Search Results - (( deviation selection research algorithm ) OR ( image classification bees algorithm ))*

Leaf lesion classification (LLC) algorithm based on artificial bee colony (ABC) by Ahmad, Faudziah, Ku-Mahamud, Ku Ruhana, Sainin, Mohd Shamrie, Airuddin, Ahmad

“…Results showed that the Leaf Lesion Classification (LLC) algorithm based on Artificial Bee colony (ABC) produced an average 96.83% of accuracy and average 1.66 milliseconds of processing time, indicating that LLC algorithm is better than algorithm such as Otsu, Canny, Roberts and Sobel. …”
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RLMD-PA: A Reinforcement Learning-Based Myocarditis Diagnosis Combined with a Population-Based Algorithm for Pretraining Weights by Seyed Vahid Moravvej, Roohallah Alizadehsani, Sadia Khanam, Zahra Sobhaninia, Afshin Shoeibi, Fahime Khozeimeh, Zahra Alizadeh Sani, Ru-San Tan, Abbas Khosravi, Saeid Nahavandi, Nahrizul Adib Kadri, N. Arunkumar, U.Rajendra Acharya, Muhammad Mokhzaini Azizan

A Standard Deviation Selection in Evolutionary Algorithm for Grouper Fish Feed Formulation by Soong, Cai Juan, Razamin, Ramli, Rosshairy, Abdul Rahman

“…Results shown that the fitness of standard deviation selection in evolutionary algorithm is applicable. …”
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A standard deviation selection in evolutionary algorithm for grouper fish feed formulation by Soong, Cai Juan, Ramli, Razamin, Abdul Rahman, Rosshairy

“…Malaysia is one of the major producer countries for fishery production due to its location in the equatorial environment.Grouper fish is one of the potential markets in contributing to the income of the country due to its desirable taste, high demand and high price.However, the demand of grouper fish is still insufficient from the wild catch.Therefore, there is a need to farm grouper fish to cater to the market demand.In order to farm grouper fish, there is a need to have prior knowledge of the proper nutrients needed because there is no exact data available.Therefore, in this study, primary data and secondary data are collected even though there is a limitation of related papers and 30 samples are investigated by using standard deviation selection in Evolutionary algorithm.Thus, this study would unlock frontiers for an extensive research in respect of grouper fish feed formulation.Results shown that the fitness of standard deviation selection in evolutionary algorithm is applicable.The feasible and low fitness, quick solution can be obtained. …”
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Grouper fish feed formulation using enhanced evolutionary algorithm with fibonacci rabbit initialization and binary-standard deviation tournament selection by Soong, Cai Juan

“…The main contribution of this research is the development of feed formulation using Evolutionary Algorithm (EA) with four variations of EA, which are Semi-Random Initialization – Binary Tournament Selection - EA (SR-BT-EA), Fibonacci Rabbit Initialization – Binary Tournament Selection - EA (FR-BT-EA), Semi-Random Initialization - Binary- Standard Deviation Tournament Selection - EA (SR-SD-EA) and Fibonacci Rabbit Initialization - Binary-Standard Deviation Tournament Selection - EA (FR-SD-EA). …”
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Dynamic Robust Bootstrap Algorithm for Linear Model Selection Using Least Trimmed Squares by Uraibi, Hassan Sami

“…The results on the model selection again signify that our proposed robust bootstrap model selection method is more robust than the classical bootstrap model selection.…”
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Neural Network Model and Finite Element Simulation of Spring back in Plane-Strain Metallic Beam Bending by Abu Khadra, Fayiz Y. M.

