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Event Management System Using Spatial and Event Attribute Information

SN computer science 2025年第3期6卷 1-9页

作者： Setia, Sonia Anjli, Km Bisht, Upasana Jyoti Raj, Dharm Department of Computer Science & Engineering Sharda School of Engineering & Technology Sharda University Uttar Pradesh Greater Noida India Department of Computer Science Institute of Innovation in Technology & Management Delhi Janakpuri India Department of Computer Science and Engineering ITS Engineering College Greater Noida India

The event management mechanism matches messages that have been subscribed to and events that have been published. To identify the subscriptions that correspond to the occurrence inside the category, it must first run a clustering operation on the subscription message data and determine the category to which the event belongs. In this work, we offer an on-the-fly publish-subscribe technique that can handle on-the-fly subscription changes and category modifications in a distributed environment, as well as the synchronization of spatial information and event attribute information. Additionally, without any prior information, clustering and matching processes can be carried out rapidly. The publish-subscribe method is implemented on a distributed system, and a load-balancing solution is proposed for the algorithm on the distributed system. We then constructed our cluster and used real data to empirically evaluate the suggested publish-subscribe technique. Experiments show that incorporating both similarity and relevance with frequency, compared to focusing on similarity alone, significantly decreases the number of categories which need to be matched for events. This leads to a 15.5% improvement in the speed of matching events and subscriptions. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Clustering algorithms Distributed systems Publish-and-subscribe systems

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Interpretable machine learning for chiral induced symmetry breaking of spin density boosting hydrogen evolution

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Journal of Energy Chemistry 2025年第4期103卷 68-78页

作者： Xin Song Zhonghua Li Li Sheng Yang Liu School of Chemistry and Chemical Engineering Key Laboratory of Microsystems and Microstructures ManufacturingMinistry of EducationHarbin Institute of TechnologyHarbin 150001HeilongjiangChina School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001HeilongjiangChina

The integration of machine learning and electrocatalysis presents nota ble advancements in designing and predicting the performance of chiral materials for hydrogen evolution reactions(HER).This study utilizes theoretical calculations and machine learning techniques to assess the HER performance of both chiral and achiral M-N-SWCNTs(M=In,Bi,and Sb)single-atom catalysts(SACs).The stability preferences of metal atoms are dependent on chirality when interacting with chiral *** HER activity of the right-handed In-N-SWCNT is 5.71 times greater than its achiral counterpart,whereas the left-handed In-N-SWCNT exhibits a 5.12-fold *** calculated hydrogen adsorption free energy for the right-handed In-N-SWCNT reaches as low as-0.02 *** enhancement is attributed to the symmetry breaking in spin density distribution,transitioning from C_(2V)in achiral SACs to C_(2)in chiral SACs,which facilitates active site transfer and enhances local spin ***-handed M-N-SWCNTs exhibit superiorα-electron separation and transport efficiency relative to left-handed variants,owing to the chiral induced spin selectivity(CISS)effect,with spin-upα-electron density reaching 3.43×10^(-3)e/Bohr^(3)at active *** learning provides deeper insights,revealing that the interplay of weak spatial electronic effects and appropriate curvature-chirality effects significantly enhances HER performance.A weaker spatial electronic effect correlates with higher HER activity,larger exchange current density,and higher turnover *** curvature-chirality effect undersco res the influence of intrinsic structures on HER *** findings offer critical insights into the role of chirality in electrocatalysis and propose innovative approaches for optimizing HER through chirality.

