International Journal of Intelligent Information Technologies

In the context of the close integration of computer network technology and daily life, big data network security faces many challenges. With its powerful information processing ability, this AI can effectively identify and handle potential threats in the network, thereby improving network security. This paper discusses the application of artificial intelligence (AI) in enhancing big data network security defense, especially in the threat detection link. This paper proposes a detection algorithm that combines support vector machine (SVM) and K nearest neighbor (KNN), namely SVM-KNN algorithm, for network attack detection and defense. When the hazard data is 60 under the test sample, the detection accuracy of KNN, SVM, and SVM-KNN is 86.88%, 89.51%, and 96.75% respectively; when the hazard data is 60 under the experimental sample, the detection accuracy of KNN, SVM, and SVM-KNN is 82.70%, 83.88%, and 89.26% respectively.response.

Haitao He

Lin Luo

Qiong Zhao

Analysis of Big Data Network Security Defense Mechanism Application of Artificial Intelligence

人工智能在大数据网络安全防御机制中的应用分析

This study introduces an interpretable imbalanced data classification method for detecting cryptocurrency transaction fraud. We address data imbalance using SMOTE oversampling and data augmentation through contrastive learning. Next, we introduce a Transformer-based deep learning model that learns sample relevance. The model undergoes pre-training with a contrastive loss and fine-tuning through Bayesian optimization to effectively extract high-dimensional, higher-order, and fraud-related features. We employ a SHAP-based interpreter along with attention scores to elucidate the role of various transaction features in fraud detection. Comparative results demonstrate the model's remarkable recall performance in identifying cryptocurrency transaction fraud. Furthermore, it achieves an excellent F1 value, striking a balance between accuracy and recall. This research not only enriches financial fraud detection but also enhances cryptocurrency transaction security, promotes market development, and contributes to economic stability and social security.

Pei Yin

Wen-long Jiang

Zi-jie Ma

Li-ke Zhang

Cryptocurrency Transaction Fraud Detection Based on Imbalanced Classification With Interpretable Analysis

基于可解释分析的不平衡分类加密货币交易欺诈检测

The explosion of multimedia content over the past years is not surprising, thus their efficient management and analysis methods are always in demand. The affectiveness of any multimedia content deals with analyzing human perception and cognition while watching it. Human attention is also one of the important parameters, as it describes the engagement and interestingness of the user while watching that content. Considering this aspect, a video tagging framework is proposed in which the EEG signals of participants are used to analyze human perception while watching videos. A rigorous analysis has been performed on different scalp locations and frequency rhythms of brain signals to formulate significant features corresponding to affective and interesting video content. The analysis presented in this paper shows that the extracted human attention-based features are generating promising results with the accuracy of 93.2% using SVM-based classification model which supports the applicability of the model for various BCI-based applications for automatic classification of multimedia content.

Shanu Sharma

Ashwani Kumar Dubey

Priya Ranjan

Affective Video Tagging Framework using Human Attention Modelling through EEG Signals

基于脑电信号的人类注意力建模情感视频标签框架

This paper presents a hybrid model that is used to measure the waste recyclability level using a convolutional neural network (CNN) and fuzzy inference system (FIS; WRL-CNNFIS). The proposed system uses waste images to train a multilayer convolutional neural network to extract the most relevant features that were used in a rule-based fuzzy system to give an accurate percentage of the recyclability level of these images. The proposed model did overcome many challenges in transfer learning models alone, like overfitting and low accuracy. The use of fuzzy rules, improved the performance even with a small data set, Results have shown the effectiveness of the proposed model in terms of all four metrics: accuracy, precision, recall, and F1 score. The performance was measured under two testing scenarios. For all evaluation measurement, in all experiments the validation was conducted using the cross validation in the last step. The proposed approach is a robust and consistent approach for classifying organic and recyclable waste types. WRL-CNNFIS has achieved accuracy rate of more than 98%.

Rawan Ghnemat

Adnan Shaout

Measuring Waste Recyclability Level Using Convolutional Neural Network and Fuzzy Inference System

使用卷积神经网络和模糊推理系统测量废物可回收性水平

Blockchain technology is one of the key technologies that have revolutionized various facets of society, such as the banking, healthcare, and other critical ecosystems. One area that can harness the usage of blockchain is the real estate sector. The most lucrative long-term investment is real estate, followed by gold, equities, mutual funds, and savings accounts. Nevertheless, it has administrative overheads such as lack of transparency, fraud, several intermediaries, title issues, paperwork, an increasing number of arbitrations, and the lack of liquidity. This paper proposes a framework that uses blockchain as an underlying technology. With the aid of blockchain and the suite of tools, it supports many of these problems that can be alleviated in the real estate investment ecosystem. These include smart contracts, immutable record management, tokenization, record tracking, and time-stamped storage. Tokenization of real estate lowers the entry barrier by fixing liquidity and interoperability and improving the interaction between various stakeholders.

