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Forecasting Ukrainian refugee employment in Ireland's Accommodation& Food Service sector Using Random Forest, Gradient Boosting, and Neural Network models
No full textWhen conflicts escalate into wars, a large scale of displacement occurs. Urging other nations to extend support to refugees. Then critical question arises like How can refugees successfully integrate into their host countries? This study delves into the employment prospects for Ukrainian refugees in Ireland, using machine learning techniques like Random Forest, Gradient Boosting and Neural Network to understand and predict employment levels among Ukrainian refugees in Ireland’s Accommodation and Food Service sector during 2022 to 2024. Comparing Random Forest, Gradient Boosting, and Neural Network models, the research evaluates their predictive accuracy for refugee employment levels. Gradient Boosting emerged as the optimal model, slightly outperforming Random Forest with an R-squared of 0.9867 (98.67% accuracy) and RMSE of 393.2624, while significantly outperforming Neural Network (R-squared: 0.6673). Despite Random Forest showing better MAE (283.0300 compared to Gradient Boosting's 332.7844), Gradient Boosting's superior learning curve convergence and generalization capabilities established it as marginally more reliable. Through Gradient Boosting's feature importance analysis, the study identified Manufacturing (Importance Score=0.204069), Information and Communication (Importance Score=0.186781), and Education (Importance Score=0.123934) as the most influential sectors affecting refugee employment in the Accommodation and Food Service sector. These quantified sector influences provide specific insights for aligning refugee employment support with sectors showing strong predictive relationships to the Accommodation and Food Service sector
Sexual Wellness Industry in Developing Country: An Examination of Personal Brands Founded by Female Entrepreneurs - A Pursuit of Financial Gain or a Feminist Movement Towards Sexual Liberation?
No full textThis study investigated the financial motivations of female entrepreneurs in developing countries who chose to start businesses in the sexual wellness industry. Through quantitative research methods, we will explore the relationship between the economic results of entrepreneurship and the feminist movement and its impact on female entrepreneurs. The results showed that financial gains were the main driving force for these women entrepreneurs to start businesses, allowing them to gain a higher status in society and family. Personal branding became a key factor, enhancing consumers' trust and loyalty to the brand, thereby ensuring the success of entrepreneurship. Through their entrepreneurial behavior in the sexual wellness industry, female entrepreneurs have promoted the acceptance of sexual wellness topics in developing countries and promoted the progress of the sexual liberation movement. Although female entrepreneurs have made great progress in the sexual wellness industry, they still face obstacles such as difficult market access, high social prejudice, and social restrictions based on traditional culture. The study recommends that industry stakeholders and policymakers need to provide more resources and support to female entrepreneurs to help them overcome these challenges. Future research should focus on the long-term impact of female entrepreneurship in this field, cross-cultural comparisons, and the effectiveness of supportive policies. This study highlights the vital role of female entrepreneurs in promoting sexual wellness awareness, promoting gender equality, and achieving economic independence, providing valuable insights for further academic exploration and practical interventions
A Medical Software proposal based on Community Pharmacy as an effective response to Pharmaceutical and Clinic Certification Requirements
No full textThe Irish healthcare system (NHS) is widely recognized due to their protocolized and immediate access to quality healthcare programs. (NHS, 2021)
Likewise, Ireland has a greater number of electronic medical records. One of the most important medical records in the last decade is the pharmacotherapeutic profile or pharmaceutical care form (PCF). The data detected contemplate, but are not limited to pharmacotherapy, allergies, ethnicities, concomitant diseases, laboratory tests, adverse events, or medication errors. (WHO., 2021) (ARS, 2006)
Currently, this format has allowed the worldwide pharmacist's to be integrated and recognized with extreme value in hospitals around the world, saving millions of dollars a year due to misdiagnoses, medical recurrences, medication errors and prevention of adverse reactions. (Anea, 2019) (OPS, 1993)
On the other hand, one of the pharma industry challenges is the consolidation of medical databases for public health decisions taking; like the impact on Periodic safety updated reports (PSUR) and Management Risk Plan (RMP) and their eventual commercialization. (EMA, 2019) (HMA, 2019)
The present research aims to propose an approach of a simulated second-level medical care unit with the pharmaceutical sector through an open, randomized, cross-sectional and post-marketing pharma study that analyzes the PCF of a simulated population group that allows the obtaining and consolidation of database software for the improvement of pharmaceutical care in pharma companies and clinic certification as a tool on the health prediction of a specific population. (Allen C. et al, 1983) (Brookhart Alan etal, 2010
Balancing short-term profit with long-term sustainability: strategies and best practices across industries
No full textThis study looks to explore the viability of short-term profitability and sustainable practices in different organizations and industries, highlighting and analyzing the best practices that this organizations have for achieving economic success while having sustainable practices. The study examines industries shaping the market's future and that can be controversial including banking, service platforms, fashion, and agricultural industries.
