Online ISSN: 2515-8260

Keywords : Neural Network


A COMPARITIVE STUDY OF MYOCARDIAL INFRACTION PROGNOSIS USING NEURAL NETWORK TECHNIQUES

Mrs.K. Gayathri; Dr.N. UmaMaheswari

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 1974-1983

Myocardial infractions are the main explanation for death within the world nowadays, significantly in India. The need to predict this is often a serious necessity for rising the country’s health-care sector. Exact and precise prognosis of the consumers illness in the main depends on graphical record of cardiogram information and clinical data. This record is being fed to a nonlinear contagion prediction model. This nonlinear cardiac function observing module is having the ability to notice cardiac abnormalities. The predictable system develops consociate in developing productive approach to gather the clinical and graphical record of cardiogram data, thus to train the simulated neural network to accurately predict the cardiac abnormalities. The proposed system can analyze the graphical record of cardiogram and clinical information to train the neural network for predicting opportunities of the myocardial infraction and it can generate a report expecting the deviations within the cardiac or its functioning.

Analysis of Power Quality Disorders in KV Transmission using ANN and CWT Methods

JUGINDER PAL SINGH; ROHIT AGRAWAL

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 468-482

With growing use of sensitive equipment, studies on power quality had developed to conduct data analysis on power quality. Wavelet transformation method has been very useful in investigating diverse types of events in power quality. Present paper associates the utilization of different wavelets at various scales and level of disintegration in examining real Power Quality (PQ) occasions from a link model or signal is produced utilizing MATLAB background. In this system voltage sag, swell, harmonics, momentary interruption, fault conditions and transient events are performed. The system proposed includes elements smaller than the traditional procurement process. In this method wavelet transform identified different power quality events and then classified them via Artificial Neural Network (ANN). Power quality disturbances are defined by the load received after neural network training. Separate MATLAB simulation model is designed to produce various power quality events like voltage sag, swell, passing disruption, harmonics, temporary and fault signals. ANN learning also wiped out MATLAB simulation using NN toolbox for power quality disturbance detection through the aliasing of voltage signals energy. Satisfactory results obtained in MATLAB simulink using such techniques.

Detection and Identification of Forest Firing using Convolution Neural Network

Himanshu Sharma; Narendra Mohan

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 126-129

Forest fire is a significant natural issue, making practical and biological harm while imperiling the human lives. The key component for controlling such marvel is fast identification .To accomplish this, one option is using neural networks to identify the fires, such that we implement Forest fire Detection. By using this convolution Neural Networks we detecting the fires that occur in the forest. Later we intimate message to forest officers then they take immediate action. CNN is a calculation that takes an input picture, assign the consequences (learnable loads and predispositions) to dissimilar perspectives in the picture and have the option to divide one from the other.

A Review of Facial Expression Recognition

Priyanka Nathawat; Dr. Vivek Chaplot

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 1232-1253

Emotions are very present in our daily life. They are manifested by physiological reactions (palpitations, heat, and acceleration of the pulse), motor reactions (gestural expressions, facial expressions) and changes in the voice. The complexity of emotions was capable to seek attention of various researchers. Differing representations of emotions were then proposed to produce a set of theories. The automation of facial expression detection can then be approached in a variety of ways. We concentrated on the face expression detection. Concerning to this the recognition is based on different visual characteristics of the expression.
This paper is dedicated at first to the exhibition of the definition of emotions, followed by the great theories that have emerged in this area. In a second step, we present the methods of extraction of the facial characteristics and the methods of selection. Finally, some databases used for automatic recognition of emotions are presented.

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.

Plant Disease Identifer Using K-Means and GLSM in Convolution Neural Network

S.P. Vijaya Vardan Reddy; T. Suresh; K. Naresh Kumar Thapa; V. Ramkumar; S. Mahabhoob Basha; Deepika. Y

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 1354-1360

Produces from agriculture which feeds the entire population is dependent on proper farming practices. The growth of technology must pay a way for increasing the produce per acre and also help in reducing the onset of frequently affecting plant disease. Timely help in detecting the diseases coupled with solution helps in productivity and quality of the produce. This paper aims to detect the plant leaf disease based on image detection and using machine learning to identify the disease with accuracy and suggest the solution. The product must cater to the needs of urban and rural farmer and also the person with only lay man knowledge of taking photo. This project mainly focuses on leaf disease like Anthracnose, Bacterial Blight, Cercospora, Alternaria Altermata diseases in the Pomegranate, Indian Beech, Tobacco, and Bitter Gourd leaves. This project aims to identify the disease even with lesser region of Interest and predict the leaf diseases using Convolutional Neural Network Algorithm

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.

A REVIEW ON VARIOUS SEGMENTATION TECHNIQUES IN IMAGE PROCESSSING

K. Jeevitha; A. Iyswariya; V. RamKumar; S. Mahaboob Basha; V. Praveen Kumar

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 1342-1348

Due to the advancement of computer technology image-processing techniques have become increasingly important in a wide variety of applications. Image segmentation plays a important role in image processing. Image segmentation refers to partition of an image into different regions that are similar and different in some characteristics like color, intensity or texture. Different algorithms and techniques have been developed for image segmentation. This paper investigates and compiles some of the technologies used for image segmentation. The various segmentation techniques like Edge Detection, Threshold, Region based, Feature Based Clustering and Neural Network Image Segmentation were discussed in this paper

Development of Fuzzy based intelligent System for Assessment of Risk Estimation in Software project for Hospitals Network

Gajanand Sharma; Ashutosh Kumar; Himanshu Sharma; Ashok Kumar Saini; Neha Janu; Blessy Thankchan

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 1433-1442

One of the major concerns of software industries is to develop invulnerable and secure software. A project is successful if it is delivered within provided time constraints with the required quality. The aim of this research is to enhance the project planning phase by risk assessment at the start of software development life cycle. The proposed model is developed to accurate estimation of risk in software project. The software risk estimation model is being made using neuro-fuzzy approach which is beneficial to the project manager in the first phase of software development life cycle. This methodology is different from the other risk assessment process as it uses the vague factors of different type of risk parameters in software projects. Two main activities of project planning phase are risk assessment and effort estimation. Effort estimation is mostly influencing with the probability of risk estimation at the early phase of software development life cycle. Implement result shows that it reduces risk caused due to vague parameters in software projects. The intelligent system so developed is also capable of identifying the risk of similar kind in any software project for hospital network system.