1. Introduction

In today’s economy, gold is becoming an essential financial commodity. Gold has become the underlying value for many reasons. Security is the first one. Gold is a robust investment instrument, capable of preserving its liquidity even in crises such as political turbulence [1] and the COVID-19 pandemic [2].              The second is that when they have had problems with their balance of payments, many nations have repeatedly used gold collateral against loans. The final reason is that for coping with inflation, gold will serve as a guide. Research on gold’s value is fundamental because gold prices can quite directly reflect the economy’s market expectations. Gold value prediction is a difficult task, primarily because of the unusual shifts in economic patterns and, on the other hand, insufficient knowledge. ARIMA is a classical approach focused on the estimation of statistical time series and a prediction model for univariate time series. The critical drawback of ARIMA is the model’s pre-assumed linear shape. With the rise of machine learning, artificial neural networks in time series forecasting have been widely studied and used.

Recurrent neural networks (RNN) [3] are often seen as the most efficient time series prediction method. RNN is a subset of artificial neural networks in which nodes are connected in a loop, and the internal state of the network can exhibit dynamic timing behavior. However, as the length of the processing time series increases, problems such as gradient disappearance often occur during the training of RNNs using conventional activation functions, such as tanh or sigmoid functions, limiting the prediction accuracy of the RNN. According to Ahmed & Bahador, the highest precision RNN is LSTM (Long Short Term Memory) Neural Network[4]. LSTM has an outstanding efficiency in natural language processing, while this model can also solve long-term dependencies very well. Since problems with long-term dependencies exist in the prediction of time series, researchers are trying to use LSTM to solve problems with time series, such as forecasting of foreign exchange [5, 6], traffic flow analysis [7, 8], and gold ETF[9]. The Gated Recurrent Unit is another type of RNN (GRU). GRU is an RNN-based network, a form of gated RNNs, which largely mitigates the gradient vanishing problem of RNNs through gating mechanism and make the structure simpler while maintaining the effect of LSTM [10]. This research’s main objective is to study RNN forecasting methods that offer the best predictions for multivariate gold price prediction concerning lower forecast errors and higher accuracy of forecasts. Research on multivariate analysis has multiple features to determine the predicted value.

The dataset for the prediction model consists of eight features after feature selection is  conducted :

1. Gold Price in IDR
2. Gold Price in USD
3. Gold Price in Euro
4. Gold Price in GBP
5. Gold Price in RMB
6. Jakarta Stock Exchange Composite (IHSG)
7. Hang Seng Index
8. NASDAQ Composite Index

Historical data on this paper is gathered from the World Gold Council website (gold.org) and investing.com. Data is collected in an interval of 20 years, start from 2001 to 2020. The author split the dataset into 70% training data and 30% testing data. The validation data is a subset of training data, and the length is 10% of the total data. The validation data is based on 20 years dataset.

2. Literature Review

2.1. Data Preprocessing

Pre-processing is a method to develop data to form good shape for data training. Data pre-processing is a fundamental stage of the machine learning application, which has been reported to significantly impact the performances of machine learning methods [11]. Data pre-processing techniques include reduction, projection, and missing data techniques (MDTs). Data reduction decreases the data size via, for example, feature selection (FS) or dimension reduction [12].

2.2. Grid Search

Grid search is a process that attempts each hyper-parameter combination extensively and selects the best as the optimal hyper-parameters[13]. The grid search method is theoretically capable of finding optimal solutions. However, it suffers from severe limitations in the following aspects. It can not provide optimal solutions within a sufficient reach with limited computational resources. However, Grid search specializes in addressing discrete hyper-parameters [14].

2.3. Feature Selection

Feature selection is referred to as obtaining a subset from an original feature set by selecting the dataset’s relevant features according to the unique feature selection criteria [15]. Feature selection has a vital role in compressing the data processing size, where the redundant and irrelevant characteristics are eliminated. Feature selection techniques can pre-process learning algorithms, and proper feature selection results can improve learning accuracy, reduce learning time, and simplify learning results [16].

2.4. Long Short Term Memory (LSTM)

Long short-term memory (LSTM) is an altered version of RNN proposed to learn long-range dependencies across time-varying patterns [17]. Generally, LSTM is a second-order recurrent neural network that solves the vanishing and exploding gradients issue by replacing RNN simple units with the memory blocks in a recurrent hidden layer. A memory cell is a complex processing unit in LSTM with many units shown in Figure 1.