Employing a qualitative research methodology, the study uses semi-structured interviews with industry experts and innovators to find insights into the practical reality of sustainable practices. The research is guided by grounded theory and interpretivism, looking to develop a theoretical framework that comes from the data to encourage individuals and organizations to include these practices. The findings reveal a context where sustainability practices are usually received with resistance because of the perceived economic risks and the stablished traditional methods. However, successful case studies highlight that integrating sustainability can bring both financial and operational benefits, if companies adopt clear goals, effective financial management, and innovative technologies.
The study shows that while resistance to change is more common for mature companies, emerging businesses built with sustainability at their core value often have a smoother transition. The research also finds that there is a need for better metrics to quantify sustainable achievements. The role of consumer demand and education is also key to thrive change. Additionally, the study talks about the importance of operational efficiency and circular economy principles in achieving both sustainability and profitability.
By offering a framework of best practices, this research looks to invite organizations to balance short-term profits with long-term sustainability
Experiences, Uses and Challenges of Digital Marketing Strategies of Small and Medium-Sized Enterprises for Brand Positioning and Success
No full textThe aim of the current study was to explore the processes and strategies of digital marketing employed by small and medium-sized fashion enterprises (SMEs) for brand positioning and success. The study addressed issues related to processes and strategies, implementation and execution of digital marketing strategies, market presence, measurement of success and the levels of awareness of artificial intelligence (AI) and its potential uses in digital marketing strategies for SMEs.
This study started from the recognition that digital marketing has changed the way of doing business, positioning itself as a crucial tool for SMEs in their modes of expansion and market penetration (Jones et al., 2015; Taiminen & Karjaluoto, 2015), which however face a number of challenges in implementing their strategies due to lack of budget, structure and professional knowledge in digital marketing (Dahnil et al., 2014; Hemann & Burbary, 2013).
A qualitative methodology design was chosen, which was carried out by developing semi-structured interviews with 10 entrepreneurs from Argentinian fashion SMEs, and the analysis was done by conducting a thematic analysis.
The findings highlighted several crucial aspects needing attention for strategic growth in SMEs. Firstly, there is a lack of professionalisation in the design and execution of digital marketing strategies, particularly in new product launches, often due to time limitations, lack of resources, or an overload of responsibilities. Secondly, while some enterprises consider profitability and financial sustainability, success is mainly understood as customer satisfaction and loyalty. Finally, the levels of awareness and use of AI tools are limited; most businesses employ only ChatGPT AI for basic tasks such as writing or brainstorming,
In conclusion the findings highlight the importance of capacity building and training in digital marketing and AI tools to improve the competitiveness, performance and efficiency of SMEs
Influence of Cryptocurrencies on Portfolio Optimization: An Analysis of Diversification Opportunities
No full textThe twenty-first century has been marked by extraordinary technological advancements that have had a profound impact on a variety of sectors, including financial services. Blockchain technology and cryptocurrencies have emerged as significant developments among these innovations. Cryptocurrencies, which were initially perceived as speculative assets, have gained the attention of both institutional and individual investors who are seeking to increase their returns in spite of their inherent volatility. This research examines the potential diversification advantages cryptocurrency, specifically Bitcoin (BTC) and Ethereum (ETH), provide when incorporated into conventional investment portfolios. This study conducts a thorough examination of the influence of BTC and ETH on portfolio performance, with a particular emphasis on the Sharpe ratios, returns, and risk of portfolios that incorporate these digital assets in addition to traditional assets. The objective of the research is to ascertain whether Ethereum offers superior diversification advantages in comparison to Bitcoin. This study utilises empirical data analysis and sophisticated portfolio optimisation techniques to provide useful insights, building on previous research that has emphasised the benefits of integrating cryptocurrencies into conventional portfolios
Predictive Modeling of Financial Distress in Indian Small-Cap Stocks
