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Gender classification based on gait analysis using ultrawide band radar augmented with artificial intelligence Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-26 Adil Ali Saleem, Hafeez Ur Rehman Siddiqui, Rukhshanda Sehar, Sandra Dudley
The identification of individuals based on their walking patterns, also known as gait recognition, has garnered considerable interest as a biometric trait. The use of gait patterns for gender classification has emerged as a significant research domain with diverse applications across multiple fields. The present investigation centers on the classification of gender based on gait utilizing data from
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Secure multi-channel information encryption based on integrated optical device Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-26 Junxiong Chai, Yiyuan Xie, Xiao Jiang, Ye Su, Lili Li
In allusion to the privacy and security problems, a secure multi-channel information protection scheme based on an integrated optical device is proposed in this paper. The information to be encrypted and shared is converted into a QR code. For supporting the multi-channel encryption, a 1- encryption algorithm is employed, which possesses the capability to map the original image information into seemingly
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Discriminative sparse subspace learning with manifold regularization Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-26 Wenyi Feng, Zhe Wang, Xiqing Cao, Bin Cai, Wei Guo, Weichao Ding
Common subspace learning methods only utilize local or global structure in feature extraction, and cannot obtain the global optimal discriminative projection matrix. For this reason, this paper proposes a discriminative sparse subspace learning method based on the manifold regularization framework (DSSL-MR), which introduces the graph Laplacian matrix that reflects the intrinsic geometric structure
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PFSC: Parameter-free sphere classifier for imbalanced data classification Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-26 Yeontark Park, Jong-Seok Lee
Imbalanced data classification is a prevalent challenge in real-world applications. While a conventional sphere-based classification algorithm, random sphere cover (RSC), evenly constructs a set of spheres for two classes in balanced data using a parameter for the minimum sphere size, it struggles with constructing minority spheres in class-imbalanced data. Although RSC can be combined with existing
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Flexible drug-target interaction prediction with interactive information extraction and trade-off Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-26 Yunfei He, Chenyuan Sun, Li Meng, Yiwen Zhang, Rui Mao, Fei Yang
Drug-target interaction (DTI) prediction refers to the use of computational methods and models to predict the interaction between drugs and biological targets. DTI can help researchers understand the mechanism of action of drugs, discover new drug targets, and screen drug candidates. Recently, a large number of DTI models integrating deep drug-target interaction features have emerged to make up for
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Uncertainty forecasting system for tropical cyclone tracks based on conformal prediction Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-26 Fan Meng, Tao Song
Tropical cyclone tracks are one of the most critical factors in tropical cyclone forecasting, but there is an inherent uncertainty in their forecasts, but there are no relevant machine learning methods to carry out uncertainty studies. This study proposes an uncertainty forecasting system using machine learning models within a conformal forecasting framework, aiming to provide reliable forecast regions
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An imbalance-aware BiLSTM for control chart patterns early detection Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-26 Mohammad Derakhshi, Talayeh Razzaghi
Digital twins-based predictive models find their roots in smart manufacturing. However, their potential applications to control chart pattern recognition (CCPR) algorithms, which lie at the heart of advanced fault detection systems, remain underexplored. A key challenge in CCPR models arises from the intrinsic imbalance between classes, which can compromise the model’s performance if left untreated
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A novel vehicle collision detection system: Integrating audio-visual fusion for enhanced performance Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-25 Kunyue Li, Zhengji Zhao, Qixuan Cai, Qin Wang, Naifeng Jing, Zhigang Mao, Jianfei Jiang
In vehicular accidents, the swift and accurate identification of car crashes is paramount, as it serves as the linchpin for prompt responses from emergency services and search-and-rescue operations. This study introduces an innovative multimodal car crash detection system that capitalizes on audio-visual data sourced from dashboard cameras, thus significantly enhancing the precision of automobile collision
