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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Wang, Zihan Song, Siyang Luo, Cheng Zhou, Yuzhi Wu, Shiling Xie, Weicheng Shen, Linlin Shenzhen University Computer Vision Institute School of Computer Science & Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China University of Leicester School of Computing and Mathematical Sciences United Kingdom

ISBN: (纸本)9798350302493

This paper presents our Facial Action Units (AUs) detection submission to the fifth Affective Behavior Analysis in-the-wild Competition (ABAW). Our approach consists of three main modules: (i) a pre-trained facial representation encoder which produce a strong facial representation from each input face image in the input sequence;(ii) an AU-specific feature generator that specifically learns a set of AU features from each facial representation;and (iii) a spatio-temporal graph learning module that constructs a spatio-temporal graph representation. This graph representation describes AUs contained in all frames and predicts the occurrence of each AU based on both the modeled spatial information within the corresponding face and the learned temporal dynamics among frames. The experimental results show that our approach outperformed the baseline and the spatio-temporal graph representation learning allows our model to generate the best results among all ablated systems. Our model ranks at the 4th place in the AU recognition track at the 5th ABAW Competition. Our code is publicly available at https://***/wzh125/ABAW-5. © 2023 IEEE.

关键词： Graphic methods

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Clause-level Relationship-aware Math Word Problems Solver

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Machine Intelligence Research 2022年第5期19卷 425-438页

作者： Chang-Yang Wu Xin Lin Zhen-Ya Huang Yu Yin Jia-Yu Liu Qi Liu Gang Zhou Anhui Province Key Laboratory of Big Data Analysis and Application School of Data ScienceUniversity of Science and Technology of ChinaHefei 230026China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230088China Laboratory of Mathematical Engineering and Advanced Computing Information Engineering UniversityZhengzhou 450001China

Automatically solving math word problems,which involves comprehension,cognition,and reasoning,is a crucial issue in artificial intelligence *** math word problem solvers mainly work on word-level relationship extraction and the generation of expression solutions while lacking consideration of the clause-level *** this end,inspired by the theory of two levels of process in comprehension,we propose a novel clause-level relationship-aware math solver(CLRSolver)to mimic the process of human comprehension from lower level to higher ***,in the lower-level processes,we split problems into clauses according to their natural division and learn their *** the higher-level processes,following human′s multi-view understanding of clause-level relationships,we first apply a CNN-based module to learn the dependency relationships between clauses from word relevance in a local ***,we propose two novel relationship-aware mechanisms to learn dependency relationships from the clause semantics in a global ***,we enhance the representation of clauses based on the learned clause-level dependency *** expression generation,we develop a tree-based decoder to generate the mathematical *** conduct extensive experiments on two datasets,where the results demonstrate the superiority of our framework.

关键词： Artificial intelligence(AI) artificial neural network(ANN) computational mathematics machine intelligence machine learning

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Innovative Video Classification Method based on Deep Learning Approach

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Recent Patents on Engineering 2025年第2期19卷 95-107页

作者： Hemamalini, V. Jayasutha, D. Vinothini, V.R. Devi, R. Manjula Kumar, A. Anitha, E. Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Arjun College of Technology Coimbatore Thamaraikulam India Department of Mathematics Bannari Amman Institute of Technology Sathyamangalam Erode638401 India Department of Computer Science and Design Kongu Engineering College Perundurai Erode638060 India Department of Civil Engineering University Institute of Engineering & Technology Maharshi Dayanand University Rohtak124001 India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore641202 India

Background: The automated classification of videos through artificial neural networks is addressed in this work. To explore the concepts and measure the results, the data set UCF101 is used, consisting of video clips taken from YouTube to recognize actions. The study is carried out with the authors' resources to determine the feasibility of independent research in the area. Methods: This work was developed in the Python programming language using the Keras library with Tensorflow as the back-end. The objective is to develop a network that presents performance compatible with the state of the art in terms of classifying videos according to the actions taken. Results: Given the hardware limitations, there is considerable distance between the implementation possibilities in this work and what is known as the state-of-the-art. Conclusion: Throughout the work, some aspects in which this limitation influenced the development are presented, but it is shown that this realization is feasible and that obtaining expressive results is possible 98.6% accuracy is obtained in the UCF101 data set, compared to the 98 percentage points of the best result ever reported, using, however, considerably fewer resources. In addition, the importance of transfer learning in achieving expressive results as well as the different performances of each architecture are reviewed. Thus, this work may open doors to carry patent-based outcomes. © 2025 Bentham science Publishers.

