1. Introduction

Aquaculture is one of the most important industries supporting the world’s food supply. Since around 1990, fisheries production has been on a flat trend, while aquaculture production has continued to increase. As a result, the share of aquaculture in the total production of the seafood industry has increased. In 2021, the production from mariculture was approximately 70 million tons, accounting for about 32% of the total production of the fisheries and aquaculture industry [1]. This is due to growing demands for edible seafood in the world. Global consumption of edible seafood is increasing and has almost doubled in the last 50 years. Also in Japan, aquaculture is an important industry. The followings are statistical data on the aquaculture industry in Japan. In 2022, the production from mariculture was about 910,000 tons, accounting for about 23% of the total production of the seafood industry [2]. Its production value was 521.1-billion-yen, accounting for about 33% of the total. These indicate that aquaculture is an indispensable industry in Japan.

Under these circumstances, one of the problems facing aquaculture farmers is the damage caused by abnormal seawater temperatures. If seawater temperature is not properly controlled, it can increase the risk of fish disease infection [3] and decrease feed efficiency [4]. To prevent such damage, aquaculture farmers need to accurately predict seawater temperatures and protect marine products in advance by moving rafts. However, temperature prediction requires years of experience and a wealth of knowledge. Furthermore, it’s getting more difficult to predict seawater temperatures due to global warming and severe weather changes. From these, water temperature management is a heavy burden for aquaculture farmers. To make these temperature management more sustainable in the future, it is necessary to provide seawater temperature predictions based on collected temperature data that can be used by anyone.

Although seawater temperature prediction is an active area of research, this is currently limited to prediction of sea surface temperature (SST) [5, 6]. One of the factors that SST prediction has been actively conducted is the abundance of SST datasets provided by satellite imagery [7]. SST prediction is used in many fields, such as marine meteorology and weather forecasting, but it is not suitable for the seawater temperature prediction required by aquaculture farmers. In aquaculture, the depth at which fish are reared depends on the type of product and the size of the fish tank. It may be appropriate to keep at a depth of several 10 meters. As seawater temperature changes with depth, it is necessary to predict using seawater temperature data from each depth. This means that abundant sea surface temperature datasets are not available, and it is necessary to measure temperatures at several depths at each location. However, since these data are measured using dedicated observation equipment, it is not easy to collect sufficiently at each aquaculture farm. Therefore, in this study, we propose and implement a seawater temperature prediction model that provides sufficient prediction accuracy even with a small amount of data for about one year.

2. Related Works

In this chapter, we introduce related studies on the prediction of seawater using measured water temperature.

In [8], the authors proposed a method based on an autoregressive model. In this method, each observation point is classified by cluster analysis, and a principal component analysis is conducted using the water temperature anomalies within each group. By autoregressive predicting using the calculated first principal component, the method enables prediction up to three months ahead. However, because they focus on a wide area such as the entire coastal area, it is difficult to predict water temperatures in a narrow area within an aquaculture farm.

In [9], the authors proposed a method for predicting water temperature in aquaculture areas. In this method, water temperature in the aquaculture area is obtained in real time by installing several small and inexpensive buoys, called ubiquitous buoys [10]. Nevertheless, it is necessary to install a large number of buoys because the prediction range covers a wide area of several kilometers.

In [11], the authors proposed a method that uses meteorological data in addition to measured water temperature data. In this method, sea temperature at multiple depths and data provided by the Japan Meteorological Agency are collected. Using these data as features, a random forest is used to make predictions for each depth. In the evaluation experiment, it was shown that using not only temperature but also wind speed improves the accuracy of the prediction. However, they did not predict for the next day or later. According to authors in [12], aquaculture farmers need to predict water temperatures up to one week ahead within an error margin of 1℃.

The authors in [13, 14] proposed a method that predicts over multiple time periods. In this method, in addition to a model that predicts up to the next day, a model that predicts daily mean water temperatures up to one week ahead is provided. These models are capable of long-term prediction using Gated Recurrent Unit (GRU) [15]. Evaluation experiments showed that the models were able to prediction with higher accuracy than existing methods. However, they required a large amount of training data, about 9 years. It is not easy to prepare such data, and it will take time to introduce them to a new farm. To solve the above issues, we develop a seawater temperature prediction method that meets the needs of aquaculture farmers even with a short period of data.

