Ali Anaissi

• Lecturer at The University of Sydney

Gastric cancer is one of the most commonly diagnosed cancers and has a high mortality rate. Due to limited medical resources, developing machine learning models for gastric cancer recognition provides an efficient solution for medical institutions. However, such models typically require large sample sizes for training and testing, which can challen...

Multimodal sentiment analysis enhances conventional sentiment analysis, which traditionally relies solely on text, by incorporating information from different modalities such as images, text, and audio. This paper proposes a novel multimodal sentiment analysis architecture that integrates text and image data to provide a more comprehensive understa...

The integration of Internet of Medical Things (IoMT) devices is critical for enabling intelligent healthcare services in smart homes. However, centralized access control methods, such as OAuth 2.0, are vulnerable to token leakage, session fixation, and unauthorized access. This study proposes a decentralized architecture leveraging Trigger-Action P...

We present an advanced approach to medical question-answering (QA) services, using fine-tuned Large Language Models (LLMs) to improve the accuracy and reliability of healthcare information. Our study focuses on optimizing models like LLaMA-2 and Mistral, which have shown great promise in delivering precise, reliable medical answers. By leveraging c...

We present a refined approach to biomedical question-answering (QA) services by integrating large language models (LLMs) with Multi-BERT configurations. By enhancing the ability to process and prioritize vast amounts of complex biomedical data, this system aims to support healthcare professionals in delivering better patient outcomes and informed d...

We present an innovative framework that integrates consumer-grade drones into bushfire management, addressing both service improvement and data privacy concerns under Australia's Privacy Act 1988. This system establishes a marketplace where bushfire management authorities, as data consumers, access critical information from drone operators, who ser...

We introduce "TMIoDT," a pioneering framework aimed at bolstering communication security in the Internet of Drone Things (IoDT) integrated with Open Radio Access Networks (Open RAN), with a specific focus on bushfire monitoring applications. Our novel contributions include the seamless integration of digital twin technology with blockchain to estab...

In the domain of spatial crowdsourcing drone services, which includes tasks like delivery, surveillance, and data collection, secure communication is paramount. The Public Key Infrastructure (PKI) ensures this by providing a system for digital certificates that authenticate the identities of entities involved, securing data and command transmission...

We introduce a privacy-preserving framework for integrating consumer-grade drones into bushfire management. This system creates a marketplace where bushfire management authorities obtain essential data from drone operators. Key features include local differential privacy to protect data providers and a blockchain-based solution ensuring fair data e...

This study introduces the Distributed Drone Reputation Management (DDRM) framework, designed to fortify trust and authenticity within the Internet of Drone Things (IoDT) ecosystem. As drones increasingly play a pivotal role across diverse sectors, integrating crowdsourced drone services within the IoDT has emerged as a vital avenue for democratizin...

Federated Learning (FL) enables local devices to collaboratively learn a shared predictive model by only periodically sharing model parameters with a central aggregator. However, FL can be disadvantaged by statistical heterogeneity produced by the diversity in each local devices data distribution, which creates different levels of Independent and I...

Identifying cancer risk groups by multi-omics has attracted researchers in their quest to find biomarkers from diverse risk-related omics. Stratifying the patients into cancer risk groups using genomics is essential for clinicians for pre-prevention treatment to improve the survival time for patients and identify the appropriate therapy strategies....

Traditionally, sentiment analysis methods rely solely on text or image data. However, most user-generated social media content includes both textual and image content. In this study, we propose a novel Dual-Pipeline based Attentional method that uses different modalities of data, including text and images, to analyse and interpret emotions and sent...

Computers have various applications in relation to the classification of weeds, including computer vision. This paper demonstrates the use of illumination invariance techniques and shadow reduction in images to improve the accuracy of machine learning (ML) models using support vector machines. The paper’s main aim is to identify the benefits of ima...

Adopting Sustainable Brand Strategies is a ‘must’ and not a ‘should’ anymore in the Business Industry. Traditional academic knowledge on how to define Brand Strategy based in the STP process (Segmentation, Target and Positioning) has been worldwide adopted by data-driven Brand Managers. However, incorporation of Sustainability motivations of consum...

