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รองศาสตราจารย์ ดร.อนุชิต จิตพัฒนกุล
อาจารย์

15 มกราคม 2564

รองศาสตราจารย์ ดร.อนุชิต  จิตพัฒนกุล

ตำแหน่ง: รองศาสตราจารย์
ห้องทำงาน: 78-1003
โทรศัพท์: 02-587-8258

อีเมล: anuchit.j@sci.kmutnb.ac.th

เว็บไซต์: https://www.researchgate.net/profile/Anuchit-Jitpattanakul

https://www.scopus.com/authid/detail.uri?authorId=36185528200

https://sciprofiles.com/profile/Anuchit



การศึกษา

  • วศ.ด. (วิศวกรรมคอมพิวเตอร์) ปีการศึกษาที่จบ พ.ศ.2554 จุฬาลงกรณ์มหาวิทยาลัย
  • วท.ม. (วิทยาการคณนา)  ปีการศึกษาที่จบ พ.ศ.2547 จุฬาลงกรณ์มหาวิทยาลัย
  • วท.บ. คณิตศาสตร์ประยุกต์ (เกียรตินิยม) ปีการศึกษาที่จบ พ.ศ.2543 สถาบันเทคโนโลยีพระจอมเกล้าพระนครเหนือ


สาขางานวิจัย

  • Wearable Sensor
  • Control Theory
  • Data Science and Innovations
  • Deep Learning and Time-Series Classification


ประสบการณ์การทำงาน 

  • 2561 – ปัจจุบัน   หัวหน้าศูนย์วิจัยเฉพาะทางนวัตกรรมอัจฉริยะและพลวัตรไม่เชิงเส้น สำนักวิจัยวิทยาศาสตร์และเทคโนโลยี มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ
  • 2558 – ปัจจุบัน   ประธานหลักสูตรคณิตศาสตร์เชิงวิทยาการคอมพิวเตอร์ ภาควิชาคณิตศาสตร์ คณะวิทยาศาสตร์ประยุกต์ มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ
  • 2562 – ปัจจุบัน   รองศาสตราจารย์ ภาควิชาคณิตศาสตร์    คณะวิทยาศาสตร์ประยุกต์ มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ
  • 2557 – 2562   ผู้ช่วยศาสตราจารย์ ภาควิชาคณิตศาสตร์ คณะวิทยาศาสตร์ประยุกต์ มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ
  • 2553 – 2557   อาจารย์ประจำ ภาควิชาคณิตศาสตร์ คณะวิทยาศาสตร์ประยุกต์ มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ


ผลงานตีพิมพ์ 

1)     Hnoohom, N.; Mekruksavanich, S.; Jitpattanakul, A. Physical Activity Recognition Based on Deep Learning Using Photoplethysmography and Wearable Inertial Sensors. Electronics 2023, 12, 693. https://doi.org/10.3390/electronics12030693 (SCIE-WOS : Impact Factor 2.690) (ที่มา: Journal Citation Reports)
2)     N. Hnoohom, S. Mekruksavanich and A. Jitpattanakul, "Pre-impact and impact fall detection based on a multimodal sensor using a deep residual network," Intelligent Automation & Soft Computing, vol. 36, no.3, pp. 3371–3385, 2023. (SCIE-WOS : Impact Factor 3.401) (ที่มา: Journal Citation Reports)
3)     S. Mekruksavanich and A. Jitpattanakul, "Automatic recognition of construction worker activities using deep learning approaches and wearable inertial sensors," Intelligent Automation & Soft Computing, vol. 36, no.2, pp. 2111–2128, 2023. (SCIE-WOS : Impact Factor 3.401) (ที่มา: Journal Citation Reports)
4)     N. Hnoohom, S. Mekruksavanich and A. Jitpattanakul, "An efficient ResNetSE architecture for smoking activity recognition from smartwatch," Intelligent Automation & Soft Computing, vol. 35, no.1, pp. 1245–1259, 2023 (SCIE-WOS : Impact Factor 3.401) (ที่มา: Journal Citation Reports)
5)     S. Mekruksavanich, A. Jitpattanakul, K. Sitthithakerngkiet, P. Youplao and P. Yupapin, "ResNet-SE: Channel Attention-Based Deep Residual Network for Complex Activity Recognition Using Wrist-Worn Wearable Sensors," in IEEE Access, vol. 10, pp. 51142-51154, 2022, doi: 10.1109/ACCESS.2022.3174124. (SCIE-WOS : Impact Factor 3.476) (ที่มา: Journal Citation Reports)
6)     Hnoohom, N.; Chotivatunyu, P.; Jitpattanakul, A. ACF: An Armed CCTV Footage Dataset for Enhancing Weapon Detection. Sensors 2022, 22, 7158. https://doi.org/10.3390/s22197158 . (SCIE-WOS : Impact Factor 3.847)(ที่มา: Journal Citation Reports)
7)     Mekruksavanich, S.; Jitpattanakul, FallNeXt: A Deep Residual Model based on Multi-Branch Aggregation for Sensor-based Fall Detection, ECTI Transactions on Computer and Information Technology, 2022, 16(4), pp. 352–364
8)     Mekruksavanich, S.; Hnoohom, N.; Jitpattanakul, A. A Hybrid Deep Residual Network for Efficient Transitional Activity Recognition Based on Wearable Sensors. Appl. Sci. 2022, 12, 4988. https://doi.org/10.3390/app12104988 (SCIE-WOS : Impact Factor 2.838)(ที่มา: Journal Citation Reports)
9)     Mekruksavanich, S.; Jitpattanakul, A. Deep Residual Network for Smartwatch-Based User Identification through Complex Hand Movements. Sensors 2022, 22, 3094. https://doi.org/10.3390/s22083094(SCIE-WOS : Impact Factor 3.847) (ที่มา: Journal Citation Reports)
10)  Mekruksavanich, S.; Jitpattanakul, A., “RNN-based deep learning for physical activity recognition using smartwatch sensors: A case study of simple and complex activity recognition”, Mathematical Biosciences and Engineering, 2022, 19(6): 5671-5698. doi: 10.3934/mbe.2022265
11)  Mekruksavanich, S.; Jitpattanakul, A., “Multimodal Wearable Sensing for Sport-Related Activity Recognition Using Deep Learning Networks”, Journal of Advances in Information Technology, 2022, 13(2), pp. 132–138
12)  Mekruksavanich, S.; Jitpattanakul, A., “Forecasting Mobility Trends in Southeast Asia during the Coronavirus (Covid-19) Pandemic by Machine Learning Approaches”, International Journal of Geoinformaticsthis link is disabled, 2021, 17(5), pp. 45–53
13)  Mekruksavanich, S.; Jitpattanakul, A. Deep Learning Approaches for Continuous Authentication Based on Activity Patterns Using Mobile Sensing. Sensors 2021, 21, 7519. https://doi.org/10.3390/s21227519 (SCIE-WOS : Impact Factor 3.576) (ที่มา: Journal Citation Reports)
14)  Mekruksavanich, S.; Jitpattanakul, A. Deep Convolutional Neural Network with RNNs for Complex Activity Recognition Using Wrist-Worn Wearable Sensor Data. Electronics 2021, 10, 1685. https://doi.org/10.3390/electronics10141685 (SCIE-WOS : Impact Factor 2.397) (ที่มา: Journal Citation Reports)
15)  Mekruksavanich, S.; Jitpattanakul, A. LSTM Networks Using Smartphone Data for Sensor-Based Human Activity Recognition in Smart Homes. Sensors 2021, 21, 1636. https://doi.org/10.3390/s21051636 (SCIE-WOS : Impact Factor 3.275) (ที่มา: Journal Citation Reports)
16)  Mekruksavanich, S., Jitpattanakul, A., “Biometric User Identification Based on Human Activity Recognition Using Wearable Sensors: An Experiment Using Deep Learning Models” Electronics, 2020, 10(3), 308 (SCIE-WOS : Impact Factor 2.412) (ที่มา: Journal Citation Reports)
17)  Mekruksavanich, S., Jitpattanakul, A., Youplao, P., Yupapin, P., “Enhanced hand-oriented activity recognition based on smartwatch sensor data using LSTMs” Symmetry, 2020, 12(9), 1570 (SCIE-WOS : Impact Factor 2.645)(ที่มา: Journal Citation Reports)
18)  Jitpattanakul A. and Pukdeboon C., “Adaptive Output Feedback Integral Sliding Mode Attitude Tracking Control of Spacecraft without Unwinding”, ADVANCES IN MECHANICAL ENGINEERING,   Volume: 9,   Issue: 7,     Article Number: 1687814017710406,   Published: JULY 21 2017, 16 pages. (SCIE-WOS : Impact Factor 0.827) (ที่มา: Journal Citation Reports)
19)  Pukdeboon C. and Jitpattanakul A., “Anti-Unwinding Attitude Control with Fixed-Time Convergence for a Flexible Spacecraft”, INTERNATIONAL JOURNAL OF AEROSPACE ENGINEERING,     Article Number: 5018323,   Published: 2017, 13 pages. (SCIE-WOS : Impact Factor 1.144) (ที่มา: Journal Citation Reports)
20)  Pukdeboon C. and Jitpattanakul A., “Disturbance Observer-based Second Order Sliding Mode Attitude Tracking Control for Flexible Spacecraft”, KYBERNETIKA,   Volume: 53,   Issue: 4,   Pages: 653-678,   Published: 2017, 26 pages. (SCIE-WOS : Impact Factor 0.379) (ที่มา: Journal Citation Reports)
21)  Amphawan K., Lenca P., Jitpattanakul A., and Surarerks A., “Mining High Utility Itemsets with Regular Occurrence”, Journal of ICT research and applications, 10 (2), pp.153 – 176, 2016
22)  Jitpattanakul A. and Surarerks A. (2013). “The Study of Learnability of the Class of K-Acceptable Languages on Gold’s Learning Model.”, Chiang Mai Journal of Science. Vol. 40, No. 2 : 248-260.
23)  Jitpattanakul A. and Pukdeboon C. (2013). “Optimal Attitude Control for Rigid Spacecraft using Successive  Approximation Approach”, Far East Journal of Mathematical Sciences, Vol. 74, No.1 : 37-52.
24)  Pukdeboon C. and Jitpattanakul A. (2013). “Finite-time Anti-disturbance Inverse Optimal Attitude Tracking Control of Flexible Spacecraft”, Mathematical Problems in Engineering,  Article ID: 967574.
25)  Jitpattanakul A. (2012) “Learnability of the Class of Strictly K-acceptable Languages", Far East Journal of Mathematical Sciences, Vol. 71, No. 1 : 169-184
26)  Jitpattanakul A. and Surarerks A. (2011). “Characteristic Sets for Learning k-Acceptable Languages.” ECTI Transactions on Computer and Information Technology, Vol. 5, No. 1 : 38-44.


