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A Survey on Graph neural networks and its applications in Various Domains

SN Computer Science 2025年第1期6卷 1-12页

作者： Murgod, Tejaswini R. Reddy, P. Srihith Gaddam, Shamitha Sundaram, S. Meenakshi Anitha, C. Department of Artificial Intelligence & Machine Learning BNM Institute of Technology Karnataka Bengaluru India Department of Computer Science and Engineering NITTE Meenakshi Institute of Technology Karnataka Bengaluru India

Graph neural networks (GNNs) are neural models that use message transmission between graph nodes to represent the dependency of graphs. Variants of Graph neural networks (GNNs), such as graph recurrent networks (GRN), graph attention networks (GAT), and graph convolutional networks (GCN), have shown remarkable results on a variety of deep learning tasks in recent years. In this study, we offer a generic design pipeline for GNN models, go over the variations of each part, classify the applications in an organized manner, and suggest four outstanding research issues. Dealing with graph data, which provides extensive connection information among pieces, is necessary for many learning tasks. A model that learns from graph inputs is required for modelling physics systems, learning molecular fingerprints, predicting protein interfaces, and identifying illnesses. Reasoning on extracted structures (such as the dependency trees of sentences and the scene graphs of photos) is an important research issue that also requires graph reasoning models in other domains, such as learning from non-structural data like texts and images. Graph neural networks (GNNs) are primarily designed for dealing with graph-structured data, where relationships between entities are modeled as edges in a graph. While GNNs are not traditionally applied to image classification problems, researchers have explored ways to leverage graph-based structures to enhance the performance of Convolutional neural networks (CNNs) in certain scenario. GNN have been increasingly applied to Natural Language processing (NLP) tasks, leveraging their ability to model structured data and capture relationships between elements in a graph. GNN are also applied for traffic related problems particularly in modeling and optimizing traffic flow, analyzing transportation networks, and addressing congestion issues. GNN can be used for traffic flow prediction, dynamic routing & navigation, Anomaly detection, public transport network

关键词： Computer vision Graph neural networks Intrusion detection Natural language processing neural networks Traffic control

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Thor: A Non-Speculative Value Dependent Timing Side Channel Attack Exploiting Intel AMX

IEEE COMPUTER ARCHITECTURE LETTERS

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IEEE COMPUTER ARCHITECTURE LETTERS 2025年第1期24卷 69-72页

作者： Dizani, Farshad Ghanbari, Azam Kalyanapu, Joshua Asher, Darsh Ajorpaz, Samira Mirbagher North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27606 USA

The rise of on-chip accelerators signifies a major shift in computing, driven by the growing demands of artificial intelligence (AI) and specialized applications. These accelerators have gained popularity due to their ability to substantially boost performance, cut energy usage, lower total cost of ownership (TCO), and promote sustainability. Intel's Advanced Matrix Extensions (AMX) is one such on-chip accelerator, specifically designed for handling tasks involving large matrix multiplications commonly used in machine learning (ML) models, image processing, and other computational-heavy operations. In this paper, we introduce a novel value-dependent timing side-channel vulnerability in Intel AMX. By exploiting this weakness, we demonstrate a software-based, value-dependent timing side-channel attack capable of inferring the sparsity of neural network weights without requiring any knowledge of the confidence score, privileged access or physical proximity. Our attack method can fully recover the sparsity of weights assigned to 64 input elements within 50 minutes, which is 631% faster than the maximum leakage rate achieved in the Hertzbleed attack.

关键词： Timing artificial neural networks Vectors Servers Program processors Microarchitecture Computational modeling Time-frequency analysis Side-channel attacks Privacy Intel advanced matrix extensions (AMX) value-dependent timing side-channel ML privacy NN sparsity

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Machine learning algorithms for manufacturing quality assurance: A systematic review of performance metrics and applications

ARRAY

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ARRAY 2025年 26卷

作者： Kausik, Ashfakul Karim Bin Rashid, Adib Baki, Ramisha Fariha Maktum, Md Mifthahul Jannat Mil Inst Sci & Technol MIST Ind & Prod Engn Dept Dhaka Bangladesh Mil Inst Sci & Technol MIST Comp Sci & Engn Dept Dhaka Bangladesh Mil Inst Sci & Technol MIST Naval Architecture & Marine Engn Dept Dhaka Bangladesh

