Article
Environmental Sciences
Weichao Liu, Hongyuan Huo, Ping Zhou, Mingyue Li, Yuzhen Wang
Summary: This paper proposes a new method for predicting soil total iron composition. The method screens abnormal samples using a Monte Carlo method based on particle swarm optimization, and adopts feature representation based on Shannon entropy for wavelet packet processing. The selected feature bands based on the correlation coefficient and the CARS algorithm are applied to the soil spectra before and after wavelet packet processing. Finally, a 1D-CNN is used to calculate the Fe content. Experimental results show that the method can effectively handle abnormal samples, improve the correlation between spectra and content, and achieve good results with few samples.
Article
Green & Sustainable Science & Technology
Prem Kumar, Mandeep Singh, Sarbjot Singh Sandhu
Summary: This research investigates the potential use of Argemone mexicana oil as a biodiesel source, optimized transesterification reaction parameters, and explored the combustion stability of a 4-cylinder turbocharged CRDI engine using AGB/diesel blended fuels. The study found that a transesterification reaction at 60 degrees C, 90 min, with a molar ratio of 6:1 produced a maximum yield of 95% AGB. The analysis of combustion parameters using wavelet transform showed that the AB10 blend exhibited the lowest cyclic fluctuations in IMEP combustion parameter at a low engine load and can be used to develop better fuel injection control strategies.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2023)
Article
Chemistry, Analytical
Haoran Li, Suyi Chen, Jisheng Dai, Xiaobo Zou, Tao Chen, Tianhong Pan, Melvin Holmes
Summary: The proposed method introduces a new fast burst-sparsity learning approach for baseline correction, utilizing downsampling strategy and pattern-coupled prior to overcome the limitations of existing baseline correction methods. The study demonstrates that burst-sparsity commonly occurs in peak zones of spectra and can be properly utilized to enhance baseline correction performance.
ANALYTICAL CHEMISTRY
(2022)
Article
Energy & Fuels
Junghwan Kim
Summary: A knock classification model was developed using machine learning algorithm in this study, with wavelet packet decomposition and ensemble empirical mode decomposition employed for signal characterization. Through experiments, it was confirmed that the trained classification model achieved a high accuracy in knock cycle detection.
Article
Automation & Control Systems
Abbas Saadatmandi, Mahmoud Reza Sohrabi, Hasan Kabiri Fard
Summary: In this study, a fast, easy, inexpensive, and precise method combining UV-Vis spectrophotometry, continuous wavelet transform, and partial least squares multivariate calibration was developed for the simultaneous determination of paracetamol, diphenhydramine, and phenylephrine in tablet dosage form without extraction. The method was validated using synthetic mixtures and showed good performance.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2023)
Article
Plant Sciences
Jingang Wang, Tian Tian, Haijiang Wang, Jing Cui, Xiaoyan Shi, Jianghui Song, Tiansheng Li, Weidi Li, Mingtao Zhong, Wenxu Zhang
Summary: Soil salinization poses a challenge to crop production in arid areas, but spectral analysis and remote sensing technology can assist in assessing the impact of salinity stress on crop photosynthesis.
FRONTIERS IN PLANT SCIENCE
(2023)
Article
Engineering, Multidisciplinary
Min Shi, Chengyi Yang, Dalu Zhang
Summary: This study proposes a sleep quality detection and management method based on EEG, which achieved high accuracy in detecting sleep quality through methods like wavelet packet decomposition and LSTM model. The mobile terminal software is utilized for managing the detected sleep quality data, including querying historical data and alerting users in case of abnormalities to ensure physical fitness.
MATHEMATICAL PROBLEMS IN ENGINEERING
(2021)
Article
Automation & Control Systems
Haoran Zhong, Elizabeth Donkor, Lisa Whitworth, Collin G. G. White, Kaushalya Sharma Dahal, Ayuba Fasasi, Thomas M. M. Hancewicz, Franklin Uba, Barry K. K. Lavine
Summary: In this study, the researchers demonstrate the collection of infrared spectra from different layers of a multilayered automotive paint chip using a Fourier transform IR imaging microscope. By applying alternating least squares to the spectral data, the researchers were able to extract the IR spectrum of each layer. To simplify the analysis, an ultramicrotome was utilized to cross section small paint chips without the need for embedding media. However, for thin peels, target testing factor analysis and modified alternating least squares were employed to recover the IR spectrum. This new sample preparation technique allows for obtaining high quality IR spectra of automotive paint layers from smaller paint fragments.
JOURNAL OF CHEMOMETRICS
(2023)
Article
Green & Sustainable Science & Technology
Fengbo Zhou, Ammar Oad, Hongqiu Zhu, Changgeng Li
Summary: The proposed method combines wavelet transform and partial least squares regression for simultaneous determination of zinc, cobalt, and nickel in industrial wastewater. It effectively preprocesses the spectra to enhance linearity and reduce noise, leading to improved accuracy and stability in detection. The results demonstrate the suitability of the WT-PLSR method for online detection of polymetallic ions in zinc industrial wastewater using UV-visible spectroscopy.
