1. Introduction

Higher education tends to be a benchmark to define student education quality as a human resource. In general, higher education is considered as a reputable institute if students are qualified in their fields and get good achievements. Conversely, student’s failure will impact negatively on students and universities. At present, the problem of student failure is known as an ongoing university challenges to investigate many factors that trigger the dropout, such as academic performance, demographic, financial support, and student behavior and etc. Dropout is determined as a consequence for students who cannot complete their education until the specified study period. It makes students’ skills and ability of dropout students in their fields less than student retention and significantly affects institution quality [1].

Drop out is not a novelty thing but still being a serious topic which attracts researchers’ attention due to its impact on decreasing higher education values and can be an adverse impact on the social environment, where other prospective students lose their opportunity to study in higher education. In the last 10 years, many research has been carried out by utilizing technology to find ways how to prevent dropout issues, which is called Education Data Mining [2]. Educational Data Mining (EDM) represents a variety of algorithmic methods to address various problems in the educational system and even generates new knowledge, to calculate student’s academic performance, predict student’s behavioral and especially to predict variables or indicators that influence dropout in higher education [3].

Some indicators are widely used by researchers to predict dropouts, such as cumulative grade point average (CGPA), internal assessment, student demographics, external assessment, extra-curricular activities, high school background, and social interaction network [4]. The most potentials variables are the cumulative grade point average (CGPA) and internal evaluation indicators because its value maximizes the measurement of the student’s skills in present and future. [5] In the first two years of study, demographic indicators, especially gender were also influence learning qualities, not only occur on conventional higher education but also online program students. Dropout possibilities are also caused by age, financial constraints, student absence, parental influence, employment opportunities, marital status [6] [7].

In Indonesia, based on data statistics in 2017 [8], the dropout rate in higher education approximately about 195,176 students. Data shows dropout students’ percentage at private universities is greater than public universities. A related work in [9], 799 dropout students at educational institutions in Jakarta was examined and found dropouts usually occur in people aged 12 to 19 years who come from suburban and rural areas with a low average economic background. Low economic indicators trigger students to choose to get a job than continuing their studies in higher education. Another similar study as shown in [10], variable age and study program are also correlated to decide dropout students through the first-year study.

The main objective of this study is to find the best modeling solution in identifying dropout student indicators especially in the first two years of the study period. We will use student data from the Faculty of Social and Political Science in one of a private university in Jakarta and measure how many demographic data had a significant influence on student dropout predictions. In this major, students tend to leave study until dropout or expelled in the first trimester. This study will focus on the demographic and academic indicator and propose a predictive modeling concept by combine Decision Tree, K-Nearest Neighbor and Naïve Bayes which are widely used as statistical models to predict dropout students and optimize results using Ensemble Classification Method. The remainder of the paper is organized as follows. In Section 2, we review previous studies on the various prediction modelling in education field and also educational data mining research. Section 3 explains our research method to find classification techniques to find student dropout predictors. Section 4 presents a discussion of the results includes the evaluation of the Ensemble Classification Method as compared to several Decision Tree, K-Nearest Neighbor and Naïve Bayes methods. Section 5 concludes the study.

2. Literature Review

Educational Data Mining (EDM) is an interdisciplinary area which related to methods development to investigate a variety of unique data in the education area, which aims to understand the student’s needs and determine properly learning methods [11]. Generally, EDM is applied to predict problems in order to improve the quality both of student performance, and teaching-learning process [12]. Its concerns about how to adapt data mining methods and find patterns that are generally very difficult to solve because of massive data in the educational dataset [13]. Data mining, as a decision-making standard, has been helped in discovering dataset with different approaches such as statistical models, mathematical methods, and also machine learning algorithms [14].

Based on a review in paper [4] some theoretical algorithms are carried out to predict student performance. In her work, she found and compare accuracy between Naïve Bayes, Neural Network and Decision Tree to predict CGPA, the students’ demographics, high school, study and social network attributes as the most critical factor student passed or failed studies. Naïve Bayes has better accuracy because of attributes more significant to predict than Neural Network and Decision Tree. Another study, paper [15] compares various and appropriate data mining methods for classification in prediction, specifically to determine dominant factors in student performance predictions. It shows predictive results of Random Forest and J48 generate classification model and find the most significant factor as a determinant on student’s attainment, such as study time, academic year, age and parent education.

