Online ISSN: 2515-8260

Keywords : Classification


Efficient Data Mining Methods For Book Review Data Sets Using Bayes, Lazy & Meta Weka Classifier

Mr. Prashant Ratan Bhagat; Dr. Yogesh Kumar Sharma

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 3, Pages 1212-1220

Data mining has become a usually utilized strategy for the examination of hierarchical information, for motivations behind summing up information in valuable manners and recognizing non-insignificant examples and connections in the information. The paper presents consequences of exploration on impact of information discretization on proficiency of Naive Bayes classifier. The investigation has been carried on datasets established on writings of two male and two female writers utilizing the WEKA Data mining programming system. The paired grouping was performed independently for both datasets for wide scope of boundaries of discretization measure so as to examine reliance between methods of discretization and nature of arrangement utilizing Naive Bayes technique. The mathematical aftereffects of tests have been thought about and examined and a few perceptions and ends planned

IMPORTANE OF THE ALTITUDE INDEX IN THE DIAGNOSTICS OF FRACTURES OF THE CALCANEUS IN CHILDREN

Zevara Karimova; Yakshimurat Kurambaev; Zulkhumor Jumaeva

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 2463-2468

Abstract. More often, the injuries happenas a result of direct trauma, and are rarely
caused by non-direct actiontraumatic force. The definition of tarano-calcaneus corner and
higher index (rentgenological) has great influence on diagnostics and on curing calcaneus
bones in children.On moderate displacement, closed reposition with the skeleton stretching
or compression apparatus distinction osteosinthesis is recommended.On crude
displacement, it is recommended to perform operative treatment and replace defects of
bones bytissues free auto-transplantation

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.

Identification and Prediction of Liver Disease using Logistic Regression

Neeraj Varshney; Ashish Sharma

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 106-110

Identification of disease at a beginning stage is very essential for higher treatment. It’s a awfully complicated task for medical researchers to predict the illness within the early stages because of delicate symptoms. Typically the symptoms turn out to be evident once it's too late. to beat this issue, this project aims to boost disease designation victimization machine learning approaches. The most objective of this analysis is to use categorization techniques to spot the liver patients from healthy people. This project conjointly aims to match the categorization techniques supported their presentation factors. To serve the medical community for the designation of disease between patients, a graphical computer interface is urbanized victimization python (Node RED). The GUI will be promptly used by doctors and medical practitioners as a screening tool for the disease.

Development of Top K-Association Rule Mining for Discovering pattern in Medical Dataset

Aakriti Sharma; Anjana Sangwan; Blessy Thankchan; Sachin Jain; Veenita Singh; Shantanu Saurabh

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 1413-1421

Association rules consist of the discovery of association between mining transaction items. This is one of the most important information mining jobs. It has been integrated into many commercial data mining software and has a wide variety of applications on a number of domains. So, computing the prediction rules in top rank data set is very difficult task. Finding the pattern in large data set require memory computational power high rate of I/O. and it is possible only on high computational machine. In this paper, selection of parameter which is used to compute is chosen based on minimum support and minimum confidence value. In this paper proposed a new algorithm which generates the association rule for the input parameters to finding the pattern in large data set. The algorithm starts searching the rules. As soon as a rule is found, it is added to the list of order rules list by support. The list is used so far to maintain top N rules found. Once valid rules are found, the minimum support for the internal minsup variable list is raised to support the rule. When the Minsup value is raised, the search space is robbed while searching for more rules. Then, every time a valid rule is found, the list is inserted into the list, the lists that are not listed in the list are excluded from the list and the minsup is raised for the price of the least fun rules in the list. Result shows that new method is efficient technique to mine data set from standard data with minimum configuration system.

ARRYTHMIA RECOGNITION AND CLASSIFICATION USING ECG MORPHOLOGY AND SEGMETATION

S. Nithyaselvakumari; M.C. Jobin Christ; B A Gowri Shankar

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 2144-2155

Cardiac arrhythmia can be identified using abnormal electrical activity of heart, this is a great menace to humans. In order to diagnose cardiac problems ECG signal is widely used. When the background noise is rejected from the ECG signal we obtain a QRS component. This QRS component consists of high frequency and high energy waves that are very easy to detect and study. Once QRS component is obtained, it is further spited into various classes that can aid in diagnosing the abnormalities. Previously extracted features are compared to find the heart abnormalities. In this paper Feed-Forward neural network is selected and data base are used to store and analyze the data.

