Home / Journals / IASC / Vol.37, No.2, 2023
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  • Open AccessOpen Access

    ARTICLE

    Instance Reweighting Adversarial Training Based on Confused Label

    Zhicong Qiu1,2, Xianmin Wang1,*, Huawei Ma1, Songcao Hou1, Jing Li1,2,*, Zuoyong Li2
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1243-1256, 2023, DOI:10.32604/iasc.2023.038241
    (This article belongs to the Special Issue: AI Powered Human-centric Computing with Cloud/Fog/Edge)
    Abstract Reweighting adversarial examples during training plays an essential role in improving the robustness of neural networks, which lies in the fact that examples closer to the decision boundaries are much more vulnerable to being attacked and should be given larger weights. The probability margin (PM) method is a promising approach to continuously and path-independently measuring such closeness between the example and decision boundary. However, the performance of PM is limited due to the fact that PM fails to effectively distinguish the examples having only one misclassified category and the ones with multiple misclassified categories, where… More >

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    ARTICLE

    Marketing Model Analysis of Fashion Communication Based on the Visual Analysis of Neutrosophic Systems

    Fangyu Ye1, Xiaoshu Xu2,*, Yunfeng Zhang3, Yan Ye4, Jingyu Dai5,*
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1257-1274, 2023, DOI:10.32604/iasc.2023.037057
    (This article belongs to the Special Issue: Neutrosophic Theories in Intelligent Decision Making, Management and Engineering)
    Abstract In order to Improvement the Neutrosophic sets as effective tools to deal with uncertain and inconsistent information. The research takes methodology of combined single-valued neutrosophic rough set and multi-scale decision systems. This paper proposes the optimal scale selection and reduction algorithms based on multi-scale single-valued neutrosophic dominance rough set model. User requirements were analyzed using KJ method to construct a hierarchical model. According to the statistics of representative studies from China and the West, we found that, on the one hand, classical theory has been expanded and supplemented in fashion culture communication and marketing. The… More >

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    ARTICLE

    A Comprehensive Evaluation of State-of-the-Art Deep Learning Models for Road Surface Type Classification

    Narit Hnoohom1, Sakorn Mekruksavanich2, Anuchit Jitpattanakul3,4,*
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1275-1291, 2023, DOI:10.32604/iasc.2023.038584
    Abstract In recent years, as intelligent transportation systems (ITS) such as autonomous driving and advanced driver-assistance systems have become more popular, there has been a rise in the need for different sources of traffic situation data. The classification of the road surface type, also known as the RST, is among the most essential of these situational data and can be utilized across the entirety of the ITS domain. Recently, the benefits of deep learning (DL) approaches for sensor-based RST classification have been demonstrated by automatic feature extraction without manual methods. The ability to extract important features… More >

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    ARTICLE

    Urban Drainage Network Scheduling Strategy Based on Dynamic Regulation: Optimization Model and Theoretical Research

    Xiaoming Fei*
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1293-1309, 2023, DOI:10.32604/iasc.2023.038607
    Abstract With the acceleration of urbanization in China, the discharge of domestic sewage and industrial wastewater is increasing, and accidents of sewage spilling out and polluting the environment occur from time to time. Problems such as imperfect facilities and backward control methods are common in the urban drainage network systems in China. Efficient drainage not only strengthens infrastructure such as rain and sewage diversion, pollution source monitoring, transportation, drainage and storage but also urgently needs technical means to monitor and optimize production and operation. Aiming at the optimal control of single-stage pumping stations and the coordinated… More >

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    ARTICLE

    Noise-Filtering Enhanced Deep Cognitive Diagnosis Model for Latent Skill Discovering

    Jing Geng1,*, Huali Yang2, Shengze Hu3
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1311-1324, 2023, DOI:10.32604/iasc.2023.038481
    (This article belongs to the Special Issue: Continual Learning Techniques for Mobile-based Smart Healthcare Information Systems)
    Abstract Educational data mining based on student cognitive diagnosis analysis can provide an important decision basis for personalized learning tutoring of students, which has attracted extensive attention from scholars at home and abroad and has made a series of important research progress. To this end, we propose a noise-filtering enhanced deep cognitive diagnosis method to improve the fitting ability of traditional models and obtain students’ skill mastery status by mining the interaction between students and problems nonlinearly through neural networks. First, modeling complex interactions between students and problems with multidimensional features based on cognitive processing theory More >

  • Open AccessOpen Access

    ARTICLE

    Stock Market Index Prediction Using Machine Learning and Deep Learning Techniques

    Abdus Saboor1,4, Arif Hussain2, Bless Lord Y. Agbley3, Amin ul Haq3,*, Jian Ping Li3, Rajesh Kumar1,*
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1325-1344, 2023, DOI:10.32604/iasc.2023.038849
    (This article belongs to the Special Issue: Smart digital education and scientific programming)
    Abstract Stock market forecasting has drawn interest from both economists and computer scientists as a classic yet difficult topic. With the objective of constructing an effective prediction model, both linear and machine learning tools have been investigated for the past couple of decades. In recent years, recurrent neural networks (RNNs) have been observed to perform well on tasks involving sequence-based data in many research domains. With this motivation, we investigated the performance of long-short term memory (LSTM) and gated recurrent units (GRU) and their combination with the attention mechanism; LSTM + Attention, GRU + Attention, and More >

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    ARTICLE

    Detection of Alzheimer’s Disease Progression Using Integrated Deep Learning Approaches