To identify dropout, this paper [16] have been used Artificial Neural Network, Decision Tree, and Bayesian Network to explore great potential factor. Conducting empirical research on a dataset of 3.59 million student data in an online training program, Tan discovered two attribute variables as test inputs, that is, student characteristics and academic performance. As a result, the Decision Tree algorithm was more precise to prove those variables are effectively used as key factors in student dropout prediction. As shown in this study [13], Marquez proposed a new method to optimize accuracy predictive modeling, called Modified Interpretable Classification Rule Mining. Marquez held an experiment in 419 schools to find the student dropout factors. The evaluation was performed in six phases using 60 different variables from 670 students. It results in Modified Classification Rule Mining more accurate than JRip.

Currently, predictive modeling challenges are efficiency and accuracy of various prediction models which are generally due to inadequate variables with the base classifier. Related work in [17], Decision Tree, Naïve Bayes, KNN, and Artificial Neural Network applied to generate predictive student dropout model and adopt ensemble clustering on student’s demographic detail, academic performance, and enrollment record. Experiment verified ensemble method which is used to transform original data to a new form can increase the accuracy of prediction models. Another similar study as shown in [18] discussed and examined the ensemble method able to reduce error and increase student performance prediction accuracy.

After reviewing background research, predictive modeling method has weakness in some way depend on attributes. In such conditions, accuracy may be misleading if we only have small attributes and data. In this study, we will compare Decision Tree, K-Nearest Neighbor, and Naïve Bayes and combine those methods to find the correlation between demographic and academic performance variables in dropout prediction. We will adopt an ensemble method to optimize accuracy results and also use Confusion Matrix to evaluate models.

2.1.  Classification Methods

Decision Tree (DT) widely known as a popular and interesting machine learning algorithm, especially in classification. It can generate or measure pattern using a tree-structured rule and describes the relationship between variables by recursively partitioning inputs into two parts. Each part forms the decision node that is linked by a branch from the root node to the leaf node [19] [20]. In data mining, several well-known decision three algorithms, namely ID3, C4.5, CART, J48, and CHAID. In this study, the CART algorithm is used to generate models.

The k-Nearest Neighbor (k-NN) is a simple classification method that is measured based on the majority vote of its neighbors [21]. The best choice of k depends upon the data; generally, larger values of k reduce the effect of noise on the classification but make boundaries between classes less distinct. However, this method has a weakness with the presence of noisy or irrelevant features, or if the feature scales are not consistent with its importance in modeling.

Naïve Bayes as a simple probabilistic classifier can be developed easily on a large amount of data because it does not need complex parameter estimation which makes it outperform over another sophisticated method [2]. Naïve Bayes was also able to learn conditional probability feature separately so it also has been very effective in classifying small datasets. In this study, Bayes’ theorem is used to predict probability dropout.

2.2.  Ensemble Classifier Method

Ensemble method is a modeling concept with multiple learners to resolve problems which called base learners. It constructs and combines a set of hypotheses to fix weakness of training data using single-learners approach [22]. We also can find solutions and collect and combine a set hypothesis from big chance hypotheses into one single prediction. As known as Importance Sampling Learning Ensembles (ISLE) framework, it shows four classic ensemble methods, namely Bagging, Random Forest, Boosting (AdaBoost) and Gradient Boosting.

This method consists of several approaches commonly used in classification to construct models that are several approaches can be used to bagging, boosting and stacking. Based on this paper [17] [18] which has been successfully used stacking approaches (stacked generalization), this study will use Gradient Boosting as an ensemble classifier and do different things to reduce error and optimize accuracy finding.