Automated Diagnosis of Malarial Parasite in Red Blood Cells

Mr K.P.K Devan; Dr G. S. Anandha Mala; Deepthi Salunkey. K; Grace Cynthia. R; Madhumitha. J

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 2718-2725

The traditional system for detecting the infection has been the manual process of diagnosing the stained slides under a microscope. This manual process might consume more time for producing the results and the availability of medical experts is not always assured. Considering this as the primary concern we proposed a strategy which limits the human error while recognizing the presence of malarial parasite in the blood sample by using Image Processing. Hence by automating the diagnosis process, results can be acquired relatively quicker and more accuracy can be expected. The technologies and techniques to patently extract the required features and efficiently classify the infected samples are surveyed. This paper presents a survey of various approaches to automate the detection and classification of infected and uninfected cells.

BRAIN COMPUTER INTERFACE SYSTEM USING DEEP CONVOLUTIONAL MACHINE LEARNING METHOD

Dr. Vikas Jain; Dr.S. Kirubakaran; Dr.G. Nalinipriya; Binny. S; Dr.M. Maragatharajan

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 3294-3301

Brain-computer interface (BCI) decoding connects the human nervous world to the external world.
People's brain signals to commands that computer devices can detect. In-depth study the performance
of brain-computer interface systems has recently increased. In this article, we will systematically
Investigate brain signal types for BCI and explore relevant in-depth study concepts for brain signal
analysis. In this study, we have a comparison of different traditional classification algorithms new
methods of in-depth study. We explore two different types Deep learning methods, i.e., traditional
neural networks Architecture with Long Short term Memory and Repetitive Neural Networks. We
check the classification Accuracy of Recent 5-Class Study-State Visual Evoked Opportunities dataset.
The results demonstrate in-depth expertise learning methods compared to traditional taxonomy
Algorithms.

An Efficient Brain Tumor Classification And Detection Using Evolutionary Approach For Healthcare System

Vinoth Kumar. V; Dr. Paluchamy. B

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 3, Pages 658-670

The growth of irregular cells within the brain region with the prearrangement of tissues is characterized as a Brain Tumor that leads death to people. Comparing to other categories of cancers, a brain tumor is the most deadly disease that has to be detected and treated in the previous stage. Due to cells' complex formation, the tumor detection process is complicated with simple image processing methodologies. Moreover, for providing proper and efficient treatment to the patients, an exact cancer segmentation and classification technique must process the input of brain images as Magnetic Resonance Imaging (MRI) scans. Based on that, this paper develops a novel approach called Soft Computing based Brain Tumor Detection and Classification (SC-BTDC) with the obtained MRI. In the present scenario of tumor detection from image processing, soft computing techniques play a significant role. Hence, it is adopted in this work. The method contains phases such as pre-processing, Fuzzy c-Means clustering-based segmentation, feature extraction, and image classification. The median pre-processing filter and edge detection methods are incorporated for noise removal and clearly define the image in the stage of the median pre-processing filter. Further, FCM based clustering is performed for the image segmentation process, following, factors of Gray Level Co-Occurrence Matrix (GLCM) found feature extraction is established. The final phase includes the classification process using the soft computing technique called Artificial Neural Network (ANN) classification. The proposed system acquires a higher accuracy rate and is compared with various existing algorithms for proving the efficiency and minimum loss of the proposed algorithm.

DETECTION AND CATEGORIZATION OF PLANT LEAF DISEASES USING NEURAL NETWORKS

V. Praveena; P. Chinnasamy; P. Muneeswari; R. Ananthakumar; Bensujitha .

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 2438-2445

-Plants are very necessary for the earth and for all living organisms. Plants maintain the atmosphere. Plant illness, an impairment of the traditional state of a plant that interrupts or modifies its very important functions. All species of plants, wild and cultivated alike, are subject to illness. These diseases occur totally on leaves, but some might also occur on stems and fruits. Leaf diseases are the foremost common diseases of most plants. Plant pathology is the science study of pathogens and environmental circumstances causing illnesses in crops. Organisms causing transmissible disease include fungi, oomycetes, bacteria, viruses, viroids, etc. The latest technique involves automated classification of diseases from plant leaf images neural networks persecution approach called hunting enhancement of microorganisms primarily focused on executing Neural system relies on planar basic principle. Throughout this article, classic neural network algorithms are used to detect and classify the areas infected with multiple illnesses on the plant leaves in order to increase the velocity and precision of the network. The region's increasing formula will improve the network's potency by searching and grouping seed points with prevalent feature extraction method characteristics. The scheduled methodology achieves greater precision in disease detection and classification.