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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Singh, Jagendra Thakur, Hardeo Kumar Sharma, Neetu Alsahlanee, Abbas Thajeel Rhaif Sahunthala, S. Shaik, Nazeer Alliance School of Advanced Computing Alliance University Bangalore India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Science and Information Technology University of Salahaddin-Erbil Erbil Iraq Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Tamil Nadu Chembarambakkam India Department of Computer Science and Engineering Srinivasa Ramanujan Institute of Technology—Autonomous Anantapur India

ISBN: (纸本)9789819763177

This study is an innovative investigation into the field of plant disease detection and plant health prediction using state-of-the-art machine learning models and Quantum Support Vector Machines (QSVM) for feature extraction. A diverse dataset of 700 images, including a variety of plant species and disease states, served as the foundation for this study. The study evaluated the performance of four machine learning models: CNN (ConvNet), RNN (Recurrent), KNN (k-Nearest Neighbors), and SVM (Support Vector Machine) for accurately identifying and classifying plant diseases. Convolutional neural network (CNN) model unexpectedly prevailed, achieving a remarkable accuracy rate of 98.77%. Accuracy, recall, and F1 score of the model further demonstrated its propensity for disease diagnosis. QSVMs were used to extract intricate features from pictures of sick leaves in order to increase the models’ precision and prognostication ability. The findings have significant impact for agriculture. And the sophisticated toolkit they offer has provided an important step forward in proactive disease management for better crop health. Carrying out an early intervention for disease can result in higher crop output, lower agricultural losses, and improved global food security. The QSVM’s application of quantum computing principles also opens up fresh frontiers for creating machine learning applications in different industries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Using mathematical model to predict the transmission trend of COVID-19 infection in global scale 3

Using mathematical model to predict the transmission trend o...

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3rd International Conference on Applied Mathematics, Modelling, and Intelligent computing, CAMMIC 2023

作者： Kong, Lingming Ou, Guanlin Tian, Tang School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin541004 China Key Laboratory of Cognitive Radio and Information Processing Ministry of Education Guilin University of Electronic Technology Guilin541004 China

ISBN: (数字)9781510667617

ISBN: (纸本)9781510667600

A deterministic model of COVID-19 transmission is established by using a differential equation system, which integrates the protective adaptive behavior of human population and the protective effect of vaccine. Based on the data of reported cases and vaccinations from February 1, 2020 to July 16, 2022, parameter fitting is performed by MCMC method to estimate the intervention effect and transmission trend. The results indicate that (1) this epidemic may level off around October 2021 if without any intervention, but in big cost of disease burden, leading to 5.46 billion cases (about 10 times of current infections);(2) 210 million people may be free of infection due to vaccination, which is 37% of the current number of infections;and (3) according to the current situation of intervention measures, the cumulative number of cases in the world would slowly increase and tend to be flat by 2024. The epidemic will be completely ended in February 2026, and the cumulative number of cases will reach 870 million. The findings show that protective behavior, vaccination and its protection rate are still the key factors to determine the global epidemic trend. © 2023 SPIE.

关键词： COVID-19

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MapExpert: Online HD Map Construction with Simple and Efficient Sparse Map Element Expert 39

MapExpert: Online HD Map Construction with Simple and Effici...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Dapeng Chen, Dayu Zhi, Peng Chen, Yinda Yuan, Zhenlong Li, Chenyang Sunjing Zhou, Rui Zhou, Qingguo School of Information Science & Engineering Lanzhou University China Robotics Technology Co. Ltd. China School of Information Science and Technology University of Science and Technology of China China Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)157735897X

