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检索条件"任意字段=2021 International Conference on Machine Learning and Intelligent Systems Engineering, MLISE 2021"

共 8431 条记录，以下是41-50 订阅

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Deepfake Video Detection: A Comprehensive Survey of Advanced machine learning and Deep learning Techniques to Combat Synthetic Video Manipulation

Deepfake Video Detection: A Comprehensive Survey of Advanced...

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Anusha, Tenali Srinagesh, A. Acharya Nagarjuna University Department of Cse Andhra Pradesh Guntur India R.V.R. & J.C College of Engineering Department of Csbs Andhra Pradesh Guntur India

ISBN: (纸本)9798331509828

The detection of deepfake videos has become a critical area of research due to the widespread use of manipulated content across digital platforms. With the increasing sophistication of generative models deepfake videos have reached a level of realism that makes them challenging to detect. This survey paper provides an extensive review of 70 research papers published from 2021 to 2024, categorizing the existing detection techniques into three primary groups such as supervised learning techniques, unsupervised learning techniques, and hybrid methods. These methods leverage labelled datasets to train models to identify subtle artefacts in manipulated videos. Notable advancements have been made by integrating CNNs with RNNs to capture both spatial and temporal patterns, enhancing detection accuracy. Unsupervised learning techniques, such as clustering algorithms and anomaly detection methods, are increasingly being explored for deepfake detection. Hybrid methods combine the strengths of both supervised and unsupervised techniques, improving detection performance. CNN-RNN architectures integrated with attention mechanisms are an example, as they enhance the focus on manipulated regions within videos. Additionally, multimodal approaches that combine both visual and audio cues have been introduced to address the complexity of detecting deepfakes across various media types. This review identifies the challenges faced by current detection systems, such as limitations in datasets, high computational costs, and a lack of robustness against evolving manipulation techniques. To address these challenges, future research should focus on developing more diverse datasets, enhancing the generalizability of models, and improving computational efficiency to support real-time applications. The most promising approach for advancing deepfake detection systems involves integrating multi-modal techniques, advanced deep learning architectures, and scalable designs. © 2025 IEEE.

关键词： Generative adversarial networks

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Research on Flood Season Runoff Forecast Based on a Novel Combination machine learning Method

Research on Flood Season Runoff Forecast Based on a Novel Co...

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2024 international conference on Mechatronics and intelligent Control, ICMIC 2024

作者： He, Feifei Wan, Qinjuan Feng, Yu Zhang, Xiaoqi Changjiang River Scientific Research Institute Changjiang Water Resources Commission of the Ministry of Water Resources of China Hubei Wuhan430010 China School of Economics and Business Administration Central China Normal University Wuhan430010 China Hubei Key Laboratory of Water Resources & Eco-Environmental Sciences Hubei Wuhan430010 China Research Center on the Yangtze River Economic Belt Protection and Development Strategy CWRC Hubei Wuhan430010 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Hydrological forecasting is of great significance for flood control and disaster reduction, water resource management, hydropower energy production, and environmental protection. This article proposes a novel approach that combines an artificial intelligence algorithm and a decomposition algorithm. The proposed method first uses a Seasonal Decomposition (SD) algorithm to decompose the runoff series into subcomponents including trend, seasonal, and residual components. Then, the Simple Recurrent Unit (SRU) algorithm is used to make predictions for each subcomponent, which are then integrated into the final prediction result. The proposed model was tested on the Dongjiang dataset along with several other common machine learning prediction models including Gated Unit Recurrent (GUR), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Backpropagation (BP), and Linear Regression (LR). The results showed that the proposed model outperformed the other models, with a coefficient of determination R2 of 88.94 and a Nash-Sutcliffe efficiency NSE of 0.936. The proposed model is particularly effective at predicting flood season runoff sequences. © 2025 SPIE.

