1.Introduction

Martial arts training is one of the preferable methods of physical exercise. It provides health & psychological benefits, such as preventing osteoporosis [1], reduces stress, depression, and increases mindfulness [2], and also has the potential to reduce aggressive behavior [3]. Basic movements of martial arts training such as stance and punches are usually done with the supervision of the instructor. However, instructors cannot always be there for one student, they must accompany other students as well. It could be a problem for new students who have just learned stances and punches. Repetitive movements without supervision could result in the students become unmotivated and stop their movement. Having the instructor to watch over them is helpful for the students to keep repeating the movement without stopping. There needs to be a method of training to make the students keep their excitement going during basic training when the instructor was not able to watch them. Watching video tutorials and reading books are some of the ways to improve the students’ training experience, however, it lacks interactions and presence needed in martial arts training.

This is when Virtual Reality (VR) technology comes into play. It is mentioned by [4] that VR is typically defined in technical terms, meaning it is associated with technical hardware such as computer systems, head-mounted display (HMD), motion trackers, etc. VR itself can be simply defined as an environment that simulates the real world and is generated by a computer system in which users can then interact with by using motion trackers [5]. The trackers are used to collect data from the real environment and translate them to the virtual environment [6].

Due to its immersive capabilities, VR technology has been used in areas, such as architecture and landscape planning [7],  dancing [8], military training [9], medical, such as stroke rehabilitation [10], and psychological treatment [11]. It has also been used for research in martial arts scope, even though there has not been much of them. The reason is that higher immersion and presence result in higher performance to the user [12].

A VR-based training simulation system could help in practicing the movement and motivates the user, due to its immersive capabilities [13]. To develop the system, capturing and classifying the hand movements is crucial, as being able to classify basic movement in Wing Chun is one of the basic training outcomes. This study uses Oculus Quest, a VR gear with a head-mounted display (HMD) and a pair of controllers. These controllers are used to capture hand movements. Variables such as time, location, rotation, angular acceleration, linear acceleration, angular velocity, and linear velocity, were recorded and used as features for classification.

The purpose of this paper is to find the best possible method of classifying Wing Chun’s basic hand movement to develop a VR-based training system. Since this paper uses Oculus Quest with a pair of hand controllers, hand movement data can be captured in the form of location in the world space. Not only that, other properties like rotation, angular acceleration, linear acceleration, angular velocity, and linear velocity can be captured as well. RapidMiner Studio [14], [15] is used to process the data, optimize models, and to analyze the result. This paper is presented as such: Section 1 is the introduction. In section 2, this paper outlines the studies and works that were related to motion and gesture recognition or classification. Section 3 outlines the method that is used in this paper. Section 4 shows the result and analysis of the study. Section 5 provides the conclusion of this paper based on the result and analysis. Section 6 provides discussions and further works that are possible to improve this paper.

2. Related Works

The study by [16] shows the approach is to segment or classify movement into distinct behaviors. The first approach is using Principal Component Analysis (PCA), which is based on the observation that simple motion exhibit lower dimensionality than complex motions. The motion is broken into frames, and each frame is represented as a point, which is the joint’s location. It is based on dimensionality, in which a motion sequence with a single behavior should have a smaller dimensionality than the one with multiple behaviors. The second approach is using Probabilistic PCA. The Probabilistic PCA ignores the noises of motion, unlike PCA. The third approach is Gaussian Mixture Model (GMM), in which the entire sequence of motion is segmented whenever two consecutive sets of frames belong to different Gaussian distributions. The Probabilistic PCA obtained the best result of all three approaches: 90% precision for 95% recall.

In [17], the authors conducted a survey on sequence classification, in which it is stated that multivariate time series classification has been used for gesture and motion classification. The research by [18] captured motion data using CyberGlove, with 22 sensors in different locations at the glove and 1 angular sensor. The data are split into 3 datasets and used K-folds validation of K = 3. The multi-attribute motion data is reduced to feature vectors with Singular Value Decomposition (SVD) and then classified using Support Vector Machine (SVM). The study results in the accuracy of 96% for dataset 1, and 100% for dataset 2 and 3.

The research by [19] used wearable accelerometers to capture movement data. Several movements such as sitting, sitting down, standing, standing up, and walking, are the subject of classification. The features that are extracted from 4 accelerometers are further pre-processed into an acceleration in x, y, and z-axis for each, resulting in 12 features. The features of the movement data are then selected with Mark Hall’s selection algorithm [20] and classified with C4.5 decision tree with AdaBoost ensemble method [21]. The study used 10 iterations of AdaBoost with C4.5 tree confidence factor of 0.25 and with 10-folds cross-validation. The overall performance recognition was 99.4%.

In [22], the authors compared the classification accuracy of several algorithms such as C4.5 decision tree, multilayer perception, Naive Bayes, logistic regression, and k-Nearest Neighbor (kNN). A smartphone with the capability of recording accelerometer and gyroscope data was used as the motion sensor. Waikato Environment for Knowledge Analysis (WEKA) machine learning tool was used to apply the algorithms and compare them. The result of the study shows that kNN algorithm obtained the highest (averaged) accuracy in all motion classification by 84.6%.

