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International Joint conference on neural networks (IJCNN)

作者： Li, Yang Sun, Yinqian He, Xiang Dong, Yiting Zhao, Dongcheng Zeng, Yi Chinese Acad Sci Inst Automat Brain inspired Cognit Intelligence Lab Beijing Peoples R China Ctr Long Term Artificial Intelligence Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Univ Chinese Acad Sci Sch Future Technol Beijing Peoples R China Chinese Acad Sci Key Lab Brain Cognit & Brain inspired Intelligenc Beijing Peoples R China

ISBN: (纸本)9798350359329;9798350359312

Efficient parallel computing has become a pivotal element in advancing artificial intelligence. Yet, the deployment of Spiking neural networks (SNNs) in this domain is hampered by their inherent sequential computational dependency. This constraint arises from the need for each time step's processing to rely on the preceding step's outcomes, significantly impeding the adaptability of SNN models to massively parallel computing environments. Addressing this challenge, our paper introduces the innovative Parallel Spiking Unit (PSU) and its two derivatives, the Input-aware PSU (IPSU) and Reset-aware PSU (RPSU). These variants skillfully decouple the leaky integration and firing mechanisms in spiking neurons while probabilistically managing the reset process. By preserving the fundamental computational attributes of the spiking neuron model, our approach enables the concurrent computation of all membrane potential instances within the SNN, facilitating parallel spike output generation and substantially enhancing computational efficiency. Comprehensive testing across various datasets, including static and sequential images, Dynamic Vision Sensor (DVS) data, and speech datasets, demonstrates that the PSU and its variants not only significantly boost performance and simulation speed but also augment the energy efficiency of SNNs through enhanced sparsity in neural activity. These advancements underscore the potential of our method in revolutionizing SNN deployment for high-performance parallel computing applications.

关键词： Spiking neural Network Parallel Computation Spiking Neuron Model Neuromorphic Computing

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SAM: cross-modal semantic alignments module for image-text retrieval

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MULTIMEDIA TOOLS AND applications 2024年第4期83卷 12363-12377页

作者： Park, Pilseo Jang, Soojin Cho, Yunsung Kim, Youngbin Chung Ang Univ Dept Artificial Intelligence Seoul 06974 South Korea Chung Ang Univ Dept Image Sci & Arts Seoul 06974 South Korea

Cross-modal image-text retrieval has gained increasing attention due to its ability to combine computer vision with natural language processing. Previously, image and text features were extracted and concatenated to feed the transformer-based retrieval network. However, these approaches implicitly aligned the image and text modalities since the self-attention mechanism computes attention coefficients for all input features. In this paper, we propose cross-modal Semantic Alignments Module (SAM) to establish an explicit alignment through enhancing an inter-modal relationship. Firstly, visual and textual representations were extracted from an image and text pair. Secondly, we constructed a bipartite graph by representing the image regions and words in the sentence as nodes, and the relationship between them as edges. Then our proposed SAM allows the model to compute attention coefficients based on the edges in the graph. This process helps explicitly align the two modalities. Finally, a binary classifier was used to determine whether the given image-text pair is aligned. We reported extensive experiments on MS-COCO and Flickr30K test sets, showing that SAM could capture the joint representation between the two modalities and could be applied to the existing retrieval networks.

关键词： image-text retrieval Cross-modal Vision-language Graph neural networks

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Spiking neural networks for Signal Classification with Digital and Analog Neuromorphic Systems: A Comparative Study

Spiking Neural Networks for Signal Classification with Digit...