การเผยแพร่ประชุมวิชาการ 

1)     S. Mekruksavanich, P. Jantawong and A. Jitpattanakul, "Pre-Impact Fall Detection Based on Wearable Inertial Sensors using Hybrid Deep Residual Neural Network," 2022 6th International Conference on Information Technology (InCIT), Nonthaburi, Thailand, 2022, pp. 450-453, doi: 10.1109/InCIT56086.2022.10067733.
2)     S. Mekruksavanich, P. Jantawong and A. Jitpattanakul, "Accuracy Improvement of Complex Sensor-based Activity Recognition Using Hybrid CNN," 2022 6th International Conference on Information Technology (InCIT), Nonthaburi, Thailand, 2022, pp. 454-457, doi: 10.1109/InCIT56086.2022.10067453.
3)     S. Mekruksavanich, P. Jantawong and A. Jitpattanakul, "ResNet-based Deep Neural Network using Transfer Learning for Animal Activity Recognition," 2022 6th International Conference on Information Technology (InCIT), Nonthaburi, Thailand, 2022, pp. 445-449, doi: 10.1109/InCIT56086.2022.10067405.
4)     P. Jantawong, S. Mekruksavanich and A. Jitpattanakul, "Monitoring System of Wearable Sensor Signal in Rehabilitation Using Efficient Deep Learning Approaches," 2022 26th International Computer Science and Engineering Conference (ICSEC), Sakon Nakhon, Thailand, 2022, pp. 361-365, doi: 10.1109/ICSEC56337.2022.10049326.
5)     P. Jantawong, N. Hnoohom, A. Jitpattanakul and S. Mekruksavanich, "Time Series Classification Using Deep Learning for HAR Based on Smart Wearable Sensors," 2022 26th International Computer Science and Engineering Conference (ICSEC), Sakon Nakhon, Thailand, 2022, pp. 357-360, doi: 10.1109/ICSEC56337.2022.10049357.
6)     Sakorn Mekruksavanich, Ponnipa Jantawong, Anuchit Jitpattanakul, “A Deep Learning-based Model for Human Activity Recognition using Biosensors embedded into a Smart Knee Bandage”, Procedia Computer Science, Volume 214, 2022, Pages 621-627, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2022.11.220.
7)     Mekruksavanich, S., Jantawong, P., Hnoohom, N., Jitpattanakul, A. (2022). Recognizing Driver Activities Using Deep Learning Approaches Based on Smartphone Sensors. 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_13
8)     Mekruksavanich, S., Jantawong, P., Hnoohom, N., Jitpattanakul, A. (2022). Wearable Fall Detection Based on Motion Signals Using Hybrid Deep Residual Neural Network. 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_19
9)     Hnoohom, N., Chotivatunyu, P., Mekruksavanich, S., Jitpattanakul, A. (2022). Multi-resolution CNN for Lower Limb Movement Recognition Based on Wearable Sensors. 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_10
10)  Hnoohom, N., Maitrichit, N., Mekruksavanich, S., Jitpattanakul, A. (2022). Hierarchical Human Activity Recognition Based on Smartwatch Sensors Using Branch Convolutional Neural Networks. 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_5
11)  N. Hnoohom, P. Chotivatunyu, S. Mekruksavanich and A. Jitpattanakul, "Recognizing Stationary and Locomotion Activities using LSTM-XGB with Smartphone Sensors," 2022 IEEE 13th International Conference on Software Engineering and Service Science (ICSESS), 2022, pp. 74-79, doi: 10.1109/ICSESS54813.2022.9930285.
12)  S. Mekruksavanich, P. Jantawong, N. Hnoohom and A. Jitpattanakul, "Badminton Activity Recognition and Player Assessment based on Motion Signals using Deep Residual Network," 2022 IEEE 13th International Conference on Software Engineering and Service Science (ICSESS), 2022, pp. 80-83, doi: 10.1109/ICSESS54813.2022.9930147.
13)  S. Mekruksavanich, P. Jantawong, N. Hnoohom and A. Jitpattanakul, "Refined LSTM Network for Sensor-based Human Activity Recognition in Real World Scenario," 2022 IEEE 13th International Conference on Software Engineering and Service Science (ICSESS), 2022, pp. 256-259, doi: 10.1109/ICSESS54813.2022.9930218.
14)  N. Hnoohom, N. Maitrichit, S. Mekruksavanich and A. Jitpattanakul, "A Deep Residual Network for Recognizing Transportation Vehicles using Smartphone Sensors," 2022 IEEE 13th International Conference on Software Engineering and Service Science (ICSESS), 2022, pp. 209-213, doi: 10.1109/ICSESS54813.2022.9930314.