It comprises one or many memory cells, adaptive multiplicative gating units (input, output, and forget), and a self-recurrent connection with a fixed weight of 1.0. It serves as a short-term memory with control from adaptive multiplicative gating units. The input and output flow of a cell activation of a memory cell is controlled by input and output gate, respectively. Forget gate was included in memory cell [18] that helps to forget or reset their previous state information when it is inappropriate.

Figure 1: LSTM Cell Unit: Image Source [19]

2.5. Gated Recurrent Unit (GRU)

The GRU is a simpler derivative of the LSTM network, and in some cases, both produce comparative results. Although there are major similarities in architecture for the purpose of solving vanishing gradient problems, there are several differences. The structure of a GRU module is shown in Figure 2.

Figure 2: GRU Cell Unit: Image Source [19]

The GRU cell does not have a separate memory cell-like LSTM cell architecture, according to [20]. In addition to having three gating layers like LSTM in each module, the GRU network only has two gating layers, a reset gate, and an update gate. The reset gate decides how much information to forget from the previous memory. The update gate acts similar to the forget and input gate of an LSTM cell. It decides how much information from previous memory can be passed along to the future.

3. Methodology

3.1. Data Collection and Preprocessing

This research collects gold price values on a daily basis. The collected data has twelve features: gold price in Indonesian Rupiah (IDR), gold price in US Dollar (USD), gold price in Euro (Euro), gold price in Pound sterling (GBP), gold price in Chinese Renminbi (RMB), gold to silver ratio, Japanese Stock Market Index (Nikkei 225), Indonesian Stock Composite Index (IHSG), Shanghai Hang Seng Index (Hang Seng), Nasdaq Index (Nasdaq), US Dollar and Indonesian Rupiah currency pair (USD/IDR), Australian Dollar and US Dollar currency pair (AUD/USD). The price and supporting data were collected from January 1, 2001, to 09:00 on December 31, 2020, through the World Gold Council (gold.org) and investing.com.

Table 1: Example of the collected raw gold price data

Table 2: Feature Correlation Value towards IDR

Table 3: Pre-processed gold data for the raw data of Table 1

3.2. Feature Selection

Feature selection is implemented by calculating Pearson’s Correlation Coefficient. The correlation value can be seen in Table 2, and the visualization of feature correlation could be inferred in Figure 3.

The defined threshold for features correlation is 0.75. All features that have correlation coefficient values below the threshold would be removed, and all features above that point will be used as selected features. An example of pre-processed data can be seen in Table 3.

3.3. Model Training and Validation

The prediction in this work is to utilize the LSTM model to forecast the gold price. As a modified version of the recurrent neural network model, the LSTM model [21] defines whether the weight value is maintained by adding cell states in an LSTM cell.

Figure 3: Pearson Correlation Matrix of All Features

The LSTM model can accept an arbitrary length of inputs, and it can be implemented flexibly and in various ways as required. The state obtained from an LSTM cell is used as input to the next LSTM cell, so the state of an LSTM cell affects the operation of the subsequent cells. The LSTM predictive model has the capability to remove or add information to the LSTM cell state.

The information that enters the cell is controlled by gates, a component that represents a way to save or forget information through the LSTM cell unit.

In order to enrich and benchmark the proposed LSTM model, this research also develops a GRU prediction model. GRU model is similar to LSTM and is known to have good forecasting performance for a shorter time period.  The same model tuning process for the GRU prediction model was also performed for the purpose of fairness. The overall training and tuning process is summarized in Figure 4.

3.4. Hyperparameter Tuning

Hyperparameter optimization is an essential step in the implementation of any machine learning model [22]. This optimization process includes regularly modifying the model’s hyperparameter values to minimize the testing error. Based on research, kernel initializer and batch size rate need to optimize for better accuracy [22]. Meanwhile, the author proposed dropout rate, neuron units, and learning optimizer [23]. Research from Google Brain Scientist studied the relation between two hyperparameters, batch size, and learning rate [24]. When the learning rate is decay, random fluctuation appears in the SGD dynamics. Instead of decaying the learning rate, that research increase the batch size. That strategy achieves near-identical model performance on the test set with the same number of training epochs but significantly fewer parameter updates. However, when the batch size is large, this often causes instabilities during the early stages of training. In consequence, the optimum batch size value must be decided for the best prediction result.

