The advent of autonomous vehicles heralds a new era in traffic management, presenting unprecedented opportunities and complex challenges. This paper aims to develop automated negotiation mechanisms for autonomous vehicles that navigate intersections without traditional traffic signals. We introduce a goal-oriented negotiation protocol grounded in the utilization of curvilinear coordinates. This approach is complemented by introducing a decision-making algorithm and a counteroffer algorithm for vehicles, both of which play pivotal roles in the negotiation protocol. Moreover, we provide evidence of the protocol's convergence and elucidate the time complexity of the underlying algorithm. We validate our algorithms with experiments using the AIM4 simulator, showcasing significant improvements in travel times compared to conventional traffic light systems and first-come-first-served methods. The results underscore our protocol's potential to reduce average travel time, enhancing overall traffic flow efficiency.

This paper is an extended abstract version of "Price of Anarchy of Traffic Assignment with Exponential Cost Functions[5]". We study a routing game where vehicles, selfish agents, independently choose routes to minimize travel delays from road congestion. We focus on exponential latency functions, unlike prior research using polynomial functions like BPR. We calculate a tight upper bound for the price of anarchy and compare it with the BPR function. Results indicate that the exponential function has a lower upper bound for traffic volumes below road capacity than the BPR function. Numerical analysis using real-world data shows that the exponential function closely approximates road latency with even tighter parameters, resulting in a relatively lower upper bound.

This chapter covers innovations in biomedical data mining and interpretations, especially using visualisations in interpretable machine learning for biomedical data analysis. Visualisations are important in presenting artificial intelligence models and validating the machine learning results. There are more new and complex machine learning methods that have been created to assist decision-making in recent years in the medical domain. Most of them are treated as "black boxes", as the training and prediction processes are hidden in complicated mathematical theories. Visualisation is a way to reveal the process and help a human understand the cause of a decision. Knowing the "why" for the prediction results and "how" the model works can improve users' trust in artificial intelligence results. The chapter introduces different visualisations used in interpreting supervised and unsupervised machine learning models for biomedical data. We also provide discussions and future work on using visualisations in interpreting data mining results in the medical domain.

A decentralized service placement policy plays a key role in distributed systems, such as fog computing, where sharing workloads fairly among active computing nodes is critical. A decentralized policy is an inherent feature of the service placement process that may improve load balancing among computers and can reduce the latency in many real‐time Internet of Things (IoT) applications. This article proposes reinforcement optimization for a decentralized service placement policy, which attempts to mitigate some of the drawbacks of existing service placement policies. Matching task size with node specifications and the allocation of less popular but time‐sensitive applications in the fog layer are the primary contributions of this study. Extensive experimental comparisons are made between the proposed algorithm and other well‐known algorithms over service latency, network usage, and computing usage using the iFogSim simulator. A microservice‐based application with varying sizes of computing requests are tested experimentally and show that the proposed algorithm effectively serves computing instances that are closer to users, reducing service latency and network usage. Compared to the existing models, the proposed modified algorithm reduces service latency by 24.1%, network usage by 4%, and computing usage by 20%, thus highlighting positive outcomes when using the proposed algorithm for fog analytics in future real‐time IoT applications.

Visual communication and user trust are always challenges in the health domain where conservativeness, precision, and domain knowledge can outweigh the validity of the outcomes in the analytical methods and processes. It is crucial to provide better awareness and understanding to domain experts, model developers, and even patients who might be conscious of their condition and how a treatment or a diagnosis is decided for them. This chapter contributes a discussion on trust and its issues in health data-driven science and how trust should be associated with analytical and computational processes, which are enhanced by visualisation and interaction. We also provide brief guidance on the models and methods for improving interpretability and trust in the health domain.

Biomedical data analytics have become a major decision-making aid for the diagnosis and treatment of diseases. Computational and visual analytics enable effective exploration and making sense of large and complex data through the deployment of appropriate machine learning and statistical analytics methods, meaningful visualisation, and human–information interaction. This chapter serves as a tutorial that provides guidelines, discussion, and reviews on methods and technologies that have been used for biomedical data analytics. We discuss the major processes of biomedical data analytics that are required to produce effective analytical outcomes. The chapter covers comprehensive discussions on computational analytics strategies, including feature selection, feature extraction, and clustering. Methods and several aspects of visual analytics and interactive visualisation in biomedical data analytics are also thoroughly explained and illustrated, including scatter plots, heat maps, parallel coordinates, network and graph visualisations, tailored visualisation, and visualisation in emerging technologies (such as virtual reality and augmented reality), as well as the human aspect of visualisation.

