Online ISSN: 2515-8260

Keywords : Detection


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.

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.


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.