15)  N. Hnoohom, N. Maitrichit, K. Wongpatikaseree, S. Yuenyong, S. Mekruksavanich and A. Jitpattanakul, "Visual Explanations of ResNet 101 for Blister Package Classification," 2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics (RI2C), 2022, pp. 148-152, doi: 10.1109/RI2C56397.2022.9910317.
16)  S. Mekruksavanich, P. Jantawong, N. Hnoohom and A. Jitpattanakul, "Classification of Physical Exercise Activity from ECG, PPG and IMU Sensors using Deep Residual Network," 2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics (RI2C), 2022, pp. 130-134, doi: 10.1109/RI2C56397.2022.9910287.
17)  S. Mekruksavanich, P. Jantawong, N. Hnoohom and A. Jitpattanakul, "Hyperparameter Tuning in Convolutional Neural Network for Face Touching Activity Recognition using Accelerometer Data," 2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics (RI2C), 2022, pp. 101-105, doi: 10.1109/RI2C56397.2022.9910262.
18)  S. Mekruksavanich, P. Jantawong, N. Hnoohom and A. Jitpattanakul, "Deep Learning Networks for Eating and Drinking Recognition based on Smartwatch Sensors," 2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics (RI2C), 2022, pp. 106-111, doi: 10.1109/RI2C56397.2022.9910318.
19)  N. Hnoohom, P. Chotivatunyu, S. Yuenyong, K. Wongpatikaseree, S. Mekruksavanich and A. Jitpattanakul, "Object Identification and Localization of Visual Explanation for Weapon Detection," 2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics (RI2C), 2022, pp. 144-147, doi: 10.1109/RI2C56397.2022.9910301.
20)  S. Mekruksavanich, P. Jantawong, N. Hnoohom and A. Jitpattanakul, "The Effect of Sensor Placement for Accurate Fall Detection based on Deep Learning Model," 2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics (RI2C), 2022, pp. 124-129, doi: 10.1109/RI2C56397.2022.9910267.
21)  N. Hnoohom, N. Maitrichit, S. Mekruksavanich and A. Jitpattanakul, "Deep Learning Approaches for Unobtrusive Human Activity Recognition using Insole-based and Smartwatch Sensors," 2022 3rd International Conference on Big Data Analytics and Practices (IBDAP), 2022, pp. 1-5, doi: 10.1109/IBDAP55587.2022.9907414.
22)  S. Mekruksavanich, P. Jantawong, N. Hnoohom and A. Jitpattanakul, "ResNet-based Network for Recognizing Daily and Transitional Activities based on Smartphone Sensors," 2022 3rd International Conference on Big Data Analytics and Practices (IBDAP), 2022, pp. 27-30, doi: 10.1109/IBDAP55587.2022.9907111.
23)  N. Hnoohom, P. Chotivatunyu, S. Mekruksavanich and A. Jitpattanakul, "Recognition of Shoulder Exercise Activity Based on EfficientNet Using Smartwatch Inertial Sensors," 2022 3rd International Conference on Big Data Analytics and Practices (IBDAP), 2022, pp. 6-10, doi: 10.1109/IBDAP55587.2022.9907217.
24)  S. Mekruksavanich, P. Jantawong, N. Hnoohom and A. Jitpattanakul, "Heterogeneous Recognition of Human Activity with CNN and RNN-based Networks using Smartphone and Smartwatch Sensors," 2022 3rd International Conference on Big Data Analytics and Practices (IBDAP), 2022, pp. 21-26, doi: 10.1109/IBDAP55587.2022.9907460.
25)  S. Mekruksavanich, P. Jantawong, N. Hnoohom and A. Jitpattanakul, "Wearable-based Activity Recognition of Construction Workers using LSTM Neural Networks," 2022 37th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC), 2022, pp. 1-4, doi: 10.1109/ITC-CSCC55581.2022.9894868.
26)  S. Mekruksavanich, P. Jantawong, N. Hnoohom and A. Jitpattanakul, "Automatic Fall Detection using Deep Neural Networks with Aggregated Residual Transformation," 2022 37th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC), 2022, pp. 811-814, doi: 10.1109/ITC-CSCC55581.2022.9895054.
27)  S. Mekruksavanich, P. Jantawong and A. Jitpattanakul, "Recognition of Human Activity from ECG and IMU Signals Using Deep Learning Networks," 2022 IEEE Region 10 Symposium (TENSYMP), 2022, pp. 1-5, doi: 10.1109/TENSYMP54529.2022.9864495.
