Road Car Accident Prediction Using a Machine-Learning-Enabled Data Analysis

Traffic accidents have become severe risks as they are one of the causes of enormous deaths worldwide. Reducing the number of incidents is critical to saving lives and achieving sustainable cities and communities. Machine learning and data analysis techniques interpret the reasons for car accidents and propose solutions to minimize them. However, this needs to take the benefits of big data solutions as the size and velocity of traffic accident data are increasingly large and rapid. This paper explores road car accident data patterns and proposes a predictive model by investigating meaningful data features, such as accident severity, the number of casualties, and the number of vehicles. Therefore, a pre-processing model is designed to convert raw data using missing and meaningless feature removal, data attribute generalization, and outlier removal using interquartile. Four classification methods, including decision trees, random forest, multinomial logistic regression, and naïve Bayes, are used and evaluated to study the performance of road accident prediction. The results address acceptable levels of accuracy for car accident prediction except for naïve Bayes. The findings are discussed through a data-driven approach to understand the factors influencing road car accidents and highlight the key ones to propose accident prevention solutions. Finally, some strategies are provided to achieve healthy and community-friendly cities

The Ethical Balance of Using Smart Information Systems for Promoting the United Nations’ Sustainable Development Goals

The Sustainable Development Goals (SDGs) are internationally agreed goals that allow us to determine what humanity, as represented by 193 member states, finds acceptable and desirable. The paper explores how technology can be used to address the SDGs and in particular Smart Information Systems (SIS). SIS, the technologies that build on big data analytics, typically facilitated by AI techniques such as machine learning, are expected to grow in importance and impact. Some of these impacts are likely to be beneficial, notably the growth in efficiency and profits, which will contribute to societal wellbeing. At the same time, there are significant ethical concerns about the consequences of algorithmic biases, job loss, power asymmetries and surveillance, as a result of SIS use. SIS have the potential to exacerbate inequality and further entrench the market dominance of big tech companies, if left uncontrolled. Measuring the impact of SIS on SDGs thus provides a way of assessing whether an SIS or an application of such a technology is acceptable in terms of balancing foreseeable benefits and harms. One possible approach is to use the SDGs as guidelines to determine the ethical nature of SIS implementation. While the idea of using SDGs as a yardstick to measure the acceptability of emerging technologies is conceptually strong, there should be empirical evidence to support such approaches. The paper describes the findings of a set of 6 case studies of SIS across a broad range of application areas, such as smart cities, agriculture, finance, insurance and logistics, explicitly focusing on ethical issues that SIS commonly raise and empirical insights from organisations using these technologies

Process Mining IPTV Customer Eye Gaze Movement Using Discrete-time Markov Chains

Human-Computer Interaction (HCI) research has extensively employed eye-tracking technologies in a variety of fields. Meanwhile, the ongoing development of Internet Protocol TV (IPTV) has significantly enriched the TV customer experience, which is of great interest to researchers across academia and industry. A previous study was carried out at the BT Ireland Innovation Centre (BTIIC), where an eye tracker was employed to record user interactions with a Video-on-Demand (VoD) application, the BT Player. This paper is a complementary and subsequent study of the analysis of eye-tracking data in our previously published introductory paper. Here, we propose a method for integrating layout information from the BT Player with mining the process of customer eye movement on the screen, thereby generating HCI and Industry-relevant insights regarding user experience. We incorporate a popular Machine Learning model, a discrete-time Markov Chain (DTMC), into our methodology, as the eye tracker records each gaze movement at a particular frequency, which is a good example of discrete-time sequences. The Markov Model is found suitable for our study, and it helps to reveal characteristics of the gaze movement as well as the user interface (UI) design on the VoD application by interpreting transition matrices, first passage time, proposed ‘most likely trajectory’ and other Markov properties of the model. Additionally, the study has revealed numerous promising areas for future research. And the code involved in this study is open access on GitHub

Sustainable Participatory Governance: Data-Driven Discovery of Parameters for Planning Online and In-Class Education in Saudi Arabia During COVID-19

