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检索条件"任意字段=1st International Conference on Pattern Recognition and Machine Intelligence"

共 4332 条记录，以下是11-20 订阅

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Research on Credit Risk Prediction Technology Based on Deep Learning 24

Research on Credit Risk Prediction Technology Based on Deep ...

引用

1st international conference on Image Processing machine Learning and pattern recognition

作者： Huang, Xinyu HSBC Bank China Co Ltd Shanghai Peoples R China

ISBN: (纸本)9798400707032

The number of institutions engaged in credit business is increasing, the types of credit products are constantly enriched, and the credit balance is growing rapidly, which has led to an increasing demand for intelligent credit risk management. Pre-loan review is the most core stage in credit risk management, and a good pre-loan risk prediction model can minimize future risks. Some small and medium-sized banks still use traditional mathematical and statistical methods for risk prediction, which can neither meet the growing business needs of credit risk prediction nor guarantee the quality of prediction. Based on the research results of domestic and foreign scholars in the field of customer credit default prediction, this paper proposes a bank credit risk prediction management model based on a multi-level deep neural network to address the limitations of small and medium-sized banks using traditional methods based on mathematical statistics for risk prediction. Using the actual loan data of a commercial bank as experimental data, the simulation experimental results show that the model has a high prediction accuracy.

关键词： Artificial intelligence machine learning deep learning credit risk prediction

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Applying machine Learning Technology for Weather Forecasting: A Case study of the Logistic Regression Model 24

Applying Machine Learning Technology for Weather Forecasting...

引用

1st international conference on Image Processing machine Learning and pattern recognition

作者： Shen, Kaiwei Beijing Inst Petrochem Technol China Big Data Management & Applicat Beijing Peoples R China

ISBN: (纸本)9798400707032

This study is focused on improving the dependability and precision of weather forecasting by employing the capabilities of Artificial intelligence. Specifically, this study utilizes Logistic Regression and machine Learning techniques to forecast weather, demonstrating the potential in optimizing weather-related activities and disaster management strategies. The study relies on comprehensive weather data observed over several years, sourced from Kaggle, and handles missing data and outliers during its pre-processing stages. The primary machine learning tool applied is Logistic Regression, followed by a stepwise feature selection to identify influential features for accurate weather prediction. The workflow also involves data collection, pre-processing, model building, training, and testing, with provisions for handling both numeric and categorical features along with imputations. The accuracy, precision, and recall of the prediction module are tested using appropriate statistical tools. The Logistic Regression model, upon implementation, demonstrated considerable accuracy, with an ability to predict rainy days and non-rainy days efficiently. An analytical approach was used to examine the model's sensitivity towards the removal of each feature, thereby ascertaining the relative importance of each. Critical predictors like 'Rainfall', 'Pressure9am', and 'WindGustSpeed' exhibited significant effects on the probability of rain. Overall, the use of Logistic Regression and machine Learning techniques notably improved rain prediction, offering potential for further advancements in the field of weather forecasting.

关键词： Artificial intelligence Data preprocessing Logistic regression model Weather forecasting

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Research on intelligent line selection and optimization scheme of transmission lines based on big data analysis and artificial intelligence 24

Research on intelligent line selection and optimization sche...

引用

1st international conference on Image Processing machine Learning and pattern recognition

作者： He, Chunhui Liu, Haitao Wang, Long Chen, Xiangjia Sun, Yongxin State Grid Shandong Elect Power Co Econ & Technol Res Inst Beijing Peoples R China

ISBN: (纸本)9798400707032

As the wave of data-driven informatization sweeps the world, society is moving from the information age (IT) to the data age (DT). GIS technology has become an important tool for complex regional development and prediction. However, the existing power grid design methods cannot meet the needs of improving production efficiency and controlling engineering costs. This paper proposes an intelligent line selection and optimization scheme for transmission lines based on big data analysis and artificial intelligence. The map model is designed using GIS multi-band raster maps, and data mining technology is used to maximize the application value of multi-factor geographic information data. The reverse dynamic programming algorithm is used for intelligent line selection, and the particle swarm optimization-convolutional neural network (PSO-CNN) model is used for engineering quantity prediction to improve the prediction accuracy and obtain more accurate optimization scheme ranking results. The scheme aims to reduce the design workload, reduce the industry design cost, improve the intelligence and standardization level of transmission and transformation engineering design, and provide strong support for the construction of smart grids.