3. Proposed Method

3.1. Subject Data

In this study, we use measured sea water temperature data and meteorological data to create a prediction model. From previous studies [11, 13, 14], it is clear that in addition to seawater temperature data, it is effective to combine this data with meteorological data for learning. In this section, as an example of the seawater temperature and meteorological data used in the proposed model, details of the data collected in this study are described.

First, the seawater temperature data is described. we prepare a dataset of seawater temperature collected at five points throughout Japan. Figure 1 shows the observation points of seawater temperature in this study. The latitude and longitude of Gokasho is (34.3461°N, 136.7050°E), Matoya is (34.8807°N, 136.8807°E), Ago is (34.3071°N, 136.8038°E), Goshoura is (32.2910°N, 130.2370°E), and Otaru is (43.1904°N, 140.9951°E). As Japan is located in the Northern Hemisphere, it is generally hot in the south and cold in the north throughout the year. Similar trends are observed not only with air temperature but also with seawater temperature. To demonstrate that the proposed model can be used universally throughout Japan despite these trends, data from various locations are used.

Table 1 shows the water depths and periods for which seawater temperatures are measured at each point. Under the conditions shown in Table 1, seawater temperatures are measured hourly at each point. At Gokasho, Ago, and Matoya, data are available for about 16 years from 2007 to 2022. On the other hand, Goshoura and Otaru have only about 8 years of data from 2012 to 2020, which means that the amount of data at each point varies. Seawater temperatures are measured simultaneously at three or four depths, which vary point site to point.

Table 1: Measurement conditions for seawater temperature

Next, the meteorological data is described. We prepare temperature and wind speed data from the meteorological database provided by the Japan Meteorological Agency [17]. We use these data measured at the nearest observation stations to the points shown in Figure 1. The periods of these data are set to be the same as the periods in which the seawater temperatures are measured at each point.

3.2. Effects of Depth on Seawater Temperature

In this section, we discuss the effects of different water depths on seawater temperature. Figure 2 shows the daily mean seawater temperature at each depth in Ago in 2015. From this graph, the difference in water temperature for each depth in Ago during the winter (December to February) is small. On the contrary, during the summer (June to August), the difference in water temperature for each depth is large. Seawater temperature tends to decrease with depth in summer. Specifically, the water temperature difference in winter is within 2℃, while that in summer ranges from 2 to 9℃.

In summary, the water temperature difference with depth in summer is larger than that in winter, and the larger the water depth, the lower the water temperature in Ago. This tendency is also observed at the other four points. For example, in Gokasho, the water temperature difference in winter is within 2℃, while the water temperature difference in summer ranges from 1℃ to 8℃. In Otaru, the water temperature difference in winter is within 2℃, while the water temperature difference in summer can be as high as 5℃.

As shown in the previous section, seawater temperatures were measured at different depths at each point. This is because the appropriate water depth for aquaculture is different at each point. The depth depends on the type of marine products to be raised and the surrounding climate.

From the above, it is necessary to account for the difference in seawater temperature associated with the water depth between points. With this in mind, the proposed model is presented in the next section.

3.3. Seawater Temperature Prediction Model

The seawater temperature prediction model proposed in this study is an extension of the long-term prediction model proposed in [13,14] by using transfer learning [18], in which water depth values are included in the input layer. This approach aims to solve the problem of the huge amount of train data required by conventional models. In addition, it aims to consider differences in water temperature due to differences in water depth.

Transfer learning is a machine learning approach which applies the knowledge obtained in the source domain to learn in the target domain [19]. In this study, the domain refers to the observation points of seawater temperature. A series of learning is performed in the source domain, and then the learned model is re-learned in the target domain. For this reason, transfer learning has the advantage that it can learn efficiently even if the amount of data in the target domain is small.

Figure 3 shows an overview of the proposed seawater temperature prediction model. The structure of the proposed model is based on Recurrent Neural Network (RNN) [20], which is suitable for time-series forecasting. The proposed model has a three-layer structure consisting of an input layer, a hidden layer, and an output layer, each of which is described separately below.

