1. Introduction

Recently, many caregivers have experienced lower back pain owing to frequent patient handling [1–2]. Previous studies have suggested that caregivers should maintain an appropriate posture based on body mechanics during patient handling [3–4]. Accordingly, previous studies have developed monitoring systems to supervise the postures of caregivers to prevent lower back pain [5–8].

Lin et al. developed a robot patient comprising an inertial measurement unit (IMU), angular position sensors, and servo motor encoders and found that the robot could classify correct and incorrect transfer methods [5]. Huang et al. developed an automatic evaluation method for patient transfer skills using two Kinect RGB-D sensors [6, 7]. Their study showed that this method could improve the patient transfer skill of students [7]. Itami et al. developed a monitoring system using electromyography, goniometer, and inclination sensor [8]. This system could monitor skills and lumbar loads of patient handling [8]. These systems are useful for monitoring caregivers’ postures; however, these systems have limitations in terms of workspace and usability because they require vision-based devices, multiple specialized sensors, or robots. Previous study reported that assistive devices for caregivers should be time-efficient, cost-effective, and suitable for various workspaces [9]. Hence, there is a need to develop wearables and simple monitoring systems for caregivers’ postures.

PostureCoach is a wearable and simple system for preventing lower back pain among caregivers [10]. This system can provide real-time biofeedback using the trunk angle obtained from only two IMUs [10]. Previous investigations have shown that PostureCoach can reduce the lumbar spine flexion in a beginner who handles patients [10]. However, this system cannot suggest caregiver ways to adjust to an appropriate posture because the system measures only the trunk angle. Several factors, such as the foot position and arm movement, are useful to achieve suitable postures to prevent lower back pain during patient handling [11–13]. Therefore, a simple and wearable system that monitors the trunk angle, foot position, and arm movement is being develop [14].

Figure 1 shows the flow of the proposed monitoring system for caregivers’ postures. The proposed system monitors a caregiver’s posture using wearable sensors mounted on the caregiver’s trunk and shoes (Step 1). The proposed method evaluates several factors such as trunk angle, foot positions, and arm movements using sensor data obtained from the wearable sensors (Step 2). If the evaluated factors are unsuitable, the proposed system informs the caregiver to adjust their posture (Step 3).

Our previous study proposed a postural recognition method during sit-to-stand assistive motions but could not consider other patient-handling aspects and various foot positions and arm movements [14]. Several patient-handling aspects, such as providing postural change to turn a patient on a bed, cause lumber load among caregivers [12, 15]. Such a type of patient handling causes lower back pain among caregivers as a patient is frequently repositioned on a bed [15]. As explained above, arm movements and foot positions are important factors for patient handling [11-13]. Thus, this study proposes the recognition method for various foot positions and arm movements of caregivers using a postural recognition method during postural change for a patient on a bed.

Figure 1: Monitoring system for patient-handling motion.

This section discusses the background, previous studies of this paper. The remainder of this paper is organized as follows. Section 2 mentions the objective and contribution of this paper. Section 3 explains the proposed method for postural recognition during postural change while handling a patient on a bed. Section 4 describes the experiments conducted to evaluate whether the proposed method can recognize a caregiver’s posture during patient handling. Section 5 presents experimental results and discussions. Finally, section 6 presents conclusions.

2. Objective and Contribution

The objective of this study is proposal of a novel posture recognition method for wearable monitoring system to prevent lower back pain among caregivers. Furthermore, this study explores appropriate combination of wearable sensors and machine learning algorithms for the proposed method.

The contribution of this study is as follows. The proposed method will be applied to suggest an appropriate posture for caregivers because this method recognizes both arm movements and foot positions. This study presents the novel posture recognition method using sensor fusion consisted of IMU and insole pressure sensor. As a scientific contribution, this method using sensor fusion of wearable sensor has a potential to be applied to posture recognition related to occupational health. In addition, the appropriate combination of wearable sensors and machine learning algorithms shown in this study will be useful in the research area of computer science and wearable systems.

3. Proposed Method

3.1. Overview

Figure 2 shows a block diagram of the proposed postural recognition method. The proposed method measured postural information by wearable sensors. Machine learning algorithm using features obtained from wearable sensors generated function for postural recognition. Caregiver’s posture such as arm movement and foot position were recognized by generated function. The details of each component are described below.

