1. Introduction

In recent years, Bitcoin has been in the spotlight not only of investors, but also in fields such as politics and media. Ever since its advent in 2008, Bitcoin has appreciated exponentially in U.S dollar terms, reaching a price of $20,000 in January of 2017. The dramatic increase in the price of Bitcoin formed an atmosphere among the public that Bitcoin may be a good means to store value. Governments around the world, on the other hand, expressed deep concerns about Bitcoin’s volatile nature. Despite the concerns of financial regulators and governments, in 2021 Bitcoin once again pumped over $64,900, though it did cool-off after Elon Musk’s negative tweets on Bitcoin.

The Bitcoin rally that was seen during 2020 and 2021 drew more attention to Bitcoin than ever before. Huge financial investment firms and multinational corporations such as Tesla started to accept Bitcoin as means of payment. According to the article[1], Moreover, rumors behind Apple accepting Bitcoin as a means of payment based on its Apple Pay seems likely to become a reality. Such interest from multinational corporations is making even those doubtful about Bitc oin enter or re-enter the crypto space.

Many estimate that Bitcoin has potential to break its all-time high in the near future and argue that its fundamentals seem to be strengthening as time passes [2]. However, as seen in the recent 50% drop, the Bitcoin market is still very volatile and can be risky to many, especially retail investors. Firms such as Tesla and Micro Strategy, unlike retail investors, have the financial stability to seek longer term returns and can manage the risks accompanied with downside volatility. The fundamental differences between the so-called Bitcoin “whales” and retail investors lead retail investors to be more elastic to downside price movements and loss from investing in Bitcoin. Considering how the cyrpto-currency market is unregulated compared to traditional assets such as real estate and equity, the fear of loss can be much bigger for retail investors than big firms [3].

There have been numerous research projects that attempted to predict the movement of this unstable market using the methods that were used to predict the price of traditional assets such as stocks. However, these studies seem to neglect the fact that there are data on Bitcoin itself that contain more information about the potential movement of Bitcoin’s price [4]. Thus, this research attempts to use data that are obtained directly from Bitcoin’s blockchain, called on-chain data. This attempt may be conducive to providing guidelines to retail investors on which data an investor should focus when investing in Bitcoin. We review related work in Section 2, discuss the background of our data and theory in Section 3, and present our data preprocessing and test analysis in Section 4. Finally, we conclude in Section 5.

2. Related Works & Literature

 As Bitcoin solidifies its position as a mainstream asset class, various studies regarding Bitcoin’s price have been conducted. Early research on Bitcoin concentrated on looking for the factors that determine Bitcoin’s price. These factors include the macroeconomic variables gold price, inflation, employment rates, and stock index. Other factors that were shown to influence Bitcoin’s price include twitter and news sentiment.

Corporate interest to own Bitcoin to hedge against inflation gave Bitcoin the famous alias ‘digital gold’. The firms that entered the Bitcoin market sparked interest in trading algorithms like it did in the equity market[5]. The use of trading algorithms in the crypto currency market also resulted in many researchers and traders making use of machine learning and deep learning algorithms to predict the price movements of Bitcoin to improve the returns of the trading algorithms in use. The studies can be categorized based on the variables that were used and the goals of each research [6].

Figure 1: The flow of the experimentation process

The studies listed in Table 1 mainly used data such as trading volume that were obtained from crypto currency exchanges, macro-economic variables, and sentiment analysis of social networking services such as Twitter. There are studies that conduct their research using blockchain data such as hash rate and mining difficulty. However, such data are what is written explicitly on the blocks and are not what are obtained using thorough analysis of the transactions recorded in the blocks. Thus, to make better use of the data from Bitcoin’s chains, this research aims to make use of LSTM (Long Short-Term Memory) based on on-chain data obtained through thorough analysis of transaction data recorded in Bitcoin’s blockchains. We predicted the price of Bitcoin based on various data such as price forecasts from FOMC announcements[7], [8]. In particular, a recently published study [9] utilized on-chain data and used LSTM, which is similar to our work. However, the data, time period, and features we used are different. We utilized market data to consider the “Kimchi Premium,” which is unique to the Korean coin market and is different from other coin exchange data.