The study by [23] used the multi-modal approach, which is using a combination of audio, video, and skeletal joints. The audio segmentation used the Hidden Markov Model (HMM) to determine the “silence” and “event” of motion. The segmentation based on skeletal joints uses the y-coordinate of hand joint location. The first step is to identify the start and end frames of gestures of either left or right hand, and the second step is to identify the same by using both hands. For the RGB and video modalities, SVM is used as the gesture classification. The audio classifier and the SVM classifier is then fused with a fusion algorithm. This study, which used 275 test samples with over 2,000 unlabeled gestures, resulted in an average edit distance of 0.2074.

The study by [24] used a signal captured from movement instead of the joint location. The study used 12 Trigno Wireless electrodes. The signal is divided into several segments, calculates the spectrogram, and then normalized. PCA is then applied to reduce the dimensionality of the data but maintaining important information. The last step is to apply SVM to the data to do the classification. Compared with the Root Mean Square (RMS) method, the method of this study results in higher accuracy of 9.75% and a reduced error rate of 12%.

Lower-limb motion classification was done by using piezoelectret sensors that apply forcemyography (FMG) which reads force distribution generated by muscle contractions [25]. study compared kNN, Linear Discriminant Analysis (LDA), and Artificial Neural Network (ANN) to classify 4 lower-limb motions, namely leg raising, leg dropping, knee extension, and knee flexion. The highest accuracy is obtained by kNN with an accuracy of 92.90%.

The study by [26] goes into more specific, classifying motions into karate stances, movements, and forms. The captured data consists of a 3-axis accelerometer, gyroscope, and magnetometer. The classification is done by averaging the dataset with Dynamic Time Warping (DTW) Barycenter Averaging (DBA) algorithm, which is an averaging method that iteratively refills an initially selected sequence, to minimize its squared distance / DTW [27] to averaged sequences. In this study, a movement template was generated from two different-styled Karate masters and then both are compared. This results in a recognition rate of 94.20%.

In [28], the authors did a study on human motion recognition by using VR. A combination of LDA, Genetic Algorithm (GA), and SVM is proposed to classify human motion. After collecting motion data, LDA is used to extract features, and then GA is used to search for optimal parameters. After optimal parameters are found, SVM is then used for classification. There are 10 motions to be classified and several averaged results are obtained: Precision of 95.65%, Accuracy of 97.05%, Specificity of 92.78%, and Sensitivity (Recall) of 94.01%.

It can be inferred from the related works that there has been various research about human motion recognition and classification for years. SVM seems to be favored among the study of motion classification. When several studies compared algorithms for movement classification, kNN seems to provide the highest accuracy. This paper performs several algorithms that were mentioned in the related works and optimizes its parameters to improve the results.

3. Proposed Method

This study utilizes the features from Oculus Quest VR gear, along with Unreal Engine 4 (UE4), which helps in extracting features of movements. RapidMiner Studio is used to pre-process the data, apply algorithms, and analyze the results.

3.1. Method of extraction

To be able to capture the hand movement, a simple game based on UE4 is developed. When the game starts, the user can press a button on the Oculus Controllers to start capturing movement data. Several movement data that are recorded are basic Wing Chun hand movements such as Straight Punch, Tan Sau, Pak Sau, Gan Sau, and Bong Sau. The recording starts from the beginning stance to the ending stance of a movement. The same movement is done 10 times within around 40 seconds with around 1 to 2 seconds interval for every time the movement is done.

3.2. Extracted Data

The movement data is generated in the form of CSV files for each movement and each hand, resulting in 10 files. The extracted features are time, location, rotation, angular acceleration, linear acceleration, angular velocity, and linear velocity. Except for time, all the features are extracted in the form of vectors, and then split into x, y, and z-axis, resulting in a total of 19 features. RapidMiner Studio is used to process the data and give labels to the data. The data is then processed again by mixing all files of each hand and movement into 1 file. The dataset consists of 8,725 rows with a total of 10 labels. For the left hand, the labels are Punch, Tan, Pak, Gan, and Bong. For the right hand, the labels are R_Punch, R_Tan, R_Pak, R_Gan, and R_Bong.

3.3. Method of optimizing algorithm

Several algorithms are performed to the dataset using RapidMiner Studio to check for their accuracy. From there, the parameter optimization process is used to make the algorithms perform better. The selected algorithms for comparison are SVM, Decision Tree, and kNN.

Figure 1 shows the flow of the algorithm optimization method. First, an algorithm is applied to the dataset with split validation to check the initial performance. The dataset is first normalized and then split into 80% of the training set and 20% of the testing set by using Split Data module. An algorithm is then applied to the dataset, and then measure the performance to obtain accuracy. After performing split validation, the initial results of the algorithms’ performances were obtained.

Then, parameter optimization is performed to get the best possible value of the parameters in the algorithm process. The algorithm is applied to the normalized dataset with 10-folds cross-validation, and then put into the Optimize Parameter module. This module executes the cross-validation using all the selected combinations of parameters that are available in the algorithm. This process results in the best accuracy of all selected combinations of parameters.

