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A Hybrid Approach of Semantic Weight Based Re-Propagation For Convolutional neural networks in Content Based Medical Image Retrieval 2

A Hybrid Approach of Semantic Weight Based Re-Propagation Fo...

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2nd IEEE International conference on distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Sood, Gourav Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350347456

Content-based image retrieval (CBIR) is a homogeneous search technology based on visual features, and information has been leaked to potential users of pharmaceutical testing, education, and research. However, the CBIR collects images related to acquisition properties, as a result of the inability to predict the similarity of images using the medical part, and the difference between the user's search destination and object space. In the existing methods, effective points of interest are searched for, and L2 normalization is applied to the spatial arrangement of the original and global features and the RGB channels in the index image by effectively detecting their points of interest. The principal component analysis is used for redundant feature sets and feature sets to provide significant effects. The existing system has certain limitations, such as information loss during the hard task completion coding process and ignores the spatial relationship among the patches in image representation. To overcome the existing method issues, the integrated feature of the images is preferred. In this work, Semantic Weight Based Feed Forward Recurrent Back Propagation (SWFFRBP) is used to retrieve the images, and then based on the semantic weight the correctness of retrieval of images is calculated. The semantic weight determines the accuracy of retrieved images. After that, the recurrent backpropagation calculates the weight error derivatives arising from this entire episode of processing in the network. The existing features compared with the proposed features were Bag of Features (BoF), SURF, and HOG. The retrieval methods taken to compare with proposed retrieval techniques are RFRM and KNN. Thus, medical image retrieval for diagnosis using SWFFRBP, accuracy, False positive rate, relevance, feedback, precision, and recall parameters were tested and proved to be efficient than other methods. © 2023 IEEE.

关键词： Semantics

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ACCELERATING CHEMICAL KINETICS CALCULATIONS WITH PHYSICS INFORMED neural networkS

ACCELERATING CHEMICAL KINETICS CALCULATIONS WITH PHYSICS INF...

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ASME ICE Forward conference (ICEF)

作者： Almeldein, Ahmed Van Dam, Noah Univ Massachusetts Lowell Lowell MA 01854 USA

ISBN: (纸本)9780791886540

Detailed chemical kinetics calculations can be very computationally expensive, and so various approaches have been used to speed up combustion calculations. Deep neural networks (DNNs) are one promising approach that has seen significant development recently. Standard DNNs, however, do not necessarily follow physical constraints such as conservation of mass. Physics Informed neural networks (PINNs) are a class of neural networks that have physical laws embedded within the training process to create networks that follow those physical laws. A new PINN-based DNN approach to chemical kinetics modeling has been developed to make sure mass fraction predictions adhere to the conservation of atomic species. The approach also utilizes a mixture-of-experts (MOE) architecture where the data is distributed on multiple subnetworks followed by a softmax selective layer. The MOE architecture allows the different sub-networks to specialize in different thermochemical regimes, such as early stage ignition reactions or post-flame equilibrium chemistry, then the softmax layer smoothly transitions between the sub-network predictions. This modeling approach was applied to the prediction of methane-air combustion using the GRI-Mech 3.0 as the reference mechanism. The training database was composed of data from 0D ignition delay simulations under initial conditions of 0.250 bar pressure, 500-2000 K temperature, an equivalence ratio between 0 and 2, and an N2-dilution percentage of up to 50%. A wide variety of network sizes and architectures of between 3 and 20 sub-networks and 6,600 to 77,000 neurons were tested. The resulting networks were able to predict 0D combustion simulations with similar accuracy and atomic mass conservation as standard kinetics solvers while having a 10-50x speedup in online evaluation time using CPUs, and on average over 200x when using a GPU.

