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Advancement in Technology (ICONAT), International Conference for

作者： K. Logeswaran S. Savitha P. Suresh M. Vimaladevi P. Saliniyan A. Sanjay A. Yeswanth Department of Artificial Intelligence Kongu Engineering College Erode Tamilnadu India Departmen of CSE K.S.R College of Engineering Tiruchengode Tamilnadu India Department of Database Systems School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamilnadu India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Successful operations in industries such as recycling, manufacturing and quality control depend on identifying and classifying different types of glass. Traditional methods, including manual analysis and traditional image processing methods, often suffer from errors and inconsistencies due to human factors and variations. This paper introduces a new automatic glass type identification system that uses advanced learning techniques, specifically K-Nearest Neighbors (KNN), CNN,RNN. By applying these algorithms, it is aimed to increase the accuracy and performance of the system’s glass type classification. Next, we delve into the design and implementation of machine learning models by discussing data collection techniques, feature extraction technology, and training methods. A comparative analysis of the performance of KNN, RNN, Naive Bayes, Logistic Regression, Random Forest, Feed Forward Network, RNN, ANN, CNN. is performed and their accuracy, performance, and robustness are evaluated in different situations. It shows the most accurate and reliable glass type classification among traditional methods. This study also explores the benefits of using such systems in the manufacturing industry and shows how work efficiency can be increased and the number of errors reduced.

关键词： Training Analytical models Recurrent neural networks Accuracy Glass Predictive models Nearest neighbor methods Robustness Recycling Random forests

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Multi-Class Gaze Detection in a Dynamic Environment

Multi-Class Gaze Detection in a Dynamic Environment

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IEEE Instrumentation and Measurement Technology Conference

作者： Aidan Lochbihler Bruce Wallace Kathleen Van Benthem Chris Herdman Will Sloan Kirsten Brightman Frank Knoefel Shawn Marshall Rafik Goubran Systems and Computer Engineering Carleton University Ottawa Canada Systems and Computer Engineering Carleton University Bruyère Research Institute SAM3 Innovation Hub Ottawa Canada Department of Cognitive Science Carleton University Ottawa Canada Bruyè re Research Institute Elisabeth-Bruyè re Hospital Systems and Computer Engineering Carleton University Ottawa Canada Bruyère Research Institute Elisabeth-Bruyère Hospital Ottawa Hospital Carleton University

ISBN: (数字)9798350380903

ISBN: (纸本)9798350380910

Developing AI tools to identify areas of interest within a dynamic field of view is essential for objective behavioural evaluation of drivers. Video image classification and specifically image segmentation is a key technology to allow for the possibility of physiological and behavioural measurement of drivers. For example, to understand driver attention, one must measure where a driver is looking when driving and this requires segmentation of their field of view into relevant areas of interest, such as windows, mirrors, and dashboard. The present work addresses the challenge of dynamic field of view classification and shows the impact of transfer learning, a new AI tool, on segmentation accuracy. Results from this study demonstrate that transfer learning improves predictive performance by 0.02 to 0.20 Dice when large training sets were used. The resulting performance was >0.80 Dice for all classification tests of driver attention segmentation. This work showed that transfer learning also supported the use of smaller training sets while still providing adequate performance. This finding is key for applications where labeled training data is limited or costly to create. The present results expand the application space for deep learning-based image segmentation models.

关键词： Training Image segmentation Transfer learning Training data Physiology Object recognition Mirrors

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Survey of Distributed Algorithms for Resource Allocation over Multi-Agent systems

arXiv

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arXiv 2024年

作者： Doostmohammadian, Mohammadreza Aghasi, Alireza Pirani, Mohammad Nekouei, Ehsan Zarrabi, Houman Keypour, Reza Rikos, Apostolos I. Johansson, Karl H. Faculty of Mechanical Engineering Semnan University Semnan Iran Department of Electrical Engineering and Computer Science Oregon State University United States Department of Mechanical Engineering University of Ottawa OttawaON Canada City University of Hong Kong China Tehran Iran Faculty of Electrical Engineering Semnan University Semnan Iran Department of Electrical and Computer Engineering Division of Systems Engineering Boston University BostonMA United States Division of Decision and Control Systems KTH Royal Institute of Technology Sweden

