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KIDNet : a knowledge-aware neural network model for academic performance prediction

- Tang, Tao, Hou, Jie, Guo, Teng, Bai, Xiaomei, Tian, Xue, Noori Hoshyar, Azadeh

  • Authors: Tang, Tao , Hou, Jie , Guo, Teng , Bai, Xiaomei , Tian, Xue , Noori Hoshyar, Azadeh
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2021, Virtual, Online14-17 December 2021, ACM International Conference Proceeding Series p. 37-44
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  • Description: Academic performance prediction and analysis in educational data mining is meaningful for instructors to know the student's ongoing learning status, and also provide appropriate help to students as early as possible if academic difficulties appear. In this paper, we first collect the basic information of students and courses as features. Then, we propose a novel knowledge extraction framework to obtain course knowledge features to reinforce feature groups. The comparative analyses of the knowledge similarity and average grades of the courses in all terms demonstrate a strong correlation between them. Furthermore, we build the Knowledge Interaction Discovery Network (KIDNet) model, based on factorization machine (FM) and deep neural network (DNN) algorithms. This model uses FM to model lower-order interactions of sparse features and employs DNN to model higher-order interactions of both dense and sparse features. The effectiveness of KIDNet has been validated by conducting experiments based on a real-world dataset. © 2021 ACM.

Analysis of failure in concrete and reinforced-concrete beams for the smart aggregate–based monitoring system

- Noori Hoshyar, Azadeh, Samali, Bijan, Liyanapathirana, Ranjith, Taghavipour, Saber

  • Authors: Noori Hoshyar, Azadeh , Samali, Bijan , Liyanapathirana, Ranjith , Taghavipour, Saber
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Structural health monitoring Vol. 19, no. 2 (2020), p. 463-480
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  • Description: Monitoring of structures and defining the severity of damages that occur under loading are essential in practical applications of civil infrastructure. In this article, we analyze failure using a smart aggregate sensor–based approach. The signals captured by smart aggregate sensors mounted on the structure under loading are de-noised using wavelet de-noising technique to prevent misdirection of the event interpretation of what is happening in the material. The performance of different mother wavelets on the de-noising process was investigated and analyzed. The objective is to identify the optimal mother wavelet for assessing and potentially reducing the effects of existing noise on signal properties for structural damage detection. In addition, we propose two innovative damage indices, entropy-based dispersion and entropy-based beta, for diagnostic purposes. The proposed entropy-based dispersion damage index is based on the modified wavelet packet tree and root mean square deviation, whereas the entropy-based beta damage index is based on the modified wavelet packet tree and slope of linear regression (beta). In both damage indices, the modified wavelet packet tree uses entropy as a high-level feature. Theoretical and experimental analyses are derived by computing indices on smart aggregate–based sensor data for concrete and reinforced-concrete beams. Validity assessment of the proposed indices was addressed through a comparative analysis with root mean square deviation damage index (benchmark) and the loading history. The proposed indices recognized the cracks faster than other measures and well before major cracking incurs in the structure. This article is expected to be beneficial for smart aggregate–based structural health monitoring applications particularly when damages occurred under loading.

Algorithm development for the non-destructive testing of structural damage

- Noori Hoshyar, Azadeh, Rashidi, Maria, Liyanapathirana, Ranjith, Samali, Bijan

  • Authors: Noori Hoshyar, Azadeh , Rashidi, Maria , Liyanapathirana, Ranjith , Samali, Bijan
  • Date: 2019
  • Type: Text , Journal article
  • Relation: Applied sciences Vol. 9, no. 14 (2019), p. 2810
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  • Description: Monitoring of structures to identify types of damages that occur under loading is essential in practical applications of civil infrastructure. In this paper, we detect and visualize damage based on several non-destructive testing (NDT) methods. A machine learning (ML) approach based on the Support Vector Machine (SVM) method is developed to prevent misdirection of the event interpretation of what is happening in the material. The objective is to identify cracks in the early stages, to reduce the risk of failure in structures. Theoretical and experimental analyses are derived by computing the performance indicators on the smart aggregate (SA)-based sensor data for concrete and reinforced-concrete (RC) beams. Validity assessment of the proposed indices was addressed through a comparative analysis with traditional SVM. The developed ML algorithms are shown to recognize cracks with a higher accuracy than the traditional SVM. Additionally, we propose different algorithms for microwave- or millimeter-wave imaging of steel plates, composite materials, and metal plates, to identify and visualize cracks. The proposed algorithm for steel plates is based on the gradient magnitude in four directions of an image, and is followed by the edge detection technique. Three algorithms were proposed for each of composite materials and metal plates, and are based on 2D fast Fourier transform (FFT) and hybrid fuzzy c-mean techniques, respectively. The proposed algorithms were able to recognize and visualize the cracking incurred in the structure more efficiently than the traditional techniques. The reported results are expected to be beneficial for NDT-based applications, particularly in civil engineering.