关键词： Chemical kinetics Physics Artificial neural networks Combustion Simulation Engineering simulation Modeling Physical laws Ignition delay Chemistry Databases Flames Ignition Methane Graphics processing units Architecture Pressure Temperature Equilibrium (Physics)

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A 16-channel Reconfigurable neural Spike Sorting System with distributed Tiles and neural network Engine Based on FPGA

A 16-channel Reconfigurable Neural Spike Sorting System with...

引用

IEEE Biomedical Circuits and Systems (BIOCAS)

作者： Tianhao Li Siyan Hu Yongfu Li Yan Liu Department of Micro-Nano Electronics Shanghai Jiao Tong University China

The increase in the amount of neural recording data will bring challenges for data communication, which has either limited bandwidth or power budget. Real-time neural spike detection and classification will greatly reduce the data bandwidth while keeping fidelity of recording, however, require manual tuning or complex algorithm for parameter optimization. In this work, we propose a customized machine-learning processing engine with distributed neural spike clustering tiles. A customized artificial neural network(ANN) module in the engine was trained offline and utilized online to optimize the initialization of existing sorting algorithm to achieve high sorting accuracy and minimized hardware complexity. By dynamically varying the data resolution of the sorting engine and distributed channels, the hardware resource occupied can be optimized. The algorithm was simulated on MATLAB and implemented on an FPGA platform. It shows that 94% sorting accuracy was achieved with 196 LUTs and 238 FFs on ZYNQ-7020 FPGA platform. The simulation results and FPGA implementation results prove the effectiveness of the sorting algorithm.

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Delay-Aware Backpressure Routing Using Graph neural networks

Delay-Aware Backpressure Routing Using Graph Neural Networks

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Zhongyuan Zhao Bojan Radojicic Gunjan Verma Ananthram Swami Santiago Segarra Rice University USA University of Novi Sad Serbia US Army’s DEVCOM Army Research Laboratory USA

We propose a throughput-optimal biased backpressure (BP) algorithm for routing, where the bias is learned through a graph neural network that seeks to minimize end-to-end delay. Classical BP routing provides a simple yet powerful distributed solution for resource allocation in wireless multi-hop networks but has poor delay performance. A low-cost approach to improve this delay performance is to favor shorter paths by incorporating pre-defined biases in the BP computation, such as a bias based on the shortest path (hop) distance to the destination. In this work, we improve upon the widely-used metric of hop distance (and its variants) for the shortest path bias by introducing a bias based on the link duty cycle, which we predict using a graph convolutional neural network. Numerical results show that our approach can improve the delay performance compared to classical BP and existing BP alternatives based on pre-defined bias while being adaptive to interference density. In terms of complexity, our distributed implementation only introduces a one-time overhead (linear in the number of devices in the network) compared to classical BP, and a constant overhead compared to the lowest-complexity existing bias-based BP algorithms.

关键词： Wireless communication Signal processing algorithms Interference Telecommunication traffic Spread spectrum communication Routing Prediction algorithms

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Automatic English Translation using Artificial Recurrent neural network with Machine Translation

Automatic English Translation using Artificial Recurrent Neu...

引用

distributed Systems, Computer networks and Cybersecurity (ICDSCNC), International conference on

作者： Jianxun Guo Beibei Zheng Lei Zhang Biao Li Haolin Guo Ming Chen Tianjin University of Technology and Education Tianjin China Zhuobei Technology (Wuhan) Co. Ltd Wuhan China Tianjin Data Development Center Tianjin China Tianjin Academy of Fine Arts Tianjin China

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

In recent years, automated recognition technique for English translation error recognition yields poor accuracy recognition results due to its limited ability for semantic analysis. Consequently, this research creates an automatic identification approach for Machine Translation (MT) error detection based on Artificial Intelligence (AI). Thus, this research developed an intelligent identification method known as Artificial Recurrent neural network with Machine Translation (ARNN-MT) in order to recognize English translation errors. Context-learning from previous words is used by ARNN sequential data to detect translation issues. MT uses these networks to predict accurate translations. These hybridized ARNN-MT techniques combined with rule-based process, and learned patterns to enhance error detection. Consequently, ARNN-MT improves phrase flow and able to resolve specific translation problems while providing a logical translation in context. When compared to existing algorithms, including Machine English Translation Errors-Multifeatured Fusion (METE-MF), Double-Recurrent neural network (Double RNN), and Radio Magnetic Pronunciation Recording Devices (RMPRD), the results demonstrated that the proposed ANN-MT method performed better in terms of accuracy, of 99.96%.

