Article
Engineering, Electrical & Electronic
Vikas Singh, Nishchal K. Verma
Summary: In recent years, intelligent condition-based monitoring of rotary machinery systems has become a major research focus. Using mRMR and deep learning models can improve fault diagnostics performance by reducing data redundancy and decreasing data dependency for training the model. The proposed frameworks show better diagnostic accuracy and faster processing of data with many features.
IEEE SENSORS JOURNAL
(2021)
Article
Computer Science, Artificial Intelligence
Arif Metehan Yildiz, Prabal D. Barua, Sengul Dogan, Mehmet Baygin, Turker Tuncer, Chui Ping Ooi, Hamido Fujita, U. Rajendra Acharya
Summary: Physical violence detection using multimedia data is important for public safety and security, and research in video-based violence detection has grown rapidly in recent years. However, verbal aggression detection technologies are still limited, leading researchers to prefer computer vision models for violence detection. To address this gap, a new automatic audio violence detection model is proposed, achieving a classification accuracy of over 89% using kNN and SVM classifiers with the proposed TreePat23 model.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Biophysics
Santiago Jimenez-Serrano, Miguel Rodrigo, Conrado J. Calvo, Jose Millet, Francisco Castells
Summary: This study proposed and validated an automated method for classifying ECG recordings, and evaluated the performance of different lead systems in detecting cardiac diseases.
PHYSIOLOGICAL MEASUREMENT
(2022)
Article
Biology
Said Abenna, Mohammed Nahid, Hamid Bouyghf, Brahim Ouacha
Summary: This work aims to improve the binary and multiclass classification of EEG signals for real-time BCI applications. With a new real-time approach, the accuracy of binary and multiclass classification is significantly increased after preprocessing. The proposed bandpass filter and common spatial pattern filter are used to further enhance the prediction models.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Engineering, Aerospace
Wim J. C. Verhagen, Bruno F. Santos, Floris Freeman, Paul van Kessel, Dimitrios Zarouchas, Theodoros Loutas, Richard C. K. Yeun, Iryna Heiets
Summary: The research aims to identify challenges and limitations in adopting CBM in aviation, as well as propose solutions and policy implications. The findings highlight the importance of addressing issues related to data quantity and quality, CBM implementation, and integration with future technologies in future research and practice.
Article
Chemistry, Analytical
Andrei S. Maliuk, Alexander E. Prosvirin, Zahoor Ahmad, Cheol Hong Kim, Jong-Myon Kim
Summary: This paper proposes a Gaussian mixture model-based method for bearing fault band selection (GMM-WBBS) in signal processing, which achieves reliable feature extraction and interference elimination. Classification is done using the Weighted KNN algorithm. Experimental results demonstrate positive effects in filtering discriminative data and improving classification performance.
Review
Engineering, Multidisciplinary
Rodrigo Barbosa de Santis, Tiago Silveira Gontijo, Marcelo Azevedo Costa
Summary: Industrial maintenance plays a crucial role in profitability and productivity, with condition-based maintenance guiding interventions and repairs based on machine health status. The application of machine learning techniques and statistical models in the hydroelectric sector for estimating useful life and prognosis of turbine-generators is a growing trend that requires further exploration.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF RISK AND RELIABILITY
(2022)
Article
Acoustics
Jederson S. Luz, Myllena C. Oliveira, Flavio H. D. Araujo, Deborah M. Magalhaes
Summary: This paper proposes a representation method for urban sound classification based on the combination of deep and handcrafted features, which outperforms most state-of-the-art CNN models in terms of classification accuracy.
Article
Engineering, Civil
Shaohua Wang, Hao Zheng, Lihua Tang, Zhaoyu Li, Renda Zhao, Yuqian Lu, Kean C. Aw
Summary: This study investigates the feasibility of using computer vision-aided health condition monitoring approach for rail track structures based on vibration signals. The proposed method converts vibration signals into grayscale images and extracts image features to establish a Visual Bag-of-Words model. The health condition of track structures is recognized by comparing the Euclidean distance between word frequency vectors. The results demonstrate high recognition accuracy and low bias, showing promising application prospects in early-stage structural defect detection.
JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING
(2023)
Article
Chemistry, Analytical
Ayman Mohamed, Mahmoud Hassan, Rachid M'Saoubi, Helmi Attia
Summary: This article reviews the latest technologies and components of TCM systems, with a focus on analyzing the advantages and limitations of wireless tool-embedded sensor nodes. It also provides a comprehensive review of dimensionality reduction techniques. Finally, it discusses attempts to generalize and enhance TCM systems and offers recommendations for future research directions.
