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  • Title/Summary/Keyword: Hybrid Intelligent Algorithm

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Development of Web-based Intelligent Recommender Systems using Advanced Data Mining Techniques (개선된 데이터 마이닝 기술에 의한 웹 기반 지능형 추천시스템 구축)

  • Kim Kyoung-Jae;Ahn Hyunchul
    • Journal of Information Technology Applications and Management
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    • v.12 no.3
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    • pp.41-56
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    • 2005
  • Product recommender system is one of the most popular techniques for customer relationship management. In addition, collaborative filtering (CF) has been known to be one of the most successful recommendation techniques in product recommender systems. However, CF has some limitations such as sparsity and scalability problems. This study proposes hybrid cluster analysis and case-based reasoning (CBR) to address these problems. CBR may relieve the sparsity problem because it recommends products using customer profile and transaction data, but it may still give rise to scalability problem. Thus, this study uses cluster analysis to reduce search space prior to CBR for scalability Problem. For cluster analysis, this study employs hybrid genetic and K-Means algorithms to avoid possibility of convergence in local minima of typical cluster analyses. This study also develops a Web-based prototype system to test the superiority of the proposed model.

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Hybrid Product Recommendation for e-Commerce : A Clustering-based CF Algorithm

  • Ahn, Do-Hyun;Kim, Jae-Sik;Kim, Jae-Kyeong;Cho, Yoon-Ho
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • 2003.05a
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    • pp.416-425
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    • 2003
  • Recommender systems are a personalized information filtering technology to help customers find the products they would like to purchase. Collaborative filtering (CF) has been known to be the most successful recommendation technology. However its widespread use in e-commerce has exposed two research issues, sparsity and scalability. In this paper, we propose several hybrid recommender procedures based on web usage mining, clustering techniques and collaborative filtering to address these issues. Experimental evaluation of suggested procedures on real e-commerce data shows interesting relation between characteristics of procedures and diverse situations.

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Design of the Hybrid Controller using the Fuzzy Switching Mode (퍼지 스위칭 모드를 이용한 하이브리드 제어기의 설계)

  • 최창호;임화영
    • Journal of the Korean Institute of Intelligent Systems
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    • v.10 no.3
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    • pp.260-269
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    • 2000
  • The fuzzy and state-feedback control systems have been applied in various areas from non-linear to linear systems. A Fuzzy controller is endowed with control rules and membership function that are constructed on the knowledge of expert, as like intuition and experience. but It is very difficult to obtain the exact values which are the membership function and consequent parameters. though apply back-propagation algorithm to the system, the convergence time a much. Besides, the state-feedback system is most widely used in industry due to its simple control structure and easily able to design the controller. but it is weak in complex system of higher degree and non-linear. In this paper presents the design of a fuzzy switching mode, it these two controllers work at different operation conditions, the advantages of both controller can be retained and the disadvantages can be removed. Between the Fuzzy and the State-feedback controlles, the good outputs are selected by the switching mode. Moreover it is powerful in complex system of higher degree and non-linear. In these sense compared with the state-feedback controller, the performance of the proposed controller was improvedin the section of linearization.

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A Study on the Image Filter using Neuro-Fuzzy (뉴로-퍼지를 이용한 영상 필터 연구)

  • 변오성;이철희;문성룡;임기영
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2001.05a
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    • pp.83-86
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    • 2001
  • In this paper, it study about the image filter applied the hybrid fuzzy membership function to the neuro-fuzzy system. Here, this system applys the genetic algorithm in order to obtain the optimal image as the iteration carry for making the data value in the error. It is removed the included noise in an image using the proposed image filter and compared the proposed image filter performance with the other filters using MATLAB. And it is found that the proposed filter performance is superior to the other filters which has the similar structure through the images. To show the superior ability, it is compared with MSE and SNR for images.

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Generalized Fuzzy Modeling

  • Hwang, Hee-Soo;Joo, Young-Hoon;Woo, Kwang-Bang
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1993.06a
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    • pp.1145-1150
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    • 1993
  • In this paper, two methods of fuzzy modeling are prsented to describe the input-output relationship effectively based on relation characteristics utilizing simplified reasoning and neuro-fuzzy reasoning. The methods of modeling by the simplified reasoning and the neuro-fuzzy reasoning are used when the input-output relation of a system is 'crisp' and 'fuzzy', respectively. The structure and the parameter identification in the modeling method by the simplified reasoning are carried out by means of FCM clustering and the proposed GA hybrid scheme, respectively. The structure and the parameter identification in the modeling method by the neuro-fuzzy reasoning are carried out by means of GA and BP algorithm, respectively. The feasibility of the proposed methods are evaluated through simulation.

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A TCP-Friendly Congestion Control Scheme using Hybrid Approach for Enhancing Fairness of Real-Time Video (실시간 비디오 스트림의 공정성 개선를 위한 TCP 친화적 하이브리드 혼잡제어기법)

  • Kim, Hyun-Tae;Yang, Jong-Un;Ra, In-Ho
    • Journal of the Korean Institute of Intelligent Systems
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    • v.14 no.3
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    • pp.285-289
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    • 2004
  • Recently, due to the high development of the internet, needs for multimedia streams such as digital audio and video is increasing much more. In case of transmitting multimedia streams using the User Datagram Protocol (UDP), it may cause starvation of TCP traffic on the same transmission path, thus resulting in congestion collapse and enormous delay because UDP does not perform TCP-like congestion control. Because of this problem, diverse researches are being conducted on new transmission schemes and protocols intended to efficiently reduce the transmission delay of real-time multimedia streams and perform congestion control. The TCP-friendly congestion control schemes can be classified into the window-based congestion control, which uses the general congestion window management function, and the rate-based congestion control, which dynamically adjusts transmission rate by using TCP modeling equations and the like. In this paper, we suggest the square-root congestion avoidance algorithm with the hybrid TCP-friendly congestion control scheme which the window-based and rate-based congestion controls are dealt with in a combined way. We apply the proposed algorithm to the existing TEAR. We simulate the performance of the proposed TEAR by using NS, and the result shows that it gives better improvement in the stability needed for providing congestion control than the existing TEAR.