关键词： Convolutional neural networks

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Real-Time Compressed Sensing for Joint Hyperspectral Image Transmission and Restoration for CubeSat

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IEEE Transactions on Geoscience and Remote Sensing 2024年 62卷 1-16页

作者： Hsu, Chih-Chung Jian, Chih-Yu Tu, Eng-Shen Lee, Chia-Ming Chen, Guan-Lin National Cheng Kung University Institute of Data Science The Department of Statistics The Miin Wu School of Computing Tainan70101 Taiwan National Cheng Kung University Department of Computer Science Tainan70101 Taiwan MobileDrive Artificial Intelligence R&d Department New Taipei City23679 Taiwan

This article addresses the challenges associated with hyperspectral image (HSI) reconstruction from miniaturized satellites, which often suffer from stripe effects and are computationally resource-limited. We propose a real-time compressed sensing (RTCS) network designed to be lightweight and require only relatively few training samples for efficient and robust HSI reconstruction in the presence of the stripe effect and under noisy transmission conditions. The RTCS network features a simplified architecture that reduces the required training samples and allows for easy implementation on integer-8-based encoders, facilitating rapid compressed sensing for stripe-like HSI, which exactly matches the moderate design of miniaturized satellites on push broom scanning mechanism. This contrasts optimization-based models that demand high-precision floating-point operations, making them difficult to deploy on edge devices. Our encoder employs an integer-8-compatible linear projection for stripe-like HSI data transmission, ensuring RTCS. Furthermore, based on the novel two-streamed architecture, an efficient HSI restoration decoder is proposed for the receiver side, allowing for edge-device reconstruction without needing a sophisticated central server. This is particularly crucial as an increasing number of miniaturized satellites necessitates significant computing resources on the ground station. Extensive experiments validate the superior performance of our approach, offering new and vital capabilities for existing miniaturized satellite systems. © 1980-2012 IEEE.

关键词： Real time systems

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Basketball Win Percentage Prediction using Ensemble-based Machine Learning 6

Basketball Win Percentage Prediction using Ensemble-based Ma...

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6th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2022

作者： Sikka, Dhruv Rajeswari, D. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur603203 India School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur603203 India

ISBN: (纸本)9781665482714

Basketball is a prominent team sport played on a rectangular court between two teams of five players each. The goal is to shoot the ball through the defender's hoop, which is high on a backboard at each end of the court, while stopping the other team from shooting through its own hoop. Predicting how sports will turn out is an interesting sports analytics problem because it gives valuable data for running the sports market. In this work, data from NBA (National Basketball Association) basketball teams is analyzed, and a machine learning model is made that uses advanced statistical measures and performance data to evaluate and predict the winning percentage of different teams. Voting Regression is used to put together algorithms like Random Forest, Decision Trees, Linear Regression, Gaussian Regression, and Gradient Boosting into a single unit that can predict a team's win percentage over the course of a season with 93.3% accuracy. © 2022 IEEE.

关键词： Forecasting

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An Efficient Capsule Based Neural Network for Detection of Grape Plant Leaf Disease and Disease Stages 5

An Efficient Capsule Based Neural Network for Detection of G...

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5th International Conference on Emerging Systems and Intelligent computing, ESIC 2025

作者： Vasudevan, N. Praveenkumar, S. Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Tamilnadu kattankulathur India

ISBN: (纸本)9798331522100

Plant infections destroy and impair the quality of crops, and the pesticides used to treat them pollute the soil, rendering it unfit for planting. Image processing and deep learning technologies may be used to identify disease spots on grape leaves. The detection's precision and efficiency, on the other hand, remain problems. In this paper, the performance of a few well-known CNN models implemented utilizing transfer learning, such as ResNet18, VGG16, and GoogleNet were compared with capsule network. The local entity characteristics are first found by the Capsule layers. For the purpose of collecting aggregate data like "disease type"and "disease stage,"the capsule layer makes use of geographic information and the frequency of local-level characteristics. Colored pictures of strong and unhealthy leaves were taken from the public dataset and then used to train the models. The proposed work focused on the novelty concept of identifying the stages of diseases. The Capsule based classification technique can give competitive benefits with an effective classification of accuracy and stages of leaf disease related to other models. © 2025 IEEE.

关键词： Agricultural engineering classifications diseases image processing neural networks

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Exploring the Potential of GPT-4 in Automated Mentoring for Programming Courses 2023

Exploring the Potential of GPT-4 in Automated Mentoring for ...

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2nd ACM Conference on Global computing Education, CompEd 2023

作者： Balse, Rishabh Prasad, Prajish Warriem, Jayakrishnan Madathil Bs Programme in Data Science and Applications Indian Institute of Technology Madras Tamil Nadu Chennai India School of Computing and Data Sciences Flame University Maharashtra Pune India Indian Institute of Technology Madras Tamil Nadu Chennai India

ISBN: (纸本)9798400703744

This research proposes an AI-assisted mentoring system for programming education, leveraging the advanced capabilities of OpenAI's GPT-4. We aim to validate students' pseudocode or algorithmic approaches to Python programming problems within the context of a Tier-1 institution in India, where the high student-to-mentor ratio presents unique challenges. The proposed system aspires to alleviate the pressures of the current mentoring system, providing a more accessible, responsive, and effective educational support system. © 2023 Owner/Author.

关键词： Students

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Augmenting the Proficiencies of GANs Through Modernizing Agriculture and Healthcare

Augmenting the Proficiencies of GANs Through Modernizing Agr...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： M. Jothi T. Veeramakali Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Nowadays, generative models with deep learning have a focus and give enormous success in various real-time experience domains. Deep learning (DL) is a subdivision of machine learning that is inspired by the structure and function of human brain activity, known as artificial neural networks. It is a data-driven technique that achieves the best accuracy while using a large number of input samples. Generative Adversarial Networks (GANs) are a robust class of neural networks used for unsupervised learning under DL categories. GAN is one of the most influential architectures demonstrating remarkable success in generating high-quality data. The reusable feature learning process from a huge number of unlabelled data paves the way for research. This survey article discusses an overview of the GAN model, such as the original GAN, Conditional GANs, and resolution GANs, along with their specific adaptations for various tasks. Additionally, we examine the situations faced by Generative Adversarial Networks depending on various applications such as image and video generation, medical image analysis, text-to-image synthesis, agriculture, and creative industries such as music and art. This survey explores diverse applications of recently developed frameworks and benchmark datasets used for development, along with their architecture and metric evaluations.

关键词： Deep learning Surveys Measurement Image analysis Accuracy Generative adversarial networks Agriculture Real-time systems Medical diagnostic imaging Farming

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Targeted View-Invariant Adversarial Perturbations for 3D Object Recognition

arXiv

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arXiv 2024年

作者： Green, Christian Ergezer, Mehmet Zeybey, Abdurrahman School of Computing and Data Science Wentworth Institute of Technology BostonMA United States

Adversarial attacks pose significant challenges in 3D object recognition, especially in scenarios involving multi-view analysis where objects can be observed from varying angles. This paper introduces View-Invariant Adversarial Perturbations (VIAP), a novel method for crafting robust adversarial examples that remain effective across multiple viewpoints. Unlike traditional methods, VIAP enables targeted attacks capable of manipulating recognition systems to classify objects as specific, pre-determined labels, all while using a single universal perturbation. Leveraging a dataset of 1,210 images across 121 diverse rendered 3D objects, we demonstrate the effectiveness of VIAP in both targeted and untargeted settings. Our untargeted perturbations successfully generate a singular adversarial noise robust to 3D transformations, while targeted attacks achieve exceptional results, with top-1 accuracies exceeding 95% across various epsilon values. These findings highlight VIAP’s potential for real-world applications, such as testing the robustness of 3D recognition systems. The proposed method sets a new benchmark for view-invariant adversarial robustness, advancing the field of adversarial machine learning for 3D object recognition. © 2024, CC BY.

关键词： Adversarial machine learning

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Automated face recognition using deep learning technique and center symmetric multivariant local binary pattern

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Neural computing and Applications 2025年第1期37卷 263-281页

作者： Sekhar, J.C. Josephson, P. Joel Chinnasamy, A. Maheswari, M. Sankar, S. Kalangi, Ruth Ramya Department of Computer Science and Engineering NRI Institute of Technology Andhra Pradesh Guntur India Department of Electronics and Communication Engineering Malla Reddy Engineering College Telangana Hyderabad India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vijayawada India

Researchers have recently created several deep learning strategies for various tasks, and facial recognition has made remarkable progress in employing these techniques. Face recognition is a noncontact, nonobligatory, acceptable, and harmonious biometric recognition method with a promising national and social security future. The purpose of this paper is to improve the existing face recognition algorithm, investigate extensive data-driven face recognition methods, and propose a unique automated face recognition methodology based on generative adversarial networks (GANs) and the center symmetric multivariable local binary pattern (CS-MLBP). To begin, this paper employs the center symmetric multivariant local binary pattern (CS-MLBP) algorithm to extract the texture features of the face, addressing the issue that C2DPCA (column-based two-dimensional principle component analysis) does an excellent job of removing the global characteristics of the face but struggles to process the local features of the face under large samples. The extracted texture features are combined with the international features retrieved using C2DPCA to generate a multifeatured face. The proposed method, GAN-CS-MLBP, syndicates the power of GAN with the robustness of CS-MLBP, resulting in an accurate and efficient face recognition system. Deep learning algorithms, mainly neural networks, automatically extract discriminative properties from facial images. The learned features capture low-level information and high-level meanings, permitting the model to distinguish among dissimilar persons more successfully. To assess the proposed technique’s GAN-CS-MLBP performance, extensive experiments are performed on benchmark face recognition datasets such as LFW, YTF, and CASIA-WebFace. Giving to the findings, our method exceeds state-of-the-art facial recognition systems in terms of recognition accuracy and resilience. The proposed automatic face recognition system GAN-CS-MLBP provides a solid basis for a

关键词： Principal component analysis

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