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An ensemble deep learning model for human activity analysis using wearable sensory data Appl. Soft Comput. (IF 8.7) Pub Date : 2024-04-16 Sheeza Batool, Muhammad Hassan Khan, Muhammad Shahid Farid
Lately, the continuous temporal data from motion sensors in wearable devices has been great interest for the research community due to its demand for analyzing human activities in several applications e.g. healthcare, sports, and surveillance. Numerous solo deep learning models have been proposed in the literature to extract an adequate feature representation from temporal sensory data, however, they
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Corrigendum to “Dual quaternion hand-eye calibration algorithm for hunter-prey optimization based on twice opposition-learning and random differential variation” Appl. Soft Comput. J. 154 (2024) 111249 Appl. Soft Comput. (IF 8.7) Pub Date : 2024-04-10 Yun-tao Zhao, Wen Li, Wei-gang Li
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Dynamic mutation late acceptance hill climbing aided red fox optimization for metabolomic biomarkers selection from lung cancer patient sera Appl. Soft Comput. (IF 8.7) Pub Date : 2024-04-09 Shuli Guo, Zhilei Zhao, Lina Han, Lei Wu, Xiaowei Song, Anil Baris Cekderi
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The selection of reform models for provincial rural credit cooperatives unions in China using an extended CPT-TODIM method based on novel type-2 fuzzy numbers Appl. Soft Comput. (IF 8.7) Pub Date : 2024-04-09 Wen Li, Luqi Wang, Zhiliang Ren, Obaid Ur Rehman
The selection of a suitable reform model for provincial rural credit cooperatives unions (PRCCUs) is a pivotal aspect of China’s ongoing financial reform endeavors. This involves optimizing internal processes, enhancing efficiency, minimizing operational costs, elevating service quality, strengthening financial innovation capabilities, and promoting the healthy growth of rural financial markets. However
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A three-way trajectory privacy-preserving model based on multi-feature fusion Appl. Soft Comput. (IF 8.7) Pub Date : 2024-04-09 Jianfeng Xu, Yiping Wei, Yingxiao Chen
The prevalent method in trajectory privacy protection through publishing -1 similar trajectories alongside a target trajectory, often relies on a single feature, which can compromise the balance between privacy, data utility, and processing efficiency. Addressing this, our study introduces a nuanced three-way decision model that integrates multiple trajectory features: staying areas, average velocity
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Research on multi-view clustering algorithm based on sequential three-way decision Appl. Soft Comput. (IF 8.7) Pub Date : 2024-04-09 Yi Xu, Guoqing Niu
Multi-view clustering has achieved many applications in recent years. But the existing multi-view clustering methods face two problems, firstly, the traditional multi-view clustering uses a hard clustering method, which cannot describe the uncertainty between the samples and the clusters, and secondly how to perform effective incremental learning on multi-view data when the number of views increases
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Self-attention and asymmetric multi-layer perceptron-gated recurrent unit blocks for protein secondary structure prediction Appl. Soft Comput. (IF 8.7) Pub Date : 2024-04-08 Dewi Pramudi Ismi, Reza Pulungan, Afiahayati
Protein secondary structure prediction (PSSP) is one of the most prominent and widely-conducted tasks in Bioinformatics. Deep neural networks have become the primary methods for building PSSP models in the last decade due to their potential to enhance PSSP performances. However, there is room for improvement in PSSP as previous studies have yet to reach the theoretical limit of PSSP model performance
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A comprehensive framework for designing and learning fuzzy cognitive maps at the granular level Appl. Soft Comput. (IF 8.7) Pub Date : 2024-04-08 Qimin Zhou, Yingcang Ma, Zhiwei Xing, Xiaofei Yang
Fuzzy Cognitive Maps (FCM) possesses interpretability and fuzzy reasoning capability. It is widely applied in addressing time series prediction problems and various algorithms for learning FCM are proposed. Information granulation method can transfer knowledge from numerical data to granular knowledge. However, how to learn FCM better at the granular level is a challenge. Therefore, we develop a comprehensive
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Optimizing public transport system using biased random-key genetic algorithm Appl. Soft Comput. (IF 8.7) Pub Date : 2024-04-08 João Luiz Alves Oliveira, Andre L.L. Aquino, Rian G.S. Pinheiro, Bruno Nogueira
Planning the public transportation system of a city is a complex process that depends on various factors, including transportation modes, origin–destination demands, service quality and reliability, and operational costs. The vehicle frequency setting (FS) problem is a particularly challenging aspect of this planning process. This work proposes a novel methodology, based on biased random-key genetic