Constructing online High-Definition (HD) maps is crucial for the static environment perception of autonomous driving systems (ADS). Existing solutions typically attempt to detect vectorized HD map elements with unified models;however, these methods often overlook the distinct characteristics of different non-cubic map elements, making accurate distinction challenging. To address these issues, we introduce an expert-based online HD map method, termed MapExpert. MapExpert utilizes sparse experts, distributed by our routers, to describe various non-cubic map elements accurately. Additionally, we propose an auxiliary balance loss function to distribute the load evenly across experts. Furthermore, we theoretically analyze the limitations of prevalent bird’s-eye view (BEV) feature temporal fusion methods and introduce an efficient temporal fusion module called Learnable Weighted Moving Descent. This module effectively integrates relevant historical information into the final BEV features. Combined with an enhanced slice head branch, the proposed MapExpert achieves state-of-the-art performance and maintains good efficiency on both nuScenes and Argoverse2 datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Expert systems

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Multi-Scale Dilated Convolution Network for SPECT-MPI Cardiovascular Disease Classification with Adaptive Denoising and Attenuation Correction

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Computer Modeling in Engineering & sciences 2025年第1期142卷 299-327页

作者： A.Robert Singh Suganya Athisayamani Gyanendra Prasad Joshi Bhanu Shrestha Department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathur603203Tamil NaduIndia School of Computing Sastra Deemed to be UniversityThanjavur613401Tamil NaduIndia Department of Artificial Intelligence&Software Kangwon National UniversitySamcheok25913Republic of Korea Department of Information Convergence System Graduate School of Smart ConvergenceKwangwoon UniversitySeoul01897Republic of Korea

Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronary artery disease(CAD).The automatic classification of SPECT images for different techniques has achieved near-optimal accuracy when using convolutional neural networks(CNNs).This paper uses a SPECT classification framework with three steps:1)Image denoising,2)Attenuation correction,and 3)Image *** denoising is done by a U-Net architecture that ensures effective image *** correction is implemented by a convolution neural network model that can remove the attenuation that affects the feature extraction process of ***,a novel multi-scale diluted convolution(MSDC)network is *** merges the features extracted in different scales and makes the model learn the features more *** scales of filters with size 3×3 are used to extract *** three steps are compared with state-of-the-art *** proposed denoising architecture ensures a high-quality image with the highest peak signal-to-noise ratio(PSNR)value of *** proposed classification method is compared with the five different CNN models,and the proposed method ensures better classification with an accuracy of 96%,precision of 87%,sensitivity of 87%,specificity of 89%,and F1-score of 87%.To demonstrate the importance of preprocessing,the classification model was analyzed without denoising and attenuation correction.

关键词： SPECT-MPI CAD MSDC denoising attenuation correction classification

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Poster: Zero Trust Driven Architecture for Blockchain-Based Access Control Delegation

Poster: Zero Trust Driven Architecture for Blockchain-Based ...

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2024 SIGCOMM Poster and Demo Sessions, SIGCOMM Posters and Demos 2024

作者： Mukta, Rahma Pal, Shantanu Hitchens, Michael Paik, Hye-Young Kanhere, Salil S Department of CSE Chittagong University of Engineering and Technology Chittagong4349 Bangladesh School of Information Technology Deakin University MelbourneVIC3125 Australia School of Computing Macquarie University SydneyNSW2109 Australia School of Computer Science and Engineering University of New South Wales SydneyNSW2052 Australia

ISBN: (纸本)9798400707179

Granting access and controlling access rights for online services is challenging in a highly dynamic and large-scale systems. In particular, the secure propagation of access right delegation is a major issue. Many proposals present access control issues for large-scale systems, however, little attention has been given to flexible and secure delegation in such dynamic systems. This article presents an approach to address such a delegation issue for large-scale systems using blockchain technology. We propose a blockchain-based delegation model that employs, i) self-sovereign identity (SSI) for user/device authentication, and ii) a restricted yet flexibly propagatable delegation process using the sanitizable signature scheme without the need for a centralized system. The goal of our primitive is to propose a Zero Trust architecture for access control delegation. To demonstrate the feasibility of our proposed architecture, we evaluate the system execution time using a sanitizable signature within a hyperledger fabric network. This work is in progress to further evaluate the system's efficiency in delegating access rights. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Large scale systems

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Monotonicity and nonmonotonicity in L3-valued propositional logic

