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8th IET Smart Cities Symposium (SCS 2024)

作者： Mahmoud Elshabrawy Faris H. Rizk Ahmed Mohamed Zaki Marwa M. Eid Ali E. Takieldeen Abdelhameed Ibrahim Abdelaziz A. Abdelhamid Laith Abualigah El-Sayed M. El-kenawy Computer Science and Intelligent Systems Research Center Blacksburg 24060 Virginia USA Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura 35111 Egypt School of ICT Faculty of Engineering Design and Information & Communications Technology (EDICT) Bahrain Polytechnic PO Box 33349 Isa Town Bahrain Department of Computer Science College of Computing and Information Technology Shaqra University 11961 Shaqra Saudi Arabia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan

ISBN: (数字)9781837243105

Treatment plant optimization plays an important role in the implementation of eco-problem solutions. This article is going to cover how machine learning algorithms can determine energy consumption, climate, and wastewater features of electricity at a wastewater treatment plant in eastern Melbourne from 2014 to 2019. This study uses data obtained from the Melbourne Water and Airport weather station, which are freely available to carry out MLPRegressor, SVM, Linear Regression, Decision Tree Regression, random forest regression, and Nearest Neighbor. Through customary measures like MSE, RMSE, EVS, and others, the best model is adequately identified, which is the Random Forest Regressor model, which has 0.889285. (Lowest MSE) With these results, the plant treatment optimization processes, such as sampling for environmental analysis and the control of energy consumption, have improved.

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Dynamic Multi-Objective Optimization Algorithm Guided by Recurrent Neural Network

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Hu, Yaru Ou, Junwei Suganthan, Ponnuthurai Nagaratnam Pedrycz, Witold Wang, Rui Zheng, Jinhua Zou, Juan Song, Yanjie School of Computer Science and School of Cyberspace Security Xiangtan University Xiangtan China College of Engineering KINDI Center for Computing Research Qatar University Doha Qatar Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada College of Systems Engineering National University of Defense Technology Changsha China School of Reliability and Systems Engineering Beihang University Beijing China

In recent years, prediction-based algorithms have attracted much attention for solving dynamic multi-objective optimization problems in the evolutionary computing community. However, this class of algorithms still has potential for further improvements by enhaneing the historical information extraction approach to balance convergence and diversity. In this paper, we propose a dynamic multi-objective optimization algorithm based on a recurrent neural network to balance the population’s convergence and diversity in dynamic environments. The recurrent neural network model in the proposed algorithm employs online learning in order to constantly improve according to the increasing evolutionary information. Meanwhile, differing from most existing prediction-based algorithms, the learning machine is not limited by assumptions, such as linear or nonlinear correlation, when it predicts new solutions for future evolutionary environments. Besides, an auxiliary strategy is performed, which adaptively introduces the random or mutated solutions according to the error losses between the prediction solutions and the optimal solutions in the whole optimization process. The experimental results show that the proposed algorithm is more effective for handling dynamic multi-objective optimization problems than several recent algorithms. IEEE

关键词： Evolutionary algorithms

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Steel surface defect detection based on lightweight model

Steel surface defect detection based on lightweight model

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Le He Jun Huang Xue Li Bolun Guan School of Computer Science and Computing Anhui University of Technology Ma’Anshan China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Agricultural Economy and Information Research InstituteAnhui Academy of Agricultural Sciences Hefei China

Steel surface defect detection, with the goal of enhancing the quality of steel within the industry, is a vital process in steel production. Over the years, a number of techniques have been developed to meet the challenge of object detection. Nonetheless, designing lightweight models that can detect defects quickly and accurately remains challenging due to a significant amount of environmental interference and limited computational resources of the edge devices used in steel factories. In this paper, In this paper, we propose an improved Lightweight Detection Network(LDN) for steel surface defect detection. First, to extract features, the lightweight MobileNetV2 model is employed as the backbone. Second, to improve the detection accuracy, a feedback mechanism from the feature pyramid is added to the backbone network to integrate the features from shallow and deep layers. Additionally, the Efficient Intersection over Union(EIoU) loss is utilized to make the regression more accurate. The experimental results indicate that the method’s accuracy in recognition surpasses that of previous models, and the parameters and computational requirements have decreased significantly, leading to a rapid and precise detection of steel surface defects.