Figure 1: Flow of algorithm optimization method

After finding the optimized value of parameters, the process of optimizing selection of features is performed to the algorithm. The process is identical to the Optimize Parameter module, but this time the algorithm is put into the Optimize Selection module. This module executes the cross-validation of the algorithm and weights the features of the dataset that further improve the accuracy. This process can be done until no more features are removed. After this process, a model from the algorithm with optimized parameters and selected features is obtained. This model is then applied to the dataset with split validation process

4. Result and Analysis

The performance indicators were based on the study by [28], in which the 4 most used indicators for action classifications are used: accuracy, precision, recall (sensitivity), and specificity.

4.1. Support Vector Machine

The parameters used to obtain the final result for SVM algorithm are SVM Type: nu-SVC [29] with the nu value of 0.1, in which nu value is an upper bound on the fraction of margin errors and a lower bound on the fraction of support vectors, and kernel type: linear, in which it is suitable when having a lot of features in the dataset [30]. The relevant features obtained from the process of optimize selection are: time, loc_x, loc_y, loc_z, rot_x, rot_y, rot_z, ang_acc_x, ang_vel_y, lin_vel_x, and lin_vel_z. The result of the optimized SVM model for each movement is shown in Table 1. The movement R_Tan and Gan obtained the highest result (100%) in all 4 indicators. R_Gan and Tan reached the highest result (100%) in the recall and specificity. The least accuracy, precision, recall, and specificity are each obtained by Punch (99.02%), Pak (95.93%), R_Bong (95.06%), and Punch (99.41%) respectively. The overall accuracy obtained from the optimized SVM model is 98.11%.

Table 1: Result of the optimized SVM model for each movement

Table 2: Result of the optimized Decision Tree model for each movement

4.2. Decision Tree

The parameters used to obtain the final result for Decision Tree algorithm are Criterion: information gain, Confidence: 0.140, and Minimal Gain: 0.019. Information gain is useful to minimize randomness in the dataset [31]. The relevant features obtained from the process of optimizing selection are loc_x, loc_y, lox_z, rot_x, rot_y, rot_z, lin_vel_y, and lin_vel_z. The result of the optimized Decision Tree model for each movement is shown in Table 2. The movement R_Tan obtained the highest result (100%) in all 4 indicators, with Gan and Tan obtained the highest precision as well. The least accuracy, precision, recall, and specificity are each obtained by Punch (98.42%), Punch (90.52%), R_Pak (91.67%), and R_Pak (99.16%) respectively. The overall accuracy obtained from the optimized Decision Tree model is 96.06%.

Table 3: Result of the optimized kNN model for each movement

4.3. k-Nearest Neighbor

The parameters used to obtain the result for kNN algorithm are Measure Type: Numerical Measures, Numerical Measure: Manhattan Distance, and k = 5. Manhattan distance is preferable when there is high dimensionality in the dataset [32]. The relevant features obtained from the process of optimizing selection are time, loc_x, rot_x, rot_y, rot_z, and ang_vel_x. The result of the optimized kNN model for each movement is shown in Table 3. All movements except for R_Bong and Bong obtained the highest result (100%) in all 4 indicators. The least accuracy, precision, recall, and specificity are each obtained by R_Bong (99.71%), R_Bong (98.16%), Bong (98.19%), and Bong (99.81%) respectively. The overall accuracy obtained from the optimized kNN model is 99.71%.

4.4. Summary of the result

By using RapidMiner Studio, 10 basic Wing Chun hand movements were classified. The result of the classification by 3 optimized algorithms are shown in Table 1-3. Figure 2 shows the comparison of the averaged results between the optimized SVM, Decision Tree, and kNN algorithms.

The averaged results of the optimized SVM and Decision Tree show that accuracy and specificity have higher results than precision and recall, while in the optimized Decision Tree, the precision and recall results are less than the optimized SVM one. From all 3 algorithms, kNN obtained the highest results in all 4 indicators, reaching more than 99%.

Figure 2: Comparison of average results between the optimized algorithms

Figure 3: Accuracy of all movements from 3 algorithms

Figure 4: Precision of all movements from 3 algorithms

The number of extracted features for optimized SVM, Decision Tree, and kNN are varied. The time feature is not present in the Decision Tree. As for angular acceleration, linear acceleration, angular velocity, and linear velocity, the features present in the 3 algorithms are varied. However, lin_vel_z is present in both SVM and Decision Tree. The rotation feature (rot_x, rot_y, and rot_z) are present in all 3 algorithms. Location (loc_x, loc_y, loc_z) are present in SVM and Decision Tree, while only loc_x that is present in kNN. This result shows that Wing Chun hand is focused more on positioning than velocity and acceleration. This corresponds according to Master Ip Chun, if the hand position and movement is not correct, it will not be useful and may be dangerous [33].