关键词： Symmetry breaking Machine learning Spin density CISS DFT Hydrogen evolution reaction

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Automated Red Deer Algorithm with Deep Learning Enabled Hyperspectral Image Classification

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Intelligent Automation & Soft Computing 2023年第2期35卷 2353-2366页

作者： B.Chellapraba D.Manohari K.Periyakaruppan M.S.Kavitha Department of Information Technology Karpagam Institute of TechnologyCoimbatore641032TamilnaduIndia Department of Computer Science and Engineering St.Joseph’s Institute of TechnologyChennai600119India Department of Computer Science&Engineering SNS College of EngineeringCoimbatore641107India Department of Computer Science&Engineering SNS College of TechnologyCoimbatore641035India

Hyperspectral(HS)image classiﬁcation is a hot research area due to challenging issues such as existence of high dimensionality,restricted training data,*** recognition of features from the HS images is important for effective classiﬁcation ***,the recent advancements of deep learning(DL)models make it possible in several application *** addition,the performance of the DL models is mainly based on the hyperparameter setting which can be resolved by the design of *** this view,this article develops an automated red deer algorithm with deep learning enabled hyperspec-tral image(HSI)classiﬁcation(RDADL-HIC)*** proposed RDADL-HIC technique aims to effectively determine the HSI *** addition,the RDADL-HIC technique comprises a NASNetLarge model with Adagrad ***,RDA with gated recurrent unit(GRU)approach is used for the identiﬁcation and classiﬁcation of *** design of Adagrad optimizer with RDA helps to optimally tune the hyperparameters of the NASNetLarge and GRU models *** experimental results stated the supremacy of the RDADL-HIC model and the results are inspected interms of different *** comparison study of the RDADL-HIC model demonstrated the enhanced per-formance over its recent state of art approaches.

关键词： Hyperspectral images image classiﬁcation deep learning adagrad optimizer nasnetlarge model red deer algorithm

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An Empirical Investigation into Machine Learning Approaches for Breast Cancer Classification 2

An Empirical Investigation into Machine Learning Approaches ...

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2nd IEEE International Conference on Recent Advances in Information technology for Sustainable Development, ICRAIS 2024

作者： Duggal, Anoushka Muhuri, Samya Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Patiala India

ISBN: (纸本)9798350354461

Breast cancer was diagnosed in 2.3 million individuals worldwide in 2022, and was the cause of 670,000 deaths. It ranks as the second most common cancer overall and the most frequently diagnosed cancer in women. Since survival is highly dependent on early detection, the development of computational models for diagnosis is crucial. Among the various models available, we have chosen to focus on three supervised machine learning models in this study: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Feed-forward Neural Networks (FFNN). Their roles in breast cancer detection are investigated. We have pre-processed the data using normalization and feature selection to enhance these models' performances. They are trained on real-life documented patient data to predict breast cancer. It is determined that the FFNN is more accurate than both SVM and KNN. This paper provides a comprehensive analysis of the various machine learning models, highlighting their positive and negative aspects in diagnosing breast cancer. The results underscore the potential of advanced neural networks in medical diagnostics and pave the way for further research and development in this domain. © 2024 IEEE.

关键词： Adversarial machine learning

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Experimental Investigation and Weibull Statistical Analysis on Tensile Behaviour of Carbon/Epoxy Composites

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Journal of The Institution of Engineers (India): Series D 2024年第3期105卷 1373-1383页

作者： Amar, Thakur Abhinav Kumar, Amit Yadav, D.K. Department of Production and Industrial Engineering Birla Institute of Technology Ranchi JH Mesra835215 India Department of Mechanical Engineering National Institute of Technology BH Patna800005 India Department of Computer Science and Engineering National Institute of Technology JH Jamshedpur831014 India

This paper is concerned with the experimental and theoretical evaluation of the tensile strength of carbon fabric-reinforced epoxy composites developed using hand lay-up and vacuum bagging techniques. The developed CFRP composite laminates were subjected to tensile tests as per ASTM D3039 at different strain rates of 1 s−1, 10 s−1, and 100 s−1. The statistical analysis of the experimental results was conducted using a two-parameter Weibull distribution function. The results showed an increase in tensile strength with an increase in strain rate for composite laminates produced using both fabrication techniques. The tensile modulus also showed an increasing trend with respect to strain rate, but the increase was marginal, while elongation at break showed a decreasing trend. For all cases of strain rate, the vacuum bag-fabricated composite laminates showed higher tensile strength and modulus values except for elongation at break when compared with the laminates fabricated with hand lay-up. Cumulative probability density data, along with Weibull parameters, were utilised to determine theoretical tensile strength. The projected tensile strength values closely matched the experimental results, with a minimal difference of approximately 6.2% observed in the context of composite laminates fabricated under vacuum bag conditions. Fracture analysis identified distinct failure mechanisms: adhesive failure at the fabric/matrix interface for the hand lay-up method and brittle fracture as the predominant failure mode for laminates produced using vacuum bagging techniques. © The Institution of Engineers (India) 2023.

