2821 research outputs found
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Enhancing educational adaptability: a review and analysis of AI-driven adaptive learning platforms
Full text linkThis study explores the transformative potential
of AI-powered adaptive learning platforms (ALPs) in
education, specifically focusing on personalized learning paths
and their impact on student engagement and outcomes.
Through a comprehensive analysis of four prominent ALPs—
Carnegie Learning, DreamBox Learning, Smart Sparrow, and
Knewton—this study investigates their approaches to content
tailoring and feedback delivery. The comparative analysis
highlights each platform’s strengths and limitations, providing educators with valuable insights for informed selection and
implementation. This study also considers the broader
landscape of ALPs, acknowledging concerns such as bias, data
privacy, and the role of educators in the tech-driven
educational environment. The findings contribute to our
understanding of how ALPs can empower educators,
personalize learning, and address achievement gaps, offering a nuanced perspective on the complex tapestry of
AI in educatio
Blockchain application with specific reference to smart contracts in the insurance sector
No full textThe term blockchain was coined in 2008 by Satoshi Nakamoto. Initially, it
was used for carrying out decentralised transactions to solve the problem of
fake transactions. In the past few years, this was explored extensively for
cryptocurrency only, but, over some time, its potential has been explored in
many areas. The major reason for the growing interest in this particular technology is that it provides a secure, reliable, and trusted platform to perform
digital activities. This is executed without the involvement of any third party.
Once the data is entered into the nodes, it is impossible to tamper it. Though
blockchain is costly, it provides better solutions to many research problems in
real- time. In recent times, researchers have explored blockchain in deep and
used it in many applications such as building smart contracts, supply chain
management, digital identity providers, voting systems, banking, and finance
applications, P2P learning, and insurance sectors. Through this chapter, the
readers will get a systematic and detailed study of blockchain in the insurance
sector and smart contracts and its current applications in the insurance sector.
This chapter will also provide a fair idea of blockchain technology in the
insurance sector and its usage in specific applications. In the end, a relevant
set of further reading references will be provided
Undergraduate nurses at risk of attrition. How far does a pilot intervention programme accelerate success for learners identified at risk of attrition in the Faculty of Health?
Full text linkOur NHS Health England research project has identified learners at risk of not completing their chosen course within the Health and Wellbeing Faculty.
Our evidence suggests learners may not engage with their programme of study and do not always take advantage of the support available to them. Further evidence shows that learners who are experiencing difficulties and struggling with their programme are the most at risk of transient levels of stress and low motivation and engagement. This is due to the perceived pressure of work especially preparing for and completing assessments while developing professional knowledge, skills, and attitudes. Consequently, learners may leave their programme of study, for a range of reasons unknown to the academic team at the time of withdrawal or may experience unsuccessful assessment leading to fail and finish decisions at progression and award examination boards
The dilemma of employee productivity measures and managerialism practices: an empirical exploration in financial institutions
Full text linkThis empirical study explored how actors in specific human resource practices (HRPs) such as line managers (LMs) impact employee productivity measures in the context of Financial Institutions (FI) banks.
This cross-country study adopted a qualitative methodology. It employed semi-structured interviews to collect data from purposefully selected 12 business-facing directors (BFDs) working in the top 10 banks in Nigeria and the United Kingdom. The data collected was analysed with the help of the trans-positional cognition approach (TPCA) phenomenological method.
The findings of a TPCA analytical process imply that in the UK and Nigerian FIs the BFDs line managers’ human resources practices (LMHRPs) resulted in a highly regulated workplace, knowledge gap, service operations challenges, and subjective quantitatively driven KPIs, considered service productivity paradoxical elements. Although the practices in the UK and Nigeria FIs had similar labels, their aggregates were underpinned by different contextual issues.
To support line managers in better understanding and managing financial institutions BFDs productivity measures and outcomes, we propose the Managerial Employee Productivity Operational Definition’ (MePoD) framework as part of their toolkit. This study will be helpful for banking sectors, their regulators, policymakers, other financial institutions’ industry stakeholders, and future researchers in the field.
Within the context of the UK and Nigeria’s FIs, this study is the first attempt to understand how line managers’ human resource practices impact BFDs productivity in this manner. It confirms LMHRPs result in service productivity paradoxical elements with perceived or lost productivity implications
Cutting-edge deep learning approaches to predict thyroid hormonal disorder for the healthcare sector
No full textSeveral researchers have used a range of
Machine Learning (ML) and a few Deep Learning (DL)
approaches to predict thyroid hormonal disorders over the
years. However, these researchers have recommended a need for
the re-evaluation of the ML models and the use of more DL
models with feature selection techniques to improve the
accuracy of predicting thyroid hormonal disorders. Therefore,
this study fills the identified gaps in the literature by
comprehensively discussing the data understanding and pre-processing of a reconciled large-sized thyroid disease dataset
obtained from Kaggle, which is a secondary source and uses the
cleaned dataset with the application of an embedded method
that is a features selection technique to develop three ML
models, a hybrid model, and four modern DL models, to
improve the accuracy of predicting thyroid hormonal disorder
by using 80% of the cleaned and balanced dataset for model
training, and 20% of the dataset for testing. Based on the
findings attained and comparisons of the performances of the
developed models using Mean Absolute Error (MAE), BiLSTM
is the best-fit model because it has a minimum MAE value of
4.9202. Therefore, this study concludes and recommends
BiLSTM as the DL model for the healthcare sector to adopt and
be deployed to produce an intelligent medical diagnosis system
for an improved prediction of thyroid hormonal disorders
Fuzzy-based prediction for suddenly expanded axisymmetric nozzle flows with microjets
Full text linkThe current research focuses on the implementation of the fuzzy logic approach for the prediction of base pressure as a function of
the input parameters. The relationship of base pressure (β) with input parameters, namely, Mach number (M), nozzle pressure ratio (η), area
ratio (α), length to diameter ratio (ξ ), and jet control (ϑ) is analyzed. The precise fuzzy modeling approach based on Takagi and Sugeno’s
fuzzy system has been used along with linear and non-linear type membership functions (MFs), to evaluate the effectiveness of the developed
model. Additionally, the generated models were tested with 20 test cases that were different from the training data. The proposed fuzzy logic
method removes the requirement for several trials to determine the most critical input parameters. This will expedite and minimize the expense
of experiments. The findings indicate that the developed model can generate accurate prediction
Improving predictive detection of leukemia using critical thinking models
No full textImproving Predictive Detection of Leukemia Using Critical Thinking Models is a comprehensive anthology that delves into the forefront of healthcare research, focusing on the critical task of accurately detecting leukemia. With leukemia ranking among the deadliest diseases globally, this book presents an essential collection of studies aimed at saving lives and ensuring proper treatment through advanced detection methods.
Drawing upon the expertise of medical professionals, researchers, and scholars, this anthology explores the utilization of cutting-edge AI/ML/DL algorithms and techniques in medical image segmentation, feature selection, and classification specific to leukemia. From algorithms for leukemia detection to image segmentation and follow-up treatment using various examinations, the book covers crucial topics that resonate with medical practitioners, policymakers, academicians, and students alike.
By providing knowledge with a groundwork of the fundamentals, and progressing to advanced levels, Improving Predictive Detection of Leukemia Using Critical Thinking Models fosters readers' critical thinking abilities and empowers them to develop innovative detection models for leukemia. This book serves as a valuable resource for both new researchers, who will gain essential insights into the field, and experienced professionals seeking to analyze algorithm performance and identify future research directions.
Catering to a diverse audience, including medical professionals, nurses, oncologists, policymakers, researchers, academicians, scholars, practitioners, instructors, and students at all levels of study, this anthology facilitates interdisciplinary collaboration and contributes to the research society's collective understanding of leukemia detection
GREAT Deliverable 2.1 Summary report and compilation of design challenges, design briefs and wireframes
Full text linkThe GREAT project explores ways of using games-based
activities to help citizens express their opinions and attitudes
to emerging policies, and making the results available to
policy makers. To this end, the task of WP2 is to work with
stakeholders on dilemmas related to climate change, carrying
out activities to develop design challenges, design briefs and
wireframes for games-based activities. This report
summarises twelve pilots carried out to inform the design of
these activities. The report consists of a summary of the
pilots, and a compendium containing the reports from each
pilot activity
From collaboration to transformation: A reflective exploration of student-staff partnerships for technology enhanced learning in Higher Education
Full text linkCollaboration between students and staff has increasingly gained recognition as a powerful avenue for enhancing the overall learning experience in Higher Education. Student-staff partnership projects offer a unique opportunity for students to actively engage with their programme of studies, influencing decision-making processes and contributing to the improvement of the learning environment. This reflective practice piece delves into the lived experiences of four students who embraced student-staff partnership in unique ways, each contributing their perspectives and invaluable insights to projects they were involved in. With an aim to shed light on the significance of reflecting upon these lived experiences, recognising the immense value they hold for students, staff and the institution as a whole, a critical narrative enquiry approach was used in addition to vignettes to understand the intricacies and dynamics of student-staff partnerships, unravelling the complexities and capturing the transformative effects of these collaborations on students. By examining the challenges and triumphs faced by these four student partners, we gain insights into the multifaceted nature of student-staff partnerships, their potential for growth, and the resulting impact on the higher education landscape
An optimal load balancing framework for fog-assisted smart grid applications
No full textThe growth of the Internet of Things (IoT) causes a significant amount of data to come in from physical devices and sensors, which adds to the latency and processing delays in smart grid applications. The pay-per-model method of transmitting gathered data that cloud computing offers improves scalability and functionality for end devices, which increases smart grid efficiency. Milliseconds matter in the crucial realms of load balancing, resource usage, and distribution systems, where any latency or jitter is unacceptable. By strategically positioning processing, networking, storage, and communication capabilities at the network edge, fog computing, an outgrowth of cloud technology, successfully addresses current issues in service groups. Three different load balancing algorithms are proposed in this research, which presents a novel hybrid model on a highly virtualized platform: throttled, round-robin, and a novel equilibrium optimizer with simulated annealing (EO-SA). In order to optimize services inside smart grids, the paper thoroughly analyzes and compares these load balancing algorithms, highlighting their significance for cost minimization and effective resource distribution