Figure 5: Recall of all movements from 3 algorithms

Figure 6: Specificity of all movements from 3 algorithms

From Figure 3-6, kNN has the highest results in all 4 indicators, while Decision Tree has the lowest. While all algorithm’s curve has some intersections with each other at some movements, the difference between them is clear enough. It can also be seen that R_Tan reached 100% in every indicator and all applied algorithms. For every indicator, the shape of the graphs for kNN is identical, except for R_Bong and Bong, which are not reaching 100%. This is understandable because even though Bong Sau is a basic hand movement, the shape and movement are more complex than other basic movements.

Figure 7: Comparison between the overall accuracy of default and optimized algorithms

The initial accuracy for SVM, Decision Tree, and kNN is 52.57%, 55.14%, and 98.06%, respectively. After the optimization process, the split validation process is done again to obtain the final accuracy. The final accuracy for SVM, Decision Tree, and kNN is 98.11%, 96.06%, and 99.71%, respectively. From the comparison seen in Figure 7, it can be seen that the optimization improves all 3 algorithms, especially SVM and Decision Tree. Both default and optimized results show that kNN obtains the highest accuracy.

      To check whether the result shows a statistically significant difference (p  0.05 and p  0.01), Wilcoxon signed-rank test is performed on all 4 indicators (accuracy, precision, recall, and specificity) of both default and optimized algorithms. The test shows that all 4 indicators in SVM have a statistically significant difference. Both Decision Tree and kNN have a statistically significant difference in all indicators except for precision. Table 4 shows the result of the test.

      Wilcoxon signed-rank test is also performed to the comparison of each algorithm in both default and optimized. The result of the comparison of default algorithms shows that SVM and Decision Tree do not have a statistically significant difference. The result of the comparison of the optimized algorithm shows that all the algorithms have a statistically significant difference. Table 5 shows the result of the test.

Table 4: Wilcoxon signed-rank test result of 4 indicators of both default and optimized algorithms

Table 5: Wilcoxon signed-rank test result of the comparison of each algorithm in both default and optimized

5. Conclusion

The purpose of this study is to find the best possible method of classifying Wing Chun basic hand movement to create a Wing Chun training simulation system in VR. The results show that movement data features such as location, rotation, and linear velocity are significant enough to classify Wing Chun basic hand movements. Right hand Tan Sau has the highest value of all 4 indicators. Meanwhile, based on the result of the optimized kNN algorithm, Bong Sau of both hands is the only movement that is not reaching 100% performance, which is understandable because Bong Sau is a more complex basic movement. Before the optimization process, only kNN algorithm that has a high-performance result out of 3 algorithms. However, after the optimization of parameters and selection of features, SVM and Decision Tree’s performances significantly improved. The comparison of the results shows that the optimized kNN algorithm obtained the highest results.

6. Discussion

Five basic movements were captured for each left and right, making it 10 movements in total. The process of capturing the movement is done with Oculus Quest VR gear that comes with a pair of handheld Oculus Controller. A simple game is developed with Unreal Engine 4 to capture motion data that consists of several features of movements, such as location, rotation, etc. RapidMiner Studio is used to pre-process the dataset, apply algorithms, and optimize the learning model. This study compares SVM, Decision Tree, and kNN algorithms since 3 of them were commonly used in the earlier study of motion classification and recognition. The performance indicators such as accuracy, precision, recall, and specificity are used to compare the overall results of the 3 algorithms.

In this study, while algorithms like SVM, Decision Tree, and kNN are compared, this study does not include combining algorithms, for example like combining SVM with kNN, etc. RapidMiner Studio has various modules that help in processing data with algorithms. AdaBoost module has been applied to the optimized kNN, but the result shows no improvement. The module like Ensemble Stacking has yet to be explored further, although attempt to use stacking to stack all 3 algorithms, the result is not as good as the obtained result of each optimized algorithm has been done. Machine learning algorithms such as Deep Learning, Neural Networks, and Spatio-temporal algorithms can also be applied and compared for future studies.

The simple game that is developed to capture the movements also has a limitation in which the user can only stand still. The game can still be developed so that it could improve the motion capture process to include the user able to move around while performing hand movements. While this study shows that the algorithms can accurately classify basic hand movements, there are more basic hand movements that are very similar to each other. These movements can only be distinguished by looking at the area of contact of the hand. This can also be a field of further study for better movement classification. Further classification of sequences could be a field to be explored as well since Wing Chun also has a more complex set of movements that require more than one type of movement.

Conflict of Interest

The authors declare no conflict of interest.

Acknowledgment

The authors would like to thank Abba Suganda Girsang S.T., M.Cs., Ph.D for the helpful suggestions and Sifu Julius Khang for his teaching of Wing Chun.