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STRPM: A Spatiotemporal Residual Predictive Model for High-Resolution Video Prediction

arXiv

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arXiv 2022年

作者： Chang, Zheng Zhang, Xinfeng Wang, Shanshe Ma, Siwei Gao, Wen School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing China National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing China

Although many video prediction methods have obtained good performance in low-resolution (64∼128) videos, predictive models for high-resolution (512∼4K) videos have not been fully explored yet, which are more meaningful due to the increasing demand for high-quality videos. Compared with low-resolution videos, high-resolution videos contain richer appearance (spatial) information and more complex motion (temporal) information. In this paper, we propose a Spatiotemporal Residual Predictive Model (STRPM) for high-resolution video prediction. On the one hand, we propose a Spatiotemporal Encoding-Decoding Scheme to preserve more spatiotemporal information for high-resolution videos. In this way, the appearance details for each frame can be greatly preserved. On the other hand, we design a Residual Predictive Memory (RPM) which focuses on modeling the spatiotemporal residual features (STRF) between previous and future frames instead of the whole frame, which can greatly help capture the complex motion information in high-resolution videos. In addition, the proposed RPM can supervise the spatial encoder and temporal encoder to extract different features in the spatial domain and the temporal domain, respectively. Moreover, the proposed model is trained using generative adversarial networks (GANs) with a learned perceptual loss (LP-loss) to improve the perceptual quality of the predictions. Experimental results show that STRPM can generate more satisfactory results compared with various existing methods. © 2022, CC BY.

关键词： Signal encoding

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Visible Light Positioning Method for Indoor Line-of-Sight Scene Based on Time Series Coding

Visible Light Positioning Method for Indoor Line-of-Sight Sc...

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IEEE International Conference on Data science in Cyberspace (DSC)

作者： Yonghao Yu Yongde Guo Dawei Zhao Kexue Fu Yongwei Tang Qinyuan Wang Faculty of Data Science City University of Macau China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center(National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China School of Mechanical Engineering Key Laboratory of High Efficiency and Clean Mechanical Manufacture Ministry of Education Shandong University Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Existing indoor visible light positioning methods, such as those based on fingerprint database algorithms, are complex to construct and consume significant resources. In response to this issue, the text proposes an end-to-end indoor visible light positioning method that samples continuous signals at a small number of random coordinate positions in indoor environments. Through a deep learning method based on attention mechanism, a nonlinear mapping between the received signal and spatial position coordinates is constructed, and the time series properties in the received signal are encoded as a feature in the data. The method is further improved by combining residual networks to improve accuracy. Through simulation experiments, it has been verified that the deep learning based localization method described in this article can achieve sub meter level accuracy on small sample datasets, and it has been proven that time series features have a significant impact on localization accuracy in indoor visible light positioning algorithms based on deep learning.

关键词： Location awareness Deep learning Meters Training Accuracy Attention mechanisms Time series analysis Training data Encoding Data models

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Myocardial Region-guided Feature Aggregation Net for Automatic Coronary artery Segmentation and Stenosis Assessment using Coronary Computed Tomography Angiography

arXiv

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arXiv 2025年

作者： Yao, Ni Liu, Xiangyu Sun, Danyang Han, Chuang Li, Yanting Nan, Jiaofen Li, Chengyang Zhu, Fubao Zhou, Weihua Zhao, Chen School of Computer Science and Technology Zhengzhou University of Light Industry Henan Zhengzhou450001 China School of Information Management and Engineering Shanghai University of Finance and Economics China Department of Applied Computing Michigan Technological University HoughtonMI United States Center for Biocomputing and Digital Health Institute of Computing and Cybersystems Health Research Institute Michigan Technological University HoughtonMI United States Department of Computer Science Kennesaw State University MariettaGA United States

Coronary artery disease (CAD) remains a leading cause of mortality worldwide, requiring accurate segmentation and stenosis detection using Coronary Computed Tomography angiography (CCTA). Existing methods struggle with challenges such as low contrast, morphological variability and small vessel segmentation. To address these limitations, we propose the Myocardial Region-guided Feature Aggregation Net, a novel U-shaped dual-encoder architecture that integrates anatomical prior knowledge to enhance robustness in coronary artery segmentation. Our framework incorporates three key innovations: (1) a Myocardial Region-guided Module that directs attention to coronary regions via myocardial contour expansion and multi-scale feature fusion, (2) a Residual Feature Extraction Encoding Module that combines parallel spatial channel attention with residual blocks to enhance local-global feature discrimination, and (3) a Multi-scale Feature Fusion Module for adaptive aggregation of hierarchical vascular features. Additionally, Monte Carlo dropout f quantifies prediction uncertainty, supporting clinical interpretability. For stenosis detection, a morphology-based centerline extraction algorithm separates the vascular tree into anatomical branches, enabling cross-sectional area quantification and stenosis grading. The superiority of MGFA-Net was demonstrated by achieving an Dice score of 85.04%, an accuracy of 84.24%, an HD95 of 6.1294 mm, and an improvement of 5.46% in true positive rate for stenosis detection compared to3D U-Net. The integrated segmentation-to-stenosis pipeline provides automated, clinically interpretable CAD assessment, bridging deep learning with anatomical prior knowledge for precision medicine. Our code is publicly available at http://***/chenzhao2023/MGFA_CCTA. © 2025, CC BY.

