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Enhancing dynamic ECG heartbeat classification with lightweight transformer model

- Meng, Lingxiao, Tan, Wenjun, Ma, Jiangang, Wang, Ruofei, Yin, Xiaoxia, Zhang, Yanchun

  • Authors: Meng, Lingxiao , Tan, Wenjun , Ma, Jiangang , Wang, Ruofei , Yin, Xiaoxia , Zhang, Yanchun
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Artificial Intelligence in Medicine Vol. 124, no. (2022), p.
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  • Description: Arrhythmia is a common class of Cardiovascular disease which is the cause for over 31% of all death over the world, according to WHOs' report. Automatic detection and classification of arrhythmia, as an effective tool of early warning, has recently been received more and more attention, especially in the applications of wearable devices for data capturing. However, different from traditional application scenarios, wearable electrocardiogram (ECG) devices have some drawbacks, such as being subject to multiple abnormal interferences, thus making accurate ventricular contraction (PVC) and supraventricular premature beat (SPB) detection to be more challenging. The traditional models for heartbeat classification suffer from the problem of large-scale parameters and the performance in dynamic ECG heartbeat classification is not satisfactory. In this paper, we propose a novel light model Lightweight Fussing Transformer to address these problems. We developed a more lightweight structure named LightConv Attention (LCA) to replace the self-attention of Fussing Transformer. LCA has reached remarkable performance level equal to or higher than self-attention with fewer parameters. In particular, we designed a stronger embedding structure (Convolutional Neural Network with attention mechanism) to enhance the weight of features of internal morphology of the heartbeat. Furthermore, we have implemented the proposed methods on real datasets and experimental results have demonstrated outstanding accuracy of detecting PVC and SPB. © 2022 Elsevier B.V.

An investigation into student satisfaction, approaches to learning and the learning context in auditing

- Chiang, Christina, Wells, Paul, Fieger, Peter, Sharma, Divesh

  • Authors: Chiang, Christina , Wells, Paul , Fieger, Peter , Sharma, Divesh
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Accounting and Finance Vol. 61, no. 1 (2021), p. 913-936
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  • Description: Arguably, the audit course is one of the most challenging as it links prior accounting knowledge with new audit knowledge that students are generally not exposed to. A mini-audit group project was implemented at a New Zealand university, and a learning approach and learning experience survey instrument was administered. Responses from 98 students suggest that they perceived the learning experience positively and were encouraged to adopt a deep approach to learning. The findings have implications for accounting educators in the design and development of learning and assessment strategies in an audit course. © 2020 Accounting and Finance Association of Australia and New Zealand

A new data driven long-term solar yield analysis model of photovoltaic power plants

- Ray, Biplob, Shah, Rakibuzzaman, Islam, Md Rabiul, Islam, Syed

  • Authors: Ray, Biplob , Shah, Rakibuzzaman , Islam, Md Rabiul , Islam, Syed
  • Date: 2020
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 8, no. (2020), p. 136223-136233
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  • Description: Historical data offers a wealth of knowledge to the users. However, often restrictively mammoth that the information cannot be fully extracted, synthesized, and analyzed efficiently for an application such as the forecasting of variable generator outputs. Moreover, the accuracy of the prediction method is vital. Therefore, a trade-off between accuracy and efficacy is required for the data-driven energy forecasting method. It has been identified that the hybrid approach may outperform the individual technique in minimizing the error while challenging to synthesize. A hybrid deep learning-based method is proposed for the output prediction of the solar photovoltaic systems (i.e. proposed PV system) in Australia to obtain the trade-off between accuracy and efficacy. The historical dataset from 1990-2013 in Australian locations (e.g. North Queensland) are used to train the model. The model is developed using the combination of multivariate long and short-term memory (LSTM) and convolutional neural network (CNN). The proposed hybrid deep learning (LSTM-CNN) is compared with the existing neural network ensemble (NNE), random forest, statistical analysis, and artificial neural network (ANN) based techniques to assess the performance. The proposed model could be useful for generation planning and reserve estimation in power systems with high penetration of solar photovoltaics (PVs) or other renewable energy sources (RESs). © 2013 IEEE.

