1. Introduction

The electromagnetic spectrum interacts with matter in a balanced relationship that indicates that the light reaching a body will be proportional to the light that is transmitted (measured through transmittance), absorbed (measured through absorbance) and reflected (measured through reflectance) in the body per in a unit of time. These measurements provide a signal that can be plotted, thus visually showing the relationship between the incident and reflected radiation flux.

These measurements are generally called spectral fingerprints or spectral signatures. Their measurement in agricultural crops is of special interest in precision agriculture, since they can provide a look into the crop health and phenological state. This information allows to determine how acceptable are the growth and development of the crop at a given planting stage.

This paper is an extension of the work originally presented in the 2019 XLV Latin American Computing Conference (CLEI) [1]. It will address other ML methods to achieve supervised classification of rice spectral signatures in Panama.

Supervised classification Machine Learning (ML) methods, such as Artificial Neural Networks (ANN), are commonly used for the analysis of Near-Infrared (NIR) spectral signatures [2]–[3]. ANN allow a way to identify relevant characteristics from a set of inputs (in this case spectral signal) and map them to corresponding output targets (physical or logical values associated with the object of under study). The model is learned through weight adjustments between connected units, called neurons, which are arranged in different layers. ANN are well regarded method. Especially in agriculture to determine quality parameters [4]–[5]. Deep neural networks

(DNNs), on the other hand a widely used in NIR spectral analysis [6]–[7]. DNNs are basically ANN with a vast number of hidden layers, which allow them to learn complex mapping relationships between inputs and outputs [8, 9].

Other ML methods rely on tree structures that partitions sample groups and the data set of features in order understand the relationship between labelled samples. Among these methods the most notable are: Decision Trees (DT) [10, 11] and Classification and Regression Trees (CART) and Random Forest (RF) [12]. The latter being of special interest since it is suited to understand massive data sets, by aggregating/averaging decision trees [13]. As ANNs and DNNs, tree based methods have been used to understand the relationships between sets of NIR spectra [14]–[15].

The objectives of this study are twofold: 1) Explore methods for the classification of spectral signatures of rice varieties, using networks and tree based algorithms; 2) Be able to train/test/validate a model that relates to field acquired spectral signatures with satellite signatures. The rest of this article is structured as follows: Section 2, presents the spectral database and the algorithms used for the classification. Section 3, shows the results obtained and discusses the implications to the model characterization. Section 4, provides overall conclusions of the article and focuses on suggesting future work that could be used as starting point for upcoming analysis.

2. Materials and Methods

2.1        Spectral Database

As described in [1], the spectral signatures were collected on-site from 3 different plots locations using a portable Spectroradiometer. The raw measurements were organized into a database consisting of signatures in the 350 nm to 1050 nm wavelength range (447 wavelengths points per signature). For each one of these 1453 signatures the database also contained information about the plot management, including crop variety, and more importantly days after planting (DAP), at which the spectral signature was collected.

The most prominent crop in the database is rice with 1348 signatures, with onion and pimento having less than 60 signatures, and other crops such as tomato, and maize having less than 15 signatures in total. Three local varieties of rice were more prominent in the plots: IDIAP-38, IDIAP 52-05 and FL137-11, the first two being experimental varieties from the Instituto de Investigaciones Agroindustriales de Panama (IDIAP), while the latter being a commercial variety.

Spectral signatures were normalized using unit variance scaling. Later, the total number of wavelengths (spectral features), was reduced from the original 447 dimensions by using Principal Components Analysis (PCA). Only the first 100 components were kept for each signature.

2.2        Satellite Derived Spectral Signature Database

This study made use of satellite images from PlanetScope, as a source of external signatures and among other reasons for models validation. The exact polygons, where field spectral signatures were acquired, were treated as a source for spectral signatures.

A successful comparison between the spectral signatures and their satellite derived counterpart was made by normalizing matching dates (or closest days available) in which there were also field measurements and by integrating (summarize) the reflectance values obtained in the field into the satellite 4 band format, as follows: blue (455 – 515 nm), green (500 – 590 nm), red (590 – 670 nm), and infrared (780 – 860 nm). Finally, all the satellite derived spectral signatures were organized into a database for later use.

