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Impact of artificial intelligence (AI) on information technology (IT) project management in Ireland
No full textThis study investigates the impact of Artificial Intelligence (AI) on IT project management in Ireland. The research aims to explore AI’s influence on project management practices, identify challenges faced by project teams, and assess the effectiveness of AI tools. Employing an interpretivist philosophy and a qualitative approach, the study uses semi-structured interviews to gather data from IT project managers in Ireland. Thematic analysis, facilitated by NVivo software, was applied to analyse the interview data. Key challenges identified include resistance to change and skill gaps, while AI tools such as machine learning and predictive analytics are examined for their effectiveness. Findings reveal that while AI enhances decisionmaking and operational efficiency, it also presents integration hurdles and ethical concerns. The results suggest that AI can significantly improve project outcomes but requires a balanced approach to implementation, considering both technological and human factors. The study contributes to understanding AI's role in project management and provides practical insights for enhancing AI implementation strategies in IT projects
Prediction Of Energy Usage In IoT Devices
No full textUnderstanding energy consumption models can enhance energy management and cut expenses. This study aims to forecast energy usage considering different temporal and environmental factors. It specifically targets predicting the energy consumption of IoT devices, utilizing data from smart homes in Greece. Data collected from the smart home is analyzed using machine learning models including Random Forest, Support Vector Machine (SVM), Logistic Regression and XGBoost. Research involves data preprocessing, feature engineering, and applying these models to produce accurate predictions. The XGBoost and Random Forest models demonstrated the highest accuracy, highlighting their effectiveness in this context. This study provides valuable insights into energy management in smart homes, facilitating the development of efficient energy strategies and improving environmental sustainability. It also highlights the importance of using advanced machine learning techniques to accurately predict energy consumption
Machine learning insight into e-commerce churn: Prediction and preventing customer loss
No full textThe customer churn prediction is the key for e-commerce companies to create the retention strategies and stay in the competition. The purpose of this study is to overcome the issue of customer churn by prediction by utilizing the means of machine learning. The study employs the CRISP-DM paradigms, implementing and evaluating machine learning models: Random Forest Classifier, Logistic Regression, XGBoost and AdaBoost. The models are built and tested on the "E-commerce Customer Behavior and Purchase Dataset”. Hyperparameter tuning and performance evaluation are carried out to get the best from each model. The XGBoost Classifier is the best out of all the models in the accuracy, precision, recall and F1 score. The research is about the problems such as skewness, parameter validation and model bias and it gives the solutions which are oversampling, under-sampling, grid search and cross-validation. The next step in this research is the improvement of feature engineering, the implementation of real-time retention strategy and the increased model interpretability for the actionable insights. The research results add to the existing body of knowledge on the prediction of customer churn in e-commerce and help to establish the basis for the development of the proactive retention strategies. The approach can be used in other industries which are also facing the same problems. This research illustrates the achievement of machine learning, especially the XGBoost model, in the prediction of customer churn and underlines the significance of data-driven decision making in the challenging e-commerce environment
The Role of Aviation in Economic in Developemnt
No full textThis paper highlights various factors that contributed to the development of interactions between air travel and several aspects of economic growth, peeling light on the multidimensional impact of aviation on development. Using a thorough analytical framework, the research explores how important aviation is to promoting investment and commerce, which in turn stimulates economic growth at the national and international levels. This research reveals the dynamic character of aviation's contribution to economic development and emphasises its relevance as a catalyst for sustained advancement through an examination of historical case studies, statistical analyses, and contemporary examples. Starting with its revolutionary impact on trade, transportation, and communication, the dissertation traces the historical development of aviation. It then explores how aviation fosters economic development, including how it supports the expansion of service industries, eases tourism, and makes supply chain management possible (Page 12, Smith, 2023).
In this research examining the positive impacts of aviation on economic development, the dissertation also acknowledges the potential challenges associated with aviation, such as environmental concerns and security risks (Page 20, Brown, 2023). The dissertation concludes by emphasizing the need for sustainable and responsible aviation practices that balance economic growth with environmental stewardship and public safety.