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10. Sutham Satthamsakul, Ari Kuswantori, Witsarut Sriratana, Worapong Tangsrirat, Taweepol Suesut, "Landmarking Technique for Improving YOLOv4 Fish Recognition in Various Background Conditions", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 3, pp. 100–107, 2023. doi: 10.25046/aj080312
11. Temsamani Khallouk Yassine, Achchab Said, Laouami Lamia, Faridi Mohammed, "Hybrid Discriminant Neural Networks for Performance Job Prediction", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 2, pp. 116–122, 2023. doi: 10.25046/aj080213
12. Abdulrasaq Jimoh, Samson Oladayo Ayanlade, Emmanuel Idowu Ogunwole, Dolapo Eniola Owolabi, Abdulsamad Bolakale Jimoh, Fatina Mosunmola Aremu, "Metaheuristic Optimization Algorithm Performance Comparison for Optimal Allocation of Static Synchronous Compensator", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 1, pp. 116–124, 2023. doi: 10.25046/aj080114
13. Sathyabama Kaliyapillai, Saruladha Krishnamurthy, Thiagarajan Murugasamy, "An Ensemble of Voting- based Deep Learning Models with Regularization Functions for Sleep Stage Classification", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 1, pp. 84–94, 2023. doi: 10.25046/aj080110
14. Fatima-Zahra Elbouni, Aziza EL Ouaazizi, "Birds Images Prediction with Watson Visual Recognition Services from IBM-Cloud and Conventional Neural Network", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 6, pp. 181–188, 2022. doi: 10.25046/aj070619
15. Penio Lebamovski, "Advantages of 3D Technology in Stereometry Training", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 6, pp. 39–48, 2022. doi: 10.25046/aj070605
16. Jabrane Slimani, Abdeslam Kadrani, Imad EL Harraki, El hadj Ezzahid, "Long-term Bottom-up Modeling of Renewable Energy Development in Morocco", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 5, pp. 129–145, 2022. doi: 10.25046/aj070515
17. Afsah Sharmin, Farhat Anwar, S M A Motakabber, Aisha Hassan Abdalla Hashim, "A Secure Trust Aware ACO-Based WSN Routing Protocol for IoT", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 3, pp. 95–105, 2022. doi: 10.25046/aj070311
18. Bougar Marieme, Ziyati El Houssaine, "Analysis Methods and Classification Algorithms with a Novel Sentiment Classification for Arabic Text using the Lexicon-Based Approach", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 3, pp. 12–18, 2022. doi: 10.25046/aj070302
19. Ilhem Bouchriha, Ali Ben Ghanem, Khaled Nouri, "Optimization of the Sliding Mode Control (SMC) with the Particle Swarm Optimization (PSO) Algorithm for Photovoltaic Systems Based on MPPT", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 1, pp. 100–106, 2022. doi: 10.25046/aj070110
20. Arakelyan Edik, Kosoy Anatoliy, Andryushin Alexander, Mezin Sergey, Yagupova Yulia, Leonov Maxim, Pashchenko Fedor, "Problems of Increasing the Intelligence of Algorithms for Optimal Distribution of the Current Load on the Combined Heat and Power Plant and Ways to Solve Them", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 369–374, 2021. doi: 10.25046/aj060542
21. Mostafa Yacoub, Amira Rezk, Mohamed Senousy, "Enhancing Decision Trees for Data Stream Mining", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 330–334, 2021. doi: 10.25046/aj060537
22. Yasuyuki Matsuura, Hiroki Takada, "Evaluation Studies of Motion Sickness Visually Induced by Stereoscopic Films", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 241–251, 2021. doi: 10.25046/aj060428
23. Liang Chen, Mo-How Herman Shen, "A New Topology Optimization Approach by Physics-Informed Deep Learning Process", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 233–240, 2021. doi: 10.25046/aj060427
24. Abdulkarim Saleh Masoud Ali, Rozmie Razif Othman, Yasmin Mohd Yacob, Haitham Saleh Ali Ben Abdelmula, "An Efficient Combinatorial Input Output-Based Using Adaptive Firefly Algorithm with Elitism Relations Testing", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 223–232, 2021. doi: 10.25046/aj060426
25. Valerii Dmitrienko, Serhii Leonov, Aleksandr Zakovorotniy, "New Neural Networks for the Affinity Functions of Binary Images with Binary and Bipolar Components Determining", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 91–99, 2021. doi: 10.25046/aj060411
26. Ali Almutawa, "Effect of Smooth Transition and Hybrid Reality on Virtual Realism: A Case of Virtual Art Gallery", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 3, pp. 231–240, 2021. doi: 10.25046/aj060325
27. Junho Chang, Mustafa Melih Pelit, Masaki Yamakita, "SLIP-SL: Walking Control Based on an Extended SLIP Model with Swing Leg Dynamics", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 3, pp. 84–91, 2021. doi: 10.25046/aj060309
28. Tamer Saraçyakupoğlu, "Usage of Additive Manufacturing and Topology Optimization Process for Weight Reduction Studies in the Aviation Industry", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 815–820, 2021. doi: 10.25046/aj060294
29. Susanto Kumar Ghosh, Mohammad Rafiqul Islam, "Convolutional Neural Network Based on HOG Feature for Bird Species Detection and Classification", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 733–745, 2021. doi: 10.25046/aj060285
