Optimizing project efficiency: impact of cloud computing solution in modern construction project management

The study is intended to determine the effect of introducing cloud computing platforms on the efficiency of current construction project management. Working with construction engineers and using SPSS statistics, this study gets the desired results of better interactions, collaboration in work, a scalability, flexibility, workflow optimization, and payroll cuts. The fact that cloud technology allows for faster sharing of data, improved resource allocation, and streamlined implementation of projects helps in the project execution and with reduced costs. The data we collected support the Thought Acceptance Model (TAM), the core of which lies in both perceived usefulness and ease of usage. Even though the limitations of the small sample size and the regional perspective were identified, this study recommends the future of research needs to go wider encompassing the cloud computing family of technologies and compare it with emerging technologies as well. The study ends by providing suggestions to industrial players so that they integrate cloud solutions to get better from the projects they conduct

Comparative analysis of ML Algorithms for effective phishing URL detection

Cybersecurity, an increasingly critical field, is under constant threat from evolving cyberattacks. Phishing websites, a prominent method for attackers to deceive users and steal sensitive information, require effective detection systems. This study, focusing on developing a highly accurate ML model to predict the phishing nature of websites, is a significant contribution to the field. The study highlights the importance of balancing accuracy with real-time applicability, emphasising the need for quick response times in practical phishing detection systems. Future improvements may include integrating incremental learning and hybrid models to enhance detection capabilities further

Strategies governing successful internal marketing communication for employee engagement in remote and hybrid work

This dissertation explores the impact of internal marketing communication strategies on employee engagement in remote and hybrid work environments. The rapid transition to these work models, accelerated by the COVID-19 pandemic, has presented both opportunities and challenges for organizations striving to maintain a connected and motivated workforce. Through qualitative research, including semi-structured interviews with professionals in marketing, internal communications, team leaders, and employees, this study investigates the effectiveness of various communication tools and strategies. Key findings highlight the critical role of leadership support, tailored communication approaches, and mental health initiatives in fostering employee engagement. The research also emphasizes the importance of segmenting employees based on demographic and geographic factors to enhance the relevance and impact of internal communication efforts. These insights contribute to a deeper understanding of how companies can successfully engage employees in increasingly digital workplaces, ensuring that they remain aligned with organizational goals despite the physical distance. The findings are discussed in relation to existing theoretical frameworks, offering practical recommendations for organizations seeking to optimize their internal communication strategies

Effect on Performance of Employees Working in Rotational Shifts within the Pharmaceutical Industry in India

The comprehensive study on rotational shift work in the Indian pharmaceutical industry illuminates the impact of irregular work schedules on employee performance, well-being, and job satisfaction, particularly among younger adults. A key finding is the negative impact of rotational shifts on work-life balance and overall health, leading to stress, sleep disturbances, and long-term health issues. These irregular hours not only affect personal lives but also professional performance, increasing the risk of errors in a precision-essential field. The study advocates for flexible scheduling, such as shift swapping and self-scheduling, to give employees more control over their work hours, enhancing job satisfaction and reducing stress. Additionally, it recommends holistic health and wellness programs, including regular health check-ups, stress management workshops, and mental health support. It also underscores the need for an inclusive, supportive work culture with open communication channels. Implementing these measures can result in a more engaged, productive, and satisfied workforce, which is vital for maintaining high productivity and quality standards in the Indian pharmaceutical industry

Can Retaining Older Workers in the Workplace Benefit Organisations in South Africa?

