The use of churn prediction to improve customer retention in grocery e-retailing

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

Ensemble approach and enhanced features for precise Bank Churn prediction analysis

Numerous studies and research work has been undertaken in the area of creating predictive models for studying Bank Churn. In these studies, the end goal was to create a high accuracy predictive model; while this is commendable, this research focuses on creating an architecture for a predictive model by aggregating the power of various predictive models. The architecture and model proposed in this paper achieved an accuracy of 91% in the test data (35% of the original data set), and an AUC of 96% - confirming the generalized nature of the model. Also, various feature extrapolation techniques were introduced which provide valuable insights to the banking sector

Non-Alcoholic fatty liver disease prediction with feature optimized XGBoost model

Non-alcoholic fatty liver disease (NAFLD) is an expanding health threat, posing significant risks for long-term complications. Early detection and intervention are crucial, but traditional diagnostic methods can be expensive and invasive. This study investigates the utilization of machine learning models for predicting liver diseases from various out-sourced datasets. .We employed Decision Trees, Random Forests, and Support Vector Machines (SVMs) to predict NAFLD based on various clinical and demographic features. Model performance was evaluated by calculating accuracy, precision, deviation and accuracy-score. All these models achieved promising accuracy levels, ranging from 80% to 90%, showcasing their potential for NAFLD prediction. Among them, XGBoost demonstrated the highest performance, with an accuracy of 90% and more. This study demonstrates the effectiveness of machine learning models in predicting NAFLD with high accuracy using readily available data. Further research with larger sized and more varied datasets will vindicate these models for real-world application in clinical settings

Time-series forecasting of crude oil production using hybrid modeling

Crude oil is the main energy source, and its demand has been usually growing over years. It has always been an issue in the petroleum industry to forecast the production of crude oil to avoid disruption of supplies and keeping the prices of oil and commodities in control and thereby manage inflation. Hence, it becomes crucial to predict the production of crude oil. This study uses time series data to forecast crude oil production. Traditional statistical Autoregressive Integrated Moving Average (ARIMA). model and deep learning models like Long Short-Term Memory (LSTM), Artificial Neural Network (ANN), and Gated Recurrent Unit (GRU) are used for prediction and comparison. A hybrid technique is used to develop an ARIMA-ANN model to forecast crude oil production more accurately

Fuzzy-based prediction for suddenly expanded axisymmetric nozzle flows with microjets

The current research focuses on the implementation of the fuzzy logic approach for the prediction of base pressure as a function of the input parameters. The relationship of base pressure (β) with input parameters, namely, Mach number (M), nozzle pressure ratio (η), area ratio (α), length to diameter ratio (ξ ), and jet control (ϑ) is analyzed. The precise fuzzy modeling approach based on Takagi and Sugeno’s fuzzy system has been used along with linear and non-linear type membership functions (MFs), to evaluate the effectiveness of the developed model. Additionally, the generated models were tested with 20 test cases that were different from the training data. The proposed fuzzy logic method removes the requirement for several trials to determine the most critical input parameters. This will expedite and minimize the expense of experiments. The findings indicate that the developed model can generate accurate prediction

GREAT Deliverable 2.1 Summary report and compilation of design challenges, design briefs and wireframes

The GREAT project explores ways of using games-based activities to help citizens express their opinions and attitudes to emerging policies, and making the results available to policy makers. To this end, the task of WP2 is to work with stakeholders on dilemmas related to climate change, carrying out activities to develop design challenges, design briefs and wireframes for games-based activities. This report summarises twelve pilots carried out to inform the design of these activities. The report consists of a summary of the pilots, and a compendium containing the reports from each pilot activity

An empirical study on the impact of effective digital customer journey management on customer satisfaction in the Nigerian Islamic banking sector