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An efficient watermarking scheme for dual color image with high security in 5G environment Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-25 Qingtang Su, Siyu Chen, Huanying Wang, Hongjiao Cao, Fangxu Hu
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Orthogonal graph regularized non-negative matrix factorization under sparse constraints for clustering Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-25 Yasong Chen, Guangwei Qu, Junjian Zhao
The standard NMF algorithm is not suitable for sampling data from low-dimensional manifolds embedded in high-dimensional environmental spaces, as the geometric information hidden in feature manifolds and sample manifolds is rarely learned. In order to obtain better clustering performance based on NMF, manifold and orthogonal constraint, a new type of model named Orthogonal Graph regularized Non-negative
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Joint low-light enhancement and deblurring with structural priors guidance Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-25 Jing Ye, Linjie Yang, Changzhen Qiu, Zhiyong Zhang
Images captured under low-light conditions usually co-exist with low light and blur degradation. Most existing cascade and joint enhancement methods may provide undesirable results, suffering from severe artifacts, deteriorating blur, and unclear details. In this paper, we propose a novel network with structural priors, including high-frequency and edge, to enable effective image representation learning
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Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-24 Mathieu Reymond, Conor F. Hayes, Lander Willem, Roxana Rădulescu, Steven Abrams, Diederik M. Roijers, Enda Howley, Patrick Mannion, Niel Hens, Ann Nowé, Pieter Libin
Infectious disease outbreaks can have a disruptive impact on public health and societal processes. As decision-making in the context of epidemic mitigation is multi-dimensional hence complex, reinforcement learning in combination with complex epidemic models provides a methodology to design refined prevention strategies. Current research focuses on optimizing policies with respect to a single objective
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A hybrid machine learning model based on ensemble methods for devices fault prediction in the wood industry Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-23 Arezoo Dahesh, Reza Tavakkoli-Moghaddam, Niaz Wassan, AmirReza Tajally, Zahra Daneshi, Aseman Erfani-Jazi
In manufacturing industries, including the wood industry, devices, and equipment are considered the basic elements and the main capital for production. That is why managers are trying to maintain and use these devices and equipment optimally. On the other hand, repurchasing device parts or repairing equipment in case of major damage can cause more damage than planned costs. Therefore, a model that
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Mobility and energy efficient services composition algorithm with QoS guarantee for large scale Cyber–Physical–Social Systems Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-23 Salma Hameche, Mohamed Essaid Khanouche, Abdelkamel Tari
Due to the mobile and random nature of services in cyber–physical–social systems (CPSSs), developing service composition approaches that ensure high availability, minimal energy consumption, and high quality of service (QoS) remains a complex challenge. Over the last two decades, several service composition approaches have been proposed in the literature to deal with this challenge. Nevertheless, the
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SAM-IE: SAM-based image enhancement for facilitating medical image diagnosis with segmentation foundation model Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-22 Changyan Wang, Haobo Chen, Xin Zhou, Meng Wang, Qi Zhang
The Segment Anything Model (SAM) is a large-scale model developed for general segmentation tasks in computer vision. Trained on a substantial dataset, SAM can accurately segment various objects in natural scene images. However, due to significant semantic differences between medical and natural images, directly applying SAM to medical image segmentation does not yield optimal results. Therefore, effectively
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Improved genetic algorithm for mobile robot path planning in static environments Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-22 Mohd Nadhir Ab Wahab, Amril Nazir, Ashraf Khalil, Wong Jun Ho, Muhammad Firdaus Akbar, Mohd Halim Mohd Noor, Ahmad Sufril Azlan Mohamed
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Affective product form bionic design based on functional analysis Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-22 Zeng Wang, Chenpeng Long, Lingyu Huang, Shijie Hu
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Grey preference for analyzing the influence of externality within the graph model for conflict resolution Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-22 Xuemei Li, Yanhui Sun, Shiwei Zhou, Benshuo Yang
Externality may have uncertain influence on decision makers (DMs). To address the conflicts with uncertain preference and externality, this study adopts the general grey number to measure the uncertain value of DMs’ option statements influenced by externality. It constructs adjustable grey preference influenced by externality (GPE), which can be dynamically adjusted according to the influence of externality
