Search Results - (( intelligence based based algorithm ) OR ( intelligence based clustering algorithm ))

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  1. 1
    Time series data intelligent clustering algorithm for landslide displacement prediction

    Time series data intelligent clustering algorithm for landslide displacement prediction by Han, Liu, Shang, Tao, Shu, Jisen, Khan Chowdhury, Ahmed Jalal

    Published 2018
    “…To address this problem, an intelligent clustering algorithm for time series data in landslide displacement prediction based on nonlinear dynamic time bending is proposed in this paper. …”
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  2. 2
    Development of an intelligent prediction tool for rice yield based on machine learning techniques

    Development of an intelligent prediction tool for rice yield based on machine learning techniques by Md. Sap, Mohd. Noor, Awan, A. M.

    Published 2006
    “…Whereas kernel-based clustering algorithm is developed for finding clusters in climate data. …”
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  3. 3
    Improvement on agglomerative hierarchical clustering algorithm based on tree data structure with bidirectional approach

    Improvement on agglomerative hierarchical clustering algorithm based on tree data structure with bidirectional approach by Dalbouh H.A., Md Norwawi N.

    Published 2024
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  4. 4
    Multi-target tracking algorithm in intelligent transportation based on wireless sensor network

    Multi-target tracking algorithm in intelligent transportation based on wireless sensor network by Lei, Yang, Wu, Yuan, Khan Chowdhury, Ahmed Jalal

    Published 2018
    “…A multi-target tracking algorithm using wireless sensors in an intelligent transportation system is proposed. …”
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  5. 5
    Metaheuristic multi-hop clustering optimization for energy-efficient wireless sensor network

    Metaheuristic multi-hop clustering optimization for energy-efficient wireless sensor network by Vincent Chung, Norah Tuah, Kit Guan Lim, Min Keng Tan, Ismail Saad, Kenneth Tze Kin Teo

    Published 2020
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  6. 6
    Experimental analysis of firefly algorithms for divisive clustering of web documents

    Experimental analysis of firefly algorithms for divisive clustering of web documents by Mohammed, Athraa Jasim, Yusof, Yuhanis, Husni, Husniza

    Published 2014
    “…This paper studies two clustering algorithms that are based on the Firefly Algorithm (FA) which is a recent swarm intelligence approach.We perform experiments utilizing the Newton’s Universal Gravitation Inspired Firefly Algorithm (GFA) and Weight-Based Firefly Algorithm (WFA) on the 20_newsgroups dataset.The analysis is undertaken on two parameters.The first is the alpha (α) value in the Firefly algorithms and latter is the threshold value required during clustering process. …”
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  7. 7
    Optimizing the Management of Knowledge Assets using Swarm Intelligence

    Optimizing the Management of Knowledge Assets using Swarm Intelligence by Yusof, Yuhanis, Baharom, Fauziah, Mohamed, Athraa Jasim

    Published 2018
    “…Hence, this indicates that the proposed swarm intelligence based clustering facilitates the grouping of knowledge assets. …”
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  8. 8
    Clustering autism spectrum disorder student’s system based on intelligence, skills and behavior using agglomerative clustering algortihm / Daarin Nadia Nordin

    Clustering autism spectrum disorder student’s system based on intelligence, skills and behavior using agglomerative clustering algortihm / Daarin Nadia Nordin by Nordin, Daarin Nadia

    Published 2020
    “…Thus, this project proposes a solution to the problems by utilizing the machine learning approach which is the Agglomerative clustering algorithm. Previous studies shows that homogenous grouping of autistics students yields positive results, therefore, this project proposes to design and develop a clustering model system known as the CASDSS (Clustering Autism Spectrum Disorder Students System) where the main goal of this system is to create a homogenous grouping of the ASD students based on their behaviour, skills and intelligence. …”
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  9. 9
    Classification and detection of intelligent house resident activities using multiagent

    Classification and detection of intelligent house resident activities using multiagent by ,, Mohd. Marufuzzaman, M. B. I., Raez, M. A. M., Ali, Rahman, Labonnah F.

    Published 2013
    “…The intelligent home research requires understanding of the human behavior and recognizing patterns of activities of daily living (ADL).However instead of understand the psychosomatic nature of human early projects in this area simply employed intelligence to the household appliance.This paper proposed an algorithm for detecting ADL.The proposed method is based on two opposite state entity extraction.The method reflects on the common data flow of smart home event sequence.The developed algorithm clusters the smart home events by isolating opposite status of home appliance. …”
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  10. 10
    Clustering Approach In Wireless Sensor Networks Based On K-Means: Limitations And Recommendations

    Clustering Approach In Wireless Sensor Networks Based On K-Means: Limitations And Recommendations by Hassan, Ali Abdul-hussian, Md Shah, Wahidah, Husien, Ali Mohamed, Talib, Mohammed Saad, Mohammed, Ali Abdul Jabbar, Othman, Mohd Fairuz Iskandar

    Published 2019
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  11. 11
    Development of an intelligent system using Kernel-based learning methods for predicting oil-palm yield.

