2821 research outputs found
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“It’s a Right Pain in the Pelvis!”: post-traumatic stress and post-traumatic growth in a sample of females experiencing chronic pelvic pain
Full text linkChronic 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
Real-time Detection of Phishing Emails Using XG Boost Machine Learning Technique
Full text linkPhishing attacks continue to pose a significant threat to individuals and organizations, making it crucial to develop effective countermeasures. Machine learning algorithms have shown promise in detecting and mitigating phishing attacks. The study evaluates the performance of four popular algorithms in the context of phishing detection and compares the effectiveness of these four different algorithms; Random Forest, Decision Tree, XGBoost, and Logistic Regression, to determine which one achieves the highest accuracy. The results show that XGBoost outperforms the other algorithms and can accurately detect phishing attacks with a high degree of precision. The algorithms are compared based on factors such as training time, test time, model size, interpretability, and explainability. To compare the effectiveness of these algorithms, the study conducted experiments using a dataset of phishing emails. The algorithms were trained on a labeled dataset and evaluated based on metrics such as accuracy, precision, and recall. The results demonstrate that XGBoost outperforms the other algorithms, achieving the highest accuracy in detecting phishing attacks. The findings of this study have significant implications for the development of antiphishing technologies. By leveraging machine learning algorithms, particularly XGBoost, organizations can enhance their ability to detect and prevent phishing attacks. This can help protect individuals' personal information, passwords, and credit card numbers from falling into the hands of cybercriminals
An empirical study on the impact of effective digital customer journey management on customer satisfaction in the Nigerian Islamic banking sector
Full text linkThis study examines the relationship between digital customer journey management and
customer satisfaction in the Nigerian Islamic banking system. The study is guided by a
conceptual framework that reflects a comprehensive and holistic account of consumers' cross-channel interactions and employs a mixed-methods approach using interviews and self-administered questionnaires to collect data from Jaiz Bank customers. Empirical evidence was
gathered from a multistage sampling methodology, and the findings showed that digital
touchpoints make a substantial contribution to the relevance of selection variables, customer
perception, and overall level of satisfaction.
This study examines the relationship between digital customer journey management and
customer satisfaction in the Nigerian Islamic banking system. The study is guided by a
conceptual framework that reflects a comprehensive and holistic account of consumers' cross-channel interactions and employs a mixed-methods approach using interviews and self administered questionnaires to collect data from Jaiz Bank customers. Empirical evidence was
gathered from a multistage sampling methodology, and the findings showed that digital
touchpoints make a substantial contribution to the relevance of selection variables, customer
perception, and overall level of satisfaction. Data were collected via interviews and self-administered questionnaire surveys using a triangulation (mixed-method) research method. The
quantitative data were analysed using Ordinal Regression Analysis and the Spearman Rank
Order Correlation, while the qualitative data were analysed using the thematic analysis approach
through NVivo.
The customer journey framework investigates how customers interact with Islamic banking
products across their omni-channel customer journeys and attempts to pinpoint at what point in
the journey this interaction takes place. The findings of this study indicate that digital
touchpoints make a substantial contribution to variances in the relevance of selection variables,
customer perception, and overall level of satisfaction. Customers of Jaiz banks ranked the
characteristics associated with the service stage of the customer journey as the most important
criteria, followed by purchase, awareness, consideration, loyalty, and advocacy stages. Most of those who participated in the survey stated that they were satisfied with Jaiz Bank. Younger
customers and those with a higher level of education had a more positive attitude towards digital
customer journey management.
Overall, this study highlights the importance of digital customer journey management in the
Nigerian Islamic banking sector and its impact on customer satisfaction. This study provides
insights into how Islamic banks can improve their digital touchpoints to enhance their customer
experience and satisfaction. It also contributes to the empirical literature on relationship
marketing and customer behaviour in the Nigerian Islamic banking sector
Cybersecurity threats detection in intelligent networks using predictive analytics approaches
Full text linkThe 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
Reassessing England and Wales' approach to forced marriage in the context of modern slavery
No full textThis chapter critically examines the legal categorization of forced marriage within the context of modern slavery, focusing on the need for legislative reform in England and Wales. It explores the intricate parallels between forced marriage and modern slavery, emphasizing shared elements of coercion, exploitation, and infringement of autonomy and consent. The discussion includes a philosophical analysis of forced marriage, considering feminist theories and the impact of patriarchal norms. By comparing the current legal frameworks of England and Wales with international standards set by the United Nations and the International Labour Organization, the chapter highlights discrepancies and advocates for harmonization. It underscores the importance of recognizing forced marriage as a form of modern slavery to ensure comprehensive legal responses, protection, and support for victims, culminating in a call for legal reform in England and Wales to align with global human rights efforts
AI and IoT for proactive disaster management
No full textIn our rapidly evolving digital landscape, the threat of natural disasters looms large, necessitating innovative solutions for effective disaster management. Integrating Artificial Intelligence (AI) and the Internet of Things (IoT) presents a transformative approach to addressing these challenges. However, despite the potential benefits, the field needs more comprehensive resources that explore the full extent of AI and IoT applications in disaster management.
