Search Results - (( using normalization based algorithm ) OR ( using optimization model algorithm ))
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1
Artificial neural network-salp-swarm algorithm for stock price prediction
Published 2024“…Additionally, the SSA-ANN model is compared with other two hybrid models: the ANN optimized by the Whale Optimization Algorithm (WOA-ANN) and Moth-Flame Optimizer (MOA-ANN), as well as a single model, namely the Autoregressive Integrated Moving Average (ARIMA). …”
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Article -
2
Development of classification model between clean water and polluted water based on capacitance properties using Levenberg Marquardt (LM) algorithm of artificial neural network / M...
Published 2020“…The accuracy from the selected most optimized models were 100%. The selected most optimized models were then can be used to classify between clean water and polluted water based on capacitance input.…”
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Student Project -
3
Taguchi?s T-method with Normalization-Based Binary Bat Algorithm
Published 2025“…Specifically, a normalization-based Binary Bat algorithm is used, where discretization of continuous solution into binary form is performed using a normalization equation. …”
Conference paper -
4
CAT CHAOTIC GENETIC ALGORITHM BASED TECHNIQUE AND HARDWARE PROTOTYPE FOR SHORT TERM ELECTRICAL LOAD FORECASTING
Published 2017“…The solution set (i.e. optimized weight/bias matrix of ANN) provided by the optimized and improved genetic algorithm and modified BP based model is extracted and used in the design and development of a prototype device of the proposed model. …”
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Thesis -
5
Modeling and multi-objective optimal sizing of standalone photovoltaic system based on evolutionary algorithms
Published 2020“…The IEM algorithm uses the attraction-repulsion mechanism to change the positions of solutions towards the optimality. …”
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Thesis -
6
One day ahead daily peak hour load forecasting by using invasive weed optimization learning algorithm based Artificial Neural Network
Published 2012“…By using 'seen' and 'unseen' of electrical energy demand data were used to test the performance of the proposed algorithm. …”
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Student Project -
7
OPTIMAL DESIGN AND ANALYSIS OF A DC–DC SYNCHRONOUS CONVERTER USING GENETIC ALGORITHM AND SIMULATED ANNEALING
Published 2009“…Abstract This paper presents the application of Genetic Algorithm (GA) and Simulated Annealing (SA) techniques for optimal design and analysis of a DC–DC converter with synchronous rectification normally used in battery charge/discharge circuits in DC uninterruptible power supply systems. …”
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Citation Index Journal -
8
OPTIMAL DESIGN AND ANALYSIS OF A DC–DC SYNCHRONOUS CONVERTER USING GENETIC ALGORITHM AND SIMULATED ANNEALING
Published 2009“…This paper presents the application of Genetic Algorithm (GA) and Simulated Annealing (SA) techniques for optimal design and analysis of a DC–DC converter with synchronous rectification normally used in battery charge/discharge circuits in DC uninterruptible power supply systems. …”
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Citation Index Journal -
9
Optimization and control of hydro generation scheduling using hybrid firefly algorithm and particle swarm optimization techniques
Published 2018“…Secondly, this approach hybridizing the FA with the rough algorithm (RA), where RA is used to control the steps of randomness for the FA while optimizing the weights of the standard BPNN model. …”
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Thesis -
10
Optimal Placement of Phasor Measurement Unit (PMU) using genetic algorithm & cuckoo search algorithm
Published 2025“…The IEEE 57-bus test system is used as the benchmark model for algorithm evaluation. …”
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Proceeding Paper -
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Flock optimization algorithm-based deep learning model for diabetic disease detection improvement
Published 2024“…Hence, the research objective is to create an improved diabetic disease detection system using a Flock Optimization Algorithm-Based Deep Learning Model (FOADLM) feature modeling approach that leverages the PIMA Indian dataset to predict and classify diabetic disease cases. …”
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13
Optimization of neural network architecture using genetic algorithm for load forecasting
Published 2014“…ANN based models are frequently used for the prediction of future load, because of their learning and mapping ability to address the non linear nature of electrical load. …”
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Conference or Workshop Item -
14
Voting algorithms for large scale fault-tolerant systems
Published 2011“…To solve this problem and gain benefits of this algorithm, we employed parallel algorithm technique and by using optimal number of processors, we could propose optimal algorithms known as Parallel Average Voting and Parallel Weighted Average Voting which both have optimal time complexity and less calculation cost. …”
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Thesis -
15
Modelling of multi-robot system for search and rescue
Published 2023“…In this project, this sensor-based algorithm is known as the Obstacle Avoidance Algorithm. …”
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Final Year Project / Dissertation / Thesis -
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Low Complexity Error Correction in Low Density Parity Check (LDPC) Code Decoder and Encoder for Decode and Forward Cooperative Wireless Communication
Published 2021“…By using the optimization min-sum belief propagation approach, a low complexity min-sum (MS) based decoding algorithm called Variable Global Optimization Min-Sum (VGOMS) has been developed. …”
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Thesis -
17
Breast cancer diagnosis through an optimization-driven multispectral gamma correction (ODMGC)
Published 2024“…The algorithm was successfully implemented in MATLAB 2020a, and the classifier was developed in Jupyter Notebook using Python. …”
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Article -
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Optimal network reconfiguration and intelligent service restoration prediction technique based on Cuckoo search spring algorithm / Mohamad Izwan Zainal
Published 2022“…In this research, Cuckoo Search Spring Algorithm (CSSA) is proposed to enhance the robustness of algorithm by constructing the optimal network reconfiguration consist of reducing power losses and improve voltage profile with the various loadability factor as the constraint according to load profile, based on single and multiobjective model. …”
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Thesis -
19
Profit-based optimal generation scheduling of a microgrid
Published 2023“…The results demonstrate the efficiency of using genetic algorithm to solve the optimization problem. �2010 IEEE.…”
Conference paper -
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