30. Yu-Chun Huang, "Analyze Performance of Enterprise Supervision System by Game Theory-Take the case of Tatung Management Rights Competition as Example", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 689–693, 2021. doi: 10.25046/aj060279
31. Khalil Ibrahim Mohammad Abuzanouneh, Khalil Hamdi Ateyeh Al-Shqeerat, "Development and Improvement of Web Services Selections using Immigrants Scheme of Multi-Objective Genetic Algorithm", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 412–422, 2021. doi: 10.25046/aj060248
32. Musa Sulaiman Jibia, Abdussamad Umar Jibia, "Fetal Electrocardiogram Extraction using Moth Flame Optimization (MFO)-Based Adaptive Filter", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 303–312, 2021. doi: 10.25046/aj060235
33. Amany Khalil, Osama Tolba, Sherif Ezzeldin, "Design Optimization of Open Office Building Form for Thermal Energy Performance using Genetic Algorithm", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 254–261, 2021. doi: 10.25046/aj060228
34. Abdelaziz Lberni, Malika Alami Marktani, Abdelaziz Ahaitouf, Ali Ahaitouf, "Multi-Objective Design of Current Conveyor using Optimization Algorithms", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 154–160, 2021. doi: 10.25046/aj060218
35. Sk. Md. Masudul Ahsan, Aminul Islam, "Visual Saliency Detection using Seam and Color Cues", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 139–153, 2021. doi: 10.25046/aj060217
36. Alisson Steffens Henrique, Anita Maria da Rocha Fernandes, Rodrigo Lyra, Valderi Reis Quietinho Leithardt, Sérgio D. Correia, Paul Crocker, Rudimar Luis Scaranto Dazzi, "Classifying Garments from Fashion-MNIST Dataset Through CNNs", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 989–994, 2021. doi: 10.25046/aj0601109
37. Mandlenkosi Shezi, Abejide Ade-Ibijola, "Deaf Chat: A Speech-to-Text Communication Aid for Hearing Deficiency", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 826–833, 2020. doi: 10.25046/aj0505100
38. Mohammed Hamim, Ismail El Moudden, Hicham Moutachaouik, Mustapha Hain, "Gene Selection for Cancer Classification: A New Hybrid Filter-C5.0 Approach for Breast Cancer Risk Prediction", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 871–878, 2021. doi: 10.25046/aj060196
39. Subash Pokharel, Aleksandar Dimitrovski, "Ferromagnetic Core Reactor Modeling and Design Optimization", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 810–818, 2021. doi: 10.25046/aj060190
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. Anass Barodi, Abderrahim Bajit, Taoufiq El Harrouti, Ahmed Tamtaoui, Mohammed Benbrahim, "An Enhanced Artificial Intelligence-Based Approach Applied to Vehicular Traffic Signs Detection and Road Safety Enhancement", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 672–683, 2021. doi: 10.25046/aj060173
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. Jesus Aguila-Leon, Cristian Chiñas-Palacios, Carlos Vargas-Salgado, Elias Hurtado-Perez, Edith Xio Mara Garcia, "Particle Swarm Optimization, Genetic Algorithm and Grey Wolf Optimizer Algorithms Performance Comparative for a DC-DC Boost Converter PID Controller", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 619–625, 2021. doi: 10.25046/aj060167
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. Broderick Crawford, Ricardo Soto, Gino Astorga, José Lemus-Romani, Sanjay Misra, Mauricio Castillo, Felipe Cisternas-Caneo, Diego Tapia, Marcelo Becerra-Rozas, "Balancing Exploration-Exploitation in the Set Covering Problem Resolution with a Self-adaptive Intelligent Water Drops Algorithm", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 134–145, 2021. doi: 10.25046/aj060115
46. Roman Dremliuga, Natalia Prisekina, Andrei Yakovenko, "New Properties of Crimes in Virtual Environments", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1727–1733, 2020. doi: 10.25046/aj0506206
47. Arwa Alshamsi, Reem Bayari, Said Salloum, "Sentiment Analysis in English Texts", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1683–1689, 2020. doi: 10.25046/aj0506200
48. Hoang Xuan Thinh, Pham Van Dong, Tran Ve Quoc, "A Study on the Tool Wear in Milling Process of the Gleason Spiral Bevel Gear", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1402–1407, 2020. doi: 10.25046/aj0506169
49. Rendani Wilson Maladzhi, Grace Mukondeleli Kanakana-Katumba, "Evolution of Teaching Approaches for Science, Engineering and Technology within an Online Environment: A Review", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1207–1216, 2020. doi: 10.25046/aj0506144
50. Rewan Kumar Dahal, Ganesh Bhattarai, Dipendra Karki, "Determinants of Technological and Innovation Performance of the Nepalese Cellular Telecommunications Industry from the Customers’ Perspective", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1013–1020, 2020. doi: 10.25046/aj0506122
51. Jinwon Cheon, Sunwoong Choi, "Hand Gesture Classification using Inaudible Sound with Ensemble Method", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 967–971, 2020. doi: 10.25046/aj0506115
52. Luisella Balbis, "Optimal Irrigation Strategy using Economic Model Predictive Control", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 781–787, 2020. doi: 10.25046/aj050693
53. Pearl Keitemoge, Daniel Tetteh Narh, "Effective Application of Information System for Purchase Process Optimization", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 594–605, 2020. doi: 10.25046/aj050673