The aim of the study was to explore whether retaining older workers in the workplace could benefit organisations in South Africa, against the backdrop of increasing numbers of people around the world being forced to work longer, or return to the workforce after retirement. This was due to socio-economic factors, which were currently being further exacerbated by the global inflationary impact on pensions and savings. Apprehension about losing meaning and purpose in life after retirement were further factors that emerged during the research for this dissertation. The objectives were thus to explore the transitioning of older, senior employees in an organisation to a coaching and mentoring role to potentially help mitigate the serous skills shortage in South Africa. Data collection took the form of seven semi-structured, online interviews with the identified participants in South Africa. The data was analysed using an inductive thematic analysis to describe and interpret the information. The interviewees confirmed that older members of the workforce proved to be a rich source of talent and experience for the organisations that engage them and have, in many instances, become catalysts for change and intergenerational diversity in the workplace. Despite the positive potential of retaining more mature members of the workforce, however, the reality of the current socio-economic situation in South Africa, proved to be far more complex than was anticipated at the outset of the study. To this end, the objectives of this study had to be revisited and some harsh truths had to be acknowledged. Thus, it was found that transitioning more mature members of the workforce to a coaching and mentoring role would require a significant mindset shift and more structured human resource management guidelines and processes to facilitate it

Comparative analysis of clustering algorithms for customer segmentation and improved marketing strategies

This study evaluates the effectiveness of K-Means, DBSCAN, and OPTICS clustering algorithms for customer segmentation. Using the Recency, Frequency, and Monetary (RFM) model, customer value was quantified, and customers were segment based on their transactional behavior. Dataset was obtained from UCI machine learning repository and contained transaction details of an online retail business. The data underwent cleaning, feature engineering, normalization, and dimensionality reduction using UMAP. The clustering algorithms were then applied and evaluated using Silhouette Scores and Davies-Bouldin Indices. K-Means effectively grouped customers, achieving a Silhouette Score of 0.445 and a Davies Bouldin Index of 0.736. DBSCAN handled noise and identified arbitrary shapes but produced scattered clusters with a lower Silhouette Score of 0.132 and a higher Davies-Bouldin Index of 1.435. Although OPTICS had similar scores to DBSCAN, it resulted in smoother clusters and handled varying densities more effectively than DBSCAN. To summarize, K-Means provided the best cluster separation. DBSCAN and OPTICS were better for noise handling and variable densities

Machine learning insight into e-commerce churn: Prediction and preventing customer loss

The 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

Investigating the Role of Cloud Computing in Enabling Digital Transformation in the Automobile Parts Manufacturing Sector

This 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

Financial performance analysis using machine learning algorithms: post-IPO of Nykaa

The research looks at how the financial performance of Nykaa which took the IPO in 2021 through 2023 in three years with machine learning models. This work aims to provide more clarity and prediction of the financial situation by using the regression analysis, time series forecasting, and clustering algorithms represented by Python and allowing this project to uncover patterns hidden within Nykaa's financial data. The literature review goes through current studies on machine learning in financial analysis which also deals with research gaps and adds to its practical concerns. The research proposal, through addressing the deficiency and taking advantage of ML-based predictive analytics, seeks to offer result-oriented insights that will be of importance to investors, general observers, and shareholders operating in the digital commerce arena. The study thus looked into the post-IPO finance and stock analysis of Nykaa that is a well-known e-commerce venture in India applying a multidisciplinary approach including clean enrichment, machine learning, and statistical modelling. Already familiarity with Python programming language and SciPy, NumPy, Pandas, Matplotlib, Seaborn, Statsmodels, TensorFlow, and scikit-learn libraries along with ARIMA and LSTM models, the research explore to a Nykaa's stock price precisely and to gain information about its financial health. It carried out the study and received historical stock price data from Yahoo Finance. The data revealed patterns in the growth of Nykaa’s revenues, profitability measures, and cash flow movements. The study used descriptive statistical approaches and visualization methods to uncover critical information about the Nykaa share price fluctuations, capitalization movement patterns, and trading volume developments. Along with this, the trainings of ARIMA and LSTM are quite good in predicting Nykaa's stock prices in the future, where it proves the real applications of machine learning in financing. Therefore, the effect of the IPO of Nykaa in the context of its financial statements' performance provides a good background of how Nykaa's investors, financial analysts, and stakeholders can understand and react responsibly to the financial market dynamics