Everything about our life is complex. It should not be so. New approaches to governance are needed to tackle these complexities and the rising global challenges. Smartization of cities and societies has the potential to unite us, humans, on a sustainable future for us through its focus on the triple bottom line (TBL) – social, environmental, and economic sustainability. Data-driven analytics are at the heart of this smartization. This study provides a case study on sustainable participatory governance using a data-driven parameter discovery for planning online, in-class, and blended learning in Saudi Arabia evidenced during the COVID-19 pandemic. For this purpose, we developed a software tool comprising a complete machine learning pipeline and used a dataset comprising around 2 million tweets in the Arabic language collected during a period of over 14 months (October 2020 to December 2021). We discovered fourteen governance parameters grouped into four governance macro parameters. These discovered parameters by the tool demonstrate the possibility and benefits of our sustainable participatory planning and governance approach, allowing the discovery and grasp of important dimensions of the education sector in Saudi Arabia, the complexity of the policy, the procedural and practical issues in continuing learning during the pandemic, the factors that have contributed to the success of teaching and learning during the pandemic times, both its transition to online learning and its return to in-class learning, the challenges public and government have faced related to learning during the pandemic times, and the new opportunities for social, economical, and environmental benefits that can be drawn out of the situation created by the pandemic. The parameters and information learned through the tool can allow governments to have a participatory approach to governance and improve their policies, procedures, and practices, perpetually through public and stakeholder feedback. The data-driven parameter discovery approach we propose is generic and can be applied to the governance of any sector. The specific case study is used to elaborate on the proposed approach

Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging.Qatar National Librar

Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging

Enhancing the Automotive E/E Architecture Utilising Container-Based Electronic Control Units

Over the past 40 years, with the advent of computing technology and embedded systems, such as Electronic Control Units (ECUs), cars have moved from solely mechanical control to predominantly digital control. Whilst improvements have been realised in terms of passenger safety and vehicle efficiency, there are several issues currently facing the automotive industry as a result of the rising number of ECUs. These include greater demands placed on power, increased vehicle weight, complexities of hardware and software, dependency on software, software life expectancy, ad-hoc methods concerning automotive software updates, and rising costs for the vehicle manufacturer and consumer. As the modern-day motor car enters the autonomous age, these issues are predicted to increase because there will be an even greater reliance on computing hardware and software technology to support these new driving functions. To address the issues highlighted above, a number of solutions that aid hardware consolidation and promote software reusability have been proposed. However, these depend on bespoke embedded hardware and there remains a lack of clearly defined mechanisms through which to update ECU software. This research moves away from these current practices and identifies many similarities between the datacentre and the automotive Electronic and Electrical (E/E) architecture, demonstrating that virtualisation technologies, which have provided many benefits to the datacentre, can be replicated within an automotive context. Specifically, the research presents a comprehensive study of the Central Processor Unit (CPU) and memory resources required and consumed to support a container-based ECU automotive function. The research reveals that lightweight container virtualisation offers many advantages. A container-based ECU can promote consolidation and enhance the automotive E/E architecture through power, weight and cost savings, as well as enabling a robust mechanism to facilitate future software updates throughout the lifetime of a vehicle. Furthermore, this research demonstrates there are opportunities to adopt this new research methodology within both the automotive industry and industries that utilise embedded systems, more broadly