关键词： Performance evaluation Translation Accuracy Recurrent neural networks Computational modeling Semantics Parallel processing Prediction algorithms Machine translation Artificial intelligence

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Research on long and short-term social recommendation based on convolutional and gated recurrent units 29

Research on long and short-term social recommendation based ...

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29th IEEE International conference on Parallel and distributed Systems, ICPADS 2023

作者： Jia, Zihe Xue, Peng Dai, Zhiqiang Gao, Qian Zhang, Xiaomeng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China

ISBN: (纸本)9798350330717

The development of the Internet has made people more closely related and has put forward higher requirements for recommendation models. Most recommendation models are studied only for the long-term interests of users. In this paper, the interaction time between the user and the item is introduced as auxiliary information in the model construction. Interaction time is used to determine users' long-term preferences and short-term preferences. In this paper, temporal features are extracted by building a convolutional gated recurrent unit with attention neural network (CNN-GRU-Attention). Firstly, for the problem of accurate feature extraction, CNN are constructed to extract higher-level and more abstract features of themselves and transform high-dimensional data into low-dimensional data;secondly, for the problem of social temporality, GRU are used to not only extract temporal information, but also effectively reduce gradient dispersion, making model convergence and training easier;finally, Graph Attention networks are used to aggregate the social relationship information of users and items respectively, which constitute the final feature representation of users and items respectively. In particular, a modified cosine similarity is used to reduce the error caused by data insensitivity when constructing the social information of the item. In this study, simulation experiments are conducted on two publicly available datasets (Epinions and Ciao), and the experimental results show that the proposed recommended model performs better than other social recommendation models, improving the evaluation metrics of MAE and RMSE by 1.06%-1.33% and 1.19%-1.37%, respectively. The effectiveness of the model innovation is proved. © 2023 IEEE.

关键词： convolutional gated recurrent unit graph attention networks neighborhood recommendation systems social networks

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Research on University Sports Information Management System Based on Big Data and Cloud Computing

Research on University Sports Information Management System ...

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Computers, Information processing and Advanced Education (CIPAE), 2020 International conference on

作者： Ming Lei Yong Wang YiBin Vocational and Technical College Yibin Sichuan China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

This paper proposes a sports information management system based on Hadoop platform, which integrates convolutional neural network (CNN) technology to achieve efficient data management and intelligent decision support. Through this architecture system can effectively process and analyze the massive data from university sports activities, so as to improve the efficiency and accuracy of data processing. Secondly, Convolutional neural network (CNN) technology is introduced to realize face recognition function. The application of this technology not only enhances the security of the system, but also improves the speed and accuracy of athlete identity verification. The automatic feature extraction capability of CNN enables the system to quickly identify key information in complex image data, which provides a strong technical support for the intelligence of sports management. In addition, the system provides rich data API interfaces, which can seamlessly connect various sports resources in colleges and universities, and realize data sharing and exchange. By building a unified sports big data environment, the system promotes the optimal allocation and efficient use of resources, and provides comprehensive data support for sports teaching, training and competition. This system builds a powerful sports information management platform by integrating Hadoop platform and CNN technology. This not only improves the ability of data processing and storage, but also greatly promotes the modernization process of college sports management through intelligent decision support.

关键词： Training Cloud computing Accuracy Face recognition distributed databases Big Data Information management Convolutional neural networks Security Sports

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Scalable CNN Accelarator with Error-Correcting Accumulator for High-Performance Deep Learning Applications

Scalable CNN Accelarator with Error-Correcting Accumulator f...

