BibliographyType,ISBN,Identifier,Author,Title,Journal,Volume,Number,Month,Pages,Year,Address,Note,URL,Booktitle,Chapter,Edition,Series,Editor,Publisher,ReportType,Howpublished,Institution,Organizations,School,Annote,Custom1,Custom2,Custom3,Custom4,Custom5
7,"","journals/jikm/BalachandranF02","Balachandran, Jayakrishnan & Foo, Schubert","Implementing KM in an Information Technology Environment: a Practical Approach.","JIKM",1,2,"","187-196",2002,"","","http://dblp.uni-trier.de/db/journals/jikm/jikm1.html#BalachandranF02","","","","","","","","","","","","","","","dblp ","",""
6,"978-3-540-69914-9","conf/icost/BiswasWFNMCPZLY08","Biswas, Jit; Wai, Aung Aung Phyo; Fook, Victor Foo Siang; Nugent, Chris D.; Mulvenna, Maurice D.; Craig, David; Passmore, Peter J.; Zhang, Daqing; Lee, Jer En & Yap, Philip Lin Kiat","Design of a Smart Continence Management System Based on Initial User Requirement Assessment.","",5120,,"","62-72",2008,"","","http://dblp.uni-trier.de/db/conf/icost/icost2008.html#BiswasWFNMCPZLY08","ICOST","","","Lecture Notes in Computer Science","Helal, Sumi; Mitra, Simanta; Wong, Johnny S.; Chang, Carl K. & Mokhtari, Mounir","Springer","","","","","","","","","dblp ","",""
6,"978-1-60558-115-6","conf/dac/CaoFHS08","Cao, Zhen; Foo, Brian; He, Lei & van der Schaar, Mihaela","Optimality and improvement of dynamic voltage scaling algorithms for multimedia applications.","",,,"","179-184",2008,"","","http://dblp.uni-trier.de/db/conf/dac/dac2008.html#CaoFHS08","DAC","","","","Fix, Limor","ACM","","","","","","","","","dblp ","",""
7,"","ding2002ora","Ding, Y. & Foo, S.","Ontology research and development. Part 1-a review of ontology generation","Journal of Information Science",28,2,"","123",2002,"","","","","","","","","CILIP","","","","","","","","","learning ontology ontology, ","",""
6,"","conf/ismb/DoFB08","Do, Chuong B.; Foo, Chuan-Sheng & Batzoglou, Serafim","A max-margin model for efficient simultaneous alignment and folding of RNA sequences.","",,,"","68-76",2008,"","","http://dblp.uni-trier.de/db/conf/ismb/ismb2008.html#DoFB08","ISMB","","","","","","","","","","","","","","dblp ","",""
6,"","conf/icip/FooS07","Foo, Brian & van der Schaar, Mihaela","Graceful Quality Degradation for Video Decoding Systems Through Priority Scheduling and Processor Power Adaptation.","",,,"","317-320",2007,"","","http://dblp.uni-trier.de/db/conf/icip/icip2007-3.html#FooS07","ICIP (3)","","","","","IEEE","","","","","","","","","dblp ","",""
7,"","journals/jikm/LeeFG06","Lee, Chu Keong; Foo, Schubert & Goh, Dion Hoe-Lian","On the Concept and Types of Knowledge.","JIKM",5,2,"","151-163",2006,"","","http://dblp.uni-trier.de/db/journals/jikm/jikm5.html#LeeFG06","","","","","","","","","","","","","","","dblp ","",""
6,"","merkel07","Merkal, Markus & Foo, Jody","Terminology Extraction and Term Ranking for Standardizing Term Banks","",,,"","349--354",2007,"Tartu, Estonia","","","Proceedings of the 16th Nordic Conference of Computational Linguistics NODALIDA-2007","","","","Nivre, Joakim; Kaalep, Heiki-Jaan; Muischnek, Kadri & Koit, Mare","","","","","","","","","","imported ","",""
6,"","conf/fuzzIEEE/TanFC07","Tan, Woei Wan; Foo, Chek Liang & Chua, Teck Wee","Type-2 Fuzzy System for ECG Arrhythmic Classification.","",,,"","1-6",2007,"","","http://dblp.uni-trier.de/db/conf/fuzzIEEE/fuzzIEEE2007.html#TanFC07","FUZZ-IEEE","","","","","IEEE","","","","","","","","","dblp ","",""
6,"3-540-66822-5","zhang:1999:GPmcod","Zhang, Mengjie & Ciesielski, Victor","Genetic Programming for Multiple Class Object Detection","",1747,,"6-10 December","180--192",1999,"Sydney, Australia","","http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-66822-5","12th Australian Joint Conference on Artificial Intelligence","","","LNAI","Foo, Norman","Springer-Verlag","","","","","","","We describe an approach to the use of genetic programming for object detection problems in which the locations of small objects of multiple classes in large pictures must be found. The evolved programs use a feature set computed from a square input field large enough to contain each of objects of interest and are applied, in moving window fashion, over the large pictures in order to locate the objects of interest. The fitness function is based on the detection rate and the false alarm rate. We have tested the method on three object detection problems of increasing difficulty with four different classes of interest. On pictures of easy and medium difficulty all objects are detected with no false alarms. On difficult pictures there are still significant numbers of errors, however the results are considerably better than those of a neural network based program for the same problems.","","Machine Neural Vision algorithms, genetic learning, networks, programming, ","",""