“…This research utilizes computer generated D-optimal designs to select training examples for both metamodeling techniques so that a comparison between the two techniques can be considered as fair. …”
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Development of a rule-based fault diagnostic advisory system for precut fractionation column by Heng, Han Yann

“…This research presents a Fault Diagnostic Advisory (FDA) System which can be used to detect and diagnose unexpected process deviation in the operation of fatty acid precut fractionation column. …”
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Statistical band selection for descriptors of MBSE and MFCC-based features for accent classification of Malaysian English / Yusnita M. A. ...[et al.] by M. A., Yusnita, M. P., Paulraj, Yaacob, Sazali, A. B., Shahriman, Mokhtar, Nor Fadzilah

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Optimal Reactive Power Dispatch Solution by Loss Minimization Using Moth-Flame Optimization Technique by Rebecca Ng, Shin Mei, Mohd Herwan, Sulaiman, Zuriani, Mustaffa, Hamdan, Daniyal

“…The statisticalanalysis of this research illustrated that MFO is able to produce competitive results by yielding lowerpower loss and lower voltage deviation than the selected techniques from literature.…”
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Feature selection in intrusion detection, state of the art: A review by Rais, H.M., Mehmood, T.

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Algorithm enhancement for host-based intrusion detection system using discriminant analysis by Dahlan, Dahliyusmanto

“…Anomaly detection algorithms model normal behavior. Anomaly detection models compare sensor data to normal patterns learned from the training data by using statistical method and try to detect activity that deviates from normal activity. …”
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An enhancement of classification technique based on rough set theory for intrusion detection system application by Noor Suhana, Sulaiman

“…In order to improve classification performance problem, feature selection and discretization algorithm are crucial in selecting relevant attributes that could improve classification performance. …”
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Comparison of mabsa, PSO and GWO of PI-PD controller for dc motor by Nur Naajihah, Ab Rahman

“…Three distinct mathematical models pertaining to the DC motor system are derived from a thorough analysis of previous research. The swarm intelligence group selected Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Modified Adaptive Bats Sonar Algorithms (MABSA) to optimize the parameters of the PI-PD controller. …”
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Optimal power flow solutions for power system operations using moth-flame optimization algorithm by Salman Ameen Ali, Abdullah Alabd

“…The comparison proves that MFO offers a better result compared to the other selected methods. In IEEE 30-bus test system, MFO outperform the other optimization methods with 967.589961$/h compared to 971.411400 $/h, 983.738069$/h, 975.346233$/h, 985.198050$/h, 1035.537820$/h for Improved Grey Wolf Optimizer (IGWO), Grey Wolf Optimizer (GWO), Ant Loin Optimizer (ALO), Whale Optimization Algorithm (WOA), and Sine Cosine Algorithm (SCA) respectively. …”
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Parameter identification of thermoelectric modules using enhanced slime mould algorithm (ESMA) by Ponnalagu, Dharswini, Mohd Ashraf, Ahmad, Jui, Julakha Jahan

“…This paper sets pioneering research which investigates the parametric identification of thermoelectric modules (TEMs) through the employment of enhanced slime mould algorithm (ESMA). …”
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Implementation of machine learning algorithms for streamflow prediction of Dokan dam by Sarmad Dashti Latif, Mr.

“…Dokan dam in Iraq was selected as the case study for this research. The data is collected from the ministry of agriculture and water resources, Kurdistan regional government, Iraq. …”

Enhancing high-dimensional streaming data analysis: optimizing Online Feature Selection for handling drift using optimization technique and ensemble learning by Kamaru-Zaman, Ezzatul Akmal

“…The research employs a structured methodology, introducing two novel methods: PSO-OSFS (Particle Swarm Optimization for Online Streaming Feature Selection), an optimized online feature selection and its enhancement, PSO-OSFS+ HEFT de-signed to handle feature drift. …”
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Hybrid Balance Artificial Potential Field Navigation System For An Autonomous Surface Vessel In Riverine Environment by Jianhong, Mei

“…Thus, the objectives of the research are: to develop a riverbanks identification algorithm for ASV navigation; and to develop a marine traffic rules compliant navigation and obstacles avoidance algorithm for ASV in the unstructured riverine environment. …”
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Automated plant recognition system based on multi-objective parallel genetic algorithm and neural network by Sefidgar, Seyed Mohammad Hossein

“…First, the best set of structures for feed forward neural network were found by multi objective parallel genetic algorithm. This approach regarded three criteria involving mean square error, Akaike information criterion and minimum description length to rate different feed forward neural network structures and to select the best set of them. …”
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