关键词： Tensile strength

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Leveraging Large Language Models and RAPIDS AI to Detect Fraudulent AI-Generated Marketplace Reviews

Leveraging Large Language Models and RAPIDS AI to Detect Fra...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Dudeja, Nishit Pundir, Arun Singh Thapar Institute of Engineering and Technology Computer Science and Engineering Department Patiala India

ISBN: (纸本)9798350350821

Consumer confidence is, in the present time, a dilemma given the steadily rising number of deceptive and inaccurate AI-generated reviews on internet marketplaces. There is an urgent need for a thorough dataset, which is nowhere to be found, to address the pervasiveness of fraudulent reviews. In order to close this gap, in this work, we propose relevant detection techniques using GPU-accelerated models from the RAPIDS AI library and new age Large Language Models like GPT-2 and BERT. By carefully constructing a unique dataset of about 54,000 reviews, we have combined human-authored reviews gleaned from web scraping with AI-generated reviews produced with the Chat-GPT 3.5 model, which supports the representative spectrum across all review polarities and offers a solid basis for precise analysis. We show significant accuracy in differentiating between reviews written by humans and those produced by AI and provide an all-inclusive solution to this new issue by using sophisticated LLMs and RAPIDS AI models. Moreover, we also compare the approaches to find the most successful one. This approach not only tackles the problem of fraudulent reviews but also establishes a new benchmark for keeping online marketplaces morally sound. © 2024 IEEE.

关键词： Marketplaces

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A Comparative Study on the Properties of Carbon Fiber-Reinforced Polymer Composites Developed by Hand Layup and Vacuum Bagging Molding Techniques

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Journal of The Institution of Engineers (India): Series D 2024年第1期106卷 263-275页

作者： Amar, Thakur Abhinav Kumar, Amit Yadav, D.K. Department of Production and Industrial Engineering Birla Institute of Technology Jharkhand Mesra Ranchi835215 India Department of Mechanical Engineering National Institute of Technology Bihar Patna800005 India Department of Computer Science and Engineering National Institute of Technology Jharkhand Jamshedpur831014 India

The present study addressed the fabrication of carbon fiber-reinforced polymer (CFRP) composites using two different techniques: manual layup and vacuum bagging molding. The purpose was to compare their unique properties. CFRP composites employ woven roving mat carbon fiber as a reinforcing material, combined with an epoxy matrix consisting of Araldite and hardener. The resin to fiber ratio has been maintained at a 1:1 ratio, while the resin to hardener ratio has been maintained at a 10:1 ratio by weight. Various stacking sequences of carbon/epoxy composites (0/90) were made using both approaches. Optimization of several process parameters was carried out to enhance the mechanical properties of the polymer composites. Every laminate was composed of four to eight layers. Various carbon/epoxy composite orientations were subjected to testing until failure using a universal testing machine, in accordance with the relevant applicable standards (ASTM-D3039), in order to determine the ultimate tensile strength, % elongation (or ductility), and elastic constant. Furthermore, the adhesion intensity between the fiber and resin was assessed by conducting microstructural studies using a scanning electron microscope. The results demonstrated that the samples fabricated using the vacuum bagging mold technique exhibited superior tensile strength compared to the samples fabricated using the hand layup approach. © 2024, The Institution of Engineers (India).

