Web Page Recommendation Using Distributional Recurrent Neural Network

In the data retrieval process of the Data recommendation system,the matching prediction and similarity identification take place a major role in the ontology.In that,there are several methods to improve the retrieving process with improved accuracy and to reduce the searching time.Since,in the data recommendation system,this type of data searching becomes complex to search for the best matching for given query data and fails in the accuracy of the query recommendation process.To improve the performance of data validation,this paper proposed a novel model of data similarity estimation and clustering method to retrieve the relevant data with the best matching in the big data processing.In this paper advanced model of the Logarithmic Directionality Texture Pattern(LDTP)method with a Metaheuristic Pattern Searching(MPS)system was used to estimate the similarity between the query data in the entire database.The overall work was implemented for the application of the data recommendation process.These are all indexed and grouped as a cluster to form a paged format of database structure which can reduce the computation time while at the searching period.Also,with the help of a neural network,the relevancies of feature attributes in the database are predicted,and the matching index was sorted to provide the recommended data for given query data.This was achieved by using the Distributional Recurrent Neural Network(DRNN).This is an enhanced model of Neural Network technology to find the relevancy based on the correlation factor of the feature set.The training process of the DRNN classifier was carried out by estimating the correlation factor of the attributes of the dataset.These are formed as clusters and paged with proper indexing based on the MPS parameter of similarity metric.The overall performance of the proposed work can be evaluated by varying the size of the training database by 60%,70%,and 80%.The parameters that are considered for performance analysis are Precision,Recall,F1-score and the accuracy of data retrieval,the query recommendation output,and comparison with other state-of-art methods.

Performance Comparison of Artificial Neural Network Models for Daily Rainfall Prediction

With an aim to predict rainfall one-day in advance, this paper adopted different neural network models such as feed forward back propagation neural network （BPN）, cascade-forward back propagation neural network （CBPN）, distributed time delay neural network （DTDNN） and nonlinear autoregressive exogenous network （NARX）, and compared their forecasting capabilities. The study deals with two data sets, one containing daily rainfall, temperature and humidity data of Nilgiris and the other containing only daily rainfall data from 14 rain gauge stations located in and around Coonoor （a taluk of Nilgiris）. Based on the performance analysis, NARX network outperformed all the other networks. Though there is no major difference in the performances of BPN, CBPN and DTDNN, yet BPN performed considerably well confirming its prediction capabilities. Levenberg Marquardt proved to be the most effective weight updating technique when compared to different gradient descent approaches. Sensitivity analysis was instrumental in identifying the key predictors.

Exploring compression and parallelization techniques for distribution of deep neural networks over Edge-Fog continuum-a review

Purpose-The trend of“Deep Learning for Internet of Things(IoT)”has gained fresh momentum with enormous upcoming applications employing these models as their processing engine and Cloud as their resource giant.But this picture leads to underutilization of ever-increasing device pool of IoT that has already passed 15 billion mark in 2015.Thus,it is high time to explore a different approach to tackle this issue,keeping in view the characteristics and needs of the two fields.Processing at the Edge can boost applications with realtime deadlines while complementing security.Design/methodology/approach-This review paper contributes towards three cardinal directions of research in the field of DL for IoT.The first section covers the categories of IoT devices and how Fog can aid in overcoming the underutilization of millions of devices,forming the realm of the things for IoT.The second direction handles the issue of immense computational requirements of DL models by uncovering specific compression techniques.An appropriate combination of these techniques,including regularization,quantization,and pruning,can aid in building an effective compression pipeline for establishing DL models for IoT use-cases.The third direction incorporates both these views and introduces a novel approach of parallelization for setting up a distributed systems view of DL for IoT.Findings-DL models are growing deeper with every passing year.Well-coordinated distributed execution of such models using Fog displays a promising future for the IoT application realm.It is realized that a vertically partitioned compressed deep model can handle the trade-off between size,accuracy,communication overhead,bandwidth utilization,and latency but at the expense of an additionally considerable memory footprint.To reduce the memory budget,we propose to exploit Hashed Nets as potentially favorable candidates for distributed frameworks.However,the critical point between accuracy and size for such models needs further investigation.Originality/value-To the best of our knowledge,no study has explored the inherent parallelism in deep neural network architectures for their efficient distribution over the Edge-Fog continuum.Besides covering techniques and frameworks that have tried to bring inference to the Edge,the review uncovers significant issues and possible future directions for endorsing deep models as processing engines for real-time IoT.The study is directed to both researchers and industrialists to take on various applications to the Edge for better user experience.