Bioinformatics has numerous approaches for evaluating the similarities between RNA-seq data for disease classification. Processing RNA-sequencing (RNA-seq) data using clustering or classification approach is extremely challenging, although analysis of ribonucleic acid (RNA-Seq) helps understand differentially expressed genes and classify the patient in a risk-free method. In this study, we present a hybrid end-to-end pipeline for analyzing, processing, and classifying the RNA-Seq data with a major focus on the covid-19 data set. The pipeline has been developed in three phases initially the raw data is normalized. Then the normalized data is pushed to a colonization algorithm to remove the noise data. The optimized data set is passed to a Deep Learning (DL) classifier. Further, a comparative analysis is performed with state of art methods discussed in the literature. The results prove that our proposed hybrid pipeline achieved the best accuracy over other methods. Gene set enrichment analysis was also performed to analyze the genes that are informative towards COVID-19 identification.

Background Visualising patient genomic data in a cohort with embedding data analytics models can provide relevant and sensible patient comparisons to assist a clinician with treatment decisions. As immersive technology is actively used around the medical world, there is a rising demand for an efficient environment that can effectively display genomic data visualisations on immersive devices such as a Virtual Reality (VR) environment. The VR technology will allow clinicians, biologists, and computer scientists to explore a cohort of individual patients within the 3D environment. However, demonstrating the feasibility of the VR prototype needs domain users’ feedback for future user-centred design and a better cognitive model of human–computer interactions. There is limited research work for collecting and integrating domain knowledge into the prototype design. Objective A usability study for the VR prototype–-Virtual Reality to Observe Oncology data Models (VROOM) was implemented. VROOM was designed based on a preliminary study among medical users. The goals of this usability study included establishing a baseline of user experience, validating user performance measures, and identifying potential design improvements that are to be addressed to improve efficiency, functionality, and end-user satisfaction. Methods The study was conducted with a group of domain users (10 males, 10 females) with portable VR devices and camera equipment. These domain users included medical users such as clinicians and genetic scientists and computing domain users such as bioinformatics and data analysts. Users were asked to complete routine tasks based on a clinical scenario. Sessions were recorded and analysed to identify potential areas for improvement to the data visual analytics projects in the VR environment. The one-hour usability study included learning VR interaction gestures, running visual analytics tool, and collecting before and after feedback. The feedback was analysed with different methods to measure effectiveness. The statistical method Mann–Whitney U test was used to analyse various task performances among the different participant groups, and multiple data visualisations were created to find insights from questionnaire answers. Results The usability study investigated the feasibility of using VR for genomic data analysis in domain users’ daily work. From the feedback, 65% of the participants, especially clinicians (75% of them), indicated that the VR prototype is potentially helpful for domain users’ daily work but needed more flexibility, such as allowing them to define their features for machine learning part, adding new patient data, and importing their datasets in a better way. We calculated the engaged time for each task and compared them among different user groups. Computing domain users spent 50% more time exploring the algorithms and datasets than medical domain users. Additionally, the medical domain users engaged in the data visual analytics parts (approximately 20%) longer than the computing domain users.

The rapid evolution of technology in connected automated and autonomous vehicles offers immense potential for revolutionizing future intelligent traffic control and management. This potential is exemplified by the diverse range of control paradigms, ranging from self-routing to centralized control. However, the selection among these paradigms is beyond technical consideration but a delicate balance between autonomous decision-making and holistic system optimization. A pivotal quantitative parameter in navigating this balance is the concept of the “price of anarchy” (PoA) inherent in autonomous decision frameworks. This paper analyses the price of anarchy for road networks with traffic of CAV. We model a traffic network as a routing game in which vehicles are selfish agents who choose routes to travel autonomously to minimize travel delays caused by road congestion. Unlike existing research in which the latency function of road congestion was based on polynomial functions like the well-known BPR function, we focus on routing games where an exponential function can specify the latency of road traffic. We first calculate a tight upper bound for the price of anarchy for this class of games and then compare this result with the tight upper bound of the PoA for routing games with the BPR latency function. The comparison shows that as long as the traffic volume is lower than the road capacity, the tight upper bound of the PoA of the games with the exponential function is lower than the corresponding value with the BPR function. Finally, numerical results based on real-world traffic data demonstrate that the exponential function can approximate road latency as close as the BPR function with even tighter exponential parameters, which results in a relatively lower upper bound.

