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Application of Transfer Learning and Ensembling Techniques for Cyber Security, Medicine, and Education Using Sensing Data

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 10 December 2024 | Viewed by 16124

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Special Issue Editors

Dr. Kamran Shaukat

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Guest Editor

School of Information and Physical Sciences, The University of Newcastle, Callaghan, NSW 2308, Australia
Interests: artificial Intelligence; data science; machine learning; cyber security; health informatics
Special Issues, Collections and Topics in MDPI journals

Dr. Suhuai Luo

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Guest Editor

School of Design Communication and IT, The University of Newcastle, Newcastle, Australia
Interests: computer vision; cyber security; data mining; image processing; bioinformatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the past decade, the rise of machine learning (ML) and deep learning (DL) evolved in various areas of life, especially in medicine, cyber security, finance, and education. In machine learning, the development of ensemble methods has gained significant attention from the scientific community. Machine learning ensemble methods combine multiple learning algorithms to obtain a better predictive performance than could be obtained from any of the constituent learning algorithms alone.

On the other hand, deep learning methods have actively been extended to other parts of machine learning, including reinforcement learning and transfer/meta-learning. At the same time, standard deep learning methods, such as convolutional neural networks (CNNs), have also been extensively studied and applied to diverse industrial fields. The training of these networks depends upon the data. The sensing data could be collected through sensors, images, actuators, virtual learning environments (for education), and IoT devices.

The aim of this Special Issue is to examine the latest theoretical and practical applications of deep and ensemble learning in various fields. Furthermore, we seek to contribute to the demonstration of new algorithms and application domains of deep learning to solve problems in various research areas. We will promote research and development of deep learning for multimodal data by publishing high-quality research articles and reviews in this rapidly growing interdisciplinary field.

Dr. Kamran Shaukat
Dr. Suhuai Luo
Guest Editors

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Keywords

transfer deep learning algorithms and models
new strategies to transfer learning
transfer learning and its application in medical informatics
relationship to multi-view learning, multi-task learning, and ensemble learning
sensors, sensor applications, and sensor platforms
federated learning
pervasive health
transfer learning (TL) applications
sensor fusion
sensing and detecting devices
implementation of ensemble learning algorithms
smart biosensors
ensemble learning methodologies for handling imbalanced data
ensemble methods in clustering
sensors and IoT devices in education
wearables and body area networks
explainable artificial intelligence (XAI)
multisensorial networks in education
graph neural networks for explainability
interpretable machine learning

Published Papers (4 papers)

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Research

21 pages, 3624 KiB

Open AccessArticle

Student Cheating Detection in Higher Education by Implementing Machine Learning and LSTM Techniques

by Waleed Alsabhan

Sensors 2023, 23(8), 4149; https://doi.org/10.3390/s23084149 - 20 Apr 2023

Cited by 6 | Viewed by 4737

Abstract

Both paper-based and computerized exams have a high level of cheating. It is, therefore, desirable to be able to detect cheating accurately. Keeping the academic integrity of student evaluations intact is one of the biggest issues in online education. There is a substantial possibility of academic dishonesty during final exams since teachers are not directly monitoring students. We suggest a novel method in this study for identifying possible exam-cheating incidents using Machine Learning (ML) approaches. The 7WiseUp behavior dataset compiles data from surveys, sensor data, and institutional records to improve student well-being and academic performance. It offers information on academic achievement, student attendance, and behavior in general. In order to build models for predicting academic accomplishment, identifying at-risk students, and detecting problematic behavior, the dataset is designed for use in research on student behavior and performance. Our model approach surpassed all prior three-reference efforts with an accuracy of 90% and used a long short-term memory (LSTM) technique with a dropout layer, dense layers, and an optimizer called Adam. Implementing a more intricate and optimized architecture and hyperparameters is credited with increased accuracy. In addition, the increased accuracy could have been caused by how we cleaned and prepared our data. More investigation and analysis are required to determine the precise elements that led to our model’s superior performance. Full article

(This article belongs to the Special Issue Application of Transfer Learning and Ensembling Techniques for Cyber Security, Medicine, and Education Using Sensing Data)

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26 pages, 3512 KiB

Open AccessArticle

Roman Urdu Hate Speech Detection Using Transformer-Based Model for Cyber Security Applications