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Frontiers of Computer science 2022年第4期16卷 33-43页

作者： Wei Li Yuefei Sui State Key Laboratory of Software Development Environment Beihang UniversityBeijing100083China Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing100049China

A sequent is a pair (Γ, Δ), which is true under an assignment if either some formula in Γ is false, or some formula in Δ is true. In L_(3)-valued propositional logic, a multisequent is a triple Δ∣Θ∣Γ, which is true under an assignment if either some formula in Δ has truth-value t, or some formula in Θ has truth-value m, or some formula in Γ has truth-value f. There is a sound, complete and monotonic Gentzen deduction system G for sequents. Dually, there is a sound, complete and nonmonotonic Gentzen deduction system G′ for co-sequents Δ: Θ: Γ. By taking different quantifiers some or every, there are 8 kinds of definitions of validity of multisequent Δ∣Θ∣Γ and 8 kinds of definitions of validity of co-multisequent Δ: Θ: Γ, and correspondingly there are 8 sound and complete Gentzen deduction systems for sequents and 8 sound and complete Gentzen deduction systems for co-sequents. Correspondingly their monotonicity is discussed.

关键词： sequent mulisequent gentzen deduction system monotonicity nonmonotonicity

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Fabric4show:real-time vision system for fabric defect detection and post-processing

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Visual Intelligence 2024年第1期2卷 146-158页

作者： Huaizhou Lin Dan Cai Zengmin Xu Jinsong Wu Lixian Sun Haibin Jia School of Materials Science and Engineering Guangxi Key Laboratory of InformationGuilin University of Electronic TechnologyGuilinChina School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and ComputationGuilin University of Electronic TechnologyCenter for Applied Mathematics of Guangxi(GUET)GuilinChina Anview.ai GuilinChina School of Artificial Intelligence Guilin University of Electronic TechnologyGuilinChina Department of Electrical Engineering University of ChileSantiagoChile

The exploration of computer vision applications for fabric defect detection has immense potential ***,current relevant research in this area has primarily focused on detection models that aim for high detection accuracy and algorithmic efficiency,while neglecting the practical industrial production ***,we propose a fabric defect detection and post-processing system that integrates an optimized region with convolutional neural network(CNN)features(i.e.,Faster R-CNN)for defect detection,defect localization and detection model *** addition,the proposed intelligent system incorporates novel approaches,such as a rearranged fabric dataset,anomaly detection,recommended clipping region division,and a replenishment *** study illustrates an example of artificial intelligence(AI)-driven automated technology in fabric *** accuracy and detection speed of different detection models under identical hardware conditions are evaluated and compared with related *** results demonstrate that the proposed approach achieves comparable performance to other models,while significantly reducing computational resource *** potential efficiency of using two-stage networks on hardware systems for fabric defect detection tasks is highlighted,which is likely to have relevant implications for the textile industry.

关键词： Computer vision Fabric manufacturing Automation Defect detection Faster R-CNN

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Extracting High-order Connectivity of EEG-based Dynamic Functional Connectivity Networks for Diagnosis of Major Depressive Disorder 24

Extracting High-order Connectivity of EEG-based Dynamic Func...

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4th Asia-Pacific Artificial Intelligence and Big Data Forum, AIBDF 2024

作者： Zhao, Feng Fan, Wenxuan Han, Zhongwei Chen, Hongyu School of Computer Science and Technology Shandong Technology and Business University Shandong Yantai China Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Shandong Yantai China Immersion Technology and Evaluation Shandong Engineering Research Center Shandong Yantai China Information Engineering College Yantai Institute of Technology Shandong Yantai China Shandong Technology and Business University Shandong Yantai China

