beneficial influence on Indian individuals’ views and thinking of foreign products. The study’s conceptual and pragmatic inference is proven by making suggestions for additional investigation. Cosmetics are becoming increasingly important for women and men worldwide as the need for beauty and personal care grows in modern life. This study provided an overview of the influence of extrinsic signals linked to cosmetic goods such as brand, price, and country-of-origin (COO) on female customers and the cosmetic business in Vietnam, based on papers with relevant theories and literature. The information might be used to develop a model or framework for future consumer behaviour research.

Aim: The purpose of this study is to use median filters and wiener filters to minimize noise in liver images in order to improve them. In addition, the output of both filters was analyzed based on their Peak Signal to Noise Ratio (PSNR). Materials and Methods: The research includes two groups; each group has a sample size of 20. Grayscale medical images collected from the kaggle website were used in this research. Samples were considered as (N=20) for guided filter and (N=20) for fast bilateral filter with total sample size 40 calculated using clinicalc.com. Image enhancement is used to enhance the niceness of a picture for the visible notion of human beings. The kaggle website was used to collect data for this study. According to clinical.com, samples were considered as size 20 for PSNR ratio of image G power of 80%, and total sample size determined. Using matlab programming and a standard data set, the Linear filtering, Median filtering were computed. Results: According to Matlab simulation results, unique median filters have a PSNR of 48.1240, while wiener filters have a PSNR of 67.8360. Comparison of PSNR values are done by independent sample test using IBM-SPSS software. There is a statistical insignificant difference between both techniques. The significant value of PSNR (Peak Signal to Noise Ratio) (0.409) and p>0.05 was found in the statistical analysis. Conclusion: On ultrasound liver pictures, the innovative median filter gives greater PSNR than the wiener filter for medical image enhancing purposes, according to this study.

India’s reputation as a country with strong abstinence culture, particularly when it comes to alcohol, is under appreciated. In recent years, the country has experienced a tremendous development of metropolitan pubs and nightclubs, and it is quickly losing its inhibitions about drinking as a lifestyle choice. This circumstance has sparked fears of an unreported surge in alcohol misuse, not only among the poorer classes, but even among hitherto dry portions of society. Drink-related disorders have already emerged as a serious public health concern in India due to increased production, distribution, and promotion of alcohol, and there has been a rapid change in patterns of alcohol consumption in India. Counseling focuses on helping people stop drinking alcohol and is followed up with life skills and social support to assist them avoid relapsing. The chance of developing an alcohol consumption disorder was considerably lowered when people were resilient. The link between resilience and alcohol use disorder was shown to be mostly due to shared genetic and environmental variables. Counseling assists persons who are addicted in setting short-term goals that are attainable and empowering, as well as long-term goals that may include repairing damaged relationships, accepting responsibility for acts, and letting go of guilt.

The prolonged increase of blood glucose give rise to complications of diabetes- premature atherosclerosis (including cardiovascular diseases and stroke), retinopathy, nephropathy and neuropathy. The lipid abnormality (dyslipidemia) associated with type 2 diabetes predominantly consists of elevated triglycerides and decreased HDL cholesterol levels. The incident of dyslipidemia in male is higher in comparison to female type 2 diabetic patients. It was also observed that type 2 diabetic patients with higher BMI has higher chance of developing dyslipidemia Diabetic nephropathy is one of the main microvascular complication of diabetes. In diabetic subjects, a major cause of morbidity and mortality is diabetic nephropathy (DN). Diabetic patients are in risk for the development of dyslipidemia and Diabetic nephropathy. Hyperglycemia causes lipid derangement and nephropathy. Elevated levels of serum cholesterol, triglycerides and lipoproteins are the main lipid derangements in diabetes which are common risk factor for coronary artery disease. It is found that elevated blood glucose levels and duration of diabetes are responsible for nephropathy and microalbuminuria. However Apo B and Lp(a) increases in the microalbuminuria and macroalbuminuria respectively.