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distributed Systems, Computer networks and Cybersecurity (ICDSCNC), International conference on

作者： Usthulamuri Penchalaiah G. Suresh K. Sravan Kumar G. Tejaswi A. Hari Krishna Dept. of ECE Geethanjali Institute of Science and Technology S.P.S.R. Nellore India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

The Multiply and Accumulate (MAC) unit is a fundamental component in Convolutional neural network (CNN) accelerators, designed to enhance the efficiency and speed of deep learning applications such as image recognition, natural language processing, and autonomous driving. These applications demand high computational power and low latency, which are critical for real-time processing and decision-making. Existing solutions often struggle with power consumption, area efficiency, and processing speed, hindering their performance in resource-constrained environments. The proposed CNN-MAC accelerator addresses these challenges by integrating a Modified Booth Multiplier (MBM) and an Error Correctable Carry Look Ahead Adder (EC-CLA). The MBM enhances multiplication efficiency by reducing the number of partial products, while the EC-CLA minimizes propagation delay and corrects errors during addition, ensuring high-speed and reliable computation. This combination significantly improves the overall performance and energy efficiency of CNN accelerators, making them suitable for deployment in various high-demand scenarios.

关键词： Deep learning Power demand Resistive RAM Memory management Real-time systems Energy efficiency Computational efficiency Convolutional neural networks Low latency communication Pipeline processing

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Research on several construction strategies of multilingual resource database 3

Research on several construction strategies of multilingual ...

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3rd International conference on Computer Science and Communication Technology, ICCSCT 2022

作者： Meng, Lingchun Kong, Shuying Li, Wenyue Cui, Lei Center of Network and Information Guangdong University of Foreign Studies Guangzhou China

ISBN: (纸本)9781510661240

In order to realize the effective use of information resources in different languages, it is necessary to establish a corresponding information resource database. Based on cross-language retrieval, this paper analyses the construction strategy of multilingual resource database, and takes language acquisition and processing as the perspective. The construction of the resource database provides a reference. In view of the construction of multilingual resource database, this paper makes an analysis from three aspects: resource collection and pre-processing, resource storage and retrieval, and multilingual translation technology, and summarizes its implementation methods. The collection, storage and retrieval of multilingual resources should adopt a distributed architecture. The resource library builds a language encoding and transcoding system for efficient collection and preprocessing of multiple languages. The translation between languages adopts machine translation technology mainly based on neural network machine translation. © 2022 SPIE.

关键词： Computer aided language translation

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Implementation of Image Caption Generation Using VGG16 and Resnet50

Implementation of Image Caption Generation Using VGG16 and R...

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distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International conference on

作者： S. Nehan A.Vijaya Lakshmi C. Shambhavi G. Chandrakanth Department of ECE Vardhaman College Of Engineering Hyderabad India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

One of the biggest problems in computer vision is getting a machine to automatically show an image's content along with a phrase in natural language. Using deep learning models, this research demonstrates two separate methods to generate picture captions. The first method employs VGG16 for feature extraction followed by training a Long Short-Term Memory (LSTM) model. In the second approach, ResNet50 is utilized for feature extraction, and a combined Convolutional neural network-Long Short-Term Memory (CNN-LSTM) model is trained for caption generation. Both VGG16 and ResNet50 are popular convolutional neural networks for extracting characteristics from images, while LSTM and CNN-LSTM are recurrent neural networks suitable for sequential data processing. By comparing these approaches, we evaluate their effectiveness in generating descriptive captions for images. Experimental results indicate the strengths and weaknesses of each method, providing insights into the interplay between feature extraction and captioning models in image understanding tasks.

关键词： Training Recurrent neural networks Computational modeling Natural languages Feature extraction Convolutional neural networks Task analysis

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