Our research will proceed as illustrated in Fig. 1. First, an explanation of the on-chain data that will go through feature engineering will be followed by an explanation of the LSTM. Next, the process of how the features were scaled and selected will be explored. Finally, the preprocessed data will be used to predict Bitcoin’s future price and will be evaluated based on certain performance measures.

Table 1: summary of related works on bitcoin prediction

3. Data and Background

The most important aspect of this research is the use of on-chain data, a type of data that were not utilized in earnest in previous studies on predicting the price of Bitcoin. Therefore, section A of Part 3 will be an explanation of on-chain data for a better understanding of the overall research. The data are subdivided into six categories.

3.1.  On-Chain Data Explanation

1)       EXCHANGE FLOWS DATA

Exchange flow data play a major role in explaining potential movements in the Bitcoin price. Unless Bitcoin is traded over the counter, most of its trading takes place in the Bitcoin exchange[10]. In this category, total reserves, reserve net flow, address count, and transaction count data will first be considered as potential factors that have influence in the predictability of the model. In any asset class, it is considered that the more assets there are in the market, the less valuable. ‘Total reserve’ and ‘total reserve net flow’ are data that show the amount of Bitcoin in the Bitcoin exchanges and the amount of Bitcoin that came into the exchange and went out. It can be inferred from the famous adage from economics, ‘the more the supply, the lower the price’, more stocks of Bitcoin in exchanges and more inflow into exchanges can lead to a lower price. The ‘address count’ and ‘transaction count’ data show the number of addresses participating and the number of transactions made in the exchange.

2)       FLOW INDICATOR

Flow indicators help assume the risks of holding Bitcoin and give insights on the value of holding Bitcoin. ‘Exchange whale ratio’ and ‘stablecoins ratio’ are two indicators that fall into this category. ‘Exchange whale ratio’ is calculated by dividing the sum of the top 10 inflows by the sum of the total inflows into exchanges. Several Bitcoin traders think that whales, who own more than a thousand bitcoins, have enough control over the market and can decide the movement of Bitcoin’s price. The ‘stablecoins ratio’ is calculated by dividing the total bitcoin reserve in exchanges by the total stablecoins reserve. Stablecoins such as USD Tether is what participants in the market use to buy cryptocurrencies. Thus, the reserve of stablecoins in exchanges can signify the amount of demand pressure in the market.

3)       MARKET INDICATOR

Indicators such as ‘estimated leverage ratio’ and ‘MVRV (Market Value to Realized Value)’ fall into this category [11]. Market indicators concentrate on showing how much the market’s investors are heated or cooled off. To be specific, ‘estimated leverage ratio’, which is calculated by dividing the open interests of exchanges by the exchanges’ Bitcoin reserves, represents the average amount of leverage of the market participants. MVRV, calculated by dividing the market value by the realized value, illustrates the relationship between short-term and long-term investors.

4)       MINER FLOW

Miner flow data focus on showing the flow of Bitcoin from the miners’ wallets. Miners are critical players in the Bitcoin ecosystem in that they are the ones who secure the network and confirm transactions and get paid with Bitcoin in return. ‘Miner’s total reserve’ and ‘miner’s net flow’ are the total amount of Bitcoins in the miner’s wallets and the net change in the miners’ reserves in a given time horizon, respectively [12].

5)       MARKET DATA

Market data are comprised of the open, close, low, and high U.S dollar value of Bitcoin in each time period

6)       NETWORK DATA

Network data consist of information recorded in each block of the Bitcoin chain, such as transaction fees, mining difficulty, and hashrate [13] , [14]. Network data were previously used along with other macro-economic metrics and social networking sentiment data to predict the price of Bitcoin. ‘Transaction fee’ shows the amount of fees that incurred from the transactions in a block. Mining difficulty shows how difficult it was to mine the specific block. Lastly, hashrate shows how quickly the miners solve hash problems to mine Bitcoin.

3.2.  LSTM Background

1)       RNN (RECURRENT NEURAL NETWORK)

Figure 2: Structure of an RNN

An RNN [15], unlike an ANN (Artificial Neural Network), forms a loop so that information persists throughout the network. As illustrated in [Fig. 2], if there is a neural network A, x and output o, the RNN consistently inputs the element in x_t to the successor output o_t. Therefore, RNNs are adequate to process data in the form of chains or lists. Due to this trait, RNNs are frequently used in speech recognition and language modeling.

