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A Stacked Autoencoder Based Meta-Learning Model for Global optimization 1

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Proceedings of the 4th international conference on Neural Computing for Advanced Applications, NCAA 2023

作者： Ma, Yue Pang, Yongsheng Li, Shuxiang Qu, Yuanju Wang, Yangpeng Chu, Xianghua College of Management Shenzhen University Shenzhen China Institute of Big Data Intelligent Management and Decision Shenzhen University Shenzhen China

ISBN: (数字)9789819958443

ISBN: (纸本)9789819958436

As optimization problems continue to become more complex, previous studies have demonstrated that algorithm performance varies depending on the specific problem being addressed. thus, this study proposes an adaptive data-driven recommendation model based on the stacked autoencoder. this approach involves the use of a meta-learning autoencoder that is stacked with multiple supervised autoencoders, generating deep meta-features. then the proper algorithms are identified to address the new problems. To verify the feasibility of this proposed model, experiments are conducted using benchmark functions. Experimental results indicate that both instance-based and model-based meta-learners are well suited to the advanced model, and the performance is promising. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Global optimization

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A Multi-Model Fusion Approach for Transformer Fault Detection 4

A Multi-Model Fusion Approach for Transformer Fault Detectio...

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4th IEEE international conference on Information Technology, Big data and Artificial Intelligence, ICIBA 2024

作者： Zhou, Ke Zhang, Wei Li, Ligang Pan, Shaoming Huang, Chengwei Electric Power Research Institute of Guangxi Power Grid CO. LTD. Guangxi Power Grid Equipment Monitoring and Diagnosis Technology Innovation Center Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment Nanning530023 China Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China Chinese Academy of Sciences Key Laboratory of Networked Control Systems China Power Supply Bureau of Guangxi Hezhou Power Grid Co. Ltd. China

ISBN: (纸本)9798350363586

Addressing the weak generalization capability and suboptimal prediction performance of single mechanistic and data-driven models, this paper proposes a transformer fault detection method based on the fusion of mechanism and data models. Firstly, vibration and current data are collected on-site to train the SVM vibration model and the Adaboost current model. Secondly, current simulation data is obtained using laboratory simulation software. this simulation data, combined with the current data collected under the same conditions on-site, is used as input to train the GBDT error compensation model. Finally, these three models are used as inputs to the GRNN, completing the overall model design. this fusion of mechanistic and data-driven models not only addresses the parameter mismatch issue of the mechanistic model under complex working conditions but also deeply mines time-series data. Experimental results show that the mean squared error of the fusion model is 0.002 lower than that of the data model, and the coefficient of determination is improved by 5.07% compared to the mechanistic model, enhancing the overall predictive accuracy of the model. © 2024 IEEE.

关键词： data accuracy

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A heterogeneous graph neural network model with multi-head attention integrating review information for recommendation 4

A heterogeneous graph neural network model with multi-head a...

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4th international conference on Advanced Algorithms and Neural Networks, AANN 2024

作者： Li, Chenyun Zhang, Guanhong Odbal, H. School of Artificial Intelligence and Big Data Hefei University Hefei230601 China School of Computing Anhui Vocational and Technical College Hefei230601 China

ISBN: (纸本)9781510686106

Recommendation systems (RS) can significantly boost the profitability of e-commerce platforms. Traditional RS methods rely on users' historical behaviors and product attributes but struggle with data sparsity, cold start problems, and complex user-item interactions. Graph neural networks (GNNs) have become popular in RS due to their ability to capture complex relationships and features within graph-structured data, improving accuracy and robustness. this paper proposes a model that combines product review information with GNNs to enhance recommendations. We design a heterogeneous graph neural network with multi-head attention integrating review information (HG-MHAR). this model constructs a user-item heterogeneous graph with ratings as edges and includes review information to build user and item feature embeddings. the model has two main components: the review feature aggregation graph learning module and the multi-head attention graph learning module. these components capture multiple relational patterns in the graph in parallel, improving the stability and robustness of feature representations. Experiments on five Amazon datasets show that HG-MHAR outperforms benchmark models, improving nearly 4% over traditional methods. this demonstrates the potential of GNNs in recommendation systems. © 2024 SPIE.