关键词： Long short-term memory

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SAF: Local Shape-aware Face-based Garment Collision Handling via Neural SDFs

SAF: Local Shape-aware Face-based Garment Collision Handling...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Tang, Minzhe Yuan, Ruisheng Kou, Dongliang Sun, Mingyang Zhang, Lihua Academy for Engineering and Technology Fudan University Shanghai China Institute of Metaverse & Intelligent Medicine Fudan University Shanghai China Jilin Provincial Key Laboratory of Intelligence Science and Engineering Changchun China Engineering Research Center of AI and Robotics Ministry of Education Shanghai China AI and Unmanned Systems Engineering Research Center of Jilin Province Changchun China

ISBN: (纸本)9798350368741

learning-based garment prediction presents an appealing alternative to physics-based methods owing to its high efficiency. However, the predicted garments can exhibit noticeable penetrations into the body. Many collision-handling methods operate in the point domain, which has an inherent limitation in addressing the penetration issues with the mesh faces. In light of this, we propose a local Shape-Aware, Face-based collision handling approach (SAF) that can be applied to garment prediction networks to achieve real-time collision handling. Considering the nature of garments, we design a compact formulation to model the continuity of the garment surface, and utilize neural Signed Distance Fields (SDFs) to accomplish penetration resolution. Recognizing the significant impact of the body's local shape on collision handling, we further propose using the angles between SDF gradients to characterize the sharpness of the body. Our approach can compensate for the inaccuracy of neural SDFs and preserve local smoothness and details. Extensive experiments demonstrate the outstanding performance and generalizability of our method. © 2025 IEEE.

关键词： collision handling computer graphics deep learning garment simulation

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Hybrid offshore wind turbines: Control-oriented modeling approaches 6th

Hybrid offshore wind turbines: Control-oriented modeling app...

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6th international conference on Renewable Energies Offshore, RENEW 2024

作者： Ahmad, I. M’zoughi, F. Aboutalebi, P. Garrido, I. Garrido, A.J. Automatic Control Group—ACG Institute of Research and Development of Processes—IIDP Department of Automatic Control and Systems Engineering Engineering School of Bilbao—EIB/BIE Faculty of Engineering of Bilbao—EIB/BIE University of the Basque Country—UPV/EHU Bilbao Spain

ISBN: (纸本)9781032905570

In the pursuit of sustainable electricity generation from offshore wind and wave energy, the combination of Floating Offshore Wind Turbines (FOWTs) and Oscillating Water Columns (OWCs) has emerged as a promising solution to enhance the working lifespan of this kind of systems. This study introduces a control-focused modeling approach, using empirical data collected from the widely accepted FAST environment, to implement advanced control laws for these integrated platforms. Leveraging Long Short-Term Memory (LSTM) and Artificial Neural Networks (ANN), the research yields predictive models that effectively capture the platform pitch dynamics so as to optimize control strategies. By integrating advanced machine learning techniques, these approaches address the challenges of modeling floating offshore structures in dynamic environments. In this sense, the neuro-estimators developed in this study closely match the original plant results, offering researchers a straightforward way to implement complex control schemes that could hardly be implemented over the original non-linear system. © 2025 The Author(s).

关键词： Offshore wind turbines

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Research on intelligent Detection Technology of Wireless Communication Signals Based on Yolov10 Model 16

Research on Intelligent Detection Technology of Wireless Com...

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16th international conference on Signal Processing systems, ICSPS 2024

作者： Zhang, Wantong Li, Jia Chu, Zhaohui Luo, Bing Li, Gui Zhang, Yulu Ran, Yuzhou Li, Jian School of Information and Communication Engineering Yibin Institute University of Electronic Science and Technology of China Chengdu611731 China The 10th Research Institute of China Electronics Technology Group Corporation Chengdu610036 China Department of IoT Technology Research China Mobile Research Institute Beijing100053 China National Defense Science and Technology Innovation Institute Academy of Military Sciences Beijing100071 China

ISBN: (纸本)9781510689251

To address the challenge of efficient communication signal detection in complex electromagnetic environments, this paper proposes an intelligent signal detection method based on the Yolov10-ghost model, incorporating deep learning techniques. Building upon the Yolov10 architecture, the method introduces the C3Ghost module and replaces conventional Conv layers with GhostConv layers to enhance feature extraction and fusion while reducing model complexity. Additionally, the loss function is optimized to WIoU-v2, addressing class imbalance and improving suitability for object detection tasks. Experimental results show that the proposed method can efficiently detect continuous and burst signals in communication data, achieving an average detection accuracy (mAP) of 98.4%. The model parameters are reduced to 1.9M, with an inference time of 80.8ms per frame, demonstrating a strong balance between detection accuracy and real-time performance. Furthermore, the model's successful deployment on embedded platforms highlights its significant engineering application value. © 2025 SPIE.

