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Evaluation of text classification using Support Vector Machine compare with Naive Bayes, Random Forest Decision Tree and K-NN
No full textThis paper aims to find the boost model which brings the best accuracy in text classification by using Support Vector Machine in comparison with other models namely Naive Bayes, Random Forest Decision Tree and K-NN. For the text classification and processing, the planned system will have to apply with the Support Vector Machine and the result is decided by major roles. Based on the Machine Learning algorithms used for the implementation of the research- the BBC news dataset- illustrates that the Support Vector Machine has better accuracy and result
The Nexus between entrepreneurial education and entrepreneurial self-competencies: a social enterprise perspective
No full textThe purpose of the study was to examine the mediation roles of student satisfaction and entrepreneurial self-efficacy in the nexus between entrepreneurial education and entrepreneurial self-competencies within a social enterprise context. The study used a cross-sectional survey design, with a sampled population of 185 business students from three universities (Accra Technical University, Cape Coast Technical University and the University of Ghana) in Ghana. A PLS-SEM approach was used to examine the relationships among the independent–dependent constructs in the study. Entrepreneurial education had positive and significant relationships to student satisfaction and entrepreneurial self-efficacy, but it showed an insignificant relationship to entrepreneurial self-competencies. Student satisfaction was also found to relate positively and significantly to entrepreneurial self-efficacy and entrepreneurial self-competencies. Furthermore, both student satisfaction and entrepreneurial self-efficacy were found to fully mediate the nexus between entrepreneurial education and entrepreneurial self-competencies. The study highlights the crucial roles of student satisfaction and self-efficacy in the implementation of entrepreneurial education in higher education institutions. In a discipline that is characterised by paucity, this study provides a unique and original assessment of the important roles of student satisfaction and student self-confidence in building entrepreneurial competencies among students
Multidimensional framework for analysing factors influencing Digital Natives' attitude towards luxury brands on social media; a comprehensive examination of digital marketing strategy
No full textThe last decade has seen a tremendous growth by the luxury brand industry as luxury brands have expanded and have been adopted all over the world. Due to its expansion marketing opportunities are presented by the accelerating demand for luxury brands, specifically by the specific demographic group of young consumers who are known as “Digital Native” population (Sandra, et al, 2022).On the other hand, in the recent years the luxury brand industry has also been strongly affected by the rapid evolution of digital technology and the internet.Social media has become a platform in which users and companies can develop a strong communication with each other, while brands can use different strategies to influence their customer’s attitude and purchase behaviour. Drawing upon the research framework, this study aims to examine the multidimensional factors that influence Digital Native’s attitude towards luxury brands on social media. Utilizing a multidimensional framework, the research focuses on factors, such as parasocial relationship, influencer marketing and E-WOM as key factors contributing to a comprehensive examination of digital marketing strategy, especially in relation to brand image, attitude, and purchase intention.Given the intrinsic link between Digital Native’s attitudes towards luxury brands and their experiences in the digital realm, this study holds significance for luxury brands seeking to capitalize on the digital native demographic. The research highlights the impact of influencers in shaping attitudes and underscores the importance of effective digital marketing strategies, including online presence, content creation and influencer collaborations.The study employs a mixed-method approach, combining quantitative and qualitative methods. The sample consists of social media-heavy users, predominantly university students which is involves in Digital Native’s category in this study. Data analysis incorporates statistical techniques to unveil the significant role of social media influencers in creating parasocial relationships, establishing credibility as opinion leaders, and influencing E-WOM, ultimately impacting Digital Natives’ attitudes and purchase intentions. Through an in-depth literature review and mixed method methodology, the research identifies and explores multidimensional factors influencing attitudes toward luxury brands on social media.The findings reveal the substantial influence of social media influencers in shaping parasocial relationships, serving as credible opinion leaders, and driving E-WOM which all these three factors will incorporate digital native’s attitude and a lead to purchase intention of luxury brand through the effect of social media, and social media influencers.This research study contributes to the consumer behaviour literature by offering a comprehensive framework to elucidate the psychological factors influencing Digital Natives’ attitudes. The implications of these findings extend to luxury brands seeking to develop more effective and targeted digital marketing strategies, with a focus on social media marketing and influencer marketing.Ultimately, this study provides valuable insights that can guid luxury brands in navigating the complex digital landscape and engaging with the discerning Digital Natives demographics
Effect of cross-linkers on the processing of lignin/polyamide precursors for carbon fibres
No full textThis work reports the use of cross-linkers in bio-based blends from hydroxypropyl-modified lignin (TcC) and a bio-based polyamide (PA1010) for possible use as carbon fibre precursors, which, while minimising their effects on melt processing into filaments, assist in cross-linking components during the subsequent thermal stabilisation stage. Cross-linkers included a highly sterically hindered aliphatic hydrocarbon (Perkadox 30, PdX), a mono-functional organic peroxide (Triganox 311, TnX), and two different hydroxyalkylamides (Primid® XL-552 (PmD 552) and Primid® QM-1260 (PmD 1260)). The characterisation of melt-compounded samples of TcC/PA1010 containing PdX and TnX indicated considerable cross-linking via FTIR, DSC, DMA and rheology measurements. While both Primids showed some evidence of cross-linking, it was less than with PdX and TnX. This was corroborated via melt spinning of the melt-compounded chips or pellet-coated TcC/PA1010, each with cross-linker via a continuous, sub-pilot scale, melt-spinning process, where both Primids showed better processability. With the latter technique, while filaments could be produced, they were very brittle. To overcome this, melt-spun TcC/PA1010 filaments were immersed in aqueous solutions of PmD 552 and PmD 1260 at 80 °C. The resultant filaments could be easily thermally stabilised and showed evidence of cross-linking, producing higher char residues than the control filaments in the TGA experiments