关键词： data modeling Performance modeling Neural networks systems modeling Modeling Convolution Equipment Deep convolutional neural networks Mathematical optimization Matrices

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A novel weight-driven ATN-based SQL sentence generator to accommodate AI-based Reinforcement Learning

A novel weight-driven ATN-based SQL sentence generator to ac...

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12th IEEE international conference on Computer, Information and Telecommunication systems (CITS)

作者： Troy, Christopher Wang, Qi Alcaraz-Calero, Jose M. Univ West Scotland CEPS Paisley Scotland

ISBN: (纸本)9798350359107;9798350359091

this paper presents a novel approach for generating SQL queries through a weight-driven framework using a modified ATN of ANTLR4's runtime components. Our objective is to enhance ATN capabilities for SQL generation by incorporating the functionality to accommodate adaptive learning solutions. We successfully designed and implemented a system that assigns weights to ATN transitions, including token weight assignment when presented with multiple valid tokens to choose from whilst traversing set-transitions. these weights have interfaces for dynamic adjustments based on heuristics and user-defined *** methodology involves modifying ANTLR4's core components to include weight management and traversal algorithms. We leverage heuristics to guide weight adjustments, addressing loop structures and recursive depth control in a system controlled by weights. Additionally, we establish mechanisms for weight persistence and optimization. Experimental evaluation using a simplistic SQL grammar demonstrates the effectiveness of our approach. We observe that weights can steer the parsing process towards desired outcomes, and that convergence occurs as the exploration-exploitation balance is optimized through parameter tuning. this research lays the groundwork for integrating reinforcement learning with our weight-driven ATN system. this holds promise for tackling complex challenges in structured data analysis that might not be readily apparent through human inspection alone. While our current work primarily focuses on heuristics, future efforts will explore the next stage of our research to further enhance the decision-making capabilities of our framework using reinforcement learning.

关键词： Automata ATN SQL Generative AI Weights Heuristics Antlr4

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4th international conference on Trends in Cognitive Computation Engineering, TCCE 2022

4th International Conference on Trends in Cognitive Computat...

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4th international conference on Trends in Cognitive Computation Engineering, TCCE 2022

ISBN: (纸本)9789811994821

the proceedings contain 43 papers. the special focus in this conference is on Trends in Cognitive Computation Engineering. the topics include: Descriptive Analysis for Electric Bus During Non-Operational Stage;the Effectiveness Level on the Electric Buses Operation: Case Study for Affordability and Accessibility;IoMT-based Android Application for Monitoring COVID-19 Patients Using Real-Time data;A Cost-Effective Unmanned Ground Vehicle (UGV) Using Swarm Robotics Technology for Surveillance and Future Combat;neural Network-Based Obstacle and Pothole Avoiding Robot;a Comparative Study of Psychiatric Characteristics Classification for Predicting Psychiatric Disorder;Material Named Entity Recognition (MNER) for Knowledge-driven Materials Using Deep Learning Approach;An Improved optimization Algorithm-Based Prediction Approach for the Weekly Trend of COVID-19 Considering the Total Vaccination in Malaysia: A Novel Hybrid Machine Learning Approach;analyzing the Effectiveness of Several Machine Learning Methods for Heart Attack Prediction;Ensemble Machine Learning Technique for Identifying COVID-19 from CT Scan Images;solving the Royalty Payment Problem through Shooting Method;ECG Signal Classification Using Transfer Learning and Convolutional Neural Networks;partitional Technique for Searching Initial Cluster Centers in K-means Algorithm;a Novel Ensemble Methodology to Validate Fuzzy Clusters of Big data;Model Analysis for Predicting Prostate Cancer Patient’s Survival: A SEER Case Study;quantum-Inspired Neural Network on Handwriting datasets;an Efficient and Secure data Deduplication Scheme for Cloud Assisted Storage systems with Access Control;priority-Based Intelligent Reflecting Surface for Uplink 6G Communication;identifying Duplicate Questions Leveraging Recurrent Neural Network;stegoPix2Pix: Image Steganography Method via Pix2Pix Networks;machine Learning-Based Tomato Leaf Disease Diagnosis Using Radiomics Features;low-Cost Energy Efficient Encryption Algorithm for P

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4th international conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2021

4th International Conference on Recent Trends in Image Proce...

