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Adaptive pelican optimization with optimized mask RCNN for automatic lung cancer detection

Multimedia Tools and Applications 2025年 1-21页

作者： Sudha, R. Maheswari, K.M. Uma Department of Computer Science and Engineering SRM Institute of Science and Technology Chengalpattu India Department of Computing Technologies SRM Institute of Science and Technology Chengalpattu India

Lung cancer is a prevalent and deadly disease worldwide, necessitating accurate and timely detection methods for effective treatment. Deep learning-based approaches have emerged as promising solutions for automated medical image analysis. This study proposes an enhanced Mask R-CNN framework tailored specifically for the automatic detection and severity analysis of lung cancer from CT images. The proposed approach consists of three stages namely, pre-processing, lung nodule detection, and segmentation using enhanced Mask R-CNN and severity analysis. Our framework employs a deep convolutional neural network architecture trained on a comprehensive dataset of annotated lung images. By incorporating a region-based convolutional neural network (R-CNN) with a mask prediction branch, our model accurately localizes lung tumors while providing precise pixel-level segmentation masks. To enhance the performance of mask RCNN, the parameter present in the classifier is optimally selected using the adaptive pelican optimization (APO) algorithm. The proposed framework detects lung tumors and provides a comprehensive severity analysis, enabling clinicians to assess cancer stage and progression accurately. Evaluation on a benchmark dataset demonstrates superior detection accuracy and robustness compared to existing methods. Our enhanced Mask R-CNN approach shows promise as a valuable tool for early diagnosis and severity assessment of lung cancer, potentially improving patient outcomes and healthcare efficiency. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Lung cancer

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The Security of Using Large Language Models:A Survey With Emphasis on ChatGPT

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 1-26页

作者： Wei Zhou Xiaogang Zhu Qing-Long Han Lin Li Xiao Chen Sheng Wen Yang Xiang the School of Science Computing and Engineering Technologies Swinburne University of Technology IEEE the School of Computer and Mathematical Sciences the University of Adelaide Adelaide the Faculty of Science and Engineering Southern Cross University the School of Information and Physical Sciences the University of Newcastle

ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential security risks that need to be carefully evaluated and addressed. In this survey, we provide an overview of the current state of research on security of using ChatGPT, with aspects of bias, disinformation, ethics, misuse,attacks and privacy. We review and discuss the literature on these topics and highlight open research questions and future *** this survey, we aim to contribute to the academic discourse on AI security, enriching the understanding of potential risks and mitigations. We anticipate that this survey will be valuable for various stakeholders involved in AI development and usage, including AI researchers, developers, policy makers, and end-users.

关键词： Artificial intelligence (AI) ChatGPT large language models (LLMs) security

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Robust federated contrastive recommender system against targeted model poisoning attack

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science China(Information sciences) 2025年第4期68卷 50-65页

作者： Wei YUAN Chaoqun YANG Liang QU Guanhua YE Quoc Viet Hung NGUYEN Hongzhi YIN School of Electrical Engineering and Computer Science The University of Queensland School of Information and Communication Technology Griffith University Deep Neural Computing Company Limited

Federated recommender systems(FedRecs) have garnered increasing attention recently, thanks to their privacypreserving benefits. However, the decentralized and open characteristics of current FedRecs present at least two ***, the performance of FedRecs is compromised due to highly sparse on-device data for each client. Second, the system's robustness is undermined by the vulnerability to model poisoning attacks launched by malicious users. In this paper, we introduce a novel contrastive learning framework designed to fully leverage the client's sparse data through embedding augmentation, referred to as CL4FedRec. Unlike previous contrastive learning approaches in FedRecs that necessitate clients to share their private parameters, our CL4FedRec aligns with the basic FedRec learning protocol, ensuring compatibility with most existing FedRec implementations. We then evaluate the robustness of FedRecs equipped with CL4FedRec by subjecting it to several state-of-the-art model poisoning attacks. Surprisingly, our observations reveal that contrastive learning tends to exacerbate the vulnerability of FedRecs to these attacks. This is attributed to the enhanced embedding uniformity, making the polluted target item embedding easily proximate to popular items. Based on this insight, we propose an enhanced and robust version of CL4FedRec(rCL4FedRec) by introducing a regularizer to maintain the distance among item embeddings with different popularity levels. Extensive experiments conducted on four commonly used recommendation datasets demonstrate that rCL4FedRec significantly enhances both the model's performance and the robustness of FedRecs.