28)  S. Mekruksavanich, P. Jantawong and A. Jitpattanakul, "Smartwatch-based Eating Detection and Cutlery Classification using a Deep Residual Network with Squeeze-and-Excitation Module," 2022 45th International Conference on Telecommunications and Signal Processing (TSP), 2022, pp. 301-304, doi: 10.1109/TSP55681.2022.9851333.
29)  S. Mekruksavanich, P. Jantawong and A. Jitpattanakul, "Deep Residual Networks for Human Activity Recognition based on Biosignals from Wearable Devices," 2022 45th International Conference on Telecommunications and Signal Processing (TSP), 2022, pp. 310-313, doi: 10.1109/TSP55681.2022.9851249.
30)  S. Mekruksavanich, P. Jantawong, N. Hnoohom and A. Jitpattanakul, "A Novel Deep BiGRU-ResNet Model for Human Activity Recognition using Smartphone Sensors," 2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE), 2022, pp. 1-5, doi: 10.1109/JCSSE54890.2022.9836276.
31)  S. Mekruksavanich, P. Jantawong, N. Hnoohom and A. Jitpattanakul, "Deep Learning Models for Daily Living Activity Recognition based on Wearable Inertial Sensors," 2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE), 2022, pp. 1-5, doi: 10.1109/JCSSE54890.2022.9836239.
32)  S. Mekruksavanich, N. Hnoohom and A. Jitpattanakul, "A Deep Residual-based Model on Multi-Branch Aggregation for Stress and Emotion Recognition through Biosignals," 2022 19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2022, pp. 1-4, doi: 10.1109/ECTI-CON54298.2022.9795449.
33)  S. Mekruksavanich, P. Jantawong, I. You and A. Jitpattanakul, "A Hybrid Deep Neural Network for Classifying Transportation Modes based on Human Activity Vibration," 2022 14th International Conference on Knowledge and Smart Technology (KST), 2022, pp. 114-118, doi: 10.1109/KST53302.2022.9729079.
34)  S. Mekruksavanich, P. Jantawong and A. Jitpattanakul, "Comparative Analysis of CNN-based Deep Learning Approaches on Complex Activity Recognition," 2022 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON), 2022, pp. 338-341, doi: 10.1109/ECTIDAMTNCON53731.2022.9720320.
35)  S. Mekruksavanich, P. Jantawong and A. Jitpattanakul, "LSTM-XGB: A New Deep Learning Model for Human Activity Recognition based on LSTM and XGBoost," 2022 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON), 2022, pp. 342-345, doi: 10.1109/ECTIDAMTNCON53731.2022.9720409.
36)  S. Mekruksavanich, P. Jantawong and A. Jitpattanakul, "Recognition of Complex Human Activities for Wellness Management from Smartwatch using Deep Residual Neural Network," 2022 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON), 2022, pp. 350-353, doi: 10.1109/ECTIDAMTNCON53731.2022.9720389.
37)  S. Mekruksavanich, P. Jantawong and A. Jitpattanakul, "Enhancement of Sensor-based User Identification using Data Augmentation Techniques," 2022 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON), 2022, pp. 333-337, doi: 10.1109/ECTIDAMTNCON53731.2022.9720293.
38)  S. Mekruksavanich, P. Jantawong and A. Jitpattanakul, "Deep Learning-based Action Recognition for Pedestrian Indoor Localization using Smartphone Inertial Sensors," 2022 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON), 2022, pp. 346-349, doi: 10.1109/ECTIDAMTNCON53731.2022.9720358.
39)  S. Mekruksavanich, P. Jantawong, A. Charoenphol and A. Jitpattanakul, "Fall Detection from Smart Wearable Sensors using Deep Convolutional Neural Network with Squeeze-and-Excitation Module," 2021 25th International Computer Science and Engineering Conference (ICSEC), 2021, pp. 448-453, doi: 10.1109/ICSEC53205.2021.9684626.
40)  S. Mekruksavanich, P. Jantawong, N. Hnoohom and A. Jitpattanakul, "Bidirectional Gate Recurrent Unit Neural Network for Recognizing Face Touching Activities using Smartwatch Sensors," 2021 25th International Computer Science and Engineering Conference (ICSEC), 2021, pp. 454-458, doi: 10.1109/ICSEC53205.2021.9684642.
41)  N. Hnoohom, N. Maitrichit, P. Chotivatunyu, V. Sornlertlamvanich, S. Mekruksavanich and A. Jitpattanakul, "Blister Package Classification Using ResNet-101 for Identification of Medication," 2021 25th International Computer Science and Engineering Conference (ICSEC), 2021, pp. 406-410, doi: 10.1109/ICSEC53205.2021.9684590.