    Jayashree Shetty1, Nisha P. Shetty1,*, Hrushikesh Kothikar1, Saleh Mowla1, Aiswarya Anand1, Veeraj Hegde2
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1345-1362, 2023, DOI:10.32604/iasc.2023.039206
    (This article belongs to the Special Issue: Artificial Intelligence based Healthcare Systems)
    Abstract Alzheimer’s disease (AD) is an intensifying disorder that causes brain cells to degenerate early and destruct. Mild cognitive impairment (MCI) is one of the early signs of AD that interferes with people’s regular functioning and daily activities. The proposed work includes a deep learning approach with a multimodal recurrent neural network (RNN) to predict whether MCI leads to Alzheimer’s or not. The gated recurrent unit (GRU) RNN classifier is trained using individual and correlated features. Feature vectors are concatenated based on their correlation strength to improve prediction results. The feature vectors generated are given as… More >

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    ARTICLE

    Using Digital Twin to Diagnose Faults in Braiding Machinery Based on IoT

    Youping Lin1, Huangbin Lin2,*, Dezhi Wei1
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1363-1379, 2023, DOI:10.32604/iasc.2023.038601
    (This article belongs to the Special Issue: Neutrosophic Theories in Intelligent Decision Making, Management and Engineering)
    Abstract The digital twin (DT) includes real-time data analytics based on the actual product or manufacturing processing parameters. Data from digital twins can predict asset maintenance requirements ahead of time. This saves money by decreasing operating expenses and asset downtime, which improves company efficiency. In this paper, a digital twin in braiding machinery based on IoT (DTBM-IoT) used to diagnose faults. When an imbalance fault occurs, the system gathers experimental data. After that, the information is sent into a digital win model of the rotor system to see whether it can quantify and locate imbalance for More >

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    ARTICLE

    A Consistent Mistake in Remote Sensing Images’ Classification Literature

    Huaxiang Song*
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1381-1398, 2023, DOI:10.32604/iasc.2023.039315
    Abstract Recently, the convolutional neural network (CNN) has been dominant in studies on interpreting remote sensing images (RSI). However, it appears that training optimization strategies have received less attention in relevant research. To evaluate this problem, the author proposes a novel algorithm named the Fast Training CNN (FST-CNN). To verify the algorithm’s effectiveness, twenty methods, including six classic models and thirty architectures from previous studies, are included in a performance comparison. The overall accuracy (OA) trained by the FST-CNN algorithm on the same model architecture and dataset is treated as an evaluation baseline. Results show that… More >

  • Open AccessOpen Access

    ARTICLE

    Performance Assessment and Configuration Analysis in the Study of SCADA System (Supervisory Control and Data Acquisition)

    R. Vanalakshmi1, S. Maragathasundari1,*, M. Kameswari1, B. Balamurugan2, C. Swedheetha3
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1399-1419, 2023, DOI:10.32604/iasc.2023.038506
    Abstract Queuing models are used to assess the functionality and aesthetics of SCADA systems for supervisory control and data collection. Here, the main emphasis is on how the queuing theory can be used in the system’s design and analysis. The analysis’s findings indicate that by using queuing models, cost-performance ratios close to the ideal might be attained. This article discusses a novel methodology for evaluating the service-oriented survivability of SCADA systems. In order to evaluate the state of service performance and the system’s overall resilience, the framework applies queuing theory to an analytical model. As a More >

  • Open AccessOpen Access

    ARTICLE

    Outsourced Privacy-Preserving kNN Classifier Model Based on Multi-Key Homomorphic Encryption

    Chen Wang1, Jian Xu1,*, Jiarun Li1, Yan Dong1, Nitin Naik2
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1421-1436, 2023, DOI:10.32604/iasc.2023.034123
    Abstract Outsourcing the k-Nearest Neighbor (kNN) classifier to the cloud is useful, yet it will lead to serious privacy leakage due to sensitive outsourced data and models. In this paper, we design, implement and evaluate a new system employing an outsourced privacy-preserving kNN Classifier Model based on Multi-Key Homomorphic Encryption (kNNCM-MKHE). We firstly propose a security protocol based on Multi-key Brakerski-Gentry-Vaikuntanathan (BGV) for collaborative evaluation of the kNN classifier provided by multiple model owners. Analyze the operations of kNN and extract basic operations, such as addition, multiplication, and comparison. It supports the computation of encrypted data… More >

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    ARTICLE

    Short-Term Wind Power Prediction Based on Combinatorial Neural Networks

    Tusongjiang Kari1, Sun Guoliang2, Lei Kesong1, Ma Xiaojing1,*, Wu Xian1
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1437-1452, 2023, DOI:10.32604/iasc.2023.037012
    Abstract Wind power volatility not only limits the large-scale grid connection but also poses many challenges to safe grid operation. Accurate wind power prediction can mitigate the adverse effects of wind power volatility on wind power grid connections. For the characteristics of wind power antecedent data and precedent data jointly to determine the prediction accuracy of the prediction model, the short-term prediction of wind power based on a combined neural network is proposed. First, the Bi-directional Long Short Term Memory (BiLSTM) network prediction model is constructed, and the bi-directional nature of the BiLSTM network is used… More >

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    ARTICLE

    Feature Fusion Based Deep Transfer Learning Based Human Gait Classification Model