This paper adds to the academic body of work in this area by considering the historical development role of aviation in economic development worldwide and the status of the industry
Predictive Analysis & Visualization of GDP of the Top Economies
No full textThis study investigates the use of the autoregressive integrated moving average (ARIMA) model to predict the gross domestic product (GDP) of the three leading global economies, which are considered possible contenders for becoming the next superpower. The data utilized in this study were gathered from the official website of the World Bank for the time frame spanning from 1990 to 2022. The study utilizes a response variable, GDP, and a time variable, Year. The ARIMA model is exclusively fitted to the response variable. The Akaike Information Criterion Corrected (AICc) and Bayesian Information Criterion (BIC) were employed to determine the optimal model from the chosen ARIMA models. The auto_arima() function from the pmdarima package was utilised to accomplish this task. Residual plots were generated to visually represent the performance of the models, and the Mean Absolute Percentage Error (MAPE) was computed. The MAPE values for the countries USA, China, and India were 3.6, 4.1, and 4.7, respectively
Strategic planning on productivity through leadership commitment and employee involvement
Full text linkEmpirical research studies that have examined the relationship between strategic planning and productivity have produced mixed results, with some studies presenting that strategic planning has a significant impact on productivity, while other studies have failed to find the relationship between strategic planning and productivity. This study attempted to clarify and understand the relationship between strategic planning and productivity by including some contingency variables considered relevant in the implementation of strategic planning namely leadership commitment and employee involvement using 156 sample data collected through questionnaire from management employees of Tanzania Zambia Railway Authority. Model fit, validity and reliability were tested using regression analysis, principal component analysis and factor analysis using Jamovi software. The study presents that strategic planning and leadership commitment have a significant impact on productivity and that leadership commitment mediates the association between strategic planning and productivity. This study provides empirical evidence on the nature of the relationship between strategic planning and productivity. This study also gives evidence that leadership commitment is very relevant in strategic planning process at all stages and that no manager should isolate him/herself from strategic planning process
My Books are my relation to society”: Transition and transformation for the arts and humanities in an open access future
No full textThis article contextualises the transition to an open access publishing future and sets it against the background of the current state of decline in arts and humanities research funding in the US and UK. It outlines the problems which have stymied and slowed the move towards open research and it highlights those issues which particularly pertain to the field of arts and humanities. It considers the demands of research assesement and to quantify value and the opportunities that open access publishing might afford to those who research in the arts and humanities
Analysing the dynamics of supply construction Gross Domestic Products in Malaysia: A comprehensive study (2015-2023)
No full textThis research study serves two purposes. First, it seeks to examine the dynamics of Malaysia's supply construction sector and its Gross Domestic Product (GDP) from 2015 to 2023. Second, it aims to identify the key macroeconomic forces regulating this sector and their influence throughout the provided timeframe. In order to achieve these goals, the study applies a variety of research approaches, including correlation and regression analysis. The primary macroeconomic indicators examined are exchange rates, base lending rates, and inflation. The study's findings show that the Base Lending Rate (BLR) has a considerable effect on Malaysia's supply construction GDP. In contrast, exchange rates and inflation rates have modest or statistically negligible impacts. The study's regression model emphasizes the significant influence of BLR and demonstrates that it is the most powerful predictor among the investigated macroeconomic factors. The importance of this research rests in its capacity to clarify the complex link between macroeconomic data and Malaysia's supply construction sector. It emphasizes BLR's crucial role in influencing the performance of this sector and, as a result, its impact on the whole economy. Furthermore, the study provides useful insights for policymakers, industry stakeholders, and future research endeavours, enabling informed economic decision-making and strategic planning in the construction sector
Real Time Facial Expression Recognition using EfficientNetB7 Model Architecture
Full text linkFacial expression recognition (FER) represents a pivotal aspect of human interaction and emotional communication. This research delves into the development and implementation of a real-time facial expression recognition system using the FER2013 dataset obtained from Kaggle. Leveraging the cutting-edge EfficientNetB7 model, the study aimed to create a robust and accurate model capable of detecting various facial expressions in real-time through webcam feeds. Employing a combination of Machine Learning and Deep Learning methodologies, the project focused on training the model to categorize facial expressions into distinct emotions. The methodologies encompassed preprocessing steps, data augmentation techniques, and the design of an EfficientNet7 architecture tailored to the nuances of facial feature recognition. The model's performance was evaluated using metrics such as accuracy, precision, recall, and loss. This research encapsulates the comprehensive approach and findings achieved in the pursuit of advancing real-time facial expression recognition technology