关键词： artificial intelligence power transmission line intelligent line selection GIS grid map data mining sequential dynamic programming PSO-CNN

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Compressed Sampling of LFM Signals Based on Fractional Fourier Domain 24

Compressed Sampling of LFM Signals Based on Fractional Fouri...

引用

1st international conference on Image Processing machine Learning and pattern recognition

作者： Liu, Wenping Huang, Kemeng Zheng, Yanxin Zhu, Jubo Sun Yat Sen Univ Sch Artificial Intelligence Guangzhou Peoples R China

ISBN: (纸本)9798400707032

In response to the challenge posed by excessively high sampling rates resulting from the wide bandwidth of linear frequency modulation (LFM) signals, we exploit the inherent sparsity of LFM signals in the fractional Fourier transform (FRFT) domain. By applying a two-step search method to estimate the optimal order, we construct an orthogonal basis dictionary for discrete fractional Fourier transform corresponding to this order. Integrating principles from compressed sensing (CS) theory, we propose a novel LFM signal compressed sampling method based on the FRFT domain. This method compresses and reconstructs LFM echo signals while simultaneously considering the impact of varying digitalizing bit depths during the analog-to-digital converter (ADC) process. Simulation results demonstrate that our approach achieves a favorable sparse representation of LFM signals, enabling signal reconstruction at approximately 10% of the Nyquist rate, while also exhibiting some attenuation of quantization noise and white Gaussian noise.

关键词： Linear Frequency Modulation Fractional Fourier Transform Sparse Representation Compressed Sensing

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Unsupervised machine Translation Based on Dynamic Adaptive Masking strategy and Multi-Task Learning 24

Unsupervised Machine Translation Based on Dynamic Adaptive M...

引用

1st international conference on Image Processing machine Learning and pattern recognition

作者： Zhang, Chuancai Qu, Dan Du, Liming Yang, Kaiyuan Univ Informat Engn Sch Informat Syst Engn Zhengzhou Peoples R China Lab Adv Comp & Intelligence Engn Wuxi Jiangsu Peoples R China Zhengzhou Univ Sch Cyberspace Secur Zhengzhou Peoples R China

ISBN: (纸本)9798400707032

This study proposes an unsupervised machine translation method based on a dynamic adaptive masking strategy and multi-task learning. Firstly, a dynamic adaptive masking strategy is introduced in masked language modeling, dynamically adjusting the masking rate based on sentence complexity and contextual information to retain more important information in complex sentences while optimizing the masking effect. Secondly, a multi-task learning framework is adopted, incorporating tasks such as translation, summarization, and question answering into the unsupervised translation model. By using a shared encoder and task-specific decoders for joint training, the model enhances both generalization and task-specific capabilities. Experimental results demonstrate that the proposed approach significantly improves translation quality and model generalization across multiple translation tasks, providing new insights and application prospects for unsupervised machine translation research.

关键词： Unsupervised machine Translation Dynamic Adaptive Masking strategy Multi-Task Learning Masked Language Modeling Translation Quality Improvement

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Defect Detection of Forming Mesh Based on YOLOV8N Model 24

Defect Detection of Forming Mesh Based on YOLOV8N Model

引用

1st international conference on Image Processing machine Learning and pattern recognition

作者： Li, Jing Wang, Lijuan Zhang, Peng Guangdong Mech & Elect Polytech Guangzhou Guangdong Peoples R China Macao Polytech Univ Macau Peoples R China

ISBN: (纸本)9798400707032

A method for detecting defects in forming mesh based on the YOLOV8N model is proposed. Firstly, an illumination system is designed, and then image data with defects is collected to construct an image dataset. The constructed image dataset is annotated and augmented to build a corresponding image sample set. The YOLOV8N model is trained using the sample set to obtain a defect recognition and localization model. The original image to be recognized is input into the defect recognition and localization model, which then outputs the corresponding defect recognition and localization results. Experimental results show that this method provides accurate localization and fast speed.

关键词： Forming mesh 1 YOLOV8N model 2 Illumination system 3 Sample set 4

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A lightweight diffusion model for image generation based on improved MobileViT 24

A lightweight diffusion model for image generation based on ...

