Article
Environmental Sciences
Lei Ao, Kaiyuan Feng, Kai Sheng, Hongyu Zhao, Xin He, Zigang Chen
Summary: The application of deep learning in remote sensing image classification has gained increasing attention. However, manually designed models for remote sensing image classification require expert knowledge and struggle to achieve high accuracy and parameter efficiency. To address this challenge, this study proposes TPENAS, a two-phase evolutionary neural architecture search framework that optimizes the model using computational intelligence techniques. Experimental results show that TPENAS outperforms state-of-the-art baselines in terms of classification accuracy and parameter reduction on three benchmark datasets.
Article
Computer Science, Artificial Intelligence
Manoj Kumar Naik, Rutuparna Panda, Ajith Abraham
Summary: The study introduces a context-sensitive entropy dependency-based multilevel thresholding method, accompanied by the opposition equilibrium optimizer. Through various testing and analysis, the method is shown to demonstrate advantages in reducing complexity, improving accuracy, and enhancing stability and scalability.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Engineering, Electrical & Electronic
Licheng Jiao, Zhongjian Huang, Xu Liu, Yuting Yang, Mengru Ma, Jiaxuan Zhao, Chao You, Biao Hou, Shuyuan Yang, Fang Liu, Wenping Ma, Lingling Li, Puhua Chen, Zhixi Feng, Xu Tang, Yuwei Guo, Xiangrong Zhang, Dou Quan, Shuang Wang, Weibin Li, Jing Bai, Yangyang Li, Ronghua Shang, Jie Feng
Summary: This article summarizes and analyzes the essential properties of brain cognize learning and the recent advance of remote sensing interpretation. It introduces the brain's structural composition and properties, and studies five representative brain-inspired algorithms. The article also summarizes the data types of remote sensing, the development of typical applications, and discusses the future direction of brain-inspired remote sensing interpretation.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)
Article
Engineering, Electrical & Electronic
Jiang Zhu, Jun Wu, Ali Kashif Bashir, Qianqian Pan, Wu Yang
Summary: This paper proposes a blockchain-empowered privacy-preserving federated learning method for remote sensing image classification, which can defend against encrypted model poisoning attacks. By using the methods of proof of accuracy and secure aggregation, the proposed scheme achieves high accuracy even in the presence of malicious attackers. Experimental results demonstrate the superiority of the scheme in defending against model poisoning attacks.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)
Article
Agronomy
Huichun Zhang, Lu Wang, Xiuliang Jin, Liming Bian, Yufeng Ge
Summary: Acquisition of plant phenotypic information is crucial for plant breeding and gene regulation, as well as optimization of agricultural and forestry product quality. Optical sensors and data processing methods enable measurement of leaf morphological, physiological, and biochemical traits at various levels, facilitating rapid and accurate acquisition of plant leaf phenotypes.
Article
Computer Science, Artificial Intelligence
Jin Li, Yanyan Liu
Summary: In this paper, a non-blind post-processing approach in the wavelet domain is proposed to improve the efficiency and performance of image compression. The method uses a high-frequency detection algorithm to guide resource allocation and remove redundancies among wavelet coefficients. Experimental results show that the proposed method achieves high calculation efficiency and compression performance, making it suitable for remote sensing images and other images.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Information Systems
Anjali Goswami, Deepak Sharma, Harani Mathuku, Syam Machinathu Parambil Gangadharan, Chandra Shekhar Yadav, Saroj Kumar Sahu, Manoj Kumar Pradhan, Jagendra Singh, Hazra Imran
Summary: Remote sensing technology has been widely used in natural resource fields, providing precise information. It is necessary to develop automatic change detection techniques to improve classification accuracy and reduce time. This study focuses on improving machine learning classification accuracy by comparing training samples and image differences.
Article
Environmental Sciences
Pei Nie, Zhenqi Cui, Yaping Wan
Summary: This paper introduces a rapid parallel remote sensing image mosaicking algorithm utilizing read filtering. By dividing the target images into blocks and storing them in a distributed file system, and using asynchronous reading and processing methods, the algorithm reduces data I/O and computing overhead and improves the efficiency of parallel computing.
