Class information for: |
Basic class information |
| Class id | #P | Avg. number of references |
Database coverage of references |
|---|---|---|---|
| 30456 | 168 | 19.1 | 29% |
Hierarchy of classes |
The table includes all classes above and classes immediately below the current class. |
Terms with highest relevance score |
| rank | Term | termType | Chi square | Shr. of publ. in class containing term |
Class's shr. of term's tot. occurrences |
#P with term in class |
|---|---|---|---|---|---|---|
| 1 | MEDICAL IMAGE RECOGNITION | authKW | 408956 | 2% | 75% | 3 |
| 2 | INTEGRATED SENSORY INTELLIGENT SYSTEM | authKW | 363518 | 1% | 100% | 2 |
| 3 | SECCIO INTELLIGENCIA ARTIFICIAL | address | 242344 | 1% | 67% | 2 |
| 4 | ABDUCTIVE MODELING | authKW | 181759 | 1% | 100% | 1 |
| 5 | ACCOMODATING NEW CLASSES | authKW | 181759 | 1% | 100% | 1 |
| 6 | ADVANCED ENGINEERED MATERIALS | authKW | 181759 | 1% | 100% | 1 |
| 7 | APPROXIMATE PIECEWISE LINEAR REGRESSION | authKW | 181759 | 1% | 100% | 1 |
| 8 | COMMON BANDWIDTH RADIAL BASIS FUNCTION | authKW | 181759 | 1% | 100% | 1 |
| 9 | COMPANHIA SIDERURG NACL | address | 181759 | 1% | 100% | 1 |
| 10 | COMPOSITE VOID ANALYSIS | authKW | 181759 | 1% | 100% | 1 |
Web of Science journal categories |
| Rank | Term | Chi square | Shr. of publ. in class containing term |
Class's shr. of term's tot. occurrences |
#P with term in class |
|---|---|---|---|---|---|
| 1 | Computer Science, Artificial Intelligence | 2527 | 32% | 0% | 54 |
| 2 | Computer Science, Theory & Methods | 386 | 14% | 0% | 23 |
| 3 | Engineering, Electrical & Electronic | 201 | 22% | 0% | 37 |
| 4 | Engineering, Industrial | 183 | 6% | 0% | 10 |
| 5 | Operations Research & Management Science | 176 | 8% | 0% | 13 |
| 6 | Computer Science, Information Systems | 109 | 7% | 0% | 12 |
| 7 | Computer Science, Interdisciplinary Applications | 82 | 7% | 0% | 11 |
| 8 | Engineering, Manufacturing | 41 | 3% | 0% | 5 |
| 9 | Engineering, Ocean | 31 | 1% | 0% | 2 |
| 10 | Materials Science, Characterization, Testing | 29 | 2% | 0% | 3 |
Address terms |
| Rank | Term | Chi square | Shr. of publ. in class containing term |
Class's shr. of term's tot. occurrences |
#P with term in class |
|---|---|---|---|---|---|
| 1 | SECCIO INTELLIGENCIA ARTIFICIAL | 242344 | 1% | 67% | 2 |
| 2 | COMPANHIA SIDERURG NACL | 181759 | 1% | 100% | 1 |
| 3 | MINES PETR GEOPHYS | 181759 | 1% | 100% | 1 |
| 4 | VISTEON CHASSIS SYST | 181759 | 1% | 100% | 1 |
| 5 | ALTANA CHAIR BIOINFORMAT INFORMAT MIN | 145405 | 1% | 40% | 2 |
| 6 | GRP INVEST SUPP S | 145405 | 1% | 40% | 2 |
| 7 | CIIPS | 90878 | 1% | 50% | 1 |
| 8 | INFORMAT MANAGEMENT SERV BRANCH | 90878 | 1% | 50% | 1 |
| 9 | MACHINE DYNAM FAILURE ANAL | 90878 | 1% | 50% | 1 |
| 10 | SCI TECHNOL ATMOSPHER PHYS GRP | 90878 | 1% | 50% | 1 |
Journals |
| Rank | Term | Chi square | Shr. of publ. in class containing term |
Class's shr. of term's tot. occurrences |
#P with term in class |
|---|---|---|---|---|---|
| 1 | MICRO | 4127 | 1% | 1% | 2 |
| 2 | INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL | 2543 | 4% | 0% | 6 |
| 3 | SOLID STATE TECHNOLOGY | 2446 | 3% | 0% | 5 |
| 4 | SYSTEMS AND COMPUTERS IN JAPAN | 2297 | 1% | 1% | 2 |