A deep learning model based on concatenation approach for the diagnosis of brain tumor

- Noreen, Neelum, Palaniappan, Sellappan, Qayyum, Abdul, Ahmad, Iftikhar, Imran, Muhammad, Shoaib, M.uhammad

  • Authors: Noreen, Neelum , Palaniappan, Sellappan , Qayyum, Abdul , Ahmad, Iftikhar , Imran, Muhammad , Shoaib, M.uhammad
  • Date: 2020
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 8, no. (2020), p. 55135-55144
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  • Description: Brain tumor is a deadly disease and its classification is a challenging task for radiologists because of the heterogeneous nature of the tumor cells. Recently, computer-aided diagnosis-based systems have promised, as an assistive technology, to diagnose the brain tumor, through magnetic resonance imaging (MRI). In recent applications of pre-trained models, normally features are extracted from bottom layers which are different from natural images to medical images. To overcome this problem, this study proposes a method of multi-level features extraction and concatenation for early diagnosis of brain tumor. Two pre-trained deep learning models i.e. Inception-v3 and DensNet201 make this model valid. With the help of these two models, two different scenarios of brain tumor detection and its classification were evaluated. First, the features from different Inception modules were extracted from pre-trained Inception-v3 model and concatenated these features for brain tumor classification. Then, these features were passed to softmax classifier to classify the brain tumor. Second, pre-trained DensNet201 was used to extract features from various DensNet blocks. Then, these features were concatenated and passed to softmax classifier to classify the brain tumor. Both scenarios were evaluated with the help of three-class brain tumor dataset that is available publicly. The proposed method produced 99.34 %, and 99.51% testing accuracies respectively with Inception-v3 and DensNet201 on testing samples and achieved highest performance in the detection of brain tumor. As results indicated, the proposed method based on features concatenation using pre-trained models outperformed as compared to existing state-of-the-art deep learning and machine learning based methods for brain tumor classification. © 2013 IEEE.

Tracing the Pace of COVID-19 research : topic modeling and evolution

- Liu, Jiaying, Nie, Hansong, Li, Shihao, Ren, Jing, Xia, Feng

  • Authors: Liu, Jiaying , Nie, Hansong , Li, Shihao , Ren, Jing , Xia, Feng
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Big Data Research Vol. 25, no. (2021), p.
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  • Description: COVID-19 has been spreading rapidly around the world. With the growing attention on the deadly pandemic, discussions and research on COVID-19 are rapidly increasing to exchange latest findings with the hope to accelerate the pace of finding a cure. As a branch of information technology, artificial intelligence (AI) has greatly expedited the development of human society. In this paper, we investigate and visualize the on-going advancements of early scientific research on COVID-19 from the perspective of AI. By adopting the Latent Dirichlet Allocation (LDA) model, this paper allocates the research articles into 50 key research topics pertinent to COVID-19 according to their abstracts. We present an overview of early studies of the COVID-19 crisis at different scales including referencing/citation behavior, topic variation and their inner interactions. We also identify innovative papers that are regarded as the cornerstones in the development of COVID-19 research. The results unveil the focus of scientific research, thereby giving deep insights into how the academic society contributes to combating the COVID-19 pandemic. © 2021 Elsevier Inc. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Jing Ren and Feng Xia" is provided in this record**
  • Description: COVID-19 has been spreading rapidly around the world. With the growing attention on the deadly pandemic, discussions and research on COVID-19 are rapidly increasing to exchange latest findings with the hope to accelerate the pace of finding a cure. As a branch of information technology, artificial intelligence (AI) has greatly expedited the development of human society. In this paper, we investigate and visualize the on-going advancements of early scientific research on COVID-19 from the perspective of AI. By adopting the Latent Dirichlet Allocation (LDA) model, this paper allocates the research articles into 50 key research topics pertinent to COVID-19 according to their abstracts. We present an overview of early studies of the COVID-19 crisis at different scales including referencing/citation behavior, topic variation and their inner interactions. We also identify innovative papers that are regarded as the cornerstones in the development of COVID-19 research. The results unveil the focus of scientific research, thereby giving deep insights into how the academic society contributes to combating the COVID-19 pandemic. © 2021 Elsevier Inc.