Auto-scaling, also known as elasticity, provides the capacity to efficiently allocate computing resources on demand, rendering it beneficial for a wide array of applications, particularly web-based ones. However, the dynamic and unpredictable nature of workloads in web applications poses considerable challenges in designing effective strategies for...

We introduce the AI-Generated Optimal Decision (AIGOD) algorithm and the Deep Diffusion Soft Actor-Critic (DDSAC) framework, marking a significant advancement in integrating Human Digital Twins (HDTs) with AI-Generated Content (AIGC) within IoMT-based smart homes. Our innovative AI-Generated Content-as-a-Service (AIGCaaS) architecture, optimized fo...

The rapid expansion of Graph Neural Networks (GNNs) in consumer electronics and Vehicular Edge Computing (VEC) enhanced Internet of Drone Things (IoDT) services highlights the need for strong defenses against cyber attacks. One significant but overlooked threat is adversarial label-flipping, where attackers slightly change training labels to disrup...

This study delves into the application of Generative Adversarial Networks (GANs) within the context of imbalanced datasets. Our primary aim is to enhance the performance and stability of GANs in such datasets. In pursuit of this objective, we introduce a novel network architecture known as Damage GAN, building upon the ContraD GAN framework which s...

Stock prices forecasting has always been a challenging task. Although many research projects adopt machine learning and deep learning algorithms to address the problem, few of them pay attention to the varying degrees of dependencies between stock prices. In this paper we introduce a hybrid model that improves stock price prediction by emphasizing...

Identifying cancer risk groups by integrative multi-omics has attracted researchers in their quest to find biomarkers from diverse risk-related omics. Stratifying the patients into cancer risk groups using genomics is essential for clinicians for pre-prevention treatment to improve the survival time for patients and identify the appropriate therapy...

In Federated Learning (FL), a shared model is learned across dispersive clients each of which often has small and heterogeneous data. As such, datasets in FL setting may suffer from the non-IID (Independent and identically distributed) problem. In this paper, we propose a BAGAN as machine learning model which has the ability to create data for mino...

Structural health monitoring (SHM) provides an economic approach which aims to enhance understanding the behavior of structures by continuously collecting data through multiple networked sensors attached to the structure. These data are then utilized to gain insight into the health of a structure and make timely and economic decisions about its mai...

Structural Health Monitoring aims to utilise sensor data to assess the integrity of structures. Machine learning is opening up the possibility for more accurate and informative metrics to be determined by leveraging the large volumes of data available in modern times. An unfortunate limitation to these advancements is the fact that these models typ...

We propose to leverage the WiFi fingerprint of people in confined areas to monitor and manage the mobility of the crowd in a smart city. We transform the indoor positioning problem into a supervised learning problem that takes as an input the WiFi fingerprint of a person and predicts their availability within a confined area. We investigate the acc...

Modern cluster management systems have effectively evolved to deal with the increasing and diverse cloud computing demands. However, several challenges including low resource utilization, high power consumption are still present that can be solved with a precise real-time usage prediction. This prediction problem is complicated since the cloud work...

Recommender systems have been successfully used in many domains with the help of machine learning algorithms. However, such applications tend to use multi-dimensional user data, which has raised widespread concerns about the breach of users’ privacy. Meanwhile, wearable technologies have enabled users to collect fitness-related data through embedde...

Different omics profiles, depending on the underlying technology, encompass measurements of several hundred to several thousand molecules in a biological sample or a cell. This study develops upon the concept of “omics imagification” as a process of transforming a vector representing these numerical measurements into an image with a one-to-one rela...

Single-cell data analysis can transform the practice of personalised medicine by facilitating characterisation of disease-associated molecular changes across every single cell. Advanced single-cell multimodal assays can now simultaneously measure different types of molecules (e.g., DNA, RNA, Protein) across hundreds of thousands of individual cells...

Multi-way data analysis has become an essential tool for capturing underlying structures in higher-order data sets where standard two-way analysis techniques often fail to discover the hidden correlations between variables in multi-way data. We propose a multi-objective variational autoencoder (MO-VAE) method for smart infrastructure damage detecti...

