Batteries are crucial for decarbonising energy storage and mitigating climate change. As the battery industry expands, it faces increasing complexity due to accelerated innovation cycles, rising competition, and growing technological diversity, emphasising the need for cost-effective strategic...

While big data analytics capabilities (BDAC) have been pivotal to retail companies, their effects on innovation capabilities and sustainable performance have not been investigated systematically thus far. Therefore, this study explores the influence of BDAC on the innovation capabilities and...

The financial sector faces mounting challenges in processing vast volumes of high-velocity data to support intelligent, real-time decision-making. Traditional machine learning models often fall short in accuracy, scalability, and responsiveness when dealing with large, dynamic financial...

Manufacturing organizations continuously need to innovative production strategies and advance their machinery to adapt to evolving business objectives. Machine learning and data mining are now essential techniques for solving various complex manufacturing problems promptly and intelligently....

The rapid advancement of Big Data technologies has significantly influenced multiple sectors, with the retail industry being a key impact area. This study explores the relationship between Big Data analytics and consumer behavior, focusing on supermarket transaction data to extract macroeconomic...

Customer lifetime value is an important marketing metric and has applications in market segmentation, strategy development, and direct marketing programs, especially when customers are not under contract. In this research, we demonstrate the prediction of the lifetime value of patients in a...

Valuing private enterprises - particularly early-stage firms - remains a significant challenge due to high variability and frequent overvaluation, often exceeding 50%. These inaccuracies undermine trust in the venture capital (VC) market and complicate investment decision-making. To address...

Imbalanced data is often encountered in scenarios where rare but critical events occur much less frequently than others, and it is particularly prominent in fields such as disease diagnosis, fraud detection, and risk management. The main problem with imbalanced data is that predictive models...

Exhaustive hyperparameter search techniques such as grid search and random search are widely used in predictive analytics but become computationally expensive and often suboptimal in high-dimensional or nonlinear settings. This challenge is particularly clear in tuning Cycle Reservoir with Jumps...

Social networks are becoming vital for people to interact with each other, which in turn represent the production of a huge amount of information that can be useful in many contexts (e.g., medicine, natural disasters, commercial purposes, tourism). Nevertheless, analyzing such Big Data for...

An adaptive analytics workflow is presented for customer churn prediction, combining Principal Component Analysis for dimensionality reduction, a hybrid Modified Particle Swarm Gravitational Search Optimization (MPSO-GSO) for feature selection and hyperparameter tuning, and an ensemble learning...

Personality prediction has become an increasingly important area in psychological computing and human-centered AI, especially with the rise of user-generated textual data from social media platforms. However, current approaches - primarily based on supervised learning - face major challenges in...

This study uses natural language processing (NLP) techniques to analyze the political discourse of the surprise presidential candidate, focusing on linguistic patterns, sentiment distribution, and recurring themes. This study addresses the problem of how TikTok virality and algorithmic...

Reducing the risk of fraud in credit card transactions is crucial for the competitiveness of companies, especially in Latin American countries. This study aims to establish measures for preventing and detecting fraud in the use of credit cards in shops through analytical methods (data mining,...

This study investigates how organisations respond to Corporate Sustainability Reporting Directive (CSRD) requirements through systematic analysis of corporate communications and sustainability reports. We employ multi-dimensional text mining analysis of 500 companies across 12 industries, using...

Background: The growing complexity of production processes in the metal-mechanical industry demands ever more effective strategies for managing machine and equipment maintenance, as unexpected failures can incur high operational costs and compromise productivity by interrupting workflows and...

In the health field, longitudinal studies involve the recording of clinical observations of the same sample of patients over successive periods, referred to as waves. This type of database serves as a valuable source of information and insights, particularly when examining the temporal aspect,...

Predicting the outcome of a sports game is a favourite pastime for sports fans and researchers. The interest has intensified in recent years due to data availability, the development and successful implementation of machine learning algorithms, and the proliferation of internet gaming. This...

The fundamental issue with a credit system for manufacturers and importers of commodities is inefficient credit assessment. Traditional techniques frequently produce inaccurate risk assessments and credit scores, resulting in financial losses for lenders, missing business growth possibilities,...

Online health consultation services are crucial for mental health support, particularly in densely populated areas. However, the heavy reliance on human expertise often leads to delays, necessitating more efficient and automated solutions. This study developed a machine learning framework to...

Association rules can offer a human-interpretable insight extracted from data. The lift measures used for evaluating association rules in classical Association Rule Mining (ARM) contexts are mainly based on traditional and well-known ones but suffer from interpretation inadequacy when dealing...

This systematic review investigates sentiment analysis of news headlines in the banking sector, a field susceptible to public sentiment, as demonstrated by phenomena like bank runs leading to rapid deposit withdrawals. We trace the evolution of analytic methods from traditional machine learning...

Global supply chains become increasingly complex due to various uncertainties associated with the constant flux. With more firms engaging in global trade, reaping the benefits of offshoring and outsourcing a multitude of business functions worldwide, the increasing complexity continuously tests...

Investing in the stock market has always been an exciting topic for people. Many specialists have tried to develop tools to predict future stock prices in order to make high profits and avoid big losses. However, predicting prices based on the dynamic characteristics of stocks seems to be a...

Background: Logistics challenges, such as driver shortages, are a major global issue, with many countries struggling to find effective solutions. YouTube, as a social networking platform, has a growing user base and is increasingly used not only for entertainment but also for social interaction,...

Small and medium enterprises around the world, and especially in emerging markets, face challenges when it comes to market segmentation. They have limited knowledge of the importance of big data customer insights for making concrete market segmentation decisions. The purpose of this study was to...

Recent developments in big data analytics (BDA) enable financial institutions to optimize their decision-making processes. However, many BDA projects fail due to a lack of understanding of the value related to the realisation process. Guided by affordance actualisation theory, this article uses...

Urban eco-environmental management is a key focus in the modernization of national governance systems and governance capacity. Advanced information technology should be used to identify ecological problems in urban areas accurately while enhancing the environmental management capacity to promote...

This paper presents a framework for collecting large datasets of hotel reviews (e.g., from Booking.com and TripAdvisor) and performing useful analytics from the data collected. This approach automates data collection, reduces manual effort, enhances data cleaning, and standardizes data...

Small and medium-sized enterprises (SMEs) face unique challenges that can be effectively addressed through the adoption of data mining and business intelligence (BI) tools. This systematic literature review scrutinizes the deployment and efficacy of BI and data mining technologies across SME...