Online ISSN: 2515-8260

Keywords : K-means


CORRELATION BETWEEN TEMPERATURE AND INCREASE IN COVID-19 CASES IN TELANGANA STATE

D.Lakshmi Padmaja; Medisetty Sujith; Sai Sruthi Bejagam; Manish Reddy Morapally

European Journal of Molecular & Clinical Medicine, 2021, Volume 8, Issue 2, Pages 2047-2052

The main aim of this paper is to know whether the temperature have any impact on the increase of corona virus. Covid-19, this name has brought a drastic change in our day-to-day life. People of Telangana have lived through 10 months of the Covid-19 pandemic and there might be more to come. Till date 2.8lakh official cases of covid-19 have been registered in Telangana and there may be many more which have gone unnoticed. In our busy life, we are neglecting our health and no one is maintaining a proper hygiene. And we have been more addicted to junk foods rather than nutrition, because of this reason covid-19 became a threat to our life. So proper precautions and awareness must be spread among people to avoid spread of virus. Fever, cough, breathing problems etc are the
symptoms of this covid-19. If we neglect these symptoms it leads to a severe problem like pneumonia, kidney failure and eventually leads to death of that person. At this moment we don’t have any vaccine to cure this disease, the only prevention or avoiding corona is to boost our immune system. To overcome this pandemic situation, firstly we need to know the important factors that increases in covid-19 cases. In this paper, machine learning techniques are used to identify how temperature varies with the increase of covid-19. Which means we find how the effect of temperature depends on the number of covid-19 cases in Telangana

Clustering Analysis from Universities in Indonesia based on Sentiment Analysis

Hendra Achmadi; Isana Meranga; Dewi Wuisan; Irwan Suarly; I Gusti Anom Yudistira; Rudy Pramono

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 10, Pages 1466-1481

There are two kind of source to determine the quality for a good university in Indonesia. First from university cluster which is publish from Ministry of Research, Technology and Higher Education issued a clustering list of Indonesian universities, the second source of data from social media, such as Twitter. In this research we use Text Mining and Data Mining Methodology to build a sentiment analysis from 50100 Tweet to assess 501 university using Python and special library in Python for Natural Language Processing a sentiment analysis , which is join the university clustering from Ministry of Research, Technology and Higher Education, so it will produce the positive, neutral and negative sentiment for each 501 universities in 2020. The next process by using R STUDIO, the process classification is continued by using K-Means, the process can be devided into two step , step 1 it will process 501 dataset university and it will build 5 cluster and secondly the similarities between Netizen cluster and cluster from Ministry of Research, Technology and Higher Education is 37 %, and step 2 after cleansing the 0 value, the result is 169 universites the similarities between Netizen cluster and cluster from Ministry of Research, Technology and Higher Education is 37 % before and after data cleansing was the same. The novelty knowledge or research finding can be derived from Netizen, firstly, the cluster can be derived based on Positive Sentiment,. Secondly, the cluster from Netizen and Cluster from Directorate General of Higher Education, Ministry of Education and Culture of higher education in Indonesia is only match around 37 % with cluster form Directorate General of Higher Education. And after data cleansing from 169 university was only match around 33 %..

HYBRID METHOD OF MRI BRAIN SEGMENTATION USING FUZZY K-MEANS

Jawwad Sami Ur Rahman, Sathish Kumar Selvaperumal, Rajasvaran Logeswaran

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 11, Pages 9144-9155

In this paper, a proposed hybrid algorithm using K-means and Fuzzy logic for brain segmentation, is developed, simulated and evaluated. The system identifies the white matter, gray matter and Cerebrospinal Fluid (CSF). The proposed system was tested using Magnetic Resonance Imaging (MRI), and evaluated in terms of the misclassification rate and percentage of clustering. The misclassification rate was found to be lesser in the proposed system as compared to the existing systems using K-means and Fuzzy logic. Further, the percentage of clustering is improved by the proposed system as compared to the existing algorithms. This work paves the way for future development of Neuro Fuzzy K-means algorithm in order to reduce the misclassification rate further in clustering the white matter, gray matter and CSF.

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.

A Novel Approach For Predicting Drug Response Similarity Using Machine Learning

M Supriya Menon; P Raja Rajeswari

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 8, Pages 796-808

Medical domain is revolutionized in terms of Diseases, Diagnosis, and Treatment Prediction thereby undergoing immense pressure due to the high dimensionality of numerable multivariate attributes, suppressing the quality of the analysis. Many techniques like Clustering and Classification have ruled over despite, rendering few hairline gaps towards attaining maximum efficiency. Our Machine Learning-based approach heads towards filling these gaps by adopting advanced K-Means in anticipating Drug likelihood in core attributes of Patients. The proposed Methodology focuses on determining Drug Response similarity by enhanced clustering technique concerning sensitive attributes of Patients. We successfully demonstrated its performance on the UCI Patient dataset reflecting enhanced results concerning Quality Parameters.

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.

DTW SIMILARITY MEASURE BASED U-SHAPELETS CLUSTERING ALGORITHM FOR TIME-SERIES DATA

Arathi M; Govardhan A

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 3378-3392

Time-Series Analysis exhibitedefficient results in delivering significant knowledge in numerous domains.
Most of the investigationon Time-Series Analysis is restrictedwith the
requirementofexpensivecategorized information. This led tothe growth of curiosity in groupingthe timeseries
informationthat does not need any access to categorized information. The clustering time-series
informationcarries out issues that donot prevail in conventional clustering methodologies.,in the
Euclidean space amongst the objects.Therefore,the authorsuggested an innovativeclustertechnique,
forTime-Seriesemploying of DTW similarity measure by extracting unsupervised shapelets. And these
extracted u-shapelets are clustered employing iterative k-means algorithm. The DTW similarity measure
provides better accuracy in formed clusters of proposed methodology compared tothe Metric
EuclidianDistance Measure. The performance of the suggested approach is evaluated employing theRand
Index (RI) Measure. The experimental for this approach was performed on four different Time-Series
data samples and the outcomes showed that the RI measure for the DTW based Time-Series Clustering
Algorithm is more when compared to the Existing ED-basedTime-Series Clustering Algorithm.