One of the areas that stand to gain the most from the adoption of Artificial Intelligence (AI) is Cyber Security. Traditional well-known system approaches may be slow and inadequate despite their virtues for a variety...
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The Greek School Network (GSN) provides support to students, teachers, and school units in secondary education across Greece. Handling numerous user queries manually can be challenging, necessitating the development o...
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ISBN:
(数字)9798350376647
ISBN:
(纸本)9798350376654
The Greek School Network (GSN) provides support to students, teachers, and school units in secondary education across Greece. Handling numerous user queries manually can be challenging, necessitating the development of an automated system for accurate categorization of these queries. This paper presents a comparative study of various transformer-based models for multi-class text categorization of Greek language queries submitted to the GSN helpdesk. We introduce a new experimental balanced dataset and extract vector representations from eleven transformer-based models. These representations are evaluated using ten classic machine learning classifiers. Our findings highlight the superior performance of the Multilingual E5 Text Embeddings model, particularly when paired with the extreme gradient-boosting classifier. This combination demonstrates a clear advantage in accurately categorizing user queries, paving the way for more efficient automated helpdesk systems.
A double output DC/DC resonant converter topology that utilizes a dual CL/LLC resonant networks for microwave magnetron application is proposed in this paper. The proposed dual CL/LLC resonant converter is able to ach...
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The DAG task model has gained significant attention and widespread usage for modeling complex real-time applications, particularly in domains like autonomous driving. To effectively utilize these models, research on t...
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According to a recent WHO study, cardiovascular diseases are on the rise. As a result of which we can see that people dies in a year is approx. 17.9 million. With the growing population, it becomes more and more diffi...
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This paper introduces an innovative approach to Retrieval-Augmented Generation (RAG) for video question answering (VideoQA) through the development of an adaptive chunking methodology and the creation of a bilingual e...
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ISBN:
(数字)9798331523114
ISBN:
(纸本)9798331523121
This paper introduces an innovative approach to Retrieval-Augmented Generation (RAG) for video question answering (VideoQA) through the development of an adaptive chunking methodology and the creation of a bilingual educational dataset. Our proposed adaptive chunking technique, powered by CLIP embeddings and SSIM scores, identifies meaningful transitions in video content by segmenting educational videos into semantically coherent chunks. This methodology optimizes the processing of slide-based lectures, ensuring efficient integration of visual and textual modalities for downstream RAG tasks. To support this work, we gathered a bilingual dataset comprising Persian and English mid- to long-duration academic videos, curated to reflect diverse topics, teaching styles, and multilingual content. Each video is enriched with synthetic question-answer pairs designed to challenge pure large language models (LLMs) and underscore the necessity of retrieval-augmented systems. The evaluation compares our CLIP-SSIM-based chunking approach against conventional video slicing methods, demonstrating significant improvements across RAGAS metrics, including Answer Relevance, Context Relevance, and Faithfulness. Fur-thermore, our findings reveal that the multimodal image-text retrieval scenario achieves the best overall performance, emphasizing the importance of integrating complementary modalities. This research establishes a robust framework for video RAG pipelines, expanding the capabilities of multimodal AI systems for educational content analysis and retrieval 1 1 The dataset is publicly available at: https://***/datasets/uIAICIEduViQA.
Image feature extraction and matching constitute fundamental steps in computer vision and image analysis, enabling various applications ranging from object recognition to scene reconstruction. Object recognition is on...
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Image feature extraction and matching constitute fundamental steps in computer vision and image analysis, enabling various applications ranging from object recognition to scene reconstruction. Object recognition is one of the major applications of artificial intelligence. Since last two decades, different techniques for object recognition were given by researchers from across the world. This paper presents a novel approach for automatic target recognition in autonomous weapons by matching connected graph of feature pixels. The Moving and Stationary Target Acquisition and Recognition (MSTAR) is taken for experimental purpose. The Proposed model has three stages for a target recognition; Image Denoising, Feature extraction & graph generation, and feature matching. Firstly, a sample image of targets taken from the MSTAR is taken as an input. Further, input sample is de-noised and features are extracted using Speeded-Up Robust Features (SURF) algorithm. Thereafter, a graph is created using feature pixels and this complete process is repeated with test image. Further, both graphs are compared using a proposed feature graph matching algorithm which results an recognition accuracy score. Proposed model is tested over the MSTAR dataset and it resulted in varying accuracy at different feature matching thresholds.
The growth in the wireless network is leading to the development of efficient WLAN enterprise that can achieve high level user requirements regarding the next generation wireless technology. Researchers always perform...
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There is growing interest in the use of additive manufacturing for the fabrication of RF devices due to fast prototyping capabilities and the use of less material as opposed to traditional fabrication techniques. In a...
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The agriculture sector in India, a cornerstone of the nation's economy, has a profound impact on social and environmental landscapes. This analysis examines the sector's social responsibility, exploring its co...
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