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Optimizing high-speed rotating shaft vibration control: Experimental investigation of squeeze film dampers and a comparative analysis using Artificial Neural Networks (ANN) and Response Surface Methodology (RSM) Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-22 Ratnesh Kumar Gupta, Ramesh Chandra Singh
This research paper presents a comprehensive experimental and statistical approach for the analysis of vibration amplitudes in a high-speed rotating shaft employing a squeeze film damper (SFD). The research combines a comprehensive analysis that connects input parameters and response parameters, with a special emphasis on vibration amplitudes along the X and Z axes. This research utilizes the rigorous
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A strategy based on statistical modelling and multi-objective optimization to design a dishwasher cleaning cycle Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-22 Korkut Anapa, Hamdullah Yücel
This study proposes a novel approach based on statistical learning and multi-objective optimization to reduce the need for experiments during the design phase of new cleaning cycles for household dishwashers. We first build regression models associated with the feature selection methods to predict the outputs of a dishwasher cleaning cycle by using the existing cleaning cycles’ program flows as input
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STP: Self-supervised transfer learning based on transformer for noninvasive blood pressure estimation using photoplethysmography Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-21 Chenbin Ma, Peng Zhang, Haonan Zhang, Zeyu Liu, Fan Song, Yufang He, Guanglei Zhang
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A synchronization-induced cross-modal contrastive learning strategy for fault diagnosis of electromechanical systems under semi-supervised learning with current signal Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-21 Qinyuan Luo, Jinglong Chen, Yanyang Zi, Jingsong Xie
Electromechanical systems is widely employed in the manufacturing industry, with fault diagnosis being critical for ensuring the reliable operation of them. Vibration signals exhibit distinct fault features, but their acquisition is subject to various limitations. Conversely, current signals, while easily measurable, typically manifest weak fault features. Therefore, selecting a signal for fault diagnosis
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Privacy-preserving federated discovery of DNA motifs with differential privacy Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-21 Yao Chen, Wensheng Gan, Gengsen Huang, Yongdong Wu, Philip S. Yu
DNA sequence motif discovery is an important issue in gene research, which helps identify transcription factor binding sites in DNA sequences to reveal the mechanisms that regulate gene expression. However, the growing awareness of privacy protection and increasingly stringent regulations pose challenges to data collection and usage. This paper proposes DP-FLMD, a privacy-preserving federated framework
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MF-Net: Multiple-feature extraction network for breast lesion segmentation in ultrasound images Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-21 Jiajia Wang, Guoqi Liu, Dong Liu, Baofang Chang
Breast lesion segmentation in ultrasound images is of great significance for qualitative breast lesions. However, blurred lesion boundaries, irregular lesion shapes, and similar intensity distributions between lesion and background bring challenges to accurately segmenting breast lesions. Recently, several U-Net-based variants and transformer-based networks have been applied in breast lesion segmentation
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X2-Softmax: Margin adaptive loss function for face recognition Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-21 Jiamu Xu, Xiaoxiang Liu, Xinyuan Zhang, Yain-Whar Si, Xiaofan Li, Zheng Shi, Ke Wang, Xueyuan Gong
Learning the discriminative features of different faces is an important task in face recognition. By extracting face features with neural networks, it becomes easy to measure the similarity of different face images, which makes face recognition possible. To enhance a neural network’s face feature separability, incorporating an angular margin during training is common practice. The state-of-the-art
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Developing a novel approach in estimating urban commute traffic by integrating community detection and hypergraph representation learning Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-21 Yuhuan Li, Shaowu Cheng, Yuxiang Feng, Yaping Zhang, Panagiotis Angeloudis, Mohammed Quddus, Washington Yotto Ochieng
The efficiency of urban traffic management and congestion alleviation relies heavily on accurate forecasting of Origin-Destination (O-D) demand matrices. Existing models primarily focus on estimating O-D demand for various travel purposes throughout the day, which is characterised by its pulsating nature. However, these models often compromise the precision of peak-hour forecasts, leading to unreliable