Article
Automation & Control Systems
Zhijun Li, Hojjat Adeli
Summary: This paper presents a new adaptive robust Hc control methodology for vibration control of large and complex structures, considering the uncertainties of both earthquake loads and structural parameters simultaneously. Through simulation experiments on benchmark buildings and shear wall buildings, the effectiveness and accuracy of the control method are demonstrated.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Engineering, Multidisciplinary
Sumika Chauhan, Manmohan Singh, Ashwani Kumar Aggarwal
Summary: A fault identification scheme for bearings is proposed in this paper, involving pre-processing vibration signals, extracting features, training SVM model, and optimizing SVM parameters for efficiency improvement. The efficacy of the proposed algorithm is verified with 100% accuracy.
Article
Neurosciences
Amir Soleymankhani, Vahid Shalchyan
Summary: This paper proposes a spike sorting method based on optimized wavelet parameter selection and validates it on simulated and publicly available datasets. The results demonstrate the superior performance of the spike sorting algorithm with optimized wavelet parameters in decoding real intracortical data.
Article
Computer Science, Information Systems
Jinhui Zhao, Tianyu Hu, Ruifang Zheng, Penghui Ba, Congli Mei, Qichun Zhang
Summary: A new method for identifying concrete defects based on stochastic configuration networks is proposed. The method decomposes detection signals using wavelet packet transform and characterizes the signals using statistical features. The stochastic configuration networks algorithm shows superior performance in recognizing defect signals.
Article
Mathematics, Applied
U. K. Mandal, Sandeep Kumar Verma, Akhilesh Prasad
Summary: The paper aims to study the composition of continuous Kontorovich-Lebedev wavelet transform and wave packet transform based on the Kontorovich-Lebedev transform. Estimates for these transforms are obtained, along with Plancherel's relation for their composition. Additionally, a reconstruction formula for WPT associated with KL-transform is derived, and Calderon's formula related to KL-transform using its convolution property is obtained.
Article
Engineering, Multidisciplinary
Tong Liu, YuCheng Jin, Shuo Wang, QinWen Zheng, Guoan Yang
Summary: This paper proposes a denoising method of acoustic emission (AE) signals based on the combination of autoencoder and wavelet packet decomposition (AE-WPD) to address the problem of weak AE signals being submerged in strong background noise in the actual operating conditions of the engine. The proposed method decomposes the engine background noise signals and noise-containing fault AE signals using wavelet packet, enhances the local analysis capability of the autoencoder, and analyzes the differences between the background noise signals and the noise-containing fault signals. The experimental results show that the proposed AE-WPD method outperforms other denoising methods at different signal-to-noise ratios (SNR).
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
Hongwei Yue, Yufeng Huang, Chi-Man Vong, Yingying Jin, Zhiqiang Zeng, Mingqi Yu, Chuangquan Chen
Summary: Scene text recognition (STR) is widely used in industrial and commercial fields. However, existing methods struggle with processing text images that have defects such as low contrast, blur, low resolution, and insufficient illumination. To address these challenges, a novel network called NRSTRNet is proposed, which effectively reduces noise and achieves superior accuracy in text recognition.
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jintao Huang, Wenbin Qian, Chi-Man Vong, Weiping Ding, Wenhao Shu, Qin Huang
Summary: This paper proposes a new multi-label feature selection algorithm that effectively resolves existing algorithms' issues through three innovative procedures. First, a new similarity relation metric is proposed to deal with hybrid data types effectively. Second, a label enhancement algorithm is designed to enhance and transform the logical labels into a label distribution by fully considering the analytic hierarchy process (AHP) embedded with label correlation, which can automatically identify the significance of different labels. Third, a feature weighting evaluation is redesigned in the feature selection process to obtain the optimal feature subset through feature ranking directly. Under these proposed procedures, multi-label feature selection can effectively utilize the abundant semantic information of the label significance and can significantly improve the operating accuracy and efficiency simultaneously.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Zhen Jia, Zhenbao Liu, Chi -Man Vong, Shengdong Wang, Yongyi Cai
Summary: This study uses a source circuit model with sufficient data to solve the problem of fault diagnosis in a target circuit with a lack of data. A deep transfer kernel extreme learning machine auto encoder (DKEA) model is designed, where the Gaussian error linear units (GELD) activation function is used to describe the probability of neuron input and the kernel extreme learning machine is employed as a classifier for the diagnosis task.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Jie Du, Yanhong Zhou, Peng Liu, Chi-Man Vong, Tianfu Wang
Summary: A parameter-free loss function is proposed for deep learning image classification tasks, which reduces training time, pays more attention to minority classes, and achieves higher accuracy compared to existing loss functions.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Erhao Zhou, Chi Man Vong, Yusuke Nojima, Shitong Wang
Summary: This study aims to enhance the generalization performance of the first-order TSK fuzzy rules by determining the weight of each rule and avoiding the intractable training of the consequent parts. A mathematically equivalent bridge is built between a Gaussian mixture model and a fully interpretable first-order TSK fuzzy system, resulting in a simpler expression and enhanced generalization performance. The proposed training method effectively provides an analytical solution to the weight of each rule.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Hongyan Li, Chi Man Vong, Zhonglin Wan
Summary: The current work presents a new multi-graph embedding collaborative disambiguation PLL algorithm (PL-MGECD) that introduces a unified framework for graph-based PLL, adopts various graph structures, and proposes an efficient optimization algorithm. Extensive experiments show that PL-MGECD has a competitive or superior performance over traditional PLL methods.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Peng Liu, Jie Du, Chi -Man Vong
Summary: In this work, a lightweight model called SMF-Net is proposed to simultaneously alleviate the issues of over-fitting and under-fitting using a novel sequential structure of multi-scale feature learning. Compared to both deep and lightweight models, the proposed sequential structure in SMF-Net can easily extract features with larger receptive fields for multi-scale feature learning with only a few and linearly increased model parameters.
