Ibtisam Mogul

The current work aims to develop computational models for the thermal characteristics of turbulent CH4 flames for varying burner dimensions. This study develops a platform for data driven analysis of temperature prediction of turbulent non-premixed flames, in which the influence of flow and geometric parameters, including burner head diameter (D), half cone angles (α), and co-flow air velocity (Ucf), have been considered. The algorithms used were ridge regressor (RR), linear regressor (LR), and three variations of support vector regression (SVR): SVR with a linear kernel (SVR-LR), SVR with a radial basis function function (SVR-RBF), and SVR with a polynomial kernel (SVR-Poly). The performance of each computational model was evaluated and contrasted based on several metrics: mean absolute error (𝑀𝐴𝐸), regression coefficient (𝑅 2 ), mean absolute percent error (𝑀𝐴𝑃𝐸), and mean Poisson deviance (𝑀𝑃𝐷). From the modelling of the output data, it was observed that the SVR-RBF predictions were more accurate compared to those from the other algorithms, as it achieved the highest training 𝑅 2 value of 0.955. The testing predictions of RR, SVR-LR, SVR-RBF, and SVR-Poly algorithms were also robust, with 𝑅 2 values ranging between 0.91-0.94. It is, therefore, established that these computational models are effectively suited for predicting sensitive turbulent CH4 flame characteristics based on varying input factors.

The current study focusses on developing a back propagation neural network model for depth of cut during the abrasive water jet machining of a Ti-6AL-4V aluminum alloy. The study analyzed depth of cut for five different water jet abrasive parameters namely, water pressure, transverse speed, abrasive mass flow rate, abrasive orifice size, and nozzle to orifice diameter. Experiments were conducted as per the L27 Taguchi-design of experiments (DoE). The back propagation neural network model comprising of one input layer, one hidden layer and an output layer with an architecture of 1–5–6 was chosen for conducting the analysis. The algorithm predicted the Taguchi based output values for the experimental depth of cut with an accuracy of up to 95 %. The neural network algorithm further automated itself, generating 50 new data sets for K-cross validation, out of which 70 %, 20 %, and 10 % of the data were used for training, testing, and validation, respectively. Confirmatory experiments were conducted for depth of cut and assessed against the data set used for validation (10 %). The results showed that as the depth of cut was small, i.e., ranging from 3 mm to 5 mm, the algorithm was unable to predict the optimized parameters, however, the prediction improved as the depth of cut increased. Overall, the consistency between the neural network predicted and the experimental depth of cut throughout the algorithm confirmed the validity of the procedure and the appropriateness of the algorithm.

As retailers embrace the online shopping experience and technology advances, it is now also vital for retailers to pay attention to customer churn since it has a detrimental impact on the company's corporate development and reputation. To mitigate the negative effects of customer churn on grocery retail businesses, this study will look at how machine learning and deep learning churn prediction models are applied, as well as data analytical findings on customer retention. The implications of customer churn and how it impacts grocery businesses will be the subject of thorough research.
Furthermore, an analysis of previously gathered data sets will reveal significant discoveries, customer preferences, and behaviours related to Churn.
The study will examine how churn prediction affects a company's profitability, reputation, and operational efficiency.
Following the study of the dataset, a thorough framework will be suggested with the main goal of proactive churn control, thereby limiting its effects on the overall growth of the company.
This thesis aims to contribute to current efforts to improve corporate company growth by studying customer behavioural patterns most associated with churn and then suggesting solutions to the challenges.

This research study aims to evaluate the significance of Technology and Industry 4.0 for Student Performance in Higher Education. Industry 4.0 is part of digital revolution which amalgamates various technologies like AI, distributed computing, virtual reality (VR), Internet of Things (IoT) & Big Data to bring a fundamental transformation in the current industry. The integration of these technologies has benefited all domains of society including Education. Education 4.0 aims to use Industry Revolution 4.0 technologies to the benefit of education field by providing means to improve the education sector using techniques like Education Mining, Prediction and Prescription of student's performance during their learning duration at universities. This study tries to highlight some important literature in the area of Industrial Revolution 4.0, Education 4.0, Big Data, Machine Learning, Descriptive, Predictive and Prescriptive analysis as well as learning analytics tools to provide a guidance for the stakeholders of the Education Industry to enrich their process for getting improved student performances at risk.