    C. S. S. Anupama1, Rafina Zakieva2, Afanasiy Sergin3, E. Laxmi Lydia4, Seifedine Kadry5,6,7, Chomyong Kim8, Yunyoung Nam8,*
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1453-1468, 2023, DOI:10.32604/iasc.2023.038321
    Abstract Gait is a biological typical that defines the method by that people walk. Walking is the most significant performance which keeps our day-to-day life and physical condition. Surface electromyography (sEMG) is a weak bioelectric signal that portrays the functional state between the human muscles and nervous system to any extent. Gait classifiers dependent upon sEMG signals are extremely utilized in analysing muscle diseases and as a guide path for recovery treatment. Several approaches are established in the works for gait recognition utilizing conventional and deep learning (DL) approaches. This study designs an Enhanced Artificial Algae… More >

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    ARTICLE

    A Single Image Derain Method Based on Residue Channel Decomposition in Edge Computing

    Yong Cheng1, Zexuan Yang2,*, Wenjie Zhang3,4, Ling Yang5, Jun Wang1, Tingzhao Guan1
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1469-1482, 2023, DOI:10.32604/iasc.2023.038251
    Abstract The numerous photos captured by low-price Internet of Things (IoT) sensors are frequently affected by meteorological factors, especially rainfall. It causes varying sizes of white streaks on the image, destroying the image texture and ruining the performance of the outdoor computer vision system. Existing methods utilise training with pairs of images, which is difficult to cover all scenes and leads to domain gaps. In addition, the network structures adopt deep learning to map rain images to rain-free images, failing to use prior knowledge effectively. To solve these problems, we introduce a single image derain model… More >

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    ARTICLE

    Intelligent Financial Fraud Detection Using Artificial Bee Colony Optimization Based Recurrent Neural Network

    T. Karthikeyan1,*, M. Govindarajan1, V. Vijayakumar2
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1483-1498, 2023, DOI:10.32604/iasc.2023.037606
    Abstract Frauds don’t follow any recurring patterns. They require the use of unsupervised learning since their behaviour is continually changing. Fraudsters have access to the most recent technology, which gives them the ability to defraud people through online transactions. Fraudsters make assumptions about consumers’ routine behaviour, and fraud develops swiftly. Unsupervised learning must be used by fraud detection systems to recognize online payments since some fraudsters start out using online channels before moving on to other techniques. Building a deep convolutional neural network model to identify anomalies from conventional competitive swarm optimization patterns with a focus… More >

  • Open AccessOpen Access

    ARTICLE

    QoS-Aware Cloud Service Optimization Algorithm in Cloud Manufacturing Environment

    Wenlong Ma1,2,*, Youhong Xu1, Jianwei Zheng2, Sadaqat ur Rehman3
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1499-1512, 2023, DOI:10.32604/iasc.2023.030484
    Abstract In a cloud manufacturing environment with abundant functionally equivalent cloud services, users naturally desire the highest-quality service(s). Thus, a comprehensive measurement of quality of service (QoS) is needed. Optimizing the plethora of cloud services has thus become a top priority. Cloud service optimization is negatively affected by untrusted QoS data, which are inevitably provided by some users. To resolve these problems, this paper proposes a QoS-aware cloud service optimization model and establishes QoS-information awareness and quantification mechanisms. Untrusted data are assessed by an information correction method. The weights discovered by the variable precision Rough Set, More >

  • Open AccessOpen Access

    ARTICLE

    Health Monitoring of Dry Clutch System Using Deep Learning Approach

    Ganjikunta Chakrapani, V. Sugumaran*
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1513-1530, 2023, DOI:10.32604/iasc.2023.034597
    Abstract Clutch is one of the most significant components in automobiles. To improve passenger safety, reliability and economy of automobiles, advanced supervision and fault diagnostics are required. Condition Monitoring is one of the key divisions that can be used to track the reliability of clutch and allied components. The state of the clutch elements can be monitored with the help of vibration signals which contain valuable information required for classification. Specific drawbacks of traditional fault diagnosis techniques like high reliability on human intelligence and the requirement of professional expertise, have made researchers look for intelligent fault More >

  • Open AccessOpen Access

    ARTICLE

    Hybrid Power Bank Deployment Model for Energy Supply Coverage Optimization in Industrial Wireless Sensor Network

    Hang Yang1,2,*, Xunbo Li1, Witold Pedrycz2
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1531-1551, 2023, DOI:10.32604/iasc.2023.039256
    Abstract Energy supply is one of the most critical challenges of wireless sensor networks (WSNs) and industrial wireless sensor networks (IWSNs). While research on coverage optimization problem (COP) centers on the network’s monitoring coverage, this research focuses on the power banks’ energy supply coverage. The study of 2-D and 3-D spaces is typical in IWSN, with the realistic environment being more complex with obstacles (i.e., machines). A 3-D surface is the field of interest (FOI) in this work with the established hybrid power bank deployment model for the energy supply COP optimization of IWSN. The hybrid… More >

  • Open AccessOpen Access

    ARTICLE

    An Optimized Implementation of a Novel Nonlinear Filter for Color Image Restoration

    Turki M. Alanazi*
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1553-1568, 2023, DOI:10.32604/iasc.2023.039686
    (This article belongs to the Special Issue: Optimization Problems Based on Mathematical Algorithms and Soft Computing)
    Abstract Image processing is becoming more popular because images are being used increasingly in medical diagnosis, biometric monitoring, and character recognition. But these images are frequently contaminated with noise, which can corrupt subsequent image processing stages. Therefore, in this paper, we propose a novel nonlinear filter for removing “salt and pepper” impulsive noise from a complex color image. The new filter is called the Modified Vector Directional Filter (MVDF). The suggested method is based on the traditional Vector Directional Filter (VDF). However, before the candidate pixel is processed by the VDF, the MVDF employs a threshold… More >