Article
Automation & Control Systems
Yichen Sun, Chi Man Vong, Shitong Wang
Summary: In this study, a fast AUC maximizing learning machine called rho-AUCCVM is proposed, which incorporates the generalized AUC metric and the core vector machine technique for simultaneous outlier detection. rho-AUCCVM has the advantages of CVM and can automatically determine the importance of the minority class or the upper bound of noises.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Nursing
Jrywan N. Huang, Margit Gerardi, Olivia Yeargain, Tracy Senterfitt, Maria Saldiva
Summary: More than half of veterans diagnosed with OUD have experienced hospitalization or death due to overdose. Telephone outreach improves access to naloxone for high-risk populations. A nurse-led intervention team successfully increased the naloxone prescription rates for at-risk veterans at the facility within three months.
ISSUES IN MENTAL HEALTH NURSING
(2023)
Article
Computer Science, Artificial Intelligence
Jintao Huang, Chi-Man Vong, Wenbin Qian, Qin Huang, Yimin Zhou
Summary: This paper proposes a novel LDL framework called OLD_RVFL+, which can effectively and accurately handle online data streams. It includes LD_RVFL+ network, a weight update module based on LD_RVFL+, and a label thresholding module for improved accuracy.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Qi Lai, Chi-Man Vong, Jianhang Zhou, Yimin Zhou, C. L. Philip Chen
Summary: Multiview multi-instance multilabel learning (M3L) is a hot research topic for modeling complex real-world objects. However, existing methods suffer from low accuracy and training efficiency for large datasets due to neglecting viewwise intercorrelation, not considering diverse correlations, and high computation burden. To address these issues, a novel framework called fast broad M3L (FBM3L) is proposed, which utilizes viewwise intercorrelation, achieves joint learning among diverse correlations, and significantly reduces training time. Experiments show that FBM3L is highly competitive in evaluation metrics (up to 64% in average precision) and much faster than most methods (up to 1030 times) on large multiview datasets.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jie Du, Xiaoci Zhang, Peng Liu, Chi-Man Vong, Tianfu Wang
Summary: Deep metric learning (DML) has been widely used in various tasks for extracting discriminant features. However, two class-imbalance learning (CIL) problems, data scarcity and data density, can lead to misclassification. Existing DML and CIL losses fail to address these issues effectively. To mitigate these challenges, we propose an IDID loss that generates diverse features within classes and preserves the semantic correlations between classes.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Qi Lai, Jianhang Zhou, Yanfen Gan, Chi-Man Vong, C. L. Philip Chen
Summary: Multi-instance multi-label learning (MIML) problems have been extensively studied in real applications, but existing methods suffer from low accuracy and training efficiency. To address these issues, a new single-stage framework called broad multi-instance multi-label learning (BMIML) is proposed, which can simultaneously learn diverse inter-correlations between whole images, instances, and labels in single stage for higher classification accuracy and faster training time through innovative modules such as auto-weighted label enhancement learning, scalable multi-instance probabilistic regression, and interactive decision optimization.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Jie Du, Peng Liu, Chi-Man Vong, Chuangquan Chen, Tianfu Wang, C. L. Philip Chen
Summary: Machine learning aims to generate predictive models from training datasets, but many real-world applications involve continuous arrival of new data, making class-incremental learning (CIL) necessary. Most current CIL methods are based on computationally expensive deep models and have issues with forgetting old knowledge. This article proposes a fast and efficient broad learning system-based CIL (BLS-CIL) method that retains old class knowledge and achieves high accuracy.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jintao Huang, Chi-Man Vong, C. L. Philip Chen, Yimin Zhou
Summary: This paper proposes a novel multi-label classifier based on a broad learning system (BLS-MLL). It improves the classification performance and training efficiency of large-scale multi-label learning by introducing kernel-based feature reduction and correlation-based label thresholding.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
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)
