Artificial Intelligence: the Application of Machine Learning and Predictive Analytics in Credit Risk

Multiple Sclerosis (MS) is a chronic neuroinflammatory disease of the Central Nervous System (CNS) in which the body's immune system attacks and destroys the myelin sheath that protects nerve fibers, leading to a wide range of debilitating symptoms and causing disruption of axonal signal transmission. Accurate prediction, diagnosis, monitoring and treatment (PDMT) of MS are essential to improve patient outcomes. Recent advances in neuroimaging technologies, particularly electroencephalography (EEG), combined with machine learning (ML) techniques - including Deep Learning (DL) models - offer promising avenues for enhancing MS management. This systematic review synthesizes existing research on the application of ML and DL models to EEG data for MS. It explores the methodologies used, with a focus on DL architectures such as Convolutional Neural Networks (CNNs) and hybrid models, and highlights recent advancements in ML techniques and EEG technologies that have significantly improved MS diagnosis and monitoring. The review addresses the challenges and potential biases in using ML-based EEG analysis for MS. Strategies to mitigate these challenges, including advanced preprocessing techniques, diverse training datasets, cross-validation methods, and explainable Artificial Intelligence (AI), are discussed. Finally, the paper outlines potential future applications and trends in ML for MS management. This review underscores the transformative potential of ML-enhanced EEG analysis in improving MS management, providing insights into future research directions to overcome existing limitations and further improve clinical practice.

This study explores the application of artificial intelligence (AI) and machine learning (ML) in predicting high school student performance during the transition to university. Recognizing the pivotal role of academic readiness, the study emphasizes the need for tailored interventions to enhance student success. Leveraging a dataset from Portuguese high schools, the research employs a comparative analysis of six ML algorithms—linear regression, decision tree, support vector regression, k-nearest neighbors, random forest, and XGBoost—to identify the most effective predictors. The dataset encompasses diverse attributes, including demographic details, social factors, and school-related features, providing a comprehensive view of student profiles. The predictive models are evaluated using R-squared, Root Mean Square Error, and Mean Absolute Error metrics. Results indicate that the Random Forest algorithm outperforms others, displaying high accuracy in predicting student performance. Visualization and residual analysis further reveal the model's strengths and potential areas for improvement, particularly for students with lower grades. The implications of this research extend to educational management systems, where the integration of ML models could enable real-time monitoring and proactive interventions. Despite promising outcomes, the study acknowledges limitations, suggesting the need for more diverse datasets and advanced ML techniques in future research. Ultimately, this work contributes to the evolving field of educational AI, offering practical insights for educators and institutions seeking to enhance student success through predictive analytics.

Technologically advanced artificial intelligence (AI) is making a significant contribution to strengthening national security. AI algorithms facilitate the processing of vast amounts of information, increasing the speed and accuracy of decision-making. Artificial intelligence and machine learning (AI/ML) are crucial for state and integrated hybrid attacks and protecting new threats in cyberspace. Existing AI capabilities have significant potential to impact national security by leveraging existing machine learning technology for automation in labor-intensive activities such as satellite imagery analysis and defense against cyber attacks. This article examines selected aspects of the impact of artificial intelligence on enhancing a state’s ability to protect its interests and its citizens., artificial intelligence through the use of neutron networks, predictive analytics and machine learning algorithms enables security agencies to analyse vast amounts of data and identify patterns indicative of potential threats. Integrating artificial intelligence into surveillance, border control and threat assessment systems enhances the ability to respond preemptively to security challenges. In addition, artificial intelligence algorithms facilitate the processing of vast amounts of information, increasing the speed and accuracy of decision-making by police authorities. The rapid development of AI raises a number of questions for its use in securing not only national security but protecting all citizens. In particular, it is worth answering the question How does artificial intelligence affect national security and clarifying the issue of how law enforcement agencies can use artificial intelligence to maximise the benefits of the new technology in terms of security and protecting communities from rising crime. The analysis is based on a descriptive method in describing the phenomenon; by explaining the concepts and applications of artificial intelligence to determine its role in the national security sphere. An analysis of the usefulness of artificial intelligence in particular in police operations is undertaken, with the aim of defending the thesis that, despite some threats to the protection of human rights from AI, it is becoming the best tool in the fight against all types of crime in the country. Technological advances in AI can also have many positive effects for law enforcement, and useful for law enforcement agencies, for example in facilitating the identification of persons or vehicles, predicting trends in criminal activities, tracking illegal criminal activities or illegal money flows, flagging and responding to fake news. Artificial intelligence (AI) has emerged as one of the biggest threats to information security, but efforts are being made to mitigate this new threat, but also to find solutions on how AI can become an ally in the fight against cyber-security, crime and terrorist threats. Artificial intelligence algorithms search huge datasets of communication traffic, satellite images and social media posts to identify potential cyber security threats, terrorist activities and organized crime. It is advisable, when analyzing the opportunities and threats that AI poses to national and public security, to gain a strategic advantage in the context of rapid technological change and also to manage the many risks associated with AI. The conclusion highlights the impact of AI on national security, creating a range of new opportunities coupled with challenges that government agencies should be prepared for in addressing ethical and security dilemmas. Furthermore, AI improves predictive analytics, thereby enabling security agencies to more accurately anticipate potential threats and enhance their preparedness by identifying vulnerabilities in the national security infrastructure

