Hybrid Approach Research Papers - Academia.edu (original) (raw)

Top Papers
Most Cited Papers
Most Downloaded Papers
Newest Papers
People
- by
- •
- Management, Marketing, Supply Chain Management, Operations Management

Connecting Multiple Criteria Decision Support (MCDS) methods with SWOT (Strengths, Weaknesses, Opportunities and Threats) analysis yields analytical priorities for the SWOT factors and makes them commensurable. Decision alternatives can also be evaluated with respect to each SWOT factor. SWOT analysis provides the basic frame for analyses of operational environments to support strategic decision-making. MCDS methods enhance SWOT analysis and its results so that alternative strategic decisions can be prioritised overall. This benefits the utilisation of the SWOT-results in the decision making process. The methods also help in defining the action line alternatives that are based on the recognition of the most important operational environmental factors and their possible interdependencies. The MCDS method applied initially and most often within the SWOT framework has been the Analytic Hierarchy Process (AHP), and the hybrid approach has been called the A'WOT. Any MCDS method, and...

- by Miika Kajanus and +1
- •
- Decision Making, Strategic Planning, Natural Resource Management, Analytic Hierarchy Process
- by Erkan Bayraktar
- •
- Management, Marketing, Supply Chain Management, Operations Management
- by Ashu Jain
- •
- Information Systems, Applied Mathematics, Hydrology, Time Series
- by David Fox
- •
- User Interface, Agile Development, Agile Methods, User Centered Design
- by Marina Vassiliou and +1
- •
- Machine Translation, System Architecture, Pattern Matching, Hybrid Approach

In this paper, we propose a novel Intrusion Detection System (IDS) architecture utilizing both anomaly and misuse detection approaches. This hybrid Intrusion Detection System architecture consists of an anomaly detection module, a misuse detection module and a decision support system combining the results of these two detection modules. The proposed anomaly detection module uses a Self-Organizing Map (SOM) structure to model normal behavior. Deviation from the normal behavior is classified as an attack. The proposed misuse detection module uses J.48 decision tree algorithm to classify various types of attacks. The principle interest of this work is to benchmark the performance of the proposed hybrid IDS architecture by using KDD Cup 99 Data Set, the benchmark dataset used by IDS researchers. A rule-based Decision Support System (DSS) is also developed for interpreting the results of both anomaly and misuse detection modules. Simulation results of both anomaly and misuse detection modules based on the KDD 99 Data Set are given. It is observed that the proposed hybrid approach gives better performance over individual approaches.

- by Murat Topallar
- •
- Anomaly Detection, Decision support system, Decision Trees, Computer Network
- by Jizhong Zhou
- •
- Microbiology, Medical Microbiology, Ecology, Bacteria
- by Selim Zaim
- •
- Information Systems, Supply Chain Management, Decision Making, Fuzzy Logic
- by Jean-yves Didier
- •
- Cognitive Science, Augmented Reality, Real Time, Pose Estimation

A hybrid Reynolds-averaged Navier–Stokes/Large-Eddy Simulation (RANS/LES) methodology has received considerable attention in recent years, especially in its application to wall-bounded flows at high-Reynolds numbers. In the conventional zonal hybrid approach, eddy-viscosity-type RANS and subgrid scale models are applied in the RANS and LES zones, respectively. In contrast, the non-zonal hybrid approach uses only a generalized turbulence model, which provides a unified simulation approach that spans the continuous spectrum of modeling/simulation schemes from RANS to LES. A particular realization of the non-zonal approach, known as partially resolved numerical simulation (PRNS), uses a generalized turbulence model obtained from a rescaling of a conventional RANS model through the introduction of a resolution control function F R , where F R is used to characterize the degree of modeling required to represent the unresolved scales of turbulent motion. A new generalized functional form ...

