Online ISSN: 2515-8260

Keywords : Detection


Family Empowerment Development Based on Health Promotion Model on Early Detection of Children's Growth And Development

Rekawati Susilanungrum; Sri Utami; Nursalam Nursalam

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 10, Pages 1167-1178

Introduction: Community empowerment has an important role for the health promotion strategies that allows the process of providing information to individuals, families or groups continuously to build awareness and shows changes in behavior. This study aims to analyze the development of family empowerment based on a health promotion model in early detection of child development.
Method: This type of research was an exploratory research, with a cross sectional design. The sample in this study were some families who have children aged 0-72 months in the Surabaya City Health Center working area. The number of the sample in this study was calculated based on the rule of the thumb sample size formula.The sampling technique was carried out by multistage random sampling. After finding the model, strategic issues will be found. Furthermore, FGDs and expert discussions were conducted to find empirical models.
Results: The results of statistical testing show that all variables show a calculated T value above 1.96 with a positive value, which indicates that these variables have an effect and increase. The results of R-Square of commitment (79.3%),cognition behavior (5.6%), Basic value (56.7%), appraisal (70.5%) and ability (46.2%), it shows that the latent variables can be explained by each of the observational variables. Conclusion: The family empowerment model can to improve the ability to detect early growth and development disorders, the highest research shows strong results on the influence of basic values on assessment and the effect of commitment on one's ability

DETECTION of SOME VIRULENCE FACTORS and ANTIBIOTIC SENSITIVITY TEST of ENTEROCOCCUS FAECALIS ISOLATED FROM SHEEP by MULTIPLEX PCR

Hala Mohammed Majeed; Bashar Sadeq Noomi; Marwan Q. AL-Samarraie

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 9, Pages 68-74

Enterococcus faecalis form an important population of commensal bacteria and have been reported to possess numerous virulence factors considered significantly important in exacerbating diseases caused by them. Objectives: The present study was conducted to evaluates the presence of virulence factors and antibiotic susceptibility among Enterococcus faecalis isolated from sheep. Methods: The study included the collection of 50 samples (25 Milk samples collected from the udder was washed and the teats were disinfected and dried using alcohol, the first milk drop removed. 5ml of milk collected on aseptic tube and 25 Feces samples collected from sheep diarrhea from rectal by aseptic gloves. (from October 2018 to March 2019 ) and transported to laboratory as soon as possible in sterile Brain heart infusion broth that incubated at 37 C for at least 24-28 hours to increasing chances of isolation. Enterococcus faecalis that were recognized by cultural characteristics, Gram stain, and biochemical reactions. Results: The results of the laboratory cultural of 50 cotton swabs used s show that the isolation rate of Enterococcus spp. were 32% and 56% from milk and feaces respectively. the result of PCR test for detection of Enterococcus faecalis: show that the Enterococcus faecalis detected in rate of 66.6% from total Enterococcus spp. While the result of Enterococcus faecalis virulence factors showed that the Surface proteins, Gelatinase and Hemolysin were 75%, 33.3%, 25.5% respectively. Results of antibiotic sensitivity test showed the most bacterial isolated sensitive Nitrofurantoin , Imipenem and Nalidixic acid were 91.6%,83.3% and 58.3% % respectively Conclusion: We report that our simple modification of the existing multiplex PCR had increased the detection of the enterococcal virulence genes. Predominance of virulence genes was in order of Surface proteins, Gelatinase and Hemolysin were 75%, 33.3%, 25.5%. This modified PCR protocol could be useful to resolve the problem of decreased detection of virulence determinants in enterococci.

CNN ANALYSIS FOR MAMMOGRAMDISEASE DETECTION

K. Kalyani

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 9, Pages 1540-1543
DOI: 10.31838/ejmcm.07.09.167

Mammography is a method for the diagnosis and screening of the human breast using low-energy X-rays. Mammograms tend to detect breast cancer early, usually by detecting standard masses, or by detecting microcalcifications.Adescriptive analysis of mammogram diagnosis using Convolutional Neural Network (CNN)for spectral detection is presented in this study.Initially, the color components are separated as red, green and blue. Only green channel is used for analysis because green is sensitive for humans. Finally,CNN is used for mammogram color spectraldetection. The performance of proposed system is analyzed by CNN in terms of accuracy.