Resource allocation and scheduling in multi-agent systems present challenges due to complex interactions and decentralization. This survey paper provides a comprehensive analysis of distributed algorithms for addressing the distributed resource allocation (DRA) problem over multi-agent systems. It covers a significant area of research at the intersection of optimization, multiagent systems, and distributed consensus-based computing. The paper begins by presenting a mathematical formulation of the DRA problem, establishing a solid foundation for further exploration. Real-world applications of DRA in various domains are examined to underscore the importance of efficient resource allocation, and relevant distributed optimization formulations are presented. The survey then delves into existing solutions for DRA, encompassing linear, nonlinear, primal-based, and dual-formulation-based approaches. Furthermore, this paper evaluates the features and properties of DRA algorithms, addressing key aspects such as feasibility, convergence rate, and network reliability. The analysis of mathematical foundations, diverse applications, existing solutions, and algorithmic properties contributes to a broader comprehension of the challenges and potential solutions for this domain. © 2024, CC BY.

关键词： Multi agent systems

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Development of a New Opto-Electrochemical Cell for Sensing Applications

Development of a New Opto-Electrochemical Cell for Sensing A...

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IEEE Sensors Applications Symposium, SAS

作者： João P. Mendes Luís C. C. Coelho José A. Ribeiro Centre for Applied Photonics INESCTEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal Department of Chemistry and Biochemistry CIQUP/IMS Faculty of Sciences of University of Porto Porto Portugal

ISBN: (数字)9798350369250

ISBN: (纸本)9798350369267

New systems with innovative design to perform measurements combining electrochemistry and surface plasmon resonance (ESPR) are currently a need to overcome the limitations of existent market solutions and expand the research possibilities of this technology. The main goal of this work was to develop a new cell to increase ESPR practical applications in several fields. To do so, a homemade SPR cell, fabricated by 3D-printing technology, was adapted for this purpose by incorporating the conventional 3-electrodes to perform the electrochemical experiments. The developed cell was fully compatible with commercial SPR substrates. After optimization of the homemade ESPR setup to perform the combined electrochemical and SPR measurements, two main applications were explored in this work. The first was the use of ESPR technology as straightforward tool to simultaneously investigate the electrical and optical properties of conducing/non-conducting polymers electrosynthetized on the SPR platforms. The conducting polymer poly(thionine) was used in this work for proof-of-concept. The second application envisaged the use of ESPR approach for simple electrodeposition ofmaterials with enhanced plasmonic properties for sensitivity enhancement of SPR biosensors. For validation of the concept, graphene oxide (GO) was electrochemically reduced on gold substrates aiming to evaluate the plasmonic properties of graphene-modified sensing surfaces.

关键词： Gold Biomedical optical imaging Sensitivity Microprocessors computer architecture Stability analysis Plastics

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Federated Cubic Regularized Newton Learning with Sparsification-amplified Differential Privacy

arXiv

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arXiv 2024年

作者： Huo, Wei Liu, Changxin Ding, Kemi Johansson, Karl Henrik Shi, Ling Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures StockholmSE-10044 Sweden School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China

This paper investigates the use of the cubic-regularized Newton method within a federated learning framework while addressing two major concerns that commonly arise in federated learning: privacy leakage and communication bottleneck. We introduce a federated learning algorithm called Differentially Private Federated Cubic Regularized Newton (DP-FCRN). By leveraging second-order techniques, our algorithm achieves lower iteration complexity compared to first-order methods. We also incorporate noise perturbation during local computations to ensure privacy. Furthermore, we employ sparsification in uplink transmission, which not only reduces the communication costs but also amplifies the privacy guarantee. Specifically, this approach reduces the necessary noise intensity without compromising privacy protection. We analyze the convergence properties of our algorithm and establish the privacy guarantee. Finally, we validate the effectiveness of the proposed algorithm through experiments on a benchmark dataset. © 2024, CC BY.

关键词： Federated learning

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Medical image segmentation approach based on hybrid adaptive differential evolution and crayfish optimizer

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computers in Biology and Medicine 2024年 180卷 109011-109011页

作者： Mostafa, Reham R. Khedr, Ahmed M. Aghbari, Zaher AL Afyouni, Imad Kamel, Ibrahim Ahmed, Naveed University of Sharjah Sharjah27272 United Arab Emirates Information Systems Department Faculty of Computers and Information Sciences Mansoura University Mansoura35516 Egypt Computer Science Department University of Sharjah Sharjah27272 United Arab Emirates Electrical & Computer Engineering Department University of Sharjah Sharjah27272 United Arab Emirates