| 5 | IEEE TRANSACTIONS ON NEURAL NETWORKS | 2258 | 4% | 0% | 6 |
| 6 | LECTURE NOTES IN ARTIFICIAL INTELLIGENCE | 1090 | 6% | 0% | 10 |
| 7 | SYSTEMS ANALYSIS MODELLING SIMULATION | 1019 | 1% | 1% | 1 |
| 8 | CHIANG MAI JOURNAL OF SCIENCE | 1014 | 1% | 0% | 2 |
| 9 | R JOURNAL | 930 | 1% | 1% | 1 |
| 10 | NEURAL NETWORKS | 918 | 2% | 0% | 4 |
Author Key Words |
Core articles |
The table includes core articles in the class. The following variables is taken into account for the relevance score of an article in a cluster c: (1) Number of references referring to publications in the class. (2) Share of total number of active references referring to publications in the class. (3) Age of the article. New articles get higher score than old articles. (4) Citation rate, normalized to year. |
| Rank | Reference | # ref. in cl. |
Shr. of ref. in cl. |
Citations |
|---|---|---|---|---|
| 1 | KONDO, T , UENO, J , (2012) FEEDBACK GMDH-TYPE NEURAL NETWORK AND ITS APPLICATION TO MEDICAL IMAGE ANALYSIS OF LIVER CANCER.INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL. VOL. 8. ISSUE 3B. P. 2285-2300 | 6 | 86% | 0 |
| 2 | LUXHOJ, JT , (1999) TRENDING OF EQUIPMENT INOPERABILITY FOR COMMERCIAL AIRCRAFT.RELIABILITY ENGINEERING & SYSTEM SAFETY. VOL. 64. ISSUE 3. P. 365-381 | 5 | 100% | 3 |
| 3 | KONDO, T , UENO, J , (2008) MULTI-LAYERED GMDH-TYPE NEURAL NETWORK SELF-SELECTING OPTIMUM NEURAL NETWORK ARCHITECTURE AND ITS APPLICATION TO 3-DIMENSIONAL MEDICAL IMAGE RECOGNITION OF BLOOD VESSELS.INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL. VOL. 4. ISSUE 1. P. 175 -187 | 4 | 80% | 4 |
| 4 | LUXHOJ, JT , WILLIAMS, TP , (1996) INTEGRATED DECISION SUPPORT FOR AVIATION SAFETY INSPECTORS.FINITE ELEMENTS IN ANALYSIS AND DESIGN. VOL. 23. ISSUE 2-4. P. 381 -403 | 6 | 75% | 2 |
| 5 | ZAKNICH, A , (2003) AN INTEGRATED SENSORY-INTELLIGENT SYSTEM FOR UNDERWATER ACOUSTIC SIGNAL-PROCES SING APPLICATIONS.IEEE JOURNAL OF OCEANIC ENGINEERING. VOL. 28. ISSUE 4. P. 750-759 | 3 | 100% | 3 |
| 6 | KONDO, T , UENO, J , (2009) MEDICAL IMAGE RECOGNITION OF ABDOMINAL MULTI-ORGANS BY RBF GMDH-TYPE NEURAL NETWORK.INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL. VOL. 5. ISSUE 1. P. 225-240 | 5 | 50% | 4 |
| 7 | LUXHOJ, JT , WILLIAMS, TP , SHYUR, HJ , (1997) COMPARISON OF REGRESSION AND NEURAL NETWORK MODELS FOR PREDICTION OF INSPECTION PROFILES FOR AGING AIRCRAFT.IIE TRANSACTIONS. VOL. 29. ISSUE 2. P. 91-101 | 5 | 71% | 6 |
| 8 | VALDES, JJ , MATEESCU, G , (2002) TIME SERIES MODEL MINING WITH SIMILARITY-BASED NEURO-FUZZY NETWORKS AND GENETIC ALGORITHMS: A PARALLEL IMPLEMENTATION.ROUGH SETS AND CURRENT TRENDS IN COMPUTING, PROCEEDINGS. VOL. 2475. ISSUE . P. 279-288 | 3 | 100% | 1 |
| 9 | SU, X , WU, YY , PEI, HJ , GAO, JS , LAN, XY , (2016) PREDICTION OF COKE YIELD OF FCC UNIT USING DIFFERENT ARTIFICIAL NEURAL NETWORK MODELS.CHINA PETROLEUM PROCESSING & PETROCHEMICAL TECHNOLOGY. VOL. 18. ISSUE 3. P. 102 -109 | 2 | 100% | 0 |
| 10 | LI, K , JIANG, DX , XIONG, K , DING, YS , (2006) APPLICATION OF RBF AND SOFM NEURAL NETWORKS ON VIBRATION FAULT DIAGNOSIS FOR AERO-ENGINES.ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 3, PROCEEDINGS. VOL. 3973. ISSUE . P. 414-419 | 4 | 57% | 0 |
Classes with closest relation at Level 1 |