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Optimising Communication Channels for Enhancing Project Management Efficiency in the IT Sector
No full textThis qualitative study explores communication channel in project management in the IT field. The study is looking to determine the main communication channels used, most effective ways of communication integration into agile processes, obstacles that prevent efficient communication, and variables that impact on communication efficiency.
IT professionals with knowledge of project management were interviewed in a semi-structured manner. Thematic analysis was used for data analysis which revealed the emerging themes.
The results show that IT teams mostly depend on digital communication channels like instant messaging, video conferencing, project management tools, email, and shared document repositories. Best practices for integrating communication into project management are holding regular meetings, using project management tool, promoting regular communication, controlling team communication, and documenting decisions.
Nevertheless, the study also points out problems of remote communication, like a delay in response, absence of live interaction and possible misunderstandings. Some of the key factors that affect the communication efficiency include team culture, tool selection and standardization, documented processes, individual preferences, remote work challenges, and cross-functional communication.
The study suggests how to solve the communication challenges in IT organizations: to invest in strong communication infrastructure, to create clear protocols of communication, to be proactive about addressing the communication problems, to develop a team culture of collaboration, and to keep adapting the communication approach.
This study adds to the expanding literature on communication in project management in the IT sector providing recommendations for practitioners and academics to improve project results and overall success in the ever-changing digital environment
Analysis on importance and effectiveness of Suspicious Activity Reporting (SAR) for the Anti-Money Laundering process of investment banks in India
No full textThe primary goal of Suspicious Activity Reporting rules at financial institutions is to identify and prevent money laundering and other crimes such as terrorist financing, tax evasion, and bribery. The Reserve Bank of India (RBI) has implemented initiatives to ensure that all financial institutions follow regulatory standards. The regulations on Suspicious Activities in India were implemented in early 2000s which is much later compared to the global initiative in in 1900s. The increasing trend of the suspicious reports in India in early years of implementation raises the concern over the effectiveness of the Anti-Money Laundering measures in Indian banks. To combat money laundering operations, investment banks must adhere to policies and processes that are consistent with national and international regulatory standards. One can identify if the client is engaging in any suspicious activities by using different mechanisms in their daily operations. Hence this study is conducted to find out the effectiveness of the Suspicious Activity Reporting in preventing the money laundering activities.
The research is conducted using the quantitative research approach and data collected through an online survey by 100 employees from Bengaluru, India, who work in investment banks using the convenience sampling method.
The results of the research analysed using descriptive and statistical reports and the results show that the efficacy Suspicious Activity Reporting on Anti-Money Laundering processes is important and helps in preventing the money laundering. However, there was knowledge gap was identified in the perceived effectiveness and importance of Suspicious Activity Reporting though research. Also, some of the investment banks are reluctant to implement the Suspicious Activity Reporting practices in place. The findings and the suggestions could assist Indian investment banks in implementing robust system for Anti-Money laundering by strengthening the Suspicious Reporting practice
Understanding Key Challenges for People with Disabilities in Digital Accessibility in Ireland and Determining Effective Measurements for Improvement
No full textAim and Objectives: This research focuses on determining the key barriers faced by people with disability in Ireland due to the inaccessibility of digital resources. Furthermore, the research aims at determining the role of assistive technologies in improving digital accessibility for people with disability, and effective strategies to enhance digital accessibility in Ireland. The research also focuses on determining the perspectives and practices of different stakeholders, which can help in improving digital accessibility in Ireland.
Research Justification: The number of internet users has tremendously increased globally, and people highly rely on digital products to carry out their day-to-day activities. However, due to the inaccessibility of digital products, people with disability are unable to carry out basic online activities and benefit from digital resources. Thus, the research focuses on bringing attention to the people with disability in Ireland and the challenges faced by them due to digital inaccessibility.
Methodology: The research is conducted using the interview method, and the data is collected from 7 stakeholders. Interview was conducted using online platform of communication (Zoom) after all the processes essential for the primary data collection, such as consent form and interview schedule information as per the participants accessibility. Semi-structured interview questionnaire was used within which open-ended questions were covered, to gain in-depth insight into research subject of digital disability.