3.2. Input (wearable sensors)

The IMU attached on the trunk and insole pressure sensors measured postural information during patient handling. The IMU (Logical Product Co., Japan) measures the three-axis acceleration and angular velocity of the trunk for features of the machine learning algorithm. Insole pressure sensors are fabricated by arranging eight FlexiForce sensors (Tekscan, USA) on each foot. Because the FlexiForce sensors are thin and flexible, they are suitable for measuring forces between various surfaces, such as an insole [16]. Furthermore, the FlexiForce sensors can be used in real time applications owing to the lack of linearity, non-repeatability, and hysteresis [17]. Front and rear forces on each foot are measured using insole pressure sensors as features for the machine learning algorithm. These sensor data were measured at a 1-kHz sampling rate.

3.3. Machine learning-based recognition

Seven features (mean, maximum, minimum, standard deviation, root mean square, kurtosis and skewness) were extracted for machine learning algorithm. These features were extracted from each sensor data using a sliding window technique with a 1.0-s window size and 50% overlap. These features, window size, and overlap were selected based on the previous researches 　　about posture recognition using wearable sensors [18–19].

The machine learning algorithm recognized three arm movements and three foot positions during postural change while handling a patient on a bed. In this study, artificial neural network, decision tree, and support vector machine were selected as machine learning algorithm for the proposed method because these three algorithms were used for wearable sensor-based posture recognition [19-20]. Details of the recognition models were described below.

Artificial neural network is applied to complex relationship between input and output by nonlinear and flexible decision boundary [19-21]. The artificial neural network-based model consists of input layer, hidden layer and output layer [19-20]. In our proposed method, the features obtained from the wearable sensors were inputted to the input layer, and the recognized posture was outputted from the output layer. Hidden layer was set function for learning the relationship between features obtained wearable sensor and postures. The specifications and parameters in artificial neural network of proposed method is shown in Table 1.

Decision tree provides if-then rules which have free of ambiguity [22-24]. If-then rules generated by decision tree consist of root, internal and leaf nodes [22]. The root and internal nodes have threshold of features for recognition. Position and features of these nodes are determined based on comparison of entropy for all features [23]. Leaf nodes are defined as recognition output via root and internal nodes. When these nodes are too many and complex, recognition model has problem such as over fitting [24]. Thus, decision tree has several parameters to adjust number of nodes [24]. The specifications and parameters of decision tree in our proposed method is shown in Table 2.

Support vector machine is applied to nonlinear data without over fitting by large margin separation and kernel function [25]. Kernel function provides efficient calculation in nonlinear feature spaces [25]. Large margin separation is required for generalization performance of support vector machine [25]. The specifications and parameters of support vector machine in our proposed method is shown in Table 3.

Comparison of these machine learning algorithms was necessary in order to determine the appropriate algorithm depends on the target postures [19–20]. In this study, the proposed method was compared to investigate which algorithm could recognize patient handling motion using these wearable sensors.

Table 1: Specifications and parameters for artificial neural network of the proposed method.

3.4. Output (recognized posture)

In the proposed method, arm movements and foot positions were recognized as postural factors. The monitoring system shown in previous section will provide caregiver the appropriate posture based on recognition results.

Figure 2: Proposed postural recognition method.

Table 2: Specifications and parameters for decision tree of the proposed method.

Table 3: Specifications and parameters for support vector machine of the proposed method.

4. Experiment

Four young healthy males (24.75 ± 0.83 years, 1.72 ± 0.05 m, 67.00 ± 10.79 kg) were participants as caregivers. One young healthy male (25.00 years, 1.69 m, 70.00 kg) participated as a simulated patient. All participants provided their verbal informed consent to the experiment.

The four participants (caregivers) were asked to perform a postural change for the simulated patient on a bed under nine postural conditions. The nine conditions were a combination of the three arm movements and three foot positions shown in Figure 3. Each participant (caregiver) repeated this motion ten times for each condition. Data for the proposed method were measured by wearable sensors (IMU and insole pressure sensors) at 1-kHz sampling frequency for each trial.