  • Open AccessOpen Access

    ARTICLE

    Computing and Implementation of a Controlled Telepresence Robot

    Ali A. Altalbe1,2,*, Aamir Shahzad3, Muhammad Nasir Khan4
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1569-1585, 2023, DOI:10.32604/iasc.2023.039124
    (This article belongs to the Special Issue: Intelligent Systems for Smart and Sustainable Healthcare)
    Abstract The development of human-robot interaction has been continuously increasing for the last decades. Through this development, it has become simpler and safe interactions using a remotely controlled telepresence robot in an insecure and hazardous environment. The audio-video communication connection or data transmission stability has already been well handled by fast-growing technologies such as 5G and 6G. However, the design of the physical parameters, e.g., maneuverability, controllability, and stability, still needs attention. Therefore, the paper aims to present a systematic, controlled design and implementation of a telepresence mobile robot. The primary focus of this paper is… More >

  • Open AccessOpen Access

    ARTICLE

    Hyper-Heuristic Task Scheduling Algorithm Based on Reinforcement Learning in Cloud Computing

    Lei Yin1, Chang Sun2, Ming Gao3, Yadong Fang4, Ming Li1, Fengyu Zhou1,*
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1587-1608, 2023, DOI:10.32604/iasc.2023.039380
    Abstract The solution strategy of the heuristic algorithm is pre-set and has good performance in the conventional cloud resource scheduling process. However, for complex and dynamic cloud service scheduling tasks, due to the difference in service attributes, the solution efficiency of a single strategy is low for such problems. In this paper, we presents a hyper-heuristic algorithm based on reinforcement learning (HHRL) to optimize the completion time of the task sequence. Firstly, In the reward table setting stage of HHRL, we introduce population diversity and integrate maximum time to comprehensively determine the task scheduling and the More >

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    ARTICLE

    Recapitulation Web 3.0: Architecture, Features and Technologies, Opportunities and Challenges

    Amtul Waheed, Bhawna Dhupia*, Sultan Mesfer Aldossary
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1609-1620, 2023, DOI:10.32604/iasc.2023.037539
    (This article belongs to the Special Issue: The Internet of Everything (IoE) for Intelligent Systems Concepts, Architecture, Standards, Tools, and Applications)
    Abstract Tim Berners-Lee developed the internet at CERN in early 1990 with fundamental technologies such as HTML, URL, and HTTP which became the foundation of the web. The contemporary web we use today has been much advanced over a period of time ever since the innovation of the World Wide Web was introduced. The static web was the first version of the web, which was the read-only web. Succeeding development in web technology is web 3.0 which is a distributed and decentralized web with emerging technologies. This article emphasizes the comparison of important details with the… More >

  • Open AccessOpen Access

    ARTICLE

    An OP-TEE Energy-Efficient Task Scheduling Approach Based on Mobile Application Characteristics

    Hai Wang*, Xuan Hao, Shuo Ji*, Jie Zheng, Yuhui Ma, Jianfeng Yang
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1621-1635, 2023, DOI:10.32604/iasc.2023.037898
    (This article belongs to the Special Issue: Emerging Trends in Big Data Driven Edge Intelligence and In-Network Computing)
    Abstract Trusted Execution Environment (TEE) is an important part of the security architecture of modern mobile devices, but its secure interaction process brings extra computing burden to mobile devices. This paper takes open portable trusted execution environment (OP-TEE) as the research object and deploys it to Raspberry Pi 3B, designs and implements a benchmark for OP-TEE, and analyzes its program characteristics. Furthermore, the application execution time, energy consumption and energy-delay product (EDP) are taken as the optimization objectives, and the central processing unit (CPU) frequency scheduling strategy of mobile devices is dynamically adjusted according to the More >

  • Open AccessOpen Access

    ARTICLE

    Prediction-Based Thunderstorm Path Recovery Method Using CNN-BiLSTM

    Xu Yang1,2, Ling Zhuang1, Yuqiang Sun3, Wenjie Zhang4,5,*
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1637-1654, 2023, DOI:10.32604/iasc.2023.039879
    (This article belongs to the Special Issue: Artificial Intelligence Algorithm for Industrial Operation Application)
    Abstract The loss of three-dimensional atmospheric electric field (3DAEF) data has a negative impact on thunderstorm detection. This paper proposes a method for thunderstorm point charge path recovery. Based on the relationship between a point charge and 3DAEF, we derive corresponding localization formulae by establishing a point charge localization model. Generally, point charge movement paths are obtained after fitting time series localization results. However, AEF data losses make it difficult to fit and visualize paths. Therefore, using available AEF data without loss as input, we design a hybrid model combining the convolutional neural network (CNN) and… More >

  • Open AccessOpen Access

    ARTICLE

    3D Model Construction and Ecological Environment Investigation on a Regional Scale Using UAV Remote Sensing

    Chao Chen1,2, Yankun Chen3, Haohai Jin4, Li Chen5,*, Zhisong Liu3, Haozhe Sun4, Junchi Hong4, Haonan Wang4, Shiyu Fang4, Xin Zhang2
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1655-1672, 2023, DOI:10.32604/iasc.2023.039057
    (This article belongs to the Special Issue: Smart digital education and scientific programming)
    Abstract The acquisition of digital regional-scale information and ecological environmental data has high requirements for structural texture, spatial resolution, and multiple parameter categories, which is challenging to achieve using satellite remote sensing. Considering the convenient, facilitative, and flexible characteristics of UAV (unmanned air vehicle) remote sensing technology, this study selects a campus as a typical research area and uses the Pegasus D2000 equipped with a D-MSPC2000 multi-spectral camera and a CAM3000 aerial camera to acquire oblique images and multi-spectral data. Using professional software, including Context Capture, ENVI, and ArcGIS, a 3D (three-dimensional) campus model, a digital More >

