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Enhancing hyperparameters of LSTM network models through genetic algorithm for virtual learning environment prediction by Ismanto, Edi, Ab Ghani, Hadhrami, Md Saleh, Nurul Izrin

“…Genetic algorithms for hyperparameter optimization significantly contributed, with the GA + LSTM + ADAGRAD model achieving 88% and 87% accuracy in the 7th and 9th models for BBB course data. …”
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Solving vehicle routing problem by using improved genetic algorithm for optimal solution by Mohammed M.A., Abd Ghani M.K., Hamed R.I., Mostafa S.A., Ahmad M.S., Ibrahim D.A.

Mental Stress Classification Among Higher Education Students In Malaysia From Electroencephalogram (Eeg) Using Convolutional Neural Network With Modified Stochastic Gradient D... by Rashid, Nur Ramizah Ramino

“…A rigorous preprocessing approach, including Independent Component Analysis (ICA) for artifact removal, was applied, followed by feature extraction focusing on key metrics such as energy, entropy, and standard deviation from both time and frequency domains. The chosen algorithm, 1D-CNN, was modified using a tailored SGD optimizer that incorporates momentum and learning rate decay to improve convergence and address challenges like vanishing gradients. …”
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Enhanced recognition methods for text and slider CAPTCHA vulnerability assessment by Xing, Wan

“…By comparing them with the benchmark models in terms of recognition accuracy, precision, computational complexity, storage space, and other metrics, the superiority of our proposed algorithm is proved. …”
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Qualitative and quantitative accuracy evaluation of 18f-fdg PET/CT with TOF and NON-TOF system on beta value in BPL reconstruction by Hui, Ng Suk

“…Methods: A National Electrical Manufacturers Association (NEMA) image quality phantom filled with Fluorine-18 fluoro-2-deoxy-D-glucose (18F-FDG) at a 5:1 tumour-to-background ratio (TBR) was scanned on a lutetium-based PET/CT scanner. The images were reconstructed using the OSEM (16 subsets, 3 iterations) and Q.Clear algorithms, both of which include Point Spread Function (PSF) modelling. …”
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Hybrid firefly and particle swarm optimization algorithm for multi-objective optimal power flow with distributed generation by Khan, Abdullah

“…A new meta-heuristic optimization technique called the Slime Mould Algorithm (SMA) approach has a high convergence rate or a few iterations and superior optimization indices analyzed against other algorithms. …”
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Improved Cheating Detection in Examinations using YOLOv8 with Attention Mechanism by YAN, ZUO

“…This system utilizes the advanced capabilities of the YOLOv8 model, optimized for speed and efficiency, making it suitable for deployment on regular- performance computers. …”
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Form four chemistry teachers’ conceptualization of pedagogical content knowledge / Chien Lee Shing by Chien, Lee Shing

“…As a whole, the teaching of the four Form Four chemistry teachers was teacher-oriented, algorithmic, using chalk-and-talk approach which did not support thoughtful and meaningful and optimal learning among the students. …”
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An enhanced opposition-based firefly algorithm for solving complex optimization problems by Ling, Ai Wong, Hussain Shareef, Azah Mohamed, Ahmad Asrul Ibrahim

“…Firefl y algorithm is one of the heuristic optimization algorithms which mainly based on the light intensity and the attractiveness of fi refl y. …”
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Machining optimization using Firefly Algorithm / Farhan Md Jasni by Md Jasni, Farhan

“…Based on the previous research on the success of Firefly Algorithm, this approach will be able to optimize the machining parameter of milling operation. …”
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Enhancing three variants of harmony search algorithm for continuous optimization problems by Alomoush, Alaa A., Alsewari, Abdulrahman A., Kamal Z., Zamli, Alrosan, Ayat, Alomoush, Waleed, Alissa, Khalid

“…Meta-heuristic algorithms are well-known optimization methods, for solving real-world optimization problems. …”
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PMT : opposition based learning technique for enhancing metaheuristic algorithms performance by Hammoudeh, S. Alamri

“…Experimentally, the PMT shows promising results by accelerating the convergence rate against the original algorithms with the same number of fitness evaluations comparing to the original metaheuristic algorithms in benchmark functions and real-world optimization problems.…”
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Optimization of milling parameters using ant colony optimization by Mohd Saupi, Mohd Sauki

“…The simulation based on ACO algorithm are successful develop and the optimization of parameters values is to maximize the production rate is obtain from the simulation.…”
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Hybrid bat algorithm-artificial neural network for modeling operating photovoltaic module temperature: article / Noor Rasyidah Hussin by Hussin, Noor Rasyidah

“…Bat algorithm was employed to optimize the training parameters such as learning rate, momentum rate and number of neurons in hidden layers. …”
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An enhanced metaheuristic approach to solve quadratic assignment problem using hybrid technique by Hameed, Asaad Shakir

“…The valuate of performance HDDETS algorithm comparison to existing hybrid-based algorithms, namely: Biogeography-Based Optimization Tabu Search (BBOTS), Whale Algorithm with Tabu Search (WAITS), Hybrid Ant System (HAS), Lexisearch and Genetic Algorithms (LSGA), and Golden Ball Simulated Annealing (GBSA) algorithms. …”
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Pipeline scour rates prediction-based model utilizing a multilayer perceptron-colliding body algorithm by Ehteram M., Ahmed A.N., Ling L., Fai C.M., Latif S.D., Afan H.A., Banadkooki F.B., El-Shafie A.

Hybrid bat algorithm hybrid-artificial neural network for modeling operating photovoltaic module temperature / Noor Rasyidah Hussin by Hussin, Noor Rasyidah

“…Bat algorithm was employed to optimize the training parameters such as learning rate, momentum rate and number of neurons in hidden layers. …”
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Firefly algorithm-based neural network for GCPV system output prediction: article / Nor Syakila Mohd Zainol Abidin by Mohd Zainol Abidin, Nor Syakila

“…FA was used to optimize the number of neurons in the hidden layer, the learning rate and the momentum rate such that the Root Mean Square Error (RMSE) was minimized. …”
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Advances of metaheuristic algorithms in training neural networks for industrial applications by Chong H.Y., Yap H.J., Tan S.C., Yap K.S., Wong S.Y.

Optimization of yeast fermentation process using genetic algorithm by Sin, Helen Ee Chuo, Yuh, Christine King Lo, Min, Keng Tan, Heng Jin Tham, Jin Tham, Sivakumar Kumaresan, Tze, Kenneth Kin Teo

“…This paper proposes genetic algorithm (GA) to optimize the productivity of yeast fermentation process. …”
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