Findings: The findings revealed that people with disability face several challenges due to digital inaccessibility. Due to digital inaccessibility, people with disability miss out on important information, which intensifies feelings of incompetency among them. Thus, there is a need to improve awareness and competency among web developers, designers, and content creators in Ireland to help them effectively add accessibility features to digital products and improve the user experience of people with disability.
Recommendations for Future Researchers: Future researchers are recommended to carry out a global context or cross-study based in two or three countries and conducts a study using the survey method to collect data from the wider population
How does diversity impact conflict between employees and relationships among employees
No full textThe increment diverse workforce of the contemporary business world has made diversity and conflict management essential components of organisations. The research was conducted to explore the role of conflict management and diversity in employee relations in the retail industry. Conflict management and managing employee relations are significant to understanding diversity management in organizations. The aim of this study is to gain an insight of how diversity impacts employee relations and conflict management among 20-40- year-old immigrant employees in Dublin retail industry.
Semi-structured interviews were employed in the qualitative approach to collect more in-depth perspective from participants’ individual experiences about diversity and conflict management. The participants are 9 immigrants, aged 20-40, who works in the Dublin retail industry. The theme in the coded documents has been developed by the application of interpretive phenomenological analysis. By giving labels or codes to real material extracted from transcripts of interviews, open coding facilitates the methodical classification and interpretation of qualitative data.
Along with the wide range of diverse workforce in Dublin retail industry, some participants shared positive experiences they came across in their workplace while others faced negative encounters and consequences.
The study findings deepen the comprehension of then complexity of the workplace diversity and conflict management in both academic and industrial areas. In addition, it underscores the challenges experienced by immigrant employees and offers ways to improve workplace inclusion and conflict resolution. The research analyses the core employee relations and conflict resolution that can take place with effective management of diversity in the workplace. The research is useful for organisations to analyse diversity, themes of diversity, management of employee relations with diversity, and the role of immigrant employees in diversity at the workplace
Human Resource and Talent Management in Ireland (Use of AI & Human Resource Information System)
No full textThis research is about addressing human resources and direct management in the digital era in Irish organizations. The primary objective is to address the impact of mobile decision management in the digital era in Ireland. The study has established a background regarding talent management and human resource management and has helped to establish the research hypothesis which has been attested in the literature review with existing research papers. Additionally, research questions and gaps in the literature based upon addressing human resource and talent management through understanding existing research papers have been established.
The literature review reveals digitalisation has positive influences on HR and talent management. Certain challenges have been identified here such as skill gaps, the existence of a generational gap, and high competition for recognizing rare talents. In this case, several factors related to the digital era such as employee satisfaction, cross-functional management, and digital transformation have been addressed. Regarding this, strategic negotiation and research-based view theory have been evaluated for addressing human resource and talent management in the digital era.
The findings of the study have showcased that data-driven HRIS and AI help to influence the talent management program and manage human resources in Irish organisations. Moreover, the survey analysis has analysed that AI and data-driven decisions are crucial to improve retention of talented employees, however, awareness of technological adaptation is crucial for this. Additionally, the findings of this study also demonstrated that employees' experience and satisfaction are crucial along with the talent management process to increase the high employee retention process and reduce attrition. Additionally, AI and HRIS help to minimise skill and communication gaps and enhance the relationship between HR and employees.