关键词： Photons

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SRv6-INT Enabled Network Monitoring and Measurement: Toward High-Yield Network Observability for Digital Twin 3rd

SRv6-INT Enabled Network Monitoring and Measurement: Toward...

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3rd international conference on machine learning, Cloud Computing and intelligent Mining, MLCCIM 2024

作者： Zhang, Xu Feng, Chuan Han, Pengchao Liu, Wei Gong, Xiaoxue Zhang, Qiupei Guo, Lei School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Institute of Intelligent Communication and Network Security Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China School of Information Engineering Guangdong University of Technology Guangzhou510006 China

ISBN: (纸本)9789819616978

Network monitoring and measurement is an important part of realizing the network digital twin. However, it introduces the problem of high cost when obtaining the status data of physical networks. Therefore, to efficiently achieve network observability, we first introduce potential techniques and network scenarios for implementing network measurements. Then, a High-Yield Segment Routing over IPv6 based In-band Network Telemetry (HY-SRv6-INT) system is proposed in this paper. In particular, we design the HY-SRv6-INT system architecture. It consists of IPv6 switches implemented by the P4 language and a Software-Defined Networking (SDN) controller containing the HY-SRv6-INT application. Next, we experimentally demonstrate a packet-level path-associated network measurement process. In addition, the performance of the HY-SRv6-INT system is also quantitatively evaluated to verify its advantages. Finally, we discuss some research challenges and open issues for future network telemetry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Internet protocols

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Maximum Entropy Deep Reinforcement learning Based Power Allocation for NOMA Maritime Network 14th

Maximum Entropy Deep Reinforcement Learning Based Power Allo...

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14th EAI international conference on Wireless and Satellite systems, WiSATS 2024

作者： He, Jiayi Zhang, Yakai Liu, Zhiyong School of Information Science and Engineering of HIT Weihai264209 China Shandong Provincial Key Laboratory of Marine Electronic Information and Intelligent Unmanned Systems Weihai264209 China Key Laboratory of Cross Domain Synergy and Comprehensive Support for Unmanned Marine Systems of Ministry of Industry and Information Technology Weihai264209 China

ISBN: (纸本)9783031862021

In order to address the challenges of high propagation delays and limited service capabilities in maritime satellite communications, unmanned aerial vehicles have been proposed as an airborne backhaul solution to enhance communications between satellites and maritime base stations. The non-orthogonal multiple access (NOMA) framework can solve the user sparsity problem in maritime networks. In this paper, a deep reinforcement learning algorithm is used to solve the nonconvex power allocation problem under NOMA. In order to mitigate the risk of overestimation of Q values and local optimal convergence of Deep Q Network (DQN) algorithm, we propose an algorithm called Soft Agent Critical Ocean Satellite Communication Power Allocation (SAC-OSCPA) based on the idea of maximum entropy and compare it with the traditional DQN algorithm. The main goal of this research is to maximize network throughput in scenarios with randomly distributed users. Simulation results show that the average system throughput is improved by 13.18% with the SAC-OSCPA algorithm, and the average throughput of the worst performing user is significantly improved by 41.59%. These results demonstrate the efficacy of the proposed algorithm in optimizing the communication performance of maritime satellite networks. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Deep reinforcement learning

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Forecasting Tax Revenue with machine learning and Granger Causality

Forecasting Tax Revenue with Machine Learning and Granger Ca...