Aim: The aim of this study is to compare and analyze linear contrast enhancement algorithm for lung image enhancement over novel histogram equalization technique. Material and Methods: In this research, different sources of lung images collected from the kaggle website were used. Samples were considered as (N=30) for linear contrast filter and (N=30) for novel histogram equalization technique with total sample size calculated using clinical.com. As a result the total number of samples was calculated to be 60. Using SPSS Software and a standard data set, the PSNR was obtained. Both linear contrast filter and novel histogram equalization technique image enhancement were implemented on lung images through Matlab coding and also PSNR values of each image were extracted. Then through SPSS software comparison and analysis has been made. Result: In the final output of image enhancement, novel histogram equalization technique shows better performance in improving PSNR of lung images than linear contrast filter. Comparison of PSNR values are done by independent sample test using IBM-SPSS software. There is a statistical difference between histogram technique and linear contrast filter. The novel histogram equalization technique showed higher results of PSNR (65.9197dB) with (p=0.04) in comparison with linear contrast filter (36.1190dB). Conclusion: Within this research study the histogram equalization image enhancement technique has greater PSNR value of lung images than in linear contrast enhancement technique.

Aim: The goal is to compare the anti-fungal characteristics of carbohydrates derivatives to commercial antibiotics, and docking will be used to see how the ligand, protein, and anti-fungal interact molecularly. Materials and methods: The protein structure was obtained from the PDB (protein data bank) website, while the ligand structure was obtained from the NCBI-PubChem website. The fungus from aspergillus parasiticus (3HQR) as a protein, gluconolactone as a ligand, and benzimidazoles as an antifungal are the three different types of chemicals employed for docking. The genuine nature of protein chemical structure may be found on the PubChem website, while the remaining two chemical structures of ligand and antifungal can be found on the PDB website. Result: To determine whether or not the interaction between the protein molecule of the fungus from aspergillus parasiticus (3HQR) and the antifungal benzimidazoles is correct. The independent sample t-test was used to compare binding affinity using SPSS software. Significant difference, one-sided p values are.18-.001, and the two-sided p-value is.689 -.001, according to statistics. Conclusion: The antifungal benzimidazoles appear to be more specific and selective in their antifungal activity against the target protein.

Objectives: To assess the knowledge on anaemia among antenatal women. To determine the adherence to iron folate supplementation among antenatal women. To identify the relationship between knowledge on anaemia and adherence to iron folate supplementation and to determine the association between knowledge and adherence with the selected background variables of antenatal women. Research Design: A descriptive cross sectional study design was used for this study. Methods: The target population of the study was antenatal women who completed 32 weeks of gestation and accessible population was antenatal women attending the outpatient department, SRH. The purposive sampling technique was used to select 240 antenatal women for the study. The instruments used for the study were standardized Morisky Medication Adherence Scale to determine the adherence to iron folate supplementation among antenatal women. In addition the investigator developed a structured questionnaire to collect data regarding background variables of the antenatal women and to assess the knowledge on anaemia among antenatal women. Interview technique was used to collect data from the antenatal women. Descriptive and inferential statistics (ANOVA, independent t’ test, correlation co-efficient, and multivariable logistic regression) were used to summarize the data. The 0.05 level of significance was used.xiii Major findings: 47.89% (113) of antenatal women had inadequate knowledge and 52.11% of antenatal women had moderately adequate knowledge. None of them had adequate knowledge on anemia. Most (63.1%) of the antenatal women had moderately adequate knowledge in the aspect of nature of the disease. In the aspect of investigation and treatment, half of the antenatal women (55.1%) had moderately adequate knowledge. In the area of prevention control and complications, 79.7% of antenatal women had inadequate knowledge. Nearly half of them (48.8%) had poor adherence to iron folate supplementation, 39.4% of them had fair adherence, only few (11.8%) had good adherence. The reasons for non adherence were: forgot to take pills (83.9%); frustration (48.7%); taste difference (45.8%); experience of side effects (27.5%). There was a statistically significant association between the knowledge on anemia and the demographic variables such as age, education and monthly family income (p< .01, p< .001). There was a statistically significant association between the adherence and maternal variable of the antenatal women such as hemoglobin level during the first trimester, after completion of 32 weeks of gestation and percentage of procurement of iron tablets.