The proposed method recognized the three arm movements and three foot positions by processing data obtained from the sensors using the machine learning algorithm. This experiment compared the recognition performance of three machine learning algorithms to determine the appropriate algorithm for the proposed method. Furthermore, the recognition performances of three feature patterns (“IMU,” “Insole Sensor,” and “All”) were compared to verify the effectiveness of the sensors and features.

The metrics of accuracy and F-measure were used to evaluate the recognition performance of the algorithms. These evaluation metrics were calculated according to

where TP denotes true positive and represents the number of correct recognition for positive samples, TN denotes true negative and represents the number of correct recognition for negative samples, FP denotes false positive represents the number of samples incorrectly recognized as positive in actual negative samples, and FN denotes false negative and represents the number of samples incorrectly recognized as negative in actual positive samples. The accuracy of the proposed method was evaluated based on the recognition performance of each algorithm using the feature patterns. The F-measure of each arm movement and foot position was calculated using the harmonic mean of precision and recall as the overall performance. These metrics were calculated by ten-fold cross validation using data obtained from all trials and participants.

Figure 3: Postural conditions in the experiment.

5. Results and Discussion

Tables 4 and 5 show the accuracy and F-measure of the proposed method. The results show that the combination of artificial neural network and all features obtained from both IMU and insole pressure sensors achieved the best performance for postural recognition. In this combination, the proposed method could recognize arm movements and foot positions with an accuracy of approximately 0.75 and 0.97, respectively. These results suggest the possibility of using the proposed recognition method in monitoring systems to prevent lower back pain.

A comparison between the machine leaning algorithms showed that the artificial neural network exhibited the best performance for both accuracy and F-measure. Therefore, the artificial neural network was deemed the most suitable machine learning algorithm for the proposed method. The artificial neural network’s best performance was attributed to its nonlinear and flexible decision boundary, which was effective for wearable sensor data [19-21].

A comparison between feature patterns showed that the proposed method yielded the best performance for accuracy and F-measure when all features obtained from both IMU and insole pressure sensors were used. These results indicated that both IMU and insole pressure sensors were effective and necessary for the proposed method. Moreover, in foot position recognition, the proposed method using only insole pressure sensors achieved a great performance. These findings indicate that the insole pressure sensors could detect the force distribution depending on the foot positions. However, the proposed method could not recognize arm movements using only the insole pressure sensors. Thus, IMU was also considered necessary for the proposed method. Table 6 shows the confusion matrices of the proposed method using the artificial neural network and all features for postural recognition. These results showed that the proposed method achieved good performance for foot position recognition. The reason for this performance, as explained earlier, was that insole pressure sensors in the shoes could detect relevant features related to foot positions. However, the proposed method incorrectly recognized arm movement between movements 1 and 3 because there was no sensor on the upper limbs. Placing additional sensors on the upper limbs could obstruct care activities. Thus, the optimal position to place an IMU on the trunk to detect arm movement without additional sensors should be explored.

The limitation of this study is that the proposed method was not evaluated in an actual workspace and under various aspects of patient handling, such as assisting transfer. Moreover, the proposed method should be tested for female participants, patients of different ages, and caregivers. Furthermore, other feature selection patterns and algorithms should be examined to improve the performance of the proposed postural recognition method. Following further tests and improvements on the proposed method, the postural monitoring system to prevent lower back pain will be implemented based on the proposed postural recognition method.

Table 4: Accuracy of the proposed method.

Table 5: F-measure of the proposed method.

In future work, the optimal placement of sensors will be explored to improve the accuracy of postural recognition in the proposed method. Moreover, other machine learning algorithms will be tested to determine a more suitable algorithm for the proposed method. Furthermore, a modified version of the proposed recognition method should be evaluated under various conditions. Finally, a monitoring system will be implemented based on the modified postural recognition method to prevent lower back pain due to patient handling.

Table 6: Confusion matrices of posture recognition
(using an artificial neural network with all features).

6. Conclusion

In this study, the posture recognition method for caregivers during postural change for a patient on a bed was proposed. The experimental results showed that the proposed method could recognize a caregiver’s posture. The proposed method using novel sensor fusion with machine learning will also be useful for posture recognition in other various application.

Conflict of Interest

The authors declare no conflict of interest.

Acknowledgment

The first author is grateful for a scholarship from the Nakatani Foundation for Advancement of Measuring Technologies in Biomedical Engineering.