Shashank Joshi

Arhan Choudhury

Tokenization of Real Estate Assets Using Blockchain

使用区块链对房地产资产进行代币化

In response to the large monitoring errors and incomplete consideration of fault locations in the operation status of wind turbines, this paper combined vibration signals and used a model constructed using nonlinear state estimation technology (NSET) to monitor and optimize the operation status of wind turbines. Firstly, it collected relevant SCADA (supervisory control and data acquisition) and vibration sensor data of Unit 5 of Guangdong Yangjiang Shaba Offshore Wind Farm on site, and used wavelet transform to denoise the vibration signal. Then, full consideration can be given to the faulty parts such as wind blades and gearboxes, and a model constructed by NSET using Markov distance to construct a process memory matrix can be used to monitor the status of the wind turbine equipment. Finally, the multi-level fuzzy comprehensive evaluation method can be used to optimize the comprehensive evaluation of the operating status of wind turbines, reducing monitoring errors.

Wei Wang

Yingchun Shi

Yahui Huang

Intelligent Monitoring and Optimization of Wind Turbine Operation Status on Basis of Vibration Signals

基于振动信号的风电机组运行状态智能监测与优化

On the basis of introducing the concept of intersection signal state phase difference, the author proposes a proportional signal phase difference design scheme for traffic signal system control, on the basis of the HCM signal delay calculation formula, a standardized signal cycle and effective green signal ratio optimization design model were compiled, thus forming a complete set of optimization design modes for the control parameters of the urban main road traffic signal group system. The experimental results indicate that, the entire simulation experiment took 6000 seconds to compare the traditional fuzzy algorithm with the controller implemented by the author's fuzzy neural network, each case was simulated 5 times, the data in the Table is the average of 5 times, compared with traditional fuzzy algorithms, the author's method shows an improvement of 20.8% to 45.67% in average vehicle delay time, especially when the traffic flow is different in the east-west and north-south directions, the improvement is more significant.

Xiuli Li

Huachang Miao

Application of Artificial Intelligence Technology in Optimizing Control Parameters of Traffic Signal Group Systems

人工智能技术在交通信号群系统优化控制参数中的应用

Based on the LSTM neural network, the author proposes a new data-driven spatial load prediction method to analyze the time series within the neural network, avoid data depression, and determine the correlation of the training data space. Establish prediction models based on different neurons, reduce the dimensionality of collected data through data preprocessing, and ensure data integrity. At the same time, provide data management base, control model input and output, unify process model, ensure training sequence model, combine LSTM neural network model, select prediction method, and finish data-driven related transportation. Experimental results show that the data driven global load forecasting model based on LSTM neural network proposed in this paper takes 1.23 seconds to complete 8000 training data, when traditional data drive spatial load forecasting method based on CNN neural network takes 3.56 seconds to finish 8000 training data. It can be seen that the prediction method proposed in this article has a good prediction accuracy.

Qing Wang

Naigen Li

A Long Short-Term Memory Neural Network Algorithm for Data-Driven Spatial Load Forecasting

一种用于数据驱动空间负荷预测的长短期记忆神经网络算法

短名	Int. J. Intell. Inf. Technol.
Journal Impact	0.78
国际分区	COMPUTER SCIENCE, INFORMATION SYSTEMS(Q4)

ISSN	1548-3657, 1548-3665
h-index	19

涉及主题	计算机科学人工智能程序设计语言操作系统数学哲学工程类机器学习经济万维网政治学法学数据挖掘生物语言学业务认识论情报检索
出版信息	出版商: IGI Publishing，出版周期: ，期刊类型: journal，开源期刊: 否
基本数据	创刊年份: 2005，原创研究文献占比: 100.00%，自引率:0.00%， Gold OA占比: 73.47%

International Journal of Intelligent Information Technologies

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Analysis of Big Data Network Security Defense Mechanism Application of Artificial Intelligence

Cryptocurrency Transaction Fraud Detection Based on Imbalanced Classification With Interpretable Analysis

Affective Video Tagging Framework using Human Attention Modelling through EEG Signals

Measuring Waste Recyclability Level Using Convolutional Neural Network and Fuzzy Inference System

Tokenization of Real Estate Assets Using Blockchain

Analysis of Big Data Network Security Defense Mechanism Application of Artificial Intelligence

Cryptocurrency Transaction Fraud Detection Based on Imbalanced Classification With Interpretable Analysis

Intelligent Monitoring and Optimization of Wind Turbine Operation Status on Basis of Vibration Signals

Application of Artificial Intelligence Technology in Optimizing Control Parameters of Traffic Signal Group Systems

A Long Short-Term Memory Neural Network Algorithm for Data-Driven Spatial Load Forecasting