In the input layer, two types of time-series data, seawater temperature data and meteorological data, and the water depth values at which seawater temperature measured are input. Since seawater temperature does not change rapidly in a short period of time, the most recent data for the prediction target date is important. Therefore, we use the daily mean seawater temperature and meteorological data for the last seven days. As meteorological data, we use daily mean air temperature and maximum and minimum wind speeds. To account for the effects of multiple depths, we also included seawater temperatures at depths other than the prediction target as an input. As mentioned in the previous section, the depths measured are different at each point, and water temperatures vary depending on the water depth. This means that when performing transfer learning, it is necessary to consider the difference in the water depths between the source and target domains. Therefore, in the proposed model, the water depth values are added to the input layer.

In the hidden layer, we use GRU, which was also used in the model by the authors in [13,14] Conventional RNNs have two problems: gradient vanishing problem and weight collision, which make it difficult to learn long-term features. On the other hand, GRU has a reset gate and an update gate, and can select a choice of information. This makes it possible to store old information, and thus it can be applied to problems that requires long-term dependence to be considered. In the proposed model, seawater temperature data and weather data are input into another GRUs to be learned separately. The results processed by each GRU and the water depth values are combined, and then passed to the output layer.

In the output layer, the results from the hidden layer are converted to prediction results. As a prediction result, seven days of daily mean seawater temperatures up to one week ahead are output. In the proposed model, this series of learning is performed at points with sufficient seawater temperature data, and then the learned model is used to re-learn at the prediction target points with only a small amount of data.

4. Experiment

4.1. Common experimental setup

In this study, we conducted two evaluation experiments to demonstrate the effectiveness of the proposed model. The first was a comparison experiment of accuracy with and without transfer learning (Experiment 1). The second was a comparison experiment of accuracy with and without the input of water depth values (Experiment 2). This section describes the experimental setup common to both experiments.

Table 2 summarizes the details of the data used in the two experiments. Of the five observation points, we set Gokasho, Matoya, and Ago as the source domain, and Goshoura and Otaru as the target domain. Three different water depth values were set at each point. The period of train data for the source domain was eight years, the target domain was one year, and the test data was one year. Because the daily mean seawater temperature changes with a cycle of one year, we chose one year for the period of train data for the target domain. In addition, by setting the period of train data for source domain to eight years, the amount of train data used at the target domain reaches the amount of train data used by the authors in [13,14].

Table 2: Data used in evaluation experiments.

As evaluation items in the two experiments, we calculated Mean Absolute Error (MAE) and the percentage of predictions with errors more than 1℃. In this study, the standard value was set to 1℃ to meet the needs of aquaculture farmers for an error less than 1℃.

4.2. Experiment 1

In this section, Experiment 1 concerning transfer learning is presented. The purpose of Experiment 1 is to evaluate whether transfer learning is valid for the seawater temperature prediction. Therefore, the proposed model with transfer learning compared with a model learned only with the train data for the prediction target points without transfer learning. The results of Experiment 1 are shown in Tables 3 and 4. Table 3 shows the results when the prediction target point is Goshoura, and Table 4 shows the results when the prediction target point is Otaru. The vertical axis of the table represents the source domain, and the horizontal axis represents the prediction target water depth. The values on the left of the table represent MAE [℃] and the values on the right represent the rate of errors above 1℃ [%].

Table 3: Results of Experiment 1 in Goshoura

Table 4: Results of Experiment 1 in Otaru

First, Table 3 shows that when the source domain was ‘none’, meaning without transfer learning, MAE ranged from 0.535°C to 0.669°C and the rate of errors above 1°C ranged from 14.5% to 23.3%. In contrast, with transfer learning, MAE ranged from 0.256°C to 0.426°C and the rate of errors above 1°C ranged from 2.5% to 7.7%. These results indicate that the proposed model has better prediction accuracy than the model without transfer learning for both evaluation items. The average MAE for each depth was roughly halved for all source domains, and the average rate of errors above 1°C for each water depth was less than one-third for all source domains.

