IJMLNCE Editorial Note Volume No 03, Issue No 03

International Journal of Machine Learning and Networked Collaborative Engineering

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Title IJMLNCE Editorial Note Volume No 03, Issue No 03
 
Creator Solanki, Vijender Kumar
 
Description The International Journal of Machine Learning and Networked Collaborative Engineering (IJMLNCE) with ISSN: 2581-3242 is now indexed in popular databases such as BASE (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 ninth volume of the journal, Volume No-03 Issue No-03, with some high-quality papers written by international authors and covering different aspects related to machine learning and collaborative engineering.
 
Phan Trong-Thanh and Doan Van Thang  published a work entitles “Joint Spatial Geometric and Max-margin Classifier Constraints for Facial Expression Recognition Using Nonnegative Matrix Factorization”. In this paper, they have presented the constrained NMF approach for problem the facial expression recognition. The proposed MNMF_SGR performs well in facial expression recognition task and its effectiveness has been proven in their model. To summarize, with many constraints allows them to build models effectively and specifically on high dimensional, sparse and noisy datasets. For their future work, more sophisticated and efficient way to tune kernel functions will be explored. They also plan to apply the proposed method to problems in other fields, such as bioinformatics and computer vision. Studying the convergence rate for MNMF_SGR and increasing the efficiency, they should be all in consideration.
 
Praneet Amul Akash Cherukuri published his article “Recommender System for Educational & Corporate Sector In Prediction of Domain Recommendations & Analysis using Machine Learning”. In this manuscript he suggest that his model has a huge impact on the educational institutions and the corporate sector of today's highly competitive world. The model proposes a simple and cross-sector solution to both the corporate and educational sectors that could result in the huge increase of employability solving the problem of wrong decision making of job aspirants as well as mistakes made by the organization whereby suffering losses from those decisions. Hence it could benefit every academician in evaluating his/her students as well as their academic performance in a more sophisticated and a single independent platform that has analysis related to current world trends and scenarios. The model has a vast scope of improvement as well as can provide great accuracy with positive results in the future.
 
Akshansh Mishra published his article “Understanding Machine Learning for Friction Stir Welding Technology”. In this manuscript, he suggest  that there is a loss of time and materials if the optimization of the Friction Stir Welding parameters is done through experimental studies which further leads to increase in the cost of the experiment. Machine Learning approach like Artificial Neural Network and image processing overcome these issues. So, it can be concluded that the mechanical and microstructure properties can be predicted and also the defects formation can also be observed by the implementation of various Machine Learning tools in the Friction Stir Welding process.
 
Anoop et al. published a work entitled “Study of Energy Efficient Algorithms for Cloud Computing based on Virtual Machine Migration Techniques”.  This survey outlined some of the very recent approaches in knowledge graph-based recommendation systems. As knowledge graph is one of the effective representation mechanisms for knowledge that has been unearthed from unstructured text, it got wider acceptance among research communities. A knowledge graph represents entities and relationships as nodes and edges respectively and a large number of meaning-aware applications and algorithms can operate on this graph. One such application is recommendation systems that suggest a user with items based on their previous interactions with the system. Knowledge graph based recommendation systems became very popular recently primarily due to its ability to supply side information for augmenting data and thus enhancing the quality of recommendations. This paper discusses some of the very prominent approaches reported very recently in the recommendation literature. Some interesting research dimensions are also discussed towards the end of this paper. This survey will be useful for the researchers and practitioners who wish to work on entity knowledge graphs based recommendation systems.
 
Finally Amrit Kaur Saggu and Shivani Agarwal published a work entitled “Performance Evaluation of LAR protocol using real dataset on Highway and City Scenario” In this work they have evaluated the performance of Location Aided Routing protocol (LAR) for Vehicular Ad-hoc Networks (VANETs) in terms of throughput, packet delivery ratio and routing overhead. They have considered two scenarios namely highway and city scenario. For highway they have taken Delhi highway data from OSM map and for city scenario taken real traces of Bologna Ringway dataset. For each of these scenarios the performance is evaluated by considering variation in terms of number of vehicles and simulation time. They observe that with the increase in simulation time the throughput increases for both highway and city scenario. The packet delivery ratio and overhead tend to decrease with increase in simulation time.
 
Publisher SR Informatics, New Delhi, India
 
Date 2020-05-16
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://www.mlnce.net/index.php/Home/article/view/120
 
Source International Journal of Machine Learning and Networked Collaborative Engineering; Vol. 3 No. 03 (2019): Volume No 03, Issue No 03
2581-3242
 
Language eng
 
Relation http://www.mlnce.net/index.php/Home/article/view/120/68
 
Rights Copyright (c) 2020 International Journal of Machine Learning and Networked Collaborative Engineering
 

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