Background. Obstructive sleep apnea (OSA) is characterized by recurrent complete (apneas) and partial (hypopneas) upper airway obstructive. The Obstructive Sleep Apnea is one of the leading risk factor for coronary heart diseases and heart failure. Aim. The aim of the study was to develop and validate the educational pamphlet on Obstructive sleep apnea (OSA) Design. A mixed method research design with quantitative research approach was adopted in this study. Methods. Educational pamphlet was developed on prevention and control of Obstructive sleep apnea. The pamphlet was developed in three phases which includes review of content, design / layout of the pamphlet and validation with expert opinion. The pamphlet was validated using Item level content validity index (I-CVI) and Scale level Content Validity Index (S-CVI). individual items which ranged from 0.94 – 1. Scale level Content Validity Index (S-CVI) was 0.9120.

The relevance of this research work lies in the fact that at present the EU policy standards are aimed at creating an ecosystem of digital (electronic) healthcare (eHealth). Digital medicine resources can radically transform healthcare, providing effective medical care, and improving diagnostics/treatment/rehabilitation methods. The aim of this article is to identify the main trends in the development and regulation of eHealth in the EU. Methods: A comprehensive review of trends and an analysis of key areas of digital health development in the EU based on the application of comparative law studies. The practical significance of the article is to identify some existing specific difficulties in the EU digital health infrastructure: those in their policy, those in accordance with regulatory requirements, in cybersecurity and exclusive rights to medical (personal) data, in the compatibility of national digital health platforms across Europe, in the principles of medical information handling, in digital therapy etc. Conclusions: Digital health in the EU is an area with an increased activity. In the EU, a common digital market is emerging, including digital health services within digital health, facilitated by the regulation and standardization of electronic prescriptions, patient data protection, digital identity and information security. The European digital health infrastructure, along with its achievements, is experiencing some difficulties, obsolete health regulations among them. At the same time, new approaches are now required to focus on digital tools and resources in order to provide full intelligent automation.

Aim: To predict the heart disease using Forest Algorithm and comparing it with Decision Tree algorithm for improving the accuracy in predicting heart disease. Methods and Materials: Anticipating coronary illness expectation was completed utilising machine learning calculations, for example, Forest Algorithm and Decision tree. Here the pretest power analysis was carried out with 80% and the sample size for the two groups are 20. Results: Forest Algorithm accuracy is 90.00% while the Decision Tree algorithm has shown an accuracy of 85.00%. There is a measurable 2-tailed significant distinction in exactness for two calculations is 0.001 (p

Background: ADA levels in PF of 290 individuals were tested to investigate pleural effusion. Based on a positive pleural biopsy, each patient was assessed for age, sex, and the presence of tuberculosis (TB). The Kruskal-Wallis one-way analysis of variance was used to analyze differences in ADA levels between groups. The likelihood of TB was also predicted using logistic regression analysis. Utilizing the receiver operating characteristic (ROC) curve, ADA cut-off levels were chosen. Aims & Objective: Our study aims to establish an ADA cutoff value for the population and explore the possibility that patient variables may affect ADA. Methods & Materials: We conducted a retrospective analysis of 309 M pleural effusion patients admitted to Santosh University, Ghaziabad, between 2016 and 2018. Information was gathered from patients’ medical records. Age, sex, and whether or not a pleural biopsy for TB was positive were all recorded. Results: When compared to non-tuberculous pleural patients, the mean PF ADA level was considerably higher in the tuberculous pleural group (63.3 ± 6.29 IU/l vs. 19.6 ± 31 IU/l, P, 0.001). Age and PF ADA levels were significantly correlated; for patients older than 745 years, the ROC curve for ADA had an area under the curve of 0.89. A 29 IU/l ADA level produced a sensitivity and specificity of 87.4% and 90.3%, respectively. Conclusion: Age and PFADA level have a substantial negative association. The quantity of false-negative outcomes is decreased by using a lower ADA cut-off.

Aim: The aim of this study is to propose smart edge detection techniques in x-ray images for improving PSNR using canny edge detection algorithm and compared with laplacian algorithm. Materials and Methods: Using the design of edge detection technique and to improve PSNR, canny edge detection algorithm is used along with gaussian filter and it is compared with laplacian algorithm. Canny edge detection algorithm and laplacian algorithm are the two groups considered in this study. For each group the sample size is 20 and the total sample size is 40. Sample size calculation was done using clinicalc. com by keeping g-power at 80%, confidence interval at 95% and threshold at 0.05%. Result: When comparing the two algorithms, it is clear that the canny edge detection algorithm has a higher mean PSNR value of 28.98db than the laplacian algorithm 27.08 db. It is observed that the canny edge detection algorithm performed better than the laplacian algorithm (p>0.05) by performing an independent sample t-test. Conclusion: Canny edge detection has insignificantly greater PSNR when compared to laplacian algorithm.