Furthermore, the dropout rate indicates the fraction of the hidden units to be dropped to transform the recurrent state in the LSTM layer. Finally, the optimization type designates the optimization algorithm to tune the internal model parameters to minimize the cross-entropy loss function [23]. This paper combines two prior research and choose the batch size, kernel initializer, dropout rate, neuron units, and learning optimizer based on the references. The proposed hyperparameter candidates for LSTM could be shown in Table 4 and GRU in Table 5.

Figure 4: Prediction model training and tuning process with grid search

The kernel initializer represents the strategy for initializing the LSTM and Dense layers weight values. The activation type represents the activation function that produces non-linear and limited output signals inside the LSTM, Dense I, and II layers.

Table 4: Candidate and optimal sets of hyper-parameters for LSTM

Table 5 : Candidate and optimal sets of hyper-parameters for GRU

3.5. Model Evaluation

In order to measure the error of the prediction model for the time series problem, the researcher utilizes Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE).

3.5.1. Root Mean Squared Error

Root Mean Squared Error (RMSE) is derived from Mean Squared Error. It represents the average difference between predicted and real value. The formula of RMSE can be seen in (1). RMSE is common practice to calculate the accuracy of the prediction model.

3.5.2. Mean Absolute Percentage Error

Mean Absolute Percentage Error (MAPE) calculates the average delta between predicted and real value and represent in percentage. The formula of MAPE can be seen in (2).

4. Results and Analysis

4.1. Prediction Result for Simple LSTM and GRU

Table 6 shows the training result of the proposed methods and some state of arts who have studied a forecasting gold price model. Inspire by that state of arts, the author utilizes RMSE and MAPE to evaluate the proposed model. The proposed models have 25 epochs, 32 batch sizes, a 0.2 dropout rate value, 32 neuron units, a uniform kernel initializer, and RMSProps Learning Optimizer. Derive from Table 6 can be known that GRU has lower error for three months until three years than LSTM. For the period above three years, LSTM has a lower error rate.

Figure 6: MAPE Value of Simple Prediction Model

Table 6: Forecasting Result with Simple Prediction Model

As presented in Figure 5, the value of RMSE for both LSTM and GRU is decreased over time. However, GRU has a lower error rate in intervals until three years, while LSTM is more accurate in the above three years intervals. RMSE indicates the average value of the difference between the predicted and real value.

Figure 5: RMSE Value of Simple Prediction Model

As presented in Figure 6, the value of MAPE for both LSTM and GRU is decreased over time. However, GRU has a lower error rate in intervals until three years, while LSTM is more accurate in the above three years intervals. MAPE indicates the percentage of the overall delta between predicted and real value.

Figure 7: LSTM Prediction for 3 Years Training Data

LSTM prediction model for three years period is accurate in train prediction but moderately inaccurate in test prediction, as seen in Figure 7. Averagely, it has around 4 million rupiah difference between predicted and real value that represented by RMSE. The reason is LSTM needs a large dataset to predict accurately.

Figure 8: GRU Prediction for 3 Years Training Data

On the contrary, for three years period, the GRU model gives good accuracy. Averagely, it has around 3 million rupiah difference between predicted and real value that represented by RMSE. It indicates that, for intervals up to 3 years, the GRU model is preferable.

Figure 9: LSTM Prediction for 20 Years Training Data

Figure 9 shown the LSTM prediction model for twenty years period. The model has pretty good accuracy. Averagely, it has around 2.7 million rupiah difference between predicted and real value that represented by RMSE.

Figure 10: GRU Prediction for 20 Years Training Data

For twenty years period, the GRU model slightly less accurate than the LSTM model. Averagely, it has around 3 million rupiah difference between predicted and real value that represented by RMSE. It indicates that, for intervals up to 20 years, it is better to use the LSTM model. According to data in Table 6, LSTM accuracy increase over time, while GRU accuracy is great for a short time period but has lower accuracy for a long period. However, the accuracy can be improved by applying Hyperparameter Tuning using Cross Validated Grid Search.

Table 7: Forecasting Result with Grid Search Optimized Prediction Model

4.2. Prediction Result for Optimized LSTM and GRU

Model Optimization for LSTM and GRU conducted by applying Hyperparameter tuning using Cross Validated Grid Search. Grid Search tries every value on the hyperparameter candidate that is stated in Table 4 for LSTM and Table 5 for GRU.  After Grid Search was conducted, the best result for each iteration becomes hyperparameter value on Optimized LSTM and GRU Model. The optimized LSTM models have 25 epochs, 16 batch sizes, a 0 dropout rate value, 128 neuron units, “ones” kernel initializer, and utilize ADAM optimizer. The training result of optimized LSTM and GRU using grid search based hyperparameter tuning can be seen in Table 7. Derive from Table 7, and it can be known that for three months until three years, GRU has lower error than LSTM. For the period above three years, LSTM has a lower error rate. Results accuracy of the optimized model can be seen in Figure 11 for RMSE and Figure 12 for MAPE.

