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Fertilizer Recommendation System using SGD on Mahout and Hadoop Platform

Soil composition encoded into vector use by classification system to trained system.  ...  Here, SGD classification system is used to train the system. Our proposed system obtained 64.08% total average accuracy.  ...  Table 1 : Literature of classification system used in the past. Author Description Guevara et al., 2011 [4] They developed a machine vision system for classification of wheat and barley grain.  ... 
doi:10.35940/ijitee.i7571.078919 fatcat:ftaqz5c6abbzxmy3g4rtom7lte

Account-based recommenders in open discovery environments

Jim Hahn, Courtney McDonald
2018 Digital Library Perspectives  
Originality/value In the age of big data and machine learning, advances in deep learning technology and data stream processing make it possible to leverage discovery system data to inform the development  ...  Findings The authors discuss the need for large data sets from which to build an algorithm; and introduce a prototype recommender service, describing the prototype's data flow pipeline and machine learning  ...  This is used to run a targeted search with related topics derived by our machine learning process.  ... 
doi:10.1108/dlp-07-2017-0022 fatcat:hhoab2pnxnajdih2o7o4uz5kz4

Ontology-based Recommender System in Higher Education

Charbel Obeid, Inaya Lahoud, Hicham El Khoury, Pierre-Antoine Champin
2018 Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18  
This paper represents an approach for developing ontology-based recommender system improved with machine learning techniques to orient students in higher education.  ...  The main objective of our ontology-based recommender system is to identify the student requirements, interests, preferences and capabilities to recommend the appropriate major and university for each one  ...  A third part for processing information using machine learning techniques, creating and clustering graduate students' profiles models and send the results to the hybrid recommendation engine.  ... 
doi:10.1145/3184558.3191533 dblp:conf/www/ObeidLKC18 fatcat:p3aac2mqgngg7h2gsszcyphfkm

Modeling human behavior in user-adaptive systems: Recent advances using soft computing techniques

2005 Expert systems with applications  
The key element of a generic adaptive hypermedia system is the user model.  ...  Traditional machine learning techniques used to create user models are usually too rigid to capture the inherent uncertainty of human behavior.  ...  Acknowledgments The work presented in this paper is funded by the UK Arts and Humanities Research Board (AHRB grant reference: MRG/AN9183/APN16300).  ... 
doi:10.1016/j.eswa.2005.04.005 fatcat:wi5eu2kzsvdotnldftwh5r3iee


Gurpinder Singh
2017 International Journal of Advanced Research in Computer Science  
A proper analysis and recommendation on the basis of data mining can help farmers in better understanding and management of their crops and land. Apart from that, patterns evaluated via.  ...  This paper emphasizes on the applications which have been developed using Data Mining and further scope of the data mining.  ...  MACHINE LEARNING Machine learning is considered to be a part of Artificial Intelligence.  ... 
doi:10.26483/ijarcs.v8i7.4307 fatcat:6rj7f34acbcabdlrqovxuwtk6q

A scalable machine learning online service for big data real-time analysis

Alejandro Baldominos, Esperanza Albacete, Yago Saez, Pedro Isasi
2014 2014 IEEE Symposium on Computational Intelligence in Big Data (CIBD)  
In order to validate the proposed architecture, two systems are developed, each one providing classical machine-learning services in different domains: the first one involves a recommender system for web  ...  This work describes a proposal for developing and testing a scalable machine learning architecture able to provide real-time predictions or analytics as a service over domain-independent big data, working  ...  ACKNOWLEDGMENT This research work is part of Memento Data Analysis project, co-funded by the Spanish Ministry of Industry, Energy and Tourism with identifier TSI-020601-2012-99.  ... 
doi:10.1109/cibd.2014.7011537 dblp:conf/cibd/BaldominosASI14 fatcat:rqd2mtvpf5huphf6vpkp53ol4i

Machine Learning Technique for Crop Recommendation in Agriculture Sector

2019 International Journal of Engineering and Advanced Technology  
Further we used Machine Learning approach for the accurate crop recommendation.  ...  In this paper, we used Naive Bayesian Classification technique to identify the class of crop and used the food grain dataset to analyze the technique over different attribute.  ...  METHODOLOGY In this approach, we used machine learning for the intended recommendation. In this modified algorithm, we use Naive Bayesian classification for crop and fertilizer recommendation.  ... 
doi:10.35940/ijeat.a1171.109119 fatcat:mswwmt74lbbblbregy47hadr3q

Recommender System based on Empirical Study of Geolocated Clustering and Prediction Services for Botnets Cyber-Intelligence in Malaysia