2)       LSTM (LONG SHORT TERM MEMORY)

An LSTM [16] can be considered as a sub-category of RNNs. Although RNNs make use of the output o_t-1, as the output gets processed and the gap gets wider, RNNs can’t connect current information and the information input in the distant past.

Figure 3: LSTM structure

An RNN, unlike an ANN (Artificial Neural Network), forms a loop so that information persists throughout the network. As illustrated in [Fig. 2], if there is a neural network A, Xt and output o, the RNN consistently inputs the element in Xt to the successor output o_t. Therefore, RNNs are adequate to process data in the form of chains or lists. Due to this trait, RNNs are frequently used in speech recognition and language modeling.

To deal with this problem, LSTMs have a rather complex structure and involve diverse linear interactions compared to their simple counterpart [17]. Fig. 3. shows the H_t of LSTMs. The upper arrow is where the cell state C_t-1 is inputted. The upper arrow seems more straightforward than the arrows below. This shows that C_t-1 faces minor interactions. Cell states are also passed through gates, structures that control whether to remove or add information to the cell state. Gates, which consist of a sigmoid layer and a pointwise multiplication operation, output values between 0 and 1. The higher the value, the more of its components are let through. An LSTM network has three such gates. To be specific, the first layer, known as the ‘forget gate layer’, receives , which is the output of the previous LSTM and x_t, the t-th input and returns values between 0 and 1, which shows how much of C_t-1 should pass through. Next, another sigmoid layer, which is also known as the input gate layer, determines the values that will be updated. The tanh layer then makes new values C̃ _t, which are candidates that can be added to the state. The sigmoid layer and the tanh layer are combined to create an update to the state. After going through the layers, the output that went through the first sigmoid layer ft is multiplied by the old cell state C_t-1. Moreover, the output of the second output layer and the tanh layer are multiplied as well. The two outputs from the multiplications are then added. Finally, to decide the output, the final sigmoid layer and tanh decide which part of the cell state will be the outcome of the process.

The fact that LSTMs consistently make use of the necessary information is the key to making predictions. Time-series data, such as the data used in this research, can make good use of the LSTM characteristic in that they do not abandon data with a large enough time gap. Moreover, since they drop the information that doesn’t seem vital to the prediction process, LSTMs suit well with Bitcoin prediction, which can contain noise in the data [18].

4. Experimentation & Evaluation

4.1.  On-Chain Data Explanation

1)       DATA PREPROCESSING & ENGINEERING

The necessary on-chain data and price data were extracted from CryptoQuant (www.cryptoquant.com) via an api access. CryptoQuant provides not only ‘Reserve’ and ‘Exchange’ data, but also provides data on the Bitcoin network and miners. However, it is important to note that the time frame of each datum can be different. Low-frequency (day) data were provided for most features, but high-frequency data such as hourly data were not provided for many features. Therefore, the experiment was based on low-frequency data to avoid having different time frames in the features. The data were first split into training, validation, and test sets. The training set consists of data from April 19th, 2019, to July 5th, 2020, which is a total of 444 days. The validation set and the test set are the data from July 6th, 2020, to November 30th, 2020, and December 1st, 2020 to April 27th, 2021, respectively, which sums up to 144 days each.

The most important part of the experiment was the scaling method. If the whole dataset is scaled at once, the 2-year length of data would be incapable of capturing the short-term trends which are apparent in the data. Thus, the scaling of the training data and the test data were done separately. The specific methodology to implement the scaling is illustrated in [Algorithm 1].

4.2.  Hyperparameter Tuning & Validation

Table 2 shows the features that were selected after scaling the training set. The 10 features were selected using sklearn.model_selection’s Select K Best setting the score function to mutual information. The prediction was done using Python’s Keras library. The data was scaled so that all values were between 0 and 1.

Table 2: 10 selected features

Moreover, hyper-parameter tuning was done to make the results as accurate as possible. The model’s performance is evaluated using RMSE, MAPE, and R-squared, all of which are used frequently to measure the performance of regression models.

