Online ISSN: 2515-8260

Keywords : Accuracy


ENERGY-EFFICIENT FILTER DESIGN USING REVERSE CARRY PROPAGATE ADDERS

G.UMA MAHESWARI; T.SRINIVASA RAO

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 11, Pages 5242-5251

The reverse propagate adder (RCPA) is presented in this project. In this project. Under the RCPA framework, the transmission signal stretches from the most important to the less significant bit in a counter-flow way; thus the transportation signal is more appropriate than the production. In the case of delay differences, this propagation process improves stability. The cell with a varying pause, capacity, energy and accuracy are implemented in three separate implementations of the reverse transportation (RCPFA). The configuration suggested can be linked along with the precise (forward) adder to shape the hybrid adders with precise tuning levels. In contrast to modern estimated additions, the specification parameters of the proposed RCPA architectures and several hybrids that have  been implemented using these frameworks are discussed. Using Reverse Carrier Adder(RCPA), the Fir Filter is applied. Project with Verilog will be created. For simulation and synthesis, the Xilinx ISE tool is used.

FOURIER–MELLIN TRANSFORM FEATURES FOR MALARIA PARASITES CLASSIFICATION USING MICROSCOPIC IMAGES

R. Charanya; J. Josphin Mary; G. Sridevi; V. Shanthi

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 9, Pages 1426-1430
DOI: 10.31838/ejmcm.07.09.150

Malaria is an infectious disease transmitted by mosquitoes that affects humans and other animals. Malaria is responsible for the effects of fever, tiredness, vomiting and headaches. Yellow skin, convulsions, a coma, or death can lead to severe cases. Symptoms usually start 10-15 days after a mosquito is bitten. The early diagnosis is required for malaria. In this study, the automatic classification of malaria system is discussed. Initially, the input images are given to Fourier–Mellin transform for feature extraction and Support Vector Machine (SVM) classifier is used for classification. The performance of malaria system produces the classification accuracy of 92%using SVM classifier.

Deep Learning System for Skin Disorder Segmentation using Neural Network

S. Ranjana; R. Manimegala; K. Priya

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 9, Pages 1515-1522
DOI: 10.31838/ejmcm.07.09.163

Skin issue is extremely normal in the day by day lives of people. Consistently a huge number of American individuals are influenced by skin issue of different types. Skin condition conclusion regularly includes a high level of information because of the scope of visual perspectives thereof. Since human judgment is constantly discretionary and infrequently reproducible, a PC helped indicative gadget ought to be considered for accomplishing an increasingly objective and precise conclusion. In this paper, we investigate the plausibility of utilizing profound Convolutional neural system (CNN) to make a widespread structure for determination of skin infection. We train the CNN engineering utilizing the Dermnet dataset's skin illness pictures and check its yield with both Dermnet and OLE, another information assortment for skin ailment, pictures. Our program can accomplish Top-1 exactness of up to 73.1 percent and Top-5 precision of 91.0 percent while running on the Dermnet dataset. Top-1 and Top-5 correctness’s for the OLE dataset check are 31.1 percent and 69.5 separately. We show that if all the more preparing pictures are utilized, those correctness’s can be additionally improved.

AUTISM SPECTRUM DISORDER USING KNN ALGORITHM

Mrs. Surya . S.R; DR. G. Kalpana

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 9, Pages 1628-1637

Autism spectrum disorder (ASD) is a psychiatric disorder which leads to
neurological anddevelopmental growth of a person which starts in early age and gets
carried throughout their life.It is a condition associated with significant healthcare costs
and early diagnosis can reduce these.Unfortunately, waiting time is lengthy for an ASD
diagnosis and it is cost effective. Due to theincrease in economy for autism prediction and
the increase in the number of ASD cases across theworld is in need of easily implemented
and effective screening methods by GUI results. Toovercome the time complexity for
identifying the disorder advanced technologies can be used suchas machine learning
algorithms to improve precision, accuracy and quality of the diagnosisprocess. Machine
learning helps us by providing intelligent techniques to discover the affectedpatient, which
can be utilized in prediction and to improve decision making. And hence, wepropose the
data set features related to autism screening of adult and child to be used for
furtheranalysis and to improve the classification of ASD cases.

A Methodology for SOFTWARE RELIABILITY BASED ON STATISTICAL MODELING

Avinash seekoli; Dr.Y. Srinivas; Dr.P. AnnanNaidu

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 804-809

Reliability is one of the quantifiable quality features of the software. Software reliability growth models (SRGMs) are used to assess the reliability achieve at different test times based on statistical learning models. Conventional time based SRGMS may not be accurate enough in all situations and such models cannot identify errors in small and medium sized applications. Numerous traditional reliability measures are used to test software errors during application development and testing. In the software testing and maintenance phase, however, new errors are taken into account in real time in order to determine the reliability estimate. In this article, we suggest using the Weibull model as a computational approach to solving the problem of software reliability modeling. In the anticipated model, a new distribution model is projected to develop the reliability estimation method. We compute the model developed and balance its presentation through additional popular software reliability increase models commencing the literature. Our test consequences demonstrate that the planned Model is greater to S-shaped Yamada, comprehensive Poisson, NHPP.

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