Cancer remains a leading cause of morbidity and mortality worldwide, necessitating innovative approaches for early detection and effective management. Predictive analytics, powered by machine learning (ML) and artificial intelligence (AI), offers a transformative potential in cancer care by integrating multi-source health data for early diagnosis, risk assessment, and personalized treatment. This presents a predictive analytics framework that leverages diverse data sources, including electronic health records (EHRs), genomic and biomarker data, medical imaging, wearable sensors, and lifestyle/environmental factors. By combining these heterogeneous data streams, AI models can identify high-risk individuals, detect cancer at its earliest stages, and optimize treatment strategies based on patient-specific insights. The framework employs advanced ML techniques, such as supervised learning models (Random Forests, Support Vector Machines), deep learning architectures (Convolutional Neural Networks, Recurrent Neural Networks), and Natural Language Processing (NLP) for analyzing clinical notes. Federated learning is also explored as a privacy-preserving approach to facilitate secure data sharing across institutions while maintaining compliance with regulatory standards such as HIPAA and GDPR. The integration of predictive analytics with telemedicine platforms and AI-driven clinical decision support systems further enhances real-time monitoring and personalized patient care. Despite its promise, challenges such as data privacy concerns, heterogeneity in multi-source data integration, model interpretability, and computational scalability must be addressed to ensure the effective implementation of AI-driven cancer diagnostics. Future advancements in Explainable AI (XAI), blockchain for secure data exchange, and multi-modal data fusion will further strengthen predictive analytics in oncology. This study underscores the critical role of big data and AI in revolutionizing cancer detection and management, paving the way for improved patient outcomes and more efficient healthcare delivery.

This paper presents a systematic review of studies related to Artificial Intelligence (AI) and Machine Learning (ML) applications in Supply Chain Management (SCM). Our systematic search studied 109 journal articles published between 2007 and 2025. It was presented definitions for AI, ML, and SCM and a bibliometric analysis was carried out to examine trends and patterns in the existing literature. The 109 articles were grouped into five clusters based on keywords and abstracts, identifying promising research areas and proposes directions for future investigations. The current literature on AI and ML in SCM is expanding, with an increasing number of publications and growing diversity in applications, although significant gaps remain warranting attention. Various AI and ML techniques, including supervised learning algorithms, neural networks, and optimization techniques, have been studied in scenarios such as demand forecasting, inventory management, and risk analysis. Additionally, AI and ML methods are expected to gain broader acceptance in SCM scenarios, such as, application of Generative AI (GenAI) and Large Multimodal Models (LMMs); Quantum ML techniques to handle with large SCM data; to implement SHAP (SHapley Additive exPlanations) for demand forecasting and fraud detection; and to use Generative AI models (like, GPT-4) to generate synthetic supply chain scenarios.