  • Open AccessOpen Access

    ARTICLE

    Leveraging Vision-Language Pre-Trained Model and Contrastive Learning for Enhanced Multimodal Sentiment Analysis

    Jieyu An1,*, Wan Mohd Nazmee Wan Zainon1, Binfen Ding2
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1673-1689, 2023, DOI:10.32604/iasc.2023.039763
    Abstract Multimodal sentiment analysis is an essential area of research in artificial intelligence that combines multiple modes, such as text and image, to accurately assess sentiment. However, conventional approaches that rely on unimodal pre-trained models for feature extraction from each modality often overlook the intrinsic connections of semantic information between modalities. This limitation is attributed to their training on unimodal data, and necessitates the use of complex fusion mechanisms for sentiment analysis. In this study, we present a novel approach that combines a vision-language pre-trained model with a proposed multimodal contrastive learning method. Our approach harnesses… More >

  • Open AccessOpen Access

    ARTICLE

    A Novel Ensemble Learning System for Cyberattack Classification

    Óscar Mogollón-Gutiérrez*, José Carlos Sancho Núñez, Mar Ávila Vegas, Andrés Caro Lindo
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1691-1709, 2023, DOI:10.32604/iasc.2023.039255
    Abstract Nowadays, IT systems rely mainly on artificial intelligence (AI) algorithms to process data. AI is generally used to extract knowledge from stored information and, depending on the nature of data, it may be necessary to apply different AI algorithms. In this article, a novel perspective on the use of AI to ensure the cybersecurity through the study of network traffic is presented. This is done through the construction of a two-stage cyberattack classification ensemble model addressing class imbalance following a one-vs-rest (OvR) approach. With the growing trend of cyberattacks, it is essential to implement techniques… More >

  • Open AccessOpen Access

    ARTICLE

    Atrous Convolution-Based Residual Deep CNN for Image Dehazing with Spider Monkey–Particle Swarm Optimization

    CH. Mohan Sai Kumar*, R. S. Valarmathi
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1711-1728, 2023, DOI:10.32604/iasc.2023.038113
    Abstract Image dehazing is a rapidly progressing research concept to enhance image contrast and resolution in computer vision applications. Owing to severe air dispersion, fog, and haze over the environment, hazy images pose specific challenges during information retrieval. With the advances in the learning theory, most of the learning-based techniques, in particular, deep neural networks are used for single-image dehazing. The existing approaches are extremely computationally complex, and the dehazed images are suffered from color distortion caused by the over-saturation and pseudo-shadow phenomenon. However, the slow convergence rate during training and haze residual is the two… More >

  • Open AccessOpen Access

    ARTICLE

    FedNRM: A Federal Personalized News Recommendation Model Achieving User Privacy Protection

    Shoujian Yu1, Zhenchi Jie1, Guowen Wu1, Hong Zhang1, Shigen Shen2,*
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1729-1751, 2023, DOI:10.32604/iasc.2023.039911
    Abstract In recent years, the type and quantity of news are growing rapidly, and it is not easy for users to find the news they are interested in the massive amount of news. A news recommendation system can score and predict the candidate news, and finally recommend the news with high scores to users. However, existing user models usually only consider users’ long-term interests and ignore users’ recent interests, which affects users’ usage experience. Therefore, this paper introduces gated recurrent unit (GRU) sequence network to capture users’ short-term interests and combines users’ short-term interests and long-term… More >

  • Open AccessOpen Access

    ARTICLE

    Attentive Neighborhood Feature Augmentation for Semi-supervised Learning

    Qi Liu1,2, Jing Li1,2,*, Xianmin Wang1,*, Wenpeng Zhao1
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1753-1771, 2023, DOI:10.32604/iasc.2023.039600
    (This article belongs to the Special Issue: AI Powered Human-centric Computing with Cloud/Fog/Edge)
    Abstract Recent state-of-the-art semi-supervised learning (SSL) methods usually use data augmentations as core components. Such methods, however, are limited to simple transformations such as the augmentations under the instance’s naive representations or the augmentations under the instance’s semantic representations. To tackle this problem, we offer a unique insight into data augmentations and propose a novel data-augmentation-based semi-supervised learning method, called Attentive Neighborhood Feature Augmentation (ANFA). The motivation of our method lies in the observation that the relationship between the given feature and its neighborhood may contribute to constructing more reliable transformations for the data, and further… More >

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    ARTICLE

    Container Instrumentation and Enforcement System for Runtime Security of Kubernetes Platform with eBPF

    Songi Gwak, Thien-Phuc Doan, Souhwan Jung*
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1773-1786, 2023, DOI:10.32604/iasc.2023.039565
    (This article belongs to the Special Issue: Advanced Achievements of Intelligent and Secure Systems for the Next Generation Computing)
    Abstract Containerization is a fundamental component of modern cloud-native infrastructure, and Kubernetes is a prominent platform of container orchestration systems. However, containerization raises significant security concerns due to the nature of sharing a kernel among multiple containers, which can lead to container breakout or privilege escalation. Kubernetes cannot avoid it as well. While various tools, such as container image scanning and configuration checking, can mitigate container workload vulnerabilities, these are not foolproof and cannot guarantee perfect isolation or prevent every active threat in runtime. As such, a policy enforcement solution is required to tackle the problem,… More >

