Role of Neural Network in Mobile Ad Hoc Networks for Mobility Prediction

The MANETs differ from traditional networks in a lot of aspects, such as high channel error rates, unusual channel features, frequent link breaks, and intense link layer contentions. These characteristics significantly reduce network connectivity, which affects overall network latency, network overhead, network throughput (i.e. the amount of data successfully transferred via a MANETs in a predetermined amount of time), and packet delivery ratio (PDR). For effective network resources preparation and organization in MANETs, the mobility forecast of MN and units is essential. This effectiveness would allow for better planning and higher overall quality - of - service, including reliable facility availability and efficient management of energy. In this research, we suggest to use ELMs, which are renowned for their ability to approximate anything, to model and forecast the mobility of each node in a MANET. Mobility-aware topology control methods and location-assisted routing both leverage mobility prediction in MANETs. It is assumed that each MN taking part in these protocols is aware of its current mobility data, including location, velocity, and movements direction angle. This approach predicts both the locations of future nodes and the distances between subsequent nodes. The interaction or relationship between the Cartesian longitude and latitude of the erratic nodes is better captured by ELMs than by multilayer perceptron’s, resulting in mobility prediction that is based on several conventional mobility models that is more precise and realistic

Role of Deep Learning in Mobile Ad-hoc Networks

The portable capability of MANETs has specially delighted in an unexpected expansion. A massive need for dynamic ad-hoc basis networking continues to be created by advancements in hardware design, high-speed growth in the wireless network communications infrastructure, and increased user requirements for node mobility and regional delivery processes. There are several challenging issues in mobile ad-hoc networks, such as machine learning method cannot analyze features like node mobility, channel variation, channel interference because of the absence of deep neural layers. Due to decentralized nature of mobile ad hoc networks, its necessitate to concentrate over some extremely serious issues like stability, scalability, routing based problems such as network congestion, optimal path selection, etc. and security

Mobile Application Based Translation of Sign Language to Text Description in Kannada Language

Sign language is a main mode of communication for vocally disabled. This language use set of representation which is finger sign, expression or mixture of both to express their information among others. This system presents a novel approach for mobile application based translation of sign action analysis, recognition and generating a text description in Kannada language. Where it uses two important steps training and testing. In training set of 50 different domains of video samples are collected, each domain contains 5 samples and assign a class of words to each video sample and it will be store in database. Where in testing test sample under goes preprocessing using median filter, canny operator for edge detection, HOG for feature extraction. SVM takes input as a HOG features and predict the class label based on trained SVM model. Finally the text description will be generated in Kannada language. The average computation time is minimum and with acceptable recognition rate and validate the performance efficiency over the conventional model.</p