Publications

Article

• [J30] Rojarath, A., Jantama, T., Srirat, W., Mookpakdee, N., khumwiso, K., Surinta, O. (2024). Factors Analysis Affecting Stroke Disease using Association Rules. Journal of Science and Technology Mahasakham University, 43(5), [In Thai] accepted (TCI 1)
• [J29] Gonwirat, S., Surinta, O. and Pawara. P. (2022). Fusion Convolutional Recurrent Neural Networks for Thai and English Video Subtitle Recognition, ICIC Express Letters, 16(12), 1331-1339. doi: 10.24507/icicel.16.12.1331 (Scopus Q3)
• [J28] Boonsirisumpun, N. and Surinta, O. (2022). Ensemble Multiple CNNs methods with partial Training Set for Vehicle Image Classification. Science, Engineering and Health Studies, 16, 22020001. (Scopus Q3)
• [J27] Gonwirat, S. and Surinta, O. (2022). DeblurGAN-CNN: Effective Image Denoising and Recognition for Noisy Handwritten Characters. IEEE Access, 10, 90133-90148. doi: 10.1109/ACCESS.2022.3201560. (ISI Q2, Impact factor 3.367)
• [J26] Noppitak, S. and Surinta, O. (2022). dropCyclic: Snapshot Ensemble Convolutional Neural Network Based on a New Learning Rate Schedule for Land Use Classification. IEEE Access, 10, 60725-60737. doi: 10.1109/ACCESS.2022.3180844. (ISI Q2, Impact factor 3.367
• [J25] Getsopon, W. and Surinta, O. (2022). Fusion Lightweight Convolutional Neural Networks and Sequence Learning Architectures for Violence Classification. ICIC Express Letters, Part B: Applications, 13(10), 1027-1035. doi: 10.24507/icicelb.13.10.1027. (Scopus Q3)
  
• [J24] Lata, S., Phiphitphatphaisit, S., Gonwirat, S. and Surinta, O.(2022). Dynamic Fingerspelling Recognition from Video Using Deep Learning Approach: From Detection to Recognition. ICIC Express Letters, Part B: Applications, 13(9), 949-957. doi: 10.24507/icicelb.13.09.949. (Scopus Q3)
• [J23] Enkvetchakul, P., Surinta, O. and Noppitak, S. (2022). Effective Data Resampling and Meta-Learning Convolutional Neural Networks for Diabetic Retinopathy Recognition. ICIC Express Letters, Part B: Applications, 13(9), 939-948. doi: 10.24507/icicelb.13.09.939. (Scopus Q3)
• [J22] Gonwirat, S. and Surinta, O. (2022). CycleAugment: Efficient Data Augmentation Strategy for Handwritten Text Recognition in Historical Document Images. Engineering and Applied Science Research, 49(4), 505-520. (Scopus Q3)
• [J21] Enkvetchakul, P. and Surinta, O. (2022). Effective Data Augmentation and Training Techniques for Improving Deep Learning in Plant Leaf Disease Recognition. Applied Science and Engineering Progress, 15(3), 3810. https://doi.org/10.14416/j.asep.2021.01.003. (Scopus Q2)
• [J20] Singkhornart, T. and Surinta, O. (2022). Multi-Language Video Subtitle Recognition with Convolutional Neural Network and Long Short-Term Memory Networks. ICIC Express Letters, 16(6), 647-655. doi: 10.24507/icicel.16.06.647. (Scopus Q3)
• [J19] Lata, S. and Surinta, O. (2022). An End-to-End Thai Fingerspelling Recognition Framework with Deep Convolutional Neural Networks. ICIC Express Letters, 16(5), 529-536. doi: 10.24507/icicel.16.05.529. (Scopus Q3)
• [J18] Enkvetchakul, P. and Surinta, O. (2022). Stacking Ensemble of Lightweight Convolutional Neural Networks for Plant Leaf Disease Recognition. ICIC Express Letters, 16(5), 521-528. doi: 10.24507/icicel.16.05.521. (Scopus Q3)
• [J17] Saichua, P. and Surinta, O. (2022). Comparative Study between Ensemble and Fusion Convolutional Neural Networks for Diabetic Retinopathy Classification. ICIC Express Letters, 16(4), 401-408. doi: 10.24507/icicel.16.04.401. (Scopus Q3)
