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A comparative study of deep learning algorithms in univariate and multivariate forecasting of the Malaysian stock market by Mohd. Ridzuan Ab. Khalil, Azuraliza Abu Bakar

“…Three deep learning algorithms, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), are used to develop the prediction model. …”
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Industry 5.0 and Education 5.0: Transforming Vocational Education through Intelligent Technology by Zhang, Hongli, Leong, Wai Yie

“…By analyzing the research gaps in personalized learning paths, emotion-driven learning, crossdisciplinary integration, and long-term learning behavior analysis, the paper proposes four improved algorithms: the adaptive learning path generation algorithm, the emotion-driven personalized learning algorithm, the cross-disciplinary knowledge graph algorithm, and the long-term learning behavior prediction algorithm. …”
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Streamflow Prediction Utilizing Deep Learning and Machine Learning Algorithms for Sustainable Water Supply Management by Latif S.D., Ahmed A.N.

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A Systematic Literature Review of Machine Learning Methods for Short-term Electricity Forecasting by Md Salleh N.S., Suliman A., Jorgensen B.N.

“…Forecasting; Investments; Machine learning; Development investment; Energy prediction; Evaluation metrics; Long term planning; Machine learning methods; Metric evaluation; Resource planning; Systematic literature review; Learning algorithms…”

Rabies Outbreak Prediction Using Deep Learning with Long Short-Term Memory by Abdulrazak Yahya, Saleh, Shahrulnizam, Medang, Ashraf, Osman Ibrahim

“…As such, biosurveillance system developers are looking for highly sensitive outbreak prediction algorithms that will minimise the number of false alarms. …”
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A Case Study of Using Long Short-Term Memory (LSTM) Algorithm in Solar Photovoltaic Power Forecasting by Lee Chin, Kho, Sze Song, Ngu, Annie, Joseph, Siti Kudnie, Sahari, Kuryati, Kipli, R., Rulaningtyas

“…The results show that the deep learning algorithm provides reliable forecasting results.…”
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SUDOKU HELPER by Abdul Razak, Muhammad Asyraf

“…In this paper research, author presents an algorithm to provide a tutorial for any Sudoku player who got stuck during the solving process. …”
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Performance comparison of CNN and LSTM algorithms for arrhythmia classification by Hassan, S.U., Zahid, M.S.M., Husain, K.

“…Analyzing the performance of these algorithms will further assist in the development of an enhanced deep learning model that offers improved performance. …”
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Real time long range (LoRa) based indoor positioning system using Deep Gaussian Process (DGP) algorithm / Ng Tarng Jian by Ng, Tarng Jian

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Concrete compressive strength prediction modeling utilizing deep learning long short-term memory algorithm for a sustainable environment by Latif S.D.

Coalition of genetic algorithms and artificial neural network for isolated spoken Malay speech recognition / Noraini Seman by Seman, Noraini

“…Genetic Algorithm (GA) based learning technique provides an alternative way that involves controlling the learning complexity by adjusting the number of weights of the ANN. …”
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Development of deep reinforcement learning based resource allocation techniques in cloud radio access network by Amjad, Iqbal

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New Approach for E-Commerce Stock Prices Prediction : Combination of Machine Learning and Technical Analysis by Kelvin Lee, Yong Ming, Mohamad, Jais, Pick Soon, Lee

“…Meanwhile, the machine learning algorithms used in this study were Random Forest (RF) and K-Neighbor Nearest (KNN). …”
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Investigating the reliability of machine learning algorithms as an advanced tool for ozone concentration prediction by Ayman Mohammed Shaher Yafouz, Mr.

“…The hybrid technique has been developed by using deep learning algorithms with the structure of multiple layers (with several neurons) of CNN and LSTM. …”

Advanced machine learning algorithm to predict the implication of climate change on groundwater level for protecting aquifer from depletion by Ahmed Osman A.I., Latif S.D., Wee Boo K.B., Ahmed A.N., Huang Y.F., El-Shafie A.

“…For all five locations, four ML algorithms have been trained, tested, and then evaluated: long short-term memory (LSTM), extreme gradient boost (XGBoost), Artificial Neural Network (ANN), and Support Vector Regression (SVR). …”

Old Jawi manuscript: Digital recognition / Zaidi Razak by Zaidi, Razak

“…The information gathered is useful for the development of a line segmentation algorithm, a new character segmentation algorithm, as well as a recognition algorithm which is based on the use of a unique code and Hamming distance calculation. …”
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Stock price monitoring system by Ng, Chun Ming

“…As stock price is time series data, a time series prediction algorithm is being utilized to build a deep learning model, namely Long Short-Term Memory (LSTM). …”
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Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli by Seyed Hamidreza , Aghay Kaboli

“…This merit is provided by balancing the exploitation of solution structure and exploration of its appropriate weighting factors through use of a robust and efficient optimization algorithm in learning process of GEP approach. To assess the applicability and accuracy of the proposed method for long-term electrical energy consumption, its estimates are compared with those obtained from artificial neural network (ANN), support vector regression (SVR), adaptive neuro-fuzzy inference system (ANFIS), rule-based data mining algorithm, GEP, linear, quadratic and exponential models optimized by particle swarm optimization (PSO), cuckoo search algorithm (CSA), artificial cooperative search (ACS) algorithm and backtracking search algorithm (BSA). …”
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Exploring machine learning algorithms for accurate water level forecasting in Muda river, Malaysia by Adli Zakaria M.N., Ahmed A.N., Abdul Malek M., Birima A.H., Hayet Khan M.M., Sherif M., Elshafie A.

“…In this study, three machine learning algorithms: multi-layer perceptron neural network (MLP-NN), long short-term memory neural network (LSTM) and extreme gradient boosting XGBoost were applied to develop water level forecasting models in Muda River, Malaysia. …”

Forecasting of fine particulate matter based on LSTM and optimization algorithm by Zaini N., Ahmed A.N., Ean L.W., Chow M.F., Malek M.A.

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