Article
Environmental Sciences
Dan Feng, Hongyun Chu, Ling Zheng
Summary: This paper proposes a frequency spectrum intensity attention network (FSIANet) for automatic building detection in high-resolution remote sensing imagery, which achieves state-of-the-art performance by introducing frequency spectrum intensity attention mechanism and atrous frequency spectrum attention pyramid.
Article
Remote Sensing
Yanfei Zhong, Xinyu Wang, Shaoyu Wang, Liangpei Zhang
Summary: This paper discusses the recent progress in Chinese spaceborne HRS, including typical satellite systems, data processing, and applications, as well as the future development trends of HRS in China.
GEO-SPATIAL INFORMATION SCIENCE
(2021)
Article
Engineering, Electrical & Electronic
Ziyi Chen, Cheng Wang, Jonathan Li, Nianci Xie, Yan Han, Jixiang Du
Summary: Automatic road extraction from remote sensing images is crucial for navigation, intelligent transportation, and road network updates, but there is a lack of standard and large dataset. This article introduces a new dataset LRSNY and a reconstruction bias U-Net model, which has been proven to outperform other state-of-the-art segmentation models in road extraction.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Physics, Multidisciplinary
Xinran Liu, Zhongju Wang, Long Wang, Chao Huang, Xiong Luo
Summary: A hybrid Rao-Nelder-Mead (Rao-NM) algorithm is proposed for image template matching, combining global search capability and local search capability to quickly and accurately search for high-quality optimal solutions while ensuring global convergence. Experimental results demonstrate the effectiveness and efficiency of the proposed method in solving image matching problems.
Article
Computer Science, Artificial Intelligence
Binghui Xu
Summary: This study improves the algorithm based on convolutional neural network and conducts experiments on multi-source remote sensing images to verify its effectiveness and scalability. The results show that the improved algorithm has certain results in remote sensing image classification, providing theoretical reference for subsequent related research.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Engineering, Electrical & Electronic
Hongfeng Yu, Chubo Deng, Liangjin Zhao, Lingxiang Hao, Xiaoyu Liu, Wanxuan Lu, Hongjian You
Summary: With the continuous development of remote sensing technology, there has been an increasing amount of high-quality remote sensing images. Content-based remote sensing image retrieval (CBRSIR) has become a popular research topic. However, previous works in this area faced challenges in cross-modal image retrieval, semantic-level retrieval, and resource constraint. To address these issues, this article introduces a lightweight nonlocal semantic fusion network based on hypergraph structure for CBRSIR. The proposed network outperforms other methods in terms of retrieval performance on a typical CBRSIR dataset.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)
Article
Engineering, Electrical & Electronic
Kun Yu, Xiao Zheng, Bin Fang, Pei An, Xiao Huang, Wei Luo, Junfeng Ding, Zhao Wang, Jie Ma
Summary: A fast and robust registration method for multimodal urban remote sensing images is proposed based on road intersection triangular features, including three main stages: road lines extraction, intersection triangular feature construction, and feature matching. The method outperforms other state-of-the-art methods, showing good robustness and efficiency.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Computer Science, Artificial Intelligence
Alan Tan Wei Min, Yew-Soon Ong, Abhishek Gupta, Chi-Keong Goh
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2019)
Article
Computer Science, Artificial Intelligence
Liang Feng, Abhishek Gupta, Yew-Soon Ong
Article
Computer Science, Artificial Intelligence
Zhenkun Wang, Yew-Soon Ong, Jianyong Sun, Abhishek Gupta, Qingfu Zhang
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2019)
Article
Computer Science, Artificial Intelligence
Wan-Yu Deng, Amaury Lendasse, Yew-Soon Ong, Ivor Wai-Hung Tsang, Lin Chen, Qing-Hua Zheng
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2019)
Article
Automation & Control Systems
Bingshui Da, Abhishek Gupta, Yew-Soon Ong
IEEE TRANSACTIONS ON CYBERNETICS
(2019)
Article
Computer Science, Artificial Intelligence
Abhishek Gupta, Yew-Soon Ong
COGNITIVE COMPUTATION
(2019)
Article
Computer Science, Artificial Intelligence
Ana Valdivia, Eugenio Martinez-Camara, Iti Chaturvedi, M. Victoria Luzon, Erik Cambria, Yew-Soon Ong, Francisco Herrera
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
(2020)
Article
Computer Science, Artificial Intelligence
Andri Ashfahani, Mahardhika Pratama, Edwin Lughofer, Yew Soon Ong
Article
Computer Science, Artificial Intelligence
Kavitesh Kumar Bali, Yew Soon Ong, Abhishek Gupta, Puay Siew Tan
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2020)