3. Research Method

The stages of this study are four-fold as shown in Figure 1. Step 1, extract variable data related to student dropouts from Academic Information System of educational institutions, construct the training data set and do feature selection using Ensemble Bagging Tree method to get the best-correlated attribute to predict dropout. Step 2, use the data to train the prediction models that were constructed based on machine learning methods such as the Artificial Neural Network (ANN), Decision Tree (DT) and Bayesian Network (BN) to derive the samples of the prediction model. Step 3, extract another section of data as a testing data set and feed it into the actual samples of the prediction model previously generated. Step 4, apply ensemble-Decision Tree to optimize and evaluate predictive modeling accuracy of student dropout.

3.1.  Data Preparation

This study used the dataset of 17.432 of student’s data from the Academic Information System in Christian University of Indonesia. Sample data in this study are comprised of relevant information from students enrolled in the Faculty of Social and Political Science from 2016-2018. This dataset was purposed for classifying higher education students that potentially dropout according to academic performance. As identified from the dataset, there was a total of 17 variables associated with student’s demographic data (Table 1). The first stage of data pre-processing is handling 2355 missing data by imputing relevant value and transform all values into numerical variables in order to improve the accuracy of prediction based on the algorithm’s requirement.

Table 1: The attributes of Datasets

A first glimpse at the data reveals that 13856 of the data indicated students were able to successfully finish their studies, while 607 data of dropout students have been observed as dropout students. There is a big difference ratio between dropout class and retained class.

In order to tackle this problem, we do partition data into 70% training dataset and 30% testing dataset and use Synthetic Minority over-sampling Technique (SMOTE) to synthetically resampled training dataset. This method can help to improve training datasets to be optimally used in classification performance [23]. Next stage, we use algorithm Learning Vector Quantization to do feature selection with 1700 balanced data on the training dataset and performed 10-fold cross-validation with 3 repetitions to reduce bias induced by sample selection. It combines clustering and classification processes based on feed forward neural network. Inputs are propagated through a variable number of hidden layers to the output nodes.

In terms of data processing, the feature selection is the necessary steps to do because machine learning can understand data and improve prediction performance if the prediction modeling used a set of properly features. In order to select features, we use the Learning Vector Quantization algorithm to prepare some vectors in the domain of observed data samples in order to be used to classify any of the hidden vectors that are unseen. As we figured out from Figure 2, it represents the attributes selection refers to the importance level of each attribute on the dependent variable. In the feature selection process, the training process is carried out and tested using 10-fold cross-validation. It aims to calculate and measure the importance feature values based on two variables distances which are identified near or close to the variable target.

The best accuracy of this selection process is 0.9757 using the value k = 6.

Figure 1. Research Method

The results of feature selection carried out on training data, two features of 13 variable input, including work status and marital status eliminated from the dataset.

Figure 2: Feature Selection Based on Importance Score

As shown in Table. 2, the features are sorted according to importance score obtained using the Learning Vector Quantization (LVQ) technique and decide to select feature with a score greater than 50%.

Table 2. Variable Importance Value

3.2.  Confusion Matrix

For evaluation, we use confusion matrix to measure classifier’s accuracy that is the ratio between correctly predicted results and the total number of samples. In this study, we will measure the precision rate, accuracy rate, sensitivity, and specificity.

Table 3: Confusion Matrix

True positive (TP) is the number of students classified as dropout students, false negative (FN) value is the number of non-dropout students classified as dropout students, true negative (TN) value is the number of non-dropout students classified as non-dropout students, false positive (FP) is the number of dropout students classified as non-dropout students. Standard formula to calculate the precision rate, accuracy rate, sensitivity, and specificity defined based on confusion matrix as shown in Eq. 1-4.

4. Result and Discussion

In this study, we use R language and R software package (version 1.2.13) to analyze data with several machine learning methods. First of all, we do the data cleaning process such as handling missing values in the dataset and facilitate dataset with the appropriate attributes. In this case, 2,355 rows of missing values of ‘student’s attendance’ variable and 1221 rows of ‘final-test grade’ variable have values less than zero -which are not relevant to the other variables value- were eliminated from the dataset. Next, we tested normalization or data distribution in order to determine whether data distribution was normal or balanced and also will help to minimize prediction error results during the modeling process. Furthermore, impute value technique is applied to the filled missing value in parent’s income feature with its mean values in order to minimize bias in the dataset. Finally, we get 13856 data with 11 variables as variable input from the data cleaning process.

