An ESG-Compliant Framework for Fraud Detection in Online Payments Using Data Privacy and Machine Learning

Coastal aquifers are vital for sustaining ecosystems and providing freshwater for agriculture and domestic use. However, rising groundwater salinity in these regions demands innovative approaches for accurate modeling and prediction. In recent decades, machine learning (ML) and deep learning (DL) techniques have been extensively used across various water resource management fields, often yielding promising outcomes. This review examines the growing usage of ML and DL techniques in modeling groundwater salinity in coastal aquifers. The analysis is based on 104 peer-reviewed journal articles including the review articles indexed in PubMed, ScienceDirect, Google Scholar, Scopus, and SpringerLink using keywords like "saltwater intrusion", "machine learning", “deep learning”, and "groundwater salinity". The review discusses recent advancements, challenges, and future directions for improving ML model accuracy and applicability. Studies reveal an increasing reliance on ML techniques to predict groundwater salinity and manage seawater intrusion across various coastal geological settings. Researchers have employed a range of ML models, from traditional methods like Artificial Neural Networks and Adaptive Neuro Fuzzy Inference Systems to advanced DL-based models like Convolutional Neural Networks and Long Short-Term Memory Networks, often using ensemble methods to enhance accuracy. Key findings include the top-tier output of ensemble and optimized models, including Random Forest and Grasshopper Optimization Algorithm-XGBoost. These studies highlight the importance of model selection, input variable ranking, and computational efficiency, demonstrating ML's global relevance in environmental management. The review highlights the prospects of ML, DL, and ensemble models to redefine groundwater management in coastal aquifers, offering more effective and sustainable solutions to the challenges posed by groundwater salinity and saltwater intrusion. Future advancements will focus on further integrating DL, explainable artificial intelligence, and ensemble approaches, along with other innovative techniques, to explore sparsely studied variables, model new or unique study areas, and apply ML methods for managing groundwater salinity.

Techno-economic estimation of a non-cover box solar still with thermoelectric and antiseptic nanofluid using machine learning models

The escalating threat of fraud in financial institutions is a global issue, with the Malaysian sector being no exception. This study focuses on the implementation and efficacy of Data Mining and Machine Learning methodologies in identifying and mitigating fraudulent activities within these institutions. The paper critically reviews existing literature, bridging the gap between advanced technology application and fraud management. Fraudulent transactions in the financial sector are dynamic and sophisticated, requiring advanced detection techniques. Traditional approaches often struggle to manage this complexity effectively, demonstrating a need for more advanced and adaptive strategies. This is where Data Mining and Machine Learning techniques, renowned for their predictive and analytical prowess, can significantly contribute. Data Mining, the process of uncovering patterns and correlations within large datasets, is a useful tool for detecting anomalies that may suggest fraud. The study assesses various data mining techniques, such as clustering, classification, and association, and explores their application in detecting fraudulent transactions. Findings indicate that these techniques can substantially enhance fraud detection rates while minimizing false positives. Furthermore, Machine Learning, an artificial intelligence subset, has shown immense potential in fraud detection. Its ability to learn from and make decisions based on data makes it a viable solution for fraud detection. This paper explores both supervised and unsupervised learning algorithms and their efficacy in identifying fraud in the Malaysian financial sector. Results suggest that machine learning models, when correctly implemented, can significantly improve the accuracy of fraud detection. The review underscores the importance of employing advanced technologies like Data Mining and Machine Learning to combat financial fraud effectively. It also suggests future research directions, emphasizing the need for context-specific, localized models considering Malaysia's unique socio-economic environment. Moreover, the development of hybrid models, integrating both data mining and machine learning, could offer improved results. In conclusion, this study sets a precedent for further exploration into the application of advanced analytical tools in fraud detection in the Malaysian financial sector. The potential these technologies offer for improving accuracy and adaptability in fraud detection systems is substantial and warrants thorough investigation. Keywords: Fraud Detection, Malaysian Financial Institutions, Data Mining, Machine Learning, Financial Fraud Management

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.

Fraud detection has become a critical concern for financial institutions seeking to safeguard their assets and maintain client trust in an increasingly digitized financial landscape. This study examines the application of machine learning (ML) techniques to enhance fraud detection systems within financial institutions. By leveraging computational algorithms and data analytics, organizations can identify patterns and anomalies in transaction data that conventional rule-based approaches often fail to detect. The efficacy of multiple ML paradigms, including supervised, unsupervised, and reinforcement learning, in identifying fraudulent activities is evaluated through a systematic review of existing literature and comparative analysis of model performance across benchmark datasets. The study highlights the critical role of feature engineering and data preprocessing in building robust ML models, as the quality of input data significantly influences predictive accuracy. The integration of real-time data processing, which enables organizations to respond to emerging threats promptly, is also examined. Key challenges are discussed, including high false positive rates, class imbalance inherent in fraud datasets, and the necessity for continuous model adaptation to track evolving fraud patterns. The findings indicate that ML-based approaches not only improve fraud detection rates but also enhance operational efficiency and customer satisfaction. This paper serves as a foundational reference for practitioners and researchers aiming to advance the application of machine learning for fraud detection in the financial sector.