No full textThis study aims at analyzing the capability of different kinds of machine learning algorithms in assessing the impact of financial distress in the context of the highly risky domain of the Indian small-cap stocks that is of significant concern to investors and financial institutions. To compare the effectiveness of the proposed method, the four models mentioned, Logistic Regression, Random Forest, Support Vector Machine (SVM), and Gradient Boosting Machine (GBM) are used to identify the best approach to use in the early identification of firms that are likely to experience financial instability in the future. The results show that the proposed model of SVM is clearly superior: the accuracy of the model for both classes is 92% and an AUC score of 0.9684. Nevertheless, it was found GBM too can achieve high accuracy, equal to 0.96 and high AUC score 0.9160, for the minority class, the performance of the model was poor for recall, which may lead to the difficulty of identifying distressed firms. Logistic Regression and Random Forest had 97% and 96% accuracy respectively but in the case of Financial Distress where accuracy of detecting the minority class is crucial, both models had a very high bias towards the majority class. The study recommends that more research should be done by including extra data sources, examining the combination of various models, and adopting the dynamic update of the model to improve the prediction performance of the model in the future
Hybrid Deep Learning Strategies for Epileptic Seizure Detection
No full textThe health challenges related to epileptic seizures are of paramount concern and accurate identification at early stages is important in order to positively influence patient outcomes. Deep learning has revolutionized the detection, monitoring, and diagnosis of epileptic seizures to a greater extent in recent years, surging towards real-time processing. In this work, a novel deep learning method is proposed to detect epileptic seizures, which combines CNN (Convolutional Neural Networks) with LSTM or GRU. The research question is whether integrating spatial and temporal feature extraction via these hybrid models in an ensembled manner can improve the accuracy and dependability of seizure detection within EEG. The solution includes training these models on a set of EEG recordings showing healthy, interictal, and ictal states with significant pre-processing to normalize input signals. The CNN layers capture spatial features, while the LSTM and GRU layers handle temporal dependencies. Evaluation determined that the CNN-LSTM model produces superior accuracy compared with alternative configurations. A Flask web service is developed for real-time seizure detection, where users can upload EEG files to preprocess signals, predict seizures, and retrieve related information from Wikipedia. These findings confirm the efficiency of combining state-of-the-art deep learning models to enhance seizure detection, which will be advantageous in healthcare. Future work will focus on further refining the model's generalization abilities, considering multiple datasets, and investigating clinical deployment scenarios
Predictive Maintenance In Industrial Sector using Machine Learning
No full textIn the industrial sector there is a need to have a reliable predictive maintenance system as it can help them to reduce downtime, unexpected machine failures can cause significant financial and safety risks. Traditional predictive maintenance methods are not effective enough to manage the increasing complexity and size of the data. Therefore, in this research the use of unsupervised machine learning algorithms is explored. The unsupervised algorithms used are Isolation Forest, One-Class Support Vector Machine and Local Outlier Factor. These models are compared against supervised algorithms like K-NN and Random Forest. The results showed that supervised learning algorithms performed better than unsupervised learning algorithms with perfect accuracy and precision. This high accuracy of K-NN and Random Forest is further justified by performing cross validation on them. On the other hand, the best performing unsupervised algorithm which is Isolation Forest showed high recall but due to low precision it leads to generating false positives. The overall findings of this research show that unsupervised algorithms have potential for anomaly detection in predictive maintenance, but they are currently less effective than supervised learning algorithms
Utilizing Advanced Machine Learning Techniques for Predicting Fetal Health Risks
No full textThe research study focuses on the application of machine learning algorithms to predict fetal health in the context of antenatal care. A predictive model is developed from such a dataset, including the baseline value, accelerations, fetal movement, uterine contractions, decelerations, without accelerations, and variability measures. Predictive models are developed, and the Boruta feature selection technique is utilized to identify the most critical features for the models. To address class imbalance, the SMOTE technique is used to increase the ability of the models to make reliable predictions across classes. Different machine learning models, such as Random Forests, GBM, Decision Trees, and K-Nearest Neighbors, are implemented on the dataset. Accuracy is combined with precision, among other performance metrics to inform the validity of the models and assess their predictive power. Among all the models implemented GBM performed well with 98% accuracy. The implications of such findings can change antenatal practices, reducing the risks associated with birthing and improving women and newborn health outcomes. The need for models capable of predicting abnormal fetal health is the research objective