IJMLNCE Editorial Note Volume No 02, Issue No 02

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IJMLNCE, Volume No 02, Issue No 02

Abstract

Preface

After more than a year, The International Journal of Machine Learning and Networked Collaborative Engineering (IJMLNCE) ISSN 2581-3242 has experienced a great growth from every possible point of view. After publishing three editions, we are now indexed in popular sources like BASE, CrossRef, CiteFactor, DRJI, Google Scholar, Index Copernicus, J-Gate, PKP-Index, ROAD, Scilit and Socolar.

We are now proud to present the fourth number, corresponding to Volume No-02 Issue No-02. On this occasion, we have five interesting papers that are framed in the scope of the journal, covering different aspects related to machine learning and collaborative engineering.

For example, Huong Thom et al. use neural networks to perform steganalysis for reversible data hiding. The aim is to restore original images after extracting information from a hidden image with secret data. Authors propose a method to improve detection rate of such type of images with 96% correct detection rates using neural networks and 94% with convolutional neural networks [1].

Maldonado et al. design a prediction model for pollutants with onboard diagnostic sensors in vehicles. The aim is to show the relation between the internal parameters of on-road vehicles and their emissions. Internal values are collected through the On-Board Diagnostics port, while values of the emissions are measured from the exhaust pipe using an Arduino board. There are observable correlations between carbon dioxide emissions and vehicle speed, as well as carbon dioxide emissions and engine revolutions per minute [2].

Choudhary et al. base their work in neural networks to process information. They extend the Leaky Integrate-and-Fire model and analyze the impact of exponentially distributed delay memory kernel on spiking activity and steady state membrane potential distribution. Authors propose their model to implement recurrent neural networks with more accuracy and as a potential way to implement chip level artificial intelligence [3].

Kiani proposes a new approach to improve automated learning methods based on the reinforcement learning technique. The effectiveness of the interactions with the environment is evaluated by the number of rewards and penalties that are taken from it. Kiani proposes three versions: simple, sequential and unstructured linear learning methods,that focus on different scenarios and areas [4].

Chatterjee work on a big data framework mixed with the Internet of Things using machine learning. Author gives an explanation about the relationship between big data and the Internet of Things, together with different issues and challenges, all from the point of view of offering solutions using an approach based on machine learning strategies [5].

References

. Huong Thom, H. T., Anh, N. K., & Vu, B. D. (2018). Steganalysis for Reversible Data Hiding Based on Neural Networks and Convolutional Neural Networks. International Journal of Machine Learning and NetworkedCollaborative Engineering, 2(2), 40-48.

https://doi.org/10.30991/IJMLNCE.2018v02i02.001

. Maldonado, B., Bennabi, M., García-Díaz, V., González García, C., &Núñez-Valdez, E. R. (2018). Prediction Model for Pollutants with Onboard Diagnostic Sensors in Vehicles. International Journal of Machine Learning and Networked Collaborative Engineering, 2(2), 49-57.

https://doi.org/10.30991/IJMLNCE.2018v02i02.002

. Choudhary, S. K., &Bharti, S. K. (2018). Information Processing in Neuron with Exponential Distributed Delay. International Journal of Machine Learning and Networked Collaborative Engineering, 2(2), 58-66.

https://doi.org/10.30991/IJMLNCE.2018v02i02.003

. Kiani, F. (2018). Improvement of Automated Learning Methods based on Linear Learning Algorithms. International Journal of Machine Learning and Networked Collaborative Engineering, 2(2), 67-74. https://doi.org/10.30991/IJMLNCE.2018v02i02.004

.Chatterjee, J. M. (2018). IoT with Big Data Framework using Machine Learning Approach. International Journal of Machine Learning and Networked Collaborative Engineering, 2(2), 75-85. https://doi.org/10.30991/IJMLNCE.2018v02i02.005

Published

2018-07-27

How to Cite

Solanki, V. K., & Diaz, V. G. (2018). IJMLNCE Editorial Note Volume No 02, Issue No 02. International Journal of Machine Learning and Networked Collaborative Engineering, 2(02). Retrieved from https://mlnce.net/index.php/Home/article/view/38

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