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An ultra-low-computation model for understanding sign languages Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-21 Mohammad K. Fallah, Mohammadreza Najafi, Saeid Gorgin, Jeong-A. Lee
In artificial intelligence applications, advanced computational models, such as deep learning, are employed to achieve high accuracy, often requiring the execution of numerous operations. Conversely, lightweight computational models are typically more resource-efficient, making them suitable for various devices, including smartphones, tablets, and wearable technology. This paper presents an ultra-low-computation
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Dynamic user profile construction and its application to smart product-service system design: A maternity-oriented case study Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-21 Xian Yang, Chu Zhang, Yijing Li, Chaolan Tang, Peiqin Liang
The human-centric philosophy is considered a crucial development direction for smart Product-Service Systems (smart PSS). Smart PSS has effectively advanced the circular economy by enhancing user experience, incorporating servitization and digital servitization, and extending product lifecycle, among other key aspects of sustainable development. However, there is currently a lack of a reasonable, accurate
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NDOrder: Exploring a novel decoding order for scene text recognition Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-21 Dajian Zhong, Hongjian Zhan, Shujing Lyu, Cong Liu, Bing Yin, Palaiahnakote Shivakumara, Umapada Pal, Yue Lu
Text recognition in scene images is still considered as a challenging task for the computer vision and pattern recognition community. For text images affected by multiple adverse factors, such as occlusion (due to obstacles) and poor quality (due to blur and low resolution), the performance of the state-of-the-art scene text recognition methods degrades. The key reason is that the existing encoder–decoder
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Latent space search approach for domain adaptation Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-21 Mingjie Gao, Wei Huang
In traditional machine learning, there is often a discrepancy in data distribution between the source and target domains. Domain adaptation (DA) was proposed to learn the robust classifier for target domain by using knowledge from different source domains. Most DA methods focus on only the geometric structure of the data or statistical properties to reduce the differences between domains. The complementarity
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A Lightweight Channel and Time Attention Enhanced 1D CNN Model for Environmental Sound Classification Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-21 Huaxing Xu, Yunzhi Tian, Haichuan Ren, Xudong Liu
One dimension convolutional neural networks (1D CNN) that directly take raw waveforms as input has less competition than 2D CNN recognizing environmental sound. In order to overcome its disadvantages, we propose a novel lightweight 1D CNN structure by employing attention mechanism, which has significant improvement in both accuracy and computational complexity. Concretely, (1) two attention modules
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Hot rolled prognostic approach based on hybrid Bayesian progressive layered extraction multi-task learning Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-21 Shuxin Zhang, Zhitao Liu, Tao An, Xiyong Cui, Xianwen Zeng, Ning Shi, Hongye Su
Hot-rolled strip products have diverse applications, and enhancing the detection, diagnostics, and prognostics of product quality during hot rolling is essential. Nevertheless, the multivariable, strong coupling, nonlinear, and time-varying nature of the production process poses a rigorous challenge for accurate hot-rolled prognostics. This paper implements a progressive layered extraction (PLE) multi-task
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Multipath 3D-Conv encoder and temporal-sequence decision for repetitive-action counting Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-21 Yicheng Qiu, Li Niu, Feng Sha
Counting repetitive actions is important in work and daily life. Automated counting using deep learning provides a more efficient, accurate alternative to manual counting, which is tedious and error-prone Deep-learning models have been proposed to automatically count repetitive actions in video content. However, for these models to be applied to realistic scenes, high-quality performance and generalization
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A noninvasive prenatal test pipeline with a well-generalized machine-learning approach for accurate fetal trisomy detection using low-depth short sequence data Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-21 Qiongrong Huang, Jianjiang Zhu, Jianbo Lu, Qiaojun Fang, Hong Qi, Bin Tu
Noninvasive prenatal test (NIPT) reduces the associated risk of procedure-related miscarriage. However, due to accuracy, special fetuses, economic and policy gaps, NIPT still cannot replace traditional surgical methods. Developing a pipeline with low cost, low technical difficulty, stability and high accuracy is a major challenge for NIPT to be widely used. This study proposes a new pipeline for the
