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|Workshop 1||Advances in Vehicular Ad Hoc Networks and Applications|
|Workshop 2||The 3nd International Workshop on Computational Intelligence for Bio-Medical Science and Engineering (CIMSE-2012)|
|Invited Session 1||Machine Learning, Data mining, and KDD|
Workshop 1: Advances in Vehicular Ad Hoc Networks and Applications
Vehicular Ad Hoc Network (VANET) is an emerging area of wireless ad hoc networks that facilitates ubiquitous connectivity between smart vehicles through Vehicle-to-Vehicle (V2V) or Vehicle-to-Roadside (V2R) and Roadside-to-Vehicle (R2V) communications. This emerging field of technology aims to improve safety of passengers and traffic flow, reduces pollution to the environment and enables in-vehicle entertainment applications. The safety-related applications could reduce accidents by providing drivers with traffic information such as collision avoidances, traffic flow alarms and road surface conditions. Moreover, the passengers could exploit an available infrastructure in order to connect to the internet for infomobility and entertainment applications.
The increasing necessity of this network is an impetus for leading car manufacturers, research communities and governments to increase their efforts toward creating a standardized platform for vehicular communications. However, VANET unique characteristics and special requirements excite new challenges to the research community. To address these challenges in both safety- and comfort-oriented applications, there is a pressing need to develop new protocols and algorithms for channel characterization and modeling, Medium Access Control (MAC), obstacle modeling, adaptive geographical routing to sparse and dense traffic conditions. This special issue aims to theme innovative research achievements in the field of vehicular networks and communications. We are seeking original and unpublished papers. Specific topics include, but are not limited to:
- Radio obstacle modeling in urban vehicular environments
- Channel characterization, modeling and simulation
- Efficient packet forwarding optimization
- Congestion control and resource management
- Medium access protocols and channel assignments
- Adaptive beaconing protocols
- Mobility management
- Mobility models
- Efficient geographical routing adapted to bipolar traffic conditions
- Delay tolerant routing protocols
- Message dissemination for safety-related applications
- Cooperative vehicular communications
- Test-beds, case studies, experimental systems and evaluations
- Security and privacy issues
Kayhan Zrar Ghafoor, Researcher(Doctor) (University of Koya, Iraq)
Jaime Lloret Mauri, Professor (Polytechnic University of Valencia, Spain)
Rashid H Khokhar, Researcher(Doctor) (University of Malaya, Malaysia)
Ali Safa Sadiq, Doctor (University Technology Malaysia, Malaysia)
Name: Dr. Kayhan
Affiliation: University of Koya
Address: Faculty of Engineering, University of Koya, Daniel Miterrand Boulevard, Koya, KOY45, Kurdistan Region-IRAQ Tel: +964 7504499850
Workshop 2: The 3nd International Workshop on Computational Intelligence for Bio-Medical Science and Engineering (CIMSE-2012)
CIMSE2012, the third in this series will held in conjunction with International Conference on New Trends in Information Science, Service Science and Data Mining (ISSDM2012), Oct. 23-25, 2012, Taipei, Taiwan (ISSDM2012 web site: http://www.aicit.org/issdm).
Computational intelligence is playing today an important role in many research areas. Computer assisted diagnosis and surgery (CAD and CAS) has become one of the major research subjects and many computer assisted diagnosis and surgery techniques have been developed. This workshop is focused on applications of computational intelligence for bio-medical science and engineering. The purpose is to exchange the new ideas, trends and current research results in these research fields. Both researches and practitioners are welcome to submit their contributed papers.
Topic of Interest:
The topics include, but are not limited to the following areas:
- Bio-medical signal and image processing
- Pattern recognition
- Machine learning
- Image segmentation and registration techniques
- Bio-medical system
- Radiological imaging
- Computer assisted diagnosis (CAD)
- Digital atlas
Authors are invited to submit papers for the workshop through E-mail (firstname.lastname@example.org) by Aug. 20, 2012. Submissions must be original and should not have been published previously.
All papers must follow the IEEE proceedings 2-column format. Regular papers are allowed to 6 pages. Proceedings for the workshops will be included in the proceedings of ICMIA2012, which will be published by the IEEE and will be included to the Ei and IEEE eXplore(IEEE Digital Library). Extended versions of accepted papers will be invited to submit to several AICIT international journals, such as JCIT, JDCTA.
- Paper submission: Aug. 20, 2012
- Acceptance notification: Aug. 31, 2012
- Final camera-ready submission: Sep. 7, 2012
Workshop Organizer and Chair:
Prof. Yen-Wei Chen (Ritsumeikan University, Japan)
Workshop Co-organizers and Co-chairs:
Prof. Akira Furukawa (Tokyo Metropolitan University, Tokyo, Japan)
Prof. Ikuko Nishikawa (Ritsumeikan University, Shiga, Japan)
Prof. Xiangyan Zeng (Fort Valley State University, USA)
Advisory Committee Co-Chairs:
Prof. Shinichi Tamura (NBL Technovator Co., Ltd, Japan)
Prof. Lahmi C. Jain (Univ. of South Australia, Australia)
Dr. Xianhua Han (Ritsumeikan University, Japan)
Name: Dr. Yen-Wei Chen
Affiliation: Ritsumeikan University
Address: College of Information Science and Engineering, Ritsumeikan University 1-1-1 Nojihigashi, Kusatsu, Shiga, 525-8577, JAPAN
Invited Session 1: Machine Learning, Data mining, and KDD
Machine Learning, a branch of artificial intelligence, is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data (sensor data or databases). Data mining and Knowledge Discovery in Databases (KDD) are interdisciplinary fields focusing on methodologies for extracting useful knowledge from data. Recent years have seen the rapid growth in the field of Machine Learning, KDD, and Data Mining.
The ongoing rapid growth of online data due to the Internet and the widespread use of databases have created an immense need for machine learning, data mining, and KDD methodologies. The challenge of extracting knowledge from data draws upon research in databases, statistics, machine learning, pattern recognition, optimization, data visualization, and high-performance computing, to deliver advanced business intelligence and web discovery solutions.
There are many companies providing machine learning/ data-mining tools and applications, consulting and seminars, and even specialized hardware. Within the next 5-10 years, the technology of machine learning, data mining, and KDD in data will become an integral part of the enterprise information technology.
Machine learning: Focuses on the prediction, based on known properties learned from the training data (Supervised learning/ Unsupervised learning/ Semi-supervised learning/ Reinforcement learning/ Transduction/ Learning to learn).
KDD: Knowledge Discovery in Databases, Which includes 1) Selection, 2) Pre-processing, 3) Transformation, 4) Data Mining, 5) Interpretation/Evaluation.
Data mining: the analysis step of KDD, focuses on the discovery of (previously) unknown properties on the data. (Anomaly detection/ Association rule learning/ Clustering/ Classification/Regression/ Summarization etc).
Hairong Lei, Researcher(Doctor) ( Trademarkia (LegalForce), United States of America)
Name: Dr. Hairong Lei
Affiliation: Trademarkia (legalforce).
Address: 1580 W. El Camino Real Suite 10, Mountain View, CA 94040 USA