This study examines the relationship between digital customer journey management and customer satisfaction in the Nigerian Islamic banking system. The study is guided by a conceptual framework that reflects a comprehensive and holistic account of consumers' cross-channel interactions and employs a mixed-methods approach using interviews and self-administered questionnaires to collect data from Jaiz Bank customers. Empirical evidence was gathered from a multistage sampling methodology, and the findings showed that digital touchpoints make a substantial contribution to the relevance of selection variables, customer perception, and overall level of satisfaction. This study examines the relationship between digital customer journey management and customer satisfaction in the Nigerian Islamic banking system. The study is guided by a conceptual framework that reflects a comprehensive and holistic account of consumers' cross-channel interactions and employs a mixed-methods approach using interviews and self administered questionnaires to collect data from Jaiz Bank customers. Empirical evidence was gathered from a multistage sampling methodology, and the findings showed that digital touchpoints make a substantial contribution to the relevance of selection variables, customer perception, and overall level of satisfaction. Data were collected via interviews and self-administered questionnaire surveys using a triangulation (mixed-method) research method. The quantitative data were analysed using Ordinal Regression Analysis and the Spearman Rank Order Correlation, while the qualitative data were analysed using the thematic analysis approach through NVivo. The customer journey framework investigates how customers interact with Islamic banking products across their omni-channel customer journeys and attempts to pinpoint at what point in the journey this interaction takes place. The findings of this study indicate that digital touchpoints make a substantial contribution to variances in the relevance of selection variables, customer perception, and overall level of satisfaction. Customers of Jaiz banks ranked the characteristics associated with the service stage of the customer journey as the most important criteria, followed by purchase, awareness, consideration, loyalty, and advocacy stages. Most of those who participated in the survey stated that they were satisfied with Jaiz Bank. Younger customers and those with a higher level of education had a more positive attitude towards digital customer journey management. Overall, this study highlights the importance of digital customer journey management in the Nigerian Islamic banking sector and its impact on customer satisfaction. This study provides insights into how Islamic banks can improve their digital touchpoints to enhance their customer experience and satisfaction. It also contributes to the empirical literature on relationship marketing and customer behaviour in the Nigerian Islamic banking sector

Entrepreneurial leadership: an approach for crisis

In the face of recurring crisis and a rapidly changing business landscape, corporate leaders are looking for new ways to address their challenges and ensure long-term sustainability. The most effective leadership style and the critical competencies required for leaders to navigate the complexities of the business environment are yet to be identified. This chapter discusses the importance of effective leadership in today’s turbulent business environment. In addition, this chapter explores how an entrepreneurial leadership style can be effective in difficult situations and how organisations can learn to adopt an entrepreneurial leadership approach to remain competitive. This chapter examines case studies of successful entrepreneurial leadership, reviews relevant literature, and provides practical recommendations for organisations to develop entrepreneurial leadership competencies

A framework for leveraging the incorporation of AI, BIM, and IoT to achieve smart sustainable cities

This study investigates the significance of leveraging the incorporation of Artificial Intelligence (AI), Building Information Modeling (BIM), and the Internet of Things (IoT) to Achieve smart sustainable cities. Understanding their applications for Architecture, Engineering, and Construction (AEC) projects. The study encompasses three key dimensions: Design Optimization and Performance Simulation, Material and Life Cycle Sustainability, and Operational Efficiency and Environmental Impact. By leveraging BIM and AI, the research explores the integration of renewable energy, sustainable material selection, and smart building controls. BIM and AI experts were given a structured questionnaire, which was then analysed using SPSS. Descriptive and correlation analyses reveal significant positive correlations between energy efficiency and design visualization, construction sustainability visualization, as well as adaptability and education through visualization. The proposed framework deepens the capabilities of the combination of different technologies towards Smart Sustainable Cities. This work not only contributes theoretical insights to the field but also provides practical implications for industry professionals striving to enhance sustainable practices in AEC projects. Further studies to encourage a combination of other recent technologies to improve smart sustainable cities' performance

CIA security for Internet of Vehicles and blockchain-AI integration

The lack of data security and the hazardous nature of the Internet of Vehicles (IoV), in the absence of networking settings, have prevented the openness and self-organization of the vehicle networks of IoV cars. The lapses originating in the areas of Confidentiality, Integrity, and Authenticity (CIA) have also increased the possibility of malicious attacks. To overcome these challenges, this paper proposes an updated Games-based CIA security mechanism to secure IoVs using Blockchain and Artificial Intelligence (AI) technology. The proposed framework consists of a trustworthy authorization solution with three layers, including the authentication of vehicles using Physical Unclonable Functions (PUFs), a flexible Proof-of-Work (dPOW) consensus framework, and AI-enhanced duel gaming. The credibility of the framework is validated by different security analyses, showcasing its superiority over existing systems in terms of security, functionality, computation, and transaction overhead. Additionally, the proposed solution effectively handles challenges like side channel and physical cloning attacks, which many existing frameworks fail to address. The implementation of this mechanism involves the use of a reduced encumbered blockchain, coupled with AI-based authentication through duel gaming, showcasing its efficiency and physical-level support, a feature not present in most existing blockchain-based IoV verification frameworks