Article
Automation & Control Systems
Tiantian He, Yang Liu, Tobey H. Ko, Keith C. C. Chan, Yew-Soon Ong
IEEE TRANSACTIONS ON CYBERNETICS
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Tiantian He, Lu Bai, Yew-Soon Ong
2019 IEEE 31ST INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2019)
(2019)
Proceedings Paper
Mathematics, Interdisciplinary Applications
Ray Lim, Yew-Soon Ong, Hanh Thi Hong Phan, Abhishek Gupta, Allan Nengsheng Zhang
PROCEEDINGS OF THE 2019 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION (GECCCO'19 COMPANION)
(2019)
Proceedings Paper
Computer Science, Theory & Methods
Mahardhika Pratama, Choiru Za'in, Andri Ashfahani, Yew Soon Ong, Weiping Ding
PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19)
(2019)
Proceedings Paper
Engineering, Electrical & Electronic
Xinghua Qu, Yew-Soon Ong, Yaqing Hou, Xiaobo Shen
2019 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
(2019)
Article
Computer Science, Artificial Intelligence
Yew-Soon Ong, Abhishek Gupta
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2019)
Article
Computer Science, Artificial Intelligence
Jin Zhang, Zekang Bian, Shitong Wang
Summary: This study proposes a novel style linear k-nearest neighbor method to extract stylistic features using matrix expressions and improve the generalizability of the predictor through style membership vectors.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qifeng Wan, Xuanhua Xu, Jing Han
Summary: In this study, we propose an innovative approach for dimensionality reduction in large-scale group decision-making scenarios that targets linguistic preferences. The method combines TF-IDF feature similarity and information loss entropy to address challenges in decision-making with a large number of decision makers.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Hegui Zhu, Yuchen Ren, Chong Liu, Xiaoyan Sui, Libo Zhang
Summary: This paper proposes an adversarial attack method based on frequency information, which optimizes the imperceptibility and transferability of adversarial examples in white-box and black-box scenarios respectively. Experimental results validate the superiority of the proposed method and its application in real-world online model evaluation reveals their vulnerability.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jing Tang, Xinwang Liu, Weizhong Wang
Summary: This paper proposes a hybrid generalized TODIM approach in the Fine-Kinney framework to evaluate occupational health and safety hazards. The approach integrates CRP, dynamic SIN, and PLTSs to handle opinion interactions and incomplete opinions among decision makers. The efficiency and rationality of the proposed approach are demonstrated through a numerical example, comparison, and sensitivity studies.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Shigen Shen, Chenpeng Cai, Zhenwei Li, Yizhou Shen, Guowen Wu, Shui Yu
Summary: To address the damage caused by zero-day attacks on SIoT systems, researchers propose a heuristic learning intrusion detection system named DQN-HIDS. By integrating Deep Q-Networks (DQN) into the system, DQN-HIDS gradually improves its ability to identify malicious traffic and reduces resource workloads. Experiments demonstrate the superior performance of DQN-HIDS in terms of workload, delayed sample queue, rewards, and classifier accuracy.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu
Summary: In this paper, we propose a Chinese text classification algorithm based on deep active learning for the power system, which addresses the challenge of specialized text classification. By applying a hierarchical confidence strategy, our model achieves higher classification accuracy with fewer labeled training data.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Kaan Deveci, Onder Guler
Summary: This study proves the lack of robustness in nonlinear IF distance functions for ranking intuitionistic fuzzy sets (IFS) and proposes an alternative ranking method based on hypervolume metric. Additionally, the suggested method is extended as a new multi-criteria decision making method called HEART, which is applied to evaluate Turkey's energy alternatives.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Fu-Wing Yu, Wai-Tung Ho, Chak-Fung Jeff Wong
Summary: This research aims to enhance the energy management in commercial building air-conditioning systems, specifically focusing on chillers. Ridge regression is found to outperform lasso and elastic net regression when optimized with the appropriate hyperparameter, making it the most suitable method for modeling the system coefficient of performance (SCOP). The key variables that strongly influence SCOP include part load ratios, the operating numbers of chillers and pumps, and the temperatures of chilled water and condenser water. Additionally, July is identified as the month with the highest potential for performance improvement. This study introduces a novel approach that balances feature selection, model accuracy, and optimal tuning of hyperparameters, highlighting the significance of a generic and simplified chiller system model in evaluating energy management opportunities for sustainable operation. The findings from this research can guide future efforts towards more energy-efficient and sustainable operations in commercial buildings.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Xiaoyan Chen, Yilin Sun, Qiuju Zhang, Xuesong Dai, Shen Tian, Yongxin Guo
