This paper demonstrates 2 methods, a reduced memory technique, and a reduced memory along with more security techniques in RSA (Rivest-Shamir-Adleman) and ElGamal which are both asymmetric cryptographic algorithms. Re...
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Remote sensing object detection plays a crucial role in environmental monitoring, yet traditional methods often face difficulties with varying object scales, intricate backgrounds, and densely populated small objects....
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Academic Atlas is a tool developed to facilitate access to academic materials such as previous year's question papers, capstone projects, and research papers. Of major concerns for the students would be that they ...
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Emergence of deep learning has significantly improved the performances of machine learning models, and various pre-trained models have made life easy for organizations that intended to accomplish their task with the h...
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The three-frame difference is a renowned tactic for detecting moving items. According to the idea, the existence of a moving object can be inferred by removing three subsequent image frames that display the moving obj...
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This paper presents a multi-agent hierarchical workflow tailored for automating data analysis, code generation, and visualization, focusing specifically on user-provided CSV datasets. The workflow integrates AlphaCodi...
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Spectral efficiency (SE) and energy efficiency (EE) are the two key performance metrics for achieving success in 6G heterogeneous networks (HetNets), especially in environments with high user density and limited spect...
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This study explores the request of Mechanism Education (ML) techniques for predicting wine quality, aiming to enhance the understanding and precision in assessing the characteristics of different wine varieties. The d...
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Egocentric activity recognition, also known as first-person vision, captures human actions and activities from the perspective of a wearable camera, providing a personalized and contextualized viewpoint. This unique p...
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ISBN:
(纸本)9798350382723
Egocentric activity recognition, also known as first-person vision, captures human actions and activities from the perspective of a wearable camera, providing a personalized and contextualized viewpoint. This unique perspective is crucial for various real-world applications, including healthcare monitoring, sports analy-sis, augmented reality, and assistive technologies. In this paper, we present a comparative analysis of various state-of-the-art algorithms for egocentric action recognition using the recently curated dataset, 'Coer-Egovision.' This dataset captures human actions and activities from the unique perspective of a wearable camera, providing a personalized and context-aware viewpoint. Such a perspective holds significant value for real-world applications, including healthcare monitoring, sports analysis, augmented reality, and assistive *** 'Coer-Egovision' dataset encompasses diverse actions, such as 'Going-Downstairs', 'Going-Upstairs', 'Texting', 'Walking' and 'Writing' allowing for a challenging benchmark to evaluate algorithms in this domain. By leveraging the wearable camera perspective, the dataset addresses the need for a more com-prehensive understanding of human interactions with the *** the evaluated algorithms, the Long-term Recurrent Convolutional Network (LRCN) emerges as the top performer, achieving an impressive accuracy of 88% in effectively modeling long-term temporal dependencies. Additionally, the Histogram of Oriented Gradients (HOG) combined with Support Vector Machine (SVM) approach demonstrates robust performance with 82% accuracy, highlighting the efficacy of feature-based methods. The study also reveals promising results for the 2-Stream Approach with 3D CNN and LSTM, achieving an accuracy of 74%. Furthermore, pretrained models like VGG16 and DenseNet201 along with LSTM exhibit competitive performances with accuracies of 80% *** results offer valuable insights into the performance of diverse algo
Water quality assessment is considered one of the most necessary measures taken to safeguard public health and maintain ecological balance. This work aims to design a machine learning-based framework for determining t...
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