In this study, five heavy metals (Zn, Fe, Pb, Cu, and Cd) concentrations were measured in samples of kerosene (locally only) that collected from different of warehouses oils in Iraq. The measurements have been done using Atomic Absorption Spectrophotometer. Non-cancer risks such as the chronic daily inhalation intake (CDIinh), a Hazard Quotient (HQ), and Hazard Index (HI) as well as Cancer Risks (CR) due to inhalation of heavy metals in the kerosene samples were calculated. The results show that, the average values of concentrations in unit ppm (mg/kg) belonging to Zn, Fe, Pb, Cu, and Cd in kerosene samples were 8.14±1.74, 0.127±0.025, 0.017±0.007, 0.15±0.064, and 0.360±0.022, respectively. While, the average value of Hazard Index (HI) due to all heave metals in the present work was 0.721± 0.16. Also, the average value of Total Cancer Risk (TCR) due to Cd and Pb were 1.12E-03±4.4E-04. When comparing the results of inhalation health risk, the present work with Worldwide average, we found that the most samples of kerosene samples are safe for using.

Infertility is frequently a secret battle, and depressive symptoms are more common in infertility patients than in cancer patients. Around 60–80 million couples worldwide have infertility, which has a wide range of effects on many different dimensions. Despite this, it is progressive in nature. Traditional conceptions of reproductive awareness included understanding of female anatomy and physiology and how it relates to family planning, as well as the possibility that couples might be somewhat exposed to the causes of infertility. The present study aims to assess the level of stress and perceived social support among women seeking infertility treatment. A quantitative approach with exploratory design was used to conduct the study at selected infertility clinic, Saveetha Medical College &Hospital with the sample size of 60 using non probability purposive sampling technique. The demographic and clinical variables of the women were collected by semi-structured questionnaire. The perceived stress scale “BDI” was used to evaluate the level of stress the and Multidimensional scale of perceived social support “MSPSS” was used for determination of perceived social support. The results the study revealed that the mean score of stress among women seeking infertility treatment was 20.97±6.85 and the mean score of perceived social support was 3.72±1.43. The Correlation value of r = -0.447 shows a mild negative correlation which was found to be statistically significant at p

Covid-19 is creating a flurry of fear and stigma across the globe. The individual fear is of discrimination at isolation centers, fears of dying alone with no funeral as per rituals. The mass fear is fueled by social and news media through negative news, disseminating medical knowledge, and confusing multiple mathematical projection models. Hence, firstly need to impede this pandemic of fear and stigma. Equally vital is to embrace the science of prevention by stringently following WHOguidelines. The general population should not fall prey to non-scientific news on social media. There should be restricted, systematic, and timely dissemination of medical knowledge to the medical fraternity. Identify limited expert spokesperson to present factual positive data and strategies of prevention. Disengaging politics and conflicts of commercial interests and educating the population for social changes and norms are required to control the pandemic. Increased GDP allocation to the health sector; Population Control, Improved Literacy rates are other long-term measures.

Aim- The current study sought to assess the fracture strength of thin monolithic zirconia crowns of varying thickness and finish line configuration. Study design- The current study was carried out to evaluate and compare the fracture strength of monolithic zirconia crowns with chamfers and radial shoulder finish lines of 0.5, 0.8, and 1.1 mm thickness. Result- Monolithic zirconia crowns with merely 0.5 mm reduction demonstrated adequate strength for use in posterior areas. On evaluation and comparison it was observed that no statistically significant difference was found in the fracture strength of monolithic zirconia crowns with different thicknesses and finish lines was investigated. Conclusion- The 0.5 mm thickness of monolithic zirconia crowns demonstrated significant fracture resistance to withstand physiological performance, regardless of finish line type.