54. Sergey Alekseevich Serebryansky, Alexander Vladimirovich Barabanov, "To the Question of Multi-Criteria Optimization of Aircraft Components in Order to Optimize its Life Cycle", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 408–415, 2020. doi: 10.25046/aj050649
55. Hao Tuan Huynh, Nghia Duong-Trung, Dinh Quoc Truong, Hiep Xuan Huynh, "Vietnamese Text Classification with TextRank and Jaccard Similarity Coefficient", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 363–369, 2020. doi: 10.25046/aj050644
56. Athraa Ali Kadhem, Noor Izzri Abdul Wahab, Ahmed Abdalla, "The Contribution of Wind Energy Capacity on Generation Systems Adequacy Reliability using Differential Evolution Optimization Algorithm", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 331–340, 2020. doi: 10.25046/aj050640
57. Munaf Salim Najim Al-Din, "Real-Time Identification and Classification of Driving Maneuvers using Smartphone", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 193–205, 2020. doi: 10.25046/aj050623
58. Simona Kirilova Filipova-Petrakieva, "Applications of the Heuristic Optimization Approach for Determining a Maximum Flow Problem Based on the Graphs’ Theory", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 175–184, 2020. doi: 10.25046/aj050621
59. Fei Gao, Jiangjiang Liu, "Effective Segmented Face Recognition (SFR) for IoT", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 36–44, 2020. doi: 10.25046/aj050605
60. Ihsan Mizher Baht, Petre Marian Nicolae, Ileana Diana, Nameer Baht, "Analysis of Green Building Effect on Micro grid Based on Potential Energy Savings and BIM", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 30–35, 2020. doi: 10.25046/aj050604
61. Daniyar Nurseitov, Kairat Bostanbekov, Maksat Kanatov, Anel Alimova, Abdelrahman Abdallah, Galymzhan Abdimanap, "Classification of Handwritten Names of Cities and Handwritten Text Recognition using Various Deep Learning Models", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 934–943, 2020. doi: 10.25046/aj0505114
62. Gökalp Çınarer, Bülent Gürsel Emiroğlu, Recep Sinan Arslan, Ahmet Haşim Yurttakal, "Brain Tumor Classification Using Deep Neural Network", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 765–769, 2020. doi: 10.25046/aj050593
63. Lana Abdulrazaq Abdullah, Muzhir Shaban Al-Ani, "CNN-LSTM Based Model for ECG Arrhythmias and Myocardial Infarction Classification", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 601–606, 2020. doi: 10.25046/aj050573
64. Alami Hamza, Noureddine En-Nahnahi, Said El Alaoui Ouatik, "Contextual Word Representation and Deep Neural Networks-based Method for Arabic Question Classification", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 478–484, 2020. doi: 10.25046/aj050559
65. Imad El Hajjami, Bachir Benhala, Hamid Bouyghf, "Shape Optimization of Planar Inductors for RF Circuits using a Metaheuristic Technique based on Evolutionary Approach", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 426–433, 2020. doi: 10.25046/aj050553
66. Nittaya Kerdprasop, Kittisak Kerdprasop, Paradee Chuaybamroong, "Economic and Environmental Analysis of Life Expectancy in China and India: A Data Driven Approach", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 308–313, 2020. doi: 10.25046/aj050539
67. Martin Marinov, Alexander Efremov, "Four-Dimensional Sparse Data Structures for Representing Text Data", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 154–166, 2020. doi: 10.25046/aj050521
68. Tran Thanh Dien, Nguyen Thanh-Hai, Nguyen Thai-Nghe, "Deep Learning Approach for Automatic Topic Classification in an Online Submission System", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 700–709, 2020. doi: 10.25046/aj050483
69. Panida Lorwongtrakool, Phayung Meesad, "Correlation-Based Incremental Learning Network for Gas Sensors Drift Compensation Classification", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 660–666, 2020. doi: 10.25046/aj050479
70. 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
71. Roberta Avanzato, Francesco Beritelli, "A CNN-based Differential Image Processing Approach for Rainfall Classification", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 438–444, 2020. doi: 10.25046/aj050452
72. Rizki Jaka Maulana, Gede Putra Kusuma, "Malware Classification Based on System Call Sequences Using Deep Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 207–216, 2020. doi: 10.25046/aj050426
73. Rand Talib, Alexander Rodrigues, Nabil Nassif, "Energy Recovery Equipment and Control Strategies in Various Climate Regions", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 47–53, 2020. doi: 10.25046/aj050407
74. Pham Van Bach Ngoc, Bui Trung Thanh, "Dynamics Model and Design of SMC-type-PID Control for 4DOF Car Motion Simulator", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 557–562, 2020. doi: 10.25046/aj050369
75. 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
76. Ayyoub El Berbri, Adil Saadi, Seddik Bri, "Design and Optimization of Dual-Band Branch-Line Coupler with Stepped-Impedance-Stub for 5G Applications", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 355–360, 2020. doi: 10.25046/aj050346