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2025 international conference on intelligent Control, Computing and Communications, IC3 2025

作者： Prasad, Rajesh Segun, Ajisola Ayotola Ajay Kumar Garg Engineering College Department of Computer Science & Engineering Ghaziabad India African University of Science and Technology Department of Computer Science Abuja Nigeria

ISBN: (纸本)9798331527495

This paper examines various tax elements that give Nigeria's government a collection basket. This work examines how taxes provide an adequate revenue mechanism and their implications for fiscal policy and macroeconomic stability. To do this, FIRS (Federal Inland Revenue Service) information on various tax components was collected monthly from 2010 to 2021. Our final model that was the best predictor of revenue was chosen after weighing models: multivariate extended short-term memory networks (MLSTM), seasonal autoregressive integrated moving average (SARIMA), multivariate linear regression (MLR) and a hybrid model (MLSTM & XGBoost). Each model was rated on predicted value and accuracy. This multiple independence of prediction factor was considered in the study. Granger Causal relationship between the Dependent and Independent was conducted for hypothesis testing. With increasing numbers of independent variables, the hybrid, MLSTM, and MLR models were better than one another. The hybrid model achieved an R2 value of 99.7%. © 2025 IEEE.

关键词： Multiple linear regression

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machine learning-Based intelligent Framework for Breast Cancer Risk Assessment

Machine Learning-Based Intelligent Framework for Breast Canc...

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international Health Informatics conference, IHIC 2023

作者： Singh, Bikesh Kumar Bodhey, Narendra Kuber Sharma, Yogesh Department of Biomedical Engineering NIT Raipur Raipur C G India Department of Radiodiagnosis AIIMS Raipur Raipur C G India

ISBN: (纸本)9789819771899

This article proposes a machine learning framework for risk assessment of breast cancer using factors such as those related to the patient’s history, anthropometric measures, clinical symptoms, etc. Breast cancer risk factor dataset including 14,382 cases from Breast Cancer Surveillance Consortium dataset was used in the experiments. The dataset consisted of a healthy group and a control group consisting of patients with breast cancer detected within a year of the screening mammography. Statistical analysis usingMann–Whitney U-test and classification using various classifiers such as support vector machine (SVM) different kernels, Gaussian (fine, medium, and course), Naïve Bayes, k-nearest neighbor (k = 5), and linear discriminant analysis (LDA) were applied on this data set for risk estimation of breast cancer under fivefold data division protocol. Experimental results show that the SVM classifier with cubic kernel achieves the superior classification accuracy of 99.43% with area under curve (AUC) equal to 1. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Risk assessment

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Lightweight design and deployment of object detection model for low-light scenes 4

Lightweight design and deployment of object detection model ...

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4th international conference on Computer Vision, Application, and Algorithm, CVAA 2024

作者： Sun, Kangjian Huo, Ju Jiang, Xiaona Gao, Yichao Cai, Chen School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China National Key Laboratory of Modeling and Simulation for Complex Systems Harbin China China FAW Corporation Limited Changchun China College of Design and Engineering National University of Singapore Singapore Signal and Communication Research Institute China Academy of Railway Sciences Beijing China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Low-light scenes are common in fields such as autonomous driving, which tests the robustness of intelligent systems. This paper tests the performance of object detection in low-light scenes based on the YOLOv8n model. In order to solve the constraint of limited computing power of embedded devices, the YOLOv8n model is adjusted through model pruning and knowledge distillation. The complexity of the model is reduced through these lightweight operations. While ensuring the detection performance as much as possible, the multiply-accumulate operations (MACs) and parameters of the model decreased by about 40% and 27% respectively. The proposed model is evaluated on the Exclusively Dark (ExDARK) dataset and achieves a mean average precision (mAP) value of 0.672. In addition, the proposed model is migrated to the embedded platform NVIDIA Jetson Xavier NX. The deployment process is optimized by multi-process scheduling and multi-thread scheduling. Experiments are also conducted on real data, and the results show that the proposed model can provide a feasible solution for vision-based nighttime autonomous driving. © 2025 SPIE.

关键词： Object detection Deep learning Image processing

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