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TinySpiking: a lightweight and efficient python framework for unsupervised learning spiking neural networks

ENGINEERING RESEARCH EXPRESS 2025年第1期7卷 015217-015217页

作者： Liu, Xin Mo, Lingfei Tang, Mengting Southeast Univ Sch Instrument Sci & Engn FutureX LAB Nanjing Peoples R China

neural computation frameworks are essential for advancing computational neuroscience and artificial intelligence, offering a robust platform for simulating intricate brain-like processes and fostering the growth of intelligent systems. This study presents TinySpiking, a novel, lightweight, and energy-efficient Python framework designed for the simulation and learning of Spiking neural networks (SNNs). Unlike traditional frameworks, TinySpiking does not depend on third-party libraries, thereby reducing computational overhead and energy consumption. The framework's innovation is that it implements unsupervised learning through Spike-Time Dependent Plasticity (STDP), a biologically inspired learning rule that adjusts synaptic weights based on the precise timing of neuronal spikes. This mechanism is crucial for constructing complex neural architectures and effectively processing spatio-temporal data, which is vital for the dynamic and intricate demands of real-world data analysis. Demonstrating the practical utility of TinySpiking, we applied it to image reconstruction tasks using the MNIST and landmark datasets. The results not only validate the network's ability to autonomously identify and enhance key visual features without external supervision but also highlight its efficiency in learning from data, mirroring the adaptability of biological neural systems. In conclusion, TinySpiking's innovative, lightweight design and its proven effectiveness in unsupervised learning tasks make it a standout computational tool for the fields of computational neuroscience and machine learning. Its low time consumption, biological plausibility, and independence from third-party libraries position it as a compelling platform for future research and applications, promising to drive advancements in unsupervised learning and intelligent system development.

关键词： spiking neural networks STDP unsupervised learning image reconstruction computational neuroscience

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Patch- and Class-Wise Hyperspectral Knowledge Learning: A Composite Consistency-Constrained Self-Ensemble Framework for Change Detection

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Zhao, Xiaoyang Li, Siyao Liu, Xinyue Song, Chuanming Wang, Xianghai Liaoning Normal Univ Sch Geog Sci Dalian 116029 Peoples R China Liaoning Normal Univ Sch Comp & Artificial Intelligence Dalian 116029 Peoples R China Dalian Univ Sch Informat Engn Dalian 116622 Peoples R China

Obtaining fine land surface change information from multitemporal hyperspectral images (HSIs) is a key goal pursued in remote sensing image processing. Recently, HSI change detection (HSI-CD) methods based on convolutional neural networks (CNNs) have achieved surprising detection results. One of the reasons is the support of large-scale labeled samples for network learning. However, the existence of mixed pixels greatly increases the difficulty of HSI interpretation, resulting in accurate pixel-level labeling work with a heavy burden and unable to meet the needs of time-sensitive applications. For this reason, achieving stable and high-precision CD with fewer samples is a difficult issue in this field. To address the above problems, a composite consistency-constrained self-ensemble framework (C3SelF) for HSI-CD is proposed, to alleviate the problems of low detection accuracy and instability caused by small samples. The framework mainly comprises two lightweight networks with the same structure aiming at accelerating the model inference process and thus improving the processing timeliness. The composite learning mode implements patch-wise classification loss, class-wise consistency loss on labeled samples, and patch-wise consistency loss on unlabeled samples under a multilevel noise perturbation strategy, which improves the classification results and reduces the labeling cost. Moreover, to exploit the multidimensional features contained in HSIs, a lightweight selective spatial-spectral feature joint network (S3Net) is designed to overcome over-fitting, and to deeply mine the discriminative information in unlabeled samples, a new sample screening strategy is designed to ensure the stability of the network during training unlabeled samples. Extensive experiments prove that the proposed C3SelF outperforms the state-of-the-art (SOTA) methods at a sampling rate of 0.1%, reaching 93.44% Kappa and 97.26% overall accuracy (OA) on the Farmland dataset. The source code of the

关键词： Accuracy Training Feature extraction Hyperspectral imaging Transformers Data mining Vectors Semisupervised learning Convolutional neural networks Unsupervised learning Change detection (CD) convolutional neural networks (CNNs) deep learning hyperspectral image (HSI) processing semi-supervised learning

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Advancements in artificial Intelligence for Better image Analysis, Recognition, and Interpretation in the Field of image processing 2

Advancements in Artificial Intelligence for Better Image Ana...

