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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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5th EAI international conference on Multimedia Technology and Enhanced learning, ICMTEL 2023

5th EAI International Conference on Multimedia Technology an...

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5th EAI international conference on Multimedia Technology and Enhanced learning, ICMTEL 2023

ISBN: (纸本)9783031505737

the proceedings contain 121 papers. the special focus in this conference is on Multimedia Technology and Enhanced learning. the topics include: Security Awareness Method of Opportunistic Network Routing Protocol Based on Deep learning and Knowledge Graph;research on Pedestrian Intrusion Detection Method in Coal Mine Based on Deep learning;personalized Recommendation Method of College Art Education Resources Based on Deep learning;global Planning Method of Village Public Space Based on Deep Neural Network;a Multi Stage Data Attack Traceability Method Based on Convolutional Neural Network for Industrial Internet;machine learning Based Method for Mining Anomaly Features of Network Multi Source Data;data Anti-jamming Method for Ad Hoc Networks Based on machine learning Algorithm;a Data Fusion Method of Information Teaching Feedback Based on Heuristic Firefly Algorithm;research on Pain Information Management System Based on Deep learning;research on Database Language Query Method Based on Cloud Computing Platform;reliability Evaluation Method of Intelligent Transportation System Based on Deep learning;forecasting Method of Power Consumption Information for Power Users Based on Cloud Computing;power Consumption Behavior Analysis Method Based on Improved Clustering Algorithm of Big Data Technology;anomaly Detection of Big Data Based on Improved Fast Density Peak Clustering Algorithm;data Privacy Access Control Method Based on Ciphertext Policy Attribute-Based Encryption Algorithm;Evaluation Method of Enterprise Circular Economy Development Level Based on AHP Fuzzy Inference;research on Performance Evaluation of Industrial Economic Management Based on Improved machine learning;application of Big Data Processing Technology in Power Consumption Information Acquisition.

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Supervised and Unsupervised Coupled Generative Adversarial Networks in Multivariate Time Series 4

Supervised and Unsupervised Coupled Generative Adversarial N...

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4th international conference on Information Science, Parallel and Distributed Systems, ISPDS 2023

作者： Wang, Qinfen Li, Yifei Xia, Yong School of Computer Science and Engineering Northwestern Polytechnical University Natl. Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an710072 China

ISBN: (纸本)9798350337181

Generative adversarial networks (GAN) have been successful in demonstrating the efficiency of generative time series. In this paper, we proposed a novel end-to-end supervised and unsupervised coupled generative adversarial networks (SUC-GAN) framework that consists of a gated recurrent unit (GRU) for generating multivariate time series. We test SUC-GAN on three datasets (i.e. Sines, Energy, Stocks) to investigate the efficacy and properties. the preliminary results show the potential power of supervised, unsupervised, and coupled learning for data generation. © 2023 IEEE.

关键词： Generative adversarial networks

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24th international conference on Product-Focused Software Process Improvement, PROFES 2023

24th International Conference on Product-Focused Software Pr...

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24th international conference on Product-Focused Software Process Improvement, PROFES 2023

ISBN: (纸本)9783031492686

the proceedings contain 16 papers. the special focus in this conference is on Product-Focused Software Process Improvement. the topics include: Speech therapy Supported by AI and Smart Assistants;integration of Classical and Quantum Services Using an Enterprise Service Bus;problem Decomposition to Leverage Quantum Computing for Optimization Problems;quantum Algorithm Cards: Streamlining the Development of Hybrid Classical-Quantum applications;simulation-Based Safety Testing of Automated Driving Systems;utilization of machine learning for the Detection of Self-admitted Vulnerabilities;closing the Loop: Towards a Complete Metamorphic Testing Pipeline;declarative Process Mining for Software Processes: the RuM Toolkit and the Declare4Py Python Library;MaREA: Multi-class Random Forest for Automotive Intrusion Detection;forecasting the Developer’s Impact in Managing the Technical Debt;development of a Desktop application to Enable Doctors to Remotely Monitor Patients’ Hematological Parameters;the Significance of Classical Simulations in the Adoption of Quantum Technologies for Software Development;Enhancing Bug-Fixing Time Prediction with LSTM-Based Approach;enhancing Code Obfuscation Techniques: Exploring the Impact of Artificial Intelligence on Malware Detection.

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Research on Ornamental Fish Recognition Base on Improved Shufflenet V2 Network 4

Research on Ornamental Fish Recognition Base on Improved Shu...

