Article
Engineering, Electrical & Electronic
Peng Wang, Zhongchen He, Ying Zhang, Gong Zhang, Hongchao Liu, Henry Leung
Summary: This study proposes a multispectral pansharpening method based on a multisequence convolutional recurrent neural network (MCRNN). The MCRNN consists of two subnetworks, namely shallow feature extraction and deep feature fusion, which effectively fuse the spatial and spectral information of the PAN and MS images. Experimental results show that the proposed MCRNN outperforms traditional pansharpening methods.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2022)
Article
Computer Science, Artificial Intelligence
Yongquan Yang, Haijun Lv, Ning Chen, Yang Wu, Jiayi Zheng, Zhongxi Zheng
Summary: Ensembles of deep CNNs play a crucial role in ensemble learning for artificial intelligence applications, but the increasing complexity of deep CNN architectures and large data dimensionality have made their usage costly. A new approach is proposed to find multiple models converging to local minima in the subparameter space of deep CNNs, which can improve generalization while being more affordable during training and testing stages.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Artificial Intelligence
Weigang Wang, Juchao Ma, Chendong Xu, Yunwei Zhang, Ya Ding, Shujuan Yu, Yun Zhang, Yuanjian Liu
Summary: The article proposes a novel feature selection model for dimension reduction and an improved version of the lightweight convolutional neural network, newCNN, to enhance the system's classification performance. By combining newCNN with Support Vector Machines (SVM) to build a hybrid classification (HC) model, the problem of overfitting in the training process is solved, and it demonstrates excellent generalization ability and robustness.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Jae-Min Lee, Min-Seok Seo, Dae-Han Kim, Sang-Woo Lee, Jong-Chan Park, Dong-Geol Choi
Summary: In this work, the authors propose a Split-and-Share Module (SSM), which splits given features into parts and shares them among multiple sub-classifiers, in order to improve the performance of image classification tasks and identify structural characteristics within the features. SSM can be easily integrated into various architectures and has been validated to show significant improvements over baseline architectures.
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
Chemistry, Multidisciplinary
Sameer Dev Sharma, Sonal Sharma, Rajesh Singh, Anita Gehlot, Neeraj Priyadarshi, Bhekisipho Twala
Summary: Predicting the stress levels of working professionals is a time-consuming and challenging research topic. Estimating their stress levels is crucial for assisting their professional growth and development. Previous studies have developed various machine learning and deep learning algorithms for this purpose, but they have limitations such as increased design complexity, high misclassification and error rates, and reduced efficiency. To address these concerns, this research proposes a sophisticated deep learning model, called the Deep Recurrent Neural Network (DRNN), for forecasting the stress levels of working professionals.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Erfan A. Shams, Ahmet Rizaner, Ali Hakan Ulusoy
Summary: A new context-aware feature extraction method was proposed for CNN-based multiclass intrusion detection, which effectively improved classification accuracy by reducing feature space and classification time. The study showed that the method performed well on multiple datasets and enhanced the performance of intrusion detection.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Biology
Md Rabiul Islam, Md Milon Islam, Md Mustafizur Rahman, Chayan Mondal, Suvojit Kumar Singha, Mohiuddin Ahmad, Abdul Awal, Md Saiful Islam, Mohammad Ali Moni
Summary: The study proposed a deep machine-learning model using Convolutional Neural Network to convert EEG data and recognize emotions on images, overcoming the challenge of emotion recognition from low amplitude variation in EEG signals.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Computer Science, Artificial Intelligence
Mustaqeem, Soonil Kwon
Summary: This paper introduces a two-stream deep convolutional neural network with iterative neighborhood component analysis for learning and selecting the most discriminative features for speech emotion recognition. Training and testing on three emotional speech corpora, the system demonstrates good performance in emotion recognition tasks.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Javier Huertas-Tato, Alejandro Martin, Julian Fierrez, David Camacho
Summary: This paper proposes an ensemble method for accurate image classification, which combines automatically detected features and statistical indicators to achieve better performance. Testing on various datasets shows that including additional indicators and using an ensemble classification approach can improve performance.
INFORMATION FUSION
(2022)
Article
Computer Science, Artificial Intelligence
Juntao Guan, Rui Lai, Huanan Li, Yintang Yang, Lin Gu
Summary: In this study, an innovative deep recurrent convolution neural network model is proposed for HSI destriping. By exploring the inner band and interband correlation, the model can effectively extract relevant features and remove strip noise, preserving scene details.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Minghao Piao, Cheng Hao Jin
Summary: The combination of feature extraction and classification methods is commonly used for wafer map failure pattern recognition. In this study, we found that using extracted features based on local property analysis, along with traditional classification methods, ensemble methods, and CNN, can improve the performance of wafer map failure pattern recognition. The decision tree showed the best performance among single traditional classification methods, and was used as the base classifier for ensemble learning. When comparing CNN on extracted feature sets and raw wafer map image data, raw image based CNN outperformed extracted features, but at a higher training cost. However, the performance of extracted features became closer to the raw image when increasing the number of epochs with a lower training cost.
JOURNAL OF INTELLIGENT MANUFACTURING
(2023)
Article
Computer Science, Interdisciplinary Applications
Hyungu Kang, Seokho Kang
Summary: This study combines two different approaches to build base classifiers and uses a stacking ensemble to improve the accuracy of wafer map pattern classification.
COMPUTERS IN INDUSTRY
(2021)
Article
Computer Science, Artificial Intelligence
Soundararajan Sankaranarayanan, Elangovan Gunasekaran, Amir Shaikh, S. Govinda Rao
Summary: Survival Analysis is crucial in manufacturing for identifying unnecessary events based on input data. Predictive maintenance is a major aspect of Survival Analysis, which helps to identify machine failures using input data from various equipment or sensors. However, existing deep learning techniques are not effective in predicting failures for unknown input data. To address this issue, this paper proposes a novel SFEC-WSA algorithm that combines Sugeno fuzzy integral ensemble model with Attention-based Bidirectional CNN-RNN Deep Model (ABCDM) to predict device failures and survival time based on input features.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Zhang-Meng Liu
Summary: This paper proposes an automatic multi-feature extraction and fusion method based on deep ensemble learning for SEI, addressing the limitations of manually extracted features and ineffective feature fusion in existing SEI methods. Experimental results demonstrate the method performs well in automatic feature extraction and significantly improves SEI performance through multi-feature fusion.
DIGITAL SIGNAL PROCESSING
(2021)
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)