Obfuscated memory malware detection in resource-constrained iot devices for smart city applications

- Shafin, Sakib, Karmakar, Gour, Mareels, Iven

  • Authors: Shafin, Sakib , Karmakar, Gour , Mareels, Iven
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Sensors Vol. 23, no. 11 (2023), p. 5348
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  • Description: Obfuscated Memory Malware (OMM) presents significant threats to interconnected systems, including smart city applications, for its ability to evade detection through concealment tactics. Existing OMM detection methods primarily focus on binary detection. Their multiclass versions consider a few families only and, thereby, fail to detect much existing and emerging malware. Moreover, their large memory size makes them unsuitable to be executed in resource-constrained embedded/IoT devices. To address this problem, in this paper, we propose a multiclass but lightweight malware detection method capable of identifying recent malware and is suitable to execute in embedded devices. For this, the method considers a hybrid model by combining the feature-learning capabilities of convolutional neural networks with the temporal modeling advantage of bidirectional long short-term memory. The proposed architecture exhibits compact size and fast processing speed, making it suitable for deployment in IoT devices that constitute the major components of smart city systems. Extensive experiments with the recent CIC-Malmem-2022 OMM dataset demonstrate that our method outperforms other machine learning-based models proposed in the literature in both detecting OMM and identifying specific attack types. Our proposed method thus offers a robust yet compact model executable in IoT devices for defending against obfuscated malware.

Adaptation of a real-time deep learning approach with an analog fault detection technique for reliability forecasting of capacitor banks used in mobile vehicles

- Rezaei, Mohammad, Fathollahi, Arman, Rezaei, Sajad, Hu, Jiefeng, Gheisarnejad, Meysam, Teimouri, Ali, Rituraj, Rituraj, Mosavi, Amir, Khooban, Mohammad-Hassan

  • Authors: Rezaei, Mohammad , Fathollahi, Arman , Rezaei, Sajad , Hu, Jiefeng , Gheisarnejad, Meysam , Teimouri, Ali , Rituraj, Rituraj , Mosavi, Amir , Khooban, Mohammad-Hassan
  • Date: 2022
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 10, no. (2022), p. 132271-132287
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  • Description: The DC-Link capacitor is defined as the essential electronics element which sources or sinks the respective currents. The reliability of DC-link capacitor-banks (CBs) encounters many challenges due to their usage in electric vehicles. Heavy shocks may damage the internal capacitors without shutting down the CB. The fundamental development obstacles of CBs are: lack of considering capacitor degradation in reliability assessment, the impact of unforeseen sudden internal capacitor faults in forecasting CB lifetime, and the faults consequence on CB degradation. The sudden faults change the CB capacitance, which leads to reliability change. To more accurately estimate the reliability, the type of the fault needs to be detected for predicting the correct post-fault capacitance. To address these practical problems, a new CB model and reliability assessment formula covering all fault types are first presented, then, a new analog fault-detection method is presented, and a combination of online-learning long short-term memory (LSTM) and fault-detection method is subsequently performed, which adapt the sudden internal CB faults with the LSTM to correctly predict the CB degradation. To confirm the correct LSTM operation, four capacitors degradation is practically recorded for 2000-hours, and the off-line faultless degradation values predicted by the LSTM are compared with the actual data. The experimental findings validate the applicability of the proposed method. The codes and data are provided. © 2013 IEEE.

Melanoma classification using efficientnets and ensemble of models with different input resolution

- Karki, Sagar, Kulkarni, Pradnya, Stranieri, Andrew

Deep matrix factorization for trust-aware recommendation in social networks

- Wan, Liangtian, Xia, Feng, Kong, Xiangjie, Hsu, Ching-Hsien, Huang, Runhe, Ma, Jianhua

  • Authors: Wan, Liangtian , Xia, Feng , Kong, Xiangjie , Hsu, Ching-Hsien , Huang, Runhe , Ma, Jianhua
  • Date: 2021
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Network Science and Engineering Vol. 8, no. 1 (2021), p. 511-528
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  • Description: Recent years have witnessed remarkable information overload in online social networks, and social network based approaches for recommender systems have been widely studied. The trust information in social networks among users is an important factor for improving recommendation performance. Many successful recommendation tasks are treated as the matrix factorization problems. However, the prediction performance of matrix factorization based methods largely depends on the matrixes initialization of users and items. To address this challenge, we develop a novel trust-aware approach based on deep learning to alleviate the initialization dependence. First, we propose two deep matrix factorization (DMF) techniques, i.e., linear DMF and non-linear DMF to extract features from the user-item rating matrix for improving the initialization accuracy. The trust relationship is integrated into the DMF model according to the preference similarity and the derivations of users on items. Second, we exploit deep marginalized Denoising Autoencoder (Deep-MDAE) to extract the latent representation in the hidden layer from the trust relationship matrix to approximate the user factor matrix factorized from the user-item rating matrix. The community regularization is integrated in the joint optimization function to take neighbours' effects into consideration. The results of DMF are applied to initialize the updating variables of Deep-MDAE in order to further improve the recommendation performance. Finally, we validate that the proposed approach outperforms state-of-the-art baselines for recommendation, especially for the cold-start users. © 2013 IEEE.

Classification of Twitter users with eating disorder engagement : learning from the biographies

- Abuhassan, Mohammad, Anwar, Tarique, Fuller-Tyszkiewicz, Matthew, Jarman, Hannah, Shatte, Adrian, Liu, Chengfei, Sukunesan, Suku

  • Authors: Abuhassan, Mohammad , Anwar, Tarique , Fuller-Tyszkiewicz, Matthew , Jarman, Hannah , Shatte, Adrian , Liu, Chengfei , Sukunesan, Suku
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Computers in Human Behavior Vol. 140, no. (2023), p.
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  • Description: Individuals with an Eating Disorder (ED) are typically reluctant to seek help via traditional means (e.g., psychologists). However, recent evidence suggests that many individuals seek assistance via social media for weight and diet related concerns. Sophisticated approaches are needed to better distinguish those who may be in need of help for an ED from those who are simply commenting on ED in online social environments. In order to facilitate effective communication between individuals with or at-risk of an ED and healthcare professionals, this research exploits a deep learning model to differentiate the users with ED engagement (e.g., ED sufferers, healthcare professionals or communicators) over social media. For this purpose, a collection of Twitter data is compiled using Twitter application programming interface (API) on the Australian Research Data Commons (ARDC) Nectar research cloud. After collecting 1,400,000 Twitter biographies in total, a subset of 4000 biographies are annotated manually. This annotation enables the differentiation of users engaged with ED-focused language on social media into five categories: ED-user, healthcare professional, communicator, healthcare professional-communicator, and other. Based on these annotated categories, a predictive deep learning model based on bidirectional encoder representations from transformers (BERT) and long short-term memory (LSTM) is developed. The model achieves an F1 score of 98.19% and an accuracy of 98.37%. It demonstrates the viability of detecting the individuals with possible ED risk and distinguishes them from other categories using their biography data. We further conducted a network analysis for investigating the communication network between these categories. Our analysis shows that ED-users are more secretive and self-protective, whereas the healthcare professionals and communicators frequently interact with each other and a wide range of other people. To the best of our knowledge, our research is the first of its kind for identifying the different user categories engaged with ED-focused communications on social media. © 2022

Impact of traditional and embedded image denoising on CNN-based deep learning

- Kaur, Roopdeep, Karmakar, Gour, Imran, Muhammad

  • Authors: Kaur, Roopdeep , Karmakar, Gour , Imran, Muhammad
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Applied sciences Vol. 13, no. 20 (2023), p.
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  • Description: In digital image processing, filtering noise is an important step for reconstructing a high-quality image for further processing such as object segmentation, object detection, and object recognition. Various image-denoising approaches, including median, Gaussian, and bilateral filters, are available in the literature. Since convolutional neural networks (CNN) are able to directly learn complex patterns and features from data, they have become a popular choice for image-denoising tasks. As a result of their ability to learn and adapt to various denoising scenarios, CNNs are powerful tools for image denoising. Some deep learning techniques such as CNN incorporate denoising strategies directly into the CNN model layers. A primary limitation of these methods is their necessity to resize images to a consistent size. This resizing can result in a loss of vital image details, which might compromise CNN’s effectiveness. Because of this issue, we utilize a traditional denoising method as a preliminary step for noise reduction before applying CNN. To our knowledge, a comparative performance study of CNN using traditional and embedded denoising against a baseline approach (without denoising) is yet to be performed. To analyze the impact of denoising on the CNN performance, in this paper, firstly, we filter the noise from the images using traditional means of denoising method before their use in the CNN model. Secondly, we embed a denoising layer in the CNN model. To validate the performance of image denoising, we performed extensive experiments for both traffic sign and object recognition datasets. To decide whether denoising will be adopted and to decide on the type of filter to be used, we also present an approach exploiting the peak-signal-to-noise-ratio (PSNRs) distribution of images. Both CNN accuracy and PSNRs distribution are used to evaluate the effectiveness of the denoising approaches. As expected, the results vary with the type of filter, impact, and dataset used in both traditional and embedded denoising approaches. However, traditional denoising shows better accuracy, while embedded denoising shows lower computational time for most of the cases. Overall, this comparative study gives insights into whether denoising will be adopted in various CNN-based image analyses, including autonomous driving, animal detection, and facial recognition.