Figure 11: RMSE Value of Optimized Prediction Model

Figure 11 shows that in the optimized model, LSTM has a better RMSE score than GRU in almost all time intervals. GRU model slightly performs better in time interval one year until three years.

Figure 12: MAPE Value of Optimized Prediction Model

Figure 12 also shows a similar result with RMSE. MAPE score in the optimized model, LSTM has better accuracy than GRU in almost all time intervals. GRU model also slightly performs better in time interval one year until three years. It indicates that, for the model that is optimized with grid search, LSTM has better accuracy than the GRU predicted model.

Forecasting gold price for three years interval using optimized LSTM and GRU model has a similar result. It has a 354942.67 RMSE value for LSTM and 343101.59 for GRU. In MAPE measurement, LSTM has 0.96 error, while GRU has 0.96 error. It indicates that for interval three years, GRU Model is slightly more accurate.

Figure 13: Optimized LSTM Prediction for 3 Years Training Data

Figure 14: Optimized GRU Prediction for 3 Years Training Data

Figure 15: Optimized LSTM Prediction for 20 Years Training Data

Figure 16: Optimized GRU Prediction for 20 Years Training Data

Forecasting gold price for twenty years intervals using optimized LSTM and GRU model also has a similar result. It has a 213036.85 RMSE value for LSTM and 216012.48 for GRU. In MAPE measurement, LSTM has 0.72 error, while GRU has 0.73 error. It indicates that for the interval of twenty years, LSTM Model is slightly more accurate. Summary prediction result accuracy for all time intervals can be seen in Table 8.

5. Conclusion

According to the training, validation, and hyperparameter tuning, the best model to use in gold price forecasting problems for time intervals under three years period is GRU. On the other hand, for time intervals above three years, LSTM is shown higher accuracy. That is shown by RMSE and MAPE Score. Grid Search based Hyperparameter tuning is proven to increase LSTM accuracy significantly by decreasing its error. Table 8 shows that

Table 8: Prediction Model Forecasting Result Summary

grid search can averagely decrease 68% RMSE and decrease 6,72 MAPE score. It also improves GRU Model, averagely decreases RMSE by 2%, and decreases 5,74 MAPE score. From that information, this research shows that hyperparameter tuning is more effective in optimizing LSTM Model than GRU Model for this research, gold price prediction problem. For future research, it is good to apply metaheuristic methods, such as Ant Colony Optimization, Genetic Algorithm, or Chaotic Metaheuristic, to conduct hyperparameter tuning with better performance.

Acknowledgment

The authors thank the World Gold Council and Investing.com for providing the historical gold data for this work also Binus University for delivering knowledge and insightful materials. This research is sponsored by Beasiswa Unggulan, a scholarship that granted from the Kementerian Pendidikan dan Kebudayaan Indonesia.

Conflict of Interest

The authors declare no conflict of interest.

