A multi-layer machine learning approach for cryptocurrency trading utilizing technical indicators and sentiment index

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.

Daily scale streamflow forecasting in multiple stream orders of Cauvery River, India: Application of advanced ensemble and deep learning models

<p>Machine Learning (ML) methods have seen widespread adoption in seismology in recent years. The ability of these techniques to efficiently infer the statistical properties of large datasets often provides significant improvements over traditional techniques when the number of data are large (>millions of examples). With the entire spectrum of seismological tasks, e.g., seismic picking and detection, magnitude and source property estimation, ground motion prediction, hypocentre determination; among others, now incorporating ML approaches, numerous models are emerging as these techniques are further adopted within seismology.</p><p>To evaluate these algorithms, quality controlled benchmark datasets that contain representative class distributions are vital. In addition to this, models require implementation through a common framework to facilitate comparison. Accessing both benchmark datasets, and integrating models built in such varying frameworks is currently a time-consuming process, hindering further advancement of ML techniques within seismology. These development bottlenecks also affect 'practitioners' seeking to deploy the latest models on seismic data, who may not want to necessarily learn entirely new ML frameworks to perform this task.</p><p>We present SeisBench as a software package to tackle these issues. SeisBench is an open-source framework for deploying ML in seismology. SeisBench standardises access to both models and datasets, whilst also integrating a range of common processing and data augmentation operations through the API. Through SeisBench, users can access several seismological ML models and benchmark datasets available in the literature via a single interface. SeisBench is built to be extensible, with community involvement encouraged to expand the package. Having such frameworks available for accessing leading ML models forms an essential tool for seismologists seeking to iterate and apply the next generation of ML techniques to seismic data.</p>

Machine learning in computational docking.

The precise identification of hydrocarbon-rich zones is crucial for optimizing exploration and production processes in the oil industry. Magnetotelluric (MT) surveys play a fundamental role in mapping subsurface geological structures. This study presents a novel methodology for automatically delineating resistivity contrasts in MT models by employing advanced machine learning and computer vision techniques. This approach commences with data augmentation to enhance the diversity and volume of resistivity data. Subsequently, a bilateral filter was applied to reduce noise while preserving edge details within the resistivity images. To further improve image contrast and highlight significant resistivity variations, contrast-limited adaptive histogram equalization (CLAHE) was employed. Finally, k-means clustering was utilized to segment the resistivity data into distinct groups based on resistivity values, enabling the identification of color features in different centroids. This facilitated the detection of regions with significant resistivity contrasts in the reservoir. From the clustered images, color masks were generated to visually differentiate the groups and calculate the area and proportion of each group within the pictures. Key features extracted from resistivity profiles were used to train unsupervised learning models capable of generalizing across different geological settings. The proposed methodology improves the accuracy of detecting zones with oil potential and offers scalable applicability to different datasets with minimal retraining, applicable to different subsurface environments. Ultimately, this study seeks to improve the efficiency of petroleum exploration by providing a high-precision automated framework with segmentation and contrast delineation for resistivity analysis, integrating advanced image processing and machine learning techniques. During initial analyses using only k-means, the resulting optimal value of the silhouette coefficient K was 2. After using bilateral filtering together with contrast-limited adaptive histogram equalization (CLAHE) and validation by an expert, the results were more representative, and six clusters were identified. Ultimately, this study seeks to improve the efficiency of petroleum exploration by providing a high-precision automated framework with segmentation and contrast delineation for resistivity analysis, integrating advanced image processing and machine learning techniques.

This survey paper explores the application of machine learning (ML) techniques in the domain of workplace injuries, focusing on three key areas: risk prediction, return to work (RTW) strategies, and demographic analysis. Through an extensive review of literature from January 2015 to July 2024, this paper examines the latest advancements in ML-driven approaches to workplace safety and identifies important research gaps. This paper highlights how classical ML techniques, such as ensemble models and decision trees, have become essential tools for identifying workplace injury risks, enabling more accurate interventions. It emphasizes the importance of leveraging ML in personalized RTW programs, which use data-driven insights to improve recovery outcomes and reduce economic demands. In the context of demographic analysis, this paper explores how ML algorithms can uncover disparities in injury rates across various age groups, industries, and occupations, underscoring the need for targeted safety measures. Moreover, research gaps are identified, particularly regarding the emerging potential of advanced ML techniques, such as deep learning and large language models (LLMs), for analyzing structured and unstructured safety data, methods that have not yet been widely applied in workplace injury research. As such, future research should apply recent advances in ML, integrating these approaches with comprehensive and accessible datasets to enhance the prediction and prevention of workplace injuries, provide more detailed analytics and insights, and improve safety protocols across all industries. This comprehensive survey is an invaluable resource for researchers and practitioners leveraging ML to address complex challenges in workplace safety.