Adopting Machine Learning (ML) in manufacturing quality assurance (QA) has accelerated with Industry 4.0, enabling automated defect detection, predictive maintenance, and real-time process optimization. However, selecting the most effective ML model remains challenging due to performance variability, scalability constraints, and inconsistent evaluation metrics across manufacturing sectors. This systematic review analyzes over 300 peerreviewed studies over the last two decades (mostly analyzing the recent works) to evaluate the effectiveness of widely used ML algorithms-artificial neural networks (ANNs), Support Vector Machines (SVMs), Random Forests (RFs), Decision Trees (DTs), and K-Nearest Neighbors (KNN)-in QA applications. Performance metrics include accuracy, precision, speed, recall, computational efficiency, scalability, and real-time processing capabilities. Findings reveal that ANNs outperform other models in image-based defect detection, while SVMs and RFs excel in predictive maintenance and process parameter optimization. DTs provide better interpretability for process control, and KNN is effective for small-scale QA implementations. In specific case scenarios, RF models showed particular strength in handling high-dimensional sensor data in fault detection in manufacturing quality assurance operations. The study presents a comparative assessment framework, guiding algorithm selection based on industry-specific requirements and operational constraints. This review provides the latest implementation of ML in QA along with quantitative evidence on which algorithm offers the most optimization in specific industrial settings, which would help in algorithm selection in manufacturing quality assurance in future for both researchers and industrial experts. Also, it offers an overview of the major and minor algorithms based on their performance metrics.

关键词： Machine learning Quality assurance artificial intelligence artificial neural networks Predictive analytics Process optimization Decision trees Object detection KNN SVM Clustering algorithms

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Large Language Models (such as ChatGPT) as Tools for Machine Learning-Based Data Insights in Analytical Chemistry

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ANALYTICAL CHEMISTRY 2025年第13期97卷 6956-6961页

作者： Duponchel, Ludovic de Oliveira, Rodrigo Rocha Motto-Ros, Vincent Univ Lille CNRS UMR 8516 LASIRE Lab Spect Interact Re?activite? & Environm F-59000 Lille France Univ Barcelona Fac Chem Chemometr Grp Barcelona 08028 Spain Univ Claude Bernard Lyon 1 Inst Lumiere Matiere CNRS UMR 5306 F-69622 Villeurbanne France

artificial intelligence (AI), especially through the development of deep learning techniques like convolutional neural networks (CNNs), has revolutionized numerous fields. CNNs, introduced by Yann LeCun in the 1990s (Hubbard, W.;Jackel, L. D. Backpropagation Applied to Handwritten Zip Code Recognition. neural Comput. 1989, 1 (4), 541- 551. https://***/10.1162/neco.1989.1.4.541), have found applications in healthcare for medical diagnostics, autonomous vehicles in transportation, stock market prediction in finance, and image recognition in computer vision to name just a few. Similarly, in analytical chemistry, deep learning has enhanced data analysis from techniques like MS spectrometry, NMR, fluorescence spectroscopy, and chromatography. Another AI branch, Natural Language processing (NLP), has surged recently with the advent of Large Language Models (LLMs), such as OpenAI's ChatGPT. This paper demonstrates the application of an LLM via a smartphone to conduct multivariate data analyses, in an interactive conversational manner, of a hyper-spectral imaging data set from laser-induced breakdown spectroscopy (LIBS). We demonstrate the potential of LLMs to process and analyze data sets, which automatically generate and execute code in response to user queries, and anticipate their growing role in the future of analytical chemistry.

