Machine Learning Optimization Research Articles

Benchmarking Digital Quantum Simulations Above Hundreds of Qubits Using Quantum Critical Dynamics

The real-time simulation of large many-body quantum systems is a formidable task, that may only be achievable with a genuine quantum computational platform. Currently, quantum hardware with a number of qubits sufficient to make classical emulation challenging is available. This condition is necessary for the pursuit of a so-called quantum advantage, but it also makes verifying the results very difficult. In this paper, we flip the perspective and utilize known theoretical results about many-body quantum critical dynamics to qualitatively benchmark digital quantum hardware and various error mitigation techniques. In particular, we benchmark against known universal scaling laws in the Hamiltonian simulation of a time-dependent transverse-field Ising Hamiltonian of up to 133 qubits. Incorporating only basic error mitigation and suppression methods, our study shows reliable control up to a two-qubit gate depth of 28, featuring a maximum of 1396 two-qubit gates, before noise becomes prevalent. These results are transferable to applications such as Hamiltonian simulation, variational algorithms, optimization, or quantum machine learning. We demonstrate this on the example of digitized quantum annealing for optimization and identify an optimal working point in terms of both circuit depth and time step on a 133-site optimization problem. Published by the American Physical Society 2024

Optimized Machine Learning Algorithms for Predicting the Punching Shear Resistance of Flat Slabs with Transverse Reinforcement

In the calculation of reinforced concrete (RC) flat slabs with transverse reinforcement, punching shear resistance is one of the most critical factors. It is true that design provisions may be implemented, but they often result in significant biases and deviations from expectations. This study aims to present an optimized machine learning (ML) algorithm for estimating the punching shear resistance. Four machine learning (ML) algorithms (SVR, DT, RF, and XGBoost) with Bayesian optimization (BO) are presented in this paper to provide accurate predictions for flat slabs. The adoptability and optimization of the models are achieved through the analysis of a database of 337 test specimens with nine design parameters. Machine learning (ML) techniques are used to estimate punching shear resistance, which is compared with design provisions and equations relating to critical shear crack theory (CSCT). According to this study, Bayesian optimization is still capable of improving the performance of conventional machine learning algorithms, while the XGBoost-based model offers advanced capabilities. Predictions based on BO-XGBoost are in good agreement with actual values (MAE, RMSE, and R2 are 0.09 MN, 0.14 MN, and 0.92, respectively) in test set. Following a detailed explanation using Shapley additive explanation (SHAP), a high-performance ML approach is used to investigate the predictive results. With the proposed optimized algorithms, it is possible to determine the punching shear resistance of flat slabs with transverse reinforcement during the preliminary stages of the construction.

Enhancing pricing strategies in the aftermarket sector with machine learning

PurposeThis research explores the application of machine learning to optimize pricing strategies in the aftermarket sector, particularly focusing on parts with no assigned values and the detection of outliers. The study emphasizes the need to incorporate technical features to improve pricing accuracy and decision-making.Design/methodology/approachThe methodology involves data collection from web scraping and backend sources, followed by data preprocessing, feature engineering and model selection to capture the technical attributes of parts. A Random Forest Regressor model is chosen and trained to predict prices, achieving a 76.14% accuracy rate.FindingsThe model demonstrates accurate price prediction for parts with no assigned values while remaining within an acceptable price range. Additionally, outliers representing extreme pricing scenarios are successfully identified and predicted within the acceptable range.Originality/valueThis research bridges the gap between industry practice and academic research by demonstrating the effectiveness of machine learning for aftermarket pricing optimization. It offers an approach to address the challenges of pricing parts without assigned values and identifying outliers, potentially leading to increased revenue, sharper pricing tactics and a competitive advantage for aftermarket companies.

Terahertz metasurface biosensor for high-sensitivity salinity detection and data encoding with machine learning optimization based on random forest regression

Terahertz metasurface biosensor for high-sensitivity salinity detection and data encoding with machine learning optimization based on random forest regression

Stacking Ensemble Technique Using Optimized Machine Learning Models with Boruta–XGBoost Feature Selection for Landslide Susceptibility Mapping: A Case of Kermanshah Province, Iran