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International Joint conference on neural networks (IJCNN)

作者： Happek, Leon Huang, Jen-Tse Gener, Alejandro Garcia Leupers, Rainer Galicia, Melvin Collins Aerosp Cork Ireland Rhein Westfal TH Aachen Aachen Germany

ISBN: (纸本)9798350359329;9798350359312

Spiking neural networks (SNNs) have suitable properties for realizing efficient processing at the edge, while having several similarities to brain-inspired computing that can be applied in neuromorphic computing systems. On the other hand, signal processing for wireless communication is an essential application that is critical at the edge. Signal modulation detection is a task that requires reliable accuracy in noisy environments, such as commercial and defense avionics applications. This task can be performed in a neuromorphic system running an SNN classification model using two different technologies: digital or analog. We present a comparative study between a digital manycore-based and an analog memristor-based neuromorphic implementation. We use Intel's LAVA and NeuroPack opensource frameworks to implement them and compare them in terms of accuracy. The results show that the digital technology achieves relevant accuracies, while the analog technology requires a more elaborate portability model to achieve comparable results.

关键词： Spiking neural networks Signal Classification Signal Modulation Digital Neuromorphic Computing Analog Neuromorphic Computing Loihi2 Memristors

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Research on Video Sample Collection and processing Methods Based on artificial Intelligence Platform 11th

Research on Video Sample Collection and Processing Methods B...

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11th International conference on applications and Techniques in Cyber Intelligence, ATCI 2023

作者： Hu, An Wang, Qi Xu, Xiaoguang Zhao, Yao Ji, Qian Pei, Lei State Grid Henan Information and Telecommunication Company Henan Province Zhengzhou450052 China State Grid Henan Electric power Company Henan Province Zhengzhou450052 China Henan Jiuyu Tenglong Information Engineering Co. Ltd Henan Province Zhengzhou450052 China

This paper summarizes the video sample collection and processing methods based on artificial intelligence platform, focusing on video noise cancellation, content segmentation and classification, and feature extraction and representation techniques. The paper believes that the deep learning technology, especially the convolutional neural network, shows great potential in image recognition and video analysis, and effectively improves the level of automation and accuracy of video processing. This paper discusses the importance of building a large-scale and high-quality video sample library, and how to improve the processing efficiency and accuracy of video data through intelligent technology. © 2024 The Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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Research on Human Resource Management and Prediction Model Based on neural Network Optimized by Genetic Algorithm 2

Research on Human Resource Management and Prediction Model B...

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2nd IEEE International conference on image processing and Computer applications, ICIPCA 2024

作者： Qi, Zexuan Yang, Sihan He, Xinjian Shi, Desheng Xinjiang University of Science & Technology Xinjiang Korla China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Engineering Guangzhou Huali College Guangzhou China Modern Agricultural College Honghe Vocational and Technical College Yunnan Mengzi China

ISBN: (纸本)9798350360240

This paper investigates the application of artificial neural networks optimized based on genetic algorithms in human resource management in hospital enterprises and constructs a human resource management and predictive analysis model. The model aims to support enterprises in achieving the optimal allocation of human resources by mining their internal human resource data and revealing the intrinsic correlation between personnel structure, business type and future development strategy. The study adopts multiple regression analysis method and combines the artificial neural network optimized by genetic algorithm to construct a human resource demand prediction model. Taking the operational data of Hospital X as an example, the model predicts the number of human resources in the hospital and analyzes in depth the demand for staff types in each department. The results show that the model has high accuracy and reliability in predicting the variables of hospital revenue, wage level, R&D investment and number of personnel, with a prediction error range between 0.09% and 1.27%. The research in this paper provides strong data support for enterprises, which helps them to realize the rational allocation of human resources and strategic transformation, and then maintain their competitive position in the market. © 2024 IEEE.

关键词： Prediction models

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artificial intelligence (AI) in textile industry operational modernization

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RESEARCH JOURNAL OF TEXTILE AND APPAREL 2024年第1期28卷 67-83页

作者： Sikka, Monica Puri Sarkar, Alok Garg, Samridhi Dr BR Ambedkar Natl Inst Technol Dept Text Technol Jalandhar Punjab India