A Study on Development of Embedded System for Speech Recognition using Multi-layer Recurrent Neural Prediction Models & HMM (다층회귀신경예측 모델 및 HMM 를 이용한 임베디드 음성인식 시스템 개발에 관한 연구)

  • Kim, Jung hoon;Jang, Won il;Kim, Young tak;Lee, Sang bae
    • Journal of the Korean Institute of Intelligent Systems
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    • v.14 no.3
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    • pp.273-278
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    • 2004
  • In this paper, the recurrent neural networks (RNN) is applied to compensate for HMM recognition algorithm, which is commonly used as main recognizer. Among these recurrent neural networks, the multi-layer recurrent neural prediction model (MRNPM), which allows operating in real-time, is used to implement learning and recognition, and HMM and MRNPM are used to design a hybrid-type main recognizer. After testing the designed speech recognition algorithm with Korean number pronunciations (13 words), which are hardly distinct, for its speech-independent recognition ratio, about 5% improvement was obtained comparing with existing HMM recognizers. Based on this result, only optimal (recognition) codes were extracted in the actual DSP (TMS320C6711) environment, and the embedded speech recognition system was implemented. Similarly, the implementation result of the embedded system showed more improved recognition system implementation than existing solid HMM recognition systems.

Design of ASM-based Face Recognition System Using (2D)2 Hybird Preprocessing Algorithm (ASM기반 (2D)2 하이브리드 전처리 알고리즘을 이용한 얼굴인식 시스템 설계)

  • Kim, Hyun-Ki;Jin, Yong-Tak;Oh, Sung-Kwun
    • Journal of the Korean Institute of Intelligent Systems
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    • v.24 no.2
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    • pp.173-178
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    • 2014
  • In this study, we introduce ASM-based face recognition classifier and its design methodology with the aid of 2-dimensional 2-directional hybird preprocessing algorithm. Since the image of face recognition is easily affected by external environments, ASM(active shape model) as image preprocessing algorithm is used to resolve such problem. In particular, ASM is used widely for the purpose of feature extraction for human face. After extracting face image area by using ASM, the dimensionality of the extracted face image data is reduced by using (2D)2hybrid preprocessing algorithm based on LDA and PCA. Face image data through preprocessing algorithm is used as input data for the design of the proposed polynomials based radial basis function neural network. Unlike as the case in existing neural networks, the proposed pattern classifier has the characteristics of a robust neural network and it is also superior from the view point of predictive ability as well as ability to resolve the problem of multi-dimensionality. The essential design parameters (the number of row eigenvectors, column eigenvectors, and clusters, and fuzzification coefficient) of the classifier are optimized by means of ABC(artificial bee colony) algorithm. The performance of the proposed classifier is quantified through yale and AT&T dataset widely used in the face recognition.

Odor Cognition and Source Tracking of an Intelligent Robot based upon Wireless Sensor Network (센서 네트워크 기반 지능 로봇의 냄새 인식 및 추적)

  • Lee, Jae-Yeon;Kang, Geun-Taek;Lee, Won-Chang
    • Journal of the Korean Institute of Intelligent Systems
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    • v.21 no.1
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    • pp.49-54
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    • 2011
  • In this paper, we represent a mobile robot which can recognize chemical odor, measure concentration, and track its source indoors. The mobile robot has the function of smell that can sort several gases in experiment such as ammonia, ethanol, and their mixture with neural network algorithm and measure each gas concentration with fuzzy rules. In addition, it can not only navigate to the desired position with vision system by avoiding obstacles but also transmit odor information and warning messages earned from its own operations to other nodes by multi-hop communication in wireless sensor network. We suggest the way of odor sorting, concentration measurement, and source tracking for a mobile robot in wireless sensor network using a hybrid algorithm with vision system and gas sensors. The experimental studies prove that the efficiency of the proposed algorithm for odor recognition, concentration measurement, and source tracking.

A Hybrid Recommendation System based on Fuzzy C-Means Clustering and Supervised Learning

  • Duan, Li;Wang, Weiping;Han, Baijing
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.15 no.7
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    • pp.2399-2413
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    • 2021
  • A recommendation system is an information filter tool, which uses the ratings and reviews of users to generate a personalized recommendation service for users. However, the cold-start problem of users and items is still a major research hotspot on service recommendations. To address this challenge, this paper proposes a high-efficient hybrid recommendation system based on Fuzzy C-Means (FCM) clustering and supervised learning models. The proposed recommendation method includes two aspects: on the one hand, FCM clustering technique has been applied to the item-based collaborative filtering framework to solve the cold start problem; on the other hand, the content information is integrated into the collaborative filtering. The algorithm constructs the user and item membership degree feature vector, and adopts the data representation form of the scoring matrix to the supervised learning algorithm, as well as by combining the subjective membership degree feature vector and the objective membership degree feature vector in a linear combination, the prediction accuracy is significantly improved on the public datasets with different sparsity. The efficiency of the proposed system is illustrated by conducting several experiments on MovieLens dataset.