Deep underground rock formations are widely used for geological carbon dioxide (CO2) storage due to their large-scale, long-term capacity. However, geophysical and petrophysical complexities can lead to challenges such as gas migration and potential leaks, posing risks to groundwater and subsurface systems. Recent advancements increasingly integrate Artificial Intelligence (AI) and Machine Learning (ML) to mitigate these risks and enhance CO2 storage efficiency. This review explores ML applications in geological CO2 storage, highlighting recent advancements and their implications. ML has demonstrated effectiveness in enhancing CO2 storage efficiency. However, the complexities of geological storage necessitate further improvements in ML model applicability, particularly in real-world projects. Since ML models depend on the availability of data, ensuring high data integrity and quality is crucial. Moreover, CO2 storage projects involve significant risks and uncertainties, making advanced probabilistic ML models essential for quantifying uncertainties and mitigating associated risks. Lastly, integrating real-time monitoring systems with sensor data and ML algorithms can enhance anomaly detection, provide early warnings, and enable timely interventions. Addressing these challenges will strengthen the adoption of advanced ML techniques in geological CO2 storage, improving efficiency, safety, and reliability.

Purpose This study aims to examine the potential effect that corporate social responsibility (CSR) practices in general and business ethics, more specifically, can exert on the financial performance of companies using advanced machine learning (ML) techniques. Design/methodology/approach Based on a sample of 523 international firms headquartered in North America and Western Europe between 2005 and 2019, the research design employs advanced machine learning algorithms including LightGBM, CatBoost, XGBoost, NGBoost, Support Vector Machine, and Random Forest to assess the relationship between business ethics, CSR practices, and financial performance. In addition, the SHapley Additive exPlanations (SHAP) technique was used to provide comprehensive insights into feature contributions to individual predictions. Findings Findings indicate a growing interest in business ethics and societal practices over the past decade. The results have revealed that the adoption of an ethical and socially responsible approach is associated with financial performance. Practical implications This study provides actionable insights for multinational companies, emphasizing the role of business ethics and social responsibility practices in enhancing financial performance. It also holds significance for practitioners aiming to improve financial efficiency. By exploring the relationship between social effectiveness and financial efficiency, this research leverages ML techniques to offer a deeper understanding of this critical connection. Originality/value This study contributes to the field by using advanced ML techniques, including SHAP, to explore the relationship between business ethics and CSR practices on Financial Performance. The findings, particularly the superior accuracy of XGBoost among algorithms, provide valuable insights into the implications of business ethics and social practices on firm financial performance.

Purpose This study aims to enhance cryptocurrency price and trend prediction by applying advanced machine learning (ML) techniques. Given the market’s high volatility and complexity, the research identifies effective models for different conditions, providing insights for investors and risk management. Design/methodology/approach This study proposes a six-stage framework for cryptocurrency price prediction, integrating advanced ML techniques. Data from ten cryptocurrencies are processed, extracting 37 key features, including return, the Fear and Greed Index and various technical indicators. The model employs Deep Q-Networks (DQN), Long Short-Term Memory (LSTM) and multiple regression methods such as linear regression, support vector regression, ridge, LASSO, decision tree, Random Forest, multi-layer perceptron, stochastic gradient descent, elastic net and Bayesian regression. Model performance is evaluated using trading strategies and metrics like accuracy, sensitivity, recall, MSE, MAE and F1-score. Findings The results indicate that complex models like DQN and LSTM excel in volatile markets due to their ability to capture intricate price patterns, whereas simpler models such as linear regression and ridge regression perform better in stable conditions. The multi-layered parallel design enhances computational efficiency, enabling independent asset evaluation. These findings highlight the potential of artificial intelligence in improving prediction accuracy and supporting informed investment decisions. Originality/value This research introduces a novel six-stage ML framework incorporating diverse predictive models and key features for cryptocurrency forecasting. The multi-layered parallel approach enhances computational efficiency, setting this study apart from existing research. The comparative analysis of models offers valuable guidance for investors, traders and financial analysts navigating volatile cryptocurrency markets.

The drilling process can result in irregular measurements due to unconsolidated geological formations, affecting the accuracy of wireline logging devices. This impacts the precision of elastic log measurements, such as velocity and density profiles, which are essential for reservoir characterization. The reliability of the wireline-logging tool is crucial in preventing inaccuracies when assessing reservoir rock properties. Previous studies have focused on applying machine learning (ML) techniques to wireline logging, but these methods have limited applicability, particularly for outlier detection and log reconstruction. In response, this study integrates both supervised and unsupervised ML techniques to enhance the accuracy of elastic log responses in reservoir characterization. Initially, density-based spatial clustering of applications with noise was applied for outlier detection, followed by feature selection to identify correlated logs for reconstructing the density log. A random forest regression model, optimized with particle swarm optimization (PSO), was then trained using the selected features. The comparative analysis showed a significant improvement in porosity estimation from the reconstructed density log compared to core data. Specifically, the comparison between core and original bulk density porosity yielded an R2 of 0.95 and a root mean squared error (RMSE) of 0.012. In contrast, the comparison between core and the rebuilt density log porosity resulted in an R2 of 0.98 and an RMSE of 0.007. The integration of advanced ML techniques with PSO-optimized random forest models represents a considerable advancement in the field of reservoir characterization. This approach enhances accuracy but also saves time and reduces manual effort, highlighting considerable potential for the advancement of methods in petroleum exploration and production.

Chemical heuristics have been fundamental to the advancement of chemistry and materials science. These heuristics are typically established by scientists using knowledge and creativity to extract patterns from limited datasets. Machine learning offers opportunities to perfect this approach using computers and larger datasets. Here, we discuss the relationships between traditional heuristics and machine learning approaches. We show how traditional rules can be challenged by large-scale statistical assessment and how traditional concepts commonly used as features are feeding the machine learning techniques. We stress the waste involved in relearning chemical rules and the challenges in terms of data size requirements for purely data-driven approaches. Our view is that heuristic and machine learning approaches are at their best when they work together.

Structural health monitoring is an indispensable procedure that is to be carried out to evaluate the serviceability of existing structures. Non-destructive testing methods are attaining increasing popularity for the assessment of concrete strength due to the ease of operation and reliability of the results. The application of machine learning in the field of engineering has increased rampantly. In this study, the compressive strength of concrete has been forecasted using support vector regression which is a machine learning technique. Artificial intelligence is nothing but the potential to impersonate human intelligence. The advantage of artificial intelligence over human intelligence is the absence of human errors that can arise due to various factors which might decrease the accuracy of results. Rebound hammer (RBH), Windsor probe penetration (WPP) and ultra-sonic pulse velocity(USV) are the non-destructive testing that has been employed to assess the concrete compressive strength. The use of multiple non-destructive testing methods than single testing methods has improved the accuracy of prediction model. Further the results of different combination models were compared using coefficient of determination which is a commonly used statistical parameter for prediction accuracy comparison. The prediction model is in accordance with the experimental results obtained. And the accuracy of prediction indicates that support vector regression can be successfully used for predicting the compressive strength of concrete.

With the popularization of machine learning (ML) techniques and the increased chipset’s performance, the application of ML to pedestrian localization systems has received significant attention in the last years. Several survey papers have attempted to provide a state-of-the-art overview, but they usually limit their scope to a particular type of positioning system or technology. In addition, they are written from the point of view of ML techniques and their practice, not from the point of view of the localization system and the specific problems that ML techniques can help to solve. This article is intended to offer a comprehensive state-of-the-art survey of the ML techniques that have been adopted over the last ten years to improve the performance of pedestrian localization systems, addressing the applicability of ML techniques in this domain, along with the main localization strategies. It concludes by indicating the underlying open issues and challenges associated with the existing systems, and possible future directions in which ML techniques could improve the performance of pedestrian localization systems. Among other open issues, most previous authors have focused their attention on position estimation accuracy, which wastes the potential of ML techniques to improve other performance parameters (e.g., response time, computational complexity, robustness, scalability or energy efficiency). This study shows that there is a strong trend towards the application of supervised learning. Consequently, there are many potential research opportunities in the use of other learning types, such as unsupervised and reinforcement learning, to improve the performance of pedestrian localization systems.