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27. Arwa Alghamdi, Graham Healy, Hoda Abdelhafez, "Machine Learning Algorithms for Real Time Blind Audio Source Separation with Natural Language Detection", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 125–140, 2021. doi: 10.25046/aj060515
28. Baida Ouafae, Louzar Oumaima, Ramdi Mariam, Lyhyaoui Abdelouahid, "Survey on Novelty Detection using Machine Learning Techniques", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 73–82, 2021. doi: 10.25046/aj060510
29. Radwan Qasrawi, Stephanny VicunaPolo, Diala Abu Al-Halawa, Sameh Hallaq, Ziad Abdeen, "Predicting School Children Academic Performance Using Machine Learning Techniques", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 08–15, 2021. doi: 10.25046/aj060502
30. Zhiyuan Chen, Howe Seng Goh, Kai Ling Sin, Kelly Lim, Nicole Ka Hei Chung, Xin Yu Liew, "Automated Agriculture Commodity Price Prediction System with Machine Learning Techniques", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 376–384, 2021. doi: 10.25046/aj060442
31. Hathairat Ketmaneechairat, Maleerat Maliyaem, Chalermpong Intarat, "Kamphaeng Saen Beef Cattle Identification Approach using Muzzle Print Image", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 110–122, 2021. doi: 10.25046/aj060413
32. Md Mahmudul Hasan, Nafiul Hasan, Dil Afroz, Ferdaus Anam Jibon, Md. Arman Hossen, Md. Shahrier Parvage, Jakaria Sulaiman Aongkon, "Electroencephalogram Based Medical Biometrics using Machine Learning: Assessment of Different Color Stimuli", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 3, pp. 27–34, 2021. doi: 10.25046/aj060304
33. Dominik Štursa, Daniel Honc, Petr Doležel, "Efficient 2D Detection and Positioning of Complex Objects for Robotic Manipulation Using Fully Convolutional Neural Network", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 915–920, 2021. doi: 10.25046/aj0602104
34. Md Mahmudul Hasan, Nafiul Hasan, Mohammed Saud A Alsubaie, "Development of an EEG Controlled Wheelchair Using Color Stimuli: A Machine Learning Based Approach", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 754–762, 2021. doi: 10.25046/aj060287
35. Antoni Wibowo, Inten Yasmina, Antoni Wibowo, "Food Price Prediction Using Time Series Linear Ridge Regression with The Best Damping Factor", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 694–698, 2021. doi: 10.25046/aj060280
36. Javier E. Sánchez-Galán, Fatima Rangel Barranco, Jorge Serrano Reyes, Evelyn I. Quirós-McIntire, José Ulises Jiménez, José R. Fábrega, "Using Supervised Classification Methods for the Analysis of Multi-spectral Signatures of Rice Varieties in Panama", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 552–558, 2021. doi: 10.25046/aj060262
37. Phillip Blunt, Bertram Haskins, "A Model for the Application of Automatic Speech Recognition for Generating Lesson Summaries", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 526–540, 2021. doi: 10.25046/aj060260
38. Byeongwoo Kim, Jongkyu Lee, "Fault Diagnosis and Noise Robustness Comparison of Rotating Machinery using CWT and CNN", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 1279–1285, 2021. doi: 10.25046/aj0601146
39. Md Mahmudul Hasan, Nafiul Hasan, Mohammed Saud A Alsubaie, Md Mostafizur Rahman Komol, "Diagnosis of Tobacco Addiction using Medical Signal: An EEG-based Time-Frequency Domain Analysis Using Machine Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 842–849, 2021. doi: 10.25046/aj060193
40. Reem Bayari, Ameur Bensefia, "Text Mining Techniques for Cyberbullying Detection: State of the Art", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 783–790, 2021. doi: 10.25046/aj060187
41. Inna Valieva, Iurii Voitenko, Mats Björkman, Johan Åkerberg, Mikael Ekström, "Multiple Machine Learning Algorithms Comparison for Modulation Type Classification Based on Instantaneous Values of the Time Domain Signal and Time Series Statistics Derived from Wavelet Transform", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 658–671, 2021. doi: 10.25046/aj060172
42. Carlos López-Bermeo, Mauricio González-Palacio, Lina Sepúlveda-Cano, Rubén Montoya-Ramírez, César Hidalgo-Montoya, "Comparison of Machine Learning Parametric and Non-Parametric Techniques for Determining Soil Moisture: Case Study at Las Palmas Andean Basin", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 636–650, 2021. doi: 10.25046/aj060170
43. Imane Jebli, Fatima-Zahra Belouadha, Mohammed Issam Kabbaj, Amine Tilioua, "Deep Learning based Models for Solar Energy Prediction", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 349–355, 2021. doi: 10.25046/aj060140
