COVID-19 is an exceptionally infectious illness that has affected the general public. Displaying such sicknesses can be critical in the expectation of their effect. The assessment of the COVID patient and its correlation with sequential age is a significant task in the clinic, however, with the help of machine learning algorithms we can effectively model the expectations. Getting factual expectation can be tedious as movements can be inclined to intra-rater changeability, the utilization of strategies that can computerize it, similar to machine learning techniques, is of value. The objective of this part is to introduce the strainer chart, patterns and holes in the examination identified with age and sex assessment that utilize machine learning techniques and also provides the COVID issues with various problems in combination with sieve methods.

Bovine infertility is a major issue in dairy industry and it is primarily addressed by hormonal therapy. In present study we compared ovarian structure palpation based minimum hormonal utilization approach (KVK-RDDC protocol) with other estrus synchronization protocols. This study was conducted on 315 infertile cows kept in different managemental conditions i.e. Commercial dairy farms, villages and Gaushala’s in different areas of Udaipur district of Rajasthan. These 315 infertile cows were divided in three feed groups mainly balanced feed without mineral and vitamin mixture, balanced feed with vitaminised chelated mineral mixture and balanced feed with vitaminised chelated oxicareovn solution. These feed groups were treated with five therapy protocols viz. No hormone, Ovosynch(GPG48), Cosynch(GPG56), Cosynch+ progesterone (GPG56 + CIDR) and KVK-RDDC. The study revealed that the group 3 which fed balanced feed with vitaminised chelated oxicareovn solution have highest conception rate i.e. 61.0 % and followed by group 2 (Balanced feed with vitaminised chelated mineral mixture) and group 1 (Balanced feed i.e without mineral and vitamin mixture) viz. 44.8% and 29.5 %, respectively. On the basis hormone protocol, the animals which were treated with KVK-RDDC protocol have highest conception rate of 61.9 % and followed by GPG+CIDR (46.0%), GPG-56(46.0%), GPG-48(42.9%) and no hormone protocol (28.6%). On the basis of different managemental conditions, highest conception rate were of at commercial dairies i.e. 55.2% and followed by village animals (43.8%) and Gaushala’s (36.9%).

Effect of beta carotene on multiparous infertile dairy cows was assessed in this study. A total of 135 dairy cows were divided into three groups, each of 45 cows. Group–I supplemented balance feed without minerals and vitamins while group II supplemented balance feed with minerals and vitamins without β-Carotene and group–III supplemented balance feed with minerals and vitamins with β-Carotene. Plasma β-Carotene concentrations were much higher in the β-Carotene group III {3•02 mg/L v. control (group 1) 1.32 mg/L} at 90 days. Conception rate was greatly improved by β-Carotene supplementation in cows (group III): conception rates at first group were 64.44% v. 37.77%.

Outbreak of brucellosis has been recorded in sheep flocks in Udaipur district of Rajasthan, India.Total 15 abortions were recorded in two flocks of 115 sheep.Samples from aborted fetus, retained placenta colostrum and sera samples were collected.Sera samples were tested by Rose Bengal plate agglutination test.Culture and isolation were done on selective media.All tested sera samples were found positive for brucellosis while Brucella melitensis was isolated from fetal abomasal content and placenta samples.Three isolated strains were subjected to molecular characterization by Real time PCR using BCSP31 gene specific oligonucliotide primer pair in real time PCR with SYBR Green chemistry.Further these isolates were confirmed by Taqman probe chemistry with IS711 gene.Species level characterization was done by Bruce ladder PCR.Outbreak of Brucellosis has been confirmed.Three strains were isolated and by molecular characterization found to be Brucella melitensis.All sheep examined found serologically positive for brucellosis.This is a primary report of culture confirmation of Brucella melitensis outbreak in southern Rajasthan.Study show the occurrence of Brucella melitensis in the area which is of major zoonotic concern.Here we are reporting first time isolation and molecular confirmation of Brucella melitensis in sheep from Udaipur district of Rajasthan K e y w o r d s

It has been a more appearance of the software development life cycle. Design stage is vital for software development. From that instant, the designers have accumulated much knowledge in the design and construction of object oriented system. However, at the present time various approaches are available to guide a design in a formal way. One important quality parameter is availability. Ethics authorize to us to analyze an easier way in which to introduce new design approach. Indirections provide availability to the pattern. In this paper, show that the concepts of availability are more significant with software and developed multiple liner equations.

The main objective of higher education is to provide quality education to students. One way to achieve highest level of quality in higher education system is by discovering knowledge for prediction regarding enrolment of students in a course. This paper presents a data mining project to generate predictive models for student retention management. Given new records of incoming students, these predictive models can produce short accurate prediction lists identifying students who tend to need the support from the student retention program most. This paper examines the quality of the predictive models generated by the machine learning algorithms. The results show that some of the machines learning algorithms are able to establish effective predictive models from the existing student retention data.

Now-a-days the amount of data stored in educational database increasing rapidly. These databases contain hidden information for improvement of students' performance. The performance in higher education in India is a turning point in the academics for all students. This academic performance is influenced by many factors, therefore it is essential to develop predictive data mining model for students' performance so as to identify the difference between high learners and slow learners student. In the present investigation, an experimental methodology was adopted to generate a database. The raw data was preprocessed in terms of filling up missing values, transforming values in one form into another and relevant attribute/ variable selection. As a result, we had 300 student records, which were used for by Byes classification prediction model construction. Keywords- Data Mining, Educational Data Mining, Predictive Model, Classification.

Knowledge Discovery and Data Mining (KDD) is a multidisciplinary area focusing upon methodologies for extracting useful knowledge from data and there are several useful KDD tools to extracting the knowledge. This knowledge can be used to increase the quality of education. But educational institution does not use any knowledge discovery process approach on these data. Data mining can be used for decision making in educational system. A decision tree classifier is one of the most widely used supervised learning methods used for data exploration based on divide & conquer technique. This paper discusses use of decision trees in educational data mining. Decision tree algorithms are applied on students' past performance data to generate the model and this model can be used to predict the students' performance. It helps earlier in identifying the dropouts and students who need special attention and allow the teacher to provide appropriate advising/counseling.

Every data has a lot of hidden information. The processing method of data decides what type of information data produce. In India education sector has a lot of data that can produce valuable information. This information can be used to increase the quality of education. But educational institution does not use any knowledge discovery process approach on these data. Information and communication technology puts its leg into the education sector to capture and compile low cost information. Now a day a new research community, educational data mining (EDM), is growing which is intersection of data mining and pedagogy. In this paper we present roadmap of research done in EDM in various segment of education sector.

The main objective of higher education institutions is to provide quality education to its students. One way to achieve highest level of quality in higher education system is by discovering knowledge for prediction regarding enrolment of students in a particular course, alienation of traditional classroom teaching model, detection of unfair means used in online examination, detection of abnormal values in the result sheets of the students, prediction about students' performance and so on. The knowledge is hidden among the educational data set and it is extractable through data mining techniques. Present paper is designed to justify the capabilities of data mining techniques in context of higher education by offering a data mining model for higher education system in the university. In this research, the classification task is used to evaluate student's performance and as there are many approaches that are used for data classification, the decision tree method is used here. By this task we extract knowledge that describes students' performance in end semester examination. It helps earlier in identifying the dropouts and students who need special attention and allow the teacher to provide appropriate advising/counseling. Keywords-Educational Data Mining (EDM); Classification; Knowledge Discovery in Database (KDD); ID3 Algorithm.