University of Bolton

University of Bolton Institutional Repository
Not a member yet
    2826 research outputs found

    Cybersecurity threats detection in intelligent networks using predictive analytics approaches

    No full text
    The modern scenario of network vulnerabilities necessitates the adoption of sophisticated detection and mitigation strategies. Predictive analytics is surfaced to be a powerful tool in the fight against cybercrime, offering unparalleled capabilities for automating tasks, analyzing vast amounts of data, and identifying complex patterns that might elude human analysts. This paper presents a comprehensive overview of how AI is transforming the field of cybersecurity. Machine intelligence can bring revolution to cybersecurity by providing advanced defense capabilities. Addressing ethical concerns, ensuring model explainability, and fostering collaboration between researchers and developers are crucial for maximizing the positive impact of AI in this critical domain

    Evaluation of venous thromboembolism risk assessment models for hospital inpatients: the VTEAM evidence synthesis

    No full text
    Background: Pharmacological prophylaxis during hospital admission can reduce the risk of acquired blood clots (venous thromboembolism) but may cause complications, such as bleeding. Using a risk assessment model to predict the risk of blood clots could facilitate selection of patients for prophylaxis and optimise the balance of benefits, risks and costs. Objectives: We aimed to identify validated risk assessment models and estimate their prognostic accuracy, evaluate the cost-effectiveness of different strategies for selecting hospitalised patients for prophylaxis, assess the feasibility of using efficient research methods and estimate key parameters for future research. Design: We undertook a systematic review, decision-analytic modelling and observational cohort study conducted in accordance with Enhancing the QUAlity and Transparency Of health Research (EQUATOR) guidelines. Setting: NHS hospitals, with primary data collection at four sites. Participants: Medical and surgical hospital inpatients, excluding paediatric, critical care and pregnancy related admissions. Interventions: Prophylaxis for all patients, none and according to selected risk assessment models. Main outcome measures: Model accuracy for predicting blood clots, lifetime costs and quality-adjusted life-years associated with alternative strategies, accuracy of efficient methods for identifying key outcomes and proportion of inpatients recommended prophylaxis using different models

    Entrepreneurial leadership: an approach for crisis

    No full text
    In the face of recurring crisis and a rapidly changing business landscape, corporate leaders are looking for new ways to address their challenges and ensure long-term sustainability. The most effective leadership style and the critical competencies required for leaders to navigate the complexities of the business environment are yet to be identified. This chapter discusses the importance of effective leadership in today’s turbulent business environment. In addition, this chapter explores how an entrepreneurial leadership style can be effective in difficult situations and how organisations can learn to adopt an entrepreneurial leadership approach to remain competitive. This chapter examines case studies of successful entrepreneurial leadership, reviews relevant literature, and provides practical recommendations for organisations to develop entrepreneurial leadership competencies

    Improved corrosion and cavitation erosion resistance of laser-based powder bed fusion produced Ti-6Al-4V alloy by pulsed magnetic field treatment

    No full text
    The application of pulsed magnetic field (PMF) treatment demonstrated enhanced corrosion resistance in saline solution and prolonged resistance to cavitation erosion in deionised water for Ti-6AI-4V alloy manufactured by laser-based powder bed fusion (LPBF) and conventional wrought processing methods. The observed outcomes were attributed to the formation of a denser protective surface oxide layer and microstructural changes, resulting in a reduction of the α’ phase by 0.13% and an increase in the presence of dislocations at the surface. Consequently, this led to an increase in the compressive residual stresses. Additionally, the application of this treatment resulted in the formation of highly refined and uniform precipitates, leading to a notable enhancement in microhardness by 5.73% and 5.85% for the conventionally manufactured (CM) and LPBF samples, respectively

    EL-RFHC: Optimized ensemble learners using RFHC for intrusion attacks classification

    No full text
    The extensive growth of mobile technology leads to magnifying the usage of digital gadgets around the world. This requires a fast-interconnecting communication medium to transfer the data between the devices. Meanwhile, the intruders attempt to make huge traffic in the network that leads to loss of data. To identify the intrusion attacks, ensemble Machine Learning (ML) classifiers are applied using the various feature variables importance. However, most of the transmitting data contains high dimensions with numerous variables leads to more execution time to classify the attacks. This study initiated the novel approach fusion of the Random Forest classifier and High Correlation (RFHC) feature selection approach to diminish the quantity of the variables. Also, the count of intrusion attacks class is lower than the normal class leads to generating an imbalanced dataset. Hence, Synthetic Minority Over-Sampling Technique (SMOTE) is suggested to create a balanced dataset for multi-class classification, and Un-upsampled data for binary-class classification respectively. The pre-processed dataset fed into the ensemble machine learners, and attention mechanism-based LSTM to classify as various intrusion attacks and normal data. This research work focused on reducing the CICIDS2017 dataset’s variable dimensions from 71 to 34 using RFHC. The performance results showed that RF classifier performed better with accuracy of 99.4 %, precision 99.4 %, average recall 99.2 % and average F1-score 99.6 % in binary-class classification, and Extreme Gradient Boosting (XGBoost) achieved better accuracy of 99.7 %, precision 98.7 %, average recall 99.5 % and average F1-score 99.2 % in multi-class classification