by Muhammad Bilal, Atif Khan, Salman Jan, Shahrulniza Musa and Shaukat Ali

Sensors 2023, 23(8), 3909; https://doi.org/10.3390/s23083909 - 12 Apr 2023

Cited by 6 | Viewed by 3662

Abstract

Social media applications, such as Twitter and Facebook, allow users to communicate and share their thoughts, status updates, opinions, photographs, and videos around the globe. Unfortunately, some people utilize these platforms to disseminate hate speech and abusive language. The growth of hate speech may result in hate crimes, cyber violence, and substantial harm to cyberspace, physical security, and social safety. As a result, hate speech detection is a critical issue for both cyberspace and physical society, necessitating the development of a robust application capable of detecting and combating it in real-time. Hate speech detection is a context-dependent problem that requires context-aware mechanisms for resolution. In this study, we employed a transformer-based model for Roman Urdu hate speech classification due to its ability to capture the text context. In addition, we developed the first Roman Urdu pre-trained BERT model, which we named BERT-RU. For this purpose, we exploited the capabilities of BERT by training it from scratch on the largest Roman Urdu dataset consisting of 173,714 text messages. Traditional and deep learning models were used as baseline models, including LSTM, BiLSTM, BiLSTM + Attention Layer, and CNN. We also investigated the concept of transfer learning by using pre-trained BERT embeddings in conjunction with deep learning models. The performance of each model was evaluated in terms of accuracy, precision, recall, and F-measure. The generalization of each model was evaluated on a cross-domain dataset. The experimental results revealed that the transformer-based model, when directly applied to the classification task of the Roman Urdu hate speech, outperformed traditional machine learning, deep learning models, and pre-trained transformer-based models in terms of accuracy, precision, recall, and F-measure, with scores of 96.70%, 97.25%, 96.74%, and 97.89%, respectively. In addition, the transformer-based model exhibited superior generalization on a cross-domain dataset. Full article

(This article belongs to the Special Issue Application of Transfer Learning and Ensembling Techniques for Cyber Security, Medicine, and Education Using Sensing Data)

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21 pages, 2975 KiB

Open AccessArticle

Human–Computer Interaction with a Real-Time Speech Emotion Recognition with Ensembling Techniques 1D Convolution Neural Network and Attention

by Waleed Alsabhan

Sensors 2023, 23(3), 1386; https://doi.org/10.3390/s23031386 - 26 Jan 2023

Cited by 8 | Viewed by 3059

Abstract

Emotions have a crucial function in the mental existence of humans. They are vital for identifying a person’s behaviour and mental condition. Speech Emotion Recognition (SER) is extracting a speaker’s emotional state from their speech signal. SER is a growing discipline in human–computer interaction, and it has recently attracted more significant interest. This is because there are not so many universal emotions; therefore, any intelligent system with enough computational capacity can educate itself to recognise them. However, the issue is that human speech is immensely diverse, making it difficult to create a single, standardised recipe for detecting hidden emotions. This work attempted to solve this research difficulty by combining a multilingual emotional dataset with building a more generalised and effective model for recognising human emotions. A two-step process was used to develop the model. The first stage involved the extraction of features, and the second stage involved the classification of the features that were extracted. ZCR, RMSE, and the renowned MFC coefficients were retrieved as features. Two proposed models, 1D CNN combined with LSTM and attention and a proprietary 2D CNN architecture, were used for classification. The outcomes demonstrated that the suggested 1D CNN with LSTM and attention performed better than the 2D CNN. For the EMO-DB, SAVEE, ANAD, and BAVED datasets, the model’s accuracy was 96.72%, 97.13%, 96.72%, and 88.39%, respectively. The model beat several earlier efforts on the same datasets, demonstrating the generality and efficacy of recognising multiple emotions from various languages. Full article

(This article belongs to the Special Issue Application of Transfer Learning and Ensembling Techniques for Cyber Security, Medicine, and Education Using Sensing Data)

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17 pages, 2234 KiB

Open AccessArticle

Collaborative Learning Based Sybil Attack Detection in Vehicular AD-HOC Networks (VANETS)

by Sofia Azam, Maryum Bibi, Rabia Riaz, Sanam Shahla Rizvi and Se Jin Kwon

Sensors 2022, 22(18), 6934; https://doi.org/10.3390/s22186934 - 13 Sep 2022

Cited by 20 | Viewed by 3071

Abstract

Vehicular Ad-hoc network (VANET) is an imminent technology having both exciting prospects and substantial challenges, especially in terms of security. Due to its distributed network and frequently changing topology, it is extremely prone to security attacks. The researchers have proposed different strategies for detecting various forms of network attacks. However, VANET is still exposed to several attacks, specifically Sybil attack. Sybil Attack is one of the most challenging attacks in VANETS, which forge false identities in the network to undermine communication between network nodes. This attack highly impacts transportation safety services and may create traffic congestion. In this regard, a novel collaborative framework based on majority voting is proposed to detect the Sybil attack in the network. The framework works by ensembling individual classifiers, i.e., K-Nearest Neighbor, Naïve Bayes, Decision Tree, SVM, and Logistic Regression in a parallel manner. The Majority Voting (Hard and Soft) mechanism is adopted for a final prediction. A comparison is made between Majority Voting Hard and soft to choose the best approach. With the proposed approach, 95% accuracy is achieved. The proposed framework is also evaluated using the Receiver operating characteristics curve (ROC-curve). Full article

(This article belongs to the Special Issue Application of Transfer Learning and Ensembling Techniques for Cyber Security, Medicine, and Education Using Sensing Data)

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