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Pruning Optimization of Deep Learning Model Based on Generalization Estimation

Pruning Optimization of Deep Learning Model Based on General...

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Artificial Intelligence, Deep Learning and Neural Networks (AIDLNN), International Conference on

作者： Xuanzhe Li Wenjun Ke Wantao Wen Shaotong Liu Xiaojuan Sheng School of Computer Science and Engineering Southeast University Beijing Institute of Computer Technology and Application

ISBN: (数字)9798331520816

ISBN: (纸本)9798331520823

Existing research has shown that deep learning models contain considerable redundancy, requiring compression optimization to eliminate unnecessary parameters and improve computational efficiency. In model compression through pruning, many existing methods often use loss values to guide the identification of sub-network models. However, low training loss does not necessarily indicate good generalization ability. Consequently, reducing model parameters while preserving strong generalization performance has become a key area of research. Addressing the challenge of ensuring strong generalization performance of submodels after optimization through deep learning model pruning, we propose a channel-structured generalization estimation model. The model architecture incorporates Fully Connected Neural Networks (FCNN) and Gated Recurrent Units (GRU) as modules for channel feature extraction. Additionally, we introduce a differentiable pruning algorithm based on generalization estimation. Experimental results demonstrate the capability of the channel-structured generalization estimation model in predicting the generalization gap via regression. Furthermore, the proposed differentiable pruning algorithm effectively safeguards the generalization capability of sub-models. We conduct pruning experiments on NiN-structured convolutional neural network models on two datasets, CIFAR-10 and SVHN. Comparative experimental results demonstrate the effectiveness of this approach: achieving a 0.72% accuracy improvement over the original model on CIFAR-10; and a 0.12% accuracy improvement over the base model on SVHN while reducing FLOPs by 50.2%, validating the effectiveness of the proposed approach.

关键词： Deep learning Training Accuracy Computational modeling Neural networks Estimation Logic gates Prediction algorithms Convolutional neural networks Optimization

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ERDD: efficient rain removal algorithm in surveillance videos via rain detection and diffusion models 9

ERDD: efficient rain removal algorithm in surveillance video...

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9th International Symposium on Advances in Electrical, Electronics, and computer Engineering, ISAEECE 2024

作者： Wen, Jiayan Zhuang, Yuansheng Deng, Junyi School of Automation Guangxi University of Science and Technology GuangXi Liuzhou545006 China The Research Center for Intelligent Cooperation and Cross-application Guangxi University of Science and Technology Liuzhou545616 China School of Computer Science and Technology Guangxi University of Science and Technology DongHuan GuangXi Liuzhou545006 China

ISBN: (纸本)9781510683303

This paper introduces a novel rain removal algorithm aimed at improving the visual quality of images and videos affected by rain. The core of ERDD lies in its sophisticated rain detection mechanism, which meticulously identifies and isolates the impact of raindrops. Subsequently, a state-of-the-art diffusion model is applied to refine and optimize the imagery, effectively mitigating the rain's visual interference. Comprehensive evaluations conducted on a variety of benchmark datasets reveal that ERDD outperforms existing methodologies in rain removal, particularly excelling in the preservation of image details and color accuracy. The results from the rain detection conducted on 1800 rainfall images showed that the average processing time for each image was 0.014 seconds, with an accuracy of 96.61%. © 2024 SPIE.

关键词： Image enhancement

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Time Series Analysis and Forecasting of Water Quality Parameters along Yamuna River in Delhi 2

Time Series Analysis and Forecasting of Water Quality Parame...

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2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Gupta, Neetu Yadav, Surendra Chaudhary, Neha School of Engineering & Technology Career Point University Kota324005 India Department of Computer Science and Application Vivekananda Global University Jaipur303012 India Department of Computer Science and Engineering Manipal University Jaipur 303007 India

The extent of pollution in areas near industrial activities and Yamuna River has increased tremendously and to determine the overall water quality status in these locations is utmost important. This paper presents a comprehensive study on assessing the water quality and gain insights into the overall water quality status in the vicinity of industries and along the Yamuna River in Delhi. To achieve this, water samples were collected from Central Pollution Control Board (CPCB) for the last eight years (2013-2021) and was converted into a machine-readable format to facilitate further analysis. These samples were analysed for several water quality parameters, including pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), nutrient levels, heavy metals, and other relevant pollutants. By understanding these factors, a deeper understanding of the pollution sources and their impact on water quality is gained. Time series methods are applied to forecast future trends and values of the water quality parameters. This allows for predicting potential changes in water quality over time. A remarkable accuracy of 93.6% is attained in predicting water quality values up to the present time. The analysis reveals that water quality in the NCR region falls below acceptable standards. However, there has been a marginal enhancement in the water quality at Khajori Paltoon (Location L3) post-COVID. To achieve a more significant improvement in water quality, it is imperative to implement new policies. © 2024 Elsevier B.V.. All rights reserved.

关键词： Water quality

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Early Diabetes Prediction Using CNN-LSTM and CNN-Bi-LSTM Models Optimized with Adam on the PIMA Indian Dataset

Early Diabetes Prediction Using CNN-LSTM and CNN-Bi-LSTM Mod...

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Advancements and Key Challenges in Green Energy and Computing (AKGEC), International Conference on

作者： Sambhu Kumar Prashant Kumar Singh Pankaj Kushwaha Sanjeev Kumar Prasad School of Computer Application and Technology Galgotias University Greater Noida India

ISBN: (数字)9798350390179

ISBN: (纸本)9798350390186

Early detection of the condition is critical to proper diabetes management and reducing risk for complications. A new concept for early prediction of diabetes is proposed in this study where the LSTM and CNN models are combined with Bi-LSTM. The target is to use the Adam optimizer for these models on top of improving this model, and testing it onto the PIMA Indian Diabetes Dataset. The CNNLSTM model is particularly well-suited for time dependent data as it has been designed to extract the spatial patterns from an input and in turn record memory of a problem across time steps. Meanwhile, with the data from both past and future, the CNN-Bi-LSTM model is capable of better prediction. From our experimental results, we can see that these hybrid models achieve superior performance over traditional machine learning algorithms and hence could serve as a powerful resource for predicting diabetes at an early stage. Thus the application of this approach is a new challenge and seems to have potential for improving early diagnosis in order to implement timely interventions in diabetes care.

关键词： Solid modeling Predictive models Solids Data models Diabetes Transient analysis Long short term memory Optimization Testing Overfitting

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Improving Connectivity of IoT-Enabled Networks Through Big Data Analytics 15

Improving Connectivity of IoT-Enabled Networks Through Big D...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kiran, K.S. Sen, Sumanta Sharma, Sunil School of Engineering and Technology Karnataka Bangalore India ARKA JAIN University Department of Pharmacy Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350370249

The net of things (IoT) has quick grow to be the backbone of many corporations, taking into account real-time statistics collection and automatic tasks. However, the connectivity of these IoT-enabled networks can become without difficulty congested when huge amounts of data are generated in a brief time frame. To be able to hold a high stage of overall performance, big facts analytics can be used to research the massive volumes of generated statistics, provide insights and locate anomalies to improve community connectivity. Massive statistics analytics will permit higher records processing and evaluation competencies, become aware of regions of improvement and optimize network operations. Additionally, predictive analytics techniques can be used to forecast upcoming community congestion and take movement that could enhance network performance in real time. This, but, requires green facts collection, garage and control gear to ensure that the big records analytics answer is effective and efficient. This paper investigates how massive records analytics may be used to improve the connectivity of IoT-enabled networks through leveraging the energy of predictive analytics. © 2024 IEEE.

关键词： Predictive analytics

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application of AmsGradP optimizer in fault diagnosis of bearing with few samples 6

Application of AmsGradP optimizer in fault diagnosis of bear...

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6th International Conference on Internet of Things, Automation and Artificial Intelligence, IoTAAI 2024

作者： Fang, Jinfa Liang, Qiong Dong, Xiangjun Wang, Qi Xiong, Jianbin Guangdong Polytechnic Normal University School of Automation Guangzhou China The Key Laboratory of Marine Environmental Survey Technology and Application Ministry of Natural Resources China Guangdong Polytechnic Normal University School of Computer Guangzhou China

ISBN: (纸本)9798350386974

Bearing fault diagnosis methods based on deep learning usually require a large number of training samples. However, obtaining large amounts of faulty bearing data can be challenging. How to diagnose faults with limited or few samples becomes a research problem. In order to solve the problem of bearing fault diagnosis with few samples, a rolling bearing fault diagnosis method based on improved Adam (Adaptive Moment Estimation) is proposed. First, it combines the advantages of both Adam and AmsGrad optimizers. Second, similar to AdamP, AmsGradP (Adaptive Gradient Optimizer with Penalty Parameters) introduces the penalty term of the parameter norm to mitigate the problem of premature decay of the learning rate. Finally, by penalizing the norm of the parameter during optimization, AmsGradP can prevent excessive parameter growth, thus further stabilizing the learning rate. The experimental results show that when only 1.58% of the data is used as the training dataset, the average accuracy of 98.26% can be achieved by using the AmsGradP optimizer, which is 6.68% higher than that by using the Adam optimizer. © 2024 IEEE.

