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Exploring the impact of content marketing through Instagram on the growth of the fitness industry in Ireland
No full textThis study investigates the impact of content marketing on the growth of the fitness industry in Ireland through Instagram, focusing on customer engagement. The research aims to explore effective content marketing strategies, with objectives including the examination of content marketing's role in customer engagement, the analysis of customer behavior on specific content types, and the evaluation of influencer collaborations' influence on engagement. The literature review highlights Instagram's pervasive influence on daily life and its advertising capabilities. Three theories—Uses and Gratifications Theory, Social Influence Theory, and Social Credibility Theory—are utilized to analyze user motivations, engagement factors, and credibility within fitness content. The methodology employs qualitative data collection through interviews with a narrative analysis & reflexive thematic analysis approach. A probability random stratified sampling method with a sample size of 5 respondents is chosen. The thematic analysis reveals key findings, including Visual Attraction, Educational Engagement, Personalized Connection, Transformation Stories, Scientific Wellness, Authentic Influencers, Influencer Trustworthiness, Community Impact, Humorous Connection, and Transparent Endorsements. The abstract concludes by underscoring the importance of visual appeal, educational value, personalized connections, and authentic storytelling in elevating engagement within the Irish fitness community on Instagram. These insights provide valuable guidance for content creators and brands aiming to refine their strategies in the dynamic landscape of fitness content marketing
How to Influence People to Migrate to Cloud Computing Systems in Developing Virtual Environment to Store Customer Data in Bangladesh
No full textThe opening section emphasizes how Bangladesh has embraced cloud computing to enhance the efficiency and security of data storage, in establishments. The adoption of cloud technology has streamlined information management, reduced costs. Transformed the way organizations operate through technology. The background section discusses the importance of using cloud computing in environments highlighting its affordability and facilitation of business. The problem statement addresses the challenges associated with data storage and advocates for migrating to the cloud. The research aims to explore why Bangladesh should adopt cloud computing as the challenges it may face and recommendations for implementation. One hypothesis suggests that storing customer data in the country's cloud is both secure and efficient.
The methodology section explains the research approach, design and strategy used to investigate how cloud computing impacts Bangladesh. Employing a philosophy this study takes an approach with a focus on collecting quantitative data through descriptive research design. Action research strategy is employed to explore migration methods while primary quantitative data collection, from professionals ensures insights. This methodology emphasizes objectivity, logical analysis and minimizes personal bias to comprehensively examine how cloud computing is evolving in Bangladesh.
In the discussion and conclusion chapters, this study interprets findings to assess the implications of adopting cloud computing in Bangladesh. It explores the impact of migration recognizes the obstacles involved and offers suggestions, for putting it into action. The final remarks underscore the importance of cloud computing highlighting its ability to revolutionize the landscape and drive growth, in the country
Examining the social media usage, body esteem, eating attitudes and life satisfaction among gym users
No full textThis study aimed to examine relationships among disordered eating attitudes, life satisfaction, body esteem (weight, appearance, attribution), social media usage for fitness-related content, and gym usage in male and female gym users. A cross-sectional, correlational design used an online survey completed by 98 gym users, incorporating demographic questions, The Disordered Eating Attitude Scale, The Body Esteem Scale, and The Life Satisfaction Scale. Findings showed no significant difference in disordered eating attitudes between genders, but a significant relationship between body esteem (weight, appearance, attribution) and life satisfaction. No significant relationship was found between social media usage for fitness content and body esteem, or between social media usage, exercise frequency, and disordered eating attitudes. Results revealed the requirement for further research with larger, gender-balanced samples, particularly exploring the positive impacts of gym usage and the variables mentioned in the current study
Unveiling patterns in employee compensation: A feature-driven analysis using machine learning algorithms
No full textEmployee compensation is a crucial aspect of organizational performance in today’s workspace. Since it is a key factor that influences employee satisfaction, attrition, which essentially influences companies’ functioning, it is crucial to revolutionize how compensation forecasting is done. This project aims to achieve this goal by applying machine learning solutions into compensation forecasting. To create powerful forecasting models, job family, department, union affiliation, and several compensation items, such as salary, overtime, benefits, and many other features are being engineered.
A detailed analysis is done to determine which variables such as job family code, department, and compensation specifics were more or less important. This involves data preprocessing and exploratory data analysis to determine the spread of each variable, cooperation patterns, and amount of contribution to its peers which helps to identify feature relationships leading to improved feature selection and engineering. Subsequently, the dataset is cleaned and engineered to be suitable for machine learning. Several regression algorithms ranging from linear regression, random forest, Gradient boosting, and XGBoost, structured such that a
gridsearch approach allows optimizing r2 and rmse by adjusting the hyperparameters of the model based on the algorithm until one achieves the best evaluation metrics possible.
The model is validated through its performance measures. An optimal R2 or Root mean square error level is achieved through several techniques such as cross-validation. Mean absolute error is used to evaluate model performance based on the total compensation measure; accurate measures make the model unbiased. Interpreting the key aspects of compensation and understanding how different factors are related is critical in coming up with a working model,
engineered through using various analytical methods
Data-driven informed decision-making for climate change mitigation: an evaluation
No full textClimate change is a complex challenge that requires informed decision-making. This applied research explores data-driven approaches to address this challenge.
The methodology involves sourcing climate datasets for India from the IMF’s online repository, a credible source. It performs descriptive and predictive analyses with Python. It explores forecasting techniques and eventually evaluates one for the specific dataset.