- by Eugene Yee and +2
- •
- Engineering, Large Eddy Simulation, Numerical Simulation, Turbulent Flow
- by Fi-john Chang
- •
- Environmental Engineering, Civil Engineering, Water resources, Management Control
- by Carla Gomes
- •
- Graph Coloring, Hybrid Approach, Constraint Satisfaction, Graph Coloring Problem
- by Jim Prentzas and +1
- •
- Cognitive Science, Expert Systems, Intelligent System, Case Based Reasoning
- by Eva González Fernández
- •
- Time Series, Forecasting, Market Research, Energy Conversion
- by Laura Morente
- •
- Engineering, Algorithms, Computer Vision, Photography
- by Jean-marie Bonnin and +1
- •
- User eXperience, Resource use, Quality of Experience, Performance Evaluation
- by Sanjay Ahire
- •
- Engineering, Quality Management, Principal Component Analysis, Pattern Recognition
- by Cheng-Lin Liu
- •
- Cognitive Science, Image Analysis, Image segmentation, Edge Detection
- by Subhes C Bhattacharyya
- •
- Engineering, Systems Analysis, Hybrid, Hybrid Approach
- by Sophie Wauquier
- •
- Speech perception, Nonlinear dynamics, Hybrid Approach, Approaches

In this paper we present a Lisp-implemented route generation algorithm (RGA) for the design of transit networks. Along with an analysis procedure and an improvement algorithm, this algorithm constitutes one of the three major components of an AI-based hybrid solution approach to solving the transit network design problem. Such a hybrid approach incorporates the knowledge and expertise of transit network planners and implements efficient search techniques using AI tools, algorithmic procedures developed by others, and modules for tools implemented in conventional languages. RGA is a design algorithm that (a) is heavily guided by the demand matrix,(b) allows the designer's knowledge to be implemented so as to reduce the search space, and(c) generates different sets of routes corresponding to different trade-offs among conflicting objectives (user and operator costs).

- by Hani Mahmassani
- •
- Engineering, Network Design, Hybrid Approach

A hybrid wavelet–bootstrap–ANN (WBANN) model is developed in this study to explore the potential of wavelet and bootstrapping techniques for developing an accurate and reliable ANN model for hourly flood forecasting. The wavelet technique is used to decompose the times series data into different components which capture useful information on various resolution levels. Five years hourly water level data for monsoon season from five gauging stations in Mahanadi River basin, India are used in this study. The observed water level time series of a particular gauging station is decomposed to sub-series by discrete wavelet transformation and then appropriate sub-series are added up to develop new time series. The bootstrap resampling method is used to generate different realizations of the newly constructed datasets using discrete wavelet transformation to create a set of bootstrap samples that are finally used as input to develop WBANN model. Performance of WBANN model is also compared with three different ANN models: traditional ANNs, wavelet based ANNs (WANNs), bootstrap based ANNs (BANNs). The results showed that the hybrid models WBANN and BANN produced better results than the traditional ANN and WANN models. WBANN model simulated the peak water level better than ANN, WANN and BANN models, and in general, the overall performance of WBANN model is accurate and reliable as compared to the other three models. This study reveals that whereas wavelet decomposition improves the performance of ANN models, bootstrap resampling technique produces more consistent and stable solutions. WBANN model is also used to assess the predictive uncertainty in forms of confidence intervals (CI) to assess the predictive uncertainty for 1–10 h lead time forecasts. Results obtained indicate that WBANN forecasting model with confidence intervals can improve their reliability for flood forecasting.

- by Mukesh Tiwari
- •
- Hydrology, Multidisciplinary, Flood Forecasting, Hybrid Approach
- by Wendell Wallach
- •
- Human Geography, Philosophy, Ethics, Applied Ethics
- by Adel S Elmaghraby
- •
- Information Systems, Bioinformatics, Mathematics, Computer Science
- by Helena Martins
- •
- Decision Making, Forest Ecology And Management, Biological Sciences, Forest Policy
- by Roger Sheldon
- •
- Algorithms, Catalysis, Kinetics, Statistical Analysis
- by Liang Gao
- •
- Engineering, Mechanical Engineering, Chemical Engineering, Tabu Search
- by Dennis McLeod
- •
- Recommender System, Time Complexity, Real Time, Hybrid Approach
- by Michael Breuer
- •
- Applied Mathematics, Adaptive Control, Classical Physics, Turbulent Flow
- by xin li
- •
- Cognitive Science, Image Processing, Computational Complexity, Computational Modeling
- by Oscar Cordon
- •
- Cognitive Science, Applied Mathematics, Soft Computing, Genetic Algorithm

