Article
Biotechnology & Applied Microbiology
Alexandra A. Portnova-Fahreeva, Fabio Rizzoglio, Maura Casadio, Ferdinando A. Mussa-Ivaldi, Eric Rombokas
Summary: Dimensionality reduction techniques are useful for simplifying complex hand kinematics. Training practices that make the relationship between low-dimensional controls and high-dimensional systems more explicit can aid learning.
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
(2023)
Article
Engineering, Biomedical
Daniele D'Accolti, Katarina Dejanovic, Leonardo Cappello, Enzo Mastinu, Max Ortiz-Catalan, Christian Cipriani
Summary: The design of prosthetic controllers using neurophysiological signals remains a significant challenge for bioengineers. Existing electromyographic (EMG) continuous pattern recognition controllers rely on assumptions of stable EMG patterns, which we challenge. We propose an algorithm that decodes wrist and hand movements based on transient EMG signals. Our offline evaluations show promising results with non-amputees achieving a median accuracy of around 96%, while amputees achieved a median accuracy of around 89%. Further assessments with domain-adaptation strategies may be needed for amputees. Overall, our results support the hypothesis that decoding transient EMG signals can be a viable pattern recognition strategy for prosthetic controllers.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
Article
Engineering, Biomedical
Ziling Zhu, Jianan Li, William J. Boyd, Carlos Martinez-Luna, Chenyun Dai, Haopeng Wang, He Wang, Xinming Huang, Todd R. Farrell, Edward A. Clancy
Summary: Recent research has made progress in achieving simultaneous, independent, and proportional control of hand-wrist prostheses using surface electromyogram signals. Two regression-based controllers were evaluated and compared with a conventional sequential controller, with the regression controllers performing better in certain tasks.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2022)
Article
Engineering, Electrical & Electronic
Antonio Vitale, Elisa Donati, Roger Germann, Michele Magno
Summary: With the emergence of edge-computing platforms, the applications of smart wearable devices are immense. This article presents two spiking neural networks (SNNs) for event-based electromyography (EMG) gesture recognition and their evaluation on Intel's research neuromorphic chip Loihi. The proposed method achieves a high accuracy of 74% on the commonly used NinaPro DB5 dataset and a low processing latency of 5.7 ms for 300-ms EMG samples while consuming only 41 mW.
IEEE SENSORS JOURNAL
(2022)
Article
Automation & Control Systems
Fady S. Botros, Angkoon Phinyomark, Erik J. Scheme
Summary: This study investigates the feasibility of hand gesture recognition using wrist EMG signals, finding that wrist signals have higher signal quality for gestures involving fine finger movements, while maintaining comparable quality for wrist gestures. The results suggest the potential of using wrist EMG signals in hand gesture recognition and the importance of incorporating knowledge from the prosthetics field into the design of EMG pattern recognition systems.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Proceedings Paper
Engineering, Biomedical
Ghinwa Masri, Hussein Harb, Nadim Diab, Ramzi Halabi
Summary: This research aims to develop a smart myoelectric prosthetic hand for wrist-disarticulated patients, allowing them to perform daily tasks efficiently. By combining mechanical design and myoelectric control, high classification accuracy in motion was achieved, and further extension of arm functionality is planned through design and optimization efforts.
2021 SIXTH INTERNATIONAL CONFERENCE ON ADVANCES IN BIOMEDICAL ENGINEERING (ICABME)
(2021)
Article
Materials Science, Multidisciplinary
Bipin Kumar Gupta, Garima Kedawat, Pawan Kumar, Mohammad A. Rafiee, Priyanka Tyagi, Ritu Srivastava, Pulickel M. Ajayan
JOURNAL OF MATERIALS CHEMISTRY C
(2015)
Article
Nanoscience & Nanotechnology
Reeti Bajpai, Soumyendu Roy, Pragyensh Kumar, Preeti Bajpai, Neha Kulshrestha, Javad Rafiee, Nikhil Koratkar, D. S. Misra
ACS APPLIED MATERIALS & INTERFACES
(2011)
Article
Chemistry, Multidisciplinary
Mohammad A. Rafiee, Tharangattu N. Narayanan, Daniel P. Hashim, Navid Sakhavand, Rouzbeh Shahsavari, Robert Vajtai, Pulickel M. Ajayan
ADVANCED FUNCTIONAL MATERIALS
(2013)
Article
Chemistry, Physical
Xiu-Zhi Tang, Wenjuan Li, Zhong-Zhen Yu, Mohammad A. Rafiee, Javad Rafiee, Faze Yavari, Nikhil Koratkar
Article
Chemistry, Physical
Sashi S. Kandanur, Mohammad A. Rafiee, Faze Yavari, Michael Schrameyer, Zhong-Zhen Yu, Thierry A. Blanchet, Nikhil Koratkar
Article
Computer Science, Artificial Intelligence
J. Rafiee, M. A. Rafiee, F. Yavari, M. P. Schoen
EXPERT SYSTEMS WITH APPLICATIONS
(2011)
Article
Chemistry, Multidisciplinary
Mohammad A. Rafiee, Fazel Yavari, Javad Rafiee, Nikhil Koratkar
JOURNAL OF NANOPARTICLE RESEARCH
(2011)
Article
Chemistry, Physical
Fazel Yavari, Hafez Raeisi Fard, Kamyar Pashayi, Mohammad A. Rafiee, Amir Zamiri, Zhongzhen Yu, Rahmi Ozisik, Theodorian Borca-Tasciuc, Nikhil Koratkar
JOURNAL OF PHYSICAL CHEMISTRY C
(2011)
Article
Nanoscience & Nanotechnology
Stephen F. Bartolucci, Joseph Paras, Mohammad A. Rafiee, Javad Rafiee, Sabrina Lee, Deepak Kapoor, Nikhil Koratkar
MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING
(2011)
Article
Chemistry, Multidisciplinary
Reeti Bajpai, Soumyendu Roy, Neha Kulshrestha, Javad Rafiee, Nikhil Koratkar, D. S. Misra
Article
Nanoscience & Nanotechnology
K. S. Hazra, J. Rafiee, M. A. Rafiee, A. Mathur, S. S. Roy, J. McLauhglin, N. Koratkar, D. S. Misra
Article
Chemistry, Physical
Javad Rafiee, Xi Mi, Hemtej Gullapalli, Abhay V. Thomas, Fazel Yavari, Yunfeng Shi, Pulickel M. Ajayan, Nikhil A. Koratkar
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