Next, Table 4 shows that without transfer learning, MAE ranged from 0.725°C to 0.941°C and the rate of errors above 1°C ranged from 27.1% to 38.6%. In contrast, with transfer learning, MAE ranged from 0.416°C to 0.608°C and the rate of errors above 1°C ranged from 6.8% to 17.5%. Compared with the model without transfer learning, the average MAE for each depth was about one-half for all source domains, and the average rate of errors above 1°C for each water depth was approximately one-third for all source domains.

In conclusion, Experiment 1 indicated that the prediction accuracy can be improved by transfer learning, regardless of whether the prediction target point is Goshoura or Otaru, where only about one year of seawater temperature data is available. In transfer learning, prediction accuracy is improved when data from the source and target domains have similar characteristics. As the accuracy increased in transfers to various regions of Japan, the proposed model has the potential to be used in a wide range of aquaculture farms of the country.

4.3. Experiment 2

In this section, Experiment 2 on inputting water depth values is presented. The purpose of Experiment 2 is to evaluate whether water depth values are effective features for improving accuracy in transfer learning. Therefore, the proposed model adding water depth values to the input layer compared with a model that does not use water depth values as input. The results of Experiment 2 were shown in Tables 5 and 6. Table 5 shows the results when the prediction target point is Goshoura, and Table 6 shows the results when the prediction target point is Otaru. The vertical axis of the table represents the source domain and whether or not a bathymetric value was input, and the horizontal axis represents the prediction target water depth. The values on the left of the table represent MAE [℃] and the values on the right represent the rate of errors above 1℃ [%].

Table 5: Results of Experiment 2 in Goshoura

Table 6: Results of Experiment 2 in Otaru

First, Table 5 showed that when the depth values were ‘none’, meaning without water depth values input, MAE ranged from 0.260°C to 0.426°C and the rate of errors above 1°C ranged from 2.7% to 9.3%. On the other hand, with depth values input, MAE ranged from 0.256°C to 0.426°C and the rate of errors above 1°C ranged from 2.5% to 7.7%. These results indicated that adding water depth values to the input layer led to almost no change in MAE and a slight improvement in the rate of errors above 1°C.

Next, Table 6 showed that without water depth values input, MAE ranged from 0.422°C to 0.577°C and the rate of errors above 1°C ranged from 7.7% to 14.5%. On the contrary, when depth values were input, MAE ranged from 0.416°C to 0.608°C and the rate of errors above 1°C ranged from 6.8% to 17.5%. Adding water depth values to the input layer resulted that the average MAE for each depth was smaller when the source domain was Ago, while larger when it was Goshoura or Matoya. However, whether these prediction accuracies improved or declined, the changes were slight.

Experiment 2 indicated that some combinations improved prediction accuracy, adding water depth values to the input layer. However, regularity between source domains and the prediction target water depth could not be confirmed. Moreover, the degree of changes in accuracy was also marginal. From these results, it was not sufficient to add water depth values to the model input to learn the water depth differences between the source and target domains.

At this point, Table 5 for Goshoura and Table 6 for Otaru were compared in the proposed model. Looking at MAE at the prediction target water depth of one meter, Goshoura is lower than Otaru by 0.056°C to 0.315°C. At other depths, similar results were found. These means that the prediction accuracy is higher when the target domain is Goshoura than Otaru. Focusing on the latitude of the five observation points, Gokasho, Ago and Matoya are about 34°C and Goshoura is 32.2910°N, while Otaru is at 43.1904°N, which is significantly different from the other points. In addition, looking at the currents in the surrounding sea, Otaru is on Tsushima Current, whereas the other points are on Kuroshio Current. Thus, it was found that the two differences mentioned above changed the similarity of the data in the source and target domains. These differences in latitude and nearshore currents could have changed the similarity of the data in the source and target domains. The reason why the prediction accuracy did not improve with the addition of water depth input was thought to be due to the above two differences. Therefore, it is necessary to consider not only depth differences, but also differences in latitude and ocean currents between points in order to further improve prediction accuracy.