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Exposing the chimp optimization algorithm: A misleading metaheuristic technique with structural bias Appl. Soft Comput. (IF 8.7) Pub Date : 2024-04-08 Lingyun Deng, Sanyang Liu
We conduct a comprehensive, component-based analysis of the highly cited chimp optimization algorithm (ChOA). Aside from disassembling ChOA into its constituent elements and establishing connections with analogous components in existing methodologies like particle swarm optimization, we scrutinize the application of metaphors that inspired ChOA. Our findings reveal that the idea introduced in ChOA
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Physics-Informed Neural Networks with skip connections for modeling and control of gas-lifted oil wells Appl. Soft Comput. (IF 8.7) Pub Date : 2024-04-07 Jonas Ekeland Kittelsen, Eric Aislan Antonelo, Eduardo Camponogara, Lars Struen Imsland
Neural networks, while powerful, often lack interpretability. Physics-Informed Neural Networks (PINNs) address this limitation by incorporating physics laws into the loss function, making them applicable to solving Ordinary Differential Equations (ODEs) and Partial Differential Equations (PDEs). The recently introduced PINC framework extends PINNs to control applications, allowing for open-ended long-range
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A matrix norm-based Pythagorean fuzzy metric and its application in MEREC-SWARA-VIKOR framework for solar panel selection Appl. Soft Comput. (IF 8.7) Pub Date : 2024-04-07 Naveen Kumar, Juthika Mahanta
Due to the escalating electricity demand from industrialization and modernization, it is imperative to explore clean and sustainable energy sources. Solar energy has become a feasible solution for this surging demand. Solar panels, vital components of solar power systems, are crucial in converting sunlight into electricity. However, selecting the most suitable solar panel is a complex task involving
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Design element extraction of plantar pressure imaging employing meta-learning-based graphic convolutional neural networks Appl. Soft Comput. (IF 8.7) Pub Date : 2024-04-06 Dan Wang, Zairan Li, Nilanjan Dey, Rubén González Crespo, Fuqian Shi, R. Simon Sherratt
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Metallic surface defect recognition network based on global feature aggregation and dual context decoupled head Appl. Soft Comput. (IF 8.7) Pub Date : 2024-04-06 Kefei Qian, Lai Zou, Zhiwen Wang, Wenxi Wang
Surface defect detection is a crucial inspection phase in ensuring industrial product quality and reliability, particularly in the context of metallic manufacturing components. While existing deep learning-based approaches have demonstrated some effectiveness, the design of certain network structures still lacks consideration for industrial scenarios. This paper proposed a complex metallic surface
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Modified jaya optimization and TOPSIS for determining the optimal frequency in LF-HVac Appl. Soft Comput. (IF 8.7) Pub Date : 2024-04-06 Mukul Anand, Swapan Kumar Goswami, Debashis Chatterjee, Anagha Bhattacharya, Md. Jalil Piran
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A metaverse framework for IoT-based remote patient monitoring and virtual consultations using AES-256 encryption Appl. Soft Comput. (IF 8.7) Pub Date : 2024-04-04 Zainab Khalid Mohammed, Mazin Abed Mohammed, Karrar Hameed Abdulkareem, Dilovan Asaad Zebari, Abdullah Lakhan, Haydar Abdulameer Marhoon, Jan Nedoma, Radek Martinek
The convergence of Internet of Things (IoT) and metaverse technologies is revolutionizing healthcare. This study introduces a pioneering framework tailored for health monitoring within the metaverse. By reshaping remote patient monitoring and virtual consultations, the framework utilizes vital parameters like heart rate, blood pressure, and body temperature. It integrates IoT sensors, augmented reality
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Optimal design of adaptive model predictive control based on improved GWO for autonomous vehicle considering system vision uncertainty Appl. Soft Comput. (IF 8.7) Pub Date : 2024-04-04 Mahmoud Elsisi
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Bearing fault diagnosis using Gradual Conditional Domain Adversarial Network Appl. Soft Comput. (IF 8.7) Pub Date : 2024-04-04 Chu-ge Wu, Duo Zhao, Te Han, Yuanqing Xia
Given the limited availability of accurately labeled data in fault diagnosis across various industrial scenarios, we proposed a Gradual Conditional Domain Adversarial Network (GCDAN) incorporating various fault categories and rotating speeds. We constructed a prototype system for collecting three-dimensional vibration data samples and modified the network structure to accommodate the input. Inspired
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Hacker group identification based on dynamic heterogeneous graph node update Appl. Soft Comput. (IF 8.7) Pub Date : 2024-04-04 Yijia Xu, Yong Fang, Cheng Huang, Zhonglin Liu, Weipeng Cao
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Automated test case generation for path coverage using Hierarchical Surrogate-Assisted Differential Evolution Appl. Soft Comput. (IF 8.7) Pub Date : 2024-04-04 Lin Gao, Songyan Bai, Mingxing Liu, Fan Li