To optimize the model’s performance, hyperparameter tuning was performed using a grid search method. Specif-ically, we used scikit-learn’s GridSearchCV function to sys-tematically search through different combinations of hy-perparameters, including lookback day, epoch, batch size, unit, dropout rate, and optimizer, in order to identify the combination that produced the best results. GridSearchCV performs a search over a pre-defined parameter space, and returns the best combination of parameters based on cross-validation scores. In our study, the best combination of hyperparameters was found to be a lookback day of 3, an epoch of 30, a batch size of 128, a unit of 1, a dropout rate of 0.4, and the optimizer set to Nadam, which pro-duced the highest scores for all three metrics.

Figure 4: Prediction Result (Full Data Length)

4.3.  Test Results & Evaluation

Table 4 shows the performance of the validation and test predictions. During the validation process, the RMSE, MAPE, and R² results were 457, 2.267%, and 0.968, respectively. In the test set prediction, the RMSE, MAPE, and R² results were 2344, 4.316%, and 0.971, respectively. There was some gap between the performance between the validation and the test prediction. A possible explanation for this would be the variation of the Bitcoin price during the validation set’s data and the test set’s data. Starting from December of 2020, Bitcoin’s price started its rally and almost tripled from 20,000 U.S dollars to 65,000 U.S dollars, while during July to November of the same year, Bitcoin price was in the price range between 10,000 U.S  dollars to 20,000 U.S  dollars. Moreover, the participation of institutions in the market might have influenced the data during the test set’s time-period.

Table 4: validation/test evaluation

Figure 5: Comparison Between Prediction Results and Actual Data

5. Conclusion

This research was based on Bitcoin’s on-chain data provided by CryptoQuant. The results show that on-chain data might be of good use when predicting the movement of Bitcoin’s price rather than traditional factors such as macro-economic data. Although data such as hashrates were used in previous studies, better performance might be drawn from the movement of Bitcoin in the exchange’s reserve or the miners’ wallets, which were data that were not previously used to forecast Bitcoin’s price movement. Although, such data are not yet utilized as much, the use of deep-learning algorithms such as LSTM with on-chain data might provide a good guideline of where Bitcoin’s price will be in the near-future. However, this work should be extended in some way, e.g., by comparing the performance of different deep learning models and/or classical non-deep learning-based models.

This research made use of various on-chain data, but there still are some on-chain data that were not available because of budget restraints but might be useful for prediction. Especially, the movement of ‘whales’ who hold more than 1,000 bitcoins is suspected to have a tremendous impact in Bitcoin’s price and can be subject to further research. Furthermore, if on-chain data and macro-economic variables are used together for prediction, there is a possibility of improvement in the prediction performance.

In addition, prediction using high-frequency data can also be a valid research topic. Especially when day-trading is such a huge part of the Bitcoin market. Using high-frequency data for prediction can lead to higher profits for those who invest in Bitcoin.

Finally, using other cryptocurrencies and their on-chain data for price prediction needs further research as well. Other cryptocurrencies have important data of their own. Cryptocurrencies that use Proof of Stake as a validation method do not have data such as hashrate and difficulty. Thus, other data in their blockchains need to be extracted to make a solid prediction of their prices.

Acknowledgment

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. NRF-2022R1F1A1074696).

