Article
Environmental Sciences
Yanling Wang, Liangsheng Shi, Xiaolong Hu, Wenxiang Song, Lijun Wang
Summary: This study proposes multiphysics-informed neural networks for soil water-heat systems, where soil moisture and temperature complement each other well. The framework improves existing soil moisture neural networks to reduce their dependency on measurement density and employs soil moisture data to promote soil temperature dynamic learning and thermal conductivity estimation. Soil temperature data assists in recovering the nonlinearity of soil hydraulic conductivity, allowing better estimations of soil water flux density.
WATER RESOURCES RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Yejin Kim, Seok Yong Lim, Kwang Yeom Kim, Tae Sup Yun
Summary: This study predicts the compressional wave velocity (V-p) of cylindrically cored cement-reinforced soils using a convolutional neural network (CNN) model. The trained network reliably predicts V(p) with reasonable performance, and it can also obtain consecutive V(p) profiles.
COMPUTERS AND GEOTECHNICS
(2023)
Article
Multidisciplinary Sciences
Aryan Mobiny, Pengyu Yuan, Supratik K. Moulik, Naveen Garg, Carol C. Wu, Hien Van Nguyen
Summary: Recent research indicates that Bayesian neural networks can help us understand when deep neural networks are more likely to make mistakes, thereby increasing the safety of deep learning technology in sensitive applications. The Monte Carlo DropConnect method provides us with a tool to represent model uncertainty with little change in the overall model structure or computational cost. Empirical results demonstrate significant improvement in both prediction accuracy and uncertainty estimation quality compared to the state of the art.
SCIENTIFIC REPORTS
(2021)
Article
Computer Science, Information Systems
Jiachun Feng, Zusheng Zhang, Cheng Ding, Yanghui Rao, Haoran Xie, Fu Lee Wang
Summary: This article introduces a Context Reinforced Neural Topic Model (CRNTM) to address the issue of feature sparsity in short texts. The proposed model infers topics for each word in a narrow range and utilizes pre-trained word embeddings for topic modeling. Extensive experiments validate the effectiveness of this model in topic discovery and text classification.
INFORMATION SCIENCES
(2022)
Article
Chemistry, Multidisciplinary
Joaquim Tinoco, Antonio Alberto S. Correia, Paulo J. Venda Oliveira
Summary: Reinforcing stabilized soils with fibers is an effective tool to overcome the weak tensile/flexural strength and brittleness of stabilized soils. Using Machine Learning techniques to predict compressive and tensile strength of soil-binder-water mixtures reinforced with fibers results in accurate estimations and useful predictive outcomes.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Nga Thanh Duong, Khiem Quang Tran
Summary: This study focuses on developing artificial neural network-based models for preliminary prediction of seepage velocity and piping resistance of fiber-reinforced soil. The results indicate that ANN models show great performance and accuracy in predicting these problems.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Multidisciplinary Sciences
Muhammad Asghar, Muhammad Faisal Javed, M. Ijaz Khan, Sherzod Abdullaev, Fuad A. Awwad, Emad A. A. Ismail
Summary: This study develops empirical models using GEP, ANN, and XG Boost to determine the CS and TS of BFRC. The results show that GEP can accurately forecast the CS and TS of BFRC. This study also investigates the ideal BF content for industrial-scale BFRC reinforcement.
SCIENTIFIC REPORTS
(2023)
Article
Computer Science, Artificial Intelligence
Kun Zhou, Sung-Kwun Oh, Jianlong Qiu, Witold Pedrycz, Kisung Seo
Summary: This paper presents a novel structure of reinforced two-stream fuzzy neural networks (TSFNNs) realized with the aid of fuzzy logic and transfer learning method. The architecture consists of a TSFNN and a fusion strategy, combining fuzzy rules-based radial basis function neural networks (FRBFNN) and convolutional neural networks (CNN). The fusion strategy concatenates the outputs of two streams using a softmax function, and a transfer learning method is used to reconstruct new data representation for the CNN. The proposed method improves the classification performance and has been validated through experimental results.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Materials Science, Multidisciplinary
Jamel Baili, Ali Raza, Marc Azab, Khawar Ali, Mohamed Hechmi El Ouni, Hammad Haider, Muhammad Ahmad Farooq
Summary: This research investigates the structural performance of glass fiber-reinforced polymer and steel-reinforced concrete columns. Experimental results show that columns with glass hybrid fibers have lower axial strengths and higher ductility indices compared to columns with steel hybrid fibers. Neural network models and theoretical equations are developed to accurately predict the performance of these materials.
MECHANICS OF ADVANCED MATERIALS AND STRUCTURES
(2023)
Article
Chemistry, Analytical
Wanqi Yang, Fusheng Li, Shubin Lyu, Qinglun Zhang, Yanchun Zhao
Summary: Soil is a significant source of potentially toxic metals that can enter the human body through the food chain. Accurate determination of elemental concentrations in soil is crucial for protecting human health, requiring reliable detection techniques. This study proposed a new quantitative analysis method that combines pre-processing and concentration prediction. The method achieved enhanced accuracy in identifying and removing spectral background, establishing instrument calibration curves, and accurately detecting the elemental concentration using a hierarchical deep neural network. The proposed method provides a new option for the elemental analysis of samples rich in potentially toxic metals.
JOURNAL OF ANALYTICAL ATOMIC SPECTROMETRY
(2023)
Article
Multidisciplinary Sciences
Richard J. Licata, Piyush M. Mehta
Summary: This article introduces the application of machine learning in space weather problems and proposes two techniques to predict thermospheric density while providing reliable uncertainty estimates.
SCIENTIFIC REPORTS
(2022)
Article
Multidisciplinary Sciences
Feng Pan, Bingyao Huang, Chunhong Zhang, Xinning Zhu, Zhenyu Wu, Moyu Zhang, Yang Ji, Zhanfei Ma, Zhengchen Li
Summary: In this study, a deep learning model called SAVSNet is proposed for student dropout prediction. By smoothing time series and integrating missingness patterns, the model is able to provide accurate predictions even in the presence of data volatility and sparsity.
Article
Computer Science, Artificial Intelligence
Guangzeng Chen, Guangke Chen, Yunjiang Lou
Summary: This article mathematically proves that the rate-independent Preisach model is a dRNN neural network and investigates the hysteresis nature and conditions of the classical dRNN with tanh activation function. Experiments show that the classical dRNN models both kinds of hysteresis more accurately and efficiently than the Preisach model.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Environmental Sciences
Dmitry Rukhovich, Polina Koroleva, Danila D. Rukhovich, Alexey D. Rukhovich
Summary: This paper proposes a method for constructing soil maps based on multi-temporal analysis of the bare soil surface (BSS) using deep machine learning. The method uses spectral neighborhood of the soil line (SNSL) technology for the detection of BSS and achieves automatic recognition using computer vision based on neural networks. The detection of degradation is based on the average long-term spectral characteristics, and the constructed maps show good correlation with ground verifications. Therefore, the proposed method is effective for generating soil degradation maps.
Article
Mechanics
Shuvayan Brahmachary, Ananthakrishnan Bhagyarajan, Hideaki Ogawa
Summary: This study combines fluid mechanics and machine learning to develop a new approach for fast and accurate prediction of internal flowfields in hypersonic airbreathing propulsion scramjet intakes. It highlights the importance of tuning parameters and using multiple reduced-order bases, as well as the potential of utilizing bias in building a reduced-order predictive framework in optimization problems.
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)