Cardiac arrhythmias is one of the cardiovascular diseases that cause more death in today’s industrial world. Among the generation of arrhythmias in cardiac myocytes, Early Afterdepolarization (EAD) and Delayed Afterdepolarization (DAD) is most significant one. Sodium -Calcium Exchanger (Na+- Ca2+ X) dysfunction is one of major source of DAD and EAD. Na+- Ca2+ X which exact mechanism is responsible for promoting EAD and DAD still not derived clearly from any cardiac models investigation. It is very important to understand the electrophysiological characteristics of Na+- Ca2+ X and its role in cardiac arrhythmias to clearly detect such arrhythmias effects. In this research article the aim is to build a strong fundamental foundation of Na+- Ca2+ X mechanism in terms of electrophysiological molecular segment (internal structure) arrangements and various Na+- Ca2+ X investigations on cardiac arrhythmias. Na+-Ca2+X is one which belongs to low chemical attraction, high transport capacity and energetically driven by the Na+ ions. The second is an L-type Ca2+ channel, which has a high affinity to Ca2+. Na+-Ca2+X eject nearly thirty (30%) of Ca2+ is required to activate the myofilaments in rabbit, guinea pig and human ventricles and a very small amount (7%) of Ca2+ is required in rat and mouse ventricles. Na+-Ca2+X transport Ca2+ up to 5000 per second (≈6μM/L), which is much greater than SR Ca2+ ATP Ca2+ transport (100 to 150 per second at ≈0.3μM/L). By using voltage patch clamp, Na+-Ca2+X current is measured in guinea pig ventricular myocyte, from that density of Na+-Ca2+X is to be found, 250 to 400 Na+-Ca2+X/μm2 . Na+-Ca2+X current (INaCa) at level [Ca2+]o = 0.50mM, Na+-Ca2+X mechanism of Ca2+ ions removal is very quickly, why because Calcium Induced Calcium Release (CICR) process does increase the cytosolic Ca2+ concentration very early. Enhanced activity of Na+-Ca2+X or increased Na+-Ca2+X channel current reduces the Action Potential (AP) and AP Duration (APD), and triggers rapid firing within cardiac pulmonary veins. Minimum intracellular calcium levels in SR is responsible for systolic dysfunction in failure heart. Similarly it increases the inward Na+-Ca2+X current, life threatening arrhythmias like DAD happen by sudden release of calcium in SR. Finally increased or enhanced or hypertrophy conditions of Na+-Ca2+X is produces EAD and DAD.

Aim: The goal of this study is to compare the accuracy, sensitivity, and specificity of a Decision tree classifier and ANN classifier in detecting Innovative Hepatitis C Detection using modern methodologies. Materials and Methods: The data for this study was collected via the kaggle website. Samples were considered as (N=22) for Decision tree and (N=22) for ANN according to clinicalc.com, by keeping alpha error-threshold value 0.05, enrollment ratio as 0.1, 95% confidence interval,G power as 80%, total sample size calculated. The accuracy, sensitivity, and specificity were calculated using MATLAB and a standard data set. Results: Comparison of accuracy, sensitivity, and specificity is done by independent sample t-test SPSS software. There is a statistically insignificant difference between Decision Tree Classifier and Artificial Neural Network Classifiers. The Decision Tree with p=0.003, p0.05-specificity (0.42%) showed better results in comparison to ANN. Conclusion: Decision Tree showed better accuracy, sensitivity, and specificity than ANN to predict Innovative Hepatitis C Detection in a faster way.

The current research study aims to address the representation of gender in animated content on children’s TV in India through the lens of political economy by understanding the perspectives of media experts. The discourse generated by capturing viewpoints of media experts who represent different roles and functions in the children’s media ecosystem will be critical in shaping a holistic overview. States that there is a considerable gap in academic investigation and scholarship that global production practices in children’s TV landscape. India, in particular, does not have an excellent scholarship to discuss children’s TV in India. This research paper aims to address the more significant gap to contribute to generating academic knowledge on children’s TV in India with particular reference to gender representations and the specific opening of analyzing industry practices in the domain through the discourse emanating from the industry experts as well as critical social and academic experts.