    “It’s a Right Pain in the Pelvis!”: post-traumatic stress and post-traumatic growth in a sample of females experiencing chronic pelvic pain

    No full text
    Chronic pelvic pain affects 38 per 1,000 women yearly (Daniels & Khan, 2010; Zondervan et al., 1999), accompanied by various psycho-logical sequelae. Positive psychology may offer new approaches to pelvic pain that complement existing interventions; these include post-traumatic growth (PTG), optimism, resilience, and models of recovery. In a sample of 132 females (aged 16 to 45þ), cross- sectional research revealed that participants with pelvic pain of unknown cause had the highest levels of post-traumatic stress dis-order (PTSD) symptoms. A regression analysis revealed that intrusive rumination, avoidant coping, and resilience were significant predictors of PTSD symptoms, and resilience and social support were predictors of PTG. Understanding the elements of positive psychology could help create positive psychology interventions focusing on chronic pelvic pain’s impact on mental health

    Empirical evaluation of deep learning approaches for predicting cervical cancer in the health care sector

    No full text
    This research paper addresses the urgent need to combat the escalating mortality rates in cervical cancer, impacting 570,000 women, with 311,000 fatalities, as reported by the World Health Organization. Recognizing the potential of digital solutions, we explore deep learning's untapped power for early diagnosis. Amidst healthcare challenges due to population growth and disease spread, traditional methods prove inadequate. To bridge this gap, we introduce novel techniques: Long Short-term Memory Networks and Bidirectional Long Short-term Memory Networks. Leveraging a comprehensive dataset of 15 attributes, including age, pregnancies, partners, smoking, cytology, and biopsy, our model achieves a noteworthy 97% accuracy, signifying a ground-breaking advancement in cervical cancer management

    Leadership During a Crisis: A Focus on Leadership Development

    No full text
    We live in uncertain times propelled by complex systems, climate change and the use of technology which possess various threats. At times of crisis, leadership that permits quick reactions to the changing organisational environment becomes necessary. However, there has been limited studies that provide a road map of leading during a crisis. What is required of leaders during a crisis? How can you develop the required leadership expertise during such turbulent periods? What are the challenges leaders will have to combat? Through this book, these questions are answered. It is no exaggeration therefore to claim that this book opens a new chapter as it seeks to advance discussions about how to lead during crisis. Drawing on empirical and conceptual evidence from the perspective of renowned authors in leadership research, it offers a robust and engaging overview of the field of leadership and leadership development in turbulent and dynamic environments. The chapters in the book support the personal and professional development of aspiring and experienced leaders and managers. The readers will be able to display critical awareness of current developments in both the theory and practice of leadership and leadership development and its importance in modern organisations

    GREAT Case Study Plan. Deliverable 4.2

    No full text
    This deliverable provides details of the Case Study Design to be applied in the GREAT project. The design is framed in the Methodology Interdisciplinary Research (MIR) Framework (Tobi and Kampen 2018) and this document provides detail of the implementation methods. This consists of an eight-step process of tasks incorporating planning, evaluation and reporting instantiated in a timeline for the GREAT case study programme

    Insights and challenges of working with perfectionism in sport

    No full text
    Perfectionism is complex and ambiguous. However, there is little known about the experiences of sport psychology practitioners when working with perfectionistic athletes. This article presents a commentary on my personal insights when working with perfectionism and the challenges that I have faced. Here, I also draw on the literature from my work and from others to help illustrate these challenges. Recommendations for sport psychology practitioners in conducting these specific challenges are then presented. The article ends by outlining a personal reflection of working with perfectionistic athletes, followed by recommendations for good practice

    0

    full texts

    2,827

    metadata records
    Updated in last 30 days.
    University of Bolton Institutional Repository is based in United Kingdom
    Access Repository Dashboard
    Do you manage University of Bolton Institutional Repository? Access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard!