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检索条件"主题词=Filtering algorithms"

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Knowledge Graph-Based Dual Feature Aggregation Recommendation Model

Knowledge Graph-Based Dual Feature Aggregation Recommendatio...

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Artificial Intelligence and Electromechanical Automation (AIEA), International Conference on

作者： Fanyang Meng Hongwei Ma Dequn Zhao Qianhua Deng Beijing University of Technology Beijing China Beijing Institute of Astronautical Systems Beijing China China Telecom Beijing China

ISBN: (数字)9798350366174

ISBN: (纸本)9798350366181

To overcome the shortcomings of current approaches in feature extraction, specifically the lack of in-depth feature extraction and the absence of high-order feature aggregation for both users and items, we propose a knowledge graph-based dual-end feature aggregation recommendation model. First, initial vectors are generated using knowledge graph embeddings. Next, a ripple network and a knowledge graph attention network are employed to extract features on the user and item sides, respectively. Then, features of various orders are aggregated and concatenated to obtain the final vectors. Finally, the matching probability between users and items is predicted. Tests performed on three datasets reveal that the new model surpasses seven other baseline models based on AUC, ACC, NDCG@K, and Recall@K metrics.

关键词： Measurement Knowledge engineering Automation Accuracy Aggregates Knowledge graphs filtering algorithms Feature extraction Vectors Graph neural networks

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An Approach to Identification of the FitzHugh-Nagumo Network Under Disturbances

An Approach to Identification of the FitzHugh-Nagumo Network...

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Neurotechnologies and Neurointerfaces (CNN), International Conference

作者： Aleksandra Rybalko Alexander Fradkov RAS Theoretical Cybernetics department SPbU Institute for Problems in Mechanical Engineering Saint Petersburg Russia

ISBN: (数字)9798350390926

ISBN: (纸本)9798350390933

The paper is devoted to the identification problem of the network composed of the FitzHugh-Nagumo single neuron models under conditions of unknown bounded disturbances. To address this problem, three versions of the speed-gradient algorithm were applied: the original one and two robustified algorithms based on the dead zone and the negative feedback. Numerical studies were conducted to examine the properties of these algorithms within the context of the considered problem.

关键词： Negative feedback Accuracy Neurons Estimation filtering algorithms Mathematical models Numerical models Control theory Tuning Neurotechnology

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Research on Image Denoising Based on Combined Adaptive GP and DCNN

Research on Image Denoising Based on Combined Adaptive GP an...

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Image Processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Xinyu Shi Gu Gong Guangjing Chen School of Computer Science and Technology Jiangsu Normal University Xuzhou China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

In the process of image acquisition and transmission, it is easy to be interfered by electronic equipment and light, resulting in noise, which affects people's acquisition and recognition of information. In image processing and computer vision, image denoising is an important step. Based on this, an image denoising algorithm combining adaptive GP (Gaussian filter, GF) and DCNN (Denoising Convolutional Neural Network, DCNN) is proposed. Through the adaptive GF preprocessing step, the noisy image is preliminarily smoothed and enhanced in detail, which provides a more favorable data basis for the subsequent DCNN deep denoising operation. Experimental results show that, compared with the traditional denoising algorithm, the proposed algorithm performs well in noise suppression, and can significantly improve the peak signal-to-noise ratio (PSNR) of the image while maintaining the image details. This achievement not only shows the excellent performance of the algorithm in the field of image denoising, but also further verifies its theoretical value in the field of image processing, and provides a strong support and reference for the development of related technologies in the future.

关键词： Computer vision PSNR Machine learning algorithms Noise reduction Noise filtering algorithms Noise measurement Convolutional neural networks Standards Image denoising

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A New Design Algorithm to Reduce the Complexity for Different 5G Waveforms

A New Design Algorithm to Reduce the Complexity for Differen...

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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Waleed Algriree Nawar Banwan Hassan Huda Hamdan Ali Nasri bin Sulaiman Mokhalad Alghrairi Bayan Mahdi Sabbar Saad Mutashar Department of Wireless Communications and Networks Engineering Faculty of Engineering University Putra Malaysia Selangor Malaysia Department of Computer Techniques Engineering Imam Alkadhim University College Baghdad Iraq Department of Electrical and Electronic Engineering Faculty of Engineering University Putra Malaysia Selangor Malaysia Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University Babylon Iraq

