the proceedings contain 85 papers. the topics discussed include: review on machine learning-based change detection and pan sharpening algorithms for remote sensing datasets;design of 4-stage single-ended ring oscillat...
ISBN:
(纸本)9798350319651
the proceedings contain 85 papers. the topics discussed include: review on machine learning-based change detection and pan sharpening algorithms for remote sensing datasets;design of 4-stage single-ended ring oscillators with quadrature-phase output;brain tumor classification on MRI images with big transfer and vision transformer: comparative study;detection of heart disease using association pattern mining;design and performance analysis of gold-coated photonic crystal fiber plasmonic biosensor;sentiment analysis on Bangla and phonetic Bangla reviews: a product rating procedure using NLP and machine learning;whale optimization algorithm for renewable energy sources integration considering solar-to-vehicle technology;and ANTIBIOLOG: an advanced tool for combat antibiotic resistance with enhanced multinomial logistic regression.
the proceedings contain 134 papers. the topics discussed include: a machine learning-based lightweight and real-time cardiac arrhythmia detection using optimum samples and features;land cover classification by decisio...
ISBN:
(纸本)9798350332506
the proceedings contain 134 papers. the topics discussed include: a machine learning-based lightweight and real-time cardiac arrhythmia detection using optimum samples and features;land cover classification by decision based classifier using dual polarimetric SAR observables;recent optimization techniques for coordinated control of electric vehicles in super smart power grids network: a state of the art;performance of Resnet-16 and inception-V4 architecture to identify COVID-19 from x-ray images;ensemble learning approaches for detecting Parkinson’s disease;a search space reduction algorithm applied for transient stability constrained optimal power flow;machine learning based selection of myocardial complications to predict heart attack;PSO based optimal placement of distributed generation for loss minimization and voltage profile improvement under contingencies;and a comprehensive study of solar energy harvesting system in wireless sensor networks.
the widespread use of deepfake technology has created previously unheard-of difficulties for digital media9;s credibility and authenticity. We describe a unique deepfake detection model that addresses this urgent p...
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Fault tolerance is an important aspect of robotics systems. In this paper, a review of some of the fault tolerant techniques used in multi robot systems is presented. A multi-level fault tolerant robotic system framew...
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the Internet of things (IoT) is an interconnection of several devices that work together to ease tasks. Cloud services are an integral part of IoT, which augments the functionality of the devices;each device can conne...
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Due to the wide scope of business, multiple audit processes, and complex event correlation in power engineering, it is difficult to achieve digital operations in the audit business. this article proposes a power scala...
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ISBN:
(纸本)9798350350227;9798350350210
Due to the wide scope of business, multiple audit processes, and complex event correlation in power engineering, it is difficult to achieve digital operations in the audit business. this article proposes a power scalable business audit method. Firstly, by constructing an audit business process based on an event framework, the workload and business expansion pressure of domain experts can be reduced. Subsequently, the Transformer combined with template matching was used for event extraction. Use the identified audit events of fixed documents as remote knowledge to generate trigger word templates with clear prompt knowledge. this method can supplement remote knowledge as needed based on different project themes, thereby improving the accuracy of event extraction. Finally, the identified audit events are assigned to different audit stages and event frameworks. this is used to support the verification and warning of related audit events during the process. Experiments have shown that in low manual labeled text, the accuracy of event extraction is higher than 93% by this method. Extracting events can accurately match various audit business processes, which supports verification warning of related events.
this research delves into the difficulty of summarizing legal documents using Natural Language Processing. It examines how cutting-edge models like XLNet and BART can be used for abstractive summarization specifically...
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In today9;s digital world, the real-time generation and analysis of data, particularly on social media platforms, are becoming increasingly important. this study aims to design and evaluate a scalable architecture ...
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Nowadays, Convolutional neural networks (CNNs) have become the benchmark technology for many computer vision applications. Face landmark detection has been an important topic over the last few decades. However, most a...
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ISBN:
(纸本)9798350372694;9798350372700
Nowadays, Convolutional neural networks (CNNs) have become the benchmark technology for many computer vision applications. Face landmark detection has been an important topic over the last few decades. However, most algorithms are designed for small to medium-sized poses of faces and lack accuracy in complex scenes such as lighting, large poses, and occlusion. there are some challenges: first, the appearance of the face varies more from the frontal view to the profile view. Second, human faces are easily affected by occlusions or illumination, so it is hard to provide the most appropriate location. In this article, we proposed using joint CNNs to improve the accuracy of facial landmark estimation. Extensive experiments conducted on large angles and severe occlusion challenging databases such as Menpo and COFW have also demonstrated the superiority of our proposed method in challenging scenarios.
Optimizing methodologies for identifying vital nodes within complex networks is an active field of study. Our findings indicate that although the traditional techniques for static undirected complex networks are effec...
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ISBN:
(纸本)9798350350227;9798350350210
Optimizing methodologies for identifying vital nodes within complex networks is an active field of study. Our findings indicate that although the traditional techniques for static undirected complex networks are effective in vital node identification, opportunities persist for enhancing both node ranking accuracy and resolution. In response, we introduce a novel approach, K-Shell Convoluted Proximal Aggregation (KSCPA). this novel method incorporates the convolution characteristic from Graph Convolutional Networks (GCNs), enabling localized node evaluation through the aggregation of adjacent node information. To evaluate the proposed method, we employed Kendall's Tau correlation index to compare withthe Susceptible Infected Recovered model (SIR) for ranking accuracy and used a monotonicity function to assess ranking resolution. At the end, empirical analysis across 12 real-world network datasets reveals that KSCPA outperforms established techniques in ranking accuracy and resolution, thereby confirming its efficacy in identifying key nodes in complex networks.
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