• [J16] Pimcharee, K. and Surinta, O. (2022). Data Mining Approaches in Personal Loan Approval. Engineering Access, 8(1), 15-21. (TCI 2)
• [J15] Boonsirisumpun, N. and Surinta, O. (2022). Fast and Accurate Deep Learning Architecture on Vehicle Type Recognition. Current Applied Science and Technology, 22(1) (January-February 2022), 1-16. (Scopus Q4)
• [J14] Bunluewong, K. and Surinta, O. (2021). Semi-Automated Mushroom Cultivation House using Internet of Things. Engineering Accees, 7(2), 181-188. (TCI 2)
• [J13] Gonwirat, S. and Surinta, O. (2021). Optimal Weighted Parameters of Ensemble CNNs Based on a Differential Evolution Algorithm for Enhancing Pornographic Image Classification. Engineering and Applied Science Research, 48(5), 560-569. (Scopus Q4)
• [J12] Phiphitphatphaisit, S. and Surinta, O. (2021). Deep Feature Extraction Technique Based on Conv1D and LSTM Network for Food Image Recognition. Engineering and Applied Science Research, 48(5), 581-592. (Scopus Q4)
• [J11] Puangsuwan, T. and Surinta, O. (2021). Enhancement of Plant Disease Classification Based on Snapshot Ensemble Convolutional Neural Network. ICIC Express Letters, 15(6), 669-680. (Scopus Q3)
• [J10] Chompookham, T. and Surinta, O. (2021). Ensemble Methods with Deep Convolutional Neural Networks for Plant Leaf Recognition. ICIC Express Letters, 15(6), 553-565. (Scopus Q3)
• [J9] Noppitak, S. and Surinta, O. (2021). Ensemble Convolutional Network Architectures for Land Use Classification in Economic Crops Aerial Images. ICIC Express Letters, 15(6), 531-543. (Scopus Q3)
• [J8] Tantrabundit, P., Wongsim, M., and Surinta, O. (2020). Technology Acceptance Model: The Evidence of NodeMCU’s Mushroom House Control System in Thailand. Journal of Accountancy and Management, 12(1), 42-54. (TCI 2)
• [J7] Sanuksan, J. and Surinta, O. (2019). Deep Convolutional Neural Networks for Plant Recognition in Natural Environment. Journal of Science and Technology Mahasarakham University, 38(2), 113-124. (TCI 1)
• [J6] Raksaard, N. and Surinta, O. (2018). Comparative Study Between Local Descriptors and Deep Learning for Silk Pattern Image Retrieval. Journal of Science and Technology Mahasarakham University, 37(6), 736-746. (TCI 1)
• [J5] Surinta, O. (2018). Local Descriptors Approach for Pornographic Image Classification. Journal of Science and Technology Mahasarakham University, 37(3), 439-448. (TCI 1)
• [J4] Surinta, O. and Bunluewong, K. (2017). Handwritten Character Recognition Using k-Nearest Neighbors Classifier – A Survey. Journal of Science and Technology Mahasarakham University, 36(1), 117-129. (TCI 1)
• [J3] Surinta, O., Karaaba, M.F., Schomaker, L.R.B., and Wiering, M.A. (2015). Recognition of handwritten characters using local gradient feature descriptors. Engineering Applications of Artificial Intelligence, (45), 405-414. (SJR Q1, 0.87; IF 2.819)
• [J2] Codreanu, V., Dröge, B., Williams, D., Yasar, B., Yang, P., Liu, B., Dong, F., Surinta, O., Schomaker, L.R.B, Roerdink, J.B.T.M., and Wiering, M.A. (2014). Evaluating automatically parallelized versions of the Support Vector Machine. Concurrency and Computation: Practice and Experience, 1-21. (SJR Q2, 0.28; IF 1.114)
• [J1] Surinta, O. and Nitsuwat, S. (2005). Handwritten Thai Character Recognition Using Fourier Descriptors and Robust C-Prototype. Information Technology Journal, 92-96.