关键词： Convolutional neural networks

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Efficient Deep Learning Infrastructures for Embedded Computing Systems: A Comprehensive Survey and Future Envision

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ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS 2025年第1期24卷 1-100页

作者： Luo, Xiangzhong Liu, Di Kong, Hao Huai, Shuo Chen, Hui Xiong, Guochu Liu, Weichen Nanyang Technol Univ Nanyang Singapore Norwegian Univ Sci & Technol Dept Comp Sci Trondheim Norway Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore

Deep neural networks (DNNs) have recently achieved impressive success across a wide range of real-world vision and language processing tasks, spanning from image classification to many other downstream vision tasks, such as object detection, tracking, and segmentation. However, previous well-established DNNs, despite being able to maintain superior accuracy, have also been evolving to be deeper and wider and thus inevitably necessitate prohibitive computational resources for both training and inference. This trend further enlarges the computational gap between computation-intensive DNNs and resource-constrained embedded computing systems, making it challenging to deploy powerful DNNs in real-world embedded computing systems towards ubiquitous embedded intelligence. To alleviate this computational gap and enable ubiquitous embedded intelligence, we focus in this survey on discussing recent efficient deep learning infrastructures for embedded computing systems, spanning from training to inference, from manual to automated, from convolutional neural networks to transformers, from transformers to vision transformers, from vision models to large language models, from software to hardware, and from algorithms to applications. Specifically, we discuss recent efficient deep learning infrastructures for embedded computing systems from the lens of (1) efficient manual network design for embedded computing systems, (2) efficient automated network design for embedded computing systems, (3) efficient network compression for embedded computing systems, (4) efficient on-device learning for embedded computing systems, (5) efficient large language models for embedded computing systems, (6) efficient deep learning software and hardware for embedded computing systems, and (7) efficient intelligent applications for embedded computing systems. We also envision promising future directions and trends, which have the potential to deliver more ubiquitous embedded intelligence. We believe th

关键词： Embedded computing systems embedded intelligence artificial intelli- gence efficient deep learning algorithms efficient network design efficient neural architecture search effi- cient model compression efficient on-device learning efficient large language models efficient deep learning software and hardware intelligent embedded applications

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IoT-Based Multisensors Fusion for Activity Recognition via Key Features and Hybrid Transfer Learning

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IEEE ACCESS 2025年 13卷 14727-14742页

作者： Jalal, Ahmad Khan, Danyal Sadiq, Touseef Alotaibi, Moneerah Alotaibi, Sultan Refa Aljuaid, Hanan Rahman, Hameedur Air Univ Fac Comp & AI Islamabad 44000 Pakistan Korea Univ Coll Informat Dept Comp Sci & Engn Seoul 02841 South Korea Univ Agder Ctr Artificial Intelligence Res Dept Informat & Commun Technol N-4879 Grimstad Norway Shaqra Univ Dept Comp Sci Coll Sci & Humanities Dawadmi Shaqra Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci POB 84428 Riyadh 11671 Saudi Arabia

Human activity recognition (HAR) has attracted significant attention in various fields, including healthcare, smart homes, and human-computer interaction. Accurate HAR can enhance user experience, provide critical health insights, and enable sophisticated context-aware applications. This paper presents a comprehensive system for HAR utilizing both RGB videos and inertial measurement unit (IMU) sensor data. The system employs a multi-stage processing pipeline involving preprocessing, segmentation, feature extraction, and classification to achieve high accuracy in activity recognition. In the preprocessing stage, frames are extracted from RGB videos, and IMU sensor data undergoes denoising. The segmentation phase applies Naive Bayes segmentation for video frames and Hamming windows for sensor data to prepare them for feature extraction. Key features are extracted using techniques such as ORB (Oriented FAST and Rotated BRIEF), MSER (Maximally Stable Extremal Regions), DFT (Discrete Fourier Transform), and KAZE for image data, and LPCC (Linear Predictive Cepstral Coefficients), PSD (Power Spectral Density), AR Coefficient, and entropy for sensor data. Feature fusion is performed using Linear Discriminant Analysis (LDA) to create a unified feature set, which is then classified using ResNet50 (Residual neural Network) to recognize activities such as using a smartphone, cooking, and reading a newspaper. The system was evaluated using the LARa and HWU-USP datasets, achieving classification accuracies of 92% and 93%, respectively. These results demonstrate the robustness and effectiveness of the proposed HAR system in diverse scenarios.

