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ORCIDSakorn Mekruksavanich
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2020 – today
- 2024
[j11]Worawith Sangkatip
, Phatthanaphong Chomphuwiset, Kaveepoj Bunluewong, Sakorn Mekruksavanich, Emmanuel Okafor, Olarik Surinta:
Improving Neural Network-Based Multi-Label Classification With Pattern Loss Penalties. IEEE Access 12: 52237-52248 (2024)- [j10]Sakorn Mekruksavanich, Anuchit Jitpattanakul:
Identifying Smartphone Users Based on Activities in Daily Living Using Deep Neural Networks. Inf. 15(1): 47 (2024) - [j9]Sakorn Mekruksavanich, Wikanda Phaphan, Narit Hnoohom, Anuchit Jitpattanakul:
Recognition of sports and daily activities through deep learning and convolutional block attention. PeerJ Comput. Sci. 10: e2100 (2024) - [j8]Sakorn Mekruksavanich, Anuchit Jitpattanakul:
Deep Residual Network with a CBAM Mechanism for the Recognition of Symmetric and Asymmetric Human Activity Using Wearable Sensors. Symmetry 16(5): 554 (2024) - [c31]Wikanda Phaphan, Anuchit Jitpattanakul, Supika Huadsri, Kamon Budsaba, Wiyada Phapan, Sakorn Mekruksavanich:
Modeling Life Insurance Business Growth in Thailand using SARIMAX and Multilayer Perceptron. ICCAE 2024: 146-151 - [c30]Sakorn Mekruksavanich, Anuchit Jitpattanakul:
Elevating Wearable Sensor Authentication with Hybrid Deep Learning and Squeeze-and-Excitation. ICCSA (2) 2024: 186-197 - [c29]Sakorn Mekruksavanich, Anuchit Jitpattanakul:
Optimizing On-Body Sensor Placements for Deep Learning-Driven Human Activity Recognition. ICCSA (2) 2024: 327-338 - [c28]Narit Hnoohom, Sakorn Mekruksavanich, Thanaruk Theeramunkong, Anuchit Jitpattanakul:
Efficient Residual Neural Network for Human Activity Recognition using WiFi CSI Signals. ICIEI 2024 - [c27]Sakorn Mekruksavanich, Wikanda Phaphan, Anuchit Jitpattanakul:
A Hybrid Residual CNN with Channel Attention Mechanism for Continuous User Identification Using Wearable Motion Sensors. TSP 2024: 143-146 - [c26]Sakorn Mekruksavanich, Wikanda Phaphan, Anuchit Jitpattanakul:
Leveraging Residual Deep Neural Networks and Multi-Device Sensors for Heterogeneous Activity Recognition. TSP 2024: 156-159 - [c25]Sakorn Mekruks Avanich, Wikanda Phaphan, Anuchit Jitpattanakul:
Deep Learning Networks for Human Knee Abnormality Detection Based on Surface EMG Signals. TSP 2024: 160-163 - [c24]Sakorn Mekruksavanich, Wikanda Phaphan, Anuchit Jitpattanakul:
Harnessing Deep Learning for Activity Recognition in Seniors' Daily Routines with Wearable Sensors. TSP 2024: 164-167 - [c23]Sakorn Mekruksavanich, Wikanda Phaphan, Anuchit Jitpattanakul:
Detecting Face-Touching Gestures with Smartwatches and Deep Learning Networks. TSP 2024: 249-252 - 2023
- [j7]Sakorn Mekruksavanich, Anuchit Jitpattanakul:
A Deep Learning Network with Aggregation Residual Transformation for Human Activity Recognition Using Inertial and Stretch Sensors. Comput. 12(7): 141 (2023) - [j6]Sakorn Mekruksavanich, Anuchit Jitpattanakul:
Effective Detection of Epileptic Seizures through EEG Signals Using Deep Learning Approaches. Mach. Learn. Knowl. Extr. 5(4): 1937-1952 (2023) - [c22]Anuchit Jitpattanakul, Sakorn Mekruksavanich:
Enhancing Sensor-Based Human Activity Recognition using Efficient Channel Attention. SENSORS 2023: 1-4 - [c21]Sakorn Mekruksavanich, Anuchit Jitpattanakul:
Classifying Activities of Electrical Line Workers Based on Deep Learning Approaches Using Wrist-Worn Sensor. JCSSE 2023: 270-274 - [c20]Sakorn Mekruksavanich, Datchakorn Tancharoen, Anuchit Jitpattanakul:
Human Activity Recognition in Logistics Using Wearable Sensors and Deep Residual Network. TENCON 2023: 194-198 - [c19]Sakorn Mekruksavanich, Anuchit Jitpattanakul:
Deep Learning Networks for Complex Activity Recognition Based on Wrist-Worn Sensor. TENCON 2023: 243-248 - [c18]Sakorn Mekruksavanich, Anuchit Jitpattanakul:
Deep Learning Approaches for Epileptic Seizures Recognition based on EEG Signal. TSP 2023: 33-36 - [c17]Sakorn Mekruksavanich, Anuchit Jitpattanakul:
Position-aware Human Activity Recognition with Smartphone Sensors based on Deep Learning Approaches. TSP 2023: 43-46 - [c16]Sakorn Mekruksavanich, Anuchit Jitpattanakul:
Free-Weight Exercise Activity Recognition using Deep Residual Neural Network based on Sensor Data from In-Ear Wearable Devices. TSP 2023: 52-55 - [c15]Sakorn Mekruksavanich, Anuchit Jitpattanakul:
A Comparative Study of Deep Learning Robustness for Sensor-based Human Activity Recognition. TSP 2023: 87-90 - 2022
- [j5]Sakorn Mekruksavanich, Anuchit Jitpattanakul, Kanokwan Sitthithakerngkiet, Phichai Youplao, Preecha P. Yupapin:
ResNet-SE: Channel Attention-Based Deep Residual Network for Complex Activity Recognition Using Wrist-Worn Wearable Sensors. IEEE Access 10: 51142-51154 (2022) - [j4]Sakorn Mekruksavanich, Anuchit Jitpattanakul:
Deep Residual Network for Smartwatch-Based User Identification through Complex Hand Movements. Sensors 22(8): 3094 (2022) - [c14]Sakorn Mekruksavanich, Ponnipa Jantawong, Anuchit Jitpattanakul:
A Deep Learning-based Model for Human Activity Recognition using Biosensors embedded into a Smart Knee Bandage. ITQM 2022: 621-627 - [c13]Sakorn Mekruksavanich, Ponnipa Jantawong, Narit Hnoohom, Anuchit Jitpattanakul:
Deep Learning Models for Daily Living Activity Recognition based on Wearable Inertial Sensors. JCSSE 2022: 1-5 - [c12]Sakorn Mekruksavanich, Ponnipa Jantawong, Narit Hnoohom, Anuchit Jitpattanakul:
A Novel Deep BiGRU-ResNet Model for Human Activity Recognition using Smartphone Sensors. JCSSE 2022: 1-5 - [c11]Sakorn Mekruksavanich, Ponnipa Jantawong, Ilsun You, Anuchit Jitpattanakul:
A Hybrid Deep Neural Network for Classifying Transportation Modes based on Human Activity Vibration. KST 2022: 114-118 - [c10]Narit Hnoohom, Nagorn Maitrichit, Sakorn Mekruksavanich, Anuchit Jitpattanakul:
Hierarchical Human Activity Recognition Based on Smartwatch Sensors Using Branch Convolutional Neural Networks. MIWAI 2022: 52-60 - [c9]Narit Hnoohom, Pitchaya Chotivatunyu, Sakorn Mekruksavanich, Anuchit Jitpattanakul:
Multi-resolution CNN for Lower Limb Movement Recognition Based on Wearable Sensors. MIWAI 2022: 111-119 - [c8]Sakorn Mekruksavanich, Ponnipa Jantawong, Narit Hnoohom, Anuchit Jitpattanakul:
Recognizing Driver Activities Using Deep Learning Approaches Based on Smartphone Sensors. MIWAI 2022: 146-155 - [c7]Sakorn Mekruksavanich, Ponnipa Jantawong, Narit Hnoohom, Anuchit Jitpattanakul:
Wearable Fall Detection Based on Motion Signals Using Hybrid Deep Residual Neural Network. MIWAI 2022: 216-224 - [c6]Sakorn Mekruksavanich, Ponnipa Jantawong, Anuchit Jitpattanakul:
Smartwatch-based Eating Detection and Cutlery Classification using a Deep Residual Network with Squeeze-and-Excitation Module. TSP 2022: 301-304 - [c5]Sakorn Mekruksavanich, Ponnipa Jantawong, Anuchit Jitpattanakul:
Deep Residual Networks for Human Activity Recognition based on Biosignals from Wearable Devices. TSP 2022: 310-313 - 2021
- [j3]Sakorn Mekruksavanich, Anuchit Jitpattanakul:
LSTM Networks Using Smartphone Data for Sensor-Based Human Activity Recognition in Smart Homes. Sensors 21(5): 1636 (2021) - [j2]Sakorn Mekruksavanich, Anuchit Jitpattanakul:
Deep Learning Approaches for Continuous Authentication Based on Activity Patterns Using Mobile Sensing. Sensors 21(22): 7519 (2021) - [c4]Sakorn Mekruksavanich, Chanon Promsakon, Anuchit Jitpattanakul:
Location-based Daily Human Activity Recognition using Hybrid Deep Learning Network. JCSSE 2021: 1-5 - 2020
- [j1]Sakorn Mekruksavanich, Anuchit Jitpattanakul, Phichai Youplao, Preecha P. Yupapin:
Enhanced Hand-Oriented Activity Recognition Based on Smartwatch Sensor Data Using LSTMs. Symmetry 12(9): 1570 (2020) - [c3]Sakorn Mekruksavanich, Anuchit Jitpattanakul:
Smartwatch-based Human Activity Recognition Using Hybrid LSTM Network. IEEE SENSORS 2020: 1-4
2010 – 2019
- 2017
- [c2]Narit Hnoohom, Sakorn Mekruksavanich, Anuchit Jitpattanakul:
Human Activity Recognition Using Triaxial Acceleration Data from Smartphone and Ensemble Learning. SITIS 2017: 408-412 - [c1]Sakorn Mekruksavanich:
Identifying Behavioral Design Flaws in Evolving Object-Oriented Software Using an Ontology-Based Approach. SITIS 2017: 424-429
Coauthor Index
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