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2nd IEEE Global conference on Communications and Information Technologies, GCCIT 2024

作者： Saravanan, V. Adaikkammai, A. Sasikala, G. Iyswariya, A. Babisha, A. Latha, M. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Nano Electronics Materials and Sensors Tamilnadu Chennai India R.M.D. Engineering College Department of Computer Science and Business Systems India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Avadi Chennai India R.M.K. Engineering College Department of Ece Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science India Srm Institute of Science and Technology Department of It India

ISBN: (纸本)9798350388916

The rapid advancement of artificial intelligence (AI) has brought about a significant revolution in image processing. This revolutionary technology has enabled the analysis, recognition, and interpretation of images in ways that were previously inconceivable. An investigation into the most recent developments in artificial intelligence techniques that have improved the efficiency and precision of image processing tasks is presented in this study. We survey the emergence of transformer architectures and the integration of deep learning models, namely Convolutional neural networks (CNNs), to explore how these technologies have transformed the understanding and application of images in several industries. An overview of existing image processing methods and the limits of those approaches is presented at the beginning of our research. This highlights the necessity of more advanced AI-driven solutions. After that, we examine the advancements that have been made in artificial intelligence, such as the incorporation of attention processes, the development of more effective CNN architectures, and the utilisation of generative adversarial networks (GANs) for the purpose of picture synthesis and augmentation. The purpose of this research is to investigate the impact that artificial intelligence has on particular applications of image processing, such as autonomous car navigation systems, facial recognition, medical imaging, and satellite images analysis. Specifically, we focus on the enhancements in diagnostic accuracy, environmental monitoring, security, and safety that arose as a result of the adoption of artificial intelligence in these sectors through the use of case studies. Furthermore, we discuss the issues that are associated with artificial intelligence in image processing. These challenges include the necessity for explainable AI models, the requirement for computational resources, and the protection of data privacy. A number of potential solutions to these problems

关键词： Convolutional neural networks

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DLSIA: Deep Learning for Scientific image Analysis

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JOURNAL OF APPLIED CRYSTALLOGRAPHY 2024年第2期57卷 392-402页

作者： Roberts, Eric J. Chavez, Tanny Hexemer, Alexander Zwart, Petrus H. Lawrence Berkeley Natl Lab Ctr Adv Math Energy Res Applicat Berkeley CA 94720 USA Lawrence Berkeley Natl Lab Mol Biophys & Integrated Bioimaging Div Berkeley CA 94720 USA Lawrence Berkeley Natl Lab Adv Light Source Berkeley CA 94720 USA Lawrence Berkeley Natl Lab Berkeley Synchrotron Infrared Struct Biol Program Berkeley CA 94720 USA

DLSIA (Deep Learning for Scientific image Analysis) is a Python-based machine learning library that empowers scientists and researchers across diverse scientific domains with a range of customizable convolutional neural network (CNN) architectures for a wide variety of tasks in image analysis to be used in downstream data processing. DLSIA features easy-to-use architectures, such as autoencoders, tunable U-Nets and parameter-lean mixed-scale dense networks (MSDNets). Additionally, this article introduces sparse mixed-scale networks (SMSNets), generated using random graphs, sparse connections and dilated convolutions connecting different length scales. For verification, several DLSIA-instantiated networks and training scripts are employed in multiple applications, including inpainting for X-ray scattering data using U-Nets and MSDNets, segmenting 3D fibers in X-ray tomographic reconstructions of concrete using an ensemble of SMSNets, and leveraging autoencoder latent spaces for data compression and clustering. As experimental data continue to grow in scale and complexity, DLSIA provides accessible CNN construction and abstracts CNN complexities, allowing scientists to tailor their machine learning approaches, accelerate discoveries, foster interdisciplinary collaboration and advance research in scientific image analysis.

关键词： deep learning convolutional neural networks X-ray scattering tomography data compression

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AutoFace: How to Obtain Mobile neural Network-Based Facial Feature Extractor in Less Than 10 Minutes?

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IEEE ACCESS 2024年 12卷 25106-25118页

作者： Savchenko, Andrey V. Sber AI Lab Moscow 117312 Russia HSE Univ Lab Algorithms & Technol Network Anal Nizhnii Novgorod 603155 Russia

Various mobile and edge devices have significantly different processing capabilities, making it challenging to develop a single universal architecture of a neural network to extract facial embeddings. In this paper, we study the automated machine learning techniques to design a neural network with the best performance on a concrete device. The novel procedure is proposed to choose the better subnetwork of the Supernet based on a genetic algorithm with a surrogate binary classifier to compare the expected accuracy of two subnetworks. The latter uses only encoding of a candidate subnetwork and does not require directly estimating its accuracy on a validation set. As a result, the most computationally efficient and accurate model in TensorFlow Lite format is obtained in less than 10 minutes for a specific device and latency constraint. An Android demo application has been developed to demonstrate the potential of designed neural networks. It is experimentally shown that the proposed approach is universal: it can extract deep embeddings for tasks such as face verification and facial expression recognition and for various types of devices, including smartphones and Raspberry Pi single-board mini-computers. Our models process one facial image in real-time and achieve much higher accuracy when compared to the best-known lightweight networks.