44. Ndiatenda Ndou, Ritesh Ajoodha, Ashwini Jadhav, "A Case Study to Enhance Student Support Initiatives Through Forecasting Student Success in Higher-Education", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 230–241, 2021. doi: 10.25046/aj060126
45. Lonia Masangu, Ashwini Jadhav, Ritesh Ajoodha, "Predicting Student Academic Performance Using Data Mining Techniques", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 153–163, 2021. doi: 10.25046/aj060117
46. Sara Ftaimi, Tomader Mazri, "Handling Priority Data in Smart Transportation System by using Support Vector Machine Algorithm", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1422–1427, 2020. doi: 10.25046/aj0506172
47. Othmane Rahmaoui, Kamal Souali, Mohammed Ouzzif, "Towards a Documents Processing Tool using Traceability Information Retrieval and Content Recognition Through Machine Learning in a Big Data Context", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1267–1277, 2020. doi: 10.25046/aj0506151
48. Puttakul Sakul-Ung, Amornvit Vatcharaphrueksadee, Pitiporn Ruchanawet, Kanin Kearpimy, Hathairat Ketmaneechairat, Maleerat Maliyaem, "Overmind: A Collaborative Decentralized Machine Learning Framework", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 280–289, 2020. doi: 10.25046/aj050634
49. Pamela Zontone, Antonio Affanni, Riccardo Bernardini, Leonida Del Linz, Alessandro Piras, Roberto Rinaldo, "Supervised Learning Techniques for Stress Detection in Car Drivers", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 22–29, 2020. doi: 10.25046/aj050603
50. Kodai Kitagawa, Koji Matsumoto, Kensuke Iwanaga, Siti Anom Ahmad, Takayuki Nagasaki, Sota Nakano, Mitsumasa Hida, Shogo Okamatsu, Chikamune Wada, "Posture Recognition Method for Caregivers during Postural Change of a Patient on a Bed using Wearable Sensors", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 1093–1098, 2020. doi: 10.25046/aj0505133
51. Khalid A. AlAfandy, Hicham Omara, Mohamed Lazaar, Mohammed Al Achhab, "Using Classic Networks for Classifying Remote Sensing Images: Comparative Study", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 770–780, 2020. doi: 10.25046/aj050594
52. Khalid A. AlAfandy, Hicham, Mohamed Lazaar, Mohammed Al Achhab, "Investment of Classic Deep CNNs and SVM for Classifying Remote Sensing Images", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 652–659, 2020. doi: 10.25046/aj050580
53. Lana Abdulrazaq Abdullah, Muzhir Shaban Al-Ani, "CNN-LSTM Based Model for ECG Arrhythmias and Myocardial Infarction Classification", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 601–606, 2020. doi: 10.25046/aj050573
54. Rajesh Kumar, Geetha S, "Malware Classification Using XGboost-Gradient Boosted Decision Tree", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 536–549, 2020. doi: 10.25046/aj050566
55. Nghia Duong-Trung, Nga Quynh Thi Tang, Xuan Son Ha, "Interpretation of Machine Learning Models for Medical Diagnosis", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 469–477, 2020. doi: 10.25046/aj050558
56. Oumaima Terrada, Soufiane Hamida, Bouchaib Cherradi, Abdelhadi Raihani, Omar Bouattane, "Supervised Machine Learning Based Medical Diagnosis Support System for Prediction of Patients with Heart Disease", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 269–277, 2020. doi: 10.25046/aj050533
57. Haytham Azmi, "FPGA Acceleration of Tree-based Learning Algorithms", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 237–244, 2020. doi: 10.25046/aj050529
58. Hicham Moujahid, Bouchaib Cherradi, Oussama El Gannour, Lhoussain Bahatti, Oumaima Terrada, Soufiane Hamida, "Convolutional Neural Network Based Classification of Patients with Pneumonia using X-ray Lung Images", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 167–175, 2020. doi: 10.25046/aj050522
59. Young-Jin Park, Hui-Sup Cho, "A Method for Detecting Human Presence and Movement Using Impulse Radar", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 770–775, 2020. doi: 10.25046/aj050491
60. Anouar Bachar, Noureddine El Makhfi, Omar EL Bannay, "Machine Learning for Network Intrusion Detection Based on SVM Binary Classification Model", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 638–644, 2020. doi: 10.25046/aj050476
61. Adonis Santos, Patricia Angela Abu, Carlos Oppus, Rosula Reyes, "Real-Time Traffic Sign Detection and Recognition System for Assistive Driving", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 600–611, 2020. doi: 10.25046/aj050471
62. Amar Choudhary, Deependra Pandey, Saurabh Bhardwaj, "Overview of Solar Radiation Estimation Techniques with Development of Solar Radiation Model Using Artificial Neural Network", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 589–593, 2020. doi: 10.25046/aj050469