5. Conclusion

What is needed in the aquaculture industry is seawater temperature prediction from several meters down a few dozen meters. However, these data are not sufficient because they are not easy to collect. The objective of this study is to enable highly accurate prediction of seawater temperature even for points with a small amount of data. Therefore, we proposed and implemented a prediction model using transfer learning, in which a model that has been learned with data from other points is re-learned with data from the target point. In addition, to account for the depth differences between the two points used in the transfer learning, we added water depth values at which seawater temperature was measured to the input layer.

In the evaluation experiment, it was shown that transfer learning improves the prediction accuracy even for points with only about one year of seawater temperature data. We also showed that the accuracy of transfer learning was not improved by simply adding water depth values to the input layer.

In the future, to solve the above issue, we aim to improve prediction accuracy by considering factors such as differences in latitude and ocean currents. Then, we aim to improve the generalization performance of the model to provide seawater temperature predictions that meet the demands of more types of aquaculture farmers in more regions.

Conflict of Interest

The authors declare no conflict of interest.

Acknowledgment

This work was supported by JSPS KAKENHI Grant Number JP21K17803.

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23. Deeptaanshu Kumar, Ajmal Thanikkal, Prithvi Krishnamurthy, Xinlei Chen, Pei Zhang, "Analysis of Different Supervised Machine Learning Methods for Accelerometer-Based Alcohol Consumption Detection from Physical Activity", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 4, pp. 147–154, 2022. doi: 10.25046/aj070419
24. Zhumakhan Nazir, Temirlan Zarymkanov, Jurn-Guy Park, "A Machine Learning Model Selection Considering Tradeoffs between Accuracy and Interpretability", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 4, pp. 72–78, 2022. doi: 10.25046/aj070410
25. Ayoub Benchabana, Mohamed-Khireddine Kholladi, Ramla Bensaci, Belal Khaldi, "A Supervised Building Detection Based on Shadow using Segmentation and Texture in High-Resolution Images", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 3, pp. 166–173, 2022. doi: 10.25046/aj070319
26. 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
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. Nuobei Shi, Qin Zeng, Raymond Shu Tak Lee, "The Design and Implementation of Intelligent English Learning Chabot based on Transfer Learning Technology", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 32–42, 2021. doi: 10.25046/aj060505
30. 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
31. 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
32. 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
33. 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
34. 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
35. 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
36. 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
37. 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
38. 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
39. 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
40. 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
41. 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
42. 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
43. 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
44. 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
45. 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
46. 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
47. 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
48. 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
49. Kin Yun Lum, Yeh Huann Goh, Yi Bin Lee, "American Sign Language Recognition Based on MobileNetV2", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 481–488, 2020. doi: 10.25046/aj050657
50. 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
51. 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
52. 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
53. 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
54. 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
55. 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
56. 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
57. 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
58. 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
59. 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
60. 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
61. Nghia Duong-Trung, Luyl-Da Quach, Chi-Ngon Nguyen, "Towards Classification of Shrimp Diseases Using Transferred Convolutional Neural Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 724–732, 2020. doi: 10.25046/aj050486
62. 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
63. 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
64. 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
65. 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
66. 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
67. 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
68. 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
69. 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
70. 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
71. 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
72. 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
73. 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
74. 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
75. Ola Surakhi, Sami Serhan, Imad Salah, "On the Ensemble of Recurrent Neural Network for Air Pollution Forecasting: Issues and Challenges", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 512–526, 2020. doi: 10.25046/aj050265
76. 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
77. Lenin G. Falconi, Maria Perez, Wilbert G. Aguilar, Aura Conci, "Transfer Learning and Fine Tuning in Breast Mammogram Abnormalities Classification on CBIS-DDSM Database", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 154–165, 2020. doi: 10.25046/aj050220
78. 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
79. 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
80. 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
81. 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
82. 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
83. 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
84. 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
85. 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
86. 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
87. 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
88. Lili Liu, Estee Tan, Zhi Qiang Cai, Xi Jiang Yin, Yongda Zhen, "CNN-based Automatic Coating Inspection System", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 6, pp. 469–478, 2018. doi: 10.25046/aj030655
89. 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
90. 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
91. 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
92. 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
93. 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
94. 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
95. 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
96. 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
97. 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
98. 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
99. 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
100. 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