77. J. Vijay Fidelis, E. Karthikeyan, "Estimation of Influential Parameter Using Gravitational Search Optimization Algorithm for Soccer", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 340–348, 2020. doi: 10.25046/aj050344
78. Jesuretnam Josemila Baby, James Rose Jeba, "A Hybrid Approach for Intrusion Detection using Integrated K-Means based ANN with PSO Optimization", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 317–323, 2020. doi: 10.25046/aj050341
79. Noraziah Adzhar, Yuhani Yusof, Muhammad Azrin Ahmad, "A Review on Autonomous Mobile Robot Path Planning Algorithms", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 236–240, 2020. doi: 10.25046/aj050330
80. Vasiliy Olonichev, Boris Staroverov, Maxim Smirnov, "Dynamic Objects Parameter Estimation Program for ARM Processors Based Adaptive Controllers", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 34–40, 2020. doi: 10.25046/aj050305
81. Jan Sikora, David Fojtík, "Classification of Timber Load on Trucks", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 683–687, 2020. doi: 10.25046/aj050284
82. 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
83. Ricardo Simões Santos, António João Pina da Costa Feliciano Abreu, Joaquim José Rodrigues Monteiro, "Using Metaheuristics-Based Methods to Provide Sustainable Market Solutions, Suitable to Consumer Needs", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 399–410, 2020. doi: 10.25046/aj050252
84. Adrian H. Hoppe, Felix Marek, Florian van de Camp, Rainer Stiefelhagen, "Extending Movable Surfaces with Touch Interaction Using the VirtualTablet: An Extended View", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 328–337, 2020. doi: 10.25046/aj050243
85. Hind El Hassani, Nour- Eddine Boutammachte, Sanae El Hassani, "Optimization of Low Temperature Differential Stirling Engine Regenerator Design", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 272–279, 2020. doi: 10.25046/aj050235
86. 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
87. Nguyen Tuan Anh, Hoang Thang Binh, Tran The Tran, "Optimization of the Stabilizer Bar by Using Total Scores Method", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 1, pp. 431–435, 2020. doi: 10.25046/aj050155
88. Airil Haimi Mohd Adnan, Mohamad Syafiq Ya Shak, Rafidah Abd Karim, Mohd Haniff Mohd Tahir, Dianna Suzieanna M Shah, "360-Degree Videos, VR Experiences and the Application of Education 4.0 Technologies in Malaysia for Exposure and Immersion", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 1, pp. 373–381, 2020. doi: 10.25046/aj050148
89. 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
90. Temitayo Olayemi Olowu, Mohamadsaleh Jafari, Arif Sarwat, "A Multi-Objective Voltage Optimization Technique in Distribution Feeders with High Photovoltaic Penetration", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 377–385, 2019. doi: 10.25046/aj040648
91. Mohd Razif Idris, Imad Mokhtar Mosrati, "Optimization of the Electrical Discharge Machining of Powdered Metallurgical High-Speed Steel Alloy using Genetic Algorithms", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 255–262, 2019. doi: 10.25046/aj040632
92. Jalal Benallal, Lekbir Cherif, Mohamed Chentouf, Mohammed Darmi, Rachid Elgouri, Nabil Hmina, "A New Wire Optimization Approach for Power Reduction in Advanced Technology Nodes", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 140–146, 2019. doi: 10.25046/aj040617
93. Bui Thanh Hung, "Integrating Diacritics Restoration and Question Classification into Vietnamese Question Answering System", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 207–212, 2019. doi: 10.25046/aj040526
94. Michael Santacroce, Daniel Koranek, Rashmi Jha, "Detecting Malicious Assembly using Convolutional, Recurrent Neural Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 46–52, 2019. doi: 10.25046/aj040506
95. Andrei Panteleev, Valentin Panovskiy, "Application of Open-Source Optimization Library “Extremum” to the Synthesis of Feedback Control of a Satellite", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 23–29, 2019. doi: 10.25046/aj040503
96. Antonio Fuduli, Pierangelo Veltri, Eugenio Vocaturo, Ester Zumpano, "Melanoma detection using color and texture features in computer vision systems", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 16–22, 2019. doi: 10.25046/aj040502
97. Houcine Marouani, Amin Sallem, Mondher Chaoui, Pedro Pereira, Nouri Masmoudi, "Multiple-Optimization based-design of RF Integrated Inductors", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 574–584, 2019. doi: 10.25046/aj040468
98. Jaya V. Gaitonde, Rajesh B. Lohani, "Material, Structural Optimization and Analysis of Visible-Range Back-Illuminated OPFET photodetector", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 485–502, 2019. doi: 10.25046/aj040459
99. Olfa Jedda, Ali Douik, "Optimal Discrete-time Sliding Mode Control for Nonlinear Systems Subject to Input Constraints", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 141–146, 2019. doi: 10.25046/aj040417
100. Tlija Amira, Istrate Dan, Badii Atta, Gattoufi Said, Bennani Az-eddine, Wegrzyn-Wolska Katarzyna, "Stress Level Classification Using Heart Rate Variability", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 3, pp. 38–46, 2019. doi: 10.25046/aj040306