关键词： federated recommender system contrastive learning model poisoning attack and defense

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When Embodied AI Meets Industry 5.0:Human-Centered Smart Manufacturing

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 485-501页

作者： Jing Xu Qiyu Sun Qing-Long Han Yang Tang IEEE the Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology the School of Science Computing and Engineering Technologies Swinburne University of Technology

As embodied intelligence(EI), large language models(LLMs), and cloud computing continue to advance, Industry5.0 facilitates the development of industrial artificial intelligence(Ind AI) through cyber-physical-social systems(CPSSs) with a human-centric focus. These technologies are organized by the system-wide approach of Industry 5.0, in order to empower the manufacturing industry to achieve broader societal goals of job creation, economic growth, and green production. This survey first provides a general framework of smart manufacturing in the context of Industry 5.0. Wherein, the embodied agents, like robots, sensors, and actuators, are the carriers for Ind AI, facilitating the development of the self-learning intelligence in individual entities, the collaborative intelligence in production lines and factories(smart systems), and the swarm intelligence within industrial clusters(systems of smart systems). Through the framework of CPSSs, the key technologies and their possible applications for supporting the single-agent, multi-agent and swarm-agent embodied Ind AI have been reviewed, such as the embodied perception, interaction, scheduling, multi-mode large language models, and collaborative training. Finally, to stimulate future research in this area, the open challenges and opportunities of applying Industry 5.0 to smart manufacturing are identified and discussed. The perspective of Industry 5.0-driven manufacturing industry aims to enhance operational productivity and efficiency by seamlessly integrating the virtual and physical worlds in a human-centered manner, thereby fostering an intelligent, sustainable, and resilient industrial landscape.

关键词： Embodied AI human-centered manufacturing Industry 5.0 internet of things large multi-mode language models

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Detecting the symptoms of COVID-19 during pandemic environment using smart spectacle thermal images and deep capsule networks

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Multimedia Tools and Applications 2025年第8期84卷 4475-4501页

作者： Dwarakanath, B. Pandimurugan, V. Mohandas, R. Sambath, M. Baiju, B.V. Chinnasamy, A. SRM Institute of Science and Technology Ramapuram Chennai India Networking and Communications School of Computing SRMIST kattankulathur Chennai India Computational Intelligence School of Computing SRMIST kattankulathur Chennai India Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Data Science and Business Systems School of Computing SRMIST kattankulathur Chennai India

The pandemic creates a more complicated providence of medical assistance and diagnosis procedures. In the world, Covid-19, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS Cov-2), and plague are widely known pandemic disease desperations. Due to the recent COVID-19 pandemic tragedies, various medical diagnosis models and intelligent computing solutions are proposed for medical applications. In this era of computer-based medical environment, conventional clinical solutions are surpassed by many Machine Learning and Deep Learning-based COVID-19 diagnosis models. Anyhow, many existing models are developing lab-based diagnosis environments. Notably, the Gated Recurrent Unit-based Respiratory Data Analysis (GRU-RE), Intelligent Unmanned Aerial Vehicle-based Covid Data Analysis (Thermal Images) (I-UVAC), and Convolutional Neural Network-based Computer Tomography Image Analysis (CNN-CT) are enriched with lightweight image data analysis techniques for obtaining mass pandemic data at real-time conditions. However, the existing models directly deal with bulk images (thermal data and respiratory data) to diagnose the symptoms of COVID-19. Against these works, the proposed spectacle thermal image data analysis model creates an easy and effective way of disease diagnosis deployment strategies. Particularly, the mass detection of disease symptoms needs a more lightweight equipment setup. In this proposed model, each patient's thermal data is collected via the spectacles of medical staff, and the data are analyzed with the help of a complex set of capsule network functions. Comparatively, the conventional capsule network functions are enriched in this proposed model using adequate sampling and data reduction solutions. In this way, the proposed model works effectively for mass thermal data diagnosis applications. In the experimental platform, the proposed and existing models are analyzed in various dimensions (metrics). The comparative results obtained in the experiments just