Cancer is a disease characterised by changes in combinations of genes within affected tumour cells. The deep understanding of genetic activity afforded to cancer specialists through complex genomics data analytics has advanced the clinical management of cancer by using deep machine learning algorithms and visualisation. However, most of the existing works do not integrate intelligent decision-making aids that can guide users in the analysis and exploration processes. This paper contributes a novel strategy that applies game theory within a VR-enabled immersive visualisation system designed as the decision support engine to mimic real-world interactions between stakeholders within complex relationships, in this case cancer clinicians. Our focus is to apply game theory to assist doctors in the decision-making process regarding the treatment options for rare-cancer patients. Nash Equilibrium and Social Optimality strategy profiles were used to facilitate complex analysis within the visualisation by inspecting which combination of genes and dimensionality reduction methods yields the best survival rate and by investigating the treatment protocol to form new hypotheses. Using a case simulation, we demonstrate the effectiveness of game theory in guiding the analyst with a patient cohort data interrogation system as compared to an analyst without a decision support system. Particularly, the strategy profile (t-SNE method and DNMT3B_ZBTB46_LAPTM4B gene) gains the highest payoff for the two doctors.

This article studies the impact of social media posts specific to a disaster incident – the Australian bushfires of 2019–2020. We analyse the social media content posted by the Australian Red Cross Organization’s Facebook page, and the user generated comments on their posts. We identify user sentiments in response to the natural disaster and towards the organization’s fundraising attempts. This study shall enable the stakeholders to understand how the general public reacts to fundraising protocols at the times of unforeseen disasters. It shall also allow policymakers to design sustainable goals to promote healthy donation behaviour through social media platforms. Further, we also analyse how benchmark Natural Language Processing tools, namely, VADER, Afinn, and TextBlob, perform in an unsupervised scenario to perform sentiment classification. Overall VADER results were best among the other algorithms Afinn and TextBlob in the term of accuracy, precision, recall and f1 score performance measure.

Uniform Manifold Approximation and Projection (UMAP) is a new and effective non-linear dimensionality reduction (DR) method recently applied in biomedical informatics analysis. UMAP's data transformation process is complicated and lacks transparency. Principal component analysis (PCA) is a conventional and essential DR method for analysing single-cell datasets. PCA projection is linear and easy to interpret. The UMAP is more scalable and accurate, but the complex algorithm makes it challenging to endorse the users' trust. Another challenge is that some single-cell data have too many dimensions, making the computational process inefficient and lacking accuracy. This paper uses linkable and interactive visualisations to understand UMAP results by comparing PCA results. An explainable machine learning model, SHapley Additive exPlanations (SHAP) run on Random Forest (RF), is used to optimise the input single-cell data to make UMAP and PCA processes more efficient. We demonstrate that this approach can be applied to high-dimensional omics data exploration to visually validate informative molecule markers and cell populations identified from the UMAP-reduced dimensionality space.

The significant advancement of inexpensive and portable virtual reality (VR) and augmented reality devices has re-energised the research in the immersive analytics field. The immersive environment is different from a traditional 2D display used to analyse 3D data as it provides a unified environment that supports immersion in a 3D scene, gestural interaction, haptic feedback and spatial audio. Genomic data analysis has been used in oncology to understand better the relationship between genetic profile, cancer type, and treatment option. This paper proposes a novel immersive analytics tool for cancer patient cohorts in a virtual reality environment, virtual reality to observe oncology data models. We utilise immersive technologies to analyse the gene expression and clinical data of a cohort of cancer patients. Various machine learning algorithms and visualisation methods have also been deployed in VR to enhance the data interrogation process. This is supported with established 2D visual analytics and graphical methods in bioinformatics, such as scatter plots, descriptive statistical information, linear regression, box plot and heatmap into our visualisation. Our approach allows the clinician to interrogate the information that is familiar and meaningful to them while providing them immersive analytics capabilities to make new discoveries toward personalised medicine.

Large-scale clinical information sharing (CIS) provides significant advantages for medical treatments, including enhanced service standards and accelerated scheduling of health services. The current CIS suffers many challenges such as data privacy, data integrity, and data availability across multiple healthcare institutions. This study introduces an innovative blockchain-based electronic healthcare system that incorporates synchronous data backup and a highly encrypted data-sharing mechanism. Blockchain technology, which eliminates centralized organizations and reduces the number of fragmented patient files, could make it easier to use machine learning (ML) models for predictive diagnosis and analysis. In turn, it might lead to better medical care. The proposed model achieved an improved patient-centered CIS by personalizing the separation of information with an intelligent "allowed list" for clinician data access. This work introduces a hybrid ML-blockchain solution that combines traditional data storage and blockchain-based access. The experimental analysis evaluated the proposed model against the competing models in comparative and quantitative studies in large-scale CIS examples in terms of model viability, stability, protection, and robustness, with improved results.