• Performance evaluation of machine learning techniques for fault diagnosis in vehicle fleet tracking modules

      Sepulevene, Luis; Drummond, Isabela; Kuehne, Bruno Tardiole; Frinhani, Rafael; Filho, Dionisio Leite; Peixoto, Maycon; Reiff-Marganiec, Stephan; Batista, Bruno; Federal University of Itajubá, Itajubá, Brazil; Federal University of Mato Grosso do Sul, Ponta Porã, Brazil; et al. (Oxford University Press, 2021-05-14)
      With industry 4.0, data-based approaches are in vogue. However, extracting the essential features is not a trivial task and greatly influences the fi nal result. There is also a need for specialized system knowledge to monitor the environment and diagnose faults. In this context, the diagnosis of faults is signi cant, for example, in a vehicle fleet monitoring system, since it is possible to diagnose faults even before the customer is aware of the fault, minimizing the maintenance costs of the modules. In this paper, several models using Machine Learning (ML) techniques were applied and analyzed during the fault diagnosis process in vehicle fleet tracking modules. Two approaches were proposed, "With Knowledge" and "Without Knowledge", to explore the dataset using ML techniques to generate classi fiers that can assist in the fault diagnosis process. The approach "With Knowledge" performs the feature extraction manually, using the ML techniques: Random Forest, Naive Bayes, Support Vector Machine (SVM) and Multi Layer Perceptron (MLP); on the other hand, the approach "Without Knowledge" performs an automatic feature extraction, through a Convolutional Neural Network (CNN). The results showed that the proposed approaches are promising. The best models with manual feature extraction obtained a precision of 99.76% and 99.68% for detection and detection and isolation of faults, respectively, in the provided dataset. The best models performing an automatic feature extraction obtained respectively 88.43% and 54.98% for detection and detection and isolation of failures.
    • Privacy verification of photoDNA based on machine learning

      Nadeem, Muhammad Shahroz; Franqueira, Virginia N. L.; Zhai, Xiaojun; University of Derby, College of Engineering and Technology; University of Essex, School of Computer Science and Electronic Engineering (The Institution of Engineering and Technology (IET), 2019-10-09)
      PhotoDNA is a perceptual fuzzy hash technology designed and developed by Microsoft. It is deployed by all major big data service providers to detect Indecent Images of Children (IIOC). Protecting the privacy of individuals is of paramount importance in such images. Microsoft claims that a PhotoDNA hash cannot be reverse engineered into the original image; therefore, it is not possible to identify individuals or objects depicted in the image. In this chapter, we evaluate the privacy protection capability of PhotoDNA by testing it against machine learning. Specifically, our aim is to detect the presence of any structural information that might be utilized to compromise the privacy of the individuals via classification. Due to the widespread usage of PhotoDNA as a deterrent to IIOC by big data companies, ensuring its ability to protect privacy would be crucial. In our experimentation, we achieved a classification accuracy of 57.20%.This result indicates that PhotoDNA is resistant to machine-learning-based classification attacks.
    • A survey of deep learning solutions for multimedia visual content analysis.

      Nadeem, Muhammad Shahroz; Franqueira, Virginia N. L.; Zhai, Xiaojun; Kurugollu, Fatih; University of Derby; University of Essex (IEEE, 2019-06-24)
      The increasing use of social media networks on handheld devices, especially smartphones with powerful built-in cameras, and the widespread availability of fast and high bandwidth broadband connections, added to the popularity of cloud storage, is enabling the generation and distribution of massive volumes of digital media, including images and videos. Such media is full of visual information and holds immense value in today’s world. The volume of data involved calls for automated visual content analysis systems able to meet the demands of practice in terms of efficiency and effectiveness. Deep Learning (DL) has recently emerged as a prominent technique for visual content analysis. It is data-driven in nature and provides automatic end-to-end learning solutions without the need to rely explicitly on predefined handcrafted feature extractors. Another appealing characteristic of DL solutions is the performance they can achieve, once the network is trained, under practical constraints. This paper identifies eight problem domains which require analysis of visual artefacts in multimedia. It surveys the recent, authoritative, and best performing DL solutions and lists the datasets used in the development of these deep methods for the identified types of visual analysis problems. The paper also discusses the challenges that DL solutions face which can compromise their reliability, robustness, and accuracy for visual content analysis.
    • A survey of interpretability of machine learning in accelerator-based high energy physics

      Turvill, Danielle; Barnby, Lee; Yuan, Bo; Zahir, Ali; University of Derby (IEEE, 2020-12-28)
      Data intensive studies in the domain of accelerator-based High Energy Physics, HEP, have become increasingly more achievable due to the emergence of machine learning with high-performance computing and big data technologies. In recent years, the intricate nature of physics tasks and data has prompted the use of more complex learning methods. To accurately identify physics of interest, and draw conclusions against proposed theories, it is crucial that these machine learning predictions are explainable. For it is not enough to accept an answer based on accuracy alone, but it is important in the process of physics discovery to understand exactly why an output was generated. That is, completeness of a solution is required. In this paper, we survey the application of machine learning methods to a variety of accelerator-based tasks in a bid to understand what role interpretability plays within this area. The main contribution of this paper is to promote the need for explainable artificial intelligence, XAI, for the future of machine learning in HEP.
    • Targeted ensemble machine classification approach for supporting IOT enabled skin disease detection

      Yu, Hong Qing; Reiff-Marganiec, Stephan; University of Derby (IEEE, 2021-03-26)
      The fast development of the Internet of Things (IoT) changes our life in many areas, especially in the health domain. For example, remote disease diagnosis can be achieved more efficiently with advanced IoT-technologies which not only include hardware but also smart IoT data processing and learning algorithms, e.g. image-based disease classification. In this paper, we work in a specific area of skin condition classification. This research work aims to provide an implementable solution for IoT-led remote skin disease diagnosis applications. The research output can be concluded into three folders. The first folder is about dynamic AI model configuration supported IoT-Fog-Cloud remote diagnosis architecture with hardware examples. The second folder is the evaluation survey regarding the performances of machine learning models for skin disease detection. The evaluation contains a variety of data processing methods and their aggregations. The evaluation takes account of both training-testing and cross-testing validations on all seven conditions and individual condition. In addition, the HAM10000 dataset is picked for the evaluation process according to the suitability comparisons to other relevant datasets. In the evaluation, we discuss the earlier work of ANN, SVM and KNN models, but the evaluation process mainly focuses on six widely applied Deep Learning models of VGG16, Inception, Xception, MobileNet, ResNet50 and DenseNet161. The result shows that each of the top four models for the major seven skin conditions has better performance for the specific condition than others. Based on the evaluation discovery, the last folder proposes a novel classification approach of the Targeted Ensemble Machine Classify Model (TEMCM) to enable dynamically combining a suitable model in a two-phase detection process. The final evaluation result shows the proposed model can archive better performance.