Proceeding

• [C31] Khruahong, S., Surinta, O., Lam, S.C. (2022). Sentiment Analysis of Local Tourism in Thailand from YouTube Comments Using BiLSTM. In: Surinta, O., Kam Fung Yuen, K. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2022. Lecture Notes in Computer Science(), vol 13651. Springer, Cham. https://doi.org/10.1007/978-3-031-20992-5_15
• [C30] Ardarsa, P. and Surinta, O. (2021). Water Quality Assessment in the Lam Pa Thao Dam, Chaiyaphum, Thailand with K-Means Clustering Algorithm, in Research, Invention and Innovation Congress (RI2C), 35-39. doi: https://doi.org/10.1109/RI2C51727.2021.9559811
• [C29] Khamket, T. and Surinta, O.(2021). Feature Extraction Efficient for Face Verification Based on Residual Network Architecture. in Multi-disciplinary Trends in Artificial Intelligence (MIWAI), The 14th International Conference on, 71-80. doi: https://doi.org/10.1007/978-3-030-80253-0_7, slide
• [C28] Khamket, T. and Surinta, O. (2020). Comparative Study Between Texture Feature and Local Feature Descriptors for Silk Fabric Pattern Image Recognition. in Information Science and System (ICISS), The 3rd International Conference on, 93-98. slide, video, doi: https://doi.org/10.1145/3388176.3388201
• [C27] Gonwirat, S. and Surinta, O. (2020). Improving Recognition of Thai Handwritten Characters with Deep Convolutional Neural Networks. in Information Science and System (ICISS), The 3rd International Conference on, 82-87. slide, video, doi: https://doi.org/10.1145/3388176.3388181
• [C26] Noppitak, S., Gonwirat, S., and Surinta, O. (2020). Instance Segmentation of Water Body from Aerial Image using Mask Region-based Convolutional Neural Network. in Information Science and System (ICISS), The 3rd International Conference on, 61-66. slide, video, doi: https://doi.org/10.1145/3388176.3388184
• [C25] Chompookham, T., Gonwirat, S., Lata, S., Phiphitphatphaisit, S. and Surinta, O. (2020). Plant Leaf Image Recognition using Multiple-grid Based Local Descriptor and Dimensionality Reduction Approach. in Information Science and System (ICISS), The 3rd International Conference on, 72-77. slide, video, doi: https://doi.org/10.1145/3388176.3388180
• [C24] Phiphitphatphaisit, S. and Surinta, O. (2020). Food Image Classification with Improved MobileNet Architecture and Data Augmentation. in Information Science and System (ICISS), The 3rd International Conference on, 51-56. slide, video, doi: https://doi.org/10.1145/3388176.3388179
• [C23] Khunthi, S., Saichua, P. and Surinta, O. (2019). Effective Face Verification Systems based on Histogram of Oriented Gradients and Deep Learning Techniques. in Artificial Intelligence and Natural Language Processing (iSAI-NLP), The 14th International Joint Symposium on, 215-219.  slide, doi: https://doi.org/10.1109/iSAI-NLP48611.2019.9045237
• [C22] Surinta, O. and Khamket, T. (2019). Gender Recognition from Facial Images using Local Gradient Feature Descriptors. in Artificial Intelligence and Natural Language Processing (iSAI-NLP), The 14th International Joint Symposium on, 346-351.  slide, doi: https://doi.org/10.1109/iSAI-NLP48611.2019.9045689
• [C21] Khruahong, S. and Surinta, O. (2019). Develop the Framework Conception for Hybrid Indoor Navigation for Monitoring Inside Building using Quadcopter. in Artificial Intelligence and Natural Language Processing (iSAI-NLP), The 14th International Joint Symposium on, 352-357.  slide, pdf, doi: https://doi.org/10.1109/iSAI-NLP48611.2019.9045445
• [C20] Surinta, O. and Khamket, T. (2019). Recognizing Pornographic Images using Deep Convolutional Neural Networks. in Digital Arts, Media and Technology (ICDAMT), The 4th International Conference on, 150-154.  slide, doi: https://doi.org/10.1109/ECTI-NCON.2019.8692296
• [C19] Roempluk, T. and Surinta, O. (2019). A Machine Learning Approach for Detecting Distributed Denial of Service Attacks. in Digital Arts, Media and Technology (ICDAMT), The 4th International Conference on, 146-149.  slide, doi: http://doi.org/10.1109/ECTI-NCON.2019.8692243
• [C18] Surinta, O. and Khruahong, S. (2019). Tracking People and Objects with an Autonomous Unmanned Aerial Vehicle using Face and Color Detection. in Digital Arts, Media and Technology (ICDAMT), The 4th International Conference on, 206-120.  slide, doi: http://doi.org/10.1109/ECTI-NCON.2019.8692269
• [C17] Wongsim, M., Sonthiprasat, R. and Surinta, O. (2018). Factors Influencing the Adoption of Agricultural Management Information Systems in Thailand. in The 3rd Technology Innovation Management and Engineering Science International Conference (TIMES-iCON), 1-4. doi: http://doi.org/10.1109/TIMES-iCON.2018.8621831
• [C16] Pawara, P., Okafor, E., Surinta, O., Schomaker, L.R.B. and Wiering, M.A. (2017). Comparing Local Descriptors and Bags of Visual Words to Deep Convolutional Neural Networks for Plant Recognition, in Pattern Recognition Applications and Methods (ICPRAM), The 6th International Conference on, 479-486.