63. Maroua Abdellaoui, Dounia Daghouj, Mohammed Fattah, Younes Balboul, Said Mazer, Moulhime El Bekkali, "Artificial Intelligence Approach for Target Classification: A State of the Art", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 445–456, 2020. doi: 10.25046/aj050453
64. Shahab Pasha, Jan Lundgren, Christian Ritz, Yuexian Zou, "Distributed Microphone Arrays, Emerging Speech and Audio Signal Processing Platforms: A Review", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 331–343, 2020. doi: 10.25046/aj050439
65. Ilias Kalathas, Michail Papoutsidakis, Chistos Drosos, "Optimization of the Procedures for Checking the Functionality of the Greek Railways: Data Mining and Machine Learning Approach to Predict Passenger Train Immobilization", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 287–295, 2020. doi: 10.25046/aj050435
66. Yosaphat Catur Widiyono, Sani Muhamad Isa, "Utilization of Data Mining to Predict Non-Performing Loan", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 252–256, 2020. doi: 10.25046/aj050431
67. Hai Thanh Nguyen, Nhi Yen Kim Phan, Huong Hoang Luong, Trung Phuoc Le, Nghi Cong Tran, "Efficient Discretization Approaches for Machine Learning Techniques to Improve Disease Classification on Gut Microbiome Composition Data", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 547–556, 2020. doi: 10.25046/aj050368
68. Ruba Obiedat, "Risk Management: The Case of Intrusion Detection using Data Mining Techniques", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 529–535, 2020. doi: 10.25046/aj050365
69. Krina B. Gabani, Mayuri A. Mehta, Stephanie Noronha, "Racial Categorization Methods: A Survey", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 388–401, 2020. doi: 10.25046/aj050350
70. Dennis Luqman, Sani Muhamad Isa, "Machine Learning Model to Identify the Optimum Database Query Execution Platform on GPU Assisted Database", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 214–225, 2020. doi: 10.25046/aj050328
71. Gillala Rekha, Shaveta Malik, Amit Kumar Tyagi, Meghna Manoj Nair, "Intrusion Detection in Cyber Security: Role of Machine Learning and Data Mining in Cyber Security", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 72–81, 2020. doi: 10.25046/aj050310
72. Ahmed EL Orche, Mohamed Bahaj, "Approach to Combine an Ontology-Based on Payment System with Neural Network for Transaction Fraud Detection", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 551–560, 2020. doi: 10.25046/aj050269
73. Bokyoon Na, Geoffrey C Fox, "Object Classifications by Image Super-Resolution Preprocessing for Convolutional Neural Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 476–483, 2020. doi: 10.25046/aj050261
74. Jude B. Rola, Cherry Lyn C. Sta. Romana, Larmie S. Feliscuzo, Ivy Fe M. Lopez, Cherry N. Rola, "A Comparative Analysis of ARIMA and Feed-Forward Neural Network Prognostic Model for Bull Services", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 411–418, 2020. doi: 10.25046/aj050253
75. Johannes Linden, Xutao Wang, Stefan Forsstrom, Tingting Zhang, "Productify News Article Classification Model with Sagemaker", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 13–18, 2020. doi: 10.25046/aj050202
76. Michael Wenceslaus Putong, Suharjito, "Classification Model of Contact Center Customers Emails Using Machine Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 1, pp. 174–182, 2020. doi: 10.25046/aj050123
77. Rehan Ullah Khan, Ali Mustafa Qamar, Mohammed Hadwan, "Quranic Reciter Recognition: A Machine Learning Approach", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 173–176, 2019. doi: 10.25046/aj040621
78. Mehdi Guessous, Lahbib Zenkouar, "An ML-optimized dRRM Solution for IEEE 802.11 Enterprise Wlan Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 19–31, 2019. doi: 10.25046/aj040603
79. Andary Dadang Yuliyono, Abba Suganda Girsang, "Artificial Bee Colony-Optimized LSTM for Bitcoin Price Prediction", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 375–383, 2019. doi: 10.25046/aj040549
80. Toshiyasu Kato, Yuki Terawaki, Yasushi Kodama, Teruhiko Unoki, Yasushi Kambayashi, "Estimating Academic results from Trainees’ Activities in Programming Exercises Using Four Types of Machine Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 321–326, 2019. doi: 10.25046/aj040541
81. Nindhia Hutagaol, Suharjito, "Predictive Modelling of Student Dropout Using Ensemble Classifier Method in Higher Education", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 206–211, 2019. doi: 10.25046/aj040425