Data-driven machine learning models, compared to numerical models, demonstrated promising improvements in detecting damage in structural health monitoring (SHM) applications. In such approaches, sensors’ data are used to train a model either in a centralized model (server) or locally inside each sensor unit node (client). The centralized learning m...

Different omics profiles, depending on the underlying technology, encompass measurements of several hundred to several thousands of molecules in a biological sample or a cell. This study develops upon the concept of "omics imagification" as a process of transforming a vector representing these numerical measurements into an image with a one-to-one...

This paper proposes a new deep learning model which replaces the softmax activation function with support vector machines. To evaluate the performance of the model, we have completed a total of four sets of codes, including the traditional svm classification model, the traditional cnn model, the model of svm behind the fully connected layer, and th...

Different omics profiles, depending on the underlying technology, encompass measurements of several hundred to several thousands of molecules in a biological sample or a cell. This study develops upon the concept of "omics imagification" as a process of transforming a vector representing these numerical measurements into an image with a one-to-one...

Recommender systems have been successfully used in many domains with the help of machine learning algorithms. However, such applications tend to use multi-dimensional user data, which has raised widespread concerns about the breach of users privacy. Meanwhile, wearable technologies have enabled users to collect fitness-related data through embedded...

Class imbalance occurs in many real-world applications, including image classification, where the number of images in each class differs significantly. With imbalanced data, the generative adversarial networks (GANs) leans to majority class samples. The two recent methods, Balancing GAN (BAGAN) and improved BAGAN (BAGAN-GP), are proposed as an augm...

Traditional approaches to recommendation systems involve using collaborative filtering and content-based techniques which make use of the similarities between users and items respectively. Such approaches evolved to encapsulate model-based latent factor (LF) algorithms that use matrix decomposition to ingest a user-item matrix of ratings to generat...

Federated Learning (FL) has recently emerged as a promising method that employs a distributed learning model structure to overcome data privacy and transmission issues paused by central machine learning models. In FL, datasets collected from different devices or sensors are used to train local models (clients) each of which shares its learning with...

This article presents a new proportional-integral-derivative-accelerated (PIDA) control with a derivative filter to improve quadcopter/drone flight stability in a noisy environment. The mathematical model is derived from having an accurate model with a high level of fidelity by addressing the problems of nonlinearity, uncertainties, and coupling. T...

Structural Health Monitoring (SHM) provides an economic approach which aims to enhance understanding the behavior of structures by continuously collects data through multiple networked sensors attached to the structure. This data is then utilized to gain insight into the health of a structure and make timely and economic decisions about its mainten...

Federated Learning (FL) has recently emerged as a promising method that employs a distributed learning model structure to overcome data privacy and transmission issues paused by central machine learning models. In FL, datasets collected from different devices or sensors are used to train local models (clients) each of which shares its learning with...

Zero-shot learning (ZSL) aims to recognize instances belonging to unseen categories which are not available at training time. Previous ZSL models learn a projection function from the visual feature space to a semantic space which contains a description of the categories. The semantic attributes are often correlated with each other at the semantic s...

The online analysis of multi-way data stored in a tensor has become an essential tool for capturing the underlying structures and extracting the sensitive features that can be used to learn a predictive model. However, data distributions often evolve with time and a current predictive model may not be sufficiently representative in the future. Ther...

Data-driven machine learning models, compared to numerical models, shown promising improvements in detecting damage in Structural Health Monitoring (SHM) applications. In data-driven approaches, sensors’ data are used to train a model either in a centralized server or locally inside each sensor unit node (decentralized model similar to edge computi...

The scheduling of multi-user remote laboratories is modeled as a multimodal function for the proposed optimization algorithm. The hybrid optimization algorithm, hybridization of the Nelder-Mead Simplex algorithm, and Non-dominated Sorting Genetic Algorithm (NSGA), named Simplex Non-dominated Sorting Genetic Algorithm (SNSGA), is proposed to optimiz...

