1,312 research outputs found
Pemilihan kerjaya di kalangan pelajar aliran perdagangan sekolah menengah teknik : satu kajian kes
This research is a survey to determine the career chosen of form four student
in commerce streams. The important aspect of the career chosen has been divided
into three, first is information about career, type of career and factor that most
influence students in choosing a career. The study was conducted at Sekolah
Menengah Teknik Kajang, Selangor Darul Ehsan. Thirty six form four students was
chosen by using non-random sampling purpose method as respondent. All
information was gather by using questionnaire. Data collected has been analyzed in
form of frequency, percentage and mean. Results are performed in table and graph.
The finding show that information about career have been improved in students
career chosen and mass media is the main factor influencing students in choosing
their career
Personalised information modelling technologies for personalised medicine
Personalised modelling offers a new and effective approach for the study in pattern recognition and knowledge discovery, especially for biomedical applications. The created models are more useful and informative for analysing and evaluating an individual data object for a given problem. Such models are also expected to achieve a higher degree of accuracy of prediction of outcome or classification than conventional systems and methodologies. Motivated by the concept of personalised medicine and utilising transductive reasoning, personalised modelling was recently proposed as a new method for knowledge discovery in biomedical applications. Personalised modelling aims to create a unique computational diagnostic or prognostic model for an individual. Here we introduce an integrated method for personalised modelling that applies global optimisation of variables (features) and an appropriate size of neighbourhood to create an accurate personalised model for an individual. This method creates an integrated computational system that combines different information processing techniques, applied at different stages of data analysis, e.g. feature selection, classification, discovering the interaction of genes, outcome prediction, personalised profiling and visualisation, etc. It allows for adaptation, monitoring and improvement of an individual’s model and leads to improved accuracy and unique personalised profiling that could be used for personalised treatment and personalised drug design
Hybrid Neuro-Fuzzy Classifier Based On Nefclass Model
The paper presents hybrid neuro-fuzzy classifier, based on NEFCLASS model, which wasmodified. The presented classifier was compared to popular classifiers – neural networks andk-nearest neighbours. Efficiency of modifications in classifier was compared with methodsused in original model NEFCLASS (learning methods). Accuracy of classifier was testedusing 3 datasets from UCI Machine Learning Repository: iris, wine and breast cancer wisconsin.Moreover, influence of ensemble classification methods on classification accuracy waspresented
ADAPTIVE SEARCH AND THE PRELIMINARY DESIGN OF GAS TURBINE BLADE COOLING SYSTEMS
This research concerns the integration of Adaptive Search (AS) technique such as the
Genetic Algorithms (GA) with knowledge based software to develop a research prototype
of an Adaptive Search Manager (ASM). The developed approach allows to utilise both
quantitative and qualitative information in engineering design decision making. A Fuzzy
Expert System manipulates AS software within the design environment concerning the
preliminary design of gas turbine blade cooling systems. Steady state cooling hole geometry
models have been developed for the project in collaboration with Rolls Royce plc. The
research prototype of ASM uses a hybrid of Adaptive Restricted Tournament Selection
(ARTS) and Knowledge Based Hill Climbing (KBHC) to identify multiple "good" design
solutions as potential design options. ARTS is a GA technique that is particularly suitable
for real world problems having multiple sub-optima. KBHC uses information gathered
during the ARTS search as well as information from the designer to perform a deterministic
hill climbing. Finally, a local stochastic hill climbing fine tunes the "good" designs. Design
solution sensitivity, design variable sensitivities and constraint sensitivities are calculated
following Taguchi's methodology, which extracts sensitivity information with a very small
number of model evaluations. Each potential design option is then qualitatively evaluated
separately for manufacturability, choice of materials and some designer's special preferences
using the knowledge of domain experts. In order to guarantee that the qualitative evaluation
module can evaluate any design solution from the entire design space with a reasonably
small number of rules, a novel knowledge representation technique is developed. The
knowledge is first separated in three categories: inter-variable knowledge, intra-variable
knowledge and heuristics. Inter-variable knowledge and intra-variable knowledge are then
integrated using a concept of compromise. Information about the "good" design solutions is
presented to the designer through a designer's interface for decision support.Rolls Royce plc., Bristol (UK
From data acquisition to data fusion : a comprehensive review and a roadmap for the identification of activities of daily living using mobile devices
This paper focuses on the research on the state of the art for sensor fusion techniques, applied to the sensors embedded in mobile devices, as a means to help identify the mobile device user’s daily activities. Sensor data fusion techniques are used to consolidate the data collected from several sensors, increasing the reliability of the algorithms for the identification of the different activities. However, mobile devices have several constraints, e.g., low memory, low battery life and low processing power, and some data fusion techniques are not suited to this scenario. The main purpose of this paper is to present an overview of the state of the art to identify examples of sensor data fusion techniques that can be applied to the sensors available in mobile devices aiming to identify activities of daily living (ADLs)
AI and OR in management of operations: history and trends
The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested
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