关键词： Feature extraction Videos image segmentation Human activity recognition Accuracy Noise Convolutional neural networks Data mining Residual neural networks Noise reduction Human activity recognition (HAR) RGB videos IMU sensor data Naive Bayes segmentation recurrent neural network (RNN) Shapley additive explanations (SHAP)

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Satellite image classification with neural quantum kernels

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MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2025年第1期6卷

作者： Rodriguez-Grasa, Pablo Farzan-Rodriguez, Robert Novelli, Gabriele Ban, Yue Sanz, Mikel Univ Basque Country UPV EHU Dept Phys Chem Apartado 644 Bilbao 48080 Spain Univ Basque Country UPV EHU Quantum Ctr Apartado 644 Bilbao 48080 Spain TECNALIA Basque Res & Technol Alliance BRTA Derio 48160 Spain GMV Dept Artificial Intelligence & Big Data Isaac Newton 11 Madrid 28760 Spain Univ Carlos III Madrid Dept Fis Avda Univ 30 Leganes 28911 Spain CSIC Inst Ciencia Mat Madrid E-28049 Madrid Spain IKERBASQUE Basque Fdn Sci Plaza Euskadi 5 Bilbao 48009 Spain Basque Ctr Appl Math BCAM Alameda Mazarredo 14 Bilbao 48009 Spain

Achieving practical applications of quantum machine learning (QML) for real-world scenarios remains challenging despite significant theoretical progress. This paper proposes a novel approach for classifying satellite images, a task of particular relevance to the earth observation industry, using QML techniques. Specifically, we focus on classifying images that contain solar panels, addressing a complex real-world classification problem. Our approach begins with classical pre-processing to reduce the dimensionality of the satellite image dataset. We then apply neural quantum kernels-quantum kernels derived from trained quantum neural networks-for classification. We evaluate several strategies within this framework, demonstrating results that are competitive with the best classical methods. Key findings include the robustness of or results and their scalability, with successful performance achieved up to 8 qubits.

关键词： quantum neural networks quantum kernels quantum machine learning satelite image classification real dataset

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Solution-processed self-assembling charge-transfer cocrystal/TIPS-pentacene heterojunctions for artificial synapses

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JOURNAL OF MATERIALS CHEMISTRY C 2025年

作者： Jin, Tao Li, Wenju Zhang, Yongyi Wang, Ruiheng Wang, Shuai Pan, Chen Wang, Guan Zhang, Jiacheng Yao, Lei Zhang, Jing Zhang, Qichun Nanjing Univ Posts & Telecommun State Key Lab Flexible Elect LoFE 9 Wenyuan Rd Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Inst Adv Mat IAM 9 Wenyuan Rd Nanjing 210023 Peoples R China City Univ Hong Kong Dept Mat Sci & Engn Hong Kong Peoples R China

artificial synapses have gained considerable attention in modeling artificial visual systems. A facile and efficient p-n heterojunction, acting as the essential component, plays a critical role in configuration optimization and processing capabilities. Herein, we introduced a novel charge-transfer cocrystal that consisted of 5,8-dihydroindolo[2,3-c]carbazole (DDCB) and 7,7,8,8-tetracyanoquinodimethane (TCNQ) via the solution-process, which demonstrated good crystallinity and typical n-type charge transport characteristics. To realize good charge storage ability, crystalline p-n heterojunctions with a phase-separated heterostructure were one-step prepared by drop-casting TIPS-pentacene and DDCB/TCNQ solutions onto the substrate. The as-prepared artificial synaptic devices exhibited remarkable paired-pulse facilitation (PPF), spike-duration dependent plasticity, spike-intensity dependent plasticity, and learning-experience behaviors similar to those of biological synapses in response to light pulses. Furthermore, based on the light potentiation and electrical depression modulation properties of these synaptic devices, we constructed artificial neural networks for image recognition, achieving a validation accuracy of over 97% on the modified national institute of standards and technology (MNIST) dataset. This work provides a new simple approach to establishing p-n heterojunction based artificial synapses for applications in light information processing, artificial vision, and memory simulation.

关键词： image coding

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Frequency-Domain Super-Resolution With Reconstruction Using Compressed Representation (FDSR-RCR) Algorithm for Remote Sensing Satellite images

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Miao, Jiaqing Zhou, Xiaobing Li, Guibing Li, Gaoping Zeng, Li Liu, Xiaoguang Tan, Ying Southwest Minzu Univ Sch Math Chengdu 610054 Peoples R China Montana Technol Univ Geol Engn Dept Butte MT 59701 USA Southwest Minzu Univ Coll Comp Sci & Artificial Intelligence Key Lab Comp Syst State Ethn Affairs Commiss Chengdu 610041 Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu 611756 Peoples R China

In remote sensing image processing for Earth and environmental applications, super-resolution (SR) is a crucial technique for enhancing the resolution of low-resolution (LR) images. In this study, we proposed a novel algorithm of frequency-domain super-resolution with reconstruction from compressed representation. The algorithm follows a multistep procedure: first, an LR image in the space domain is transformed to the frequency domain using a Fourier transform. The frequency-domain representation is then expanded to the desired size (number of pixels) of a high-resolution (HR) image. This expanded frequency-domain image is subsequently inverse Fourier transformed back to the spatial domain, yielding an initial HR image. A final HR image is then reconstructed from the initial HR image using a low-rank regularization model that incorporates a nonlocal smoothed rank function (SRF). We evaluated the performance of the new algorithm by comparing the reconstructed HR images with those generated by several commonly used SR algorithms, including: 1) bicubic interpolation;2) sparse representation;3) adaptive sparse domain selection and adaptive regularization;4) fuzzy-rule-based (FRB) algorithm;5) SR convolutional neural networks (SRCNNs);6) fast SR convolutional neural networks (FSRCNNs);7) practical degradation model for deep blind image SR;8) the frequency separation for real-world SR (FSSR);and 9) the enhanced SR generative adversarial networks (ESRGANs). The algorithms were tested on Landsat-8 and Moderate Resolution Imaging Spectroradiometer (MODIS) multiresolution images over various locations, as well as on images with artificially added noise to assess the robustness of each algorithm. Results show that: 1) the proposed new algorithm outperforms the others in terms of the peak signal-to-noise ratio, structure similarity, and root-mean-square error and 2) it effectively suppresses noise during HR reconstruction from noisy low-resolution (LR) images, overcoming a key

关键词： Compressed representation frequency domain low-rank regularization model low-rank regularization model remote sensing images remote sensing images smoothed rank function (SRF) smoothed rank function (SRF) super-resolution (SR) super-resolution (SR) super-resolution (SR)

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Automated Extraction and Classification of Fossil Whorl Patterns Using Deep Learning: A Novel Approach to Paleontological image Analysis

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TRAITEMENT DU SIGNAL 2025年第1期42卷 67-77页

作者： Bi, Ruifang Shi, Hualin Shanxi Inst Technol Dept Art & Design Sci Yangquan 045000 Peoples R China Huayuan Int Land Port Grp Co Ltd Enterprise Technol Ctr Taiyuan 030000 Peoples R China Key Lab Intelligent Logist Shanxi Prov Taiyuan 030000 Peoples R China

The rapid advancement of artificial Intelligence (AI) has led to the displacement of traditional fossil pattern recognition methods in paleontological studies, particularly through the application of image processing technologies. This study focuses on the fossilized whorls of ancient organisms from the Yangquan region, employing state-of-the-art AI-driven techniques to identify and extract distinctive features from these fossils for automated pattern recognition. Existing paleontological databases of whorl fossils were reviewed, and a deep learning model was developed using convolutional neural networks (CNNs) to facilitate the extraction and classification of fossil whorl patterns. The model incorporates multi-level feature abstraction through various image preprocessing techniques to enhance both the accuracy and robustness of the recognition process. A transfer learning strategy based on CNNs was introduced, allowing for rapid adaptation to new fossil patterns despite limited sample sizes. Furthermore, an improved feature extraction algorithm leveraging Scale-Invariant Feature Transform (SIFT) for feature point matching was implemented, significantly accelerating the speed and accuracy of the feature extraction process. In the experimental phase, over 300 images of fossilized whorls were utilized for model training and validation, achieving a recognition accuracy exceeding 95%, Awhich represents an improvement of nearly 30% over traditional manual methods. The generalization ability of the model was also evaluated, confirming its stability and reliability across diverse fossil data sets. This research underscores the transformative potential of AI-based image processing technologies in the extraction and analysis of paleontological patterns, offering new tools for the study of Yangquan fossils while also contributing to broader applications in cultural heritage preservation and scientific education. This work provides a solid foundation for the further integrati

关键词： paleontological whorl fossils AI image processing CNN feature point extraction pattern recognition

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