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33. Seok-Jun Bu, Hae-Jung Kim, "Ensemble Learning of Deep URL Features based on Convolutional Neural Network for Phishing Attack Detection", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 291–296, 2021. doi: 10.25046/aj060532
34. Caglar Arslan, Selen Sipahio?lu, Emre ?afak, Mesut Gözütok, Tacettin Köprülü, "Comparative Analysis and Modern Applications of PoW, PoS, PPoS Blockchain Consensus Mechanisms and New Distributed Ledger Technologies", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 279–290, 2021. doi: 10.25046/aj060531
35. Marko Suvajdzic, Dragana Stojanovic, "Discover DaVinci: Blockchain, Art and New Ways of Digital Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 273–278, 2021. doi: 10.25046/aj060530
36. Osaretin Eboya, Julia Binti Juremi, "iDRP Framework: An Intelligent Malware Exploration Framework for Big Data and Internet of Things (IoT) Ecosystem", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 185–202, 2021. doi: 10.25046/aj060521
37. 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
38. Fatima-Ezzahra Lagrari, Youssfi Elkettani, "Traditional and Deep Learning Approaches for Sentiment Analysis: A Survey", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 01–07, 2021. doi: 10.25046/aj060501
39. 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
40. Anjali Banga, Pradeep Kumar Bhatia, "Optimized Component based Selection using LSTM Model by Integrating Hybrid MVO-PSO Soft Computing Technique", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 62–71, 2021. doi: 10.25046/aj060408
41. Nadia Jmour, Slim Masmoudi, Afef Abdelkrim, "A New Video Based Emotions Analysis System (VEMOS): An Efficient Solution Compared to iMotions Affectiva Analysis Software", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 990–1001, 2021. doi: 10.25046/aj0602114
42. Bakhtyar Ahmed Mohammed, Muzhir Shaban Al-Ani, "Follow-up and Diagnose COVID-19 Using Deep Learning Technique", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 971–976, 2021. doi: 10.25046/aj0602111
43. Showkat Ahmad Dar, S Palanivel, "Performance Evaluation of Convolutional Neural Networks (CNNs) And VGG on Real Time Face Recognition System", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 956–964, 2021. doi: 10.25046/aj0602109
44. Abraham Adiputra Wijaya, Inten Yasmina, Amalia Zahra, "Indonesian Music Emotion Recognition Based on Audio with Deep Learning Approach", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 716–721, 2021. doi: 10.25046/aj060283
45. Erick Fernando, Meyliana Meyliana, Harco Leslie Hendric Spits Warnars, Edi Abdurachman, Surjandy Surjandy, "Blockchain Technology-Based Good Distribution Practice Model of Pharmacy Industry in Indonesia", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 267–273, 2021. doi: 10.25046/aj060230
46. Sebastianus Bara Primananda, Sani Muhamad Isa, "Forecasting Gold Price in Rupiah using Multivariate Analysis with LSTM and GRU Neural Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 245–253, 2021. doi: 10.25046/aj060227
47. Meng-Chang Jong, Chin-Hong Puah, Ann-Ni Soh, "The Impact of COVID-19 Pandemic and Commodities Prices on Booking.com Share Price", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 185–189, 2021. doi: 10.25046/aj060221
48. Dejun Chen, Congcong Xiong, Li Guo, Ming Zhong, "A Hybrid NMF-AttLSTM Method for Short-term Traffic Flow Prediction", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 175–184, 2021. doi: 10.25046/aj060220
49. Prasham Shah, Mohamed El-Sharkawy, "A-MnasNet and Image Classification on NXP Bluebox 2.0", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 1378–1383, 2021. doi: 10.25046/aj0601157
50. 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
51. Meyliana, Cadelina Cassandra, Yakob Utama Chandra, Surjandy, Erick Fernando, Henry Antonius Eka Widjaja, Harjanto Prabowo, "Recording of Academic Transcript Data to Prevent the Forgery based on Blockchain Technology", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 1273–1278, 2021. doi: 10.25046/aj0601145
52. Alisson Steffens Henrique, Anita Maria da Rocha Fernandes, Rodrigo Lyra, Valderi Reis Quietinho Leithardt, Sérgio D. Correia, Paul Crocker, Rudimar Luis Scaranto Dazzi, "Classifying Garments from Fashion-MNIST Dataset Through CNNs", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 989–994, 2021. doi: 10.25046/aj0601109
53. 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
54. Erick Fernando, Meyliana, Harco Leslie Hendric Spits Warnars, Edi Abdurachman, "Blockchain Technology for Tracing Drug with a Multichain Platform: Simulation Method", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 765–769, 2021. doi: 10.25046/aj060184