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Datasets, clues and state-of-the-arts for multimedia forensics: An extensive review Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-21 Ankit Yadav, Dinesh Kumar Vishwakarma
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Learning legal text representations via disentangling elements Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-21 Yingzhi Miao, Fang Zhou, Martin Pavlovski, Weining Qian
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BTWM-HF: A behavioral three-way multi-attribute decision-making method with hesitant fuzzy information Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-21 Chao Fu, Keyun Qin, Kuo Pang, Jing Wu, Erlong Zhao
Three-way decision (TWD) theory offers a novel paradigm for solving hesitant fuzzy multi-attribute decision-making problems. However, many existing TWD approaches follow the principle of minimum risk, thereby overlooking the influence of the psychological factors of decision-makers (DMs) on decision outcomes when faced with losses and gains. To address this challenge, we propose a behavioral three-way
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Graph-aware multi-feature interacting network for explainable rumor detection on social network Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-21 Chang Yang, Xia Yu, JiaYi Wu, BoZhen Zhang, HaiBo Yang
At present, rumors are growing wantonly with the convenience and influence of social media, becoming a problem that may severely impact social stability and development. The rumor is not an objective judgment but a process of multi-dimensional subjective value superposition and a collective transaction of people’s thoughts on social networks. How to fully mine the critical features of rumor detection
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A combination prediction model based on Theil coefficient and induced continuous aggregation operator for the prediction of Shanghai composite index Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-21 Yixiang Wang, Zhicheng Hu, Kai Zhang, Jiayi Zhou, Ligang Zhou
This paper proposes an interval combination prediction model for Shanghai composite index, utilizing the Theil coefficient and the induced continuous generalized ordered weighted logarithmic harmonic averaging (ICGOWLHA) operator. The effectiveness of the proposed model under specific weight conditions and the existence of its analytical solution are demonstrated. Shanghai composite index's case analysis
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A hybrid optimization algorithm for improving load frequency control in interconnected power systems Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-21 Md. Shahid Iqbal, Md. Faiyaj Ahmed Limon, Md. Monirul Kabir, Md Khurram Monir Rabby, Md. Janibul Alam Soeb, Md. Fahad Jubayer
The objective of this study is to develop an algorithm named Modified Artificial Bee Colony and Particle Swarm Optimization (MHABC-PSO) to address load frequency control (LFC) challenges in a two-area interconnected power system. The proposed MHABC-PSO algorithm is designed with two key modifications to enhance global exploration capability and improve convergence speed. Hence, a decision block is
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An automatic feedback educational platform: Assessment and therapeutic intervention for writing learning in children with special educational needs Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-21 Luis J. Serpa-Andrade, José J. Pazos-Arias, Alberto Gil-Solla, Yolanda Blanco-Fernández, Martín López-Nores
In this paper we present the SPRF (Sensorised Pen with Real time Feedback) platform that aims to objectively detect handwriting-related graphomotor disorders in children with Special Educational Needs (SEN), and to treat them through game-guided intervention plans performed with the same sensorised system. We have developed two sensorised devices (wristband and pen) for the SPRF to be able to quantify
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Lameness detection system for dairy cows based on instance segmentation Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-20 Qian Li, Zhijiang He, Xiaowen Liu, Mengyuan Chu, Yanchao Wang, Xi Kang, Gang Liu
Lameness is one of the major health problems on dairy farms, which seriously affects dairy cow welfare and increases the risk of premature culling. Accuracy lameness detection ensures timely treatment of hooves and improves the level of health management on dairy farms. Most of the existing lameness detection methods detect one dairy cow, and it is difficult to detect lameness in multiple dairy cows
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A long-tail alleviation post-processing framework based on personalized diversity of session recommendation Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-20 Dunlu Peng, Yi Zhou
Session-based recommendation leverages the short-term interaction sequence to predict the next item a user is most likely to click on. Generally, in real applications, users often click on different types of items in the same session, which makes the items in the sequence present diversity, and degree of diversity vary with different sequences, that is, there is a phenomenon of personalized diversity