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    ARTICLE

    Anomaly Detection for Cloud Systems with Dynamic Spatiotemporal Learning

    Mingguang Yu1,2, Xia Zhang1,2,*
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1787-1806, 2023, DOI:10.32604/iasc.2023.038798
    Abstract As cloud system architectures evolve continuously, the interactions among distributed components in various roles become increasingly complex. This complexity makes it difficult to detect anomalies in cloud systems. The system status can no longer be determined through individual key performance indicators (KPIs) but through joint judgments based on synergistic relationships among distributed components. Furthermore, anomalies in modern cloud systems are usually not sudden crashes but rather gradual, chronic, localized failures or quality degradations in a weakly available state. Therefore, accurately modeling cloud systems and mining the hidden system state is crucial. To address this challenge,… More >

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    ARTICLE

    A Low-Cost and High-Performance Cryptosystem Using Tripling-Oriented Elliptic Curve

    Mohammad Alkhatib*, Wafa S. Aldalbahy
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1807-1831, 2023, DOI:10.32604/iasc.2023.038927
    Abstract Developing a high-performance public key cryptosystem is crucial for numerous modern security applications. The Elliptic Curve Cryptosystem (ECC) has performance and resource-saving advantages compared to other types of asymmetric ciphers. However, the sequential design implementation for ECC does not satisfy the current applications’ performance requirements. Therefore, several factors should be considered to boost the cryptosystem performance, including the coordinate system, the scalar multiplication algorithm, and the elliptic curve form. The tripling-oriented (3DIK) form is implemented in this work due to its minimal computational complexity compared to other elliptic curves forms. This experimental study explores the… More >

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    ARTICLE

    An Update Method of Decision Implication Canonical Basis on Attribute Granulating

    Yanhui Zhai1,2,*, Rujie Chen1, Deyu Li1,2
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1833-1851, 2023, DOI:10.32604/iasc.2023.039553
    (This article belongs to the Special Issue: Cognitive Granular Computing Methods for Big Data Analysis)
    Abstract Decision implication is a form of decision knowledge representation, which is able to avoid generating attribute implications that occur between condition attributes and between decision attributes. Compared with other forms of decision knowledge representation, decision implication has a stronger knowledge representation capability. Attribute granularization may facilitate the knowledge extraction of different attribute granularity layers and thus is of application significance. Decision implication canonical basis (DICB) is the most compact set of decision implications, which can efficiently represent all knowledge in the decision context. In order to mine all decision information on decision context under attribute More >

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    ARTICLE

    A Trailblazing Framework of Security Assessment for Traffic Data Management

    Abdulaziz Attaallah1, Khalil al-Sulbi2, Areej Alasiry3, Mehrez Marzougui3, Neha Yadav4, Syed Anas Ansar5,*, Pawan Kumar Chaurasia4, Alka Agrawal4
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1853-1875, 2023, DOI:10.32604/iasc.2023.039761
    Abstract Connected and autonomous vehicles are seeing their dawn at this moment. They provide numerous benefits to vehicle owners, manufacturers, vehicle service providers, insurance companies, etc. These vehicles generate a large amount of data, which makes privacy and security a major challenge to their success. The complicated machine-led mechanics of connected and autonomous vehicles increase the risks of privacy invasion and cyber security violations for their users by making them more susceptible to data exploitation and vulnerable to cyber-attacks than any of their predecessors. This could have a negative impact on how well-liked CAVs are with… More >

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    ARTICLE

    Railway Passenger Flow Forecasting by Integrating Passenger Flow Relationship and Spatiotemporal Similarity

    Song Yu*, Aiping Luo, Xiang Wang
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1877-1893, 2023, DOI:10.32604/iasc.2023.039132
    (This article belongs to the Special Issue: Artificial Intelligence for Smart Cities)
    Abstract Railway passenger flow forecasting can help to develop sensible railway schedules, make full use of railway resources, and meet the travel demand of passengers. The structure of passenger flow in railway networks and the spatiotemporal relationship of passenger flow among stations are two distinctive features of railway passenger flow. Most of the previous studies used only a single feature for prediction and lacked correlations, resulting in suboptimal performance. To address the above-mentioned problem, we proposed the railway passenger flow prediction model called Flow-Similarity Attention Graph Convolutional Network (F-SAGCN). First, we constructed the passenger flow relations… More >

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    ARTICLE

    Real-Time Multi Fractal Trust Evaluation Model for Efficient Intrusion Detection in Cloud

    S. Priya1, R. S. Ponmagal2,*
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1895-1907, 2023, DOI:10.32604/iasc.2023.039814
    Abstract Handling service access in a cloud environment has been identified as a critical challenge in the modern internet world due to the increased rate of intrusion attacks. To address such threats towards cloud services, numerous techniques exist that mitigate the service threats according to different metrics. The rule-based approaches are unsuitable for new threats, whereas trust-based systems estimate trust value based on behavior, flow, and other features. However, the methods suffer from mitigating intrusion attacks at a higher rate. This article presents a novel Multi Fractal Trust Evaluation Model (MFTEM) to overcome these deficiencies. The… More >