In the highly competitive e-commerce landscape, retaining customers and predicting their behaviors are critical for sustainable growth. This review explores the advancement of machine learning (ML) frameworks for customer retention and propensity modeling, emphasizing their transformative potential in e-commerce platforms. Customer retention is vital, as retaining an existing customer is significantly more cost-effective than acquiring a new one. Propensity modeling further complements retention strategies by predicting customer actions, such as purchases or churn, enabling businesses to tailor their marketing efforts effectively. Advanced ML techniques, including deep learning, reinforcement learning, and natural language processing (NLP), are reshaping how businesses approach these challenges. These models leverage diverse data sources, such as transaction history, browsing behavior, and customer feedback, to identify actionable insights. Key advancements in feature engineering, real-time data processing, and hyperparameter optimization have enhanced the accuracy and scalability of these frameworks, making them indispensable for e-commerce platforms handling vast datasets. Despite these advancements, challenges persist, such as ensuring data privacy, addressing the interpretability of complex models, and achieving real-time scalability. Successful implementations, including case studies from leading e-commerce platforms, demonstrate the potential of ML to improve customer engagement, increase lifetime value, and drive business growth. Looking forward, integrating AI and ML for enhanced personalization, leveraging real-time predictive analytics, and addressing ethical considerations like bias and fairness are crucial for advancing these frameworks. This review provides a comprehensive overview of state-of-the-art ML techniques for customer retention and propensity modeling, highlighting their practical applications and future prospects in e-commerce. By adopting these innovations, e-commerce platforms can achieve a competitive edge, fostering long-term customer loyalty and business success.

Breast cancer is one of the most prevalent and chronic disease that affect women. To overcome this disease, effective medical treatment is required. Early detection of the disease plays an important role for suitable medication and survival of patient. To identify the breast cancer in the patients, standard imaging technique mammography is used. Due to the subtle and varied nature of cancer tissues interpreting mammogram images can be a challenge to doctors. Machine learning (ML) and Deep Learning (DL) techniques offer promising solutions that provide efficient breast cancer detection from mammograms. In this review paper a comprehensive review of ML and DL algorithms and their applications in mammogram image analysis are presented. Various supervised and unsupervised learning techniques, such as convolutional neural networks (CNNs), support vector machines (SVMs), random forests, and other popular ML and DL models are discussed in paper. The integration of these DL methods that are efficiently used in image preprocessing techniques, feature extraction, and classification strategies. The overall survey focusses on various performance metrics, datasets, and benchmarks used in existing studies. Further the strengths and limitations of different approaches used by various researchers are identified. By understating current research trends this paper aims to contribute to the ongoing development of more accurate and reliable breast cancer detection systems using advanced ML techniques.

Waste marble, an industrial byproduct generated from marble cutting and polishing processes, can be effectively utilized as a partial replacement in concrete mixtures. Incorporating waste marble in concrete not only addresses environmental concerns related to marble waste disposal but also contributes to the sustainability of construction materials. Using machine learning (ML) to predict the impact of waste marble on the compressive strength of traditional concrete offers several advantages over repeated laboratory experiments. ML offers a powerful alternative to costly and time-consuming laboratory experiments, enabling faster and more sustainable exploration of the potential of waste marble in improving concrete’s compressive strength. This research has focused on evaluating the impact of waste marble on the compressive strength of traditional concrete using machine learning (ML). Advanced ML techniques such as the Group Methods Data Handling Neural Network (GMDH-NN), Support Vector Regression (SVR), K-Nearest Neighbors (kNN) and Adaptive Boosting (AdaBoost) have been applied in this research work. The GMDH-NN model was created using GMDH Shell 3.0 software, while AdaBoost, SVR and kNN models were created using “Orange Data Mining” software version 3.36. Error indices such as the sum of squared error (SSE), mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and Error (%), and performance metrics such as Accuracy % and the R2 between predicted and calculated compressive strength parameters were used to evaluate the overall behavior of the models. Finally, the Hoffman sensitivity analysis procedure was applied to determine the individual relative impact of the input variables on the output. At the end of the processes, a total of 1135 waste marble concrete entries were collected containing constituents such as the cement density (C), waste marble (WM), fine aggregate (FAg), coarse aggregate (CAg), water (W), superplasticizer (PL) and curing age (Age) used as input variables of the waste marble concrete model. The records were divided into training set (900 records = 80%) and validation set (235 records = 20%) following standard partitioning pattern reported in the literature. The kNN and AdaBoost, with SSE of 1408.5 MPa2 and 1397 MPa2 respectively and a tie Accuracy of 95.5% and R2 of 0.985 showed the best models suggesting excellent model performance while GMDH-NN showed the worst. Conversely, RF balances accuracy and model complexity, making it a practical alternative to kNN and AdaBoost. And lastly, Age, Coarse Aggregates, Water, and Plasticizer play the most significant roles in determining the compressive strength, while Cement, Waste Marble, and Fine Aggregates have comparatively smaller impacts. However, considering the standard proportion required for waste marble powder to replace cement, it showed a remarkable influence on the behavior of the concrete thus a recommended potential for its used as replacement for cement.