Landslides cause significant human and financial losses in different regions of the world. A high-accuracy landslide susceptibility map (LSM) is required to reduce the adverse effects of landslides. Machine learning (ML) is a robust tool for LSM creation. ML models require large amounts of data to predict landslides accurately. This study has developed a stacking ensemble technique based on ML and optimization to enhance the accuracy of an LSM while considering small datasets. The Boruta–XGBoost feature selection was used to determine the optimal combination of features. Then, an intelligent and accurate analysis was performed to prepare the LSM using a dynamic and hybrid approach based on the Adaptive Fuzzy Inference System (ANFIS), Extreme Learning Machine (ELM), Support Vector Regression (SVR), and new optimization algorithms (Ladybug Beetle Optimization [LBO] and Electric Eel Foraging Optimization [EEFO]). After model optimization, a stacking ensemble learning technique was used to weight the models and combine the model outputs to increase the accuracy and reliability of the LSM. The weight combinations of the models were optimized using LBO and EEFO. The Root Mean Square Error (RMSE) and Area Under the Receiver Operating Characteristic Curve (AUC-ROC) parameters were used to assess the performance of these models. A landslide dataset from Kermanshah province, Iran, and 17 influencing factors were used to evaluate the proposed approach. Landslide inventory was 116 points, and the combined Voronoi and entropy method was applied for non-landslide point sampling. The results showed higher accuracy from the stacking ensemble technique with EEFO and LBO algorithms with AUC-ROC values of 94.81% and 94.84% and RMSE values of 0.3146 and 0.3142, respectively. The proposed approach can help managers and planners prepare accurate and reliable LSMs and, as a result, reduce the human and financial losses associated with landslide events.

Daily Runoff Forecasting Using Novel Optimized Machine Learning Methods

Daily Runoff Forecasting Using Novel Optimized Machine Learning Methods

A gentle introduction to gradient-based optimization and variational inequalities for machine learning

Abstract The rapid progress in machine learning in recent years has been based on a highly productive connection to gradient-based optimization. Further progress hinges in part on a shift in focus from pattern recognition to decision-making and multi-agent problems. In these broader settings, new mathematical challenges emerge that involve equilibria and game theory instead of optima. Gradient-based methods remain essential—given the high dimensionality and large scale of machine-learning problems—but simple gradient descent is no longer the point of departure for algorithm design. We provide a gentle introduction to a broader framework for gradient-based algorithms in machine learning, beginning with saddle points and monotone games, and proceeding to general variational inequalities. While we provide convergence proofs for several of the algorithms that we present, our main focus is that of providing motivation and intuition.

Comparative analysis of hybrid cognitive radar techniques for enhanced target detection and tracking: A performance evaluation perspective

This paper introduces an innovative hybrid algorithmic approach for cognitive radar systems, by integrating unique machine learning optimization techniques such as, YOLO (You Only Look Once), Mask R-CNN, and Recurrent Neural Networks (RNNs). Through extensive simulations, the integrated approach demonstrates notable enhancements in target detection, instance segmentation, and target tracking within radar systems. Leveraging deep learning models, the framework facilitates adaptive and intelligent processing of radar data, augmenting system performance in dynamic environments. By seamlessly integrating state-of-the-art techniques, the proposed framework showcases a comprehensive solution to the challenges faced by traditional radar systems. This research represents a significant stride forward in radar technology, promising transformative impacts across various domains, including surveillance, remote sensing, and autonomous navigation. As radar systems continue to evolve, the adoption of advanced deep learning techniques offers unprecedented opportunities for enhancing situational awareness and decision-making capabilities in complex operational scenarios.

Sentiment classification for insider threat identification using metaheuristic optimized machine learning classifiers

This study examines the formidable and complex challenge of insider threats to organizational security, addressing risks such as ransomware incidents, data breaches, and extortion attempts. The research involves six experiments utilizing email, HTTP, and file content data. To combat insider threats, emerging Natural Language Processing techniques are employed in conjunction with powerful Machine Learning classifiers, specifically XGBoost and AdaBoost. The focus is on recognizing the sentiment and context of malicious actions, which are considered less prone to change compared to commonly tracked metrics like location and time of access. To enhance detection, a term frequency-inverse document frequency-based approach is introduced, providing a more robust, adaptable, and maintainable method. Moreover, the study acknowledges the significant impact of hyperparameter selection on classifier performance and employs various contemporary optimizers, including a modified version of the red fox optimization algorithm. The proposed approach undergoes testing in three simulated scenarios using a public dataset, showcasing commendable outcomes.

Large language models to facilitate pregnancy prediction after invitro fertilization.