Background: The field of sports science has been fundamentally transformed by the integration of artificial intelligence (AI) technologies. AI has enabled advances in areas such as performance optimization, training personalization, injury risk assessment and prevention, talent recognition, rehabilitation, athlete monitoring, and wellness optimization. Objective: This review aims to explore the diverse impact of AI in sports science, highlighting advances in AI techniques, the challenges and limitations of integrating AI tools, and the emerging role of AI chatbots in shaping the future of sports research and applications. Methods: The review examines the existing literature on the application of machine learning, deep learning and other AI techniques in various aspects of sports science research and practice. It provides an overview of how these technologies have been used to improve personalized training programs, video analysis, injury risk prediction, talent identification, and rehabilitation. Results: The article describes how AI-powered tools and techniques have revolutionized sports science, enabling personalized, data-driven and efficient approaches to performance optimization. Sophisticated machine learning algorithms such as artificial neural networks, decision trees and support vector machines have been used to develop predictive models for injury risk assessment and prevention, leading to improvements in athletes' wellbeing and long-term performance. AI-driven talent identification and selection processes have also shown promise in recognizing exceptional athletes with greater accuracy. In addition, the integration of AI into athlete monitoring and rehabilitation has led to greater personalization, better decision making and faster return to play. Conclusion: The integration of AI and machine learning techniques into sports science has the potential to transform the field and lead to improved athlete performance, reduced risk of injury, improved well-being, and more personalized and effective interventions. The emergence of AI chatbots expands the applications of these technologies in sports science and offers new opportunities to streamline research processes, provide personalized advice to athletes and support sports medicine and rehabilitation

Liberalized electricity markets have been studied for the past few decades with different mathematical techniques. Operating these markets under the growing uncertainties has been challenging in many jurisdictions. Given the recent advances in machine learning techniques and big-data analysis, applications of such techniques have been growing in recent years. In this paper we review state-of-the-art developments of machine learning techniques and their applications in electricity market studies. We briefly provide current market challenges around the world. Then we show how these challenges are addressed using different machine learning approaches. Later, we provide a comparative table where all relevant papers are compared. This table can guide future studies on the machine learning applications in electricity markets by highlighting the promising potential areas. Then, we suggest directions for future researches to pursue machine learning applications in electricity market. Consequently, these approaches can be employed to resolve the uncertainties.

Machine learning (ML) is revolutionizing industries by providing innovative solutions that drive sustainable economic growth and market efficiency. The integration of ML techniques in various sectors, including finance, healthcare, manufacturing, and energy, offers significant potential for enhancing productivity, optimizing resource allocation, and fostering innovation. This review explores the transformative impact of ML on economic growth and market efficiency, emphasizing its role in predictive analytics, decision-making, and automation. In finance, ML algorithms are employed to predict market trends, manage risks, and detect fraudulent activities. By analyzing vast amounts of data in real-time, ML models enable financial institutions to make informed decisions, reduce operational costs, and improve customer experiences. In healthcare, ML applications range from diagnosing diseases and predicting patient outcomes to optimizing treatment plans and managing healthcare resources, thereby improving patient care and reducing costs. The manufacturing sector benefits from ML through predictive maintenance, quality control, and supply chain optimization. ML models analyze data from sensors and production lines to predict equipment failures, ensuring timely maintenance and minimizing downtime. This leads to increased productivity and reduced waste, contributing to economic sustainability. Energy management is another critical area where ML drives efficiency. ML algorithms optimize energy consumption, forecast renewable energy production, and enhance grid management. By integrating ML into energy systems, organizations can reduce their carbon footprint and operational costs while promoting sustainable energy practices. Moreover, ML enhances market efficiency by enabling businesses to understand consumer behavior and preferences. Retailers use ML to personalize marketing strategies, optimize pricing, and manage inventory effectively. This not only boosts sales but also improves customer satisfaction and loyalty. Despite the significant benefits, the adoption of ML presents challenges such as data privacy concerns, algorithmic bias, and the need for skilled professionals. Addressing these challenges requires robust regulatory frameworks, ethical guidelines, and investment in education and training. In conclusion, harnessing ML techniques is pivotal for driving sustainable economic growth and market efficiency. By leveraging ML's capabilities in predictive analytics, decision-making, and automation, industries can achieve higher productivity, optimized resource utilization, and innovative solutions that contribute to long-term economic sustainability and competitiveness. As ML continues to evolve, its potential to transform various sectors and promote sustainable development remains immense.