AI and IoT for Proactive Disaster Management fills that gap by examining how AI and IoT can revolutionize disaster preparedness, response, and recovery. It offers a deep dive into AI frameworks, IoT infrastructures, and the synergy of these technologies in predicting and managing natural disasters. By showcasing cutting-edge research and practical applications, this book equips readers with the knowledge and tools to harness AI and IoT for more efficient and effective disaster management strategies.
Targeted at undergraduate and postgraduate students, academicians, research scholars, industry professionals, and technology enthusiasts, this book serves as a comprehensive guide to understanding the intersection of AI, IoT, and disaster management. It offers insights into emerging trends, ethical considerations, and best practices, making it an essential resource for anyone interested in leveraging technology to mitigate the impact of natural disasters
Exploring the concept of Mini Data Sprints as a methodology to assess data validity and stimulate climate conversation
Full text linkThe GREAT (Games Realising Effective and Affective Transformation) project explores new approaches that foster climate change discussion and stimulate citizen reflection. However, some citizens have limited resources for participation, even though their engagement and contributions are crucial. To address this challenge, the authors present two studies that have deployed mini data sprints (MDS). The MDS approach uses interactive data applications and visualisations to provoke citizens’ feelings, knowledge, and perspectives towards the climate conversation and presented data. These studies highlight how the MDS approach can provide data set recommendations, facilitate efficient and focused climate conversation, and improve the data literacy of the cohort
Deep temporal convolutional neural network for predicting electricity consumption
No full textThis study addresses the critical research domain of
electricity consumption prediction, emphasizing its importance in
energy production, distribution, and related aspects such as load
balancing, cost optimization, energy efficiency, and carbon
emissions reduction. Various models have been explored to tackle
the challenges of prediction accuracy. The research introduces a
Temporal Convolution Network (TCN) as a base model, aiming to
enhance accuracy in predicting electricity consumption using a
United Kingdom (UK) dataset spanning 2009 to 2023. Models like
ARIMA, Linear Regression (LR), Random Forest (RF), Support
Vector Regression (SVR), LR-SVR Hybrid, and Long Short-Term
Memory (LSTM) were compared using Mean Absolute Error
(MAE), with the proposed TCN demonstrating superior accuracy
over other model
Risking it all: authentic leadership in crisis
No full textThe COVID-19 pandemic has revealed the vulnerability of organisations, institutions, and society, underscoring the need for new leadership approaches to develop them. This chapter examines authentic leadership, highlighting the complexity and problematic nature of the construct. Although scholars have acknowledged its significance and linked it to positive organisational outcomes, the construct has not progressed beyond its current conceptualisation. Consequently, the chapter explores the rise of authentic leadership and its limitations, accentuating the lack of a clear path for developing the construct. To contribute to the literature on authentic leadership theory, the chapter proposes the authentic leadership plumb line and discusses its role in facilitating its resurgence within organisations. The chapter concludes that authentic leadership should be embraced during crises as it enables the alignment of the leader and follower interaction
Predicting acute clinical deterioration with interpretable machine learning to support emergency care decision making
Full text linkThe emergency department (ED) is a fast-paced environment responsible for large volumes of
patients with varied disease acuity. Operational pressures on EDs are increasing, which creates the
imperative to efficiently identify patients at imminent risk of acute deterioration. The aim of this
study is to systematically compare the performance of machine learning algorithms based on logistic
regression, gradient boosted decision trees, and support vector machines for predicting imminent
clinical deterioration for patients based on cross-sectional patient data extracted from electronic
patient records (EPR) at the point of entry to the hospital. We apply state-of-the-art machine learning
methods to predict early patient deterioration, based on their first recorded vital signs, observations,
laboratory results, and other predictors documented in the EPR. Clinical deterioration in this study
is measured by in-hospital mortality and/or admission to critical care. We build on prior work by
incorporating interpretable machine learning and fairness-aware modelling, and use a dataset
comprising 118, 886 unplanned admissions to Salford Royal Hospital, UK, to systematically compare
model variations for predicting mortality and critical care utilisation within 24 hours of admission.
We compare model performance to the National Early Warning Score 2 (NEWS2) and yield up to
a 0.366 increase in average precision, up to a 21.16% reduction in daily alert rate, and a median
0.599 reduction in differential bias amplification across the protected demographics of age and
sex. We use Shapely Additive explanations to justify the models’ outputs, verify that the captured
data associations align with domain knowledge, and pair predictions with the causal context of
each patient’s most influential characteristics. Introducing our modelling to clinical practice has the
potential to reduce alert fatigue and identify high-risk patients with a lower NEWS2 that might be
missed currently, but further work is needed to trial the models in clinical practice. We encourage
future research to follow a systematised approach to data-driven risk modelling to obtain clinically
applicable support tool