82. Priyamvada Chandel, Tripta Thakur, "Smart Meter Data Analysis for Electricity Theft Detection using Neural Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 161–168, 2019. doi: 10.25046/aj040420
83. Fernando Hernández, Roberto Vega, Freddy Tapia, Derlin Morocho, Walter Fuertes, "Early Detection of Alzheimer’s Using Digital Image Processing Through Iridology, An Alternative Method", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 3, pp. 126–137, 2019. doi: 10.25046/aj040317
84. Abba Suganda Girsang, Andi Setiadi Manalu, Ko-Wei Huang, "Feature Selection for Musical Genre Classification Using a Genetic Algorithm", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 2, pp. 162–169, 2019. doi: 10.25046/aj040221
85. Konstantin Mironov, Ruslan Gayanov, Dmiriy Kurennov, "Observing and Forecasting the Trajectory of the Thrown Body with use of Genetic Programming", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 1, pp. 248–257, 2019. doi: 10.25046/aj040124
86. Bok Gyu Han, Hyeon Seok Yang, Ho Gyeong Lee, Young Shik Moon, "Low Contrast Image Enhancement Using Convolutional Neural Network with Simple Reflection Model", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 1, pp. 159–164, 2019. doi: 10.25046/aj040115
87. Zheng Xie, Chaitanya Gadepalli, Farideh Jalalinajafabadi, Barry M.G. Cheetham, Jarrod J. Homer, "Machine Learning Applied to GRBAS Voice Quality Assessment", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 6, pp. 329–338, 2018. doi: 10.25046/aj030641
88. Richard Osei Agjei, Emmanuel Awuni Kolog, Daniel Dei, Juliet Yayra Tengey, "Emotional Impact of Suicide on Active Witnesses: Predicting with Machine Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 5, pp. 501–509, 2018. doi: 10.25046/aj030557
89. Sudipta Saha, Aninda Saha, Zubayr Khalid, Pritam Paul, Shuvam Biswas, "A Machine Learning Framework Using Distinctive Feature Extraction for Hand Gesture Recognition", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 5, pp. 72–81, 2018. doi: 10.25046/aj030510
90. Charles Frank, Asmail Habach, Raed Seetan, Abdullah Wahbeh, "Predicting Smoking Status Using Machine Learning Algorithms and Statistical Analysis", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 2, pp. 184–189, 2018. doi: 10.25046/aj030221
91. Sehla Loussaief, Afef Abdelkrim, "Machine Learning framework for image classification", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 1, pp. 1–10, 2018. doi: 10.25046/aj030101
92. Ruijian Zhang, Deren Li, "Applying Machine Learning and High Performance Computing to Water Quality Assessment and Prediction", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 6, pp. 285–289, 2017. doi: 10.25046/aj020635
93. Batoul Haidar, Maroun Chamoun, Ahmed Serhrouchni, "A Multilingual System for Cyberbullying Detection: Arabic Content Detection using Machine Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 6, pp. 275–284, 2017. doi: 10.25046/aj020634
94. Yuksel Arslan, Abdussamet Tanıs, Huseyin Canbolat, "A Relational Database Model and Tools for Environmental Sound Recognition", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 6, pp. 145–150, 2017. doi: 10.25046/aj020618
95. Loretta Henderson Cheeks, Ashraf Gaffar, Mable Johnson Moore, "Modeling Double Subjectivity for Gaining Programmable Insights: Framing the Case of Uber", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 1677–1692, 2017. doi: 10.25046/aj0203209
96. Moses Ekpenyong, Daniel Asuquo, Samuel Robinson, Imeh Umoren, Etebong Isong, "Soft Handoff Evaluation and Efficient Access Network Selection in Next Generation Cellular Systems", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 1616–1625, 2017. doi: 10.25046/aj0203201
97. Rogerio Gomes Lopes, Marcelo Ladeira, Rommel Novaes Carvalho, "Use of machine learning techniques in the prediction of credit recovery", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 1432–1442, 2017. doi: 10.25046/aj0203179
98. Daniel Fraunholz, Marc Zimmermann, Hans Dieter Schotten, "Towards Deployment Strategies for Deception Systems", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 1272–1279, 2017. doi: 10.25046/aj0203161
99. Arsim Susuri, Mentor Hamiti, Agni Dika, "Detection of Vandalism in Wikipedia using Metadata Features – Implementation in Simple English and Albanian sections", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 4, pp. 1–7, 2017. doi: 10.25046/aj020401
100. Adewale Opeoluwa Ogunde, Ajibola Rasaq Olanbo, "A Web-Based Decision Support System for Evaluating Soil Suitability for Cassava Cultivation", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 1, pp. 42–50, 2017. doi: 10.25046/aj020105