In the domain of automated test case generation for path coverage (ATCG-PC), commonly used evolutionary optimization algorithms face significant computational overhead due to the necessity of executing the test program for individual fitness evaluations. To address this challenge and enhance efficiency while ensuring comprehensive coverage, this paper proposes a Hierarchical Surrogate-Assisted Differential
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Opinion formation over dynamic hierarchical networks with acquaintances and strangers: A genetic variation based double-mechanism framework Appl. Soft Comput. (IF 8.7) Pub Date : 2024-04-04 Jianglin Dong, Jiangping Hu, Yiyi Zhao, Yuan Peng
Inspired by the psychological phenomenon that agents generally adopt different opinions or action-update mechanisms when faced with different types of neighbors, we propose a novel double-mechanism framework over dynamic hierarchical networks to fill this gap. First, a novel multi-attribute genetic variation-based leader-influencer-follower (LIF) dynamic hierarchical network is developed. Second, we
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Integral reinforcement learning-based angular acceleration autopilot for high dynamic flight vehicles Appl. Soft Comput. (IF 8.7) Pub Date : 2024-04-04 Yingxin Liu, Yuhui Hu, Kai Shen, Jiatai Qiu, Konstantin A. Neusypin
During the synthesis of acceleration autopilots for high dynamic flight vehicles (HDFV), autopilots with feedback of angular acceleration (AFAA) have become more perspective with stringent requirements on response speed and high maneuverability, compared with autopilots with feedback of angular rate (AFAR). Integral reinforcement learning (IRL) method has now proved to be an effective technique for
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Structure-aware preserving projections with applications to medical image clustering Appl. Soft Comput. (IF 8.7) Pub Date : 2024-04-04 Keyang Yu, Yike Zhu, Xuesong Yin, Ting Shu, Yigang Wang, Enliang Hu
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Maximum consensus model with individual tolerance and mixed DEA prospect cross-efficiency for multi-attribute group decision-making Appl. Soft Comput. (IF 8.7) Pub Date : 2024-04-04 Huayou Chen, Longlong Shao, Ligang Zhou, Jinpei Liu
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Ranking products through online opinions: A text analysis and regret theory-based approach Appl. Soft Comput. (IF 8.7) Pub Date : 2024-04-04 Kejia Chen, Jingjing Zheng, Jian Jin
With the development of e-shopping, a list of similar products can be found with a large volume of valuable customer reviews online. However, it is generally difficult to compare various aspects of similar products effectively by understanding all relevant online opinions. To help consumers, in this study, how products are ranked according to online reviews is investigated. Firstly, an SC-LDA (Seed
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Social-aware graph contrastive learning for recommender systems Appl. Soft Comput. (IF 8.7) Pub Date : 2024-04-03 Yuanyuan Zhang, Junwu Zhu, Yonglong Zhang, Yi Zhu, Jialuo Zhou, Yaling Xie
Recommender systems usually encounter the issue of sparse interaction data, which is commonly alleviated by social recommendation models based on graph neural networks. However, these models overlook the collaborative similarity relationship among items and fail to effectively integrate and process various graph structures. To address these issues, we propose a novel ocial-aware raph ontrastive earning
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A Fuzzy Minkowski Distance-based fusion of convolutional neural networks for gastrointestinal disease detection Appl. Soft Comput. (IF 8.7) Pub Date : 2024-04-03 Sohaib Asif, Qurrat-ul-Ain
Accurate classification of gastrointestinal (GI) conditions from medical images is a critical task for facilitating timely diagnosis and effective treatment. However, the reliance on manual diagnosis introduces the possibility of human errors. In response, researchers have been tirelessly working to develop robust computerized methods that can significantly enhance diagnostic accuracy. In this study
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An End-to-end Deep Clustering Method with Consistency and Complementarity Attention Mechanism for Multisensor Fault Diagnosis Appl. Soft Comput. (IF 8.7) Pub Date : 2024-04-03 Zhangjun Wu, Gang Fang, Yifei Wang, Renli Xu
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Modeling of limit order book data with ordered fuzzy numbers Appl. Soft Comput. (IF 8.7) Pub Date : 2024-04-02 Adam Marszałek, Tadeusz Burczyński
This paper presents a novel approach to representing the Limit Order Book data at a given timestamp using the Ordered Fuzzy Numbers concept. The limit order book contains all buy and sell orders placed by investors, updated in real-time, for the most liquid securities, even several hundred times a minute. Due to its irregular nature (different and dynamic changes in the number of buy and sell orders)