    Development of an intelligent system using Kernel-based learning methods for predicting oil-palm yield. by Md. Sap, Mohd. Noor, Awan, A. Majid

    Published 2005
    “…Intelligent systems based on machine learning techniques, such as classification, clustering, are gaining wide spread popularity in real world applications. …”
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  12. 12
    Development Of Human Skin Detection Algorithm Using Multilayer Perceptron Neural Network And Clustering Method

    Development Of Human Skin Detection Algorithm Using Multilayer Perceptron Neural Network And Clustering Method by Al-Mohair, Hani Kaid Saif

    Published 2017
    “…The performance of the developed system has been compared with the existing intelligent skin detection systems. The experimental results reveal that the developed algorithm is able to achieve an accuracy of 87.82% F1-measure based on images from the ECU database. …”
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  13. 13
    An initial state of design and development of intelligent knowledge discovery system for stock exchange database

    An initial state of design and development of intelligent knowledge discovery system for stock exchange database by Che Mat @ Mohd Shukor, Zamzarina, Khokhar, Rashid Hafeez, Md Sap, Mohd Noor

    Published 2004
    “…Generally our clustering algorithm consists of two steps including training and running steps.The training step is conducted for generating the neural network knowledge based on clustering.In running step, neural network knowledge based is used for supporting the Module in order to generate learned complete data, transformed data and interesting clusters that will help to generate interesting rules.…”
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  14. 14
    Software module clustering based on the fuzzy adaptive teaching learning based optimization algorithm

    Software module clustering based on the fuzzy adaptive teaching learning based optimization algorithm by Kamal Z., Zamli, Fakhrud, Din, Nazirah, Ramli, Ahmed, Bestoun S.

    Published 2019
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  15. 15
    Analyzing CT images for detecting lung cancer by applying the computational intelligence-based optimization techniques

    Analyzing CT images for detecting lung cancer by applying the computational intelligence-based optimization techniques by Pethuraj, Mohamed Shakeel, Mohd Aboobaider, Burhanuddin, Salahuddin, Lizawati

    Published 2022
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  16. 16
    Sustainable energy management: Artificial intelligence-based electricity consumption prediction in limited dataset environment for industry applications

    Sustainable energy management: Artificial intelligence-based electricity consumption prediction in limited dataset environment for industry applications by Chuan, Zun Liang, Tan, Lit Ken, Wee, Angel Chi Chyin, Yim Hin, Tham, Shao, Jie Ong, Jia, Yi Low, Chong, Yeh Sai

    Published 2024
    “…The proposed AI-based predictive algorithm aimed to provide predictive insights and interpret the impact of significant economic, environmental, and social clustered determinants on electricity consumption in Malaysia. …”
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  17. 17
    A Center-Based Stable Evolving Clustering Algorithm With Grid Partitioning And Extended Mobility Features For VANETs

    A Center-Based Stable Evolving Clustering Algorithm With Grid Partitioning And Extended Mobility Features For VANETs by Talib, Mohammed Saad, Abdullah, Nihad Ibrahim, Hassan, Aslinda, Abal Abas, Zuraida, Mohammed Al-Khazraji, Ali Abdul-Jabbar, Alamery, Thamer, Ibrahim, Ali Jalil

    Published 2020
    “…An improvement percentage of the efficiency in (CEC-GP) over the benchmarks Center based stable clustering (CBSC) and evolving data clustering algorithm (EDCA) is 65% and 394% respectively.…”
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  18. 18
    Cauchy Density-based Algorithm for VANETs Clustering in 3D Road Environments

    Cauchy Density-based Algorithm for VANETs Clustering in 3D Road Environments by AL-Obaidi, A.S., Jubair, M.A., Aziz, I.A., Ahmad, M.R., Mostafa, S.A., Mahdin, H., AL-Tickriti, A.T., Hassan, M.H.

    Published 2022
    “…In addition, a clustering algorithm that defines mobility vector and uses Cauchy-based density for enabling adding vehicles to their respective clusters. …”
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  19. 19
    Development of intelligent hybrid learning system using clustering and knowledge-based neural networks for economic forecasting : First phase

    Development of intelligent hybrid learning system using clustering and knowledge-based neural networks for economic forecasting : First phase by Che Mat @ Mohd Shukor, Zamzarina, Md Sap, Mohd Noor

    Published 2004
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  20. 20
    Minimizing the number of stunting prevalence using the euclid algorithm clustering approach

    Minimizing the number of stunting prevalence using the euclid algorithm clustering approach by Zarlis, Muhammad, Oktavia, Tanty, Buaton, Relita, Ernawan, Ferda, Andrian, Kevin

    Published 2023
    “…The algorithm used is Euclid. The Euclid algorithm can cluster stunting prevalence data into 4 clusters with the very little category at 79%, the little category at 67%, the many categories at 51%, and the very much category at 21%. …”
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