e17570 Background: Ovarian cancer(OC) is the leading cause of death from gynecologic malignancy. Current challenges include lack of diagnostic tools, predictive biomarkers, and identifying appropriate surgical candidates. Machine learning(ML) is an emerging field that can make accurate projections by making inferences on data and may play a crucial role in OC.The objective of the current study was to review the literature on application of ML in OC and report the most commonly used algorithms and their performance in comparison to existing prediction tools and traditional regression models. Methods: This is a systematic review of published literature from January 1985 to March 2021 on the use of ML in OC. An extensive search of electronic library databases was conducted. Four independent reviewers screened the articles initially by title then full text. Quality was assessed using the MINORS criteria. P-values were generated using the Pearson’s Chi-squared(x2) test to compare performance of ML models with traditional statistics. No p-values were reported if only one study was available. Results: Among 4,295 articles screened, 88 studies on ML in OC were included. The mean age of OC patients was 54.7 years(11-90) and the most common stages at diagnosis were:Stage III (39.9%) and IV (34%). Applications of ML were in clinical datasets(33%, n = 29), preoperative diagnostics(30.7%, n = 27), serum biomarkers (21.6%, n = 19), genomics (12.5%, n = 11), and prediction of cytoreductive outcomes (2.3%, n = 2). The most commonly applied algorithms were Support Vector Machine [SVM](28%, n = 33)and Neural Networks[NN] (25.28%). Over the past decades, the number of publications on ML in OC increased three-fold from 20(1994-2010) to 67 (2011–2021). Only 9 (10%) studies compared ML techniques with existing prediction tools, or traditional regression models. Among 29 clinical dataset studies, 4 compared ML with traditional logistic regression(LR). Two studies reported better performance with ML compared to LR but not significant(accuracy: 0.88 vs 0.84, p = 0.15), one study performed comparably(accuracy: 0.1 vs 0.1) while one study performed worse(accuracy: 0.1 vs 0.97). Only one preoperative diagnostic study compared ML techniques with LR. SVM classifiers outperformed LR in classifying ovarian masses as benign or malignant(sensitivity: 0.88 vs. 0.70). One serum biomarker study compared LR with ML algorithms; LR performed better using two biomarkers for predicting OC(accuracy: 0.97 vs. 0.94). Among five studies reporting overall survival outcomes, only one study compared survival ML techniques using NN with LR and showed that NN classifiers outperformed LR in predicting overall survival(AUC: 0.72 vs. 0.62). Conclusions: This is the first systematic review exploring the literature on ML algorithms in OC. Most ML models outperformed traditional models. However, larger datasets would be required to validate findings.

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.

The increasing integration of telecommunications with financial services has brought about significant advancements in the accessibility and efficiency of financial transactions. However, this convergence has also led to a rise in fraudulent activities, posing substantial risks to both service providers and users. The application of machine learning (ML) in detecting fraud within telecommunication-based financial transactions offers a promising solution to these challenges. This abstract explores the potential of ML techniques to enhance the detection and prevention of fraud in this domain. Machine learning algorithms, particularly those specializing in anomaly detection, pattern recognition, and predictive modeling, are well-suited to identifying fraudulent activities in real-time. These algorithms can analyze vast amounts of transaction data to detect irregularities that may indicate fraud, such as unusual transaction patterns, deviations from normal behavior, and other red flags that traditional rule-based systems might overlook. By continuously learning from new data, ML models can adapt to emerging fraud tactics, making them highly effective in a rapidly evolving threat landscape. Furthermore, the integration of ML with big data analytics allows for the processing and analysis of large-scale transactional data, enhancing the accuracy and speed of fraud detection. Techniques such as supervised learning, unsupervised learning, and reinforcement learning are particularly effective in categorizing transaction types and identifying potential frauds with minimal human intervention. The use of ML also enables the automation of fraud detection processes, reducing operational costs and increasing the efficiency of fraud management systems. This abstract highlights the critical role of machine learning in enhancing the security of telecommunication-based financial transactions. The ability of ML to detect and prevent fraud in real-time not only mitigates risks but also improves trust and reliability in telecommunication financial services. As fraudsters continue to develop sophisticated methods, the ongoing refinement of ML algorithms will be essential in maintaining robust defenses against financial fraud in the telecommunication sector. Keywords: ML, Detecting, Fraud, Telecommunication-Based, Financial Transactions.

The application of machine learning (ML) techniques for understanding and predicting organic matter (OM) and harmful algal blooms (HABs) in freshwater systems has increased significantly with the availability of abundant data and advanced monitoring technologies. However, there is a lack of comprehensive reviews concentrating on practical applications and delving into the potential risks associated with misrepresentation or inflation in constructing ML models. This review aims to bridge these gaps by providing a comprehensive overview of various aspects of ML applications in the context of OM and HABs in freshwater systems. It covers practical ML applications for rapid assessment, early warning, and driver analysis, highlighting the diverse range of techniques employed in these areas. Furthermore, it discusses the challenges and considerations associated with data handling, including using in situ and remote sensing data and the importance of appropriate data-splitting techniques to avoid data leakage. To ensure unbiased and reproducible results, this review offers recommendations for model improvement, such as utilizing explainable ML techniques to gain insights into model behavior and avoiding overreliance on a single ML algorithm. It also emphasizes the significance of deploying ML models through user-friendly interfaces, enabling non-experts in ML to effectively utilize these models in real-world water environments.

