Welcome to Journal of Network Communications and Emerging Technologies (JNCET)

Volume 10, Issue 8, August (2020)                                       Cover Page and Table of Contents

S.No	Title & Authors	Full Text
1	An Innovative Method of Interruption Revelation Structure for Portable Ad Hoc Networks using SVM and RST S. Ravichandran Abstract - Portable Ad Hoc Networks has all the more testing vulnerabilities contrasted and wired systems. Portable specially appointed systems administration takes to develop an important innovation in current years on account of the fast expansion of remote gadgets. They are exceptionally defenseless against assaults because of the open medium, progressively changing system geography, and absence of brought together observing point. It is critical to look through the new design and components to secure these systems. Interruption identification framework (IDS) instruments are appropriate for making sure about such systems. The primary assignment of IDS remains to find that interruption after gathered evidence respectively. An allotment of these highlights of gathered evidence might be repetitive or underwrite slight to this discovery procedure. So the situation is fundamental to choose that significant highlights to expand the recognition rate. A large portion of the current interruption recognition frameworks recognizes the interruption by utilizing an enormous number of information highlights gathered from organizing separately. Now that effort, to suggest peculiarity constructed interruption discovery framework toward recognizing these vindictive exercises thru gathering insights from organizing. Likewise, we use the SVM AI procedure then Coarse Group Assumption which remains utilized to recognize that assaults inside a productive manner. The harsh set hypothesis preprocesses the component information to decrease this calculation unpredictability. These help path mechanism stands prepared thru utilizing highlight group after the coarse group hypothesis pro distinguishing irregular conduct.	Download
2	Diagnosis of Liver Diseases Using Neural Network Ensemble Bihter Das Abstract - In the last couple of decades, the diagnosis of liver diseases using computational techniques was heavily investigated. This study focuses on the using of a Neural Network ensemble-based method for effective diagnosis of liver diseases. In this study, a Neural Network ensemble-based method was proposed to predict liver diseases. Although ANN and other classification approaches have been heavily investigated in recent years, the using of ensemble-based approaches to predict liver diseases has not been thoroughly investigated. The proposed model relies on using five different ANN nodes. Using the commonly used Indian Liver Patient Dataset, which is provided by the University of California, Irvine, and SAS software suite evaluates the accuracy of the proposed model. The obtained results indicate that in the validation phase the classification accuracy of the proposed model to predict liver diseases is 74.35%. Also, in terms of important classification metrics such as specificity, sensitivity, precision, false-positive rate, false-negative rate and F_1(F-measure) in the training phase the proposed model achieved the rates 36.36%, 89.28%, 78.12%, 63.64 %, 10.72% and 83.33%, respectively. The results are very promising for researchers and practitioners working in related fields.	Download