Online ISSN: 2515-8260

Keywords : Artificial Neural Network


Optimal Allocation Of DSTATCOM In The Distribution Network Using Optimization Algorithm

Purushottam Jaipuria; Yuvaraj. T

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 3, Pages 1247-1257

The expansion of power system has prompted the increase of Distributed Generation to satisfy the load demand, due to the development of power grid the efficient utilisation of electricity is most important. Since the significant expense for construction and development of power networks, mitigation alleviation of existing issues that are excessive power losses, voltage profile problems, voltage instabilities, reliability issues. To hinder all these issues Distribution Synchronous Static Compensator D-STATCOM can be connected as a shunt device to provide reactive power compensation in the low voltage distribution network. It is very important to figure out based on reliability, economic usage, availability. This paper provides an up to date survey of the literature on the optimal allocation of D-STATCOM in distribution networks.

SEGMENTATION OF PANCREATIC CYSTS AND ROI EXTRACTION FROM PANCREATIC CT IMAGES USING MACHINE LEARNING

Mrs. R.Reena Roy; Dr. G.S. Anandha Mala; C. Sarika; S. Shruthi; S. Sripradha

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 2981-2991

Segmentation of Pancreas with high accuracy in computerized tomography (CT) results is considered to be a basic issue in both medical image processing and computer-aided diagnosis (CAD). Pancreas segmentation is considered as a difficult task due to its uncertainity in location and in analysis of organs, while it takes very minute division of the entire abdominal CT scans. Because of the accelerated development of the CAD system and therefore the serious need for antiseptic treatments, pancreas segmentation with high accuracy of results is demanded. A new approach is used in this paper, for automated pancreas segmentation of CT images using inter-/intra-slice circumstancial instruction with preprocessing, segmentation, feature extraction, classification.

Identification of Speech Signal in Moving Objects using Artificial Neural Network System

DIWAKAR BHARDWAJ; RAKESH KUMAR GALAV

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 418-424

The speech signal moving objects regarding the speaker’s personality. A speaker recognition field is about retrieving the name of the individual voicing the speech. The effectiveness of accurately identifying a speaker is focused solely on vocal features, as voice contact with machines is becoming more prevalent in tasks like telephone, banking transactions, and the transformation of data from speech databases. This review illustrates the detection of text-dependent speakers, which identifies a single speaker from a known population. The program asks the user to utter voice. Program recognizes the person through evaluating the voice utterance codebook with the voice utterance codebook held in the database and records that may have provided the voice speech. Furthermore, the features are removed; the speech signal is registered for 6 speakers. Extraction of the function is achieved using LPC coefficients, AMDF calculation and DFT. By adding certain features as input data, the neural network is equipped. For further comparison the characteristics are stored in models. The characteristics that need to be defined for the speakers were obtained and analyzed using Back Propagation Algorithm to a template image. Now this framework trained correlates to the outcome; the source is the characteristics retrieved from the speaker to be described. The weight adjustment is done by the system, and the similarity score is discovered to recognize the speaker. The number of iterations needed for achieving the goal determines the efficiency of the network.