An Improved GA-Based Recommendation System for Soil Fertilization

Author(s):  
Usman Ahmed ◽  
Jerry Chun-Wei Lin ◽  
Gautam Srivastava ◽  
Jimmy Ming-Tai Wu
2021 ◽  
Vol 189 ◽  
pp. 106407
Author(s):  
Usman Ahmed ◽  
Jerry Chun-Wei Lin ◽  
Gautam Srivastava ◽  
Youcef Djenouri

Author(s):  
Htay Htay Win ◽  
Aye Thida Myint ◽  
Mi Cho Cho

For years, achievements and discoveries made by researcher are made aware through research papers published in appropriate journals or conferences. Many a time, established s researcher and mainly new user are caught up in the predicament of choosing an appropriate conference to get their work all the time. Every scienti?c conference and journal is inclined towards a particular ?eld of research and there is a extensive group of them for any particular ?eld. Choosing an appropriate venue is needed as it helps in reaching out to the right listener and also to further one’s chance of getting their paper published. In this work, we address the problem of recommending appropriate conferences to the authors to increase their chances of receipt. We present three di?erent approaches for the same involving the use of social network of the authors and the content of the paper in the settings of dimensionality reduction and topic modelling. In all these approaches, we apply Correspondence Analysis (CA) to obtain appropriate relationships between the entities in question, such as conferences and papers. Our models show hopeful results when compared with existing methods such as content-based ?ltering, collaborative ?ltering and hybrid ?ltering.


2010 ◽  
Vol 130 (2) ◽  
pp. 317-323
Author(s):  
Masakazu Takahashi ◽  
Takashi Yamada ◽  
Kazuhiko Tsuda ◽  
Takao Terano

2020 ◽  
Vol 16 (7) ◽  
pp. 1095
Author(s):  
Gao Yuan ◽  
Zhang Youchun ◽  
Lu Wenpen ◽  
Luo Jie ◽  
Hao Daqing

2008 ◽  
Vol 63 (4) ◽  
Author(s):  
Jan Buczek ◽  
Barbara Kryńska ◽  
Renata Tobiasz-Salach

2020 ◽  
Author(s):  
Nathaniel Park ◽  
Dmitry Yu. Zubarev ◽  
James L. Hedrick ◽  
Vivien Kiyek ◽  
Christiaan Corbet ◽  
...  

The convergence of artificial intelligence and machine learning with material science holds significant promise to rapidly accelerate development timelines of new high-performance polymeric materials. Within this context, we report an inverse design strategy for polycarbonate and polyester discovery based on a recommendation system that proposes polymerization experiments that are likely to produce materials with targeted properties. Following recommendations of the system driven by the historical ring-opening polymerization results, we carried out experiments targeting specific ranges of monomer conversion and dispersity of the polymers obtained from cyclic lactones and carbonates. The results of the experiments were in close agreement with the recommendation targets with few false negatives or positives obtained for each class.<br>


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