Instruction cognitive one-shot malware outbreak detection

- Park, Sean, Gondal, Iqbal, Kamruzzaman, Joarder, Oliver, Jon

  • Authors: Park, Sean , Gondal, Iqbal , Kamruzzaman, Joarder , Oliver, Jon
  • Date: 2019
  • Type: Text , Conference paper
  • Relation: 26th International Conference on Neural Information Processing [ICONIP 2019] December 12-15 2019, Proceedings, Part IV Vol. 1142, p. 769-778
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  • Description: New malware outbreaks cannot provide thousands of training samples which are required to counter malware campaigns. In some cases, there could be just one sample. So, the defense system at the firing line must be able to quickly detect many automatically generated variants using a single malware instance observed from the initial outbreak by tatically inspecting the binary executables. As previous research works show, statistical features such as term frequency-inverse document frequency and n-gram are significantly vulnerable to attacks by mutation through reinforcement learning. Recent studies focus on raw binary executable as a base feature which contains instructions describing the core logic of the sample. However, many approaches using image-matching neural networks are insufficient due to the malware mutation technique that generates a large number of samples with high entropy data. Deriving instruction cognitive representation that disambiguates legitimate instructions from the context is necessary for accurate detection over raw binary executables. In this paper, we present a novel method of detecting semantically similar malware variants within a campaign using a single raw binary malware executable. We utilize Discrete Fourier Transform of instruction cognitive representation extracted from self-attention transformer network. The experiments were conducted with in-the-wild malware samples from ransomware and banking Trojan campaigns. The proposed method outperforms several state of the art binary classification models.
  • Description: E1

Subgraph adaptive structure-aware graph contrastive learning

- Chen, Zhikui, Peng, Yin, Yu, Shuo, Cao, Chen, Xia, Feng

  • Authors: Chen, Zhikui , Peng, Yin , Yu, Shuo , Cao, Chen , Xia, Feng
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Mathematics (Basel) Vol. 10, no. 17 (2022), p. 3047
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  • Description: Graph contrastive learning (GCL) has been subject to more attention and been widely applied to numerous graph learning tasks such as node classification and link prediction. Although it has achieved great success and even performed better than supervised methods in some tasks, most of them depend on node-level comparison, while ignoring the rich semantic information contained in graph topology, especially for social networks. However, a higher-level comparison requires subgraph construction and encoding, which remain unsolved. To address this problem, we propose a subgraph adaptive structure-aware graph contrastive learning method (PASCAL) in this work, which is a subgraph-level GCL method. In PASCAL, we construct subgraphs by merging all motifs that contain the target node. Then we encode them on the basis of motif number distribution to capture the rich information hidden in subgraphs. By incorporating motif information, PASCAL can capture richer semantic information hidden in local structures compared with other GCL methods. Extensive experiments on six benchmark datasets show that PASCAL outperforms state-of-art graph contrastive learning and supervised methods in most cases.

