A Malicious Webpage Detection Algorithm Based on Image Semantics

Li, Xiangjun; Li, Sifan; Liu, Shengnan; Liu, Lingfeng; He, Daojing

doi:10.18280/ts.370115

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…The ANN is a technique that spans across different disciplines, such as biology, computer science, and mathematics. It is extensively applied in machine learning, and target recognition [29,30]. The ANN, composed of artificial nodes that simply and mimic biological neurons, simulates how the biological neural system responds to specific objects [31].…”

Section: Methodsmentioning

confidence: 99%

Amos-Based Risk Forecast of Manufacturing Supply Chain

Wan¹

2021

Int. j. simul. model.

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Manufacturing supply chain is vulnerable to various risks, because of its complex network structure, as well as the strong sensitivity of manufacturing to the dynamic market changes. Therefore, the management of supply chain risks has become the focus of manufacturers. To help Chinese enterprises reduce or eliminate supply chain risks, this paper puts forward several hypotheses and a risk forecast model for manufacturing through theoretical analysis. By the Amos method, the hypotheses were tested through path analysis with empirical data. Finally, an artificial neural network (ANN) was adopted to verify the effectiveness of the proposed Amos model. The study provides the reference for preventing supply chain risks and promoting the healthy development of manufacturing enterprises.

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Section: Methodsmentioning

confidence: 99%

Amos-Based Risk Forecast of Manufacturing Supply Chain

Wan¹

2021

Int. j. simul. model.

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“…2.2 and 25 of the Chinese Patent Code are closely related, and patent examiners frequently refer to both of them when examining applications relating to computer programs. 70 Naturally, AI-related inventions are no exception. For example, supervised learning and semi-supervised learning are typical learning methods for machine learning, a sub-field of AI that is concerned with the automated detection of meaningful patterns in data and using the patterns for specific tasks.…”

Section: Basics Of the Chinese Patent Codementioning

confidence: 99%

Smart Grids and Machine Learning in Chinese and Western Intellectual Property Law

Papastefanou

2021

IIC

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Having huge power grids successfully integrate sustainable energy sources requires a smart and flexible power grid management system. Such smart systems have to adapt fast and accurately to a great amount of data input – a task which is made easier by applying modern machine learning technology. Solutions crafted by dynamic and powerful computing algorithms have the potential to surpass human cognitive capabilities. The question arises whether and how intellectual property law can be used to set the right incentives. This paper initially describes the basic functions of smart grids and the corresponding necessity of machine learning. Subsequently, it will analyze the current approaches of the most relevant patent offices in dealing with the challenges of AI-related smart grid inventions. Ultimately, it will be demonstrated that the contemporary discussions fail to focus on practical considerations of market entry possibilities that might be more promising than the approach of creating new exclusionary intellectual property rights.

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“…In 2020, a study of malicious webpage detection algorithm based on image semantics that derived by the backpropagation neural network (BPNN) to obtain the target image, and combine the image with other functions of the malicious website, so that the malicious website can be detected [14]. In 2019, Fenjiro and Benbrahim proposed an optimal combination of imitation and reinforcement learning for selfdriving cars, carry out self-driving training through deep learning [15].…”

Section: Introductionmentioning

confidence: 99%

Sound Visualization and Convolutional Neural Network in Fault Diagnosis of Electric Motorbike

Wu¹,

Hsieh²,

Luo³

2021

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This study proposed convolutional neural network (CNN) training for different figure recognition to diagnose electric motorbike faults. Traditional motorbike maintenance is usually carried out by technicians to find the problem step by step. Many resources are wasted and time consumed in diagnosing maintenance problems. Due to rising environmental protection awareness, motorbike power systems gradually transformed from combustion engines into the electric motor. The sound amplitude generated by the combustion engine is great and may cover other faulty sounds. The electric power system sound amplitude is greatly decreased, permitting various fault diagnosis to be performed by extracting the electric motor sound. With the development of computers and image processing, deep learning neural network for picture recognition technology becomes more feasible. This study presents the motor system sound visualization for fault diagnosis. First obtain the sound signals of the motor in the five different states of the operation in the laboratory and the road test, and draw the time domain graph, frequency domain graph and spectrogram graph to be used as the test database. The results graphs of various states were trained through a CNN. The signal states were then classified to achieve fault diagnosis. Experiments and identification results show that the spectrogram and CNN method can identify motorbike faults most effectively compared to the time domain graph and the frequency domain graph.

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A Malicious Webpage Detection Algorithm Based on Image Semantics

Cited by 3 publications

References 15 publications

Amos-Based Risk Forecast of Manufacturing Supply Chain

Amos-Based Risk Forecast of Manufacturing Supply Chain

Smart Grids and Machine Learning in Chinese and Western Intellectual Property Law

Sound Visualization and Convolutional Neural Network in Fault Diagnosis of Electric Motorbike

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