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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Sivakumar, T.B. Malakar, Antara Lekshmi, S. Shailaja, G. Kalaivani, E. Babu, Dhananjaya K. Department of CSE School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai India Department of Computer Science & Engineering The Assam Royal Global University Guwahati India Basic Science and Humanities Department Mangalam College of Engineering Kottayam Ettumanoor India Department IT CVR College of Engineering Hyderabad India Department of Computer Science and Engineering Bannari Amman Institute of Technology sathyamangalam India Rajampet India

ISBN: (纸本)9798350383867

Type II diabetes, which nowadays has become one of the most common chronic metabolic illnesses, needs cloud-based information systems with great predictive models to ensure timely intervention and proper management. Given the fact that EHRs and deep learning are quite advanced serves as a solution to the given problem. The study is about the application of the improved diabetes prediction model that makes use of prevailing deep learning architecture and EHR data. The framework constitutes a three-step process: It encapsulates a preprocessing step, a feature extraction stage, and a training procedure for a deep autoencoders that learns levels of representation. Demonstration of the system' s effectiveness becomes evident when it shows its improved ability of prediction compared to baseline models which in turn outperform in basic indicators such as accuracy, precision, recall, F1 score, and AUC-ROC. The result of the sensitivity check demonstrates that the model is able to perform well even under different hyperparameter arrangements. Latent feature analysis shows some unequivocal ways of hidings that reflect diabetes risk factors thus making it easy to understand. The clinical validation in a healthcare setting presents high accuracy, the specificity and the predictive values that are known in indicating those who are at the risk of diabetes onset. This research aims at deepening the understanding for the diagnosis of diabetes through the implementation of deep learning methods and generating real-world medical data. It attempts to provide better prognosis, lower healthcare expenditures and alleviate diabetes burden on patients and society. © 2024 IEEE.

关键词： Electronic health record

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An IoT Architecture Leveraging Digital Twins: Compromised Node Detection Scenario

arXiv

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arXiv 2023年

作者： Alanezi, Khaled Mishra, Shivakant Department of Computing College of Basic Education PAAET Kuwait Computer Science Department University of Colorado Boulder United States

Modern IoT (Internet of Things) environments with thousands of low-end and diverse IoT nodes with complex interactions among them and often deployed in remote and/or wild locations present some unique challenges that make traditional node compromise detection services less effective. This paper presents the design, implementation and evaluation of a fog-based architecture that utilizes the concept of a digital-twin to detect compromised IoT nodes exhibiting malicious behaviors by either producing erroneous data and/or being used to launch network intrusion attacks to hijack other nodes eventually causing service disruption. By defining a digital twin of an IoT infrastructure at a fog server, the architecture is focused on monitoring relevant information to save energy and storage space. The paper presents a prototype implementation for the architecture utilizing malicious behavior datasets to perform misbehaving node classification. An extensive accuracy and system performance evaluation was conducted based on this prototype. Results show good accuracy and negligible overhead especially when employing deep learning techniques such as MLP (multilayer perceptron). © 2023, CC BY.

关键词： Internet of things

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Elsa: Energy-based Learning for Semi-supervised Anomaly Detection 32

Elsa: Energy-based Learning for Semi-supervised Anomaly Dete...

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32nd British Machine Vision Conference, BMVC 2021

作者： Han, Sungwon Song, Hyeonho Lee, Seungeon Park, Sungwon Cha, Meeyoung School of Computing Korean Advanced Institute of Science and Technology Daejeon Korea Republic of Data Science Group Institute of Basic Science Daejeon Korea Republic of

Contrastive learning has brought important advances in improving anomaly detection. Yet these techniques rely on clean training data, which cannot be guaranteed in real-world scenarios. This paper presents a theoretical interpretation of when and how contrastive learning alone fails to detect anomalies under data contamination. To address the shortcomings, we propose Elsa, a novel semi-supervised anomaly detection approach, that unifies the concept of energy-based models with unsupervised contrastive learning. Elsa instills robustness against various practical scenarios by a carefully designed fine-tuning step that uses the energy function to divide the normal data into prototype classes or subclasses that reflect heterogeneity of the data distribution. By using a small set of anomaly labels, Elsa improves anomaly detection performance in both clean and contaminated data scenarios by 0.9 and 6.6 AUROC, respectively. © 2021. The copyright of this document resides with its authors.

关键词： Anomaly detection

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Deep Learning Oriented Smart Constructional Material and Face Recognition System for the Site Safety

Deep Learning Oriented Smart Constructional Material and Fac...