Multitask deep learning for cost-effective prediction of patient's length of stay and readmission state using multimodal physical activity sensory data

- Ali, Sajid, El-Sappagh, Shaker, Ali, Farman, Imran, Muhammad, Abuhmed, Tamer

  • Authors: Ali, Sajid , El-Sappagh, Shaker , Ali, Farman , Imran, Muhammad , Abuhmed, Tamer
  • Date: 2022
  • Type: Text , Journal article
  • Relation: IEEE Journal of Biomedical and Health Informatics Vol. 26, no. 12 (2022), p. 5793-5804
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  • Description: In a hospital, accurate and rapid mortality prediction of Length of Stay (LOS) is essential since it is one of the essential measures in treating patients with severe diseases. When predictions of patient mortality and readmission are combined, these models gain a new level of significance. Therefore, the most expensive components of patient care are LOS and readmission rates. Several studies have assessed readmission to the hospital as a single-task issue. The performance, robustness, and stability of the model increase when many correlated tasks are optimized. This study develops multimodal multitasking Long Short-Term Memory (LSTM) Deep Learning (DL) model that can predict both LOS and readmission for patients using multi-sensory data from 47 patients. Continuous sensory data is divided into eight sections, each of which is recorded for an hour. The time steps are constructed using a dual 10-second window-based technique, resulting in six steps per hour. The 30 statistical features are computed by transforming the sensory input into the resulting vector. The proposed multitasking model predicts 30-day readmission as a binary classification problem and LOS as a regression task by constructing discrete time-step data based on the length of physical activity during a hospital stay. The proposed model is compared to a random forest for a single-task problem (classification or regression) because typical machine learning algorithms are unable to handle the multitasking challenge. In addition, sensory data combined with other cost-effective modalities such as demographics, laboratory tests, and comorbidities to construct reliable models for personalized, cost-effective, and medically acceptable prediction. With a high accuracy of 94.84%, the proposed multitask multimodal DL model classifies the patient's readmission status and determines the patient's LOS in hospital with a minimal Mean Square Error (MSE) of 0.025 and Root Mean Square Error (RMSE) of 0.077, which is promising, effective, and trustworthy. © 2013 IEEE.

Robust Mobile Malware Detection

- Khoda, Mahbub

  • Authors: Khoda, Mahbub
  • Date: 2020
  • Type: Text , Thesis , PhD
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  • Description: The increasing popularity and use of smartphones and hand-held devices have made them the most popular target for malware attackers. Researchers have proposed machine learning-based models to automatically detect malware attacks on these devices. Since these models learn application behaviors solely from the extracted features, choosing an appropriate and meaningful feature set is one of the most crucial steps for designing an effective mobile malware detection system. There are four categories of features for mobile applications. Previous works have taken arbitrary combinations of these categories to design models, resulting in sub-optimal performance. This thesis systematically investigates the individual impact of these feature categories on mobile malware detection systems. Feature categories that complement each other are investigated and categories that add redundancy to the feature space (thereby degrading the performance) are analyzed. In the process, the combination of feature categories that provides the best detection results is identified. Ensuring reliability and robustness of the above-mentioned malware detection systems is of utmost importance as newer techniques to break down such systems continue to surface. Adversarial attack is one such evasive attack that can bypass a detection system by carefully morphing a malicious sample even though the sample was originally correctly identified by the same system. Self-crafted adversarial samples can be used to retrain a model to defend against such attacks. However, randomly using too many such samples, as is currently done in the literature, can further degrade detection performance. This work proposed two intelligent approaches to retrain a classifier through the intelligent selection of adversarial samples. The first approach adopts a distance-based scheme where the samples are chosen based on their distance from malware and benign cluster centers while the second selects the samples based on a probability measure derived from a kernel-based learning method. The second method achieved a 6% improvement in terms of accuracy. To ensure practical deployment of malware detection systems, it is necessary to keep the real-world data characteristics in mind. For example, the benign applications deployed in the market greatly outnumber malware applications. However, most studies have assumed a balanced data distribution. Also, techniques to handle imbalanced data in other domains cannot be applied directly to mobile malware detection since they generate synthetic samples with broken functionality, making them invalid. In this regard, this thesis introduces a novel synthetic over-sampling technique that ensures valid sample generation. This technique is subsequently combined with a dynamic cost function in the learning scheme that automatically adjusts minority class weight during model training which counters the bias towards the majority class and stabilizes the model. This hybrid method provided a 9% improvement in terms of F1-score. Aiming to design a robust malware detection system, this thesis extensively studies machine learning-based mobile malware detection in terms of best feature category combination, resilience against evasive attacks, and practical deployment of detection models. Given the increasing technological advancements in mobile and hand-held devices, this study will be very useful for designing robust cybersecurity systems to ensure safe usage of these devices.
  • Description: Doctor of Philosophy