Aim: The intent of this research is to analyze and compare ventricular cardiac arrhythmia classification using sodium potassium pump (Na+/K+) channel parameters with Artificial Neural Network (ANN) and K-Nearest Neighbor (KNN) classifiers. Materials and Methods: P.J.Noble and A.V.Panfilov model (PJAV) is used for human ventricular study based on the action potential distance. PJAV uses alternative methods of computer simulations which include major ions, pumps and exchangers. Sample was calculated by keeping threshold 0.05, G Power 80%, confidence interval 95% and enrolment ratio as 1. Number of samples considered is 20. According to these data the accuracy is obtained from the classifiers by training novel ANN and KNN classifiers by alternating the Cross fold validation to obtain 20 different samples. These samples are imported to Statistical Package for the Social Science (SPSS) software for graphical representation and overall accuracy. Result: The concluded results shows that ANN has accuracy of 12.25% with standard deviation (4.0911) and Standard error mean (0.9148). Similarly KNN produces an accuracy value of 4.54 % with standard deviation (2.5732) and Standard error mean (0.5754). Conclusion: As of the results, it clearly shows that ANN has better accuracy for classification than KNN.

Digitization and pop culture has come a long way. Making and taking experiences online has given new spectrums for fans to participate and integrate. The Hallyu wave, one of the recent flagbearers of the pop culture phenomenon, facilitating the global rise of K-pop, has brought in new lessons for the music industry. With cultural similarity and fascination that draws audiences to K-pop, the Korean music industry relies on a solid audience presence for fans to interact, consumer, and garner intimacy from them as a two-way exchange. India is one of the Asian countries that has caught on to this wave through rapid digitization, giving the country a strong base of consumers. Content consumption through videos and articles is one way that fans learn more about Korea’s music, culture, and people. The following paper examines the nature of content consumption amongst Indian K-pop fans based on three parameters, the quantity of consumption, the preference of platform, and the perceived intimacy, given between the fans and K-pop by these platforms.

COVID-19 has created a misbalance in the global economy by affecting multiple sectors. It has also affected the mindset and, consequently, the behavior of economic agents, one of whom is the investors. This study aims to learn about investors’ investment behavior in COVID-19 by looking at their preferred sources of information, which influence investment decisions, and their preferred investment avenues. A deep learning model is also proposed for accurately predicting stock market movement. For this study, data was collected from 50 investors. Factors such as the respondents’ age, salary, educational background, investment experience, and income were considered when selecting the sample. According to the findings, most investors trust modern investment vehicles such as stocks and mutual funds. For most investors, liquidity and fund safety have emerged as the most important factors influencing their investment goals. A deep learning model proposed is a combination of CNN network plus bidirectional LSTM network.

Introduction: Dyspepsia is a common gastrointestinal (GI) condition around the world. Dyspepsia is a symptom complex that includes a variety of upper GI symptoms such as epigastric pain or burning, early satiety, and post-prandial fullness. Treatment with complementary and alternative medicines, particularly herbal remedies, is one option for treating dyspepsia symptoms. Objectives: The objectives of the study were to evaluate the effectiveness of ginger tea on symptoms of dyspepsia among adults Methods: A one-group pre-test and post-test research design with 30 samples who met the inclusion criteria was used. Demographic and clinical data were collected using a self-structured questionnaire. During the pre-test, a questionnaire (LDQ) was used to assess the severity of dyspepsia symptoms using an interview method. Following the pre-test, 100ml of freshly prepared ginger tea was given once a day, seven days a week, in the morning before meals. The same tool was used for the post-test after seven days of ginger tea administration. Data has been subjected to descriptive and inferential statistics Result: During the pre-test, 13(43.33 %) of the 30 samples had moderate dyspepsia, 9(30%) had severe dyspepsia, and 8(26.67%) had mild dyspepsia, whereas in the post-test, 14(46.67%) had moderate dyspepsia, 12(40%) had mild dyspepsia, and 4(13.33%) had severe dyspepsia. Ginger tea is effective when the pre-test and post-test values are compared. At the p

The world witnessed the biggest pandemic in almost 100 years, the impact of which is felt in all walks of life. It will be early days to predict too many things. But it is essential to foresee the economic impact of COVID-19. In India, which was already in economic turmoil, it remains to be seen how this new blow will affect the economic cycle. The paper attempts to do this in various sectors like agriculture, tourism, and industrial sectors. Secondary data is reviewed for the study. The paper also discusses innovations in this reference, and it also discusses the steps that the country is taking to sustain this situation. It uses estimation techniques for predicting the economic impact in the various sectors. The paper identifies and analyses the various factors that led to the significant disturbance of farming systems and the agricultural sector as a whole following the lockout, using quantitative and qualitative sources of knowledge with an emphasis on the Indian state of Uttar Pradesh, including expert elicitation and a farmer survey. Our research found that a shortage of migrant labor in some regions and a surplus of jobs in others had a significant impact on the April crop, resulting in a decrease in agricultural wages in some communities and an increase in others, as well as significant production losses.