55. Achmad Ichwan Yasir, Gede Putra Kusuma, "Deep Deterministic Policy Gradients for Optimizing Simulated PoA Blockchain Networks Based on Healthcare Data Characteristics", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 757–764, 2021. doi: 10.25046/aj060183
56. Meyliana, Yakob Utama Chandra, Cadelina Cassandra, Surjandy, Erick Fernando, Henry Antonius Eka Widjaja, Andy Effendi, Ivan sangkereng, Charles Joseph, Harjanto Prabowo, "Recording of Student Attendance with Blockchain Technology to Avoid Fake Presence Data in Teaching Learning Process", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 742–747, 2021. doi: 10.25046/aj060181
57. Nakhoon Choi, Heeyoul Kim, "A Novel Blockchain-Based Authentication and Access Control Model for Smart Environment", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 651–657, 2021. doi: 10.25046/aj060171
58. 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
59. Minkyung Kim, Kangseok Kim, Jai-Hoon Kim, "Analysis of Real-time Blockchain Considering Service Level Agreement (SLA)", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 218–223, 2021. doi: 10.25046/aj060124
60. Devika K N, Ramesh Bhakthavatchalu, "Modified Blockchain based Hardware Paradigm for Data Provenance in Academia", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 66–77, 2021. doi: 10.25046/aj060108
61. Qazi Mudassar Ilyas, Muhammad Mehboob Yasin, "An Anonymity Preserving Framework for Associating Personally Identifying Information with a Digital Wallet", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 36–42, 2021. doi: 10.25046/aj060104
62. Meyliana, Yakob Utama Chandra, Cadelina Cassandra, Surjandy, Erick Fernando, Henry Antonius Eka Widjaja, Harjanto Prabowo, "Education Value Chain Model for Examination, Grading, and Evaluation Process in Higher Education based on Blockchain Technology", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1698–1703, 2020. doi: 10.25046/aj0506202
63. Xianxian Luo, Songya Xu, Hong Yan, "Application of Deep Belief Network in Forest Type Identification using Hyperspectral Data", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1554–1559, 2020. doi: 10.25046/aj0506186
64. Fernando Richter Vidal, Feliz Gouveia, Christophe Soares, "Blockchain Application in Higher Education Diploma Management and Results Analysis", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 871–882, 2020. doi: 10.25046/aj0506104
65. Lubna Abdelkareim Gabralla, "Dense Deep Neural Network Architecture for Keystroke Dynamics Authentication in Mobile Phone", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 307–314, 2020. doi: 10.25046/aj050637
66. Helen Kottarathil Joy, Manjunath Ramachandra Kounte, "A Comprehensive Review of Traditional Video Processing", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 274–279, 2020. doi: 10.25046/aj050633
67. Kailerk Treetipsounthorn, Thanisorn Sriudomporn, Gridsada Phanomchoeng, Christian Dengler, Setha Panngum, Sunhapos Chantranuwathana, Ali Zemouche, "Vehicle Rollover Detection in Tripped and Untripped Rollovers using Recurrent Neural Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 228–238, 2020. doi: 10.25046/aj050627
68. Andrea Generosi, Silvia Ceccacci, Samuele Faggiano, Luca Giraldi, Maura Mengoni, "A Toolkit for the Automatic Analysis of Human Behavior in HCI Applications in the Wild", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 185–192, 2020. doi: 10.25046/aj050622
69. 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
70. Daniyar Nurseitov, Kairat Bostanbekov, Maksat Kanatov, Anel Alimova, Abdelrahman Abdallah, Galymzhan Abdimanap, "Classification of Handwritten Names of Cities and Handwritten Text Recognition using Various Deep Learning Models", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 934–943, 2020. doi: 10.25046/aj0505114
71. 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
72. 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
73. Chigozie Enyinna Nwankpa, "Advances in Optimisation Algorithms and Techniques for Deep Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 563–577, 2020. doi: 10.25046/aj050570
74. Alami Hamza, Noureddine En-Nahnahi, Said El Alaoui Ouatik, "Contextual Word Representation and Deep Neural Networks-based Method for Arabic Question Classification", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 478–484, 2020. doi: 10.25046/aj050559
75. Fatma Mallouli, Aya Hellal, Fatimah Abdulraheem Alzahrani, Abdulsalam Ali Almadani, Nahla Sharief Saeed, "Comparative Study of Cryptocurrency Algorithms: Coronavirus Towards Bitcoin’s Expansion", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 452–459, 2020. doi: 10.25046/aj050556
76. Andi Aljabar, Suharjito, "BISINDO (Bahasa Isyarat Indonesia) Sign Language Recognition Using CNN and LSTM", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 282–287, 2020. doi: 10.25046/aj050535