In this work, we present a hybrid classification technique combining an expert system and an object‐oriented
approach. The expert system allows the integration of a knowledge base built through a series of deductive rules, that will
guide the classification whose primitives requires informations on the highest level and will be represented by semantic
objects, not pixels. Instead of the original bands only, other derived data combining textural, spectral information and
shapes, are included in the classification process. The result is then combined with an expert system whose rules use
variables such as vegetation index (NDVI), shading of building objects and other indicators. In conclusion, this approach has
allowed us to improve the accuracy of the feature extraction method by extracting objects like, roads, trees, grass, bare soil
and shadow on a very high‐resolution image of the city of Rabat.

- by Alays Kacimi and +1
- •
- Remote Sensing, Urban Planning, Rule based systems, High Resolution Images
- by Niraj Aswani
- •
- Word alignment, Parallel Corpora, Hybrid Approach, Edit Distance
- by Daoxiong Gong
- •
- Genetics, Control Engineering, Genetic Algorithms, Ant Colony Optimization
- by mekki ksouri
- •
- Engineering, Simulated Annealing, Product Design, Genetic Algorithm
- by Oscar Castillo
- •
- Information Systems, Applied Mathematics, Fuzzy Logic, Genetic Algorithms

Carboxyl methyl cellulose (CMC) as a water-soluble metal-binding polymer in combination with ultrafiltration (UF) was used in a hybrid approach for selective removal and recovery of copper from water. In the complexation-UF process the... more

- by Valentin Nenov
- •
- Engineering, Mine Water, Desalination, Copper
- by Dagan Feng
- •
- Algorithms, Biomedical Engineering, Fuzzy Logic, Magnetic Resonance Imaging
- by Todd Arbogast
- •
- spe paper x plot, Reservoir Simulation, Numerical Simulation, Multiscale Simulation

E-commerce and the continuous growth of the WWW has seen the rising of a new generation of e-retail sites. A number of commercia l agent-based systems has been developed to help Internet shoppers decide what to buy and wh ere to buy it from. In such systems, ontologies play a crucial role in supporting the ex change of business data, as they provide a formal vocabulary for the information and unify dif ferent views of a domain in a shared and safe cognitive approach. In CROSSMARC (a European research project supporting development of an agent-based multilingual/multi-domain system for information extraction (IE) from web pages), a knowledge based approach has been combined with machine learning techniques (in particular, wrapper induction based components) in order to design a robust system for extracting information from relevant web sites. In the ever-changing Web framework this hybrid approach supports adaptivity to new emerging concepts and a certain degree of independence from ...

- by Michele Vindigni and +1
- •
- Machine Learning, Agent Based, Information Extraction, Knowledge base
- by sooyoung kim
- •
- Linear Programming, Production Planning, Multidisciplinary, Simulation

Urdu script-based languages’ character recognition has some technical issues not existing in other languages and makes these languages more complicated. Segmentation-based character recognition approach for handwritten Urdu, both Nasta’liq and Nasakh script-based languages, incorporates number of overhead and very less accurate as compared to segmentation free. This paper presents a segmentation-free approach for recognition of online Urdu handwritten script using hybrid classifier, HMM and fuzzy logic. Trained data set consisting of HMMs for each stroke is further classified into 62 sub-patterns based on the primary stroke shape at the beginning and end using fuzzy rule. Fuzzy linguistic variables based on language structure are used to model features and provide suitable result for large variation in handwritten strokes. Twenty-six time variant structural and statistical features are extracted for the base strokes. The fuzzy classification into sub-patterns increases the efficiency and decreases the computational complexity due to reduction in data set size. The hybrid HMM–fuzzy technique is efficient for large and complex data set. It provided 87.6% and 74.1% for Nasta’liq and Nasakh, respectively, on 1800 ligatures.

- by Afaq H Syed
- •
- Computational Complexity, Fuzzy Logic, Character Recognition, Knowledge Based Systems
- by Anne Tarantino
- •
- Mechanical Engineering, Time Series, Energy, Sensitivity Analysis