42)  P. Jantawong, N. Hnoohom, A. Jitpattanakul and S. Mekruksavanich, "A Lightweight Deep Learning Network for Sensor-based Human Activity Recognition using IMU sensors of a Low-Power Wearable Device," 2021 25th International Computer Science and Engineering Conference (ICSEC), 2021, pp. 459-463, doi: 10.1109/ICSEC53205.2021.9684631.
43)  N. Hnoohom, P. Chotivatunyu, N. Maitrichit, V. Sornlertlamvanich, S. Mekruksavanich and A. Jitpattanakul, "Weapon Detection Using Faster R-CNN Inception-V2 for a CCTV Surveillance System," 2021 25th International Computer Science and Engineering Conference (ICSEC), 2021, pp. 400-405, doi: 10.1109/ICSEC53205.2021.9684649.
44)  S. Mekruksavanich and A. Jitpattanakul, "Detection of Freezing of Gait in Parkinson's Disease by Squeeze-and-Excitation Convolutional Neural Network with Wearable Sensors," 2021 15th International Conference on Open Source Systems and Technologies (ICOSST), 2021, pp. 1-5, doi: 10.1109/ICOSST53930.2021.9683890.
45)  N. Hnoohom, A. Jitpattanakul, I. You and S. Mekruksavanich, "Deep Learning Approach for Complex Activity Recognition using Heterogeneous Sensors from Wearable Device," 2021 Research, Invention, and Innovation Congress: Innovation Electricals and Electronics (RI2C), 2021, pp. 60-65, doi: 10.1109/RI2C51727.2021.9559773.
46)  P. Jantawong, A. Jitpattanakul and S. Mekruksavanich, "Enhancement of Human Complex Activity Recognition using Wearable Sensors Data with InceptionTime Network," 2021 2nd International Conference on Big Data Analytics and Practices (IBDAP), 2021, pp. 12-16, doi: 10.1109/IBDAP52511.2021.9552133.
47)  S. Mekruksavanich, P. Jantawong and A. Jitpattanakul, "A Lightweight Deep Convolutional Neural Network with Squeeze-and-Excitation Modules for Efficient Human Activity Recognition using Smartphone Sensors," 2021 2nd International Conference on Big Data Analytics and Practices (IBDAP), 2021, pp. 23-27, doi: 10.1109/IBDAP52511.2021.9552111.
48)  S. Mekruksavanich, K. Sooksomsatarn and A. Jitpattanakul, "Flooding Forecasting System Based on Water Monitoring with IoT Technology," 2021 IEEE 12th International Conference on Software Engineering and Service Science (ICSESS), 2021, pp. 247-250, doi: 10.1109/ICSESS52187.2021.9522266.
49)  S. Mekruksavanich and A. Jitpattanakul, "Recognition of Real-life Activities with Smartphone Sensors using Deep Learning Approaches," 2021 IEEE 12th International Conference on Software Engineering and Service Science (ICSESS), 2021, pp. 243-246, doi: 10.1109/ICSESS52187.2021.9522231.
50)                 S. Mekruksavanich and A. Jitpattanakul, "Sensor-based Complex Human Activity Recognition from Smartwatch Data using Hybrid Deep Learning Network," 2021 36th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC), 2021, pp. 1-4, doi: 10.1109/ITC-CSCC52171.2021.9501477.
51)  S. Mekruksavanich, C. Promsakon and A. Jitpattanakul, "Location-based Daily Human Activity Recognition using Hybrid Deep Learning Network," 2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE), 2021, pp. 1-5, doi: 10.1109/JCSSE53117.2021.9493807.
52)  P. Rojanavasu, A. Jitpattanakul and S. Mekruksavanich, "Comparative Analysis of LSTM-based Deep Learning Models for HAR using Smartphone Sensor," 2021 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunication Engineering, 2021, pp. 269-272, doi: 10.1109/ECTIDAMTNCON51128.2021.9425733.
53)  S. Wanriko, N. Hnoohom, K. Wongpatikaseree, A. Jitpattanakul and O. Musigavong, "Risk Assessment of Pregnancy-induced Hypertension Using a Machine Learning Approach," 2021 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunication Engineering, 2021, pp. 233-237, doi: 10.1109/ECTIDAMTNCON51128.2021.9425764.
54)  S. Mekruksavanich and A. Jitpattanakul, "A Multichannel CNN-LSTM Network for Daily Activity Recognition using Smartwatch Sensor Data," 2021 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunication Engineering, 2021, pp. 277-280, doi: 10.1109/ECTIDAMTNCON51128.2021.9425769.
