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Optimization of neural network through genetic algorithm searches for the prediction of international crude oil price based on energy products prices by Chiroma, Haruna, Ya’u Gital, Abdulsalam, Abubakar, Adamu, Usman, Mohammed Joda, Waziri, Usman

“…This study investigated the prediction of crude oil price based on energy product prices using genetically optimized Neural Network (GANN). …”
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A variable-capacity-based fuzzy random facility location problem with VaR objective by Wang, Shuming, Watada, Junzo, Shamsul Bahar, Yaakob, Prof. Madya

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Optimal design of three-phase static var compensation system by George M., Bakar M.B.B.A., Basu K.P.

“…An optimal design for three-phase SVC system based on SDM is considered. …”

Sensitivity-based fuzzy multi-objective portfolio model with Value-at-Risk by Zhang, H., Watada, J., Wang, B.

“…In addition, compared with the VaR-FMOPSM model, our sensitivity-based improved model with the IPSO algorithm also performs better than Genetic Algorithm and Simulate Anneal Algorithm (SA), it provides the same performance on this point. …”
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Transmission loss minimization using SVC based on particle swarm optimization by Jumaat, S.A., Musirin, I., Othman, M.M., Mokhlis, Hazlie

“…This paper describes optimal sizing of static var compensator (SVC) based on Particle Swarm Optimization for minimization of transmission losses considering cost function. …”
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Optimal location and sizing of SVC using Particle Swarm Optimization technique by Jumaat, S.A., Musirin, I., Othman, M.M., Mokhlis, Hazlie

“…This paper describes optimal location and sizing of static var compensator (SVC) based on Particle Swarm Optimization for minimization of transmission losses considering cost function. …”
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Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection by Saputra , Dhany, Rambli, Dayang R.A., Foong, Oi Mean

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Optimal operation and control of hybrid power systems with stochastic renewables and FACTS devices: An intelligent multi-objective optimization approach by Premkumar M., Hashim T.J.T., Ravichandran S., Sin T.C., Chandran R., Alsoud A.R., Jangir P.

Subjects:

Optimal Placement Of FACTS Devices In Power System Using Computational Intelligence Based Method by Mohammad Haikal Bin Aziz

Genetic algorithm based method for optimal location placement of flexible ac transmission system devices for voltage profile improvement by Karami, Mahdi

“…This thesis present a genetic algorithm based method for placement of FACTS devices for voltage profile improvement. …”
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Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

“…Using the J48 tree algorithm implemented through WEKA API on a Java Servlet, data provided is processed to derive a health index of the plant, with the possible outcomes set to “Good,” “Okay”, or “Bad”. …”
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Evaluating Adan vs. Adam: an analysis of optimizer performance in deep learning by Ismail, Amelia Ritahani, Azhary, Muhammad Zulhazmi Rafiqi, Hitam, Nor Azizah

“…With various optimization algorithms available, choosing the one that best suits the deep learning model and dataset can make a substantial difference in achieving optimal results. …”
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An Empirical Study on the Construction of A Non-Convex Risk Parity Portfolio using a Genetic Algorithm by Kusumawati, Rosita, Rosadi, Dedi, Abdurakhman, Abdurakhman

“…Risk-based portfolio optimization has become increasingly crucial due to the limitations and underperformance of traditional Mean-Variance (MV) portfolios. …”
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Hyper-heuristic strategies for optimal power flow problem with FACTS devices allocation in wind power integrated system by Mohd Herwan, Sulaiman, Zuriani, Mustaffa

“…These low-level metaheuristics comprise the Moth-Flame Optimizer (MFO), Barnacles Mating Optimizer (BMO), Teaching-Learning Based Optimization (TLBO) and Gradient-Based Optimizer (GBO). …”
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On the Application of heuristic Method and Saddle Node Bifurcation for Optimal Placement of FACTS Devices in Power System by Mariun, Norman, Ab Kadir, Mohd Zainal Abidin, Karami, Mehdi

“…This study focuses on an optimal placement of five major types of FACTS devices, namely, Static Var Compensator (SVC), Thyristor Controlled Series Compensator (TCSC), Static Synchronous Compensator (STATCOM), Static Synchronous Series Compensator (SSSC) and Unified Power Flow Controller (UPFC) in power system network using a well-known and applicable heuristic method known as genetic algorithm to seek the optimum location and setting of these controllers in the system. …”
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Mining Sequential Patterns using I-PrefixSpan by Saputra , Dhany, Dayang R.A. Rambli, Foong, Oi Mean

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Computational intelligence based technique for congestion management and compensation scheme in power system / Nur Zahirah Mohd Ali by Mohd Ali, Nur Zahirah

“…CEPSO integrates the element of cloning and swarm in the original Evolutionary Programming algorithm. CEPSO is initially used to optimize the location and sizing of FACTS devices for compensation scheme. …”
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Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde by Ogunfolajin Maruff , Tunde

“…This thesis is based on the application of sentiment classification algorithm to tweet data with the goal of classifying messages based on the polarity of sentiment towards a particular topic (or subject matter). …”
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Mining Sequential Patterns Using I-PrefixSpan by Dhany , Saputra, Rambli Dayang, R.A., Foong, Oi Mean

“…In this paper, we propose an improvement of pattern growth-based PrefixSpan algorithm, called I-PrefixSpan. The general idea of I-PrefixSpan is to use the efficient data structure for general tree-like framework and separator database to reduce the execution time and memory usage. …”
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AI powered asthma prediction towards treatment formulation: an android app approach by Murad, Saydul Akbar, Adhikary, Apurba, Md Muzahid, Abu Jafar, Sarker, Md Murad Hossain, Khan, Md. Ashikur Rahman, Hossain, Md. Bipul, Bairagi, Anupam Kumar, Masud, Mehedi, Kowsher, Md

“…We utilized eight robust machine learning algorithms to analyze this dataset. We found that the Decision tree classifier had the best performance, out of the eight algorithms, with an accuracy of 87%. …”
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