Retinal optical coherence tomography image enhancement via deep learning

- Halupka, Kerry, Antony, Bhavna, Lee, Matthew, Lucy, Katie, Rai, Ravneet, Ishikawa, Hiroshi, Wollstein, Gadi, Schuman, Joel, Garnavi, Rahil

  • Authors: Halupka, Kerry , Antony, Bhavna , Lee, Matthew , Lucy, Katie , Rai, Ravneet , Ishikawa, Hiroshi , Wollstein, Gadi , Schuman, Joel , Garnavi, Rahil
  • Date: 2018
  • Type: Text , Journal article
  • Relation: Biomedical Optics Express Vol. 9, no. 12 (2018), p. 6205-6221
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  • Description: Optical coherence tomography (OCT) images of the retina are a powerful tool for diagnosing and monitoring eye disease. However, they are plagued by speckle noise, which reduces image quality and reliability of assessment. This paper introduces a novel speckle reduction method inspired by the recent successes of deep learning in medical imaging. We present two versions of the network to reflect the needs and preferences of different end-users. Specifically, we train a convolution neural network to denoise cross-sections from OCT volumes of healthy eyes using either (1) mean-squared error, or (2) a generative adversarial network (GAN) with Wasserstein distance and perceptual similarity. We then interrogate the success of both methods with extensive quantitative and qualitative metrics on cross-sections from both healthy and glaucomatous eyes. The results show that the former approach provides state-of-the-art improvement in quantitative metrics such as PSNR and SSIM, and aids layer segmentation. However, the latter approach, which puts more weight on visual perception, outperformed for qualitative comparisons based on accuracy, clarity, and personal preference. Overall, our results demonstrate the effectiveness and efficiency of a deep learning approach to denoising OCT images, while maintaining subtle details in the images.

Efficient anomaly recognition using surveillance videos

- Saleem, Gulshan, Bajwa, Usama, Raza, Rana, Alqahtani, Fayez, Tolba, Amr, Xia, Feng

  • Authors: Saleem, Gulshan , Bajwa, Usama , Raza, Rana , Alqahtani, Fayez , Tolba, Amr , Xia, Feng
  • Date: 2022
  • Type: Text , Journal article
  • Relation: PeerJ Computer Science Vol. 8, no. (2022), p.
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  • Description: Smart surveillance is a difficult task that is gaining popularity due to its direct link to human safety. Today, many indoor and outdoor surveillance systems are in use at public places and smart cities. Because these systems are expensive to deploy, these are out of reach for the vast majority of the public and private sectors. Due to the lack of a precise definition of an anomaly, automated surveillance is a challenging task, especially when large amounts of data, such as 24/7 CCTV footage, must be processed. When implementing such systems in real-time environments, the high computational resource requirements for automated surveillance becomes a major bottleneck. Another challenge is to recognize anomalies accurately as achieving high accuracy while reducing computational cost is more challenging. To address these challenge, this research is based on the developing a system that is both efficient and cost effective. Although 3D convolutional neural networks have proven to be accurate, they are prohibitively expensive for practical use, particularly in real-time surveillance. In this article, we present two contributions: a resource-efficient framework for anomaly recognition problems and two-class and multi-class anomaly recognition on spatially augmented surveillance videos. This research aims to address the problem of computation overhead while maintaining recognition accuracy. The proposed Temporal based Anomaly Recognizer (TAR) framework combines a partial shift strategy with a 2D convolutional architecture-based model, namely MobileNetV2. Extensive experiments were carried out to evaluate the model's performance on the UCF Crime dataset, with MobileNetV2 as the baseline architecture; it achieved an accuracy of 88% which is 2.47% increased performance than available state-of-the-art. The proposed framework achieves 52.7% accuracy for multiclass anomaly recognition on the UCF Crime2Local dataset. The proposed model has been tested in real-time camera stream settings and can handle six streams simultaneously without the need for additional resources. © Copyright 2022 Saleem et al.

