In today’s quandary, the effort required to prevent the disease occurrence should be more as compared to the methodologies followed for identifying it. In this paper list down the different number of diseases are affected in distinct regions are extracted and by fixing disease based coupling and cohesion range metric the severity level of each disease and its dependence level will be confirmed. By preparing the coupling and cohesion metrics, additional tests are performed to measure its updating as the value is ‘1’ then the disease severity level will be very high otherwise consider as low. © RJPT All right reserved.

Iyapparaja M

Department of Digital Communications

School of Information Technology and Engineering

Vellore Campus

Sivakumar P.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Metrics Based Evaluation for Disease Affection in Distinct Cities

Research Journal of Pharmacy and Technology

Polycystic ovary syndrome (PCOS) disorder is identified by the presence of a number of follicles present in the ovary of female reproductive system. Ultrasound imaging of the ovary contains essential information about the size, number of follicles and its position. In real time, the detection of PCOS is a difficult task for radiologists due to the various sizes of follicles and is highly connected with blood vessels and tissues. This often results in error diagnosis. For preprocessing various standard filtering techniques are applied on ovary image. Based on the performance, appropriate filter is chosen to remove the noise from the image. This paper presents an effectual active contour with modified Otsu threshold value to automated discovery of follicles from the ultrasound images. The performances of the proposed method illustrate the betterments of the proposed approach over other techniques. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

Multimedia Tools and Applications

Active contour with modified Otsu method for automatic detection of polycystic ovary syndrome from ultrasound image of ovary

Several novel applications and services of networking for the IoT are supported by the Social Internet of Things (SIoT) in a more productive and powerful way. SIoTs are the recent hot topics rather than other extensions of IoTs. In this research, the authors have extracted the Big Data SIoT using the well-known model named MapReduce framework. Moreover, the unwanted data and noise from the database are reduced using the Gabor filter, and the big databases are mapped and reduced using the Hadoop MapReduce (HMR) technique for improving the efficiency of the proposed GA-EHO. Furthermore, the feature selection using GA-EHO is processed on the filtered dataset. The implementation of the proposed system is done by using some machine learning classifiers for classifying the data and the efficiency is predicted for the proposed work. From the simulation results, the specificity, maximum accuracy, and sensitivity of the proposed GA-EHO are produced about 87.88%, 99.1%, and 81%. Also, the results are compared with other existing techniques.

Journal	Research Journal of Pharmacy and Technology
Publisher	Diva Enterprises Private Limited
ISSN	0974-3618
Open Access	0