Image segmentation plays a pivotal role in medical image analysis, particularly for accurately isolating tumors and lesions. Effective segmentation improves diagnostic precision and facilitates quantitative analysis, which is vital for medical professionals. However, traditional segmentation methods often struggle with multilevel thresholding due to the associated computational complexity. Therefore, determining the optimal threshold set is an NP-hard problem, highlighting the pressing need for efficient optimization strategies to overcome these challenges. This paper introduces a multi-threshold image segmentation (MTIS) method that integrates a hybrid approach combining Differential Evolution (DE) and the Crayfish Optimization Algorithm (COA), known as HADECO. Utilizing two-dimensional (2D) Kapur's entropy and a 2D histogram, this method aims to enhance the efficiency and accuracy of subsequent image analysis and diagnosis. HADECO is a hybrid algorithm that combines DE and COA by exchanging information based on predefined rules, leveraging the strengths of both for superior optimization results. It employs Latin Hypercube Sampling (LHS) to generate a high-quality initial population. HADECO introduces an improved DE algorithm (IDE) with adaptive and dynamic adjustments to key DE parameters and new mutation strategies to enhance its search capability. In addition, it incorporates an adaptive COA (ACOA) with dynamic adjustments to the switching probability parameter, effectively balancing exploration and exploitation. To evaluate the effectiveness of HADECO, its performance is initially assessed using CEC’22 benchmark functions. HADECO is evaluated against several contemporary algorithms using the Wilcoxon signed rank test (WSRT) and the Friedman test (FT) to integrate the results. The findings highlight HADECO's superior optimization abilities, demonstrated by its lowest average Friedman ranking of 1.08. Furthermore, the HADECO-based MTIS method is evaluated using M

关键词： Image segmentation

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Circumnavigation of UAVs integrating QP-Based CLF and CBF in a Cooperative Path-Following Framework

Circumnavigation of UAVs integrating QP-Based CLF and CBF in...

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IEEE Conference on Decision and Control

作者： Pallov Anand A. Pedro Aguiar P. B. Sujit SYSTEC-ARISE Research Center for Systems and Technologies Faculdade de Engenharia Universidade do Porto Porto Portugal Department of Electrical Engineering and Computer Science Indian Institute of Science Education and Research Bhopal Bhopal India

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Unmanned Aerial Vehicles (UAVs) are increasingly utilized in various applications often requiring coordinated operations of multiple UAVs. Circular formation control has garnered significant attention due to its diverse relevance in many situations. In this paper, we propose a Quadratic Programming (QP) based Control Lyapunov Function (CLF) and Control Barrier Function (CBF) framework for circumnavigating stationary and moving targets for single and multiple UAVs while avoiding collision with a known obstacle. By integrating CLF and CBF constraints in a cooperative path-following framework, our approach offers robustness and scalability for complex multi-agent systems, and ensures asymptotic tracking of the target while maintaining closed-loop stability of the error dynamics and safety margins around the obstacle. Through theoretical analysis and simulation studies, we demonstrate the effectiveness and versatility of our proposed framework in challenging environments.

关键词： Target tracking Scalability Simulation Stability analysis Robustness Trajectory Safety Quadratic programming Collision avoidance Vehicle dynamics

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Channel-Wise and Spatial Feature Recalibration Network for Nuclear Cataract Classification

Channel-Wise and Spatial Feature Recalibration Network for N...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Zhang, Xiaoqing Xu, Gelei Shen, Junyong Xiao, Zunjie Yan, Qiuyang Yuan, Jin Higashita, Risa Liu, Jiang Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China Sun Yat-sen University State Key Laboratory of Ophthalmology Guangzhou China Tomey Corporation Japan Cixi Institute of Biomedical Engineering Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Nuclear cataract (NC) is a prior age-related disease for blindness and vision impairment globally. Anterior segment optical coherence tomography (AS-OCT) image is a new ophthalmology image, which can capture the lens nucleus region clearly compared with other ophthalmic images, e.g., slit lamp images. Clinical research has suggested that features e.g., mean from AS-OCT images have varying correlations with NC severity levels. However, existing convolutional neural network (CNN) based NC classification works have not incorporated the clinical features into the network design to improve the performance. To this end, we propose a novel channel-wise and spatial feature recalibration network (CSFR-Net) to predict NC severity levels automatically, which is built on a stack of channel-wise and spatial feature recalibration (CSFR) modules. In each CSFR module, we construct a channel-wise feature recalibration block and a spatial feature recalibration block to recalibrate intermediate feature maps dynamically. This feature recalibration strategy enables CSFR-Net to highlight feature representations and suppress unnecessary ones in a global-and-local manner. We conduct extensive experiments on a clinical AS-OCT image dataset and CIFAR benchmarks. The results show that our CSFR-Net achieves better performance than state-of-the-art methods with less model complexity. © 2022 IEEE.