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4th international conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2023

作者： Zhang, Xingyun Chen, Hongming Zhang, Longqing Chen, Qian Zhejiang Ocean University Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhoushan316022 China Guangdong University of Science and Technology School of Computer Science Dongguan523083 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China

ISBN: (纸本)9798350329490

With the advancement of computer vision technology, fish recognition plays a crucial role in various fields such as aquaculture processing and disseminating fish knowledge. In this study, we propose an enhanced and lightweight fish recognition algorithm to accomplish fish classification recognition with limited data samples. Based on the ShuffleNet V2 network, we incorporate Transfer learning and the SE attention mechanism. Additionally, we replace the ReLu activation function with the AM-Softmax activation function. Comparative evaluations are conducted against Resnet50, MobileNet V2, MobileNet V3, and the original ShuffleNet V2 network model. Our experimental findings demonstrate that the proposed algorithm outperforms the aforementioned models, resulting in recognition accuracy improvements of 16.01%, 19.05%, 15.04%, and 14.44% respectively. the final recognition accuracy achieved is 97.20%, successfully attaining the desired outcome. © 2023 IEEE.

关键词： computer vision

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24th international conference on Product-Focused Software Process Improvement, PROFES 2023

24th International Conference on Product-Focused Software Pr...

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24th international conference on Product-Focused Software Process Improvement, PROFES 2023

ISBN: (纸本)9783031492655

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Intelligent Deep learning Framework for Breast Cancer Prediction using Feature Ensemble learning 4

Intelligent Deep Learning Framework for Breast Cancer Predic...

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4th IEEE Global conference for Advancement in Technology, GCAT 2023

作者： Rao, Chintala Nageswara Chatrapathy, K. Jainul Fathima, A. Sathish, Goski Mukherjee, Saptarshi Reddy, Pundru Chandra Shaker CSE-AI&ML Geethanjali College of Engineering and Technology Hyderabad India School of Computing&Information Technology REVA University Karnataka Bengaluru India Information Technology Francis Xavier Engineering College TamilNadu Tirunelveli India Information Technology St. Martins Engineering College Dhullapally Hyderabad India M.Tech-Bioinformatics Maulana Abul Kalam Azad University of Technology Uttarakhand India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350305258

the mortality and morbidity rates associated with breast cancer are extremely high. In the fight against breast cancer (BC), a correct diagnosis is crucial. the diagnostic process can benefit from the application of machine-learning (ML) and deep-learning (DL) strategies. In this paper, a deep-recurrent-neural-network (DRNN) technique optimized for breast cancer analysis using RNN and the Keras-Tuner optimization technique is proposed. the input layer, five hidden layers, five dropout layers, and the output layer make up the optimized DRNN. It fine-tuned the dropout layer's neurologist count and firing rate for each successive hidden layer. the most crucial data points have been elected utilizing three different feature-selection methods. the optimized DRNN was contrasted to five standard ML strategies: decision-tree (DT), support-vector-machine (SVM), random-forest (RF), Naïve-Bayes (NB), and the K-nearest neighbor algorithm (KNN). the elected features have been applied to both standard ML models and optimized DRNN. the outcomes showed that, compared to the other models, the cross-validation (CV) and testing outcomes were best achieved (99.16%) by the optimized DRNN with the features picked by univariate. © 2023 IEEE.

关键词： Breast cancer Carcinoma Deep learning KNN machine learning RNN SVM

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A Comparative Study of machine learning and Deep learning Models for Weather Image Classification 4

A Comparative Study of Machine Learning and Deep Learning Mo...

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2023 4th international conference on computer Engineering and Intelligent Control, ICCEIC 2023

作者： Liu, Dun Shi, Wen Jiang, Linfeng Sun, Jiazhi Xin, Changyi Beihang University School of Astronautics Beijing102206 China Fose University of Nottingham Ningbo China Ningbo315100 China Chongqing Nankai Secondary School Chongqing400030 China University of California Davis College of Letter and Science Davis95616 United States The University of Manchester Department of Computer Science ManchesterM139PL United Kingdom