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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. Khalid A. AlAfandy, Hicham Omara, Mohamed Lazaar, Mohammed Al Achhab, "Using Classic Networks for Classifying Remote Sensing Images: Comparative Study", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 770–780, 2020. doi: 10.25046/aj050594
51. Khalid A. AlAfandy, Hicham, Mohamed Lazaar, Mohammed Al Achhab, "Investment of Classic Deep CNNs and SVM for Classifying Remote Sensing Images", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 652–659, 2020. doi: 10.25046/aj050580
52. Rajesh Kumar, Geetha S, "Malware Classification Using XGboost-Gradient Boosted Decision Tree", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 536–549, 2020. doi: 10.25046/aj050566
53. Nghia Duong-Trung, Nga Quynh Thi Tang, Xuan Son Ha, "Interpretation of Machine Learning Models for Medical Diagnosis", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 469–477, 2020. doi: 10.25046/aj050558
54. Oumaima Terrada, Soufiane Hamida, Bouchaib Cherradi, Abdelhadi Raihani, Omar Bouattane, "Supervised Machine Learning Based Medical Diagnosis Support System for Prediction of Patients with Heart Disease", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 269–277, 2020. doi: 10.25046/aj050533
55. Haytham Azmi, "FPGA Acceleration of Tree-based Learning Algorithms", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 237–244, 2020. doi: 10.25046/aj050529
56. Hicham Moujahid, Bouchaib Cherradi, Oussama El Gannour, Lhoussain Bahatti, Oumaima Terrada, Soufiane Hamida, "Convolutional Neural Network Based Classification of Patients with Pneumonia using X-ray Lung Images", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 167–175, 2020. doi: 10.25046/aj050522
57. Young-Jin Park, Hui-Sup Cho, "A Method for Detecting Human Presence and Movement Using Impulse Radar", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 770–775, 2020. doi: 10.25046/aj050491
58. Anouar Bachar, Noureddine El Makhfi, Omar EL Bannay, "Machine Learning for Network Intrusion Detection Based on SVM Binary Classification Model", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 638–644, 2020. doi: 10.25046/aj050476
59. Adonis Santos, Patricia Angela Abu, Carlos Oppus, Rosula Reyes, "Real-Time Traffic Sign Detection and Recognition System for Assistive Driving", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 600–611, 2020. doi: 10.25046/aj050471
60. Amar Choudhary, Deependra Pandey, Saurabh Bhardwaj, "Overview of Solar Radiation Estimation Techniques with Development of Solar Radiation Model Using Artificial Neural Network", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 589–593, 2020. doi: 10.25046/aj050469
61. Maroua Abdellaoui, Dounia Daghouj, Mohammed Fattah, Younes Balboul, Said Mazer, Moulhime El Bekkali, "Artificial Intelligence Approach for Target Classification: A State of the Art", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 445–456, 2020. doi: 10.25046/aj050453
62. Shahab Pasha, Jan Lundgren, Christian Ritz, Yuexian Zou, "Distributed Microphone Arrays, Emerging Speech and Audio Signal Processing Platforms: A Review", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 331–343, 2020. doi: 10.25046/aj050439
63. Ilias Kalathas, Michail Papoutsidakis, Chistos Drosos, "Optimization of the Procedures for Checking the Functionality of the Greek Railways: Data Mining and Machine Learning Approach to Predict Passenger Train Immobilization", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 287–295, 2020. doi: 10.25046/aj050435
64. Yosaphat Catur Widiyono, Sani Muhamad Isa, "Utilization of Data Mining to Predict Non-Performing Loan", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 252–256, 2020. doi: 10.25046/aj050431
65. Hai Thanh Nguyen, Nhi Yen Kim Phan, Huong Hoang Luong, Trung Phuoc Le, Nghi Cong Tran, "Efficient Discretization Approaches for Machine Learning Techniques to Improve Disease Classification on Gut Microbiome Composition Data", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 547–556, 2020. doi: 10.25046/aj050368
66. Ruba Obiedat, "Risk Management: The Case of Intrusion Detection using Data Mining Techniques", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 529–535, 2020. doi: 10.25046/aj050365
67. Krina B. Gabani, Mayuri A. Mehta, Stephanie Noronha, "Racial Categorization Methods: A Survey", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 388–401, 2020. doi: 10.25046/aj050350
68. Dennis Luqman, Sani Muhamad Isa, "Machine Learning Model to Identify the Optimum Database Query Execution Platform on GPU Assisted Database", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 214–225, 2020. doi: 10.25046/aj050328
