IJMLNCE Editorial Note Volume No 03, Issue No 02

Abstract

The International Journal of Machine Learning and Networked Collaborative Engineering (IJMLNCE) with ISSN: 2581-3242is now indexed in popular databasessuch asBASE (Bielefeld Academic Search Engine), CNKI Scholar, CrossRef, CiteFactor, Dimensions, DRJI, Google Scholar, Index Copernicus, JournalTOCs, J-Gate, Microsoft Academic, PKP-Index, Portico, ROAD, Scilit, Semantic Scholar, Socolar or WorldCat-OCLC.We are now proud to present the eighth volume of the journal, Volume No-03 Issue No-02, with some high-qualitypapers written by international authors and covering different aspects related to machine learning and collaborative engineering.

Puri et al. published a work entitled “Cloudbin: Internet of Things based waste monitoring system”.In this paper, authors present an IoT-based waste management system called Cloudbin to monitor and control waste garbage in urban areas. To that end, authors use different elements like an ultrasonic sensor, a GPS module or a methane detection mechanism. The problem of waste management is one of the key elements in which governments must take an active part.

Rimal published a work entitled “Machine Learning Prediction of Wikipedia Time Series Data using: R Programming”. In this work, author explains how prediction of automatic learning of Wikipedia time series work using the R environment. To that end, author focused on real data from Cristina Ronaldo, a famous football player, presenting, according to the author, the simplest way to predict times series data and its strengths for data analysis.

Sen et al. published a work entitled “Study of Energy Efficient Algorithms for Cloud Computing based on Virtual Machine Migration Techniques”. This study describes how energy efficiency in cloud computing is one of the most important features to be considered to measure the efficiency of such services, balancing power and quality of the service. Thus, authors discuss how virtual machine migration techniques can help to achieve energy efficiency.

Choudhary published a work entitled “Information Processing in GLIF Neuron Model with Noisy Conductance”. Authors investigate the generalized leaky integrate-and-fire neuron model with stochastic synaptic conductance and investigate the effect of varying concentration of electro-chemicals at the synapse in a single neuron model. To that, they developed a simulation-based study with the temporal encoding technique to analyze the encoding mechanism.

Finally, Kothandan and Sujatha published a work entitled “Deep Neural Network with Stacked Denoise Auto Encoder for Phishing Detection”. In this paper, authors present and discuss a deep neural network to detect phishing uniform resource locators. They use a feature vector with a stacked denoise auto encoder. In addition, the noisy data is trained to reconstruct a clean input feature vector. Experiments are based on the Ham, Phishing Corpus and Phishload datasets to prove its effectiveness.