ISBN: (纸本)9798350308877

the study of identifying and categorizing weather images is closely connected to our daily routines and the economy. It affects decisions such as whether to bring an umbrella for the day or the agricultural earnings of an entire country for the year. However, the complexities within the domain of weather classification are manifold, owing to the subtle nature of weatherrelated image features and the multitude of factors contributing to image variability. In this context, we explore the efficacy of three distinct deep learning models: Convolutional Neural Networks (CNNs), Visual Geometry Group (VGG) networks, and Vision Transformer (ViT) models, to address the challenges posed by ambiguous attributes of weather images. the above models exhibit notable differences in their architectural nuances. CNNs excel at capturing local features through convolutional layers. VGG networks are adept at learning intricate feature hierarchies via their deep architecture. ViT, leveraging self-attention mechanisms, excels in capturing global context from images. these models collectively tackle the issue of indistinct weather features by extracting and learning relevant patterns at different scales and levels of abstraction. Our experimentation employs a diverse weather dataset encompassing ten categories, including sunny, cloudy, rainy, snowy, and others, each comprising 500 images. the results are promising, with all three models achieving classification accuracies exceeding 90%. this study underscores the potential of CNNs, VGG networks, and ViT models in effectively categorizing weather images despite the inherent challenges, contributing to improved weather understanding and forecasting, and ultimately benefiting various sectors of society. © 2023 IEEE.

关键词： Convolutional neural networks

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the application of Simplified Strassen Algorithm to Snow Simulation with Material Point Method 4

The Application of Simplified Strassen Algorithm to Snow Sim...

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4th international conference on machine learning and computer application, ICMLCA 2023

作者： Wang, Mingrui St. Olaf College NorthfieldMN55057 United States

ISBN: (纸本)9798400709449

As a beautiful yet complicated material, snow and its simulations are significantly challenging in the computer graphics community. Since the introduction of the material point method, abbreviated as MPM, the performance of snow simulations has been augmented in many aspects, including accuracy, stability, and efficiency. Nowadays, researchers work on developing simpler mathematical models or incorporating better hardware design to accelerate the snow simulations, but the overall running speed remains a problem. Instead of improving on the simulation methods, this paper presents a novel approach to optimize the efficiency of snow simulations by applying the simplified Strassen algorithm, an abridgment of a famous fast matrix multiplication algorithm, to snow simulations implemented with MPM. Rather than straightforwardly showing the triumph of the simplified Strassen algorithm over the standard matrix multiplication algorithm in runtime, the results present the evidence that leads to the inference that for simulations with a certain level of disorder, the simplified Strassen algorithm will surpass the standard matrix multiplication algorithm as the simulations progress. © 2023 ACM.

关键词： Snow

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Refined Meta-learning Approach Leveraging Residual Networks for Enhanced Performance 4

Refined Meta-Learning Approach Leveraging Residual Networks ...

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4th international conference on Advances in Electrical Engineering and computer applications, AEECA 2023

作者： Wang, Yihan Wu, Bo Guo, Hongchen School of Cyberspace and Technology Beijing Institute of Technology Beijing China Information Technology Center Beijing Institute of Technology Beijing China

ISBN: (纸本)9798350308082

Meta-learning, as a new way of small-sample learning, corrects the network parameters of the overall model for classification problems by treating tasks as inputs, thus obtaining a more rapid iteration effect and more generalizable network parameters. For current image classification problems, after model training, learning for new tasks often requires retraining with a large amount of new and past data, requiring the entire model parameters to be updated. Using the meta-learning method, the overall model parameters can be updated through sub-tasks, so as to quickly update the parameters according to new tasks, reducing the required training time and data. We propose an improved model based on the MAML algorithm, which has the characteristic of continuously updating the parameters through sub-tasks to obtain better initial training parameters, and focuses on obtaining higher accuracy on new tasks while ensuring training speed. this balances the accuracy and iteration speed of the model, and experimental results show that meta-learning can achieve a good balance between convergence speed and accuracy on new samples. © 2023 IEEE.

关键词： Image classification

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Multi-Label Feature Selection Based on Improved Fisher Score with Label Correlation 13

Multi-Label Feature Selection Based on Improved Fisher Score...

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13th international conference on Information Technology in Medicine and Education, ITME 2023

作者： Mao, Yu Cheng, Yuxuan Shi, Chunyu Lin, Yaojin School of Computer Science Minnan Normal University Zhangzhou China Minnan Normal University Lab of Data Science and Intelligence Application Zhangzhou China

ISBN: (纸本)9798350319156

Feature selection for multi-label classification has received extensive attention in the fields of machine learning and data mining. However, some feature selection methods fail to incorporate label correlations, resulting in the degradation of learning performance. In this paper, we dedicate to fuse the Fisher score model and neighborhood mutual information model for multi-label learning. the neighborhood mutual information is initially constructed to identify the coefficients between labels, by measuring the granularity of each instance. After that, an improved Fisher score model based on label coefficients is formed to select features with multiple crucial labels. Extensive experiments demonstrate the effectiveness and stability of the proposed method. © 2023 IEEE.

关键词： Feature Selection

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