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

With the exponential growth of wireless communications, there is a great need for innovative telecommunication services throughout the frequency ranges where the present scarcity of spectrum might be addressed. As a result, to enhance the use of spectrum, a Fifth generation -MIMO CR system of communication that utilizes the spectrum sense (SS) methodology was developed. Based on the two-stage method, this suggested SS algorithm uses a two-stage approach: first, it filters the traffic signal using the Cosine law, and then it applies the Welch algorithm to enable segmentation. In addition, wave forms for filter bank multi-carrier (FBMC), universal filtering multi-carrier (UFMC), filtered-orthogonal multiplexing with frequency division (F-OFDM), as well as non-orthogonal multiple access (NOMA) are included here (GFDM). Evaluations of the computational difficulty and other operational factors, including SNR, antenna count, power, as well as signal span, were also conducted. A remarkable accomplishment was observed for the parameters with SNR values less than 0 dB, minimal difficulty, global identification percentage $> 99\%$ ,global false alarm a likelihood $< 0.0 7\%$ ;, universal system error a likelihood $< 0.0 5\%$ , according to the simulation findings. The factors of the suggested system were evaluated, indicating its superiority over other systems that had already been established. It was discovered that this system could recognize not just one form of5G signal, but all of them. Keywords: MIMO, 5G, computational complexity, spectrum sensing (ss)

关键词： NOMA Filters Accuracy 5G mobile communication Filter banks Interference filtering algorithms Computational efficiency Computational complexity Signal to noise ratio

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A tracking filter algorithm for migratory birds based on group situation constraints

A tracking filter algorithm for migratory birds based on gro...

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Signal, Information and Data Processing (ICSIDP), IEEE International Conference on

作者： Biao Li Rui Wang Jiong Cai The Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Aerial animal migration is closely related to human life, and radar is one of the most effective means of observing it. However, scanning radar has a low data rate, a short integration time, and lacks prior knowledge of target speed, while migratory animals exhibit high mobility. These factors make traditional tracking filter algorithms ineffective at predicting their movement. To address this issue, this paper proposes a tracking filter algorithm based on group dynamics constraints, leveraging the characteristic behavior of migratory animals flying in groups. First, the dynamic state of the group is calculated using the group as the research unit. Then, tracking filtering is performed under the constraint of group dynamics. Simulation and field data have verified the effectiveness of the proposed algorithm.

关键词： Target tracking Heuristic algorithms Dynamics Radar filtering algorithms Radar tracking Prediction algorithms Birds Information filters Radar signal processing

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A Multi-Noise Multi-Channel ANC System using Relative Transfer Matrix-Based Approach

A Multi-Noise Multi-Channel ANC System using Relative Transf...

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International Workshop on Acoustic Signal Enhancement (IWAENC)

作者： Yile Angela Zhang Thushara D. Abhayapala Huiyuan Sun Prasanga N. Samarasinghe Amy Bastine Audio and Acoustic Signal Processing Group The Australian National University Australia Computing and Audio Research Lab The University of Sydney NSW Australia

ISBN: (数字)9798350361858

ISBN: (纸本)9798350361865

Remote microphone-based virtual sensing (RM-VS) methods are used in active noise control (ANC) systems to avoid the physical presence of microphones inside the region of interest (ROI). Conventionally, this is achieved by estimating an observation filter to obtain the primary noise signals at virtual microphones inside the ROI, with the physical microphones recording outside the ROI. In this paper, we present a novel structure of the RM technique, where we estimate the relative transfer matrix (ReTM) between the signals at virtual microphones and physical microphones with both noise sources and secondary sources active. This ReTM can be directly applied to the recorded residual error signals during noise control, as opposed to the conventional observation filter which is typically applied to the estimated primary noise signals. Simulation results show that the proposed ReTM-based approach achieves similar performance to the conventional RM method in a stationary environment, while being more robust to changes in the secondary paths.

关键词： Soft sensors Simulation Noise Noise reduction filtering algorithms Robustness Acoustics Recording Monitoring Microphones

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Improved Genetic Algorithm in the Model Construction of Automated Data Acquisition and Management Platform

Improved Genetic Algorithm in the Model Construction of Auto...

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Data Science and Information System (ICDSIS), IEEE International Conference on

作者： Xiaoying Lv School of Business Information Yunnan Land and Resources Vocational College Kunming Yunnan China

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

abstract-This research focuses on the optimization of the model construction of automated data acquisition and management platform, with special attention to improving the genetic algorithm to improve the model performance. The problems of traditional genetic algorithm in this field include slow convergence speed and easy to fall into local optimal solution, which limit the effect of model construction. In order to solve these challenges, this paper introduces adaptive parameter adjustment, multi-objective optimization strategy, evolutionary strategy introduction, population dynamic adjustment and other methods to improve the convergence speed, global search ability, and deal with multi-objective optimization problems. By comparing the time of pattern construction algorithm, the time of improved genetic algorithm reaches 49.3 seconds. The experimental evaluation shows that the prediction accuracy of genetic algorithm in the model construction of automatic data acquisition and management platform is high, and the performance of the improved genetic algorithm even exceeds the traditional algorithm. Compared with the traditional genetic algorithm, the improved algorithm performs better in the convergence speed, can quickly reach $\mathbf{9 5 \%}$ convergence speed, and shows better adaptability in different problem scenarios.