关键词： COVID-19

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Revealing the effect of external pressure on solid-electrolyte interphase and lithium plating in lithium-ion batteries

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Journal of Energy Chemistry 2025年第3期102卷 734-744页

作者： Rui Xiong Haocheng Li Bing-Ang Mei Hongwen He Weixiang Shen National Engineering Research Center of Electric Vehicles School of Mechanical EngineeringBeijing Institute of TechnologyBeijing 00081China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyHawthornVictoria 3122Australia

Lithium plating is a detrimental phenomenon in lithium-ion cells that compromises both functionality and *** study investigates electro-chemo-mechanical behaviors of lithium plating in lithium iron phosphate pouch cells under different external *** force microscopy nanoindentation is performed on the graphite electrode to analyze the influence of external pressure on solid-electrolyte interphase(SEI),revealing that the mechanical strength of SEI,indicated by Young's modulus,increases with the presence of external ***,an improved phase field model for lithium plating is developed by incorporating electrochemical parameterization based on nonequilibrium *** results demonstrate that higher pressure promotes lateral lithium deposition,covering a larger area of ***,electrochemical impedance spectroscopy and thickness measurements of the pouch cells are conducted during overcharge,showing that external pressure suppresses gas generation and thus increases the proportion of lithium deposition among galvanostatic overcharge *** integrating experimental results with numerical simulations,it is demonstrated that moderate pressure mitigates SEI damage during lithium plating,while both insufficient and excessive pressure may exacerbate *** study offers new insights into optimizing the design and operation of lithium iron phosphate pouch cells under external pressures.

关键词： Lithium-ion batteries Overcharge Lithium plating Electrochemical mechanics Synergistic effects

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Design and Development of Defected Ground Structure Fractal Antenna based on Osgood Curve for C-band Modern Wireless Communications

Advanced Electromagnetics

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Advanced Electromagnetics 2025年第1期14卷 23-29页

作者： Singh, Gurmeet Kumar, Ashish School of Computing Science and Engineering Galgotias University Greater Noida India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Multifunctional behavioral antennas are one of the significant features of the modern wireless communication systems. This is due to the fact that the miniaturized sizes of the devices demand compact and low-profile antennas that cover limited space and provide multi-frequency functionalities over a wide range of the spectrum. In view of these prerequisites, this paper presents the two design approaches utilizing Osgood Curve Fractal structure as the antenna element with different feeding techniques. The proposed antennas resonate at multiple frequencies covering S-and C-band with excellent impedance matching. The designs have been optimized in terms of scaling factor and position of microstrip feed line with peak gain of 4.5 dBi. Design 1 has significant bandwidth for required applications;however, design 2 possesses low bandwidth with small frequency ratio. Both the proposed designs are fabricated, and results are quite promising. The proposed designs can be utilized for various sub-6 GHz 5G automotive applications as it can easily integrated in the various parts of the modern passenger car. © 2025, Advanced Electromagnetics. All rights reserved.