关键词： Clinical research

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The Impact of SBOM Generators on Vulnerability Assessment in Python: A Comparison and a Novel Approach

arXiv

引用

arXiv 2024年

作者： Benedetti, Giacomo Cofano, Serena Brighente, Alessandro Conti, Mauro Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Italy IMT School for Advanced Studies Lucca Italy Department of Mathematics University of Padua Italy Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Netherlands

The Software Supply Chain (SSC) security is a critical concern for both users and developers. Recent incidents, like the SolarWinds Orion compromise, proved the widespread impact resulting from the distribution of compromised software. The reliance on open-source components, which constitute a significant portion of modern software, further exacerbates this risk. To enhance SSC security, the Software Bill of Materials (SBOM) has been promoted as a tool to increase transparency and verifiability in software composition. However, despite its promise, SBOMs are not without limitations. Current SBOM generation tools often suffer from inaccuracies in identifying components and dependencies, leading to the creation of erroneous or incomplete representations of the SSC. Despite existing studies exposing these limitations, their impact on the vulnerability detection capabilities of security tools is still unknown. In this paper, we perform the first security analysis on the vulnerability detection capabilities of tools receiving SBOMs as input. We comprehensively evaluate SBOM generation tools by providing their outputs to vulnerability identification software. Based on our results, we identify the root causes of these tools' ineffectiveness and propose PIP-sbom, a novel pip-inspired solution that addresses their shortcomings. PIP-sbom provides improved accuracy in component identification and dependency resolution. Compared to best-performing state-of-the-art tools, PIP-sbom increases the average precision and recall by 60%, and reduces by ten times the number of false positives. © 2024, CC BY-SA.

关键词： Input output programs

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Multimodal brain tumor segmentation and classification from MRI scans based on optimized DeepLabV3+ and interpreted networks information fusion empowered with explainable AI

引用

computers in Biology and Medicine 2024年 182卷 109183-109183页

作者： Ullah, Muhammad Sami Khan, Muhammad Attique Albarakati, Hussain Mubarak Damaševičius, Robertas Alsenan, Shrooq Department of Computer Science HITEC University Taxila47080 Pakistan Department of Artificial Intelligence College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al Khobar Saudi Arabia Computer and Network Engineering Department College of Computing Umm Al-Qura University Makkah24382 Saudi Arabia Faculty of Applied Mathematics Silesian University of Technology Gliwice44-100 Poland Information Systems Department College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Explainable artificial intelligence (XAI) aims to offer machine learning (ML) methods that enable people to comprehend, properly trust, and create more explainable models. In medical imaging, XAI has been adopted to interpret deep learning black box models to demonstrate the trustworthiness of machine decisions and predictions. In this work, we proposed a deep learning and explainable AI-based framework for segmenting and classifying brain tumors. The proposed framework consists of two parts. The first part, encoder-decoder-based DeepLabv3+ architecture, is implemented with Bayesian Optimization (BO) based hyperparameter initialization. The different scales are performed, and features are extracted through the Atrous Spatial Pyramid Pooling (ASPP) technique. The extracted features are passed to the output layer for tumor segmentation. In the second part of the proposed framework, two customized models have been proposed named Inverted Residual Bottleneck 96 layers (IRB-96) and Inverted Residual Bottleneck Self-Attention (IRB-Self). Both models are trained on the selected brain tumor datasets and extracted features from the global average pooling and self-attention layers. Features are fused using a serial approach, and classification is performed. The BO-based hyperparameters optimization of the neural network classifiers is performed and the classification results have been optimized. An XAI method named LIME is implemented to check the interpretability of the proposed models. The experimental process of the proposed framework was performed on the Figshare dataset, and an average segmentation accuracy of 92.68 % and classification accuracy of 95.42 % were obtained, respectively. Compared with state-of-the-art techniques, the proposed framework shows improved accuracy. © 2024 Elsevier Ltd

关键词： Data fusion

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