55)  S. Mekruksavanich, A. Jitpattanakul and P. Thongkum, "Metrics-based Knowledge Analysis in Software Design for Web-based Application Security Protection," 2021 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunication Engineering, 2021, pp. 281-284, doi: 10.1109/ECTIDAMTNCON51128.2021.9425703.
56)  S. Mekruksavanich, A. Jitpattanakul and P. Thongkum, "Placement Effect of Motion Sensors for Human Activity Recognition using LSTM Network," 2021 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunication Engineering, 2021, pp. 273-276, doi: 10.1109/ECTIDAMTNCON51128.2021.9425719.
57)  Hnoohom, N., Jitpattanakul, A., Mekruksavanich, S. “Real-life Human Activity Recognition with Tri-axial Accelerometer Data from Smartphone using Hybrid Long Short-Term Memory Networks”, 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing and 2020 International Conference on Artificial Intelligence and Internet of Things (iSAI-NLP-AIoT), pp. 161-166.
58)  S. Mekruksavanich and A. Jitpattanakul, "Smartwatch-based Human Activity Recognition Using Hybrid LSTM Network," 2020 IEEE Sensors, Rotterdam, Netherlands, 2020, pp. 1-4, doi: 10.1109/SENSORS47125.2020.9278630.
59)  Mekruksavanich, S. and Jitpattanakul, A., “Improving Biometric User Identification based on Walking Patterns using Convolutional Neural Network and Data Augmentation Techniques”, 2020 International Conference on ICT for Sustainable Development, India, 2020.
60)  Mekruksavanich, S., Jitpattanakul, A., “Exercise Activity Recognition with Surface Electromyography Sensor using Machine Learning Approach”, 2020 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2020, 2020, pp. 75–78, 9090711
61)  Mekruksavanich, S., Jitpattanakul, A., Huoohom N., “Negative Emotion Recognition using Deep Learning for Thai Language”, 2020 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2020, 2020, pp. 71–74, 9090768
62)  Mekruksavanich S., Jitpattanakul A., “Classification of gait pattern with wearable sensing data”, ECTI DAMT-NCON 2019 - 4th International Conference on Digital Arts, Media and Technology and 2nd ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, 2019, pp. 137–141, 8692229
63)  Mekruksavanich S., Huoohom N., Jitpattanakul A., “Smartwatch-based Sitting Detection with Human Activity Recognition for Office Workers Syndrome”, The International ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI-NCON 2018), : p.183-187.
64)  Huoohom N., Jitpattanakul A., Inluergsri P., Wongbudsri P., Ployput W., “Multi-Sensor based Fall Detection and Activity Daily Living Classification by using Ensemble Learning”, The International ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI-NCON 2018), : p.63-67.
65)  Inluergsri P., Wongbudsri P., Ployput W., Jitpattanakul A., "Comparison of Machine Learning Algorithms for Human Physical Activity Recognition on Smartphones", International Conference of Creative Media and Innovation Conference (CMIC2017).
66)  Huoohom N., Jitpattanakul A., “Comparison of Ensemble Learning Algorithms for Cataract Detection from Fundus Images”, Proceeding of the 21sh International Computer Science and Engineering Conference 2017,: p.56-59.
67)  Huoohom N., Mekruksavanich S., Jitpattanakul A., “Human Activity Recognition Using Triaxial Acceleration Data from Smartphone and Ensemble Learning”, Proceeding of the 13th International Conference on Signal-Image Technology and Internet-Based Systems 2017,: p.408-412.
68)  Jitpattanakul A., "Closure properties of the strictly k-acceptable". The Eleventh International Symposium on Natural Language Processing (SNLP-2016), 2016.
69)  Jitpattanakul A., "Finite automata for ECG signal classification by using grammatical inference". The 8th National Science Research Conference, 2016.
70)  Jitpattanakul A. and Surarerks A. (2009). “An algorithm for learning k-DFA from informant.”, Proceeding of the 13th International Annual Symposium on Computational Science and Engineering, : 31-36.


