Online ISSN: 2515-8260

Keywords : Random Forest


S. Ranjana; R. Manimegala; K. Priya

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 9, Pages 1530-1534
DOI: 10.31838/ejmcm.07.09.165

SCA is a genetic category of red diseases of the blood cells. People in their red blood cells contain an abnormal protein. Part of a group of SCA diseases is sickle cell anaemia (SCA). Sickle cell anaemia is a red blood cell condition that has not been inherited in the body with ample red cells to hold oxygen.. It is dangerous because it can cause extreme pain, anemia and other symptoms. The early diagnosis is required for sickle cell anemia. In this study, the automatic classification of SCA system is discussed. Initially, the input images are given to median filter for pre-processing. Then the Gray Level Co-occurrence Matrix (GLCM) and Haralick features are extracted. Finally, Random Forest (RF) classifier is used for Prediction. The performance of SCA system produces the classification accuracy of 95%using RF classifier.


Vibin chandar; Dr. Krishnapriya . V

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 9, Pages 856-865

The rapid growth of genomics and proteomics in science has led to the
exponential development of information that requires a complex computational analysis to
find details. Review of statistical science or bioinformatics using knowledge mining centres
using bioinformatics to resolve a range of certifiable problems in the field of medical services.
Breast cancer malignant growth is the second most deadly form of disease that causes a
woman to die. Numerous experts have led to the early detection, visualisation and improved
management of malignancy in the breast cancer over the last 20 years, contributing to a
reduction in the rate of death. However the problem of malignancy in the breast
cancer remains concerning and requires further study in the territory of the development of
locations and forecasts other than treatment methods. This article explore the present
situation with the technique of estimating breast cancer disease status, which includes the
study on breast cancer malignancy, breast cancer, the prediction of the risk of malignant
growth, and the prediction of survival for breast cancer disease.

Crop Value Forecasting using Decision Tree Regressor and Model s

AkshayPrassanna S; B A Harshanand, B Srishti; Chaitanya R; KirubakaranNithiyaSoundari .; SwathiSriram .; V Manoj Kumar; VarshithaChennamsetti .; Venkateshwaran G; Dr.Pramod Kumar Maurya

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 3702-32722

Machine Learning is an emerging research field which can be used for the analysis of crop
price prediction and accurately provide solutions for the same. We can use this system as a backhand
while we decide what a farmer should plant while considering factors such as annual rainfall, WPI
and so on which is provided from the dataset and produce a logical conclusion on which products
would give a more reliable outcome. The performance between Random forest ensemble learning and
decision tree regressor is compared and it has been observed that the Random Forest Ensemble
learning method gives a higher accuracy. In this system there are 23 crops whose information can be
accessed upon for deciding collaborated with a simple user friendly UI

Detection of Human Activity Performance Analysis Utilizing Machine Learning Algorithms

Ashish Sharma; Dilip Kumar Sharma

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 82-87

Human Activity detection is a talented region has the capacity to earn the human culture by creating assistive advances so that assist old, incessantly sick and for those with exceptional requirements. Precise movement acknowledgment is testing since human action is mind boggling and profoundly assorted. Writing overview acted approximately that has exposed data mining technique are utilized for grouping of exercises. Data mining methods, Naive Bayes with SVM and KNN with Neural Network are end up by proficient in ordering the accelerometers understanding data. This datasets have huge preparation of occurrence by numerous earnings by values. Building categorisers the group like data is as yet a difficult errand. Arbitrary woodland is known for accomplishing high precision in characterization. Its strength in arranging enormous informational indexes is capable. Present paper projects random forest representation for characterizing/anticipating the way of performance. Present data is pre handled to complete stability. Occurrences by organizing dataset are attracted irregular for n tests, and n choice tree are built. Thus, a random based forest is built for ordering initiates depended accelerometers information esteems. To anticipate unlabeled exercise information, total of n trees is presented. Exploratory investigations are led to consider the action acknowledgment capacity of the representation; the outcomes are contrasted and well known managed order strategies. It is seen that the projected representation hits the other grouping methods in relative examination. The planned grouping representation is constrained to perform movement acknowledgment with regards to weight lifting works out. Human Activity acknowledgment is can be applied to some reality, human-driven issues

Prediction of Admission Process for Gradational Studies using Al Algorithm

Saurabh Singhal; Ashish Sharma

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 116-120

In the present time there are plenty of scholars seeking after their instruction away from their nations of origin. The fundamental nation focused through these worldwide scholars is The United States of America. The popular of the universal scholars in the United States of America are from India and China. With the expansion in the quantity of worldwide scholars concentrating in the USA, every candidate needs to confront extreme rivalry to get admission to their fantasy college. This work is to build up a framework utilizing AI algorithms, named it as Graduate Admission Prediction(GAP). GAP will assist the scholars by predicting the chance to get seat in Fantasy College. This paper compares and recognizes which AI algorithm is going to give precise outcome. A straightforward UI will be created for clients to get to the framework

Movie Prior Release Box Office Prediction A Machine Learning Based Approach

V. Gangadhara Reddy; K. Radheer Reddy; A. Krishnamoorthy; R. Kannadasan; P. Boominathan

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 5016-528

The “Movie Box Office Prediction” includes different factors that influence the movie revenue at the Box Office. Some of the factors include Budget, Genres, Spoken languages, Cast, Crew. In this paper, various plots are made in order to understand and observe the relations between the variables and the amount of effect of factors on the Revenue. Linear Regression, Random Forest and XGBoost are the models used for Training and Testing the Data.

Weather Forecasting Using An Extreme Learning Algorithm

Saikiran .; Dr. Rama

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 3, Pages 2190-2193

In this paper we have applied various classification algorithm to demonstrate better classifier to produce a hybrid selection of classifier. It is enlarged with weighted balloting on the idea of Out_Of_Bag blunders charge of man or woman decision bushes. As pre work, we first done evaluation of Random Forest the usage of five one-of-a-kind break up procedures; a unmarried split quantity is used at a time for whole forest. In this paper, we to start with proposed an superior random Forest that utilizes polluting affect optimization strategies just like the bushes in random forests. If there have to be an occurrence of accuracy development, inquire approximately is finished using exceptional belongings assessment procedures and consolidate capacities. A pass breed selection tree model alongside weighted balloting is proposed which recovers the accuracy. Development in getting to know time principally issues on diminishing range of base choice bushes in Random Forest with the aim that learning and for this reason, class is quicker. The methodologies proposed in the bearing of this direction are separate parcels of schooling datasets to get acquainted with the bottom choice bushes, and ranking of training bootstrap samples primarily based on first rate range. Both these methodologies are prompting efficient gaining knowledge of Random Forest classifier.