Based on distribution data, 66% of student’s data was dominated by women while men were only 34% of total data. Every student generally comes from an urban area (87%), which means most students come from urban areas while the percentage of students from suburban areas are relatively small. In addition, 95% of students are dominantly 18-23 years old while others are over 23 years old. In this case, work status is not determined as predictors because its correlation is relatively small about 250 students which are only 2% of all student’s data. The dataset also shown that almost 100% of students are single with the majority parent’s education were ‘high school’ and ‘undergraduate’ level with parents financial is predominantly low that is less than IDR 5000000. These data distribution, especially demographic features, describe that dataset has a fairly good variation to be used during student dropout prediction.

By using 9700 training data, we demonstrated also compared and discussed 3 different common classifiers performance, which is K-Nearest Neighbor (KNN), Decision Tree (DT), and Naıve Bayes (NB) as shown in Table 4 and Table 5.

Table 4: Comparison Prediction Results

The first prediction modeling was carried out using the K-Nearest Neighbor method. The specified k-value was used with k = 5, k = 7, k = 9, and k = 11. Its best k-value was k=5 which predict with accuracy rate about 0.9820 and recall rate of prediction was 0.8497. Next prediction model, we use the Decision Tree CART method and obtain prediction accuracy about 0.9791 and recall rate of prediction of 0.9060.

Table 5: Evaluation of Prediction Results

The last method was Naive Bayes which is not much different from Decision Tree, its prediction accuracy is 0.9824 with recall rate about 0.9360. To improve accuracy and predictive precision values, we implement Ensemble Stacking Classification Method to obtain better predictive accuracy. Two things are required in build prediction model using ensemble stacking method, that is weak-learner as a base-layer classifier and meta-model as a top-layer classifier that will combine K-Nearest Neighbour (KNN), Decision Tree (DT), and Naive Bayes (NB). In this paper, the algorithm iterates to find the best rules that predict student dropout using probability results of each classification methods as describes below:

The first step, we do training data with base classifier and evaluate them with 10-fold cross-validation. Next, the predictive probability is accommodated as the new input value (x) in either training or testing data so we can use it in the next modeling stage. Three new X variables will be used as predictors on modeling using Ensemble Stacking Classification Method by combining the three base-classifiers. In the last step, prediction modeling is held by using the Gradient Boosting algorithm as a meta-classifier that will classify each prediction probabilities as predictors and variable ‘student status’ as a target variable. The prediction using Ensemble Stacking Classification shown in Table 6.

Table 6: Confusion matrix for Ensemble Stacking Classification

Figure 3: Comparison performance prediction between models

We get the highest accuracy rate with at 98.82%, followed by the second best method was Naïve Bayes at 98.24%. In last, the K-Nearest Neighbor method achieved an accuracy of about 98.20%, which was not much different from Naïve Bayes. As shown in Figure 3, the results of the precision predictions of the Ensemble Stacking method are not much different from the K-Nearest Neighbor even though the value was successfully increased precision percentage up to 79.12%.

Furthermore, the testing process is also found recall rates of prediction. Recall rate is a benchmark to measure modeling successfully predict rediscovering information. If we compared with its precision value, the recall rate of Ensemble Stacking method was high enough that is at 92.9%. However, the recall rate of Naïve Bayes as a single classifier is better although not much different, it gets about 93.60%.

5. Conclusion

This work aimed to describe possibilities to use data in order to help to deal with the dropout problem. Many algorithms have been involved and give a qualified insight of from simple dataset until the dataset with high complexities. In this study, the Ensemble Stacking Classification method with the Boosting Gradient algorithm as a meta-classifier can increase the accuracy of dropout predictions when it compared to a single classifier, such as K-Nearest Neighbor, Decision Tree, and Naïve Bayes. By combining those three algorithms, this method can achieve an accuracy rate of 98.82%, the precision of 79.12% and a recall rate of 92.90%. In addition, the number of false prediction called False Positive (FP) is greater than the number of false negatives (FN) prediction. It means, the performance of the Ensemble Stacking Classification method is good enough at prediction student dropouts. In this study, we also found that features that influence prediction student dropout include the percentage of student attendance, assignment scores, total credits, UTS scores, UAS scores, GPA, parental income, parent’s education, gender and age of students. However, there is an indication that academic performance is not the only reason that potentially influenced student’s dropout, but also the existence of external reasons such as study program selection and environmental influences.

There are still many shortcomings in this study, for further work we suggest to increase the number of variations correlative feature and large dataset so it will help to improve performance more better than this research, i.e. external assessment features. It also needs to do more research about feature selection method so each feature is more significant and very optimal to use in prediction modeling.