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4th international conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2021

ISBN: (纸本)9783031070044

the proceedings contain 33 papers presendted at a virtual meeting. the special focus in this conference is on Recent Trends in Image Processing and Pattern Recognition. the topics include: Real-Time Face Recognition for Organisational Attendance systems;Harnessing Sustainable Development in Image Recognition through No-Code AI Applications: A Comparative Analysis;evaluating Performance of Adam optimization by Proposing Energy Index;an Alignment-Free Fingerprint Template Protection Technique Based on Minutiae Triplets;early Prediction of complex Business Processes Using Association Rule Based Mining;A Framework for Masked-Image Recognition System in COVID-19 Era;A Deep-Learning Based Automated COVID-19 Physical Distance Measurement System Using Surveillance Video;Detection of Male Fertility Using AI-driven Tools;face Mask Detection Using Deep Hybrid Network Architectures;a Super Feature Transform for Small-Size Image Forgery Detection;UHTelHwCC: A dataset for Telugu Off-line Handwritten Character Recognition;inflectional and Derivational Hybrid Stemmer for Sentiment Analysis: A Case Study with Marathi Tweets;adaptive threshold-Based database Preparation Method for Handwritten Image Classification;a Graph-Based Holistic Recognition of Handwritten Devanagari Words: An Approach Based on Spectral Graph Embedding;Imagined Object Recognition Using EEG-Based Neurological Brain Signals;a Fast and Efficient K-Nearest Neighbor Classifier Using a Convex Envelope;single Channel Speech Enhancement Using Masking Based on Sinusoidal Modeling;extraction of Temporal Features on Fibonacci Space for Audio Based Vehicle Classification;an Empirical Study of Vision Transformers for Cervical Precancer Detection;An Improved Technique for Preliminary Diagnosis of COVID-19 via Cough Audio Analysis;agricultural Field Analysis Using Satellite Hyperspectral data and Autoencoder;Development of NDVI Prediction Model Using Artificial Neural Networks;time Series Forecasting of Soil Moisture Using Sa

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Research on Topological Deformation of 3D Human Image Based on Laplace optimization 4

Research on Topological Deformation of 3D Human Image Based ...

引用

4th international conference on High Performance Big data and Intelligent systems, HDIS 2022

作者： Zhou, Dongdong Chen, Xiaobing Zhang, Chuangchuang Guo, Shuxin Ashim, Khadka Lin, Jianchu Huaiyin Insititute of Technology Faculty of Computer and Software Engineering Huai'an China Gandaki College of Engineering and Science Pokhara University Pokhara Nepal

ISBN: (数字)9781665491440

ISBN: (纸本)9781665491440

Image deformation technology is an important technology in computer graphics image processing, which can satisfy people's requirements for visual and sensory effects, and its research results have been widely used in film and television animation, game development, virtual reality and other fields. Mesh-based deformation technology has been a research emphasis in image deformation technology, through the virtual model for the mesh division, the use of mesh nodes control, to achieve the deformation of the model. However, the more complex the model is, the greater number of mesh control nodes, which greatly increases the complexity of mesh deformation. In this paper, the deformation of 3D virtual character model is studied based on the method of image topological deformation. In order to solve the problem of large number of network nodes in 3D model, the Laplace deformation method is further combined to reduce the number of control nodes in the network. the experimental results show that the deformation can be effectively completed using a small number of control nodes by using the laplace coordinate system for adjacent topological mesh node control. © 2022 IEEE.

关键词： Virtual reality

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Innovization - Based Evolutionary Large-Scale Multiobjective optimization

Innovization - Based Evolutionary Large-Scale Multiobjective...