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    ARTICLE

    Computational Analysis for Computer Network Model with Fuzziness

    Wafa F. Alfwzan1, Dumitru Baleanu2,3,4, Fazal Dayan5,*, Sami Ullah5, Nauman Ahmed4,6, Muhammad Rafiq7,8, Ali Raza4,9
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1909-1924, 2023, DOI:10.32604/iasc.2023.039249
    (This article belongs to the Special Issue: Human Behaviour Analysis using Fuzzy Neural Networks)
    Abstract A susceptible, exposed, infectious, quarantined and recovered (SEIQR) model with fuzzy parameters is studied in this work. Fuzziness in the model arises due to the different degrees of susceptibility, exposure, infectivity, quarantine and recovery among the computers under consideration due to the different sizes, models, spare parts, the surrounding environments of these PCs and many other factors like the resistance capacity of the individual PC against the virus, etc. Each individual PC has a different degree of infectivity and resistance against infection. In this scenario, the fuzzy model has richer dynamics than its classical counterpart More >

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    ARTICLE

    Missing Value Imputation Model Based on Adversarial Autoencoder Using Spatiotemporal Feature Extraction

    Dong-Hoon Shin1, Seo-El Lee2, Byeong-Uk Jeon1, Kyungyong Chung3,*
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1925-1940, 2023, DOI:10.32604/iasc.2023.039317
    Abstract Recently, the importance of data analysis has increased significantly due to the rapid data increase. In particular, vehicle communication data, considered a significant challenge in Intelligent Transportation Systems (ITS), has spatiotemporal characteristics and many missing values. High missing values in data lead to the decreased predictive performance of models. Existing missing value imputation models ignore the topology of transportation networks due to the structural connection of road networks, although physical distances are close in spatiotemporal image data. Additionally, the learning process of missing value imputation models requires complete data, but there are limitations in securing More >

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    ARTICLE

    A Novel Ego Lanes Detection Method for Autonomous Vehicles

    Bilal Bataineh*
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1941-1961, 2023, DOI:10.32604/iasc.2023.039868
    Abstract Autonomous vehicles are currently regarded as an interesting topic in the AI field. For such vehicles, the lane where they are traveling should be detected. Most lane detection methods identify the whole road area with all the lanes built on it. In addition to having a low accuracy rate and slow processing time, these methods require costly hardware and training datasets, and they fail under critical conditions. In this study, a novel detection algorithm for a lane where a car is currently traveling is proposed by combining simple traditional image processing with lightweight machine learning… More >

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    ARTICLE

    Multimodal Sentiment Analysis Using BiGRU and Attention-Based Hybrid Fusion Strategy

    Zhizhong Liu*, Bin Zhou, Lingqiang Meng, Guangyu Huang
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1963-1981, 2023, DOI:10.32604/iasc.2023.038835
    Abstract Recently, multimodal sentiment analysis has increasingly attracted attention with the popularity of complementary data streams, which has great potential to surpass unimodal sentiment analysis. One challenge of multimodal sentiment analysis is how to design an efficient multimodal feature fusion strategy. Unfortunately, existing work always considers feature-level fusion or decision-level fusion, and few research works focus on hybrid fusion strategies that contain feature-level fusion and decision-level fusion. To improve the performance of multimodal sentiment analysis, we present a novel multimodal sentiment analysis model using BiGRU and attention-based hybrid fusion strategy (BAHFS). Firstly, we apply BiGRU to More >

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    ARTICLE

    Classifying Hematoxylin and Eosin Images Using a Super-Resolution Segmentor and a Deep Ensemble Classifier

    P. Sabitha*, G. Meeragandhi
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1983-2000, 2023, DOI:10.32604/iasc.2023.034402
    Abstract Developing an automatic and credible diagnostic system to analyze the type, stage, and level of the liver cancer from Hematoxylin and Eosin (H&E) images is a very challenging and time-consuming endeavor, even for experienced pathologists, due to the non-uniform illumination and artifacts. Albeit several Machine Learning (ML) and Deep Learning (DL) approaches are employed to increase the performance of automatic liver cancer diagnostic systems, the classification accuracy of these systems still needs significant improvement to satisfy the real-time requirement of the diagnostic situations. In this work, we present a new Ensemble Classifier (hereafter called ECNet)… More >

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    ARTICLE

    Flow Direction Level Traffic Flow Prediction Based on a GCN-LSTM Combined Model

    Fulu Wei1, Xin Li1, Yongqing Guo1,*, Zhenyu Wang2, Qingyin Li1, Xueshi Ma3
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2001-2018, 2023, DOI:10.32604/iasc.2023.035799
    Abstract Traffic flow prediction plays an important role in intelligent transportation systems and is of great significance in the applications of traffic control and urban planning. Due to the complexity of road traffic flow data, traffic flow prediction has been one of the challenging tasks to fully exploit the spatiotemporal characteristics of roads to improve prediction accuracy. In this study, a combined flow direction level traffic flow prediction graph convolutional network (GCN) and long short-term memory (LSTM) model based on spatiotemporal characteristics is proposed. First, a GCN model is employed to capture the topological structure of… More >

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    ARTICLE

    Hyperparameter Optimization for Capsule Network Based Modified Hybrid Rice Optimization Algorithm

    Zhiwei Ye1, Ziqian Fang1, Zhina Song1,*, Haigang Sui2, Chunyan Yan1, Wen Zhou1, Mingwei Wang1
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2019-2035, 2023, DOI:10.32604/iasc.2023.039949
    Abstract Hyperparameters play a vital impact in the performance of most machine learning algorithms. It is a challenge for traditional methods to configure hyperparameters of the capsule network to obtain high-performance manually. Some swarm intelligence or evolutionary computation algorithms have been effectively employed to seek optimal hyperparameters as a combinatorial optimization problem. However, these algorithms are prone to get trapped in the local optimal solution as random search strategies are adopted. The inspiration for the hybrid rice optimization (HRO) algorithm is from the breeding technology of three-line hybrid rice in China, which has the advantages of… More >