• [C15] Okafor, E., Pawara, P., Karaaba, F., Surinta, O., Codreanu, V., Schomaker, L.R.B. and Wiering, M.A. (2016). Comparative Study Between Deep Learning and Bag of Visual Words for Wild-Animal Recognition, in Computational Intelligence (SSCI), IEEE Symposium Series on, 1-8.
• [C14] Karaaba, F., Surinta, O., Schomaker, L.R.B. and Wiering, M.A. (2016). Robust Face Identification with Small Sample Sizes using Bag of Words and Histogram of Oriented Gradients, in Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAAP), The 10th International Joint Conference on, 582-589.
• [C13] Karaaba, F., Surinta, O., Schomaker, L.R.B., and Wiering, M.A. (2015). Robust Face Recognition by Computing Distances From Multiple Histograms of Oriented Gradients, in Computational Intelligence in Biometrics and Identity Management (IEEE CIBIM), IEEE Symposium Series on, 2015, 203-209.
• [C12] Surinta, O., Karaaba, M.F., Mishra, T.K., Schomaker, L.R.B., and Wiering, M.A. (2015). Recognizing Handwritten Characters with Local Descriptors and Bags of Visual Words, in Engineering Applications of Neural Networks (EANN), The 16th International Conference on, 255-264. slide
• [C11] Karaaba, F., Surinta, O., Schomaker, L.R.B., and Wiering, M.A. (2015). In-Plane Rotational Alignment of Faces by Eye and Eye-Pair Detection, in Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAAP), The 10th International Joint Conference on, 392-399. poster
• [C10] Surinta, O., Holtkamp, M., Karaaba, M.F., van Oosten, JP., Schomaker, L.R.B., and Wiering, M.A. (2014). A* Path Planning for Line Segmentation of Handwritten Documents, in Frontiers in Handwriting Recognition (ICFHR), The 14th International Conference on, 175-180. poster
• [C9] Surinta, O., Schomaker, L.R.B., and Wiering, M.A. (2013) A comparison of feature and pixel-based methods for recognizing handwritten Bangla digits, in Document Analysis and Recognition (ICDAR), The 12 International Conference on, 165-169. poster
• [C8]Surinta, O., Schomaker, L.R.B. and Wiering, M.A. (2012). Handwritten Character Classification Using the Hotspot Feature Extraction Technique, in Pattern Recognition Applications and Methods (ICPRAM), The 1st International Conference on, 261-264. poster
• [C7] Surinta, O. (2009). Optimization of Line Segmentation Techniques for Thai Handwritten Documents, in Natural Language Processing (SNLP), The 8th International Symposium on, 180-183.
• [C6] Surinta, O. and Chamchong, R. (2008). Image Segmentation of Historical Handwriting from Palm Leaf Manuscripts, in Intelligent Information Processing, (288), 182-189.
• [C5] Leesom, N. and Surinta, O. (2007). Thai Handwritten Character Segmentation from Digital Image Documents, the 3rd Mahasarakham University Research Conference, 123-132.
• [C4] Surinta, O. (2007). Patient Record System, in Computing and Information Technology (NCCIT), The 3rd National Conference on, 1-6.
• [C3] Chamchong, R., Surinta, O., Udom, T., and Thongchan, P. (2007). Text Line Segmentation from Palm Leaf Manuscripts, in Computing and Information Technology (NCCIT), The 3rd National Conference on, 1-6.
• [C2] Surinta, O. and Jareanpon, C. (2006). Comparison of Image Analysis for Thai Handwritten Character Recognition, in Intelligent Information Processing, (228), 373-382.
• [C1] Jareanpon, C. and Surinta, O. (2006). Handwritten Recognition with Neural Network, in Advanced Image Technology (IWAIT), The International Workshop on, 1-6.