2.3        Regression and Supervised Classification of Spectral Signatures

Since one of the goals of this study was to find a model capable of predicting the days after planting (DAP), just by using the spectral signatures two families of models were selected. The first being a (continuous) regression models family in which the targets were numerical values representing the DAP. The second being a (discrete) binary classification model family, which makes possible to make rice predictions from non-rice related signatures, coded as 0 and 1,

Three regression models were tested to predict the DAP of the signatures:

1. Multilayer Perceptron Regressor (MLPR)
2. Decision Tree Regression (DTR)
3. Random Forest Regression (RFR)

Two classification models were test to predict the rice and nonrice:

1. Multilayer Perceptron Classification (MLPC)
2. Random Forest Classification (RFC).

In all models, spectral signatures were divided as 70% and 30% for training and testing purposes, respectively.

2.4        Cost functions and Evaluation Metrics

Two cost functions were compared for all models, the Mean Square Error – MSE (1), and the Mean Absolute Error – MAE (2). Two measures were used to evaluate the performance of all models, the Root Mean Square Error – RMSE (3), and the coefficient of determination,

For binary classification models, evaluation of their performance was made via a different set of metrics. Generally, the performance of these models are tied to the model capacity to provide true predictions: true positive (TP) and true negative (TN), but also, prediction errors or false prediction is accounted via: false positive (FP) and false negative (FN). These counts are organized into a confusion matrix, shown in Table 1.

Table 1: Theoretical confusion matrix between actual and predicted classes.

Using the confusion matrix, metrics that use the true predictions and prediction errors can be determined, among them:

• Accuracy: a measure of the number of true predictions made by the model. That can be calculated with the following

formula: #TP+##TNTP++##TNFP+#FN .

• Precision: a measure that can be used to evaluate the proportion of positive predictions that were correctly classified. It can be calculated using the formula: #TP#TP+#FP.
• Sensitivity (Recall): a measure that can be used to evaluate the proportion of the actual positive predictions (observations) that were correctly classified. It can be calculated with the formula: #TP#TP+#FN .
• F1 Score: a measure of the model accuracy, but considering both the precision and recall. In general, a model can have a bad F1 Score (closer to 0) or a good one (closer to 1). It can be calculated employing the formula: 2 ∗ PrecisionPrecision+∗RecallRecall.

2.5        Software Implementation

All plots were made using matplotlib. Database normalization, dimensionality reduction, regressions and classification methods and their errors were implemented using the Scikit-Learn library in Python [16]. The trees structures generated by Decision Tree and Random Forest algorithms were plotted using graphviz (https://graphviz.readthedocs.io/).

3. Results and Discussion

3.1        Characteristics of the Spectral Signatures

Figure 1 shows the complete set of reflectance spectral signatures present in the database. Figure 2 shows the reflectance spectral response of the local varieties for the Juan Hombron plot.

Figure 2A, shows the spectral signal variation of the FL137-11 material, from 48 to 116 DAP. Figure 2B, shows the variation of the IDIAP-38 material in 13 and 19 DAP, and also the variation of the IDIAP 52-05 material from 13 to 116 DAP. In all cases, the spectral signature has less variation in the visible range (400-700 nm) and more activity in the near-infrared part of the spectrum (700-100 nm) as the DAP increases.

Figure 1: Rice Spectral Signatures in the Database

Figure 2: Spectral signatures of Rice varieties in Juan Hombron. A) FL137-11, B) IDIAP 38 and C) IDIAP 52-05.

Figure 3 shows the average spectra of rice (red) as compared against other crops in the database. It is notable that rice and onion have similar spectral signatures, with lower reflectance values in the NIR region. While tomato, maize and onion have a similar signature with higher reflectance values in the same range.

Figure 3: Comparison between Spectral Signatures of Crops in the Database

3.2        Regression Models applied to Rice Spectral Signatures

The Multilayer Perceptron Regressor network was set up to have

500 neurons in the hidden layer, tolerance of 1e⁻⁴ and using as ADAM as optimizer. For the Decision Tree Regressor, MSE was set to be the criterion, and the default settings were followed. The Random Forest Regressor was set up to build 50 trees (estimators), as in in the Decision tree, the default settings were followed. Error metrics for the three algorithms are shown in Table 2.

Table 2: Resulting Errors for Regression Models

The MLPR model achieved the higher R² with 0.92 and the lower RMSE values with 8.78. The two tree based regression models showed to behave similarly with R² of 0.56 and 0.57, respectively. Also, having RMSE values of around 19, in both cases. Figure 4 shows the loss curve per iterations for the MLPR model, with a final loss of 0.08. The network training was stopped after 1492 iterations and resulted in an overall MLP training score of 0.99.

