Search Results - (( intelligence _ ((bee algorithm) OR (tree algorithm)) ) OR ( intelligence based real algorithm ))
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1
Performance Enhancement Of Artificial Bee Colony Optimization Algorithm
Published 2013“…Artificial Bee Colony (ABC) algorithm is a recently proposed bio-inspired optimization algorithm, simulating foraging phenomenon of honeybees. …”
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2
An intelligent DDoS attack detection tree-based model using Gini index feature selection method
Published 2023“…This paper proposes a novel intelligent DDoS attack detection model based on a Decision Tee (DT) algorithm and an enhanced Gini index feature selection method. …”
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An Intelligent System Approach to the Dynamic Hybrid Robot Control
Published 1996“…A one-pass smoothing algorithm was used to generate a practical smooth trajectory path in near real time. …”
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4
A conceptual framework for multi-objective optimization of building performance: Integrating intelligent algorithms, simulation tools, and climate adaptation
Published 2025“…A thematic analysis of 40 peer-reviewed articles was conducted using ATLAS.ti, revealing three dominant research themes: intelligent algorithms, building performance simulation techniques, and adaptive design for climate change. …”
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An efficient and effective case classification method based on slicing
Published 2006“…The algorithms are: Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5). …”
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Modeling of static and dynamic components of bio-nanorobotic systems
Published 2012“…In addition, a graph algorithm based on greedy methods is employed to compute a new set of optimal weighted electronic properties of the fullerenes via computing their Minimum Weight Spanning Trees (MWSTs). …”
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7
Fruit-Fly Based Searching Algorithm For Cooperative Swarming Robotic System
Published 2013“…A number of benchmark function processes were conducted to assess the performance of proposed FOA (Fly Optimisation Algorithm).…”
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Green building valuation based on machine learning algorithms / Thuraiya Mohd ... [et al.]
Published 2021“…This experiment used five common machine learning algorithms namely 1) Linear Regressor, 2) Decision Tree Regressor, 3) Random Forest Regressor, 4) Ridge Regressor and 5) Lasso Regressor tested on a real estate data-set of covering Kuala Lumpur District, Malaysia. 3 set of experiments was conducted based on the different feature selections and purposes The results show that the implementation of 16 variables based on Experiment 2 has given a promising effect on the model compare the other experiment, and the Random Forest Regressor by using the Split approach for training and validating data-set outperformed other algorithms compared to Cross-Validation approach. …”
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9
Insomnia audio therapy mobile application with music recommender system / Nur Azmina Mohamad Zamani, Nasiroh Omar and Nur Damira Huda Azmi
Published 2022“…The research focused on investigating the performances of three machine learning algorithms namely Random Forest, Decision Tree and Support Vector Machine to be selected as the music recommendation tool. …”
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ABC-PSO for vertical handover in heterogeneous wireless networks
Published 2017“…This study proposes a hybrid intelligent handover decision algorithm primarily founded on two main heuristic algorithms: Artificial Bee Colony or ABC as well as Particle Swarm Optimization or PSO named ABC-PSO to select best wireless network during vertical handover process. …”
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Application of machine learning algorithms to predict removal efficiency in treating produced water via gas hydrate-based desalination
Published 2025“…These findings demonstrate that integration of ML with GHBD significantly improved predictive capabilities, enabled real time application, reduce experimental effort, as well as improve the development of intelligent, sustainable, and scalable water treatment technology. © 2025 Elsevier B.V.…”
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Time series forecasting of energy commodity using grey wolf optimizer
Published 2015“…The ability to model and perform decision making is an essential feature of many real-world applications including the forecasting of commodity prices.In this study, a forecasting model based on a relatively new Swarm Intelligence (SI) behaviour, namely Grey Wolf Optimizer (GWO), is developed for short term time series forecasting.The model is built upon data obtained from the West Texas Intermediate (WTI) crude oil and gasoline price.Performance of the GWO model is compared against two other models which are developed based on Evolutionary Computation (EC) algorithms, namely the Artificial Bee Colony (ABC) and Differential Evolution (DE).Results showed that the GWO model outperformed DE in both crude oil and gasoline price forecasting. …”
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13
Time Series Forecasting of Energy Commodity using Grey Wolf Optimizer
Published 2015“…Performance of the GWO model is compared against two other models which are developed based on Evolutionary Computation (EC) algorithms, namely the Artificial Bee Colony (ABC) and Differential Evolution (DE). …”
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Application of Bee Colony Optimization (BCO) in NP-Hard Problems
Published 2011“…Bee-Inspired algorithms were presumed to bring the new direction in the field of Swann Intelligence. …”
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Content-based indexing of low resolution documents
Published 2016“…This approach, which is signature-based are considered for fast and efficient matching to fulfil the needs of real-time applications. …”
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17
Optimal design of step – cone pulley problem using the bees algorithm
Published 2021“…It is commonly known as Swarm Intelligence (SI). Examples of algorithm categorized under SI are Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and the Bees Algorithm (BA). …”
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Application of the bees algorithm to the selection features for manufacturing data
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A true annealing approach to the marriage in honey-bees optimization algorithm
Published 2003“…Marriage in Honey-Bees Optimization is a new swarm intelligence technique inspired by the marriage process of honey-bees. …”
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