关键词： Image segmentation

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Exploring The Impact Of Artificial Intelligence On Visual Communication In Unmasking Deep Fake And Ensuring Public Trust

Exploring The Impact Of Artificial Intelligence On Visual Co...

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Intelligent Systems and Machine Learning Conference (ISML)

作者： Jagendra Singh Neeraj Abbas Thajeel Rhaif Alsahlanee Abha Kiran Rajpoot Gaurav Agrawal Mohit Tiwari Computer Science Engineering Alliance School of Advanced Computing Alliance University Banglore India School of Computer Science Engineering and Technology Bennett University Greater Noida India Development and Continuous Education Center University of Thi-Qar Thi-Qar Iraq Department of CSE(AIML) KIET Group of Institution Ghaziabad India Department of CSE(AI) KIET Group of Institution Ghaziabad India Dept. of Computer Science & Engineering Bharati Vidyapeeth’s College of Engineering Delhi India

ISBN: (数字)9798350343878

ISBN: (纸本)9798350343885

In this groundbreaking study, we delve into the pivotal role of the Vision Transformer (V-iT) in the realm of deepfake detection, seeking to unravel its effectiveness in navigating the intricate landscape of manipulated visual content. As image-generating technologies advance, the proliferation of Dfakes poses profound challenges to the integrity of digital media, sparking concerns about misinformation and eroding trust in visual communication. Against this backdrop, V-iT emerges as a cutting-edge solution, leveraging its unique ability to comprehend intricate patterns in visual data. Our research is anchored in a meticulous methodology, involving comprehensive experiments designed to assess V-iT's performance in deepfake detection. The results, spanning 10 diverse samples, reveal consistently high accuracy, precision, recall, and F1 rate values. V-iT's capacity to achieve accurate classifications, minimize false positives, and sensitively detect manipulated content underscores its robustness in addressing the complexities of deceptive media practices. This paper goes beyond a mere validation of V-iT's capabilities, offering a nuanced understanding of its performance metrics. The high precision values emphasize V-iT's accuracy in identifying manipulated content, while exceptional recall values highlight its sensitivity in capturing true positive instances. The balanced F1 rate provides a harmonious measure of V-iT's overall effectiveness, demonstrating its potential as a dependable tool in upholding the authenticity of visual communication.

关键词： Deepfakes Visualization computer vision Accuracy Sensitivity Visual communication Weapons Resists Transformers Robustness

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CAC-EMVT: Efficient Coronary Artery Calcium Segmentation with Multi-scale Vision Transformers

CAC-EMVT: Efficient Coronary Artery Calcium Segmentation wit...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Ning, Yang Zhang, Shouyi Xi, Xiaoming Guo, Jie Liu, Peide Zhang, Caiming Shandong Jianzhu University School of Computer Science and Technology Jinan China The University of Edinburgh School of Informatics Edinburgh United Kingdom Shandong University School of Software Jinan China Shandong Co-Innovation Center of Future Intelligent Computing Yantai China Shandong Jianzhu University School of Computer Science and Technology Jinan China School of Management Science and Engineering Shandong University of Finance and Economics Jinan China

