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An improvement of stochastic gradient descent approach for mean-variance portfolio optimization problem by S. W. Su, Stephanie, Kek, Sie Long

“…Furthermore, the applicability of SGD, Adam, AdaMax, Nadam, AMSGrad, and AdamSE algorithms in solving the mean-variance portfolio optimization problem is validated.…”
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Integrated combined layer algorithm of jamming detection and classification in manet / Ahmad Yusri Dak by Dak, Ahmad Yusri

“…It involves development of Max-Min Rule-Based Classification Algorithm. The fourth stage is to design evaluation methodology of Max-Min Rule-Based Classification Algorithm using classifier model. …”
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Modern fuzzy min max neural networks for pattern classification by Al Sayaydeh, Osama Nayel Ahmad

“…To build an efficient classifier model, researchers have introduced hybrid models that combine both fuzzy logic and artificial neural networks. Among these algorithms, Fuzzy Min Max (FMM) neural network algorithm has been proven to be one of the premier neural networks for undertaking the pattern classification problems. …”
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Enhanced handover decision algorithm in heterogeneous wireless network by Abdullah, Radhwan Mohammed, Ahmad Zukarnain, Zuriati

“…The algorithm consists of three technology interfaces: Long-Term Evolution (LTE), Worldwide interoperability for Microwave Access (WiMAX) and Wireless Local Area Network (WLAN). …”
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New approaches to normalization techniques to enhance k-means clustering algorithm by Dalatu, Paul Inuwa, Midi, Habshah

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Automatic algorithm applied for calculating thermal conductivity by transient plane source method by Jia, Zhijie, Yang, Liping, Cao, Chengcheng, Li, Huidong, Luo, Caiyun, Tao, Te, Zhong, Qiu, Xu, Zijun, Chen, Zezhong

“…Additionally, the integration of the time window function max/tθ, typically utilized solely for result validation in conventional methods, further enhances the objectivity and reproducibility of the results obtained by the automatic algorithm…”
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Electric vehicle battery state of charge estimation using metaheuristic-optimized CatBoost algorithms by Mohd Herwan, Sulaiman, Zuriani, Mustaffa, Ahmad Salihin, Samsudin, Amir Izzani, Mohamed, Mohd Mawardi, Saari

“…A comprehensive data preprocessing pipeline was implemented, including missing value treatment, outlier removal, and feature normalization using Min-Max scaling. …”
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Malaysian license plate recognition system using Convolutional Neural Network (CNN) on web application / Nur Farahana Mahmud by Mahmud, Nur Farahana

“…Nowadays, there are numerous license plate recognition systems that have been developed and analysed effectively by previous researchers using different machine learning algorithms. However, according to a recent study, ANN algorithms require a huge amount of training data while BPFFNN algorithms only have an average success rate of 70% in recognizing all the characters. …”
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Real time self-calibration algorithm of pressure sensor for robotic hand glove system by Almassri, Ahmed M. M.

“…The model was tested using an untrained input data set in order to verify the Proposed model’s capability for implementing a self-calibration algorithm. …”
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MiMaLo: advanced normalization method for mobile malware detection by Sriyanto, Sahib @ Sahibuddin, Shahrin, Abdollah, Mohd Faizal, Suryana, Nanna, Suhendra, Adang

“…An application used to be mounted on mobile gadget to gather facts and processed them to get dataset. This research used data mining classification approach method and validates it using ten fold cross validation. …”
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Machine learning for mapping and forecasting poverty in North Sumatera: a datadriven approach by Marpaung, Faridawaty, Ramadhani, Fanny, Dinata, Dewan

“…The best model was created using the grid search cross-validation, while the best prediction results were created using the RF algorithm, with the following parameters: n-estimator = 50, max depth = 10, min samples split = 2, and min samples leaf = 1. …”
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The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

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Development of colorization of grayscale images using CNN-SVM by Abualola, Abdallah, Gunawan, Teddy Surya, Kartiwi, Mira, Ambikairajah, Eliathamby, Habaebi, Mohamed Hadi

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Self-calibration algorithm for a pressure sensor with a real-time approach based on an artificial neural network by M. Almassri, Ahmed M., Wan Hasan, Wan Zuha, Ahmad, Siti Anom, Shafie, Suhaidi, Wada, Chikamune, Horio, Keiichi

“…To verify the proposed model’s capability to build a self-calibration algorithm, the model was tested using an untrained input data set. …”
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Machine learning methods to predict and analyse unconfined compressive strength of stabilised soft soil with polypropylene columns by Hoque, Md. Ikramul, Muzamir, Hasan, Islam, Md Shofiqul, Houda, Moustafa, Abdallah, Mirvat, Sobuz, Md. Habibur Rahman

“…This research work generates new data and applies several machine learning algorithms for the analysis of UCS. …”
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The Impact of Normalization Techniques on Performance Backpropagation Networks by Norlida, Hassan

“…To explore the impact of normalization technique on the performance on NN, medical datasets with Boolean target were preprocessed, trained, validated and tested using backpropagation learning algorithm. …”
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Embedded Artificial Intelligent (AI) To Navigate Cart Follower by Tang, Khai Luen

“…The training algorithm may also vary with different sets of parameters, number of neurons and activation function. …”
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New correlation for oil formation volume factor by Sulaimon, A.A., Ramli, N., Adeyemi, B.J., Saaid, I.M.

“…In this work, a new correlation for estimating oil formation volume factor (βo) using a Group Method of Data Handling (GMDH) technique was developed. It is a family of inductive algorithms which executes computer-based mathematical modeling of multi-parametric data sets. …”
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Performance comparison of GA and PSO based ANN training on medical dataset / Muhammad Amirul Danish Jamal by Jamal, Muhammad Amirul Danish

“…The assessment incorporates vital performance metrics such as accuracy, precision, sensitivity, Mean Square Error (MSE), Mean Absolute Error (MAE), and training efficiency. Data preprocessing was carried out using min-max normalization, and an ANN architecture featuring 20 hidden neurons was created and optimized with MATLAB. …”
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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush