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Solution of the family traveling salesman problem using a hyper-heuristic approach Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-13 Venkatesh Pandiri, Alok Singh
This article is concerned with a recently introduced variant of the generalized traveling salesman problem (GTSP) called the family traveling salesman problem (FTSP). Given a set of nodes partitioned into multiple clusters termed as families, the FTSP consists in finding a tour visiting a pre-specified number of nodes from each of these families in such a manner that the total distance traveled is
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A comprehensive review and experimental comparison of deep learning methods for automated hemorrhage detection Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-13 A.S. Neethi, Santhosh Kumar Kannath, Adarsh Anil Kumar, Jimson Mathew, Jeny Rajan
Hemorrhagic stroke poses a critical medical emergency that necessitates prompt and accurate diagnosis to prevent irreversible brain damage. The emergence of automated deep learning methods for identifying hemorrhagic stroke on medical imaging scans has garnered significant interest in recent years. These methods are specifically designed to assist radiologists in identifying and highlighting abnormalities
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A frequency and topology interaction network for hyperspectral image classification Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-12 Shuaishuai Fan, Qikang Liu, Weiming Li, Hongyang Bai
Most existing Convolutional Neural Networks (CNNs), Transformers, and their variants have limitations in capturing relationships between hyperspectral image (HSI) data, leading to unclear descriptions of region boundaries and limited generalization abilities. While semi-supervised Graph Neural Networks (GNNs) come with higher computational costs. Therefore, this paper proposes a method interacting
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Neural natural language processing for long texts: A survey on classification and summarization Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-12 Dimitrios Tsirmpas, Ioannis Gkionis, Georgios Th. Papadopoulos, Ioannis Mademlis
The adoption of Deep Neural Networks (DNNs) has greatly benefited Natural Language Processing (NLP) during the past decade. However, the demands of long document analysis are quite different from those of shorter texts, while the ever increasing size of documents uploaded online renders automated understanding of lengthy texts a critical issue. Relevant applications include automated Web mining, legal
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Combined analysis of thermofluids and electromagnetism using physics-informed neural networks Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-12 Yeonhwi Jeong, Junhyoung Jo, Tonghun Lee, Jihyung Yoo
A physics-informed neural network was developed for estimating a solution to a multi-physics problem involving electromagnetism, fluid dynamics, and heat transfer. The multi-physical phenomenon was modeled on a cylindrical conductor with electrical and magnetic field, as well as heat transfer between the conductor and the surrounding. For improved performance, the physics-informed neural network was
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Semi-supervised nonnegative matrix factorization with label propagation and constraint propagation Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-12 Yuanjian Mo, Xiangli Li, Jianping Mei
Semi-supervised nonnegative matrix factorization (SNMF) improves the clustering effect of nonnegative matrix factorization (NMF) by integrating label information. However, as one of the most commonly used semi-supervised learning methods, label propagation algorithm (LP) has some limitations due to its heavy dependence on the predefined similarity matrix. To address this deficiency and effectively
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Selection of the structural severest design ground motions based on big data and random forest Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-12 Xiaohong Long, Chunde Lu, Xiaopeng Gu, Yongtao Ma, Zonglin Li
When calculating the time history of the seismic response, the conventional approach only processes a limited number of ground motions because of the elevated computational costs associated with nonlinear time history analysis and the lack of useable earthquake data. To solve this problem, a selection method for the severest design ground motions based on big data and Random Forest is proposed. Firstly
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A complete artificial intelligence pipeline for radio frequency energy prediction in cellular bands for energy harvesting systems Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-12 Shaimaa H. Mohammed, Ashraf S. Mohra, Ashraf Y. Hassan, Ahmed F. Elnokrashy
Radio Frequency (RF) energy harvesting has been used to power wireless and low-powered devices. However, RF energy harvesting has limitations in terms of the amount of power that can be collected based on signal availability. Hence, energy prediction is essential to improve energy harvesting circuits' performance. Previous research has mainly focused on improving power harvesting policies or theoretically
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Grey prediction of carbon emission and carbon peak in several developing countries Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-12 Kai Cai, Lifeng Wu
The carbon emissions in BRICS countries (Brazil, Russia, India, China and South Africa) account for nearly half of global carbon emissions. To explore the future carbon emissions in BRICS countries and the carbon peak in China and India, the grey Gompertz model with new information priority accumulation is proposed. Intelligent algorithms are used in the parameter optimization. The new model not only
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Enhancing sustainability in supply chain management using softmax Schweizer-Sklar information aggregation Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-11 Yasir Yasin, Muhammad Riaz, Rukhsana Kausar, Muhammad Aslam
Selecting sustainable suppliers is crucial to achieving sustainability objectives within the supply chain. However, the inherent complexity and uncertainty associated with evaluating supplier performance necessitate the use of fuzzy decision-making tools. This paper proposes the use of cubic intuitionistic fuzzy sets (CIFS) with four aggregation operators (AOs) based on Schweizer-Sklar (SS) t-norm
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A new Takagi–Sugeno–Kang model to time series forecasting Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-11 Kaike Sa Teles Rocha Alves, Caian Dutra de Jesus, Eduardo Pestana de Aguiar
A fuzzy inference system consists of a machine learning concept that combines accuracy and interpretability. They are divided into two main groups: Mamdani and Takagi–Sugeno-Kang. While Mamdani models favor interpretability, Takagi–Sugeno-Kang models provide more accurate results because of their ability to approximate a nonlinear system through a collection of linear subsystems. The evolving Takagi
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A hybrid deep learning model for urban expressway lane-level mixed traffic flow prediction Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-11 Heyao Gao, Hongfei Jia, Qiuyang Huang, Ruiyi Wu, Jingjing Tian, Guanfeng Wang, Chao Liu
Precise real-time traffic flow prediction is crucial for route guidance and traffic fine control. With the development of autonomous driving, the mixed traffic flow state composed of Connected Automated Vehicles (CAVs) and Human-driven Vehicles (HVs) provides new insight into traffic flow prediction. In this paper, we innovatively consider the interaction between heterogeneous traffic flow as well
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A self-supervised contrastive change point detection method for industrial time series Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-11 Xiangyu Bao, Liang Chen, Jingshu Zhong, Dianliang Wu, Yu Zheng
Manufacturing process monitoring is crucial to ensure production quality. This paper formulates the detection problem of abnormal changes in the manufacturing process as the change point detection (CPD) problem for the industrial temporal data. The premise of known data property and sufficient data annotations in existing CPD methods limits their application in the complex manufacturing process. Therefore
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Defect recognition in sonic infrared imaging by deep learning of spatiotemporal signals Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-11 Jinfang Xie, Zhi Zeng, Yue Ma, Yin Pan, Xinlin Wu, Xiaoyan Han, Yibin Tian
In the realm of active thermography, most studies have used spatial-domain images as the primary input for defect recognition, while some have explored the utilization of temporal-domain one-dimensional signals. However, Sonic Infrared Imaging (SonicIR), as an active thermography non-destructive testing method, presents unique challenges in automatically distinguishing defect signals from background
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Learning to disentangle and fuse for fine-grained multi-modality ship image retrieval Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-11 Wei Xiong, Zhenyu Xiong, Pingliang Xu, Yaqi Cui, Haoran Li, Linzhou Huang, Ruining Yang
Multi-modality ship image retrieval aims to retrieve ship images from a large dataset, encompassing various modalities, when provided with a query ship image. One of the key challenges is addressing intra-modality variations and cross-modality discrepancies resulting from complex image content and different types of imaging systems. Current multi-modality retrieval methods primarily focus on extracting
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Adaptive goal recognition using process mining techniques Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-10 Zihang Su, Artem Polyvyanyy, Nir Lipovetzky, Sebastian Sardiña, Nick van Beest
Goal Recognition (GR) is a research problem that studies ways to infer the goal of an intelligent agent based on its observed behavior and knowledge of the environment in which the agent operates. A common assumption of GR is that the environment is static. However, in many real-world scenarios, for example, recognizing customers’ preferences, it is necessary to recognize the goals of multiple agents
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A survey on semi-supervised graph clustering Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-10 Fatemeh Daneshfar, Sayvan Soleymanbaigi, Pedram Yamini, Mohammad Sadra Amini
Semi-Supervised Graph Clustering (SSGC) has emerged as a pivotal field at the intersection of graph clustering and semi-supervised learning (SSL), offering innovative solutions to intricate data analysis problems. However, despite its significance and wide-ranging applications, there exists a notable void in the literature—a comprehensive survey specifically dedicated to SSGC techniques and their diverse
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Incremental learning with multi-fidelity information fusion for digital twin-driven bearing fault diagnosis Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-10 Xufeng Huang, Tingli Xie, Shuyang Luo, Jinhong Wu, Rongmin Luo, Qi Zhou
Digital twin (DT)-driven intelligent fault diagnosis (IFD) has been a hot topic, which can support personalized monitoring of critical machinery. A central challenge is that diagnostic models using deep learning (DL) suffer from the problem of catastrophic forgetting if personalized faults occur in dynamic environments. To deal with this issue, this article presents a class-incremental learning method
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Attribute granules-based object entropy for outlier detection in nominal data Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-10 Chang Liu, Dezhong Peng, Hongmei Chen, Zhong Yuan
Concept lattice theory, which is one of the key mathematical models of granular computing, is capable of successfully dealing with uncertain information in nominal data. It has been applied to machine learning tasks such as data reduction, classification, and association rule mining. For the problem of outlier detection in nominal data, this paper presents a concept lattice theory-based approach for
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Spatial air quality prediction in urban areas via message passing Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-10 Sergio Calo, Filippo Bistaffa, Anders Jonsson, Vicenç Gómez, Mar Viana
Air pollution in urban areas poses a significant and pressing challenge for modern society. Unfortunately, the existing network of pollution detectors in many cities is limited in scope and fails to adequately cover the entire geographical area. Consequently, the implementation of spatial prediction algorithms becomes essential to generate high-resolution data. In this paper, we introduce two significant
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A multi-task learning model for recommendation based on fusion of dynamic and static neighbors Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-10 Bo Huang, Sirui Zheng, Hamido Fujita, Jin Liu
To improve recommendation performance, this study introduces self-supervised learning into recommendation systems and proposes a multi-task learning recommendation framework that combines static neighbor and dynamic neighbor contrastive learning. Specifically, this study considers node relationships at both the graph and embedding levels, which can be defined in two aspects: (1) Static neighbors, which
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Load forecasting model considering dynamic coupling relationships using structured dynamic-inner latent variables and broad learning system Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-10 Ziwen Gu, Yatao Shen, Zijian Wang, Jiayi Qiu, Wenmei Li, Chun Huang, Yaqun Jiang, Peng Li
Integrated energy systems (IES) can effectively regulate and optimize dynamic loads by utilizing load forecasting, which intelligently manages energy scheduling. Nevertheless, the insufficient attention given by existing research to loads with multiple frequency scales and dynamic coupling relationships in the IES may lead to a reduced accuracy in load forecasting. To address this issue, a novel load
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An unsupervised anomaly detection framework for onboard monitoring of railway track geometrical defects using one-class support vector machine Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-10 Ramin Ghiasi, Muhammad Arslan Khan, Danilo Sorrentino, Cassandre Diaine, Abdollah Malekjafarian
Track geometry is one of the critical indicators of railway tracks’ condition which requires continuous monitoring and maintenance over time. In this paper, a novel artificial intelligence (AI) based framework is proposed for railway track geometry inspection using vibration data collected from a dedicated measuring high-speed train. This AI-based anomaly track detection approach consists of two main
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A regularized constrained two-stream convolution augmented Transformer for aircraft engine remaining useful life prediction Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-10 Zhu Jiangyan, Jun Ma, Jiande Wu
Remaining Useful Life (RUL) prediction is of great significance for maintaining the reliability and safety of industrial equipment. To address the challenges faced by existing methods in simultaneously extracting local and global degradation information from monitoring data. This paper proposes a Two-Stream Convolution Augmented Transformer (TACT) model based on regularization constraint. Specifically
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Event-centric hierarchical hyperbolic graph for multi-hop question answering over knowledge graphs Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-10 Xun Zhu, Wang Gao, Tianyu Li, Wenguang Yao, Hongtao Deng
Question Answering over Knowledge Graphs (KGQA) blends natural language processing with structured knowledge representation. While much attention of existing research has been given to entity-centric representations, the significance of events has not been fully explored. This paper introduces a novel Event-centric Hierarchical Hyperbolic Graph system for KGQA that effectively integrates entity and
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Soft computing methods in the solution of an inverse heat transfer problem with phase change: A comparative study Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-08 Tomáš Mauder, Jakub Kůdela, Lubomír Klimeš, Martin Zálešák, Pavel Charvát
Inverse heat transfer problems are ill-posed problems and their solution is challenging. Conventional (hard computing) solution methods were developed for this purpose in the past, but they are not well applicable in cases including phase change, which contain strong non-linearity and bring additional computational difficulties. Soft computing methods, which currently experience very rapid development
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Multilayer adaptive critic design with digital twin for data-driven optimal tracking control and industrial applications Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-08 Ding Wang, Hongyu Ma, Junfei Qiao
In this paper, an optimal trajectory tracking control problem for general nonlinear systems is investigated. An adaptive critic control method with the digital twin (DT) theory is developed. Divergent from the existing tracking control methods, the advantages of adaptive dynamic programming (ADP) and the theory of DT are combined in this paper, and the novel multilayer artificial system structure is
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Fault diagnosis of mine main ventilator based on multi-eigenvalue selection and data fusion Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-08 Zuzhi Tian, Xiankang Huang, Fangwei Xie, Xiangfan Wu, Jinjie Ji, Yangyang Guo
As a strong nonlinear complex system, the mine main ventilator is difficult to select eigenvalues of the ventilator fault. This study proposed a multiple eigenvalue selection method based on ensemble empirical modal decomposition (EEMD). Firstly, an experimental platform of axial flow fan was built to simulate the bearing fault and fan blade fault. Secondly, the vibration and wind pressure data of
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A hybrid deep learning method for the prediction of ship time headway using automatic identification system data Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-08 Quandang Ma, Xu Du, Cong Liu, Yuting Jiang, Zhao Liu, Zhe Xiao, Mingyang Zhang
Ship Time Headway (STH) is used in maritime navigation to describe the time interval between the arrivals of two consecutive ships in the same water area. This measurement may offer a straightforward way to gauge the frequency of ship traffic and the likelihood of congestion in a particular area. STH is an important factor in understanding and managing the dynamics of ship movements in busy waterways
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A fast intrusion detection system based on swift wrapper feature selection and speedy ensemble classifier Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-08 Ezgi Zorarpaci
Due to the widespread use of the internet, computer network systems may be exposed to different types of attacks. For this reason, the intrusion detection systems (IDSs) are often used to protect the network systems. Network traffic data (i.e., network packets) includes many features. However, most of them are irrelevant and can lead to a decrease in the runtime and/or the detection performance of
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Development of optimized machine learning models for predicting flat plate solar collectors thermal efficiency associated with Al2O3-water nanofluids Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-08 Omer A. Alawi, Haslinda Mohamed Kamar, Sinan Q. Salih, Sani Isah Abba, Waqar Ahmed, Raad Z. Homod, Mehdi Jamei, Shafik S. Shafik, Zaher Mundher Yaseen
Predictions of thermal performance (η) of flat plate solar collectors (FPSCs) can provide essential information for diverse engineering applications such as thermal and energy areas. Several thermal and operating parameters influence η, and its prediction and quantification are highly complex and challenging. The current research was adopted to investigate the potential of different machine learning
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A dual strategy of adaptive knee-point guidance and niche sampling for non-cyclic dynamic multiobjective optimization problems Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-08 Hao Sun, Cong Wang, Ziyu Hu
Current theoretical studies on dynamic multiobjective optimization problems (DMOPs) are based on periodic problems, while most DMOPs in real life have non-cyclic variations. To better reflect real-life problems, a non-cyclic benchmark test suite was designed to better test the performance of algorithms. The proposed test suite contains 15 problems that introduce difficult and complex geometric features
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Residual attention enhanced Time-varying Multi-Factor Graph Convolutional Network for traffic flow prediction Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-08 Yinxin Bao, Qinqin Shen, Yang Cao, Weiping Ding, Quan Shi
Precise and timely traffic flow prediction holds significant importance in alleviating traffic congestion. Despite the success of graph convolution traffic flow prediction methods, there is still room for improvement in global spatial feature extraction and external factor measurement. To address this challenge, a novel Residual attention enhanced Time-varying Multi-factor Graph Convolutional Network
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Dynamic normalization supervised contrastive network with multiscale compound attention mechanism for gearbox imbalanced fault diagnosis Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-08 Yutong Dong, Hongkai Jiang, Wenxin Jiang, Lianbing Xie
Deep learning has gained significant success in fault diagnosis. However, the number of gearbox health samples is inevitably much larger than that of fault samples in real-world engineering, which severely limits the diagnostic performance of such methods. Thus, this paper put forward a dynamic normalization supervised contrastive network (DNSCN) with a multiscale compound attention mechanism to recognize
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Customer preference analysis integrating online reviews: An evidence theory-based method considering criteria interaction Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-08 Mei Cai, Chen Yang
Conventional preference analysis methods often implicitly neglect the influence of online review reliability and cannot fully consider customer psychological needs, e.g., reference-level dependence and interactions among criteria, which results in insufficient explanatory power and prediction of customer behavior. In this paper, an evidence theory-based multicriteria group decision-making (MCGDM) method
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On the application of symbolic regression in the energy sector: Estimation of combined cycle power plant electrical power output using genetic programming algorithm Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-07 Nikola Anđelić, Ivan Lorencin, Vedran Mrzljak, Zlatan Car
This paper focuses on the estimation of electrical power output () in a combined cycle power plant (CCPP) using ambient temperature (AT), vacuum in the condenser (V), ambient pressure (AP), and relative humidity (RH). The study stresses accurate estimation for better CCPP performance and energy efficiency through responsive control to changing conditions. The novelty lies in applying genetic programming
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Unpaired robust hashing with noisy labels for zero-shot cross-modal retrieval Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-07 Kailing Yong, Zhenqiu Shu, Zhengtao Yu
With new social media concepts emerging, zero-shot cross-modal retrieval methods have gained significant attention. Most of the existing methods assume that the labels of training data are correct and the different modalities are perfectly matched, which is unrealistic in real-life retrieval scenarios. This paper presents a novel approach, termed unpaired robust hashing with noisy labels (URHNL), for
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Enhancing interval-valued time series forecasting through bivariate ensemble empirical mode decomposition and optimal prediction Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-07 Zhifu Tao, Wenqing Ni, Piao Wang
Interval-valued time series (ITS) has been widely concerned by the academic community due to its outstanding performance in dealing with the uncertainty of systems. Numerous ITS forecasting studies have emerged, while the most popular method is based on “divide and conquer”. For ITS analysis, bivariate empirical mode decomposition (BEMD) is currently the main tool that can simultaneously consider the
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Design of a personalized cognitive layered framework for optimal extraction of mathematical teaching techniques Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-06 Srivani M., Abirami S.
Mathematical teaching techniques encompass a diverse range of approaches for students to grasp mathematical concepts. Currently, various mathematical teaching techniques exist, and it is crucial to extract personalized teaching techniques based on students’ knowledge level. Cognitive Computing systems in the learning domain open a substantial range of possible alternatives, thereby helping both students
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Evaluation of Artificial Intelligence-Based Solid Waste Segregation Technologies through Multi-Criteria Decision-Making and complex q-rung picture fuzzy Frank aggregation operators Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-06 Fathima Banu M., Subramanian Petchimuthu, Hüseyin Kamacı, Tapan Senapati
In the 21st century, global waste challenges worsen in developing nations relying on manual sorting. This improper waste disposal poses significant threats to human health and the environment, necessitating the adoption of Artificial Intelligence-Based Solid Waste Segregation Technology (AIBSWST). In this context, the advanced Frank t-norm captures nuanced relationships in fuzzy logic, crucial in scenarios
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Bayesian Quality-Diversity approaches for constrained optimization problems with mixed continuous, discrete and categorical variables Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-06 Loïc Brevault, Mathieu Balesdent
Complex system design problems, such as those involved in aerospace engineering, require the use of numerically costly simulation codes in order to predict the performance of the system to be designed. In this context, these codes are often embedded into an optimization process to provide the best design while satisfying the design constraints. Recently, new approaches, called Quality-Diversity, have
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An analysis of effect of higher order endothermic/exothermic chemical reaction on magnetized casson hybrid nanofluid flow using fuzzy triangular number Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-06 M. Shanmugapriya, R. Sundareswaran, S. Gopi Krishna, Madhumangal Pal
In this study, we considered higher-order endothermic/exothermic chemical reactions with activation energy on Casson hybrid nanofluid flow over a moving wedge under the fuzzy atmosphere, which includes the influence of thermal radiation, thermophoresis and Brownian diffusion. The main purpose of this analysis is to investigate the significance of fuzzy volume percentage on Casson hybrid nanofluid flow
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A multi-fidelity deep operator network (DeepONet) for fusing simulation and monitoring data: Application to real-time settlement prediction during tunnel construction Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-06 Chen Xu, Ba Trung Cao, Yong Yuan, Günther Meschke
Ground settlement prediction during mechanized tunneling is of paramount importance and remains a challenging research topic. Typically, two paradigms are existing: a physics-driven approach utilizing numerical simulation models for prediction, and a data-driven approach employing machine learning techniques to learn mappings between influencing factors and the settlement. To integrate the advantages
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Rank-based multimodal immune algorithm for many-objective optimization problems Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-05 Hainan Zhang, Jianhou Gan, Juxiang Zhou, Wei Gao
The immune algorithm (IA) is a prestigious heuristic algorithm based on a model of an artificial immune system, and the IA has shown promising results in the multi-objective optimization field. However, the algorithm’s low search ability in high-dimensional space and the clone assignment metric problem must be addressed. Thus, to solve these problems, we propose a rank-based multimodal immune algorithm
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An efficient skeleton learning approach-based hybrid algorithm for identifying Bayesian network structure Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-05 Niantai Wang, Haoran Liu, Liyue Zhang, Yanbin Cai, Qianrui Shi
Bayesian network (BN) structure learning is the basis of BN applications and plays a pivotal role in many machine learning tasks. Whereas remarkable progress in structure learning has been achieved in the past, making further improvements in the efficiency and accuracy of structure learning is a significant challenge. In this paper, we propose an efficient skeleton learning approach-based hybrid algorithm
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Identification of multiple-input and single-output Hammerstein controlled autoregressive moving average system based on chaotic dynamic disturbance sand cat swarm optimization Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-05 Junhong Li, Kang Xiao, Hongrui Zhang, Liang Hua, Juping Gu
This paper mainly studies the parameter identification problem of multiple-input and single-output Hammerstein controlled autoregressive moving average (MISO-HCARMA) system in multivariable systems. A parameter identification method based on chaotic dynamic disturbance sand cat swarm optimization (CDD-SCSO) algorithm is proposed to solve the identification problem of a large number of unknown parameters
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A co-evolutionary algorithm based on sparsity clustering for sparse large-scale multi-objective optimization Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-05 Yajie Zhang, Chengming Wu, Ye Tian, Xingyi Zhang
Sparse large-scale multi-objective optimization problems (LSMOPs), which are characterized by high dimensional search space and sparse Pareto optimal solutions, have a widespread existence in academic research and practical applications. While the high dimensional decision space poses challenges to multi-objective evolutionary algorithms (MOEAs), the difficulty of solving sparse LSMOPs can be alleviated
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Variable scale multilayer perceptron for helicopter transmission system vibration data abnormity beyond efficient recovery Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-05 Chao Fan, Yanfeng Peng, Yiping Shen, Yong Guo, Sibo Zhao, Jie Zhou, Sai Li
In the task of health monitoring of helicopter transmission systems, data transmission is often abnormal due to harsh working environments, sparse wire connections, and other factors. This paper addresses the problem of recovering abnormal vibration data, focusing on common Bias anomalies and precision degradation anomalies in the collected vibration data. The Transformer model has gained popularity
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A secure and accurate localization algorithm for mobile nodes in underwater acoustic network Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-04 Mingru Dong, Haibin Li, Yuhua Qin, Yongtao Hu, Haocai Huang
The autonomous mobile nodes in underwater acoustic sensor network can be widely used in underwater rescue, mine clearance and many other important applications. And the precise localization for the mobile nodes is the basis of these applications. However, in the complex underwater environment, the mobile nodes localization error is easily caused by malicious nodes attacking anchor nodes and inaccurate
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Acoustic tomography temperature reconstruction based on improved sparse reconstruction model and multi-scale feature fusion network Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-04 Xianghu Dong, Lifeng Zhang, Lifeng Qian, Chuanbao Wu, Zhihao Tang, Ao Li
Acoustic tomography is a widely used non-contact method for visualizing temperature distribution. A temperature distribution reconstruction algorithm based on an improved sparse reconstruction model and multi-scale feature fusion network is proposed. First, the acoustic temperature measurement sparse reconstruction model is improved by combining the error function (ERF) and the iterative reweighting
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Extending a human error identification and assessment method considering the uncertainty information for human reliability analysis of robot-assisted rehabilitation Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-04 Duojin Wang, Jiawan Liu, Hongliu Yu
The demand for rehabilitation treatments in today’s society is surging, but the increasing incidence of human-machine interaction accidents involving rehabilitation robots highlights the need for a proactive approach to mitigate human errors. Robot-assisted rehabilitation introduces new modes of human error, necessitating a focus on human reliability analysis (HRA) to reduce medical errors and associated
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Collaborative and Discriminative Subspace Learning for unsupervised multi-view feature selection Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-04 Jian-Sheng Wu, Yanlan Li, Jun-Xiao Gong, Weidong Min
