International Journal of Intelligent Information Technologies

The semantic web is a global initiative which employs ontologies to offer rich, semantic-based knowledge representation. Concepts in these ontologies are explored to find (dis)similarities between them using (dis)similarity measures. Despite the existence of numerous (dis)similarity measures, none have dynamically determined the quantum of information required to discover (dis)similarities between concepts. In this article, a new, efficient, feature-based semantic dissimilarity measure is proposed where the prime novelty lies in the dynamic selection of the semantic neighourhood (features) of the concepts. The neighbourhood is dynamically selected in accordance with the local density of the concept and the density of the ontology determined by the proposed density coefficient. Further, the proposed measure also scales down the dissimilarity value in accordance with the depth of the concept pair, using the novel Depth Coefficient.

Sathiya Balasubramanian

None Geetha T. V.

A New Dynamic Neighbourhood-Based Semantic Dissimilarity Measure for Ontology

基于新动态邻域的本体语义相异度量

Recognizing food images presents unique challenges due to the variable spatial layout and shape changes of ingredients with different cooking and cutting methods. This study introduces a new approach for recognizing multiple ingredients segmented from food images. The method localizes the candidate regions of the ingredients, and then, these regions are assigned into ingredient classes using a CNN-based single-ingredient classification model trained on a dataset of single-ingredient images covering 110 kinds of foundational ingredient categories. Subsequently, the multi-ingredient identification is achieved through a decision-making scheme, incorporating a novel top n algorithm with integrating the classification results from various candidate regions to improve the ingredient recognition accuracy. Experimental results validate the effectiveness and efficiency of our method, particularly highlighting its competitive performance in recognizing multiple ingredients compared to state-of-the-art (SOTA) methods, while offering high interpretability.

Kun Fu

Ying Dai

Recognizing Multiple Ingredients in Food Images Using a Single-Ingredient Classification Model

使用单成分分类模型识别食品图像中的多种成分

To solve the problem of low detection accuracy of deep learning targets for crack detection in complex backgrounds, The objective of this study is to propose a fusion (CBAM) attention mechanism to enhance the YOLOv5 model for high-precision bridge crack recognition. At the same time, a consistent bridge crack dataset covering different lighting conditions and humidity environments is constructed. To validate the effectiveness of the proposed model, it was compared and analyzed with current mainstream deep learning models (e.g., CNN, VGG19,etc.).The experimental results indicate that incorporating the CBAM attention mechanism, the proposed model was improved by 5.6% in precision, 1.2% in recall, and 13% in optimization of the integrated discrimination parameters compared to the original YOLOv5 model. In addition, in terms of width detection. The experimental results indicate that the model can reach 0.01 mm in measurement accuracy, The research contributes to the efficiency, precision and intelligence of concrete bridge crack detection.

Yingjun Wu

Junfeng Shi

Wenxue Ma

Bin Liu

Bridge Crack Recognition Method Based on Yolov5 Neural Network Fused With Attention Mechanism

基于注意力机制融合Yolov5神经网络的桥梁裂缝识别方法

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

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

Abbreviation	Int. J. Intell. Inf. Technol.
Journal Impact	0.78
Quartiles(Global)	COMPUTER SCIENCE, INFORMATION SYSTEMS(Q4)

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

Publication Information	Publisher: IGI Publishing，Publishing cycle: ，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2005，Proportion of original research papers: 100.00%，Self Citation Rate:0.00%， Gold OA Rate: 73.47%

International Journal of Intelligent Information Technologies

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A New Dynamic Neighbourhood-Based Semantic Dissimilarity Measure for Ontology

Recognizing Multiple Ingredients in Food Images Using a Single-Ingredient Classification Model

Bridge Crack Recognition Method Based on Yolov5 Neural Network Fused With Attention Mechanism

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

Cryptocurrency Transaction Fraud Detection Based on Imbalanced Classification With Interpretable Analysis

Recognizing Multiple Ingredients in Food Images Using a Single-Ingredient Classification Model

Bridge Crack Recognition Method Based on Yolov5 Neural Network Fused With Attention Mechanism

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