This study derives insights using correlation matrices and climate variable plots. It uses ARIMA modelling for forecasting, whose parameters are determined by recommended approaches.
The results are summarised, followed by interpretations of the data patterns, assessments of efforts to mitigate climate crisis, and evaluations of ARIMA statistics applied in this context.
The ARIMA model is evaluated using a combination of automated statistics and visualisation of its results.
Ultimately, this study advocates for a proactive, fact-based approach to addressing the climate crisis, using data as a powerful tool for informed decision-making and effective action
Detecting Bank Account Opening Fraud Using Machine Learning
No full textThis research explores machine learning techniques for detecting fraudulent bank account openings using a recently published large-scale benchmark dataset. Multiple classification algorithms are evaluated, including Logistic Regression, Decision Trees, Random Forests, and LightGBM. Following standard data preprocessing and feature engineering, these models are trained on an imbalanced dataset of one million account applications over eight months. Adaptive synthetic oversampling is utilized to mitigate the extreme rarity of positive fraud cases.
Various performance metrics assess model accuracy, real-world feasibility constraints, and fairness across protected demographic groups. Initial results indicate LightGBM achieved the best overall recall of 62%, capturing most fraudulent instances. However, enforcing a 5% false positive rate threshold is necessary for practical usage but severely impacts recall. Predictive equality analysis also exposes some algorithms that inadvertently introduce bias against seniors. These findings indicate that combining sampling techniques with gradient boosting methods has the potential to balance performance, operational constraints, and ethical considerations in identifying criminal account openings. More hyperparameter tuning and model ensembling could enhance performance. This study establishes a rigorous methodology and a benchmark for future research into machine learning for fraud detection in the banking sector
Analyzing differences in motivations, perfectionism, and subjective well-being of recreational, vocational, and professional dancers
No full textThis study sought to further research within the dance domain by analyzing specific motivations and well-being of recreational, vocational, and professional dancers. Furthermore, the research addressed gender differences in perfectionism and examined whether perfectionism predicts mastery motivation. Mood enhancement and self-confidence motives were analyzed as predictors of well-being. A total of 218 participants across Ireland and the UK completed an online survey. Measures included The Dance Motivation Inventory, The Frost Multidimensional Perfectionism Scale-Brief, and The Mental Health Continuum Short-Form. Data analysis was conducted using SPSS. Statistical analysis revealed a significant difference in motivations between groups. Well-being did not differ significantly between groups. Perfectionism was not found to significantly predict mastery motivation, and no significant differences in perfectionism among genders were found. Mood enhancement and self-confidence motivations were found to predict well-being. The results provide valuable insights for dance educators in acknowledging what drives participation in dance class while sustaining engagement
Predicting CO2 Emission from Power Industry using Machine Learning
No full textUsing a variety of forecasting models, this thesis provides a thorough examination of CO2 emissions from coal-fired electric generating facilities in the United States. The main dataset, which spans the period from January 1973 to August 2023, comes from the U.S. Energy Information Administration. To anticipate CO2 emissions, models such as Prophet, Holt Winter's Exponential Smoothing, and ARIMA were used. After a performance evaluation, ARIMA was shown to be the most efficient model, with the lowest RMSE (2.23), MAE (1.69), and highest R2-score (0.83). Every model was put through a thorough pre-treatment procedure that included exploratory data analysis, feature extraction, and data cleaning. The study
examines these model’s advantages and disadvantages extensively. Notably, Holt Winter's Exponential Smoothing and Prophet showed benefits in managing trend patterns and capturing seasonality, even though ARIMA showed better predicted accuracy overall. The use of these models and its consequences for environmental policymaking are ethical issues that are also covered in the thesis. This study identifies gaps in the literature, especially regarding the possibility for hybrid modelling techniques, ethical frameworks, scalability across different contexts, and the inclusion of external elements. To contribute to more reliable and responsible forecasting frameworks for CO2 emissions from coal-fired power plants, the findings indicate directions for future study aimed at improving forecasting accuracy and ethical applicability
Investigating the Role of Cloud Computing in Enabling Digital Transformation in the Automobile Parts Manufacturing Sector
No full textThis all-round analysis examines the role of cloud computing in guiding digital transformation across our car parts manufacture industry. The study, intended to help them understand the diverse nature of cloud technologies in terms of how they transform operational processes and fit in with wider digital transformation initiatives. The study takes a systematic approach, combining quantitative and qualitative analysis methods with the main statistical evaluation method being SPSS.
Other important observations include that cloud computing is very helpful for improving operational efficiency, costs and innovation as well as effectively managing the supply chain. However, these ben efits are accompanied by difficulties in adapting manpower and integrating organizations-such is the double face of technological development. In addition to the above, research on cloud computing is trying to shed light onto its strategic significance. The paper emphasizes that cloud characteristics promote service transformation, and suit Industry 4.0 concepts well.
The possible policy implications include the pursuit of strategic frameworks covering technological use, workforce training, and data security. Limitations and recommendations The report points out that the first shortcoming of its study is that it has been limited to large Czech companies, while in fact small and medium-sized enterprises form a far more important element.
In sum, the study adds fresh perspectives on cloud computing's role in industrial digital transformation that can help researchers and practitioners alike. This highlights the need for a rational perspective on how best to introduce these cloud technologies, which are changing in rapid succession. Both their revolutionary potential and mission critical complexities put them at opposite ends of the scale