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    ARTICLE

    A Robust Model for Translating Arabic Sign Language into Spoken Arabic Using Deep Learning

    Khalid M. O. Nahar1, Ammar Almomani2,3,*, Nahlah Shatnawi1, Mohammad Alauthman4
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2037-2057, 2023, DOI:10.32604/iasc.2023.038235
    Abstract This study presents a novel and innovative approach to automatically translating Arabic Sign Language (ATSL) into spoken Arabic. The proposed solution utilizes a deep learning-based classification approach and the transfer learning technique to retrain 12 image recognition models. The image-based translation method maps sign language gestures to corresponding letters or words using distance measures and classification as a machine learning technique. The results show that the proposed model is more accurate and faster than traditional image-based models in classifying Arabic-language signs, with a translation accuracy of 93.7%. This research makes a significant contribution to the More >

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    ARTICLE

    Two-Layer Information Granulation: Mapping-Equivalence Neighborhood Rough Set and Its Attribute Reduction

    Changshun Liu1, Yan Liu1, Jingjing Song1,*, Taihua Xu1,2
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2059-2075, 2023, DOI:10.32604/iasc.2023.039592
    (This article belongs to the Special Issue: Cognitive Granular Computing Methods for Big Data Analysis)
    Abstract Attribute reduction, as one of the essential applications of the rough set, has attracted extensive attention from scholars. Information granulation is a key step of attribute reduction, and its efficiency has a significant impact on the overall efficiency of attribute reduction. The information granulation of the existing neighborhood rough set models is usually a single layer, and the construction of each information granule needs to search all the samples in the universe, which is inefficient. To fill such gap, a new neighborhood rough set model is proposed, which aims to improve the efficiency of attribute… More >

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    ARTICLE

    Competitive and Cooperative-Based Strength Pareto Evolutionary Algorithm for Green Distributed Heterogeneous Flow Shop Scheduling

    Kuihua Huang1, Rui Li2, Wenyin Gong2,*, Weiwei Bian3, Rui Wang1
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2077-2101, 2023, DOI:10.32604/iasc.2023.040215
    (This article belongs to the Special Issue: Artificial Intelligence Algorithm for Industrial Operation Application)
    Abstract This work aims to resolve the distributed heterogeneous permutation flow shop scheduling problem (DHPFSP) with minimizing makespan and total energy consumption (TEC). To solve this NP-hard problem, this work proposed a competitive and cooperative-based strength Pareto evolutionary algorithm (CCSPEA) which contains the following features: 1) An initialization based on three heuristic rules is developed to generate a population with great diversity and convergence. 2) A comprehensive metric combining convergence and diversity metrics is used to better represent the heuristic information of a solution. 3) A competitive selection is designed which divides the population into a… More >

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    ARTICLE

    Fine-Grained Action Recognition Based on Temporal Pyramid Excitation Network

    Xuan Zhou1,*, Jianping Yi2
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2103-2116, 2023, DOI:10.32604/iasc.2023.034855
    Abstract Mining more discriminative temporal features to enrich temporal context representation is considered the key to fine-grained action recognition. Previous action recognition methods utilize a fixed spatiotemporal window to learn local video representation. However, these methods failed to capture complex motion patterns due to their limited receptive field. To solve the above problems, this paper proposes a lightweight Temporal Pyramid Excitation (TPE) module to capture the short, medium, and long-term temporal context. In this method, Temporal Pyramid (TP) module can effectively expand the temporal receptive field of the network by using the multi-temporal kernel decomposition without More >

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    ARTICLE

    Forecasting Energy Consumption Using a Novel Hybrid Dipper Throated Optimization and Stochastic Fractal Search Algorithm

    Doaa Sami Khafaga1, El-Sayed M. El-kenawy2, Amel Ali Alhussan1,*, Marwa M. Eid3
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2117-2132, 2023, DOI:10.32604/iasc.2023.038811
    (This article belongs to the Special Issue: Optimization Algorithm for Intelligent Computing Application)
    Abstract The accurate prediction of energy consumption has effective role in decision making and risk management for individuals and governments. Meanwhile, the accurate prediction can be realized using the recent advances in machine learning and predictive models. This research proposes a novel approach for energy consumption forecasting based on a new optimization algorithm and a new forecasting model consisting of a set of long short-term memory (LSTM) units. The proposed optimization algorithm is used to optimize the parameters of the LSTM-based model to boost its forecasting accuracy. This optimization algorithm is based on the recently emerged… More >

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    ARTICLE

    Predicting Lumbar Spondylolisthesis: A Hybrid Deep Learning Approach

    Deepika Saravagi1, Shweta Agrawal2,*, Manisha Saravagi3, Sanjiv K. Jain4, Bhisham Sharma5, Abolfazl Mehbodniya6,*, Subrata Chowdhury7, Julian L. Webber6
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2133-2151, 2023, DOI:10.32604/iasc.2023.039836
    (This article belongs to the Special Issue: Intelligent Systems for Diversified Application Domains)
    Abstract Spondylolisthesis is a chronic disease, and a timely diagnosis of it may help in avoiding surgery. Disease identification in x-ray radiographs is very challenging. Strengthening the feature extraction tool in VGG16 has improved the classification rate. But the fully connected layers of VGG16 are not efficient at capturing the positional structure of an object in images. Capsule network (CapsNet) works with capsules (neuron clusters) rather than a single neuron to grasp the properties of the provided image to match the pattern. In this study, an integrated model that is a combination of VGG16 and CapsNet… More >

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