关键词： Adaptation models Accuracy Data acquisition filtering algorithms Predictive models Prediction algorithms Data models

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Comparative Analysis of Eminent algorithms for Detecting Ham and Spam Contents in OSN

Comparative Analysis of Eminent Algorithms for Detecting Ham...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Kardeepa Ponnuchamy N. Subbulakshmi Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Virudhunagar India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Social or Communal media platforms is a lay of exchanging the thoughts or ideas of online users through various data formats like text content, image content or through audio, video, Graphics Interchange Format (GIF) etc and user can dynamically share real-time interactive events in a cost effective manner. Nowadays, with emerging fast-growing tools and technology, online socio-communal networks have shattered popularity as the central thought is to offer a framework for virtual users to link with their associative friends, subordinates and family members that blowouts their messages instantly which in turn paves a way for attackers to gain information or hack online user's data by unauthorized access. Extraction of contents is not accurate due to inflexible rule sets and some contents which are furnished in short texts are more difficult to correlate with their semantics and thereby it is difficult to prevent the display of undesired data in the existing system. Hence, the trained model system needs architecture with Enhanced Adaptive Rule-sets to overcome the inflexibility. Adaptation rule is employed by incorporating the blacklist mechanism along with eminent Machine learning algorithms to predict and prevent the undesired data. Information filtering system and Content filter removes redundant, irrelevant or unwanted information using user feedbacks with multiclass data labels. Furthermore, self-protective approaches are emphasized to achieve fidelity and ensure the trustworthiness in any micro blogging services or in online social networks.

关键词： Graphics Machine learning algorithms Social networking (online) Semantics Predictive models Media filtering algorithms Prediction algorithms Real-time systems Expert systems

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Determination of Optimal Component Values in Low-Pass Filter Design Using Red Fox Optimization Algorithm

Determination of Optimal Component Values in Low-Pass Filter...

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International Conference on Electrical and Electronics Engineering, ELECO

作者： Pelin DOĞAN SEKRETER Ahmet Erdem YILMAZ Atilla UYGUR Elektronik Mühendisliği Bölümü Gebze Teknik Üniversitesi

ISBN: (数字)9798331518035

ISBN: (纸本)9798331518042

In this study, the optimization of sixth-order Butterworth low- pass filter components was performed using the recently introduced Red Fox Optimization (RFO) algorithm in the literature. The RFO is a meta-heuristic optimization method inspired by nature, specifically modeling the hunting strategies of foxes. The primary objective of RFO is to optimize the search process effectively, enabling the attainment of optimal results. In this study, the RFO algorithm was implemented in MATLAB to optimize the resistance and capacitance values required for the low-pass filter, taking into account the specified cutoff frequency and quality factors. The E24 industrial series was employed to realize the optimized filter components. To validate the accuracy of the optimized results, the obtained values were simulated and analyzed using LTSpice. The simulations demonstrated that the RFO algorithm achieved an error rate of 0.0113 in the optimal filter design, and these findings were compared with those reported in the literature.

关键词： Resistance Q-factor Error analysis Metaheuristics Cutoff frequency Low-pass filters Optimization methods filtering algorithms Search problems MATLAB

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Disease-Free Survival Prediction in Recurrent Cervical Cancer using Naive Bayes Machine Learning Algorithm

Disease-Free Survival Prediction in Recurrent Cervical Cance...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： S. Geeitha P. Renuka K. Poongothai S. Ananth K. Sinduja K.R Sangeetha Department of Information Technology M. Kumarasamy College of Engineering Karur India Department of AI&DS Mahendra Engineering College Namakkal India Department of Information Technology Sri Krishna College of Engineering and Technology Coimbatore India Information Science and Engineering S.E.A College of Engineering and Technology Bangalore India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Cervical cancer ranks amongst the most common causes of death for women. The primary objective of this research is to use artificial intelligence based Naive bayes machine learning algorithm to use of different procedures that affect patients chances of surviving if their disease recurs. The Naive Bayes approach, that analyze clinical details and estimate disease-free survival in recurrent cervical carcinoma, is utilized throughout this work. If an initial treatments are unsuccessful for the medical professionals then they want to know how the other methods may affect the long term survival of patients. From the dataset by selecting the most important features through feature selection methods this give the progress to select the better treatment and results for patients. Surgical sample is collected from the certain reoccurring location to verify the recurrence by diagnostic. The clinical research’s results may offer significant details regarding the root cause and characteristics of the recurrent illness. Machine learning algorithms can use Disease free survival (DFS) information from cervical cancer patients to forecast how long patients would likely be free from disease progression. The predictive algorithms determined the chance of achieving prolonged DFS through examining multiple variables, which includes as methods of treatment, recurring sites, and histological classifications. Disease free survival is predicted using Naive Bayes classifier with high level of accuracy. This model is tested with 5 different classifiers and Naive Bayes attains high level of accuracy.

关键词： Machine learning algorithms Accuracy Surgery filtering algorithms Prediction algorithms Naive Bayes methods Classification algorithms Cervical cancer Medical diagnostic imaging Diseases

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