关键词： Face recognition Task analysis Training Computer architecture Feature extraction artificial neural networks Performance evaluation Mobile communication Genetic algorithms Search methods Evolutionary computation Emotion recognition Mobile applications edge devices genetic algorithms evolutionary search face verification facial expression recognition

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Topological Signal processing Over Generalized Cell Complexes

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IEEE TRANSACTIONS ON SIGNAL processing 2024年 72卷 687-700页

作者： Sardellitti, Stefania Barbarossa, Sergio Sapienza Univ Rome Dept Informat Engn Elect & Telecommun I-00184 Rome Italy

Topological Signal processing (TSP) over simplicial complexes is a framework that has been recently proposed, as a generalization of graph signal processing (GSP), to extend GSP to analyzing signals defined over sets of any order (i.e., not only vertices of a graph) and to capture multiway relations of any order among the data. However, simplicial complexes are required to satisfy the so-called inclusion property, according to which, if a set belongs to the complex, then all its subsets must also belong to the complex. In some applications, this is a severe limitation. To overcome this limit, in this paper we extend TSP to deal with signals defined over cell complexes and we also generalize the concept of cell complexes to include hollow cells. We show that, even if the algebraic formulation does not change significantly, the extension to the generalized cell complexes considerably broadens the number of applications. Most important, the new representation provides a much better trade-off between the complexity of the representation and its accuracy. In addition, we propose a method to infer the structure of the cell complex from data and we propose distributed filtering strategies, including a method to retrieve the sparsest representation of the harmonic component. We quantify the advantages of using cell complexes instead of simplicial complexes, in terms of the complexity/accuracy trade-off, for different applications such image segmentation and recovering of real flows measured on data traffic and transportation networks.

关键词： Signal processing Data mining Topology Finite impulse response filters artificial neural networks Transportation Partitioning algorithms Topological signal processing algebraic topology cell complexes simplicial complexes FIR filters topology inference

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Enhanced Medicare Fraud Detection Using Graph Convolutional networks 4

Enhanced Medicare Fraud Detection Using Graph Convolutional ...

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4th International conference on artificial Intelligence and Signal processing

作者： Rakesh, Molkam Shetty, Pushparaj D. Natl Inst Technol Karnataka Dept Math & Computat Sci Surathkal India

ISBN: (纸本)9798350350661;9798350350654

This paper explores the applications of Graph neural networks (GNN) for enhancing Medicare fraud detection. Graph convolutional network (GCN) is a type of graph neural network. Governments and insurance companies are continuously adapting new technologies to detect and prevent fraud activities and trying to minimize financial losses and improve services because every year they lose billions of dollars due to Medicare fraud. Machine learning algorithms fail to analyze the graph data structure but Graph neural networks are good at analyzing the complex relational data and they directly integrate with the learning process. Machine learning algorithms are facing scalability and generalization across diverse graphs. GNN works on graph data structure, using unique IDs as nodes in a graph, with edges illustrating their relationships. Graph neural networks is used to improve the accuracy and efficiency of fraud detection by learning the complex relational information obtained from providers, beneficiaries, and physicians. We created a graph database based on the healthcare provider dataset. In this graph database, two types of heterogeneous nodes are there that are beneficiary and medicare provider nodes. The connection between the beneficiary and medicare providers is a power edge and the connection between providers is a shared-physician edge. We developed a fraud detection model using both machine learning and graph neural networks. Our Graph convolutional Network (GCN) model performed well compared to the basic machine learning (Logistic regression) model. The complex relationships between provider and beneficiary, provider and physician helped to detect medicare fraud using our model.

关键词： Graph convolutional networks Graph neural networks Deep learning Machine learning Logistic regression Medicare fraud detection

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image Classification with Regularized Likelihood Estimation and Large Interval Discriminant Dimensionality Reduction

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2025年第2期41卷 385-400页

作者： Shen, Ying Zhu, Wei-hua Fu, Si-yong Wu, Guang-sheng Xinyu Univ Zhongxing Commun & Informat Coll Xinyu 338004 Peoples R China Xinyu Univ Sch Math & Comp Sci Xinyu 338004 Peoples R China