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Uncertainty-Aware Online Learning of Dynamic Thermal Control in Data Center with Imperfect Pretrained Models Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-20 Qingang Zhang, Chin-Boon Chng, Chee-Kong Chui, Poh-Seng Lee
The growing demand for cloud computing and storage necessitates the expansion of Data Centers, thereby increasing energy consumption and environmental footprint. The data-driven dynamic thermal control presents a promising solution to mitigate this issue, as it can offer improved thermal control strategies and address the intricate and stochastic nature of the thermal environment. Nonetheless, existing
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Attention-based ConvNeXt with a parallel multiscale dilated convolution residual module for fault diagnosis of rotating machinery Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-20 Baosu Guo, Zhaohui Qiao, Ning Zhang, Yongchun Wang, Fenghe Wu, Qingjin Peng
Convolutional Neural Networks have promoted development of the fault diagnosis in the machine prognostics and health management. However, the existing methods have limited applicability under strong noisy conditions. We propose an attention-based ConvNeXt with a parallel multiscale dilated convolution residual module for the rotor fault diagnosis. The parallel multiscale dilated convolution residual
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Deep reinforcement learning approach with hybrid action space for mobile charging in wireless rechargeable sensor networks Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-20 Chengpeng Jiang, Wencong Chen, Xingcan Chen, Sen Zhang, Wendong Xiao
Mobile charging is feasible to deal with the energy-constrained problem in wireless rechargeable sensor networks (WRSNs). The mobile chargers (MCs) are usually employed to charge the sensors sequentially according to the charging schemes. Existing studies assume that each sensor should be charged to its maximum energy capacity or to a fixed upper threshold before the next one can be charged. However
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Harnessing the power of radiomics and deep learning for improved breast cancer diagnosis with multiparametric breast mammography Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-20 Tariq Mahmood, Tanzila Saba, Amjad Rehman, Faten S. Alamri
Breast cancer, with its high mortality, faces diagnostic challenges due to variability in mammography quality and breast densities, leading to inconsistencies in radiological interpretations. Computer-aided diagnostic (CAD) systems, while helpful, struggle with accurately interpreting lesion characteristics such as morphology, density, and size. To address this, our study developed advanced deep-learning
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A two-stage frequency-domain generation algorithm based on differential evolution for black-box adversarial samples Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-20 Xianfang Song, Denghui Xu, Chao Peng, Yong Zhang, Yu Xue
Adversarial sample generation problem is a hot issue in the security field of deep learning. Evolutionary algorithm has been widely used to solve this problem in recent years because of its good global search ability. However, existing methods still suffer from the “curse of dimensionality” when attacking high-resolution images. In this paper, a two-stage frequency domain generation algorithm of black-box
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Direct prediction for oceanic mesoscale eddy geospatial distribution through prior statistical deep learning Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-20 Huan Tang, Jianmin Lin, Dongfang Ma
Mesoscale Eddy (ME) is a widely recognized and significant oceanic phenomenon characterized by extensive energy exchange. Accurate predictions of the future geospatial distribution of ME are crucial for maritime activities. Deep learning (DL) methods for ME prediction have consistently outperformed classical mathematical models and numerical modeling approaches. However, current DL methods based on
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A multiple risk coupled propagation model for emergency information considering government information and government mandatory measures Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-20 Jing Zhang, Ning Wang
In the era of new media, it is crucial to investigate the transmission paths of emergency information due to the rising risk of such information. We take the risks caused by the dissemination of various heterogeneous negative information public opinion arising from the emergencies as the research object. Starting from the coupling of multiple risks and considering the behavioral impact of different
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Data-model-interactive enhancement-based Francis turbine unit health condition assessment using graph driven health benchmark model Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-20 Fengyuan Zhang, Jie Liu, Yujie Liu, Haoliang Li, Xingxing Jiang