A suboptimal management or system malfunction can often lead to abnormal energy consumptions in buildings, which result in a significant waste of energy. For this reason, the adoption of advanced monitoring systems, based on Machine Learning (ML) and visualization techniques, is crucial to avoid possible deviations from the baseline energy consumption. However, the historical data on which analyses are based generally do not report the occurrence of anomalies. Therefore, the application of supervised ML techniques is limited and unsupervised approaches are favored. Moreover, domain experts find most Machine Learning (ML) techniques hard to interpret, and thus find it difficult to contextualize anomalies. To overcome these issues, this work proposes a machine learning-based Anomaly Detection Framework (ADF) that involves the use of two complementary semi-supervised ML applications to obtain a highly interpretable and accurate detection of anomalies. Both techniques use Symbolic Aggregate approXimation (SAX) encoding to extract the most relevant information from load profiles. The aim of the first approach is to maximize the interpretability of the definition and distinction between anomalous and normal behavior. This is achieved using a Classification And Regression Tree (CART), albeit at the expense of a coarser output granularity. The second approach exploits an Multi-Layer Perceptron (MLP) algorithm to obtain a higher and more accurate output resolution, although it leads to a less interpretable definition of any anomalous behavior. The ADF has been applied to a real case study using electricity consumption data provided by a large telecommunications service provider. The results show that combining both ML models enhances the accuracy and interpretability of the detected anomalies.

Background: Application of Machine Learning (ML) techniques in the prediction of Neurological Disorders has been growing rapidly. ML techniques require novel computer programming skills along with domain knowledge to produce a useful tool. We used Google Auto ML and Google ML Tables, a Cloud based front-end tools to develop, validate and deploy a prognostic model for Intracerebral Hemorrhage (ICH) without a single line of programming code. Methods: Data from Kasturba Medical College (KMC) Hospital ICH Stroke Registry between January 2015 and May 2019 was accessed. Google Auto ML Tables splits the dataset into training, validation and testing sets. 80% of the dataset was used for training, 10% for validation, and 10% for testing. Cramers V correlation statistic ( ϕ c ) between 30 variables and outcome was used to select 20 variables to predict the dichotomous outcome of good (mRS 0-3) vs bad outcome (mRS 4-6). The ML model generated was evaluated using Area Under the Curve (AUC) and Receiver Operating Characteristic (ROC) measures and by confusion/error matrix. Logistic Regression model was developed in parallel using Wizard Pro with the same set of 20 variables. Results: 1000 patients from KMC Registry were analysed. Cramers V correlation was highest for Glasgow Coma Scale (GCS) on admission ( ϕ c =0.6), followed by hematoma volume ( ϕ c =0.57). The AUC ROC of the model for good and bad outcome was 0.89 with an accuracy of 85.6%. Confusion matrix predicted 84% patients with good outcome and 87% with poor outcome. Feature importance chart showed that GCS had the highest relative importance at 25.7, followed by hematoma volume and age. A similar model created with simple logistic regression yielded a ROC of 0.87 with comparable results to the machine learning platform. Conclusion: Useful ML models can be easily developed and implemented by clinicians possessing domain expertise using Google Auto ML without computer programming experience.

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.

Systematic literature review of machine learning based software development effort estimation models

Aim: The majority of machine learning (ML) models in healthcare are built on retrospective data, much of which is collected without explicit patient consent for use in artificial intelligence (AI) and ML applications. The primary aim of this study was to evaluate whether clinicians and scientific researchers themselves consent to provide their own data for the training of ML models. Materials and Methods: This survey was conducted through an anonymous online survey, utilizing platforms such as Telegram, LinkedIn, and Viber. The target audience comprised specific international groups, primarily Russian, German, and English-speaking, of clinicians and scientific researchers. These participants ranged in their levels of expertise and experience, from beginners to veterans. The survey centered on a singular, pivotal question: “Do You Consent to the Use of Your Biological and Private Data for Training Machine Learning and AI Models?” Respondents had the option to choose from three responses: “Yes” and “No”. Results: The survey was conducted in January 2024. A total of 119 unique and verified individuals participated in the survey. The results revealed that only 50% of respondents (63 persons) expressed consent to provide their own data for the training of ML and AI models. Conclusion: In the development of ML and AI models, particularly open-source ones, it is crucial to ascertain whether participants are willing to provide their private data. While ML algorithms can transform the nature of data, it is important to remember that the primary owner of this data is the individual. Our findings show that in 50% of the cases, even participants from scientific research and clinical backgrounds – individuals typically accountable for ensuring data quality in AI and ML model development – do not consent to the use of their data in AI and ML settings. This highlights the need for more stringent consent processes and ethical considerations in the utilization of personal data in AI and ML research.

A comprehensive review on detection of plant disease using machine learning and deep learning approaches