ISBN: (纸本)9798400710865

Functional connectivity networks (FCNs), particularly those utilizing the phase lag index (PLI) method, have been instrumental in elucidating the pathological features of Major Depressive Disorder (MDD) by assessing the synchrony and lag of electroencephalogram (EEG) signal waveforms. Currently, the more prevalent methods are traditional FCNs that quantify phase synchronization as scalar values and dynamic FCNs (D-FCNs) that employ sliding window approaches to capture the instantaneous variations in functional connectivity. However, these two methods merely capture the low-level functional connections between paired regions of interest (ROIs) from both the static and dynamic perspectives, and fail to reflect the high-level functional connections involving multiple ROIs. Moreover, the feature dimension of the dynamic FCN is excessively high, and there exists a phase mismatch issue between different samples, which renders it impossible to be directly applied to the classification task, and no better solution is available at present. To mitigate these issues, we proposed a comprehensive framework that includes segmenting EEG sequences based on PLI to construct dynamic FCNs;employing seventh-order central moments for feature extraction to form low-order FCNs (Lo-FCNs);and establishing high-order FCNs (Ho-FCNs) based on Pearson correlation theory derived from Lo-FCNs. We conducted classification experiments across four distinct EEG frequency bands using our proposed framework while analyzing relevant features through graph theory and brain topological mapping techniques. The experiments showed that the proposed framework has a high classification accuracy rate in the classification experiments of MDD and NC, and proved the importance of high-order features in classification experiments, indicating that they may serve as physiological markers for MDD diagnosis. © 2024 Copyright held by the owner/author(s).

关键词： Electroencephalography

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Workload Prediction of Virtual Machines Using Integrated Deep Learning Approaches Over Cloud Data Centers 1st

Workload Prediction of Virtual Machines Using Integrated Dee...

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1st International Conference on Human-Centric Smart computing, ICHCSC 2022

作者： Leka, Habte Lejebo Fengli, Zhang Kenea, Ayantu Tesfaye Sharma, Durga P. Tegene, Abebe Tamrat School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Science and Engineering School of Electrical Engineering and Computing Adama Science and Technology University Adama1888 Ethiopia AMUIT MOEFDRE Under UNDP & MAISM RTU Kota Kota India

ISBN: (纸本)9789811954023

Exponential growth in the use of cloud computing services makes it difficult to forecast loads of virtual machines (VMs). Accurate virtual machine (VM) workload forecasting is the most critical task in appropriately managing cloud resources such as memory and central processing units while minimizing energy usage. To address this problem, an integrated deep learning model is proposed in this research paper. The model employs two popular neural networks: a bidirectional Long Short-Term Memory Network (BiLSTM) with a convolutional neural networks (CNN). The CNN component pulls high-level attributes from all VM workload data, whereas the BiLSTM component forecasts future VM workload. The experimental results reveal that the suggested model outperforms commonly used workload prediction methods in terms of forecasting accuracy of VMs workloads in cloud computing environments. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cloud computing

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Tactile Data Comics: A Step-by-step Multimodal Presentation Method on a Refreshable Tactile Display for Blind and Visually Impaired Individuals

Tactile Data Comics: A Step-by-step Multimodal Presentation ...

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2025 CHI Conference on Human Factors in computing Systems, CHI EA 2025

作者： Sun, Ruoting Luo, Rong Yao, Xiwen She, Xinran Hara, Kotaro Jiao, Yang Tsinghua University Beijing China University of Science and Technology Beijing Beijing China South China University of Technology GuangZhou China School of Computing and Information Systems Singapore Management University Singapore Singapore

ISBN: (纸本)9798400713958

Refreshable tactile displays (RTDs) could present dynamic tactile graphics to support blind and low-vision individuals in learning. However, existing research offers limited evidence on the effectiveness of dynamic tactile graphics on RTDs in supporting blind students’ learning outcomes. To study the value of presenting dynamic contents in the learning context, we designed tactile data comics, a method that combines step-by-step presentation of tactile graphics with verbal narration. We conducted a user study with 16 visually impaired students to compare tactile data comics against verbal-only and static tactile graphic presentation approaches. Our findings show that tactile data comics significantly improves participants’ content comprehension. The results highlight the potential of tactile data comics to benefit the visually impaired community, particularly in educational applications. © 2025 Copyright held by the owner/author(s).

关键词： Students

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