PAPlace: Performance-Driven Differentiable Analog Placement

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.

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

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.

Machine learning in computational docking.

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.

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 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.

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.

The Meadusa (Multiple Readout Ultra-High Segmentation) Detector Concept is an innovative approach to address the unique challenges and opportunities presented by the future lepton colliders and beyond. The Meadusa concept prioritizes ultra-high segmentation and multi-modal data acquisition to achieve ultra-high spatial, timing and event structure precision in particle detection. By combining a diverse array of active materials and readout technologies, Meadusa design is intended to be optimized for specific single particle and jet energy resolution, single particle identification and flavour tagging capabilities. The Meadusa concept is based on bringing together multiple, highly granular active elements with complementary sensitivities to different particle species in a single detector layer. The Meadusa detector is expected to embed cutting edge technologies and recent findings in optical, solid-state and gaseous detectors. The conceptual development has started as an initial design and is expected to evolve with the advancement of relevant technologies and following the performance estimation and optimization with advanced machine learning and artificial intelligence techniques and experimental validation. Here we report on the foundations of the concept, the description of the initial design and the preliminary performance parameters using novel machine learning techniques.

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.

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.

With the advances in computational power and numerical methods, analysts can now rely on first-principle simulations to predict ultra-fine details in a variety of applications. Advances in machine learning (ML) have produced algorithms that can now learn high-level abstractions via hierarchical models. This project aims to leverage advances in ML techniques and the available high-resolution simulation data to develop a novel modeling and simulation (M\&S) methodology for reactor safety analysis. While application-agnostic ML techniques are available, complex physics constraints need to be incorporated into ML techniques to build ML-based closures for computationally efficient predictive simulations. This project intends to develop a physics-guided data-driven multi-scale methodology for M\&S of advanced reactors. The project focuses on thermal fluid (T/F) phenomena, which play major roles in advanced reactor safety. Specifically, we propose a data-driven coarse-mesh turbulence model based on local flow features for the transient analysis of thermal mixing and stratification in a sodium-cooled fast reactor (SFR). The model has a coarse-mesh setup to ensure computational efficiency, while it is trained by fine-mesh computational fluid dynamics (CFD) data with Reynolds-averaged Navier-Stokes (RANS) turbulence model to ensure accuracy. Three different neural networks are developed and tested for loss-of-flow transients in the hot pool of SFR, i.e. the densely connected convolutional neural network (DCNN), long-short-term-memory network based on proper orthogonal decomposition (POD-LSTM), and the DCNN informed by LSTM (DCNN-LSTM). The performances of these three neural networks are evaluated based on baseline models. The DCNN-LSTM model has been chosen for further hyperparameter optimization. Furthermore, based on a simplified two-dimensional case, uncertainty quantification (UQ) of the developed ML-based closure are investigated with three methods, i.e. Monte Carlo dropout, deep ensemble, and Bayesian neural network. The developed ML-based turbulent viscosity closure relation based on deep ensemble is then integrated into the system analysis module SAM and serves as a term in the conservation equations. Such a SAM-ML based procedure guarantees that the obtained results are consistent with the physical constraints of the thermal-fluid system. The SAM-ML simulation on the same loss-of-flow transient showed comparable accuracy with the CFD simulation but with a much coarser mesh setup. Last but not least, the ML-based closure improvement with the support of higher-fidelity data from large eddy simulation (LES) is discussed. As a first step towards this direction, a baseline LES simulation is performed to obtain comparable data with RANS results. Based on the early results, future investigation on further improving the ML-based closure is discussed. We believe the developed approach that combines scientific machine learning with nuclear system analysis code can benefit the advanced reactor community as more accurate safety analyses will better characterize reactor safety margins and reduce licensing efforts.

<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>

The purpose of this study is to develop predictive and optimization models to reduce patients' waiting time for diagnostic tests and medical treatments. Long wait times for receiving medical care is a pressing issue in the Canadian healthcare system. These wait times are spread over different phases of treatment, such as diagnostic tests, physicians' treatments, and appointments with specialists and surgeons. An integrated system to monitor all the phases of treatment along with efficient workload distribution in each phase can reduce patients' waiting time at each of these phases. This study investigates the impact of efficient resource allocation and workload distribution in the medical system. This research also explores the effect of optimized resource allocation on the patients' waiting time in Medicare settings. Resource allocation planning is directly related to the number of patient-arrival, and it is hard to predict such uncertain parameters in the future time frame. The number of patient-arrival also varies across different medical departments and different timeframes which makes the patient-arrival prediction challenging. The goal of this study is to investigate the forecasting effect on patients' waiting time and physicians' workload. To achieve this goal, advanced machine learning technique is integrated with the optimization model. The machine learning technique is used to predict the uncertain parameters of the optimization model for a shorter time span. To predict time-dependent uncertain parameters, such as patient arrival is a major issue, as the prediction may suffer from the concept drift problem. Besides, real-time data are commonly prone to errors due to irregular fluctuations, seasonal biases, and missing values in the data. On the other hand, predicting for shorter intervals requires lower execution time combined with higher accuracy. The developed predictive ensemble model in this research has addressed these issues legitimately with four research contributions. In the first contribution (Chapter 2), we have investigated methodologies for predicting Radiologists’ workload in a short time interval by adopting a machine learning technique. An ensemble model is proposed with the fixed batch training method in this part. To excel in the execution time, a fixed batch training method is used. Secondly, in Chapter 3, an Adaptive Batched-Ranked Ensemble (ABRE) model that reduces the effect of fluctuation using the time-variant windowing technique. Besides, a data aggregation technique is developed and integrated with the offline training phase of the proposed model to tackle the concept drift problem. In the third contribution (Chapter 4), a novel Ensemble of Pruned Regressor Chain (EPRC) method is developed and trained offline to predict uncertain parameters, such as patients’ arrival. Finally, the fourth contribution in Chapter 4, the EPRC method is integrated with a novel multi-objective optimization model to reduce patients’ waiting time, and to determine workload allocation for future timespan. This research enables enhanced decision-making with effective resource allocation and workload scheduling, as well as assists in reducing healthcare expenditure.