By effectively exploiting the consistent information of multi-view data, multi-view feature selection seeks to select crucial features from multiple heterogeneous data views to improve the clustering and classification accuracy of multi-view data in pattern recognition, data mining, and machine learning. However, most unsupervised multi-view feature selection models investigate the consistent information
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A robust semi-supervised learning scheme for development of within-batch quality prediction soft-sensors Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-04 Yi Shan Lee, Junghui Chen
The pivotal factor to regulate and enhance within an operating batch is the quality process, a challenging variable to monitor online. Soft sensors offer an immediate alternative for providing real-time insights into process quality, yet persisting issues include the imbalance between process and quality measurements, noisy measurements, and the intricate 3-dimensional dynamic batch data structure
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Intersecting reinforcement learning and deep factor methods for optimizing locality and globality in forecasting: A review Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-04 João Sousa, Roberto Henriques
Operational forecasting often requires predicting collections of related, multivariate time series data that are high-dimensional in nature. This can be tackled by fitting a single function to all series (global approach) or assuming each series as a separate prediction problem and fitting one function to each (local approach) – . Deep learning models inspired by different data generation processes
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A Multi-User-Multi-Scenario-Multi-Mode aware network for personalized recommender systems Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-04 Yingshuai Wang, Dezheng Zhang, Aziguli Wulamu
User personalized recommendation is increasingly vital in many industrial applications. How to precisely mine user’s dynamic interests from multiple scenarios is a challenge task in Click-Through Rate (CTR) prediction. Existing works obtain representations based on feature engineering from training data. However, the CTR models are often sensitive to different users and scenarios. To address these
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A scenario-based genetic algorithm for controlling supercapacitor aging and degradation in the industry 4.0 era Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-04 Imtiaz Ahmed Khan, Masoud Khaleghiabbasabadi, Daniele Silvestri, Adnan Ahmed Mazari, Stanisław Wacławek, Benyamin Chahkandi, Mohammad Gheibi
Electric double layer capacitors (EDLCs) are promising energy storage solutions, yet aging and degradation issues impede reliability and lifespan. The research proposes integrated simulation, modeling, and optimization to actively control EDLC degradation during charge-discharge cycles, mathematically modeling and simulating electrical and aging dynamics. These aging simulations are coupled with a
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Single and simultaneous fault diagnosis of gearbox via wavelet transform and improved deep residual network under imbalanced data Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-04 Suiyan Wang, Jiaye Tian, Pengfei Liang, Xuefang Xu, Zhuoze Yu, Siyuan Liu, Delong Zhang
Playing a vital role in keeping gearbox working reliably and safely, smart fault diagnosis (FD) technology has attracted much attention in recent years. However, in practical industrial applications, owing to the imbalance of healthy state data and fault state data and various unpredictable compound fault modes, it is still extremely challenging to fulfill the high-accuracy and effective FD of gearbox
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A novel partial grey prediction model based on traffic flow wave equation and its application Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-03 Huiming Duan, Qiqi Zhou
Due to the spatiotemporal, periodic, and wave characteristics of traffic flow, this paper considers the continuous traffic flow on the road as a special kind of fluid, and uses the wave equation in fluid mechanics to describe the fluctuation and undulation characteristics of the traffic flow data. From the traffic flow wave equation, using the partial grey prediction model can effectively reflect the
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A two-stage network framework for topology optimization incorporating deep learning and physical information Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-02 Dalei Wang, Yun Ning, Cheng Xiang, Airong Chen
The advent of deep learning provides a promising opportunity to improve the efficiency of topology optimization. However, existing methods make it difficult to achieve a balance between efficiency, accuracy, and generalization ability. To tackle this challenge, we propose a novel method based on a two-stage network framework. In the network, the partial convolution block and shifted windows attention
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StainSWIN: Vision transformer-based stain normalization for histopathology image analysis Eng. Appl. Artif. Intell. (IF 8.0) Pub Date : 2024-03-02 Elif Baykal Kablan, Selen Ayas
Stain normalization is a key preprocessing step that has been shown to significantly improve the segmentation and classification performance of computer-aided diagnosis (CAD) systems. In recent advancements, numerous approaches have demonstrated significant progress in the domain of stain normalization; however, the most of these approaches are based on Generative Adversarial Networks. In this paper