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Information granule optimization and co-training based on kernel method Appl. Soft Comput. (IF 8.7) Pub Date : 2024-04-02 Yuzhang Bai, Jusheng Mi, Leijun Li
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Wireless sensor networks-based adaptive differential evolution for multimodal optimization problems Appl. Soft Comput. (IF 8.7) Pub Date : 2024-04-02 Yi-Biao Huang, Zi-Jia Wang, Yu-Hui Zhang, Yuan-Gen Wang, Sam Kwong, Jun Zhang
In wireless sensor networks (WSN), we often detect the monitoring areas among different sensors so that the sensors can be switched on and off adaptively to save energy and extend their lifetime. Inspired by the principle of WSN, a WSN-based adaptive differential evolution (WSNADE) algorithm is proposed in this paper, together with a WSN-based adaptive niching technique (WANT) and two novel strategies
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Directional optimization of elevator scheduling algorithms in complex traffic patterns Appl. Soft Comput. (IF 8.7) Pub Date : 2024-04-01 Yu Wu, Jianjun Yang
Elevator systems in buildings face challenges due to unpredictable passenger flow, which can make scheduling elevators complicated to optimize their operation. Most of the existing algorithms are developed based on pattern recognition and may not be effective in scenarios where patterns are difficult to classify, especially when elevator operations involve uncertain human behaviors. To address this
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Hidden Markov guided Deep Learning models for forecasting highly volatile agricultural commodity prices Appl. Soft Comput. (IF 8.7) Pub Date : 2024-04-01 G. Avinash, V. Ramasubramanian, Mrinmoy Ray, Ranjit Kumar Paul, Samarth Godara, G.H. Harish Nayak, Rajeev Ranjan Kumar, B. Manjunatha, Shashi Dahiya, Mir Asif Iquebal
Predicting agricultural commodity prices accurately is of utmost importance due to various factors such as perishability, seasonality, production uncertainty . Moreover, the substantial volatility that may be exhibited in time series further adds to the complexity and constitutes a significant challenge. In this paper, a Hidden Markov (HM) guided Deep Learning (DL) models has been developed on nonlinear
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Incorporating temporal multi-head self-attention convolutional networks and LightGBM for indoor air quality prediction Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-31 Yifeng Lu, Jinyong Wang, Dongsheng Wang, ChangKyoo Yoo, Hongbin Liu
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Risk measurement of aggregation approaches in multiple attribute decision making under uncertain information Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-30 Jiajia Jiang, Gaocan Gong, Lin Wang, Quanbo Zha
The decision-maker's judgment deviates from uncertain attribute information will lead to decision risk in multiple attribute decision making (MADM), and different aggregation approaches result in different risk levels. This paper aims to study the risk levels of aggregation operators in MADM with uncertain attribute information. We use the signal detection theory to characterize the decision-maker's
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Dynamic metaheuristic selection via Thompson Sampling for online optimization Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-30 Alain Nguyen
It is acknowledged that no single heuristic can outperform all the others in every optimization problem. This has given rise to hyper-heuristic methods for providing solutions to a wider range of problems. In this work, a set of five non-competing low-level heuristics is proposed in a hyper-heuristic framework. The multi-armed bandit problem analogy is efficiently leveraged and Thompson Sampling is
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A binary sparrow search algorithm for feature selection on classification of X-ray security images Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-30 Ahmet Babalik, Aybuke Babadag
In today's world, especially in public places, strict security measures are being implemented. Among these measures, the most common is the inspection of the contents of people's belongings, such as purses, knapsacks, and suitcases, through X-ray imaging to detect prohibited items. However, this process is typically performed manually by security personnel. It is an exhausting task that demands continuous
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An operator-inspired framework for metaheuristics and its applications on job-shop scheduling problems Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-30 Jiahang Li, Xinyu Li, Liang Gao
The job-shop scheduling problem (JSP) is a well-known combinatorial optimization problem in manufacturing systems. For the past two decades, real-number metaheuristics have been widely used to solve the JSP using the real-number transform methods. A limitation of the real-number metaheuristics is the premature convergence due to the stochasticity of the transform methods. To eliminate this limitation
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Carbon emission price point-interval forecasting based on multivariate variational mode decomposition and attention-LSTM model Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-29 Liling Zeng, Huanling Hu, Huajun Tang, Xuejing Zhang, Dabin Zhang
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Effective combining source code and opcode for accurate vulnerability detection of smart contracts in edge AI systems Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-28 Huakun Huang, Longtao Guo, Lingjun Zhao, Haoda Wang, Chenkai Xu, Shan Jiang
Automating transactions using smart contracts extends the functionality of blockchains and secures the decentralization of blockchains in edge AI systems. Whereas, since plenty of smart contracts are deployed to support various decentralized edge applications, the security vulnerabilities of smart contracts will lead to huge irreversible losses. To deal with this problem, many deep learning-based methods
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A federated recommendation algorithm based on user clustering and meta-learning Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-28 Enqi Yu, Zhiwei Ye, Zhiqiang Zhang, Ling Qian, Meiyi Xie
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A parallel chimp optimization algorithm based on tracking-learning and fuzzy opposition-learning behaviors for data classification Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-28 Zhaolin Lai, Guangyuan Li, Xiang Feng, Xiaochun Hu, Caoqing Jiang
Chimp optimization algorithm (ChOA), which simulates the social behaviors of chimps, is a novel swarm intelligence algorithm for solving global optimization problems. ChOA has the advantages of fast convergence and avoiding falling into local optimum. However, the global search capability is weakened and the time overhead is too large when solving complex optimization problems. In order to improve
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Ensemble classifiers using multi-objective Genetic Programming for unbalanced data Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-27 Wenyang Meng, Ying Li, Xiaoying Gao, Jianbin Ma
Genetic Programming (GP) can be used to design effective classifiers due to its built-in feature selection and feature construction characteristics. Unbalanced data distributions affect the classification performance of GP classifiers. Some fitness functions have been proposed to solve the class imbalance problem of GP classifiers. However, with the evolution of GP, single-objective GP classifiers
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A solution to multi Objective Stochastic Optimal Power Flow problem using mutualism and elite strategy based Pelican Optimization Algorithm Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-27 Bimal Kumar Dora, Sunil Bhat, Sudip Halder, Ishan Srivastava
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A novel network for semantic segmentation of landslide areas in remote sensing images with multi-branch and multi-scale fusion Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-26 Kai Wang, Daojie He, Qingqiang Sun, Lizhi Yi, Xiaofeng Yuan, Yalin Wang
Landslides pose significant risks as natural disasters, highlighting the importance of accurate mapping using remote sensing images for various practical applications. However, due to the challenges arising from incomplete and inaccurate boundary information of foreground landslide polygons, existing methods can only achieve suboptimal performance. To this premise, in this paper, we propose a segmentation
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Optimizing investment portfolios with a sequential ensemble of decision tree-based models and the FBI algorithm for efficient financial analysis Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-26 Jui-Sheng Chou, Ke-En Chen
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Improved learning efficiency of deep Monte-Carlo for complex imperfect-information card games Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-26 Qian Luo, Tien-Ping Tan
Deep Reinforcement Learning (DRL) has achieved considerable success in games involving perfect and imperfect information, such as Go, Texas Hold’em, Stratego, and DouDiZhu. Nevertheless, training a state-of-the-art model for complex imperfect-information card games like DouDiZhu and Big2 remains resource and time-intensive. To address this challenge, this paper introduces two innovative methods: the
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Particle Filter based on Jaya optimisation for Bayesian updating of nonlinear models Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-26 Amardeep Amavasai, Jelke Dijkstra
Particle filter (PF) is a powerful and commonly used filtering technique based on Sequential Monte Carlo framework. The main challenge in using PF for nonlinear state and parameter estimation is the degeneracy of particles. Although resampling techniques can solve this to some extent, it would still result in particle impoverishment when a limited number of particles are used thereby affecting the
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Sentiment analysis on a low-resource language dataset using multimodal representation learning and cross-lingual transfer learning Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-26 Aruna Gladys A., Vetriselvi V.
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Credit risk prediction based on an interpretable three-way decision method: Evidence from Chinese SMEs Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-26 Meng Pang, Fengjuan Wang, Zhe Li
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A dynamic graph structure identification method of spatio-temporal correlation in an aluminum electrolysis cell Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-26 Yubo Sun, Xiaofang Chen, Lihui Cen, Weihua Gui, Chunhua Yang, Zhong Zou
The dynamic correlation analysis of cell-spatial information (distributed anode current signal, DACS) is of great significance in the regional-refined control of industrial aluminum electrolysis cell. Due to the strong-dynamic spatio-temporal correlation of DACS and the complex dynamic cell noise, the existing methods are difficult to effectively obtain the spatio-temporal correlation analysis results
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Multi-head attention ResUnet with sequential sliding windows for sea surface height anomaly field forecast: A regional study in North Atlantic Ocean Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-26 Zeguo Zhang, Jianchuan Yin, Lijun Wang
Accurate and efficient prediction of Sea surface height anomaly (SSHA) field is very important for operational marine monitoring and engineering. It is also a vital indicator of better understanding global climate changes and ocean dynamics. Many traditional SSHA forecasting methods focused primarily on single grid-point based predictions, and some classical Recurrent Neural Network/Long-Short-Term-Memory
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Complex q-rung orthopair fuzzy Yager aggregation operators and their application to evaluate the best medical manufacturer Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-26 Shumaila Javeed, Mubashar Javed, Izza Shafique, Muhammad Shoaib, Mansoor Shaukat Khan, Lirong Cui, Sameh Askar, Ahmad M. Alshamrani
In contemporary healthcare systems, the selection and judicious choice of the right medical device becomes paramount consideration for accurate diagnosis, patient care, mitigate risk and cost incurred on diagnosis of disease. Moreover, medical devices are requisite indispensable tools to help healthcare professionals for effective diagnosis, selection of the right treatment amongst different options
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Parameter-efficient fine-tuning large language model approach for hospital discharge paper summarization Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-24 Joyeeta Goswami, Kaushal Kumar Prajapati, Ashim Saha, Apu Kumar Saha
Text summarization in medical domain is one of the most crucial chores as it deals with the critical human information. Consequently the proper summarization and key point extraction from medical deeds using pre-trained Language models is now the key figure to be focused on for the researchers. But due to the considerable amount of real-world data and enormous amount of memory requirement to train
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Brownian motion based multi-objective particle swarm optimization methodology and application in binary classification Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-24 Shiwei Liu, Yong Liu, Qiaohua Wang, Weiguo Lin, Yanhua Sun, Lingsong He
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Research orientation and novelty discriminant for new metaheuristic algorithms Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-24 Zhongbo Hu, Qian Zhang, Yujie Wang, Qinghua Su, Zenggang Xiong
The rapid rate of generating a new metaheuristic algorithm almost every month is causing increasing concerns and disputes about their novelty. To stop the disputes and steer algorithm design in a healthy direction, this article presents a discriminant method of novelty and a research orientation for metaheuristic algorithms. The novelty discriminant is implemented by two novel mathematical definitions
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Augmented support vector regression with an autoregressive process via an iterative procedure Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-23 Jinran Wu, You-Gan Wang, Hao Zhang
The Support Vector Regression (SVR) technique can approximate intricate systems by addressing learning and estimation challenges within a reproducing kernel Hilbert space, devoid of reliance on specific parameter assumptions. However, when dealing with correlated data like time series, the SVR method often falls short in accounting for underlying temporal structures, leading to limited enhancements
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A multi-granularity hierarchical network for long- and short-term forecasting on multivariate time series data Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-23 Hong Yu, Zongqiang Wang, Yongfang Xie, Guoyin Wang
Multivariate time series forecasting is a significant research problem in many fields such as economics, finance and transportation, where simultaneous long- and short-term forecasting is required. However, current techniques are typically limited to a single short-term or a long-term forecast. To address the limitation, a novel multi-granularity hierarchical network, GNet-LS, is proposed for long-
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A GAN-based method for diagnosing bodywork spot welding defects in response to small sample condition Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-23 Chen Geng, Sheng Buyun, Fu Gaocai, Chen Xiangxiang, Zhao Guangde
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Heuristic computing performances based Gudermannian neural network to solve the eye surgery corneal model Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-23 Zulqurnain Sabir, Muhammad Umar, Hafiz Abdul Wahab, Shahid Ahmad Bhat, Canan Unlu
The current work is related to present the solutions of the corneal shape-based eye surgery model (CSESM) by applying the novel procedures of Gudermannian neural network (GNN) along with the hybrid optimization of the global and local approaches of heuristic genetic algorithm (GA) and sequential quadratic programing (SQP), i.e., GNN-GASQP. An error function is constructed using the terminologies of