关键词： Resins

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Spatial Dynamics for Identification of Individuals through Gait and Other Locomotion Activities

Spatial Dynamics for Identification of Individuals through G...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Anusha, R. Sanshi, Shridhar Bapuji Institute of Engineering and Technology Dept. of Computer Science and Engineering Davanagere India National Institute of Technology Dept. of Computer Science and Engineering Karnataka Surathkal India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379525

Gait, the pattern of walking, has been extensively studied and various methods have been developed to use it as a biometric for individual recognition. Despite this, the potential to identify individuals through running videos has not been thoroughly explored. The paper introduces a novel method that expands the feature-based approach for identifying individuals based on their running style. This work focuses on extracting the mutual information and location specific metric from the key gait poses of subjects in the testing and training datasets. Later on, the assignment of a testing sample to the training sample is accomplished using the proposed classification method. The experiments are conducted on KTH, OU-ISIR A, and Weizmann database. The efficiency of this method is demonstrated by the obtained experimental results. © 2024 IEEE.

关键词： Feature extraction

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An IoMT-based Federated Learning Survey in Smart Transportation

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Recent Advances in computer science and Communications 2024年第4期17卷 20-39页

作者： Karnam, Geetha Vani Maddikunta, Praveen Kumar Reddy School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India

Internet of Medical Things (IoMT) is a technology that encompasses medical devices, wearable sensors, and applications connected to the Internet. In road accidents, it plays a crucial role in enhancing emergency response and reducing the impact of accidents on victims. Smart Transportation uses this technology to improve the efficiency and safety of transportation systems. The current Artificial Intelligence applications lack transparency and interpretability which is of utmost importance in critical transportation scenarios, such as autonomous vehicles, air traffic control systems, and traffic management systems. Explainable Artificial Intelligence (XAI) provides a clear, transparent explanation and actions. Traditional Machine Learning techniques have enabled Intelligent Transportation systems by performing centralized vehicular data training at the server where data sharing is needed, thus introducing privacy issues. To reduce transmission overhead and achieve privacy, a collaborative and distributed machine learning approach called Federated Learning (FL) is used. Here only model updates are transmitted instead of the entire dataset. This paper provides a comprehensive survey on the prediction of traffic using Machine Learning, Deep Learning, and FL. Among these, FL can predict traffic accurately without compromising privacy. We first present the overview of XAI and FL in the introduction. Then, we discuss the basic concepts of FL and its related work, the FL-IoMT framework, and motivations for using FL in transportation. Subsequently, we discuss the applications of using FL in transportation and open-source projects. Finally, we highlight several research challenges and their possible directions in FL. © 2024 Bentham science Publishers.

关键词： Intelligent systems

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A Cascaded Dynamic Attention-Based Deep Learning Model Segments Brain Tumors 12

A Cascaded Dynamic Attention-Based Deep Learning Model Segme...

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12th International Conference on Current Trends in Advanced Computing, ICCTAC 2024

作者： Choudhary, Kartik Singh, Kaptan Saxena, Amit Computer Science & Engineering Truba Institute of Engineering & Information Technology Bhopal India

ISBN: (纸本)9798350395808

Brain tumor diagnosis and treatment need segmentation, Precision automated segmentation of brain tumors is difficult due to their size, shape, unexpected placements, and fuzzy boundaries. U-Net is a popular medical picture segmentation model due to its simple architecture and high performance. Underutilized contextual information, limited local receptive fields, and lost spatial information are issues. For brain tumor segmentation, CDAU-Net, a novel model with Dynamic Convolution and Non-Local Attention Mechanism, addresses these issues. Two-stage cascaded three-dimensional U-Nets rebuild brain tumor spatial information at better resolution. The cascaded network's lateral connections receive maximal attention, improving its ability to collect long-distance interconnections and use tumor context information. Finally, dynamic convolutions with local adaptive capabilities replace normal convolutions in the cascaded network to increase local features capture. Extensive experiments were conducted on publicly available BraTS 2019 and 2020 datasets, comparing the proposed method with other representative approaches. Proposed method segments brain tumors. Brain tumor segmentation dice values of 0.897/0.903 for whole tumor, 0.826/0.828 for tumor core, and 0.781/0.786 for enhanced tumor segmentation worked in the BraTS 2019/2020 validation set. © 2024 IEEE.

关键词： Convolution

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