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data-driven optimization of complex systems (docs), international conference on

作者： Songbai Liu Qianqiang Yan Zeyi Wang College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

How to handle the high dimensional search space effectively is a burning research question in evolutionary computation when solving large-scale multiobjective optimization problems. To alleviate it, we propose a new evolutionary algorithm with a tactic of innovization-based representation learning, which aims to discover innovative solution principles from compressed representations. Specifically, a neural net, including an encoder and a decoder, is iteratively learned to transform input solutions into the predictions that dominate them. the encoder thus can capture their compressed representations that retain the information necessary for making good predictions. the decoder can create innovative promising solutions from newly explored representations by evolutionary search. Finally, the proposed algorithm is tested on synthetic and real-world benchmarks with the dimension of variables up to $10^th$ . Comparison results show the superior performance of the proposed optimizer in solving these benchmarks within $10^th$ function evaluations.

关键词： Representation learning Neural networks Evolutionary computation Transforms Benchmark testing Search problems Prediction algorithms Distance measurement Decoding optimization

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4th international Workshop on Field-Programmable Logic and Applications, FPL 1994

4th International Workshop on Field-Programmable Logic and A...

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4th international Workshop on Field-Programmable Logic and Applications, FPL 1994

ISBN: (纸本)9783540584193

the proceedings contain 63 papers. the special focus in this conference is on Field-Programmable Logic and Applications. the topics include: A test methodology applied to cellular logic programmable gate arrays;influence of logic block layout architecture on FPGA performance;a global routing heuristic for FPGAs based on mean field annealing;power dissipation driven FPGA place and route under delay constraints;FPGA technology mapping for power minimization;specification and synthesis of complex arithmetic operators for FPGAs;a speed-up technique for synchronous circuits realized as LUT-based FPGAs;an efficient technique for mapping RTL structures onto FPGAs;a testbench design method suitable for FPGA -based prototyping of reactive systems;using consensusless covers for fast operating on Boolean functions;formal verification of timing rules in design specifications;a high-speed rotation processor;a reprogrammable processor for fractal image compression;implementing GCD systolic arrays on FPGA;formal cad techniques for safety-critical FPGA design and deployment in embedded subsystems;FPGA based reconfigurable architecture for a compact vision system;a new FPGA architecture for word-oriented datapaths;image processing on a custom computing platform;a superscalar and reconfigurable processor;a fast FPGA implementation of a general purpose neuron;data-procedural languages for FPL-based machines;implementing on line arithmetic on PAM;constraint-based hierarchical placement of parallel programs;simulating static and dynamic faults in BIST structures with a FPGA based emulator;meaningful benchmarks for logic optimization of table-lookup FPGAs and educational use of field programmable gate arrays.

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Improved YOLOv5 for Road Disease Detection

Improved YOLOv5 for Road Disease Detection

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data-driven optimization of complex systems (docs), international conference on

作者： Guangfu Wu Longxin Liang Hao Liu Yun Li School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing China Chongqing Wukang Technology Co. Ltd Chongqing China

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

Rapid detection and high accuracy are crucial for effective road disease detection in road maintenance. this study proposes an improved YOLOv5 algorithm to address the issues of poor efficiency and low accuracy in traditional road disease detection methods. First, an enhanced attention module, Mixed Attention Squeeze-and-Excitation (MASE), is integrated into the backbone network. this module improves feature extraction in complex backgrounds by enhancing foreground and background information discrimination. It significantly refines detail processing in scenarios where diverse and intricate background elements obscure or confuse disease features. Second, the original PANet feature fusion framework is enhanced with a cross-layer enhancement network (CLEN), improving the fusion of small-scale features. this enhancement makes it more efficient at processing feature information across different scales, thereby addressing the issue of tiny disease features disappearing during multiple downsampling stages. A new target detection bounding box loss function, Hybrid IoU Loss (HIoU), is also designed to provide a more comprehensive loss calculation. this function effectively addresses the challenge of detecting irregularly shaped diseases. Experimental results demonstrate that the improved algorithm significantly outperforms the original algorithm, with mAP values increased by 10.4 % on the CWNU(China West Normal University) dataset and 2.3 % on the RDD2022 dataset.

关键词： YOLO Cross layer design Accuracy Roads Feature extraction Maintenance complex systems optimization Diseases

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