Figure 4: Loss Curves for MLPR Model

3.3        Classification Models applied to Rice Spectral Signatures

The Multilayer Perceptron Classifier network was set up to have 200 neurons in the hidden layer, tolerance of 1e⁻⁴ and using ReLU as an activation function. As for the Random Forest Classifier was set up to build 50 trees (estimators), here also, the default settings were followed. Error metrics for the two algorithms are shown in Table 3.

Table 3: Resulting Errors for Classification Models

The MLPC model achieved a better R² than the RFC, with a value of 0.94 and the lower RMSE of the two with a value of 0.006. The two algorithms perform in the 90% having accuracy values of 0.99 and 0.96, respectively. Also having high F1 scores, with values of 0.99 and 0.97, respectively. Figure 5 shows the loss curve per iterations for the MLPC model, with a final loss of 0.006, after 166 iterations.

Figure 5: Loss Curve for MLPC Model

Figure 6 shows the final tree structure of the RFC. In warmer colors it shows the statistically significant decision nodes. In general, the RFC models were not as accurate as MLPC models, but they help us determine the most relevant wavelengths for classification, Table 4 provides a list of the Top-5 wavelengths that could be used as to classify the signatures. Interestingly, all these wavelengths are in the visible range between 400 and 600 nm.

A partial conclusions that can be addressed is that when predicting the DAP from the spectral signatures, it is very important to reduce the parameters to increase the precision of the model. It is important to notice that also, that when classifying crops (rice or other), there was no need to apply PCA since the model is quite accurate and has fewer losses. It even starts at a loss value of 0.4, close enough to 0, in the first iterations, as shown in Figure 5.

Figure 6: Resulting Tree and Rules learned by the Random Forest Classifier

Table 4: Summary of Top-5 Predictors from RFC model

3.4        Regression and Classification of Satellite Derived Signatures

The database for this experiment was comprised of the satellite spectral signatures and also the field (on site) spectral signals. The satellite spectral signatures are the ones compared using the Spectral Angle Mapper (SAM) method, as described in [1]. As for the (on site) spectral signals they had to be integrated to four wavelength bands that are used to represent satellite images. Table 5, shows the distribution of the spectral signature in the expanded database, with both spectral signatures collected on site and from the satellite image.

Table 5: Distribution of Spectral Signatures

A neural network model capable of distinguishing between the rice, and not-rice (namely, forest and road satellite signatures) was trained using a Multilayer Perceptron Classifier. The network was set up to have 600 neurons in the hidden layer, tolerance of 1e⁻⁴ and using ReLU as an activation function. Figure 7A shows the loss curve for the MLPC model, having a final loss value of 0.11 after 733 iterations.

Subsequently, a model was trained with the field spectral signatures reduced to 4 bands and the satellite spectral signatures. The field collected spectral signatures were used for training and testing (calibration), while the satellite spectral signatures were used for validation of the model. A Multilayer Perceptron Regressor was used. The network was set up to have 1000 neurons in the hidden layer, tolerance of 1e⁻⁴ and using as ADAM as optimizer.

Figure 7: Model Loss Curve fror the MLPC (A) and MLPR (B) methods

Figure 7A shows the loss curve for the MLPC model, having a final loss value of 0.11 after 733 iterations. Figure 7B shows the loss curve of the MLPR model, it had final loss value of 23.74 after 2884 iterations.

As it is shown in Table 6, the MLPR model achieved the highest R² with 0.88 and an overall score of 0.90. However, the MLPC model had a lower RMSE value with 0.216.

Table 6: Resulting Errors for Satellite Derived Models

The validation of the model, using satellite spectral signatures resulted in having a R² of 0.54, a MAE of 14.998, MSE of 413.237 and RMSE of 20.3282.

A result from this subsection is that when validating the model to predict days after planting , the error is still quite large. It might be possible to make it lower by having a larger amount of field spectral signatures, since this would give more information to the model longer learning time .

4. Conclusions

The aim of this work was to be able to train supervised classification Machine Learning models in order to further understand rice reflectance signatures. A database of spectral signatures collected using a portable spectroradiometer and a set of satellite image derived signatures were employed as input for these models. As output or targets for the training of these models, few variables were used, among them: days after planting (DAP), and the possibility of distinguishing rice from other crops or from other geographical features.

In order to develop these regression and classification models, neural network based algorithms (Multilayer Perceptrons) and tree based algorithms (decision trees and random forest), were used.

For both cases, regression and classification of rice spectral signatures, the use of Multilayer Perceptron models are recommended. Our results suggest that MLPR is the best method to make a model able to regress on the DAP just from the spectral signatures. For the classification of rice and non-rice signatures MLPC was one again the best resulting method. The tree based models, although providing less useful models, were able t suggest a list of most relevant predictors and a visualization of their results. It seems that the most useful wavelengths to build this models are found mostly in the 500-600 nm wavelength range, which corresponds to the green and yellow colors of the visible spectrum. These wavelengths are in line to changes in the panicle, as described by [17].