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IEEE UP Section Conference on Electrical Computer and Electronics (UPCON)

作者： Megha Nain Shilpa Sharma Sandeep Chaurasia Department of Computer Applications School of Basic Science Manipal University Jaipur Jaipur India Department of Computer Science and Engineering School of Computing & Information Technology Manipal University Jaipur Jaipur India

ISBN: (数字)9798350378726

ISBN: (纸本)9798350378733

Ensuring worker safety and compliance on construction sites is critical due to the high-risk nature of the environment and the need for efficient material handling. This paper presents a deep learning-based system for real-time detection and recognition of construction materials and equipment to enhance safety and optimize workflows. The model integrates pre-trained facial recognition networks (VGG Face) and object detection models (YOLO) to achieve accurate worker identification and material classification. A custom dataset was created, comprising 52 images for each of the 24 material/equipment classes and 20 worker profiles from 7 trades, including carpenters, metalworkers, and heavy machinery operators. The system successfully detects and classifies 24 distinct material classes, achieving 88.4 % precision and 63.7 % recall. By verifying workers' presence in designated environments and detecting materials in real-time, the proposed system ensures compliance with safety regulations and addresses critical challenges in construction site monitoring and safety enforcement.

关键词： Deep learning YOLO Surveys Accuracy Reviews Face recognition Real-time systems Safety Object recognition Monitoring

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Research and Implementation of an Embedded Image Classification Method Based on ZYNQ 11

Research and Implementation of an Embedded Image Classificat...

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11th International Conference of Information and Communication Technology, ICTech 2022

作者： Wang, Jiangbo Yin, Zhenyu Xu, Fulong Zhang, Feiqing Xu, Guangyuan Shenyang Institute of Computing Technology Chinese Academy of Science Shenyang110168 China Liaoning Key Lab. of Domestic Industrial Control Platform Technology on Basic Hardware Software 110168 China

ISBN: (数字)9781665496940

ISBN: (纸本)9781665496940

FPGA was a programmable chip with powerful high parallel computing capabilities. Through the FPAG&ARM collaborative processing of neural networks, it can improve computing efficiency and reduce energy consumption. Aiming at a large number of matrix operation problems involved in the AlexNet network, this paper uses the OPENBLAS acceleration algorithm to optimize the matrix operation, and proposes a research and implementation of an embedded image classification method based on ZYNQ. This paper uses FPGA to realize the real-time image acquisition, takes the acquired image as the input of the convolutional neural network model, uses the AlexNet network on the ARM side to realize the classification of the image, and finally deploys the neural network model to the ZYNQ-7000 platform. The experimental results show that in the case of ensuring that the accuracy is not reduced during the image classification process, compared with using the AlexNet network to achieve image classification in 23.4s, using the OPENBLAS acceleration algorithm to accelerate the matrix calculation in the AlexNet network, the image classification consumes time is about 4.5s, and the classification performance has been improved by nearly 5.2 time. © 2022 IEEE.

关键词： Image classification

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Propagation of incompatible opinions on social networks

Propagation of incompatible opinions on social networks

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第43届中国控制会议

作者： Shuli Zhang Jingyuan Ma Jingying Ma School of Mathematics and Statistics Ningxia University Ningxia Key Laboratory of Interdisciplinary Mechanics and Scientific Computing Ningxia Basic Science Research Center of Mathematics Ningxia University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This paper investigates a model for the propagation of opinions over time in a social network where two competing opinions are considered. The social network is divided into multiple communities based on the interests of agents, and rules for updating agent opinions are established. The mean-field approximation theory is used to establish dynamic equations for the evolution of opinions. Additionally, the paper analyzes and proves the sufficient conditions for opinions to reach consensus under certain rules in social networks, including the conditions for one opinion to prevail and another to be "eradicated". The stability of the dynamic system is analyzed using the Lyapunov asymptotic stability theory, demonstrating that the equilibrium state is stable. Furthermore, the paper uses the Lyapunov function to theoretically analyze the stability conditions when two opposite opinions coexist in social networks. The equilibrium stability of the system has been proven by combining nonlinear system theory. Finally, the reliability and effectiveness of the research content are confirmed through numerical simulation.

关键词： Social networks Opinion dynamic Competing opinions Lyapunov stability theory

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Robust Optimization in Protein Fitness Landscapes Using Reinforcement Learning in Latent Space

arXiv

引用

arXiv 2024年

作者： Lee, Minji Vecchietti, Luiz Felipe Jung, Hyunkyu Ro, Hyunjoo Cha, Meeyoung Kim, Ho Min School of Computing KAIST Daejeon Korea Republic of Center for Mathematical and Computational Sciences Institute for Basic Science Daejeon Korea Republic of Center for Biomolecular and Cellular Structure Institute for Basic Science Daejeon Korea Republic of Department of Biological Sciences KAIST Daejeon Korea Republic of

Proteins are complex molecules responsible for different functions in nature. Enhancing the functionality of proteins and cellular fitness can significantly impact various industries. However, protein optimization using computational methods remains challenging, especially when starting from low-fitness sequences. We propose LatProtRL, an optimization method to efficiently traverse a latent space learned by an encoder-decoder leveraging a large protein language model. To escape local optima, our optimization is modeled as a Markov decision process using reinforcement learning acting directly in latent space. We evaluate our approach on two important fitness optimization tasks, demonstrating its ability to achieve comparable or superior fitness over baseline methods. Our findings and in vitro evaluation show that the generated sequences can reach high-fitness regions, suggesting a substantial potential of LatProtRL in lab-in-the-loop scenarios. © 2024, CC BY.

关键词： Proteins

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Partonic collinear structure by quantum computing 31

Partonic collinear structure by quantum computing

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31st International Workshop on Deep Inelastic Scattering, DIS 2024

作者： Li, Tianyin Xing, Hongxi Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter Institute of Quantum Matter South China Normal University Guangzhou510006 China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Guangdong Provincial Key Laboratory of Nuclear Science Southern Nuclear Science Computing Center South China Normal University Guangzhou510006 China Institute of Modern Physics Chinese Academy of Sciences Huizhou516000 China

We present a systematic quantum algorithm that combines hadronic state preparation with the evaluation of real-time light-front correlators to investigate the parton distribution function (PDF) and light cone distribution amplitude (LCDA). As a proof of concept, we simulate both the PDF and LCDA in the 1+1 dimensional Nambu-Jona-Lasinio model. The results obtained from exact diagonalization and quantum computation on classical hardware show strong agreement, validating the effectiveness of the proposed algorithm. Our findings highlight the promising potential for calculating partonic collinear structure on current and near-term quantum devices. This quantum algorithm is anticipated to have broad applications in high-energy particle and nuclear physics. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).

关键词： Quantum computers

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Outcome of a Magnetic Field on Heat Transfer of Carbon Nanotubes (CNTs)-Suspended Nanofluids by Shooting Type Laplace–Adomian Decomposition Method (LADM) 1

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International Conference on Sustainable Building Materials and Construction, ICSBMC 2021

作者： Vennila, B. Nithya, N. Kabilan, M. Department of Mathematics School of Basic Sciences College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Computer Science Engineering School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

ISBN: (数字)9789811684968

ISBN: (纸本)9789811684951

This paper focuses on the impact of carbon nanotubes (CNTs) on nanofluid flow and heat transfer in the occurrence of a magnetic field. Various parameters’ consequences on fluid velocity and temperature have been addressed. To obtain the precise analytic solution for the velocity and temperature distribution, a shooting form Laplace–Adomian decomposition method is used. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Carbon nanotubes

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YOLO-MFE: towards more accurate object detection using multiscale feature extraction 6

YOLO-MFE: towards more accurate object detection using multi...

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6th International Conference on Intelligent computing, Communication, and Devices, ICCD 2023

作者： Li, Hui Yin, Zhenyu Fan, Chao Wang, Xiaohui School of Computer Science and Technology Shenyang University of Chemical Technology Shenyang110142 China Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China Liaoning Key Lab. of Domestic Industrial Control Platform Technology on Basic Hardware and Software Shenyang110168 China

ISBN: (数字)9781510666269

ISBN: (纸本)9781510666252

YOLOv3, as a multi-scale object detection algorithm, has a simple structure and can be detected quickly. However, during the training process, as the bottom convolutional layer contains more object detail, and the information in the underlying layer will be gradually faded out through continuous convolution. To address this, this paper adds a normalized attention mechanism behind the feature layers at different scales to further suppress insignificant features and enhance the discriminability and robustness of the features. Moreover, an improved pyramid structure is leveraged to fuse the detail of the shallow feature maps with the semantic feature information of the higher layers, aiming to improve the model performance. In particular, a YOLOv3 object detection algorithm named YOLO-MFE based on improved multi-scale feature extraction is proposed. Experimental results on the Pascal VOC dataset demonstrate that our algorithm outperforms the original YOLOv3 algorithm in terms of object detection accuracy, with a mAP on the Pascal VOC 2007 test set that is 1.66% higher. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Object detection

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