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27. Arwa Alghamdi, Graham Healy, Hoda Abdelhafez, "Machine Learning Algorithms for Real Time Blind Audio Source Separation with Natural Language Detection", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 125–140, 2021. doi: 10.25046/aj060515
28. Baida Ouafae, Louzar Oumaima, Ramdi Mariam, Lyhyaoui Abdelouahid, "Survey on Novelty Detection using Machine Learning Techniques", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 73–82, 2021. doi: 10.25046/aj060510
29. Radwan Qasrawi, Stephanny VicunaPolo, Diala Abu Al-Halawa, Sameh Hallaq, Ziad Abdeen, "Predicting School Children Academic Performance Using Machine Learning Techniques", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 08–15, 2021. doi: 10.25046/aj060502
30. Zhiyuan Chen, Howe Seng Goh, Kai Ling Sin, Kelly Lim, Nicole Ka Hei Chung, Xin Yu Liew, "Automated Agriculture Commodity Price Prediction System with Machine Learning Techniques", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 376–384, 2021. doi: 10.25046/aj060442
31. Hathairat Ketmaneechairat, Maleerat Maliyaem, Chalermpong Intarat, "Kamphaeng Saen Beef Cattle Identification Approach using Muzzle Print Image", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 110–122, 2021. doi: 10.25046/aj060413
32. Md Mahmudul Hasan, Nafiul Hasan, Dil Afroz, Ferdaus Anam Jibon, Md. Arman Hossen, Md. Shahrier Parvage, Jakaria Sulaiman Aongkon, "Electroencephalogram Based Medical Biometrics using Machine Learning: Assessment of Different Color Stimuli", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 3, pp. 27–34, 2021. doi: 10.25046/aj060304
33. Dominik Štursa, Daniel Honc, Petr Doležel, "Efficient 2D Detection and Positioning of Complex Objects for Robotic Manipulation Using Fully Convolutional Neural Network", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 915–920, 2021. doi: 10.25046/aj0602104
34. Md Mahmudul Hasan, Nafiul Hasan, Mohammed Saud A Alsubaie, "Development of an EEG Controlled Wheelchair Using Color Stimuli: A Machine Learning Based Approach", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 754–762, 2021. doi: 10.25046/aj060287
35. Antoni Wibowo, Inten Yasmina, Antoni Wibowo, "Food Price Prediction Using Time Series Linear Ridge Regression with The Best Damping Factor", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 694–698, 2021. doi: 10.25046/aj060280
36. Javier E. Sánchez-Galán, Fatima Rangel Barranco, Jorge Serrano Reyes, Evelyn I. Quirós-McIntire, José Ulises Jiménez, José R. Fábrega, "Using Supervised Classification Methods for the Analysis of Multi-spectral Signatures of Rice Varieties in Panama", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 552–558, 2021. doi: 10.25046/aj060262
37. Phillip Blunt, Bertram Haskins, "A Model for the Application of Automatic Speech Recognition for Generating Lesson Summaries", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 526–540, 2021. doi: 10.25046/aj060260
38. 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
39. 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
40. 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
41. 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
42. 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
43. 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
44. Ndiatenda Ndou, Ritesh Ajoodha, Ashwini Jadhav, "A Case Study to Enhance Student Support Initiatives Through Forecasting Student Success in Higher-Education", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 230–241, 2021. doi: 10.25046/aj060126
45. Lonia Masangu, Ashwini Jadhav, Ritesh Ajoodha, "Predicting Student Academic Performance Using Data Mining Techniques", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 153–163, 2021. doi: 10.25046/aj060117
46. Sara Ftaimi, Tomader Mazri, "Handling Priority Data in Smart Transportation System by using Support Vector Machine Algorithm", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1422–1427, 2020. doi: 10.25046/aj0506172
47. Othmane Rahmaoui, Kamal Souali, Mohammed Ouzzif, "Towards a Documents Processing Tool using Traceability Information Retrieval and Content Recognition Through Machine Learning in a Big Data Context", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1267–1277, 2020. doi: 10.25046/aj0506151
48. Puttakul Sakul-Ung, Amornvit Vatcharaphrueksadee, Pitiporn Ruchanawet, Kanin Kearpimy, Hathairat Ketmaneechairat, Maleerat Maliyaem, "Overmind: A Collaborative Decentralized Machine Learning Framework", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 280–289, 2020. doi: 10.25046/aj050634
49. Pamela Zontone, Antonio Affanni, Riccardo Bernardini, Leonida Del Linz, Alessandro Piras, Roberto Rinaldo, "Supervised Learning Techniques for Stress Detection in Car Drivers", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 22–29, 2020. doi: 10.25046/aj050603
50. Kodai Kitagawa, Koji Matsumoto, Kensuke Iwanaga, Siti Anom Ahmad, Takayuki Nagasaki, Sota Nakano, Mitsumasa Hida, Shogo Okamatsu, Chikamune Wada, "Posture Recognition Method for Caregivers during Postural Change of a Patient on a Bed using Wearable Sensors", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 1093–1098, 2020. doi: 10.25046/aj0505133
51. Khalid A. AlAfandy, Hicham Omara, Mohamed Lazaar, Mohammed Al Achhab, "Using Classic Networks for Classifying Remote Sensing Images: Comparative Study", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 770–780, 2020. doi: 10.25046/aj050594
52. Khalid A. AlAfandy, Hicham, Mohamed Lazaar, Mohammed Al Achhab, "Investment of Classic Deep CNNs and SVM for Classifying Remote Sensing Images", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 652–659, 2020. doi: 10.25046/aj050580
53. 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
54. 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
55. 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
56. 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
57. 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
58. 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
59. 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
60. 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
61. 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
62. 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
63. 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
64. 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
65. 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
66. 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
67. 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
68. 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
69. Reda El Abbadi, Hicham Jamouli, "Trajectory Tracking Control of a DC Motor Exposed to a Replay-Attack", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 264–269, 2020. doi: 10.25046/aj050334
70. Dennis Luqman, Sani Muhamad Isa, "Machine Learning Model to Identify the Optimum Database Query Execution Platform on GPU Assisted Database", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 214–225, 2020. doi: 10.25046/aj050328
71. Gillala Rekha, Shaveta Malik, Amit Kumar Tyagi, Meghna Manoj Nair, "Intrusion Detection in Cyber Security: Role of Machine Learning and Data Mining in Cyber Security", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 72–81, 2020. doi: 10.25046/aj050310
72. Ahmed EL Orche, Mohamed Bahaj, "Approach to Combine an Ontology-Based on Payment System with Neural Network for Transaction Fraud Detection", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 551–560, 2020. doi: 10.25046/aj050269
73. Bokyoon Na, Geoffrey C Fox, "Object Classifications by Image Super-Resolution Preprocessing for Convolutional Neural Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 476–483, 2020. doi: 10.25046/aj050261
74. 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
75. 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
76. 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
77. 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
78. 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
79. 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
80. 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
81. 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
82. 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
83. 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
84. 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
85. 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
86. 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
87. 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
88. 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
89. 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
90. 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
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. 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
96. 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
97. 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