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.

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.

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.

Background Peripheral Arterial Disease(PAD) is a prevalent cardiovascular condition affecting millions worldwide, and the advent of machine learning(ML) techniques offers promising opportunities to enhance risk prediction and personalise patient management in this complex disease. This systematic review and meta-analysis aimed to evaluate the performance of ML models in predicting adverse outcomes in PAD patients compared to traditional statistical approaches. Methods A comprehensive search of major databases(MEDLINE,EMBASE,CINAHL,Cochrane CENTRAL) was conducted for studies published between 2000 and 2024. Two reviewers independently screened studies, extracted data, and assessed risk of bias using the PROBAST tool. The quality of included studies was also assessed using criteria outlined by Qiao et al. A modified Hierarchical Summary Receiver Operating Characteristic(HSROC) analysis was performed to compare the predictive performance of various ML models and traditional regression methods. Results Thirteen studies met the inclusion criteria. Gradient boosted models demonstrated the highest predictive performance with a diagnostic odds ratio(DOR) of 36.593(95% CI:24.44-54.79), sensitivity of 0.853, and specificity of 0.863. The best performing ML models for outcomes within 2 years, showed a DOR of 34.169(95% CI:22.840-51.117). Traditional regression models consistently underperformed compared to ML approaches, with the lowest DOR of 3.326(95% CI:1.814-6.097). The quality assessment revealed a mix of methodological rigor, with 54% of studies rated as low risk of bias and 46% as unclear. Conclusion Advanced ML techniques demonstrate superior predictive power for adverse outcomes in PAD patients compared to traditional regression methods.

<abstract><p>In this paper, a distributed machine-learning strategy, i.e., federated learning (FL), is used to enable the artificial intelligence (AI) model to be trained on dispersed data sources. The paper is specifically meant to forecast cryptocurrency prices, where a long short-term memory (LSTM)-based FL network is used. The proposed framework, i.e., <italic>F-LSTM</italic> utilizes FL, due to which different devices are trained on distributed databases that protect the user privacy. Sensitive data is protected by staying private and secure by sharing only model parameters (weights) with the central server. To assess the effectiveness of <italic>F-LSTM</italic>, we ran different empirical simulations. Our findings demonstrate that <italic>F-LSTM</italic> outperforms conventional approaches and machine learning techniques by achieving a loss minimal of $ 2.3 \times 10^{-4} $. Furthermore, the <italic>F-LSTM</italic> uses substantially less memory and roughly half the CPU compared to a solely centralized approach. In comparison to a centralized model, the <italic>F-LSTM</italic> requires significantly less time for training and computing. The use of both FL and LSTM networks is responsible for the higher performance of our suggested model (<italic>F-LSTM</italic>). In terms of data privacy and accuracy, <italic>F-LSTM</italic> addresses the shortcomings of conventional approaches and machine learning models, and it has the potential to transform the field of cryptocurrency price prediction.</p></abstract>

This study applies machine learning (ML) techniques to model and predict Assam’s agricultural Gross State Domestic Product (GSDP). Three predictive models—multiple linear regression, random forest regression, and gradient boosting—are evaluated. The random forest model achieved the best fit, exhibiting the highest R² and the lowest mean squared error (MSE) and Akaike information criterion (AIC), along with statistically significant coefficients. Ensemble methods (random forest and gradient boosting) markedly improve forecast accuracy of agricultural growth trends compared to traditional regression, yielding more reliable predictions of productivity and GSDP contributions. The findings underscore the vital role of agricultural productivity in driving economic growth, strengthening GSDP, and supporting food security and employment. Integrating advanced ML techniques with statistical analysis provides insights for policymakers to make data-driven decisions that foster sustainable agricultural development and economic prosperity in Assam. Objectives: Predict Assam’s agricultural sector performance using selected machine learning models. Evaluate and compare the effectiveness of these models in assessing the state’s agricultural economy. Methods: Data preprocessing involved handling outliers (using interquartile range and mean-max scaling) and feature selection via correlation heatmaps. Predictive models (multiple linear regression, random forest regression, and gradient boosting) were implemented in Python. Results: The gradient boosting model emerged as the most effective, achieving the highest accuracy and generalization (testing R² = 0.9867). Farm area, labour, maize yield, and autumn rice yield were the most significant positive contributors to GSDP. The random forest model performed similarly well (R² = 0.9867), while the multiple linear regression model was least accurate (R² = 0.9521), likely due to its inability to capture nonlinear relationships. Conclusions: Machine learning models offer transformative potential for Assam’s agricultural sector. Leveraging data-driven insights from these models can empower policymakers to design targeted interventions, promoting inclusive and sustainable economic growth in the region.