引用

1st international conference on Image Processing machine Learning and pattern recognition

作者： Liu, Yifei Sun, Wenyun Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing Peoples R China

ISBN: (纸本)9798400707032

Aiming at the problem that the structural complexity of the backbone architecture of denoising diffusion probabilistic model (DDPM) leads to the inefficiency of the training of the model, a lightweight image generation method (MobileDiT) based on MobileViT is proposed, which takes MobileViT block as the backbone architecture of DDPM, and improves the computational efficiency while keeping the image quality by combining lightweight convolution with Transformer. The conditional mechanism for introducing conditional information into the model is also improved by replacing the traditional layer normalization in the Transformer block with adaptive layer normalization and initialising each block as a constant function, allowing the model to process conditional information more efficiently. The experiment results show that the proposed model reduces the FID-50K to 2.15 and improves the IS value compared with models such as style Generative Adversarial Network (styleGAN) and Ablative Diffusion Model (ADM). This shows that the proposed model can not only improve the computational efficiency, but also enhance the quality of the generated images.

关键词： Image generation Denoising Diffusion Probabilistic Model (DDPM) Computational complexity Lightweight convolution Conditional mechanism

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An FPGA-Accelerated Approach to Direct Waveform Processing for Low-Power Keyword Spotting 24

An FPGA-Accelerated Approach to Direct Waveform Processing f...

引用

1st international conference on Image Processing machine Learning and pattern recognition

作者： Yang, Yucheng Duan, Shukai Wang, Lidan Southwest Univ Coll Artificial Intelligence Chongqing Peoples R China

ISBN: (纸本)9798400707032

Natural language processing is typically high-power-consuming, hence we require a gating switch to activate the natural language processing system only when needed. The gating switch is the keyword-spotting system, which is always kept in an open state;therefore, it is essential to ensure the system operates with low power consumption. To address this issue, this paper firstly introduces a convolutional algorithm that directly convert voice signals into Mel-frequency cepstral coefficients features. Compared to traditional feature extractors, this algorithm significantly reduces computational load, thereby lowering power consumption. Subsequently, in order to further reduce computational load and lower power consumption, we introduced a classifier with weight binarization. To enhance the accuracy of this classifier, a two-step distillation method was proposed. Finally, this article presents an FPGA-based hardware accelerator that implements accelerated inference computation for a KWS engine within an FPGA. Compared to a high-performance general-purpose processor (Intel Core i5-12400F), the FPGA-based pattern recognition engine hardware accelerator achieves a speedup of 28.6 times, while its power consumption is only 4.2% of that of the i5-12400F.

关键词： FPGA accelerator keyword-spotting

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PoC Implementation of Neuromorphic Wireless Cognition: Gesture recognition for Robotic Control 1

PoC Implementation of Neuromorphic Wireless Cognition: Gestu...

引用

1st IEEE international conference on machine Learning for Communication and Networking (IEEE ICMLCN)

作者： Ke, Yuzhen Heshmati, Mehdi Utkovski, Zoran Dommel, Johannes stanczak, Slawomir Fraunhofer Heinrich Hertz Inst Berlin Germany Tech Univ Berlin Berlin Germany

ISBN: (纸本)9798350343205;9798350343199

Neuromorphic computing is emerging as a brain-inspired paradigm for low-power artificial intelligence on edge devices through event-based data processing. In this paper, we present a proof-of-concept (PoC) implementation of an architecture for device-edge co-inference that integrates neuromorphic computing and wireless communication. To demonstrate the concept, we present a demo setup for gesture recognition in a robotic control application. The demo setup integrates a neuromorphic sensor (event-based camera), on-device neuromorphic processor performing joint feature extraction (i.e. semantic coding) and channel coding, impulse-radio-based transmission/reception over a wireless channel, and neuromorphic processor for edge inference. In the considered architecture, learning is performed in an end-to-end fashion via the directed information bottleneck principle, which allows to trade task accuracy for communication overhead and implementation complexity.

关键词： neuromorphic computing semantic communication spiking neural networks edge intelligence directed information bottleneck

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2024 1st international conference on Communications and Computer Science, InCCCS 2024

2024 1st International Conference on Communications and Comp...

引用

1st IEEE international conference on Communications and Computer Science, InCCCS 2024

ISBN: (纸本)9798350358858

The proceedings contain 34 papers. The topics discussed include: digital sculpting in the new age: a bibliometric analysis of artificial intelligence and virtual reality integration;Bharatanatyam mudra recognition using deep learning and meta-learning techniques;public safety surveillance system using deep learning;trust based minimum spanning tree routing algorithm in WSNs;antenna design for collision avoidance system in autonomous driving cars;silkworm cocoon assessment using GRNN on X-rays;framework for evaluating the carbon footprint of academic machine learning endeavor;transfer learning in endoscopic imaging: a machine vision approach to git disease identification;and exploring the impact of financial news sentiment on stock price forecasting: a comparative deep learning approach.

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