As the data-driven Francis turbine units (FTUs) deterioration assessment method is widely investigated, the data quality in actual industrial scene has become an important prerequisite to restrict the method performance. However, existing data augmentation methods often lie in simply mixing real data and simulated data, ignoring the inherent cross-domain state relationships. In this paper, a data-model-interactive
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LCDFormer: Long-term correlations dual-graph transformer for traffic forecasting Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-20 Jiongbiao Cai, Chia-Hung Wang, Kun Hu
Traffic forecasting has always been a critical component of intelligent transportation systems. Due to the complexity of traffic prediction models, most research just only consider short-term historical data in the temporal dimension. However, learning temporal patterns necessitates the involvement of long-term historical data. Additionally, many models are limited in capturing spatial features by
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PPTtrack: Pyramid pooling based Transformer backbone for visual tracking Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-20 Jun Wang, Shuai Yang, Yuanyun Wang, Guang Yang
In visual tracking, Convolutional Neural Network (CNN) is usually used as feature extractor, and can fully explore local dependencies of image blocks, which is help for improving tracking performance. However, CNN ignores global dependencies in image blocks. The global modeling is crucial in visual tracking. Recently, Transformer has gained attention to fully explore global dependencies on sequential
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Learning-based image steganography and watermarking: A survey Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-20 Kun Hu, Mingpei Wang, Xiaohui Ma, Jia Chen, Xiaochao Wang, Xingjun Wang
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Revolutionizing flame detection: Novelization in flame detection through transferring distillation for knowledge to pruned model Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-20 Hongkang Tao, Jiansheng Liu, Zan Yang, Guhong Wang, Jie Shang, Haobo Qiu, Liang Gao
Traditional flame sensors have demonstrated suboptimal detection performance in complex environments, prompting researchers to integrate deep neural network algorithms into these sensors to enhance detection accuracy. However, these algorithms usually rely on sizeable neural networks, resulting in excessive parameterization, which limits their adaptability to different devices. To effectively compress
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Energy efficient FPGA implementation of an epileptic seizure detection system using a QDA classifier Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-20 Md Shamshad Alam, Umamah Khan, Mohd Hasan, Omar Farooq
Epilepsy is a severe neurological disorder that causes seizures. It is detected by analyzing the electrical impulses of the human brain. Monitoring the brain is commonly done using an electroencephalogram (EEG). Seizure detection from the large recorded EEG dataset is a demanding task. However, numerous machine learning classifiers and the appropriate features can detect seizures. The Hjorth and statistical
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Near-repeat terrorism: Identifying and analyzing the spatiotemporal attack patterns of major terrorist organizations Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-20 Kyle Hunt, Brandon Behlendorf, Steven Wang, Sayanti Mukherjee, Jun Zhuang
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Air cargo load and route planning in pickup and delivery operations Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-20 A.C.P. Mesquita, C.A.A. Sanches
In the aerial pickup and delivery of goods in a distribution network, transport aviation faces risks of load imbalance due to the urgency required for loading, immediate take-off, and mission accomplishment. Transport planners deal with trip itineraries, prioritization of items, building up pallets, and balanced loading, but there are no commercially available systems that can integrally assist in
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A hybrid similarity model for mitigating the cold-start problem of collaborative filtering in sparse data Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-20 Jiewen Guan, Bilian Chen, Shenbao Yu
Similarity is a vital component for neighborhood-based collaborative filtering (CF). To improve the quality of recommendation, many similarity methods have been proposed and analyzed in recent decades. However, nearly all traditional similarity methods and many advanced similarity methods only utilize corated items among users to compute their similarity, which provides limited information in cold-start/sparse
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Multi-geometry embedded transformer for facial expression recognition in videos Expert Syst. Appl. (IF 8.5) Pub Date : 2024-03-20 Dongliang Chen, Guihua Wen, Huihui Li, Pei Yang, Chuyun Chen, Bao Wang
Dynamic facial expressions in videos express more realistic emotional states, and recognizing emotions from in-the-wild facial expression videos is a challenging task due to the changeable posture, partial occlusion and various light conditions. Although current methods have designed transformer-based models to learn spatial–temporal features, they cannot explore useful local geometry structures from