One-shot malware outbreak detection using spatio-temporal isomorphic dynamic features

- Park, Sean, Gondal, Iqbal, Kamruzzaman, Joarder, Zhang, Leo

A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion

- Ali, Farman, El-Sappagh, Shaker, Islam, S., Kwak, Daehan, Ali, Amjad, Imran, Muhammad, Kwak, Kyung-Sup

  • Authors: Ali, Farman , El-Sappagh, Shaker , Islam, S. , Kwak, Daehan , Ali, Amjad , Imran, Muhammad , Kwak, Kyung-Sup
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Information Fusion Vol. 63, no. (2020), p. 208-222
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  • Description: The accurate prediction of heart disease is essential to efficiently treating cardiac patients before a heart attack occurs. This goal can be achieved using an optimal machine learning model with rich healthcare data on heart diseases. Various systems based on machine learning have been presented recently to predict and diagnose heart disease. However, these systems cannot handle high-dimensional datasets due to the lack of a smart framework that can use different sources of data for heart disease prediction. In addition, the existing systems utilize conventional techniques to select features from a dataset and compute a general weight for them based on their significance. These methods have also failed to enhance the performance of heart disease diagnosis. In this paper, a smart healthcare system is proposed for heart disease prediction using ensemble deep learning and feature fusion approaches. First, the feature fusion method combines the extracted features from both sensor data and electronic medical records to generate valuable healthcare data. Second, the information gain technique eliminates irrelevant and redundant features, and selects the important ones, which decreases the computational burden and enhances the system performance. In addition, the conditional probability approach computes a specific feature weight for each class, which further improves system performance. Finally, the ensemble deep learning model is trained for heart disease prediction. The proposed system is evaluated with heart disease data and compared with traditional classifiers based on feature fusion, feature selection, and weighting techniques. The proposed system obtains accuracy of 98.5%, which is higher than existing systems. This result shows that our system is more effective for the prediction of heart disease, in comparison to other state-of-the-art methods. © 2020

Electricity theft detection for energy optimization using deep learning models

- Pamir, Javaid, Nadeem, Javed, Muhammad, Houran, Mohamad, Almasoud, Abdullah, Imran, Muhammad

  • Authors: Pamir , Javaid, Nadeem , Javed, Muhammad , Houran, Mohamad , Almasoud, Abdullah , Imran, Muhammad
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Energy Science and Engineering Vol. 11, no. 10 (2023), p. 3575-3596
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  • Description: The rapid increase in nontechnical loss (NTL) has become a principal concern for distribution system operators (DSOs) over the years. Electricity theft makes up a major part of NTL. It causes losses for the DSOs and also deteriorates the quality of electricity. The introduction of advanced metering infrastructure along with the upgradation of the traditional grids to the smart grids (SGs) has helped the electric utilities to collect the electricity consumption (EC) readings of consumers, which further empowers the machine learning (ML) algorithms to be exploited for efficient electricity theft detection (ETD). However, there are still some shortcomings, such as class imbalance, curse of dimensionality, and bypassing the automated tuning of hyperparameters in the existing ML-based theft classification schemes that limit their performances. Therefore, it is essential to develop a novel approach to deal with these problems and efficiently detect electricity theft in SGs. Using the salp swarm algorithm (SSA), gate convolutional autoencoder (GCAE), and cost-sensitive learning and long short-term memory (CSLSTM), an effective ETD model named SSA–GCAE–CSLSTM is proposed in this work. Furthermore, a hybrid GCAE model is developed via the combination of gated recurrent unit and convolutional autoencoder. The proposed model comprises five submodules: (1) data preparation, (2) data balancing, (3) dimensionality reduction, (4) hyperparameters' optimization, and (5) electricity theft classification. The real-time EC data provided by the state grid corporation of China are used for performance evaluations via extensive simulations. The proposed model is compared with two basic models, CSLSTM and GCAE–CSLSTM, along with seven benchmarks, support vector machine, decision tree, extra trees, random forest, adaptive boosting, extreme gradient boosting, and convolutional neural network. The results exhibit that SSA–GCAE–CSLSTM yields 99.45% precision, 95.93% F1 score, 92.25% accuracy, and 71.13% area under the receiver operating characteristic curve score, and surpasses the other models in terms of ETD. © 2023 The Authors. Energy Science & Engineering published by Society of Chemical Industry and John Wiley & Sons Ltd.