Article
Engineering, Industrial
Rui He, Zhigang Tian, Yifei Wang, Mingjian Zuo, Ziwei Guo
Summary: This study aims to optimize maintenance decisions for multi-component systems using prognostics information, considering the continuous prediction of remaining useful life (RUL) for some components. It also investigates the impact of system degradation on the benefits of maintenance decisions and extends the model with prognostic error modeling. A case of wind turbine farms is used to demonstrate and validate the proposed method.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Computer Science, Artificial Intelligence
Ayan Seal, Rishabh Bajpai, Mohan Karnati, Jagriti Agnihotri, Anis Yazidi, Enrique Herrera-Viedma, Ondrej Krejcar
Summary: This study presents a dataset that includes EEG data and Patient Health Questionnaire scores for the diagnosis and classification of depression. The results demonstrate the effectiveness of traditional supervised machine learning algorithms and feature selection methods in distinguishing healthy subjects from depressed individuals.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Bianca Sousa Soares, Jederson Sousa Luz, Valderlandia Francisca de Macedo, Romuere Rodrigues Veloso e Silva, Flavio Henrique Duarte de Araujo, Deborah Maria Vieira Magalhaes
Summary: Monitoring and detecting the health of beehives is crucial for ecosystem conservation and sustainable beekeeping. This study explores machine learning and deep learning techniques to detect the presence of queen bees through the analysis of hive sounds. By using feature extraction and selection techniques, efficient and compact descriptors are obtained, enabling accurate classification in real-time monitoring scenarios.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Erdal Tasci, Aybars Ugur
Summary: With the increasing number of digital images, computer-aided classification of image types is widely used. The feature extraction and selection stages play a crucial role in improving classification performance. In this study, a novel pattern recognition framework combining diverse and large-scale handcrafted feature extraction methods and the selection stage is developed. Genetic algorithms are used for feature selection. Experimental results show high accuracy rates on different datasets, making the proposed method competitive with existing state-of-the-art methods.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Biochemical Research Methods
Fengsheng Wang, Leyi Wei
Summary: In this study, we propose a novel multi-scale end-to-end deep learning model, MSTLoc, for identifying protein subcellular locations in the imbalanced multi-label immunohistochemistry (IHC) images dataset. We demonstrate that the proposed MSTLoc outperforms current state-of-the-art models in multi-label subcellular location prediction. Through feature visualization and interpretation analysis, we show that the multi-scale deep features learned from our model exhibit better ability in capturing discriminative patterns underlying protein subcellular locations, and the features from different scales are complementary for the improvement in performance. Case study results indicate that our MSTLoc can successfully identify some biomarkers from proteins that are closely involved in cancer development.
Article
Environmental Sciences
Xuecheng Wu, Weiwei Huang, Yongxin Zhang, Chenghang Zheng, Xiao Jiang, Xiang Gao, Kefa Cen
AEROSOL AND AIR QUALITY RESEARCH
(2015)
Article
Mathematics, Interdisciplinary Applications
Xiaofeng Li, Limin Jia, Xin Yang
DISCRETE DYNAMICS IN NATURE AND SOCIETY
(2015)
Article
Engineering, Environmental
Christine L. Lemieux, Alexandra S. Long, Iain B. Lambert, Staffan Lundstedt, Mats Tysklind, Paul A. White
ENVIRONMENTAL SCIENCE & TECHNOLOGY
(2015)
Article
Engineering, Industrial
Simon Malinowski, Brigitte Chebel-Morello, Noureddine Zerhouni
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2015)
Article
Engineering, Industrial
Damien Burlet-Vienney, Yuvin Chinniah, Ali Bahloul, Brigitte Roberge
Article
Engineering, Industrial
Yuvin Chinniah
Review
Food Science & Technology
F. Waliyar, M. Osiru, B. R. Ntare, K. Vijay Krishna Kumar, H. Sudini, A. Traore, B. Diarra
WORLD MYCOTOXIN JOURNAL
(2015)
Article
Engineering, Mechanical
Van Tung Tran, Faisal AlThobiani, Tiedo Tinga, Andrew Ball, Gang Niu
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE
(2018)
Article
Multidisciplinary Sciences
Ted W. Simon, Robert A. Budinsky, J. Craig Rowlands
Article
Engineering, Mechanical
Gang Niu, Yajun Zhao, Van Tung Tran
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE
(2015)
Article
Energy & Fuels
Josh Kusnick, Douglas E. Adams, D. Todd Griffith
Proceedings Paper
Automation & Control Systems
Doan Ngoc Chi Nam, Tran Van Tung, Edward Yapp Kien Yee
Summary: This paper explores the feasibility of using a semi-supervised approach in quality monitoring for plastic injection moulding processes. With only 8% labelled data and 92% unlabelled data, the effectiveness of the Co-trained_Top MLP model was demonstrated through benchmarking.
IECON 2021 - 47TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY
(2021)
Proceedings Paper
Automation & Control Systems
Van Tung Tran, Jihoon Hong
IECON 2020: THE 46TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY
(2020)
Proceedings Paper
Engineering, Mechanical
El Mehdi Semma, Ahmed Mousrij, Hassan Gziri
AVE2014 - 4IEME COLLOQUE ANALYSE VIBRATOIRE EXPERIMENTALE / EXPERIMENTAL VIBRATION ANALYSIS
(2015)
Proceedings Paper
Engineering, Environmental
Radoslaw Zimroz, Monika Hardygora, Ryszard Blazej
PROCEEDINGS OF THE 12TH INTERNATIONAL SYMPOSIUM CONTINUOUS SURFACE MINING - AACHEN 2014
(2015)
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)