A Técnica de Profiling Criminal: Uma abordagem Meta-Analítica

A dissertação a que nos propusemos surge no âmbito do curso de mestrado em Ciências Policiais, na especialização de Criminologia e Investigação Criminal, do Instituto Superior de Ciências Policiais e Segurança Interna, sob a orientação científica do Professor Doutor Nuno Caetano Lopes de Barros Poiares. A especialização acima referida permitiu-nos optar por um tema com relevância para a comunidade académica, mas sobretudo com grande interesse para as forças e serviços de segurança portugueses. Os comportamentos desviantes fazem parte da história da humanidade, suscitando sempre um misterioso interesse por tudo o que envolve. O conhecimento científico relativo ao fenómeno da criminalidade tem vindo a adicionar fatores de análise em diversas áreas, que pretendem ajudar a detetar as causas destes comportamentos e, se possível, preveni-las, uma vez que a segurança das sociedades é uma preocupação do Estado. Atendendo à necessidade de reforço da identidade profissional do criminólogo, em Portugal, bem como a sua ligação às forças policiais, resolvemos proceder a uma investigação que permitisse fornecer uma visão global da produção de conhecimento autónomo, designadamente sobre o Profiling Criminal, uma técnica auxiliar de investigação criminal. Assim, procurou-se fornecer uma explicação detalhada desta técnica, o seu enquadramento histórico, os diversos autores que contribuíram para a sua evolução, as diferentes metodologias, bem como o seu enquadramento legal. Em termos metodológicos, recorreu-se ao processo de revisão da literatura com base na meta-análise, comprovando-se a falta de estudos de caso na área do Profiling Criminal que demonstrem cientificamente a validade das técnicas utilizadas e que o reforço da identidade profissional do criminólogo passa pela sua contribuição efetiva para o trabalho das forças e serviços de segurança. Os resultados obtidos com o presente estudo demonstram que existe uma necessidade de fortalecimento do conhecimento científico para que se possa alcançar um nível de fiabilidade que retire a técnica do Profiling Criminal do escopo do ceticismo.The dissertation to which we proposed arises in the scope of the master's course in Police Sciences, in the field of Criminology and Criminal Investigation, of the Superior Institute of Police Sciences and Internal Security, under the scientific guidance of Full Professor Nuno Caetano Lopes de Barros Poiares. The mentioned specialization allowed us to select a theme with relevance to the academic community and above all with great interest to Portuguese security forces and services. Deviant and violent behavior is part of human history, always arousing a mysterious interest in everything it involves. Scientific knowledge regarding the phenomenon of crime has been adding factors of analysis in several areas, which aims to help detecting the causes of these behaviors and, if possible, prevent them, since the security of societies is a concern of the State. Bearing in mind the need to strengthen the criminologist's professional identity in Portugal, as well as his connection to security forces and services, we decided to carry out an investigation that would allow us to provide an overview of the production of autonomous knowledge, namely on Criminal Profiling, an auxiliary criminal investigation technique. With this research, we tried to provide a detailed explanation of this technique, its historical background, the different authors who contributed to its evolution, the various methodologies, as well as its legal framework. In methodological terms, using the literature review process based on meta analysis, it was verified that there is a lack of case studies in the area of Criminal Profiling which scientifically demonstrate the validity of the techniques used and that the reinforcement of the professional identity of the criminologist will pass through its effective contribution to the work of security forces and services. The results obtained with the present study demonstrate that there is a need to strengthen scientific knowledge in order to achieve a level of reliability that removes the Criminal Profiling technique from the scope of skepticism

Data Science in Healthcare

Data science is an interdisciplinary field that applies numerous techniques, such as machine learning, neural networks, and deep learning, to create value based on extracting knowledge and insights from available data. Advances in data science have a significant impact on healthcare. While advances in the sharing of medical information result in better and earlier diagnoses as well as more patient-tailored treatments, information management is also affected by trends such as increased patient centricity (with shared decision making), self-care (e.g., using wearables), and integrated care delivery. The delivery of health services is being revolutionized through the sharing and integration of health data across organizational boundaries. Via data science, researchers can deliver new approaches to merge, analyze, and process complex data and gain more actionable insights, understanding, and knowledge at the individual and population levels. This Special Issue focuses on how data science is used in healthcare (e.g., through predictive modeling) and on related topics, such as data sharing and data management

“Be a Pattern for the World”: The Development of a Dark Patterns Detection Tool to Prevent Online User Loss

Dark Patterns are designed to trick users into sharing more information or spending more money than they had intended to do, by configuring online interactions to confuse or add pressure to the users. They are highly varied in their form, and are therefore difficult to classify and detect. Therefore, this research is designed to develop a framework for the automated detection of potential instances of web-based dark patterns, and from there to develop a software tool that will provide a highly useful defensive tool that helps detect and highlight these patterns