关键词： Roller bearings

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Content-Based Book Recommender System Using Supervised Learning 2nd

Content-Based Book Recommender System Using Supervised Learn...

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Rahul, Mayur Singh, Akhilesh Verma, Shekhar Shukla, Himanshu Yadav, Vikash Department of Computer Application School of Engineering and Technology CSJM University Uttar Pradesh Kanpur India Department of Technical Education Government Polytechnic Bighapur Unnao Uttar Pradesh Manpur India

ISBN: (纸本)9789819735938

Online shopping in India has already got more attention in the last few decades. The Indians are more interested in online shopping which has changed all the concepts of the market scenario. There are so many online shopping websites available nowadays like Flipkart, Amazon, Junglee, Snapdeal, etc. With the increase in the number of traders and buyers, efficient and effective techniques are used to manage the vast amount of data formed day by day. Recommender systems give a platform to filter out the given data and able to provide relevant information to the end users. The methods used in the recommender systems are content-based, collaborative-based, and demographic-based but there are some issues that fail in producing efficient information for the end users. Thus, it is very important to find the most important features that are capable of optimizing recommender systems. This paper proposed a content-based book recommender system using PCA and SVM with the help of two publicly available datasets like Amazon Book Reviews (AB) and BookCrossing (BX). The results of the proposed recommender system prove the superiority of the system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Recommender systems

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Track-before-detect algorithm based optimized interactive multiple model particle filtering for tracking maneuvering weak target

Track-before-detect algorithm based optimized interactive mu...

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2024 International Conference on Image Processing and Artificial Intelligence, ICIPAl 2024

作者： Wu, Xiaojia Yang, Jinlong School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education Beijing100039 China

ISBN: (纸本)9781510681514

This paper proposes an optimized algorithm for tracking maneuvering weak radar targets in complex sea environments. In the proposal, we first preprocesses radar echo images using the fractional Fourier transform (FrFT) to reduce clutter interference. Then, Feedback factor and residual resampling are introduced to optimize the interactive multiple model particle filtering (IMMPF) algorithm to achieve effective tracking of maneuvering targets. Additionally, the optimized IMMPF is combined with track before detect algorithm (TBD) to suppress clutter and accumulate target trajectory. Simulation experiment results demonstrate that the proposed algorithm can effectively track targets compared to other TBD algorithms. © 2024 SPIE.

关键词： Monte Carlo methods

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Metal surface Feature Fusion for Defect Recognition 3

Metal surface Feature Fusion for Defect Recognition

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3rd International Conference on Robotics, Automation and Intelligent Control, ICRAIC 2023

作者： Lyu, Wenkai Yang, Chengzhuan Zhejiang Normal University School of Computer Science and Technology Zhejiang China Zhejiang Normal University Key Laboratory of Intelligent Education Technology and Application Zhejiang Province Zhejiang China

ISBN: (纸本)9798350360813

Metal sheets have been widely used in various industrial production and production life aspects. However, the surface of metal plates is prone to multiple defects, which affect the electromagnetic characteristics and coating effect of metal plates. Currently, mainstream inspection uses YOLO, PaddleClas, and other methods to detect flat metal surfaces. These algorithms are all very potent in their classification capabilities, but as generalized defect handling algorithms, they cannot be specific to each type of defect in a real problem. In this paper, we propose a multi-information fusion method for defect recognition. We consider both low-level texture features and high-level convolutional features of metal surfaces for defect recognition. We use a convolutional neural network as the main body and add extracted and processed prompts during training. Finally, we test the proposed method extensively on Camshaft and Kaggle metal surface defects datasets, and the experimental results demonstrate that our method achieves a state-of-the-art recognition performance that surpasses previous defect recognition methods. © 2023 IEEE.

关键词： Convolution

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Cryptocurrency Price Prediction with Deep Neural Networks: A Comparative Analysis of Machine Learning Approaches

Cryptocurrency Price Prediction with Deep Neural Networks: A...

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International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Pallavi Jain Aryan Kumar Nikhil kumar Pathak Manvi Chaudhary School of Computer Application & Technology Galgotias University Greater Noida India

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

In the current study, the immediate correlation coefficient and root mean square error (RMSE) are combined to create a fusion model that can accurately predict cryptocurrency prices. Multivariate linear regression, MARS, artificial neural networks (ANN), random forests, support vector machines (SVM), bootstrap aggregation, decision trees, and extreme gradient boosting with XG Boost are just a few of the deep learning and machine learning models that we use. Utilizing long short-term memory (LSTM) is a crucial element. LSTM emerges as the most accurate model for predicting cryptocurrency prices during January 1, 2023, to March 31, 2023.

关键词： Support vector machines Deep learning Predictive models Cryptocurrency Indexes Reliability Forecasting

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