Excellence in Research and Innovation for Humanity

ICKDDM 2018 : 20th International Conference on Knowledge Discovery and Data Mining

London, United Kingdom
January 18 - 19, 2018

Conference Code: 18UK01ICKDDM

Conference Proceedings

All submitted conference papers will be blind peer reviewed by three competent reviewers. The peer-reviewed conference proceedings are indexed in the International Science Index (ISI), Google Scholar, Semantic Scholar, Zenedo, OpenAIRE, BASE, WorldCAT, Sherpa/RoMEO, and other index databases. Impact Factor Indicators.

Special Journal Issues

ICKDDM 2018 has teamed up with the Special Journal Issue on Knowledge Discovery and Data Mining. A number of selected high-impact full text papers will also be considered for the special journal issues. All submitted papers will have the opportunity to be considered for this Special Journal Issue. The paper selection will be carried out during the peer review process as well as at the conference presentation stage. Submitted papers must not be under consideration by any other journal or publication. The final decision for paper selection will be made based on peer review reports by the Guest Editors and the Editor-in-Chief jointly. Selected full-text papers will be published online free of charge.

Conference Sponsor and Exhibitor Opportunities

The Conference offers the opportunity to become a conference sponsor or exhibitor. To participate as a sponsor or exhibitor, please download and complete the Conference Sponsorship Request Form.

Important Dates

Abstracts/Full-Text Paper Submission Deadline   November 30, 2017
Notification of Acceptance/Rejection   December 5, 2017
Final Paper (Camera Ready) Submission & Early Bird Registration Deadline   December 19, 2017
Conference Dates   January 18 - 19, 2018

Important Notes

Please ensure your submission meets the conference's strict guidelines for accepting scholarly papers. Downloadable versions of the check list for Full-Text Papers and Abstract Papers.

Please refer to the Paper Submission GUIDE before submitting your paper.

Selected Conference Papers

1) Noise Reduction in Web Data: A Learning Approach Based on Dynamic User Interests
Julius Onyancha, Valentina Plekhanova
2) Evaluating Machine Learning Techniques for Activity Classification in Smart Home Environments
Talal Alshammari, Nasser Alshammari, Mohamed Sedky, Chris Howard
3) Cognition of Driving Context for Driving Assistance
Manolo Dulva Hina, Clement Thierry, Assia Soukane, Amar Ramdane-Cherif
4) Hybrid Knowledge Approach for Determining Health Care Provider Specialty from Patient Diagnoses
Erin Lynne Plettenberg, Jeremy Vickery
5) Hybrid Reliability-Similarity-Based Approach for Supervised Machine Learning
Walid Cherif
6) Hybrid Approach for Software Defect Prediction Using Machine Learning with Optimization Technique
C. Manjula, Lilly Florence
7) Optimized Preprocessing for Accurate and Efficient Bioassay Prediction with Machine Learning Algorithms
Jeff Clarine, Chang-Shyh Peng, Daisy Sang
8) Comparative Evaluation of Accuracy of Selected Machine Learning Classification Techniques for Diagnosis of Cancer: A Data Mining Approach
Rajvir Kaur, Jeewani Anupama Ginige
9) Missing Link Data Estimation with Recurrent Neural Network: An Application Using Speed Data of Daegu Metropolitan Area
JaeHwan Yang, Da-Woon Jeong, Seung-Young Kho, Dong-Kyu Kim
10) Automatic Landmark Selection Based on Feature Clustering for Visual Autonomous Unmanned Aerial Vehicle Navigation
Paulo Fernando Silva Filho, Elcio Hideiti Shiguemori
11) Adaption Model for Building Agile Pronunciation Dictionaries Using Phonemic Distance Measurements
Akella Amarendra Babu, Rama Devi Yellasiri, Natukula Sainath
12) Hand Gesture Detection via EmguCV Canny Pruning
N. N. Mosola, S. J. Molete, L. S. Masoebe, M. Letsae
13) A Comprehensive Evaluation of Supervised Machine Learning for the Phase Identification Problem
Brandon Foggo, Nanpeng Yu
14) Web Proxy Detection via Bipartite Graphs and One-Mode Projections
Zhipeng Chen, Peng Zhang, Qingyun Liu, Li Guo
15) Development of Prediction Models of Day-Ahead Hourly Building Electricity Consumption and Peak Power Demand Using the Machine Learning Method
Dalin Si, Azizan Aziz, Bertrand Lasternas

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