Facial expression is the most powerful, natural and universal nonverbal means for human beings to convey their emotional state and intention in the process of communication. Face expression recognition can make robots...
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As Unmanned Aerial Vehicles (UAVs) become more accessible to the public, they become a common tool for malicious purposes. As a result, there is an increasing demand for Counter Unmanned Aerial systems (CUAS) that can...
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ISBN:
(纸本)9798331521561;9798331521554
As Unmanned Aerial Vehicles (UAVs) become more accessible to the public, they become a common tool for malicious purposes. As a result, there is an increasing demand for Counter Unmanned Aerial systems (CUAS) that can detect UAVs. Existing CUAS solutions often rely on high-priced radar systems or advanced technologies, primarily designed for military purposes. In this paper, a low-cost, effective, non-military CUAS that uses inexpensive smartphones' microphones and camera, along with machine learning models, is proposed to detect and track a malicious UAV (MUAV) in real-time. Our proposed CUAS is designed to be affordable and accessible to the general public, operating automatically to detect and track MUAVs in real-time.
作者:
Nandhini, J.T.thinakaran, K.
Department of Computer Science and Engineering Saveetha School of Engineering Chennai India
the field of computer vision stands to benefit significantly from automated crime scene detection. In this work, we demonstrate the application of DNN (Deep Neural Network) to identify a knife, blood, and gun in a pic...
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Node classification in complex networks plays an important role including social network analysis and recommendation systems. Some graph neural networks such as Graph Convolutional Networks (GCN) and Graph Attention N...
Node classification in complex networks plays an important role including social network analysis and recommendation systems. Some graph neural networks such as Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) have emerged as effective approaches for achieving high-performance classification in such tasks. However, constructing a graph neural network architecture is challenging particularly due to the complex task of determining the optimal number of layers. this study presents a mathematical formula for determining the optimal number of GCN and GAT hidden layers. the experiment was conducted on ten benchmark datasets, evaluating performance metrices such as accuracy, precision, recall, F1-score, and MCC for identifying the best estimation of number of hidden layers. According to the experimental findings, the number of GAT and GCN layers selected has a substantial impact on classification accuracy. Studies show that adding extra layers after the optimum number of layers has a negative or no impact on the classification performance. Our proposed approximation technique may provide valuable insights for enhancing efficiency and accuracy of the Graph Neural Network algorithms.
Withthe increasing usage of networks, the frequency of cyber attacks are expected to increase. To prepare against these attacks, modern organizations require effective ways to ensure cyber security. Twitter is one of...
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Withthe increasingly fierce competition in the automobile industry, the demand for digital transformation is increasingly strong, and the requirements for system operation and maintenance are also increasingly high. ...
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Current signals are commonly used as output signals of sensors. For sensors with output signals ranging from 4 to 20mA, a data acquisition system based on STM32F103 microcontroller of ARM Cortex-M3 kernel is proposed ...
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this study proposes a new fuzzy control method based on Improved Genetic Algorithm (IGA) that employs the Parallel Distributed Compensation technology to achieve the exponential synchronization of two chaotic systems ...
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Withthe recent rise of large language models, learning assistance systemsthat utilize natural language processing capabilities are gaining attention. However, existing learning assistance systems mainly aim to provi...
Withthe recent rise of large language models, learning assistance systemsthat utilize natural language processing capabilities are gaining attention. However, existing learning assistance systems mainly aim to provide answers to learners' questions, and have limitations in accurately identifying individuallearning needs and analyzing questions to provide customized answers. therefore, in this study, we compare chatGPT and mentor responses in the process of learning a programming language and find out the limitations of each and develop a system to assist programming language learning using chatGPT. the developed system allows learners to get realtime answers from chatGPT immediately, and can also provide additional personalized guidance and answers based on the mentor's judgment. In addition, through the analysis of learners' responses and chatGPT's answers, we aim to evaluate the appropriateness and accuracy of the answers and explore and suggest technical limitations and improvement measures of chatGPT. By analyzing and discussing the differences between the traditional mentoring method and the learning assist method using chatGPT, we aim to diagnose the limitations of chatGPT in programming language learning. through this study, we expect to develop an effective programming language learning assistance system by integrating the advantages of chatGPT and mentoring to improve learners' understanding and learning efficiency.
the aim of this research study is to present a new YouTube downloader software tool that leverages the power of both artificial intelligence and the Python programming language. this innovative solution combines cutti...
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