On the threshold of a new technological era, Sixth Generation (6G) networks promise to revolutionize global connectivity, bringing mobile communications to data speeds in the terabits per second range and ultra-low la...
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Estimating the suitability of individuals for a vocation via leveraging the knowledge within cognitive factors comes with numerous applications: employment resourcing, occupation counseling, and workload management. A...
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Optimizing camera information storage is a critical issue due to the increasing data volume and a large number of daily surveillance videos. In this study, we propose a deep learning-based system for efficient data st...
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Optimizing camera information storage is a critical issue due to the increasing data volume and a large number of daily surveillance videos. In this study, we propose a deep learning-based system for efficient data storage. Videos captured by cameras are classified into four categories: no action, normal action, human action, and dangerous action. Videos without action or with normal action are stored temporarily and then deleted to save storage space. Videos with human action are stored for easy retrieval, while videos with dangerous action are promptly alerted to users. In the paper, we propose two approaches using deep learning models to address the video classification problem. The first approach is a separate approach, where pretrained CNN models extract features from video frame images. These features are then passed through RNN, Transformer models to extract relationships between them. The goal of this approach is to delve into extracting features of objects in the video. The proposed models include VGG16, InceptionV3 combined with LSTM, BiLSTM, Attention, and Vision Transformer. The next approach combines CNN and LSTM layers simultaneously through models like ConvLSTM and LRCN. This approach aims to help the model simultaneously extract object features and their relationships, with the goal of reducing model size, accelerating the training process, and increasing object recognition speed when deployed in the system. In Approach 1, we construct and refine network architectures such as VGG16+LSTM, VGG16+Attention+LSTM, VGG16+BiLSTM, VGG16+ViT, InceptionV3+LSTM, InceptionV3+Attention+LSTM, InceptionV3+BiLSTM. In Approach 2, we build a new network architecture based on the ConvLSTM and LRCN model. The training dataset, collected from real surveillance cameras, comprises 3315 videos labeled into four classes: no action (1018 videos), actions involving people (832 videos), dangerous actions (751 videos), and normal actions (714 videos). Experimental results show t
Hyperspectral images have a lot of spatial and spectral information, which makes them great for fine crop classification and detection. The hyperspectral remote sensing is more capable than panchromatic remote sensing...
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In this work, we formulate an accelerated image fusion algorithm for Unmanned Aerial Vehicle (UAV) application which is based on image stitching using invariant features. By utilizing parallel computing techniques for...
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Orchestrating next-gen applications over heterogeneous resources along the Cloud-IoT continuum calls for new strategies and tools to enable scalable and application-specific managements. Inspired by the self-organisat...
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The proliferation of Internet of Things (IoT) devices has introduced significant security challenges due to the increased attack surface and the inherent vulnerabilities of interconnected systems. This paper proposes ...
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The growth of internet used automobile shopping has been tremendous in recent years. It is important for sellers to correctly evaluate the price of the used cars. From the literature, classification is an approach tha...
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As social media continues to gain popularity, offensive language on the Internet is on the rise, and how to effectively detect it has become a hot spot in the academic community. Due to the strong context-dependence o...
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mathematics is often considered a difficult and tedious subject as most of its concepts consist of symbolic definitions, properties, laws, and theorems. According to research, logarithms and exponents are some of thos...
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ISBN:
(数字)9781905824731
ISBN:
(纸本)9798350356595
mathematics is often considered a difficult and tedious subject as most of its concepts consist of symbolic definitions, properties, laws, and theorems. According to research, logarithms and exponents are some of those mathematics concepts students frequently encounter difficulties with, such as interpreting the notation used to express logarithms, understanding the notion of logarithms, and considering the laws and principles when working with logarithmic and exponential equations. This study aimed to investigate how augmented reality tools can help University of the Western Cape pre-calculus students better understand the rules and regulations for working with logarithmic and exponential equations and functions. An Augmented Reality (AR) mobile application called logAR(ithms) was developed using the agile scrum methodology to ensure that the project was completed successfully, and that the development process was in line with the application's specified requirements. The application has immersive features such as the viewing of graphing functions in an immersive and interactive environment. Future recommendations for the logAR(ithms) application include implementing user testing to collect user experience data while incorporating a database of results of the application's assessment.
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