For the work on satellite derived signatures both MLPC and MLPR show interesting results over 0.7 and 0.88, respectively. However, the error metrics on the MLPR model are quite large. A large error that can be attributed to normalization effect between satellite and on site measurements, and total reflectance correction that exist between both spectral signatures. Suggesting that more samples should be added to the model to produce a more robust model.

One of the limitations of this study was the relatively small size of the database of spectral signatures employed. It is known that Machine Learning algorithms need more samples in order to have robust results. The need of more spectral signatures is evident by the poor generalization obtained, especially for the validation of satellite derived spectral signals.

For future work, few changes to the approach employed should be done. For instance, having different percentages in the training and testing splits. This work shows results only with 70%/30% split. It could be beneficial to test the limit of prediction by changing the percentages and/or using cross-validation techniques. Second, a class imbalance is evident in the spectral database, due to the focus on rice signatures by the original objective , leaving other crops or other spectral signatures astray. In order to make this database more balanced pseudo replicates need to be made from the non-rice signatures. In [18], the author presents an interesting approach for feature selection and replication using Variational Auto-Encoder (VAE) type of network, that could be pursued. Also in this line, the author presents a complete framework for Data Augmentation (DA) approach for convolutional neural network (CNN) based deep learning chemometrics [19], that seems worth exploring for this application. Third, recent literature shows a shift from deriving spectral signals from satellite images, to working directly over the Hyperspectral images (HSI) [20]. Even using deep learning frameworks for identifying soil characteristics [21], rice varieties [22], rice phenology [23], and even focusing on data imbalance in the image of domain [24].

The value of this work rest about on the fact that it presents a practical example of using supervised classification algorithms for the determination of regression and classification models from reflectance spectral signatures of a local crop. This study provides a basis for future works in deep learning based on chemometrics, and in particular the possibility of predicting crop conditions and characteristics from spectral signatures or satellite images.

Conflict of Interest

The authors declare no conflict of interest.

Acknowledgment

The authors wish to thank the Secretar´ıa Nacional de Ciencia, Tecnolog´ıa e Innovacion de Panam´ a (SENACYT)´ for its support through the project IDDS 15-184. The Sistema Nacional de Investigacion (SNI) of SENACYT supports research´ activities by E.Q-M., J.R.F. and J.E.S.-G.. The authors acknowledge administrative support provided by Universidad Tecnologica´ de Panama (UTP).´

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22. 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
23. 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
24. 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
25. 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
26. 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
27. 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
28. 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
29. 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
30. 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
31. Adewoyin Olusegun Oladotun, Omeje Maxwell, Joel Emmanuel Sunday, Mojisola Rachael Usikalu, Zaidi Embong, Alam Saeed, "Evaluation of Radiological Health Risk Associated with Indian Tiles Available in Nigerian Markets", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 3, pp. 271–276, 2021. doi: 10.25046/aj060330
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. 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
37. 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
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. 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
44. 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
45. 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
46. 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
47. 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
48. 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
49. 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
50. 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
51. 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
52. 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
53. 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
54. 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
55. 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
56. 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
57. 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
58. 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
59. 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
60. 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
61. 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
62. 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
63. 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
64. 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
65. 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
66. 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
67. 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
68. 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
69. 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
70. 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
71. 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
72. 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
73. 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
74. 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
75. 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
76. 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
77. 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
78. 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
79. 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
80. 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
81. 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
82. 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
83. 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
84. 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
85. 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
86. 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
87. 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
88. 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
89. 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
90. Faten Ayadi, Mongi Lahiani, Nabil Derbel, "Kalman filter Observer for SoC prediction of Lithium cells", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 4, pp. 180–188, 2017. doi: 10.25046/aj020424
91. 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
92. 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
93. 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
94. 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
95. Che Hafizah Che Noh, Nor Fadhillah Mohamed Azmin, Azura Amid, "Principal Component Analysis Application on Flavonoids Characterization", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 435–440, 2017. doi: 10.25046/aj020356
96. 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
97. 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
98. Arsim Susuri, Mentor Hamiti, Agni Dika, "The Class Imbalance Problem in the Machine Learning Based Detection of Vandalism in Wikipedia across Languages", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 1, pp. 16–22, 2016. doi: 10.25046/aj020103