This paper presents a new Proportional-Integral-Derivative-Accelerated (PIDA) control with a derivative filter to improve quadcopter flight stability in a noisy environment. The mathematical model is derived from having an accurate model with a high level of fidelity by addressing the problems of non-linearity, uncertainties, and coupling. These un...

Designing an efficient and reliable airport security screening system is a critical and challenging task. It is an essential element of airline and passenger safety which aims to provide the expected level of confidence and to ensure the safety of passengers and the aviation industry. In recent years, security at airports has gone through noticeabl...

This paper presents a new framework to use images as the inputs for the controller to have autonomous flight, considering the noisy indoor environment and uncertainties. A new Proportional-Integral-Derivative-Accelerated (PIDA) control with a derivative filter is proposed to improves drone/quadcopter flight stability within a noisy environment and...

Augmented Reality (AR) is investigated as a unique technology to combine virtual and real world together. The key feature of AR is to demonstrate the extra information in the field of view for those who interact with the authentic environment. Two new schemes of AR are proposed, including technical terms as well as psychological approaches. The fin...

The scheduling of multi-user remote laboratories is modeled as a multimodal function for the proposed optimization algorithm. The hybrid optimization algorithm, hybridization of the Nelder-Mead Simplex algorithm and Non-dominated Sorting Genetic Algorithm (NSGA), is proposed to optimize the timetable problem for the remote laboratories to coordinat...

Multi-way data analysis has become an essential tool for capturing underlying structures in higher-order datasets stored in tensor $\mathcal{X} \in \mathbb{R} ^{I_1 \times \dots \times I_N} $. $CANDECOMP/PARAFAC$ (CP) decomposition has been extensively studied and applied to approximate $\mathcal{X}$ by $N$ loading matrices $A^{(1)}, \dots, A^{(N)}...

Multi-way data analysis has become an essential tool for capturing underlying structures in higher-order data sets where standard two-way analysis techniques often fail to discover the hidden correlations between variables in multi-way data. We propose a multi-objective variational autoencoder (MVA) method for smart infrastructure damage detection...

The online analysis of multi-way data stored in a tensor $\mathcal{X} \in \mathbb{R} ^{I_1 \times \dots \times I_N} $ has become an essential tool for capturing the underlying structures and extracting the sensitive features which can be used to learn a predictive model. However, data distributions often evolve with time and a current predictive mo...

Airport security screening processes are essential to ensure the safety of passengers and the aviation industry. Security at airports has improved noticeably in recent years through the utilisation of state-of-the-art technologies and highly trained security officers. However, maintaining a high level of security can be costly to operate and implem...

Despite its success for anomaly detection in the scenario where only data representing normal behavior are available, one-class support vector machine (OCSVM) still has challenge in dealing with non-stationary data stream, where the underlying distributions of data are time-varying. Existing OCSVM-based online learning methods incrementally update...

Multi-label classification is defined as the problem of identifying the multiple labels or categories of new observations based on labeled training data. Multi-labeled data has several challenges, including class imbalance, label correlation, incomplete multi-label matrices, and noisy and irrelevant features. In this article, we propose an integrat...

A new heuristic optimization algorithm is presented to solve the nonlinear optimization problems. The proposed algorithm utilizes a stochastic method to achieve the optimal point based on simplex techniques. A dual simplex is distributed stochastically in the search space to find the best optimal point. Simplexes share the best and worst vertices o...

We propose a multi-objective autoencoder method for fault detection and diagnosis in multi-way data based on the reconstruction error of autoencoder deep neural network (ADNN). Multi-way data analysis has become an essential tool for capturing underlying structures in higher-order data sets. Our method fuses data from multiple sources in one ADNN a...

Airport security screening processes are essential to ensure the safety of both passengers and the aviation industry. Security at airports has improved noticeably in recent years through the utilisation of state-of-the-art technologies and highly trained security officers. However, maintaining a high level of security can be costly to operate and i...

Road networks are critical assets supporting economies and communities. Despite budget and time constraints, road authorities strive to maintain them to ensure safety, ongoing service, and economic productivity. This paper proposes a virtual road network inspector (VRNI), which continuously monitors road conditions and provides decision support to...

Data mining techniques have been widely applied in several domains to support a variety of business-related applications such as market basket analysis. For instance, basket market transaction accumulate large amounts of customer purchase data from their day-to-day operations. This paper delivers a strategy for the implementation of a systematic an...

Machine learning algorithms have been employed extensively in the area of structural health monitoring to compare new measurements with baselines to detect any structural change. One-class support vector machine (OCSVM) with Gaussian kernel function is a promising machine learning method which can learn only from one-class data and then classify an...

In this paper, we focused on the development and verification of a solid and robust framework for structural condition assessment of real‐life structures using measured vibration responses, with the presence of multiple progressive damages occurring within the inspected structures. A self‐tuning learning method for structural condition assessment w...

One-class support vector machine (OCSVM) has been widely used in the area of structural health monitoring, where only data from one class (i.e., healthy) are available. Incremental learning of OCSVM is critical for online applications in which huge data streams continuously arrive and the healthy data distribution may vary over time. This article p...

High-dimensional highly correlated data exist in several domains such as genomics. Many feature selection techniques consider correlated features as redundant and therefore need to be removed. Several studies investigate the interpretation of the correlated features in domains such as genomics, but investigating the classification capabilities of t...

Structural Health Monitoring (SHM) is a condition-based maintenance technology using sensing systems. In SHM, the use of domain knowledge is essential: it motivates the use of machine learning approaches; it can be used to extract damage sensitive features and interpret the results by machine learning. This work focuses on two SHM problems: damage...

Early damage detection is critical for a large set of global ageing infrastructure. Structural Health Monitoring systems provide a sensor-based quantitative and objective approach to continuously monitor these structures, as opposed to traditional engineering visual inspection. Analysing these sensed data is one of the major Structural Health Monit...

Civil infrastructures are key to the flow of people and goods in urban environments. Structural Health Monitoring (SHM) is a condition-based maintenance technology, which provides and predicts actionable information on the current and future states of infrastructures. SHM data are usually multi-way data which are produced by multiple highly correla...

Incremental One-Class Support Vector Machine (OCSVM) methods provide critical advantages in practical applications, as they are able to capture variations of the positive samples over time. This paper proposes a novel self-advised incremental OCSVM algorithm, which decides whether an incremental step is required to update its model or not. As oppos...

Machine learning algorithms have been employed extensively in the area of structural health monitoring to compare new measurements with baselines to detect any structural change. One-class support vector machine (OCSVM) with Gaussian kernel function is a promising machine learning method which can learn only from one class data and then classify an...

The identification of a subset of genes having the ability to capture the necessary information to distinguish classes of patients is crucial in bioinformatics applications. Ensemble and bagging methods have been shown to work effectively in the process of gene selection and classification. Testament to that is random forest which combines random d...

Childhood Leukaemia Dataset. This dataset contains data for 60 patients with expression values for 22,277 probes. It is generated from the U133A platform and collected from The Children’s Hospital at Westmead. (CSV)

Background: The process of retrieving similar cases in a case-based reasoning system is considered a big challenge for gene expression data sets. The huge number of gene expression values generated by microarray technology leads to complex data sets and similarity measures for high-dimensional data are problematic. Hence, gene expression similarit...

The wealth of gene expression values being generated by high throughput microarray technologies leads to complex high dimensional datasets. Moreover, many cohorts have the problem of imbalanced classes where the number of patients belonging to each class is not the same. With this kind of dataset, biologists need to identify a small number of infor...

Gene expression data is a very complex data set characterised by abundant numbers of features but with a low number of observations. However, only a small number of these features are relevant to an outcome of interest. With this kind of data set, feature selection becomes a real prerequisite. This paper proposes a methodology for feature selection...

Microarray analysis and visualization is very helpful for biologists and clinicians to understand gene expression in cells and to facilitate diagnosis and treatment of patients. However, a typical microarray dataset has thousands of features and a very small number of observations. This very high dimensional data has a massive amount of information...