Predicting acute clinical deterioration with interpretable machine learning to support emergency care decision making
No full textThe 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
Specifying an innovative route in confronting youth unemployment in Ghana: a policy brief for the Ministry of Trade, Government of Ghana
No full textThis policy brief has been developed from a funded British Council Innovation for African Universities programme (IAU). In this document the authors provide an executive summary of the current youth unemployment landscape in Ghana, and a research synopsis of the fieldwork data collection. The policy brief also devises a number of recommendations on entrepreneurship, innovation, training and skills for young people
Victim–Survivor–Warrior–Healer: An autoethnographic account of a male childhood sexual violence survivor’s activist journey
No full textIt has been argued that stories inform our perceptions of reality and social change is driven by stories (Sarbin, 1986; Bochner, 2012; Frank, 2011/2013). Sexual violence is a complex cultural challenge for societies (Rape Crisis, 2020). Individual survivor identity is formed in that complexity and personal posttraumatic growth (PTG) can be forged in such challenges (Tedeschi & Calhoun, 2004). Activism is one way the survivor can help forge social change both for themselves and the ‘community of interest’ they belong to (Raskovic, 2020; Herman, 1992). This article uses autoethnography to explore one male survivor’s story of childhood sexual violence and his 22-year journey of activism. It adopts a novel approach weaving metaphors taken from episodes of the long-running British television series Doctor Who. It attempts to link social action to PTG in its reflections on meaning and redemption beyond shame via activism and lived experience witnessing (Bruner, 2002).The power of lived experience can powerfully bring the ‘unspeakable’ to society’s conscious awareness (Herman, 1992; Balfour, 2013). By sharing the raw reality of victim blaming when challenging the status quo. The reality of political and professional agents’ resistance to change is evidenced. It uses psychological and other theories, aiming to weave them through the story and illuminate one activist’s journey. Its limitation is its just one story, However, within that lies an authentic strength. It does not claim to be objective. Instead, it knits both the subjective and objective together to allow you to experience something as old as humans, a real story told in a new form (Gottschall, 2012
Addiction recovery stories: Rebecca Kippax in conversation with Lisa Ogilvie
No full textPurpose: The purpose of this paper is to examine recovery through lived experience. It is part of a series that explores candid accounts of addiction and recovery to identify the important components in the recovery process. Design/methodology/approach: The G-CHIME model comprises six elements important to addiction recovery (Growth, Connectedness, Hope, Identity, Meaning in life and Empowerment). It provides a standard against which to consider addiction recovery, having been used in this series, as well as in the design of interventions that improve well-being and strengthen recovery. In this paper, a first-hand account is presented, followed by a semi-structured e-interview with the author of the account. Narrative analysis is used to explore the account and interview through the G-CHIME model. Findings: This paper shows that addiction recovery is a remarkable process that can be effectively explained using the G-CHIME model. The significance of each component in the model is apparent from the account and e-interview presented. Originality/value: To the best of the authors' knowledge, each account of recovery in this series is unique, and as yet, untold
Geographies of exclusion: rebuilding collective responsibility in a fragmented school system
No full textThe goal of equity in education in England is damaged by regional disparities in outcomes and a marked social gradient in school exclusion. The most vulnerable groups are disproportionately represented in in-year transfers. Drawing on 24 interviews with school leaders and education decision-makers in a socioeconomically deprived area, this study examined institutional strategies to promote inclusion by reducing pupil mobility in an area-based initiative. The analysis highlights the interaction of administrative, professional and market logics, and the significance of the ‘middle tier’ in mediating inter-local tensions. Further research is needed on ‘hidden’ pupil moves and diverse forms of within-school segregation-reintegration
Evaluating the effects of strategic planning on IT Higher Education in Saudi Arabia by the perspective of students
No full textAccording to the main strategic document in Saudi Arabia, Strategic Vision 2030, one of commitments is to continue investing in education so that young people are equipped for the jobs of the future. Currently, in Saudi Arabia there is no clear picture about the influence of that proclaimed strategical focus to the reality in public education, specifically in IT studies. Based on an empirical investigation in IT universities in Saudi’s capital city Riyadh, this paper reveals characteristics of the IT studies and its alignment to the job market needs, from the perspective of students. The research is conducted among students at the final years in public universities with IT department in Riyadh. Primary data is obtained by on-line survey, individual interviews and focus group discussions. Research findings showed that strategic planning decisions made by public authorities during previous years strongly influenced the part of the higher public education of information technology in Universities in Riyadh. It confirmed that large investments in education have yielded results in a high students’ satisfaction with: a) faculty equipment (education tools, libraries and computing facilities); b) computer based labs where research and hands-on activities are performed and c) the high level of applicability of the knowledge and skills gained during the study. Graduated students are flexible and ready to accept the job either in private or public organisation, with flexible working options or as lifetime employments. However, there are still low interests of students to build own business through entrepreneurial activities and they are relatively low interested to study, build career and live outside of Riyadh and other big cities in the country. According to student’s opinions, there is space for modernization and innovation of the study curriculum through the use of interactive software applications and e-learnin