With the expansion of image application scale and the development of artificial intelligence industry, the demand for high precision and high robustness of image feature expression is gradually increasing. Convolutional neural networks (CNNS) have attracted much attention in the field of computer vision in recent years because of their excellent image feature learning ability. In order to further improve the accuracy and robustness of image classification, a second-order statistical convolutional neural network with multilayer fusion and cross-convolutional layer pooling are proposed in this study to obtain feature information of different dimensions in different convolutional layers. Due to the increase of computing and storage costs, an image classification method based on robust covariance of deep convolution features is proposed, and two regularized maximum likelihood estimators are introduced. Finally, the proposed method is tested by fine-grained experiments, image retrieval experiments and image classification experiments. The results show that MFBP (4,5) structure achieves 92.4%, 74.2% and 72.9% accuracy in finegrained image classification. Compared with MFBP (3,4,5), despite a slight decrease in accuracy, FPS performance is improved by more than 40%, demonstrating the advantages of this method in real-time applications. In addition, RMIE-VND and RMIE-IM methods improved by 4.1%, 8.4%, 7.8% and 7.1%, 12%, 9.8%, respectively, compared with LW methods on different data sets. The experimental results of image classification show that MFBP+RLME (VND+IM) has better performance and applicability than the current mainstream classification methods. This research is of great value for improving the efficiency and accuracy of image processing in big data environment, and provides a new research direction and practical tool for future real-time image application and computer vision system development.

关键词： image classification image dimensionality reduction convolutional neural net- work bilinear pooling regularized likelihood estimation

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Sparse Auto Encoder Based Deep neural Network for image processing Technology 2

Sparse Auto Encoder Based Deep Neural Network for Image Proc...

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2nd IEEE International conference on networks, Multimedia and Information Technology, NMITCON 2024

作者： Yang, Zufang Zeng, Heqiong Wuhan Technology and Business University College of Artificial Intelligence Wuhan China Wuhan Technology and Business University Institute of Information and Intelligent Engineering Applications Wuhan China

ISBN: (纸本)9798350372892

The image processing technology has been widely used in various field including video and image surveillance automatic capture the various application in image way representing. However, image process in different way and maximum amount of linear layer then easily fall into local optimum and occur overfitting in learn the data. This paper proposed Sparse Auto Encoder based Deep neural Network (SAE-DNN) for image classification is efficiently achieve high accuracy especially noisy data and dimensionality reduces. The SAE-DNN technique help to remove noise in data by learning the image pattern efficiently, which improve classification accuracy in noisy environments. Initial data is obtained from CIFAR-100 dataset and Pre-processing involves data augmentation, where transformations like translation and rotation are frequently applied during training phase of neural networks for image classification. The MobileNetV2 involved feature extraction, which efficiently extracts relevant features from training data. The proposed SAE-DNN technique are evaluated achieve better outcomes such as accuracy of 92.02% on CIFAR100 dataset existing technique like Fully Convolutional neural Network (FCNN) and Mobile Net V2 with Wavelet-Attention Convolutional neural Network (WA-CNN) are evaluated for proposed method. © 2024 IEEE.

关键词： Convolutional neural networks

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From methods to datasets: A survey on image-Caption Generators

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MULTIMEDIA TOOLS AND applications 2024年第9期83卷 28077-28123页

作者： Agarwal, Lakshita Verma, Bindu Delhi Technol Univ Dept Informat Technol Delhi 110042 India

image - Caption Generator is a popular artificial Intelligence research tool that works with image comprehension and language definition. Creating well-structured sentences requires a thorough understanding of language in a systematic and semantic way. Being able to describe the substance of an image using well-structured phrases is a difficult undertaking, but it can have a significant impact in terms of assisting visually impaired people in better understanding the images' content. image captions has gained a lot of attention as a study subject for various computer vision and natural language processing (NLP) applications. The goal of image captions is to create logical and accurate natural language phrases that describes an image. It relies on the caption model to see items and appropriately characterise their relationships. Intuitively, it is also difficult for a machine to see a typical image in the same way that humans do. It does, however, provide the foundation for intelligent exploration in deep learning. In this review paper, we will focus on the latest in-depth advanced captions techniques for image captioning. This paper highlights related methodologies and focuses on aspects that are crucial in computer recognition, as well as on the numerous strategies and procedures being developed for the development of image captions. It was also observed that Recurrent neural networks (RNNs) are used in the bulk of research works (45%), followed by attention-based models (30%), transformer-based models (15%) and other methods (10%). An overview of the approaches utilised in image captioning research is discussed in this paper. Furthermore, the benefits and drawbacks of these methodologies are explored, as well as the most regularly used data sets and evaluation processes in this sector are being studied.

关键词： image- Caption Generator Natural language processing Computer vision Intelligent exploration Deep learning

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