บทความวิชาการ

1. อนุชิต จิตพัฒนกุล, “การเรียนรู้ภาษาสม่ำเสมอโดยการอนุมานเชิงไวยากรณ์”,   วารสารวิชาการเทคโนโลยีสารสนเทศและการสื่อสาร ปีที่ 1 ฉบับที่ 1 ตุลาคม 2556 – มีนาคม 2557, คณะเทคโนโลยีสารสนเทศและการสื่อสาร, มหาวิทยาลัยพะเยา, หน้า 56-67.
2. อนุชิต จิตพัฒนกุล, “เทคนิคการลดรูปสำหรับการพิสูจน์ความสามารถการเรียนรู้ของระดับชั้นภาษารูปนัย”, วารสารวิทยาศาสตร์ประยุกต์ ปีที่ 11 ฉบับที่ 2 [2555], คณะวิทยาศาสตร์ประยุกต์, มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ, 16 หน้า.
3. Jitpattanakul A., "Closure properties of the classes of languages recognized by k-edge finite state automata". Journal of Information and Communication Technology University of Prayao, Vol.1 No.2, 2014.


ตำรา

1. ทฤษฎีการคำนวณ, ศูนย์ผลิตตำราเรียน มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ, พฤษภาคม 2562
2.  คณิตศาสตร์เต็มหน่วยและการประยุกต์, ศูนย์ผลิตตำราเรียน มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ, กุมภาพันธ์ 2561


รางวัลที่ได้รับ

    1. Gold Medal Award จากงานประกวด “The International Exhibition of Invention of Geneva 2021”
    2. รางวัลผู้ที่สร้างชื่อเสียงให้กับมหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ ประจำปี 2560
    3. Best Paper Award, ECTI DAMT-NCON 2019 - 4th International Conference on Digital Arts, Media and Technology and 2nd ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, 2019