77. Sathyabama Kaliyapillai, Saruladha Krishnamurthy, "Differential Evolution based Hyperparameters Tuned Deep Learning Models for Disease Diagnosis and Classification", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 253–261, 2020. doi: 10.25046/aj050531
78. 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
79. Tran Thanh Dien, Nguyen Thanh-Hai, Nguyen Thai-Nghe, "Deep Learning Approach for Automatic Topic Classification in an Online Submission System", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 700–709, 2020. doi: 10.25046/aj050483
80. Mohammed Qbadou, Intissar Salhi, Hanaâ El fazazi, Khalifa Mansouri, Michail Manios, Vassilis Kaburlasos, "Human-Robot Multilingual Verbal Communication – The Ontological knowledge and Learning-based Models", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 540–547, 2020. doi: 10.25046/aj050464
81. 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
82. Sreela Sreekumaran Pillai Remadevi Amma, Sumam Mary Idicula, "Keyword Driven Image Description Generation System", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 405–411, 2020. doi: 10.25046/aj050447
83. Samir Allach, Mohamed Ben Ahmed, Anouar Abdelhakim Boudhir, "Deep Learning Model for A Driver Assistance System to Increase Visibility on A Foggy Road", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 314–322, 2020. doi: 10.25046/aj050437
84. Rizki Jaka Maulana, Gede Putra Kusuma, "Malware Classification Based on System Call Sequences Using Deep Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 207–216, 2020. doi: 10.25046/aj050426
85. 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
86. Neptali Montañez, Jomari Joseph Barrera, "Automated Abaca Fiber Grade Classification Using Convolution Neural Network (CNN)", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 207–213, 2020. doi: 10.25046/aj050327
87. Arif Furkan Mendi, Tolga Erol, Emre Şafak, "Generating a Blockchain Smart Contract Application Framework", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 191–197, 2020. doi: 10.25046/aj050325
88. Basem Assiri, "Using Leader Election and Blockchain in E-Health", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 46–54, 2020. doi: 10.25046/aj050307
89. Segundo Moisés Toapanta Toapanta, Daniela Monserrate Moreira Gamboa, Luis Enrique Mafla Gallegos, "Analysis of the Blockchain for Adoption in Electronic Commerce Management in Ecuador", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 762–768, 2020. doi: 10.25046/aj050295
90. Aditi Haresh Vyas, Mayuri A. Mehta, "A Comprehensive Survey on Image Modality Based Computerized Dry Eye Disease Detection Techniques", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 748–756, 2020. doi: 10.25046/aj050293
91. Bayan O Al-Amri, Mohammed A. AlZain, Jehad Al-Amri, Mohammed Baz, Mehedi Masud, "A Comprehensive Study of Privacy Preserving Techniques in Cloud Computing Environment", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 419–424, 2020. doi: 10.25046/aj050254
92. Shweta Mittal, Om Prakash Sangwan, "Big Data Analytics Using Deep LSTM Networks: A Case Study for Weather Prediction", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 133–137, 2020. doi: 10.25046/aj050217
93. Daihui Li, Chengxu Ma, Shangyou Zeng, "Design of Efficient Convolutional Neural Module Based on An Improved Module", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 1, pp. 340–345, 2020. doi: 10.25046/aj050143
94. Saleh Albahli, Rehan Ullah Khan, Ali Mustafa Qamar, "A Blockchain-Based Architecture for Smart Healthcare System: A Case Study of Saudi Arabia", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 1, pp. 40–47, 2020. doi: 10.25046/aj050106
95. 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
96. Shun Yamamoto, Momoyo Ito, Shin-ichi Ito, Minoru Fukumi, "Verification of the Usefulness of Personal Authentication with Aerial Input Numerals Using Leap Motion", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 369–374, 2019. doi: 10.25046/aj040548
97. Kevin Yudi, Suharjito, "Sentiment Analysis of Transjakarta Based on Twitter using Convolutional Neural Network", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 281–286, 2021. doi: 10.25046/aj040535
98. Bui Thanh Hung, "Integrating Diacritics Restoration and Question Classification into Vietnamese Question Answering System", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 207–212, 2019. doi: 10.25046/aj040526
99. Anang Hudaya Muhamad Amin, Nazrul Muhaimin Ahmad, Subarmaniam Kannan, "Event Monitoring using Distributed Pattern Recognition Approach on Integrated IoT-Blockchain Network", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 256–264, 2019. doi: 10.25046/aj040432
100. 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