Excellence in Research and Innovation for Humanity

ICCKM 2019 : 21st International Conference on Computer and Knowledge Management

London, United Kingdom
March 14 - 15, 2019

Conference Code: 19UK03ICCKM

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

ICCKM 2019 has teamed up with the Special Journal Issue on Computer and Knowledge Management. 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   October 26, 2018
Notification of Acceptance/Rejection   November 9, 2018
Final Paper (Camera Ready) Submission & Early Bird Registration Deadline   February 14, 2019
Conference Dates   March 14 - 15, 2019

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) Hybrid Reliability-Similarity-Based Approach for Supervised Machine Learning
Walid Cherif
2) Thermalytix: An Advanced Artificial Intelligence Based Solution for Non-Contact Breast Screening
S. Sudhakar, Geetha Manjunath, Siva Teja Kakileti, Himanshu Madhu
3) Knowledge Reactor: A Contextual Computing Work in Progress for Eldercare
Scott N. Gerard, Aliza Heching, Susann M. Keohane, Samuel S. Adams
4) Performance Evaluation of Parallel Surface Modeling and Generation on Actual and Virtual Multicore Systems
Nyeng P. Gyang
5) An Improved K-Means Algorithm for Gene Expression Data Clustering
Billel Kenidra, Mohamed Benmohammed
6) Energy Efficiency Analysis of Crossover Technologies in Industrial Applications
W. Schellong
7) CoP-Networks: Virtual Spaces for New Faculty’s Professional Development in the 21st Higher Education
Eman AbuKhousa, Marwan Z. Bataineh
8) Road Traffic Accidents Analysis in Mexico City through Crowdsourcing Data and Data Mining Techniques
Gabriela V. Angeles Perez, Jose Castillejos Lopez, Araceli L. Reyes Cabello, Emilio Bravo Grajales, Adriana Perez Espinosa, Jose L. Quiroz Fabian
9) SeCloudBPMN: A Lightweight Extension for BPMN Considering Security Threats in the Cloud
Somayeh Sobati Moghadam
10) Concept for Knowledge out of Sri Lankan Non-State Sector: Performances of Higher Educational Institutes and Successes of Its Sector
S. Jeyarajan
11) Unstructured-Data Content Search Based on Optimized EEG Signal Processing and Multi-Objective Feature Extraction
Qais M. Yousef, Yasmeen A. Alshaer
12) Real-Time Data Stream Partitioning over a Sliding Window in Real-Time Spatial Big Data
Sana Hamdi, Emna Bouazizi, Sami Faiz
13) Accelerating the Uptake of Smart City Applications through Cloud Computing
Panagiotis Tsarchopoulos, Nicos Komninos, Christina Kakderi
14) Personal Knowledge Management: Systematic Review and Future Direction
Kuribachew Gizaw Tohiye, Monica Garfield
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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