Aim: The aim of this work is to evaluate the accuracy and precision in predicting heart disease using Decision Tree (DT) and Novel Random forest (RF) Classification algorithms. Materials and Methods: Novel Random forest is appealed on a heart dataset which consists of 150 records. A framework for predicting heart disease in the medical field comparing the proposed and developed RF and DT classifiers. Sample Size Calculated as 55 in every group by using 80% G power. Sample Size Calculated using clinical analysis, with Alpha and Beta values of 0.05 and 0.5, the confidence level. confidence is 95%, nicest strength is 80% and registration rate is 1. Results: The Decision Tree classifier produces 96.42% accuracy in predicting the heart disease on the data set, whereas the Random forest classifier predicts the same at the rate of 78.45% of the time with a statistically significant difference between the two groups (p=0.004;p

Introduction: Dental Treatment for an anxious child has always been a challenge to the pediatric dentist. Nitrous oxide has been documented as a safe and effective pharmacological mean of behaviour management which results in anxiolytic and analgesic effects along with slight amnesic effect. The objective of the study is to evaluate the amnesic effects of nitrous oxide during treatment at 30% and 50% concentrations of nitrous oxide and after 100% oxygen for 3 minutes. Materials and method: 30 Healthy Children (ASA I) of age group 7-12 years undergoing extraction were included in the study. Amnesic effects were evaluated visually by showing various cartoon pictures before the start of the treatment (A1) and subsequently asking the child to recognise them after 30% (A2), 50% (A3) nitrous oxide and after 100% oxygen (A4). Results: Nitrous oxide showed amnesic effects at various titration levels. Discussion: Any invasive dental intervention makes even a cooperative child uncooperative in the subsequent visits. However, any substance having amnesic effect results in no negative memory of the painful procedure performed.Nitrous oxide seems to be promising as a safe and potent amnesic agent. Conclusion: Nitrous oxide shows amnesic effects which can be helpful as it won’t let the child create any traumatic memory of the treatment.

COVID-19 crisis has changed the way we work. Work-fromhome, which was a possibility in some sectors, has become a compulsion for all, which has affected the work-life balance of working professionals. Depending on where the employees are working, the office hours have noticeably increased by one to five hours longer as this supplementary time is spent in conferences and follow-ups, or an endeavor to improve productivity, or just being unaware of the time as there is no clear boundary that exists between work and life now. During this unprecedented time, the augmented center of attention should be towards the welfare of the employees and the acknowledgment, recognition, and acceptance of the fact that work and personal/ family needs will overlap. This paper intends to investigate the various factors impacting work-life balance during the lockdown imposed by the government in light of the coronavirus, which is spreading at a rapid pace. Key insights were obtained by conducting a factor analysis and extracting four main factors, which have been explained further below. Data was collected from 225 employees across India belonging to various sectors. The findings of this study can be a crucial contributor to organizations by taking inputs. Then this prescience will help organizations to be prepared during unforeseen events by further refining their policies to benefit their employees.

Autism spectrum disorder (ASD) is a behavioural condition that affects the child’s social interaction, communication, and behaviour. The early identification of ASD is critical for the effective and timely therapies. This study presents an enhanced prediction model for Autism Spectrum Disorder (ASD). It is based on facial features extracted from the face image. Mallat’s multi-resolution algorithm is employed in this work for extracting facial features. Two distance based classifiers such as Euclidean Distance Classifier (EDC) and Absolute Distance Classifier (ADC) are employed for the ASD prediction. The proposed ASD prediction system is evaluated on face images of autistic and non-autistic children. The database is obtained from the Kaggle data repository. A total of 2940 facial images (1470 autistic and 1470 non-autistic) are employed for performance analysis. Experimental results show that the proposed ASD prediction system provides promising results with an accuracy of 97.01% by EDC and 96.87% by ADC classifiers. Keywords Autism spectrum disorder, Computer aided diagnosis, Machine learning, Image based diagnosis, Prediction system.