Purpose With the help of basic physics, the application of computer algorithms in the form of recent advances such as machine learning and neural networking in textile Industry has been discussed in this review. Scientists have linked the underlying structural or chemical science of textile materials and discovered several strategies for completing some of the most time-consuming tasks with ease and precision. Since the 1980s, computer algorithms and machine learning have been used to aid the majority of the textile testing process. With the rise in demand for automation, deep learning, and neural networks, these two now handle the majority of testing and quality control operations in the form of image processing. Design/methodology/approach The state-of-the-art of artificial intelligence (AI) applications in the textile sector is reviewed in this paper. Based on several research problems and AI-based methods, the current literature is evaluated. The research issues are categorized into three categories based on the operation processes of the textile industry, including yarn manufacturing, fabric manufacture and coloration. Findings AI-assisted automation has improved not only machine efficiency but also overall industry operations. AI's fundamental concepts have been examined for real-world challenges. Several scientists conducted the majority of the case studies, and they confirmed that image analysis, backpropagation and neural networking may be specifically used as testing techniques in textile material testing. AI can be used to automate processes in various circumstances. Originality/value This research conducts a thorough analysis of artificial neural network applications in the textile sector.

关键词： artificial intelligence neural networking Machine learning Deep learning AI in dyeing processing

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Efficient neural Architecture Search: Computational Cost Reduction Mechanisms in DeepGA 1

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17th Workshop of Hybrid Intelligent Systems, HIS 2024, 17th Workshop on Intelligent Learning Environments, WILE 2024 and 6th Workshop on New Trends in Computational Intelligence and applications, CIAPP 2024, held in conjunction with Mexican International conference on artificial Intelligence, MICAI 2024

作者： Barradas-Palmeros, Jesús-Arnulfo López-Herrera, Carlos-Alberto Acosta-Mesa, Héctor-Gabriel Mezura-Montes, Efrén Artificial Intelligence Research Institute University of Veracruz Xalapa Mexico

ISBN: (数字)9783031838828

ISBN: (纸本)9783031838811

neural Architecture Search (NAS) aims to automate the design process of Deep neural networks (DNN) without requiring profound domain knowledge. The Deep Genetic Algorithm (DeepGA) was proposed to find the architectures of Convolutional neural networks (CNNs) for image processing, and its applications have covered a variety of data domains. Nonetheless, one of the main impediments of NAS is its computational cost, which is produced by evaluating candidate architectures. This work proposes using two cost-reduction mechanisms applied to DeepGA: using memory to avoid repeated evaluations and reducing the number of epochs for training in a low-fidelity estimation scheme for evaluations. The results indicate that the execution time of the algorithm was extensively reduced without diminishing the accuracy performance of the resulting architecture. The previous derives in a more efficient NAS procedure. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Optoelectronic Synapse Enabled by Defect Engineering of Tellurene for Neuromorphic Computing

IEEE ELECTRON DEVICE LETTERS

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IEEE ELECTRON DEVICE LETTERS 2025年第1期46卷 68-71页

作者： Guo, Junxiong Huang, Junyan Gu, Shuyi Lin, Lin Zhang, Yafei Wang, Xiang Liu, Yu Gong, Tianxun Lin, Yuan Yu, Bin Huang, Wen Zhang, Xiaosheng Univ Elect Sci & Technol China Sch Integrated Circuit Sci & Engn Chengdu 611731 Peoples R China Sch Elect Informat & Elect Engn Chengdu 610106 Peoples R China Nanjing Univ Posts & Telecommun Coll Integrated Circuit Sci & Engn Nanjing 210023 Peoples R China Univ Elect Sci & Technol China Sch Mat & Energy Chengdu 610054 Peoples R China Zhejiang Univ ZJU Hangzhou Global Sci & Technol Innovat Ctr Hangzhou 311215 Peoples R China

Emerging optoelectronic synapses hold immense potential for advancing neuromorphic computing systems. However, achieving precise control over selective responses in optoelectronic memory and clarifying tunable synaptic weights has remained challenging. This study reports an optoelectronic synapse utilizing oxygen plasma-assisted defect engineering in tellurene for artificial neural networks. Through DFT calculations and experimental analyses, we demonstrate that tellurene conductance can be modulated by controlling plasma-defined defect engineering, allowing a transition from short-term to long-term synaptic plasticity, largely determined by intrinsic large-lattice-relaxation effects. Our artificial synapses exhibit high linearity, a broad dynamic range, and tunable synaptic weights. Additionally, our optoelectronic synapses display selective sensitivity to multi-spectral light and achieve a pattern recognition accuracy of up to 96.7% across five typical datasets, surpassing even the ideal synapse. These tunable spectral responses, combined with high-performance neuromorphic applications using spike coding, establish a foundation for developments in brain-inspired machine learning, robotics, and real-time data processing.

