Search Results - (( intelligence data modeling algorithm ) OR ( intelligence system clustering algorithm ))

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    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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    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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    Expectation maximization clustering algorithm for user modeling in web usage mining system by Mustapha, Norwati, Jalali, Manijeh, Jalali, Mehrdad

    Published 2009
    “…To provide intelligent personalized online services such as web recommender systems, it is usually necessary to model users’ web access behavior. …”
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    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
    “…Data mining is a challenging matter in research field for the last few years.Researchers are using different techniques in data mining.This paper discussed the initial state of Design and Development Intelligent Knowledge Discovery System for Stock Exchange (SE) Databases. …”
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    Improved Transportation Model with Internet of Things Using Artificial Intelligence Algorithm by Ayman Khallel Al-ani, Shams Ul Arfeen Laghari, Hariprasath Manoharan, Shitharth Selvarajan, Mueen Uddin

    Published 2023
    “…Furthermore, a route management technique is combined with Artificial Intelligence (AI) algorithm to transmit the data to appropriate central servers. …”
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    Adaptive Neural Subtractive Clustering Fuzzy Inference System for the Detection of High Impedance Fault on Distribution Power System by Tawafan, Adnan, Sulaiman , Marizan, Ibrahim, Zulkifilie

    Published 2012
    “…This paper proposes an intelligent algorithm using an adaptive neural- Takagi Sugeno-Kang (TSK) fuzzy modeling approach based on subtractive clustering to detect high impedance fault. …”
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    Detecting High Impedance Fault in Power Distribution Feeder with Fuzzy Subtractive Clustering Model by Sulaiman , Marizan, Adnan, Tawafan, Ibrahim, Zulkifilie

    Published 2013
    “…This paper proposes an intelligent algorithm using the Takagi Sugeno- Kang (TSK) fuzzy modeling approach based on subtractive clustering to detect the high impedance fault. …”
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    Clustering Based on Customers’ Behaviour in Accepting Personal Loan using Unsupervised Machine Learning by Lim, Wai Ping, Goh, Ching Pang

    Published 2023
    “…This research contributes novel insights into the application of clustering algorithms in banking, proposing pragmatic solutions for efficient data analysis and campaign optimization. …”
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    Broken Conductor Detection on Power Distribution Feeder by Sulaiman , Marizan, Tawafan, Adnan, Ibrahim, Zulkifilie

    Published 2013
    “…It proposes an intelligent algorithm using the Fuzzy Subtractive Clustering Model (FSCM) to detect the high impedance fault. …”
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    High Impedance Fault Detection on Power Distribution Feeder by Sulaiman , Marizan, Tawafan, Adnan, Ibrahim, Zulkifilie

    Published 2012
    “…This paper presents an intelligent algorithm using a Takagi Sugeno-Kang (TSK) fuzzy modeling approach based on subtractive clustering to detect high impedance fault. …”
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    Advanced biomedical imaging for plaque lesion diagnosis focusing on Malaysian subjects / Hadzli Hashim by Hashim, Hadzli

    Published 2006
    “…The system which is based on primary color components from digital images employed new algorithms of data acquisition, data processing, data extraction and application of artificial neural network (ANN) as the decision model to discriminate plaque from other major psoriasis. …”
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    A QoS Aware Cluster Head Selection and Hybrid Cryptography Routing Protocol for Enhancing Efficiency and Security of VANET by JUBAIR, MOHAMMED AHMED, Mostafa, Salama A., ZEBARI, DILOVAN ASAAD, HARIZ, HUSSEIN MUHEE, ABDULSATTAR, NEJOOD FAISAL, HASSAN, MUSTAFA HAMID, ABBAS, ALI HASHIM, ABBAS, FATIMA HASHIM, ALASIRY, AREEJ, ALOUANE, M. TURKI-HADJ

    Published 2022
    “…Nowadays, VANET (Vehicular Ad hoc Network) is one of the key aspects of developing advanced intelligent transportation systems. Due to its huge mobility and rapid topology alteration, the network exposes to link failure that affects the firmness of the network and causes delay and congestion. …”
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    A QoS Aware Cluster Head Selection and Hybrid Cryptography Routing Protocol for Enhancing Efficiency and Security of VANETs by JUBAIR, MOHAMMED AHMED, A. MOSTAFA, SALAMA, DZEBARI, DILOVAN ASAAD, MUHEE HARIZ, HUSSEIN, ABDULSATTAR, NEJOOD FAISAL, HASSAN, MUSTAFA HAMID, ABBAS, ALI HASHIM, HASHIM ABBAS, FATIMA, ALASIRY, AREEJ, ALOUANE, M. TURKI-HADJ

    Published 2023
    “…Nowadays, VANET (Vehicular Ad hoc Network) is one of the key aspects of developing advanced intelligent transportation systems. Due to its huge mobility and rapid topology alteration, the network exposes to link failure that affects the firmness of the network and causes delay and congestion. …”
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    Article
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    Artificial intelligence system for pineapple variety classification and its quality evaluation during storage using infrared thermal imaging by Mohd Ali, Maimunah

    Published 2022
    “…For each variety, 30 samples were randomly selected for data collection at every seven days intervals (Day 0, Day 7, Day 14, and Day 21). …”
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