ISBN: (纸本)9781665401265

In clinical practice, as a powerful and independent risk indicator of cardiovascular disease (CVD), accurate coronary artery calcium (CAC) segmentation can provide important information for the early diagnosis of CVD. However, due to the small and inconsistent CAC usually has fuzzy boundaries, which leads existing segmentation methods to suffer from unsatisfactory performance. To tackle this challenge, we propose a novel Efficient Multi-scale Vision Transformers for CAC segmentation (CAC-EMVT), which uses both the local and global branches to jointly model short- and long-range dependencies. CAC-EMVT is mainly composed of three modules: 1) a key factor sampling (KFS) module, which is used to mine the key factors of the image to perform low-rank reconstruction of highly structured features;2) a non-local sparse context fusion (NSCF) module, which is used to efficiently model the global context information of shallow texture features;and 3) a non-local multi-scale context aggregation (NMCA) module, which can be applied to cross-level features to collect long-range dependencies from multiple scales. Undeniably, the newly proposed decomposable positional encoding plays a vital role in the performance improvement of the above modules. Extensive experiments are conducted on the CT scans of 130 CVD patients under 4-fold cross-validation and have demonstrated our CAC-EMVT notably outperforms the state-of-the-art methods in terms of both the mean Dice similarity coefficient (mDice) of 75.39%± 3.17 and mean surface distance (MSD) of 1.93%± 0.46. This reveals the effectiveness and the potential of our model in the clinical setting. © 2021 IEEE.

关键词： computerized tomography

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IRS-Enhanced UAV Communication Networks: Securing Data with Hybrid Genetic and Gradient Descent Algorithms

IRS-Enhanced UAV Communication Networks: Securing Data with ...

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IEEE Conference on Wireless Communications and Networking

作者： Zina Chkirbene Ala Gouissem Ridha Hamila Unal Devrim Arafat Al-Dweik Kaya Kuru Electrical Engineering Qatar University Qatar College of Computing and Information Technology University of Doha for Science and Technology Qatar KINDI Center for Computing Research College of Engineering Qatar University Qatar Department of Computer and Information Engineering 4 KU 6G Research Center Khalifa University Abu Dhabi UAE School of Engineering University of Central Lancashire Preston UK

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In the rapidly advancing field of wireless communication, Unmanned Aerial Vehicles (UAVs) have become indispensable due to their extensive coverage capabilities and ability to access remote locations. Whether deployed as mobile base stations (BSs) or relays, UAVs significantly enhance network throughput and reliability. Alongside UAVs, Intelligent Reflecting Surfaces (IRS) have emerged as a cost-effective solution for improving communication quality through passive modulation arrays. Despite these advancements, the potential misuse of UAVs poses serious security risks, particularly in the form of communication eavesdropping. To address these challenges, this paper introduces a novel communication framework that integrates a UAV equipped with an adaptive IRS. The primary aim is to boost communication secrecy between BSs and multiple users, even in the presence of several UAV eavesdroppers. This objective is formulated as an optimization problem focused on maximizing the secrecy rate while considering UAV mobility constraints. To solve this non-convex problem, we propose a hybrid strategy that combines Genetic Algorithms and Gradient Descent techniques. This innovative approach efficiently determines suboptimal reflection angles and UAV trajectories for IRS-equipped UAVs, thereby enhancing the security of the communication network. This method not only addresses the complexity of the optimization but also provides a practical pathway to secure communications in environments with high eavesdropping risks.

关键词： Wireless networks Autonomous aerial vehicles Throughput Security Communication networks Reliability Relays Trajectory optimization Eavesdropping Testing

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Vflh: A Following-the-Leader-History Based Algorithm for Adaptive Online Convex Optimization with Stochastic Constraints

SSRN

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SSRN 2022年

作者： Yang, Yifan Chen, Lin Zhou, Pan Ding, Xiaofeng Department of Computer Science University of California Santa BarbaraCA93106 United States National Engineering Research Center for Big Data Technology and System Lab Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan430072 China

This paper considers online convex optimization (OCO) with generated i.i.d. stochastic constraints, where the distribution of environment is changing and the performance is measured by \textit{adaptive regret}. The stochastic constraints are disclosed at each round to the learner after the decision is made. Different from the previous non-adaptive constrained OCO algorithm which is generalized based on the traditional online gradient descent algorithm, we propose a novel \textit{Virtual queue based Following-the-Leader-History} (VFLH) strategy to make the constrained OCO algorithm adaptive and design the VFLH algorithm. In this setting, the algorithm firstly generalizes all the experts in the specified time interval, then decides the optimal decision with each expert and gives out the final choice by combining the predictions of active experts. In terms of the algorithm performance, there are two performance metrics to be evaluated, i.e., the bounds of constraint violation and regret. For the constraint violation, we bring in the multi-objective drift analysis approach to deal with the constraints via a virtual queue which provides a new method to bound the constraints violations. For the regret, we give out its upper bound and then achieve a corresponding lower bound that proves the upper bound is sufficiently tight. Finally, we provide some numerical experiments to validate the feasibility of our algorithm. © 2022, The Authors. All rights reserved.

关键词： Stochastic systems

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