关键词： Optoelectronic synapse defect engineering image recognition neural network Optoelectronic synapse defect engineering image recognition neural network

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A Method Generating Adversarial Mark Based on Convolutional neural networks 13th

A Method Generating Adversarial Mark Based on Convolutional ...

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13th International conference on Computer Engineering and networks (CENet)

作者： Deng, Zhengjie Liu, Meijun Li, Xiyan Hainan Normal Univ Sch Informat Sci & Technol Haikou Hainan Peoples R China Haikou Key Lab Artificial Intelligence & Digital Haikou Hainan Peoples R China Guangxi Key Lab Image & Graph Intelligent Proc Guilin Peoples R China

ISBN: (纸本)9789819992454;9789819992430;9789819992423

Deep neural networks (DNNs) have achieved impressive results in image classification tasks. Recent studies have shown that adding imperceptible perturbations to original images can cause image recognition models to make erroneous judgments. Additionally, visible marks, which add text or image information to images, serve as a reminder of copyright ownership and can help preventing image theft and infringement. In a sense, visible text mark can be seen as meaningful noise added to clean images. In this article, we propose a method (GAM) that combines text mark and adversarial examples by focusing perturbations on meaningful text that does not affect human judgment, while causing image classification models to produce incorrect results.

关键词： Adversarial mark Convolutional neural networks Adversarial samples Perturbation

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DNCS: artificial Intelligence Enabled Food Recognition and Associated Calorie Level Estimation System Using Deep neural Classification Scheme

DNCS: Artificial Intelligence Enabled Food Recognition and A...

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2024 International conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Ramesh, A.P. Prasad, Ovss Venkat, S. Jagadeeshwaran, A. Lakshmi, P. Adi Singaravelu, R. Sri Ramakrishna College of Arts and Science Department of Electronics Coimbatore India Qis College of Engineering and Technology Department of Ece AndhraPradesh Ongole India S.A. Engineering College Department of Ece Chennai India Sona College of Technology Department of Eee Salem India Qis College of Engineering and Technology Department of Cse AndhraPradesh Ongole India Vel Tech Multi Tech Dr. Rangarajan Dr Sakunthala Engineering College Department of Ece Chennai India

ISBN: (纸本)9798331543617

The DNCS (Cascaded CNN-ANN) enabled Food Recognition (FR) and Associated Calorie Level Estimation System presents a novel approach to food classification and calorie estimation using a cascaded neural network architecture. The system combines Convolutional neural networks (CNN) for food image recognition with artificial neural networks (ANN) for calorie estimation, leveraging fine-tuned pre-trained models such as InceptionV3 and EfficientNet. The system demonstrated high FR accuracy across various datasets, achieving a peak accuracy of 96.2% for common food items like burgers and pizzas, with an overall accuracy range between 89.2% and 96.2%. For calorie estimation, the system achieved a Mean Absolute Error (MAE) of less than 6.1%, demonstrating reliability in predicting calorie content based on food portion size. The use of image segmentation techniques like graph-cut and GrabCut further enhanced the accuracy of FR. The system was tested for real-time applications, achieving an average processing time of 350 to 450 milliseconds per image, making it suitable for integration into mobile applications. User feedback highlighted ease of use, speed, and accuracy as key strengths, with an overall satisfaction rating of 4.7 out of 5. These results suggest that the DNCS system offers an efficient solution for dietary management through real-time calorie tracking. © 2024 IEEE.

关键词： Convolutional neural networks

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