69. Gillala Rekha, Shaveta Malik, Amit Kumar Tyagi, Meghna Manoj Nair, "Intrusion Detection in Cyber Security: Role of Machine Learning and Data Mining in Cyber Security", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 72–81, 2020. doi: 10.25046/aj050310
70. Ahmed EL Orche, Mohamed Bahaj, "Approach to Combine an Ontology-Based on Payment System with Neural Network for Transaction Fraud Detection", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 551–560, 2020. doi: 10.25046/aj050269
71. Bokyoon Na, Geoffrey C Fox, "Object Classifications by Image Super-Resolution Preprocessing for Convolutional Neural Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 476–483, 2020. doi: 10.25046/aj050261
72. Johannes Linden, Xutao Wang, Stefan Forsstrom, Tingting Zhang, "Productify News Article Classification Model with Sagemaker", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 13–18, 2020. doi: 10.25046/aj050202
73. Michael Wenceslaus Putong, Suharjito, "Classification Model of Contact Center Customers Emails Using Machine Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 1, pp. 174–182, 2020. doi: 10.25046/aj050123
74. Rehan Ullah Khan, Ali Mustafa Qamar, Mohammed Hadwan, "Quranic Reciter Recognition: A Machine Learning Approach", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 173–176, 2019. doi: 10.25046/aj040621
75. Mehdi Guessous, Lahbib Zenkouar, "An ML-optimized dRRM Solution for IEEE 802.11 Enterprise Wlan Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 19–31, 2019. doi: 10.25046/aj040603
76. Toshiyasu Kato, Yuki Terawaki, Yasushi Kodama, Teruhiko Unoki, Yasushi Kambayashi, "Estimating Academic results from Trainees’ Activities in Programming Exercises Using Four Types of Machine Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 321–326, 2019. doi: 10.25046/aj040541
77. Nindhia Hutagaol, Suharjito, "Predictive Modelling of Student Dropout Using Ensemble Classifier Method in Higher Education", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 206–211, 2019. doi: 10.25046/aj040425
78. Fernando Hernández, Roberto Vega, Freddy Tapia, Derlin Morocho, Walter Fuertes, "Early Detection of Alzheimer’s Using Digital Image Processing Through Iridology, An Alternative Method", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 3, pp. 126–137, 2019. doi: 10.25046/aj040317
79. Abba Suganda Girsang, Andi Setiadi Manalu, Ko-Wei Huang, "Feature Selection for Musical Genre Classification Using a Genetic Algorithm", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 2, pp. 162–169, 2019. doi: 10.25046/aj040221
80. Konstantin Mironov, Ruslan Gayanov, Dmiriy Kurennov, "Observing and Forecasting the Trajectory of the Thrown Body with use of Genetic Programming", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 1, pp. 248–257, 2019. doi: 10.25046/aj040124
81. Bok Gyu Han, Hyeon Seok Yang, Ho Gyeong Lee, Young Shik Moon, "Low Contrast Image Enhancement Using Convolutional Neural Network with Simple Reflection Model", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 1, pp. 159–164, 2019. doi: 10.25046/aj040115
82. Callum Owen-Bridge, Stewart Blakeway, Emanuele Lindo Secco, "An Integrated & Secure System for Wearable Devices", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 6, pp. 432–437, 2018. doi: 10.25046/aj030651
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. Mostafa Haghi, Kerstin Thurow, Regina Stoll, "A Ubiquitous and Configurable Wrist-Worn Sensor Node in Hazardous Gases Detection", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 5, pp. 248–257, 2018. doi: 10.25046/aj030530
86. 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
87. Sherif Said, Samer Al-Kork, Vishnu Nair, Itta Gowthami, Taha Beyrouthy, Xavier Savatier, M Fayek Abdrabbo, "Experimental Investigation of Human Gait Recognition Database using Wearable Sensors", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 4, pp. 201–210, 2018. doi: 10.25046/aj030418
88. 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
89. 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
90. 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
91. 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
92. 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
93. 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
94. 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
95. 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
96. 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
97. 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
98. 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
99. 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