The discussion section of the research helps in analysing potential key findings from the survey session, the literature review part along with overall analysis of the findings related to digital transformation essentialities for HR and talent management in organisations of Ireland. Moreover, objective-based information analysis with the alignment of key findings has been also analysed along with the information theoretical re-establishment for the current research
Classifying AI-Generated images using EfficientNet-B0, ResNet50 and VGG16 CNN ML models
No full textThe creation of AI-Generated images has been democratised with the proliferation of online and scalable tools. These tools enable users to easily create high quality, fidelity and realistic fake images for multiple use cases and purposes. Detecting, labelling and classifying AI-Generated images is crucial in a wide range of circumstances and applications. This research evaluates the performance of three CNN ML models (EfficientNet-B0, RestNet50 and VGG16) on four public image datasets belonging to three themes: miscellaneous; shoes; and, fruit. The VGG16 model proved to have higher accuracy and performance compared to the other two models
Hybrid Skin Disease Diagnosis Using StyleGAN2 and EfficientNet
No full textAutomated skin disease detection is a transformative application of machine learning in healthcare, addressing the challenges of early diagnosis and effective treatment planning. This study introduces a hybrid framework that integrates StyleGAN2 and UNet for synthetic data generation and EfficientNet-B5 for classification, tackling issues such as data scarcity, class imbalance, and variability in dermatological datasets. The curated dataset includes three skin disease categories eczema, psoriasis, and fungal infections chosen for its diagnostic complexity and clinical relevance. Through synthetic data augmentation, the framework achieved significant improvements in classification performance, with accuracy increasing from 68.5% to 82.3% and F1-score rising from 0.72 to 0.85. Synthetic images generated using the StyleGAN2-UNet hybrid model exhibited a low Fr´echet Inception Distance (FID) score of 18.7, validating their quality and utility. Evaluation metrics such as precision, recall, and F1-scores were supplemented by visual tools like confusion matrices, ROC curves, and Class Activation Maps (CAMs), ensuring both reliability and interpretability. This study contributes a scalable, robust, and interpretable solution for automated skin disease detection, with the potential for broader applications in medical diagnostics
From Data to Dollars
No full textCryptocurrencies and Stock Markets are a hot new way of investing into and earning some big bucks. They have changed the way of how we perceive finances and how the traditional transactional or investment approaches are now replaced by thousands of stock options, and 13,000+ cryptocurrencies around the world. With a market cap of several hundred Trillion dollars, these financial instruments have attracted researchers to analyse trends, and read patterns convert that data into dollars. Capturing price values changing with time, hours after hours and weeks after weeks sometimes may not be enough, and here we also show how social sentiments affect the actual market along with the financial market trends themselves. We demonstrate the pros and cons of various Time Series models and how they perform with and without Twitter sentiments for predicting prices of a stock, generalising over capabilities for all Time Series analysis
Impact of Direct Marketing Strategies on Consumer Behavior in the Banking Sector
No full textDigitalization has radically changed the entire business of banking, increased competition, and increased consumer expectations. This paper discusses the efficiency of some popular direct marketing strategies for the banking industry using machine learning algorithms like logistic regression, decision trees, random forest, and gradient boosting. Some of the major objectives of this paper were to check the performance of these models on customer response prediction, examine demographic factors which may make a difference, and analyze the most important features contributing towards marketing campaign success. Conducted on detailed data from Kaggle, extensive data preprocessing, feature scaling, model training, and model evaluation formed part of the research. The findings revealed that Gradient Boosting and Random Forest models achieved the highest overall performance, with accuracies around 90.2% and ROC AUC scores above 92 %, making them highly effective in distinguishing between positive and negative customer responses. The study also highlighted the role of feature engineering and demographic factors in determining marketing outcomes. Despite the fact that such models have been successful, there are still some areas to improve, such as enhancing recall rates and class imbalance. This study sets a path toward individualized, efficient, and successful campaigns that aim at better customer engagement and retention in the highly competitive banking industry of today
Optimizing Movie Recommendations with MLOps in AWS
No full textIn the age of streaming services and online content, there has been a significant increase in the need for personalized movie suggestions. This has led to the creation of complex recommendation systems capable of managing large amounts of data and quickly adapting to evolving user preferences. This study explores enhancing movie recommendation systems by incorporating Machine Learning Operations (MLOps) in Amazon Web Services (AWS) environments. The research focuses on the increasing demand for efficient and easily expandable recommendation systems due to the growth of digital content. Conventional machine learning methods frequently encounter difficulties when being implemented in real-world situations, especially when dealing with vast amounts of data and adjusting to user behavior. The Project highlights the significance of operational elements when it comes to implementing and upkeeping machine learning models in a production environment. Utilizing AWS’s strong cloud infrastructure, the project seeks to develop a streamlined MLOps pipeline to enable ongoing integration, delivery, and monitoring of machine learning models. This method guarantees that the recommendation system will be able to grow, stay dependable, and respond quickly, leading to enhanced user happiness. The project utilizes different AWS services such as SageMaker for constructing models, Lambda for executing serverless functions, and S3 for storing data, in order to establish a smooth, automated pipeline which results emphasize how MLOps can improve the performance and scalability of movie recommendation systems, offering valuable insights for future applications in this area