关键词： Bandwidth

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CENTRAL LIMIT THEOREM FOR TEMPORAL AVERAGE OF BACKWARD EULER-MARUYAMA METHOD

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Journal of Computational Mathematics 2025年第3期43卷 588-614页

作者： Diancong Jin School of Mathematics and Statistics Hubei Key Laboratory of Engineering Modeling and Scientific ComputingHuazhong University of Science and TechnologyWuhan 430074China

This work focuses on the temporal average of the backward Euler-Maruyama(BEM)method,which is used to approximate the ergodic limit of stochastic ordinary differential equations(SODEs).We give the central limit theorem(CLT)of the temporal average of the BEM method,which characterizes its asymptotics in *** the deviation order is smaller than the optimal strong order,we directly derive the CLT of the temporal average through that of original equations and the uniform strong order of the BEM *** the case that the deviation order equals to the optimal strong order,the CLT is established via the Poisson equation associated with the generator of original *** experiments are performed to illustrate the theoretical *** main contribution of this work is to generalize the existing CLT of the temporal average of numerical methods to that for SODEs with super-linearly growing drift coefficients.

关键词： Central limit theorem Temporal average Ergodicity Backward Euler-Maruyama method

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Human Drawing-Driven Automated Evaluation for Parkinson's Disease Detection

Human Drawing-Driven Automated Evaluation for Parkinson's Di...

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2025 IEEE International Students' Conference on Electrical, Electronics and Computer science, SCEECS 2025

作者： Pareek, Keshav Jain, Garima Jain, Sanat Bhargav, Vaibhav Vit Bhopal University School of Computing Science Engineering and Artificial Intelligence Sehore India Vit Bhopal University School of Computing Science and Engineering Sehore India

ISBN: (纸本)9798331529833

Globally, Parkinson's Disease is the second most prevalent neurodegenerative disorder, which is identified by distinct symptoms like tremors, bradykinesia, rigidity, and postural instability. Early signs may include reduced facial expression, impaired hand-eye coordination, and decreased muscle-brain coordination. As PD progresses, symptoms worsen, potentially leading to severe disability. Although there is no cure, early detection and intervention can improve patient outcomes. Traditional PD diagnosis methods, involving clinical assessments and neurological tests, are often time-consuming and subjective. Machine Learning (ML) provides a promising alternative for automating PD detection using data-driven algorithms. This paper explores various ML methods, including Logistic Regression, K-Nearest Neighbors (KNN), Linear Discriminant Analysis, Random Forest, Gaussian Naive Bayes, Decision Tree, and Support Vector Classifier (SVC), to predict early-stage PD symptoms. After experiments, the Gaussian Naive Bayes model scored the highest accuracy of 78%, showcasing the potential of ML to improve early detection. © 2025 IEEE.

关键词： Neurodegenerative diseases

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Bridging Theory and Practice in Outcome-Based Assessment: Developing a Web Application for Learning Outcome Alignment 14

Bridging Theory and Practice in Outcome-Based Assessment: De...

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14th International Conference on Educational and Information Technology, ICEIT 2025

作者： Alhazmi, Abdulsalam K. University of Science and Technology Faculty of Engineering & Computing Aden Yemen

ISBN: (纸本)9798331540883

Assessment remains a cornerstone of the educational process, with standardized testing often serving as a primary method for evaluating learning. However, as pedagogical approaches continue to evolve, Outcome-Based Assessment (OBA) has gained prominence as a more comprehensive model that aligns learning outcomes with teaching strategies. This paper explores the significance of OBA, grounded in frameworks of domain learning, and highlights its global adoption as a measure of institutional quality. By focusing on measurable learning outcomes across cognitive domains, OBA enhances student engagement, critical thinking, and skill development. Extending this theoretical foundation, the paper presents an Outcome Based Assessment System (OBAS) developed to facilitate the practical implementation of OBA. OBAS provides a structured framework that links program learning outcomes to course learning outcomes, integrating considerations of learning domains and difficulty levels. By supporting the creation of standardized and authentic OBA assessments, this application empowers educators and institutions to uphold educational quality, foster accountability, and align assessments with targeted learning objectives. © 2025 IEEE.

关键词： Teaching

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