SPAM DETECTION OF PHISHING WEBSITES USING ML

Dr. J. Selvakumar; Mr. R. Prithiviraj; Mr. Joshua Jafferson; Mr.S. Bashyam

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 2184-2190

In today’s internet era various websites through which a number of individuals purchase items. There are certain online forums which request their users to provide confidential data such as card number, cvv, pin number etc. for various malicious practices. These websites are referred as Phishing Websites. Therefore, to distinguish between the authentic website and the malicious website we suggested an intelligent, adaptable, and efficient model that utilizes Machine learning techniques. We carry through the project using the algorithm of classification and different methods to gather the phishing websites dataset to verify its validity. These spoofing websites are differentiated on certain significant attribute such as encryption standards, Domain Identity, URL and security. The project will utilize machine learning concept thus informing the user if the website is legal or not. This software is highly secured and can be utilized by many E-commerce ventures so as to provide hassle free transaction. Machine Learning design utilized in the project gives good results when compared with other standard classification algorithms. Detection of Phishing web site is ML intelligent and effective model that’s supported victimization classification or association data processing algorithms. The algorithms we are using here is logistic regression. We are also using decision tree classifier so that we can make a point-to-point comparison between them which will help us to know parameters like accuracy and time taken.

Plant Curl Disease Detection And Classification Using Active Contour And Fourier Descriptor

M. Bala Naga Bhushanamu; M. Purnachandra Rao; K. Samatha

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 5, Pages 1088-1105

Automatic plant leaf curl detection is an important step towards the development of Computer-aided crop damage analysis systems. It helps in analyzing the health condition of the plants through leaf images. Image processing techniques are recently being used to analyze the condition of the leaf and identify the disease that inflicted the crop. Leaf curl disease can be identified by analyzing the edges of the leaf. This paper presents a procedure to identify the curl disease occurring in plant leaves using active contour, Fourier feature descriptor, and deep learning. Active contour is used to identify the shape of the leaf. The edge contour of the leaf is then given to the Fourier feature descriptor. The feature extracted using the Fourier descriptor is invariant to the angle and size of the leaf. The same feature vector is produced in any given angle and size of the leaf in the image. The features are trained using 1D CNN. The model can then be used to classify new images and automatically identify the leaf have curl disease or not. The experimental results prove that the proposed algorithm produces good results in identifying the leaf curl disease.

Review on Mitochondrial Disease, Therapies and Preventive Measures

Shailza Verma; Sneha Mohan Singh

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 6, Pages 1343-1350

Essential mitochondrial ailment incorporates an amazing scope of acquired vitality insufficiency issue having exceptionally factor sub-atomic etiologies just as clinical beginning, seriousness, movement, and reaction to treatments of assorted multi-framework appearances. Huge advancement has been made in essential mitochondrial sickness demonstrative methodologies, clinical administration, restorative alternatives, and safeguard techniques that are custom fitted to major mitochondrial illness phenotypes also, subclasses. A large portion of the disease cases were grown-ups, and the conclusion of a mitochondrial issue in a grown-up quiet turned out to be moderately direct. Adults present with very much characterized "mitochondrial disorders" and for the most part convey mitochondrial DNA changes that are effortlessly recognized. Kids with mitochondrial scatters are a lot harder to characterize. The purpose of this paper is to review the advancements made for the treatment of mitochondrial diseases so as to prevent the victims from any adverse calamities.

MOVING MULTI OBJECT DETECTION AND TRACKING IN VIDEO

G Ahmed Zeeshan; Dr. R Sundaraguru; Dr.P. Vijayakarthick

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 1, Pages 2380-2388

A decade ago we are encountering more applications in video surveillance to deliver issues identified with social needs. The significance of open security is developing, and video observation frameworks are progressively far reaching devices for checking, the board, and law implementation in open zones. In this paper, we proposes computer vision techniques to recognize moving objects from video to follow continuously as articles experienced in the indoor and outside condition. Nearness is a reality of being close to other and legitimizes closeness. Framework tracks grouped items against a domain comprising of objects of shifting sizes, shapes and hues. At first foundation demonstrating is performed utilizing the capacity which gathered the foundation outlines from mean and standard deviation of first N outlines. Each critical change in the article appearance from that point, because of new object, old item vanishing is followed dependent on the vicinity of the objective. The visual similarity is resolved as for the recognized item in the past video outline and the last edge detection