Proposed machine learning techniques for bridge structural health monitoring : a laboratory study

- Noori Hoshyar, Azadeh, Rashidi, Maria, Yu, Yang, Samali, Bijan

  • Authors: Noori Hoshyar, Azadeh , Rashidi, Maria , Yu, Yang , Samali, Bijan
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Remote Sensing Vol. 15, no. 8 (2023), p.
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  • Description: Structural health monitoring for bridges is a crucial concern in engineering due to the degradation risks caused by defects, which can become worse over time. In this respect, enhancement of various models that can discriminate between healthy and non-healthy states of structures have received extensive attention. These models are concerned with implementation algorithms, which operate on the feature sets to quantify the bridge’s structural health. The functional correlation between the feature set and the health state of the bridge structure is usually difficult to define. Therefore, the models are derived from machine learning techniques. The use of machine learning approaches provides the possibility of automating the SHM procedure and intelligent damage detection. In this study, we propose four classification algorithms to SHM, which uses the concepts of support vector machine (SVM) algorithm. The laboratory experiment, which intended to validate the results, was performed at Western Sydney University (WSU). The results were compared with the basic SVM to evaluate the performance of proposed algorithms. © 2023 by the authors.

Graph learning for anomaly analytics : algorithms, applications, and challenges

- Ren, Jing, Xia, Feng, Lee, Ivan, Noori Hoshyar, Azadeh, Aggarwal, Charu

  • Authors: Ren, Jing , Xia, Feng , Lee, Ivan , Noori Hoshyar, Azadeh , Aggarwal, Charu
  • Date: 2023
  • Type: Text , Journal article
  • Relation: ACM Transactions on Intelligent Systems and Technology Vol. 14, no. 2 (2023), p.
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  • Description: Anomaly analytics is a popular and vital task in various research contexts that has been studied for several decades. At the same time, deep learning has shown its capacity in solving many graph-based tasks, like node classification, link prediction, and graph classification. Recently, many studies are extending graph learning models for solving anomaly analytics problems, resulting in beneficial advances in graph-based anomaly analytics techniques. In this survey, we provide a comprehensive overview of graph learning methods for anomaly analytics tasks. We classify them into four categories based on their model architectures, namely graph convolutional network, graph attention network, graph autoencoder, and other graph learning models. The differences between these methods are also compared in a systematic manner. Furthermore, we outline several graph-based anomaly analytics applications across various domains in the real world. Finally, we discuss five potential future research directions in this rapidly growing field. © 2023 Association for Computing Machinery.

Deepfake video detection : challenges and opportunities

- Kaur, Achhardeep, Noori Hoshyar, Azadeh, Saikrishna, Vidya, Firmin, Sally, Xia, Feng

  • Authors: Kaur, Achhardeep , Noori Hoshyar, Azadeh , Saikrishna, Vidya , Firmin, Sally , Xia, Feng
  • Date: 2024
  • Type: Text , Journal article
  • Relation: Artificial Intelligence Review Vol. 57, no. 6 (2024), p.
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  • Description: Deepfake videos are a growing social issue. These videos are manipulated by artificial intelligence (AI) techniques (especially deep learning), an emerging societal issue. Malicious individuals misuse deepfake technologies to spread false information, such as fake images, videos, and audio. The development of convincing fake content threatens politics, security, and privacy. The majority of deepfake video detection methods are data-driven. This survey paper aims to thoroughly analyse deepfake video generation and detection. The paper’s main contribution is the classification of the many challenges encountered while detecting deepfake videos. The paper discusses data challenges such as unbalanced datasets and inadequate labelled training data. Training challenges include the need for many computational resources. It also addresses reliability challenges, including overconfidence in detection methods and emerging manipulation approaches. The research emphasises the dominance of deep learning-based methods in detecting deepfakes despite their computational efficiency and generalisation limitations. However, it also acknowledges the drawbacks of these approaches, such as their limited computing efficiency and generalisation. The research also critically evaluates deepfake datasets, emphasising the necessity for good-quality datasets to improve detection methods. The study also indicates major research gaps, guiding future deepfake detection research. This entails developing robust models for real-time detection. © The Author(s) 2024.
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