Examining Successful Attributes for Academic Performance using Machine Learning
Authors: AMRUTHA N S, Mrs. PUSHPALATHA S
Abstract
Identification of different factors which affects a student’s learning behavior and performance during academic career. Analyzing student mental issues and low academic performances is a complex task in the current education sector. In current system it is difficult to track the students behavior and characteristics. There is no automation or tool which predicts or shows how to improvise the students academic performances. Finding the correlation between factors that affects student’s performance and academic results pays a vital role in the current education sector. System uses data science technique called as “Association Learning” to find the patterns. We use either “apriori algorithm” or “apriori TID algorithm” or “Eclat algorithm” to find patterns. Proposed system build as real time application useful to colleges and lecturers to know the students behaviour patterns. System also aims at predicting the individual performance. System uses machine learning algorithm called as “naive bayes” algorithm for student’s performance prediction individually.
Detection of Leukemia in Human Blood Cell Images Using Image Processing with SVM classifier
Authors: Singh Ananya, Mandal Binita, Vaghasiya Mohit, Khambhati Dimpal
Abstract
Leukemia is commonly referred for different types of blood cancer of different stages. It occurs when our bone marrow forms more white blood cells tissue. Due to irregular and incomplete growth the cells become deadly and harmful for healthy body functioning. Hence it is necessary to diagnose this lethal disease at early stage. Many procedures are being researched regarding this field which aids in detection of this disease at very early stage. One such methodology is used in this proposed system of using image processing with SVM which helps in detection of Leukemia at early stage with reliable results. The proposed methodology uses different techniques of image processing such as segmentation, edge detection and feature extraction and to classify the results it uses the SVM which gives more precise results and are reliable. All this is performed in MATLAB software. For feature extraction step we have preferred to take 5 features which are referred as contrast, correlation, homogeneity, energy and local binary patterns. These features help classifier to give output precisely.
SYNTHESIS OF SOME MIXED LIGAND COMPEXES OF CADMIUM AND THEIR BIOMEDICINAL STUDIES
Authors: Nagendra Singh, Arvind Kumar
Abstract
The present manuscript deals with the synthesis of some novel mixed ligand complexes of Cadmium and their characterization on the basis of melting point, elemental and spectral analysis. The newly synthesized complexes were also tested for their antibacterial, antifungal and antitumor activity in-vitro and their results of screening clearly indicating them as potent biomedicinal agents.
EVALUATION OF WOUND HEALING ACTIVITY OF THE METHANOLIC EXTRACT ON RIVEA HYPOCRATERIFORMIS (Desr.) Choisy
Authors: Iswarya S, Mary kensaV
Abstract
Wound is defined as a disruption of cellular, anatomical and functional continuity of a living tissue. Several drugs of plants, minerals and animals origins are described in ayurvedic for their wound healing potential. Thus, the methanol extract of Rivea hypocrateriformis (whole plant) was investigated for the evaluation of their wound healing potential on excision wound model in wistar rats. The present study consequence the ointment of plant mixture has significant effects on wound contraction, wound closure time, tensile strength and regeneration of tissues on wound site, among other effects. All of these effects were comparable to those of the standard drug Povidone iodine ointment 5%. Thus, this wound healing evaluation study demonstrates the R. hypocrateriformis is effective in stimulating the closure of wounds.