Summary: In this study, a method for dynamically non-destructive grasping of thin-skinned fruits is proposed. It utilizes a multi-modal depth fusion convolutional neural network for image processing and segmentation, and combines the evaluation mechanism of optimal grasping stability and the forward-looking non-destructive grasp control algorithm. The proposed method greatly improves the comprehensive performance of grasping delicate fruits using flexible hands.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Yuxuan Yang, Siyuan Zhou, He Weng, Dongjing Wang, Xin Zhang, Dongjin Yu, Shuiguang Deng
Summary: The study proposes a novel model, POIGDE, which addresses the challenges of data sparsity and elusive motives by solving graph differential equations to capture continuous variation of users' interests. The model learns interest transference dynamics using a time-serial graph and an interval-aware attention mechanism, and applies Siamese learning to directly learn from label representations for predicting future POI visits. The model outperforms state-of-the-art models on real-world datasets, showing potential in the POI recommendation domain.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
S. Karthika, P. Rathika
Summary: The widespread development of monitoring devices in the power system has generated a large amount of power consumption data. Storing and transmitting this data has become a significant challenge. This paper proposes an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. It utilizes multi-objective particle swarm optimization (MO-PSO) to select the optimal threshold. The algorithm has been tested and outperforms other existing algorithms.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jiaqi Guo, Haiyan Wu, Xiaolei Chen, Weiguo Lin
Summary: In this study, an adaptive SV-Borderline SMOTE-SVM algorithm is proposed to address the challenge of imbalanced data classification. The algorithm maps the data into kernel space using SVM and identifies support vectors, then generates new samples based on the neighbors of these support vectors. Extensive experiments show that this method is more effective than other approaches in imbalanced data classification.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qiumei Zheng, Linkang Xu, Fenghua Wang, Yongqi Xu, Chao Lin, Guoqiang Zhang
Summary: This paper proposes a new semantic segmentation network model called HilbertSCNet, which combines the Hilbert curve traversal and the dual pathway idea to design a new spatial computation module to address the problem of loss of information for small targets in high-resolution images. The experiments show that the proposed network performs well in the segmentation of small targets in high-resolution maps such as drone aerial photography.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Mojtaba Ashour, Amir Mahdiyar
Summary: Analytic Hierarchy Process (AHP) is a widely applied technique in multi-criteria decision-making problems, but the sheer number of AHP methods presents challenges for scholars and practitioners in selecting the most suitable method. This paper reviews articles published between 2010 and 2023 proposing hybrid, improved, or modified AHP methods, classifies them based on their contributions, and provides a comprehensive summary table and roadmap to guide the method selection process.
APPLIED SOFT COMPUTING
(2024)
Review
Computer Science, Artificial Intelligence
Gerardo Humberto Valencia-Rivera, Maria Torcoroma Benavides-Robles, Alonso Vela Morales, Ivan Amaya, Jorge M. Cruz-Duarte, Jose Carlos Ortiz-Bayliss, Juan Gabriel Avina-Cervantes
Summary: Electric power system applications are complex optimization problems. Most literature reviews focus on studying electrical paradigms using different optimization techniques, but there is a lack of review on Metaheuristics (MHs) in these applications. Our work provides an overview of the paradigms underlying such applications and analyzes the most commonly used MHs and their search operators. We also discover a strong synergy between the Renewable Energies paradigm and other paradigms, and a significant interest in Load-Forecasting optimization problems. Based on our findings, we provide helpful recommendations for current challenges and potential research paths to support further development in this field.
APPLIED SOFT COMPUTING
(2024)