Nazri Ahmad Zamani, Aswami Fadillah, Siti Norul
2018 International Journal of Advanced Computer Science and Applications  
The machine learning is based on K-Means and DBSCAN clustering. The result is a recommendation of top potential attacks based on ranks from a given geolocations coordinates.  ...  The system responds to users preferences in goods and services and gives recommendations via Machine Learning algorithms deployed catered specifically for such services.  ...  A recommender system is one of the popular applications that is built on top of a Machine Learning predictive analytics algorithms.  ... 
doi:10.14569/ijacsa.2018.091266 fatcat:7kzcjl7qzjezdddgrxuc3lne2u

Personalized Intelligent Recommendation Algorithm Design for Book Services Based on Deep Learning

Weiwei Yang, Zhiguo Qu
2022 Wireless Communications and Mobile Computing  
The framework of the personalized intelligent service model for libraries based on user portraits is proposed, and intelligent technologies such as machine learning are applied to analyze and mine users  ...  Machine learning is one of the important branches of artificial intelligence, which provides new technical means for analyzing users, understanding users, and gaining insight into users.  ...  Conflicts of Interest e authors declare that they have no conflicts of interest regarding this work.  ... 
doi:10.1155/2022/9203665 fatcat:3bmbztwxkzaxnkwm2g4es7j2c4

Mobile apps meet the smart energy grid: A survey on consumer engagement and Machine Learning applications

Spiros Chadoulos, Iordanis Koutsopoulos, George C. Polyzos
2020 IEEE Access  
It does not necessarily reflect the opinion of DG CNECT or the European Commission (EC). DG CNECT or the EC are not responsible for any use that may be made of the information contained herein.  ...  Evgenia Faltaka for helping with an initial exploration of available mobile apps. The sole responsibility for the content lies with the authors.  ...  Clustering and classification are two of the most wellknown and studied machine learning techniques.  ... 
doi:10.1109/access.2020.3042758 fatcat:xptweaczcvf33etdodebdp5xb4

Advanced Technologies in Blockchain, Machine Learning, and Big Data

Ji Su Park, Jong Hyuk Park
2020 Journal of Information Processing Systems  
In this paper, we present a summary of 18 high-quality accepted articles following a rigorous review process in the fields of Blockchain, machine learning, and big data.  ...  Blockchain, machine learning, and big data are among the key components of the future IT track. These technologies are used in various fields; hence their increasing application.  ...  A recommendation technology based on the personal information of a user or a best-selling product is generally used in a movie recommendation system. Vilakone et al.  ... 
doi:10.3745/jips.01.0052 dblp:journals/jips/ParkP20 fatcat:fasmcganvzfuhngr6ddeujemmq

Big Data Analytics as a Service for Affective Humanoid Service Robots

Ming Jiang, Li Zhang
2015 Procedia Computer Science  
A user preference based children toy recommendation application is introduced as a use case study of the DCCL mechanism and platform.  ...  Based on the latest Big Data Analytics tools with distributed machine learning technologies integrated as services, a novel DCCL middleware platform is developed to facilitate the realisation of the DCCL  ...  Recommendation, clustering and classification are the main machine learning techniques implemented by the Mahout libraries.  ... 
doi:10.1016/j.procs.2015.07.288 fatcat:zpvrv4vvazcunakmpumfodjozq

Survey on Recommender System using Distributed Framework

2016 International Journal of Science and Research (IJSR)  
In this paper, all the approaches of recommender system is explained. Classification of data is vital part of recommender system.  ...  The Bayesian Classifier is one of the most successful machine learning algorithms in many classification domains.  ...  Conclusion Data in the form of reviews, opinions, feedback, remarks, and complaint treated as Big Data cannot be used directly for recommendation system.  ... 
doi:10.21275/v5i1.nov153104 fatcat:yyrbzzu5ujdbpkqxqkko6gebcq

Evaluating systematic transactional data enrichment and reuse

Jim Hahn
2019 Proceedings of the Conference on Artificial Intelligence for Data Discovery and Reuse - AIDR '19  
A library account-based recommender system was developed using machine learning processing over transactional data of 383,828 check-outs sourced from a large multi-unit research library.  ...  The purpose of this paper is to evaluate the results of systematic transactional data reuse in machine learning.  ...  With these topic metadata clusters a rule set for the recommender system was developed.  ... 
doi:10.1145/3359115.3359116 dblp:conf/aidr/Hahn19 fatcat:oq2r3xfsezdgtizyha5b2smyra

Context-aware rule learning from smartphone data: survey, challenges and future directions

Iqbal H. Sarker
2019 Journal of Big Data  
Thus, machine learning based context-aware rules can be used to make such recommendation system more intelligent.  ...  In addition to these approaches, a number of authors use machine learning techniques such as clustering, genetic algorithm etc.  ... 
doi:10.1186/s40537-019-0258-4 fatcat:hnbskalstzaexaf3fptvxf6qey
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