We evaluated the efficacy of large language models (LLMs), specifically, generative pre-trained transformer-4 (GPT-4), in predicting pregnancy following invitro fertilization (IVF) treatment and compared its accuracy with results from an original published study. Our findings revealed that GPT-4 can autonomously develop and refine advanced machine learning models for pregnancy prediction with minimal human intervention. The prediction accuracy was 0.79, and the area under the receiver operating characteristic curve (AUROC) was 0.89, exceeding or being at least equivalent to the metrics reported in the original study, that is, 0.78 for accuracy and 0.87 for AUROC. The results suggest that LLMs can facilitate data processing, optimize machine learning models in predicting IVF success rates, and provide data interpretation methods. This capacity can help bridge the knowledge gap between data scientists and medical personnel to solve the most pressing clinical challenges. However, more experiments on diverse and larger datasets are needed to validate and promote broader applications of LLMs in assisted reproduction.

Metasurface Based Surface Plasmon Resonance (SPR) Biosensor for Cervical Cancer Detection with Behaviour Prediction using Machine Learning Optimization Based on Support Vector Regression

Metasurface Based Surface Plasmon Resonance (SPR) Biosensor for Cervical Cancer Detection with Behaviour Prediction using Machine Learning Optimization Based on Support Vector Regression

Conditional Generative Adversarial Networks with Optimized Machine Learning for Fault Detection of Triplex Pump in Industrial Digital Twin

In recent years, digital twin (DT) technology has garnered significant interest from both academia and industry. However, the development of effective fault detection and diagnosis models remains challenging due to the lack of comprehensive datasets. To address this issue, we propose the use of Generative Adversarial Networks (GANs) to generate synthetic data that replicate real-world data, capturing essential features indicative of health-related information without directly referencing actual industrial DT systems. This paper introduces an intelligent fault detection and diagnosis framework for industrial triplex pumps, enhancing fault recognition capabilities and offering a robust solution for real-time industrial applications within the DT paradigm. The proposed framework leverages Conditional GANs (CGANs) alongside the Harris Hawk Optimization (HHO) as a metaheuristic method to optimize feature selection from input data to enhance the performance of machine learning (ML) models such as Bagged Ensemble (BE), AdaBoost (AD), Support Vector Machine (SVM), K-Nearest Neighbors (KNNs), Decision Tree (DT), and Naive Bayes (NB). The efficacy of the approach is evaluated using key performance metrics such as accuracy, precision, recall, and F-measure on a triplex pump dataset. Experimental results indicate that hybrid-optimized ML algorithms (denoted by “ML-HHO”) generally outperform or match their classical counterparts across these metrics. BE-HHO achieves the highest accuracy at 95.24%, while other optimized models also demonstrate marginal improvements, highlighting the framework’s effectiveness for real-time fault detection in DT systems, where SVM-HHO attains 94.86% accuracy, marginally higher than SVM’s 94.48%. KNN-HHO outperforms KNNs with 94.73% accuracy compared to 93.14%. Both DT-HHO and DT achieve 94.73% accuracy, with DT-HHO exhibiting slightly better precision and recall. NB-HHO and NB show near-equivalent performance, with NB-HHO at 94.73% accuracy versus NB’s 94.6%. Overall, the optimized algorithms demonstrate consistent, albeit marginal, improvements over their classical versions.

Forecasting the trend of tuberculosis incidence in Anhui Province based on machine learning optimization algorithm, 2013–2023

Tuberculosis has been one of the most common communicable diseases raising global concerns. Accurately predicting the incidence of Tuberculosis remains challenging. Here we constructed a time-series analysis and fusion tool using multi-source data, and aimed to more accurately predict the incidence trend of tuberculosis of Anhui Province from 2013 to 2023. Random forest algorithm (RF), Feature Recursive Elimination (RFE) and Least absolute shrinkage and selection operator (LASSO) were implemented to improve the derivation of features related to infectious diseases and feature work. Based on the characteristics of infectious disease data, a model of RF-RFE-LASSO integrated particle swarm optimization multiple inputs long short term memory recurrent neural network (RRL-PSO-MiLSTM) was created to perform more accurate prediction. Results showed that the PSO-MiLSTM achieved excellent prediction results compared with common single-input and multi-input time-series models (test set MSE:42.3555, MAE: 59.3333, RMSE: 146.7237, MAPE: 2.1133, R2: 0.8634). PSO-MiLSTM enriches and complements the methodological research content of calibrating the time-series predictive analysis of infectious diseases using multi-source data, and can be used as a brand-new benchmark for the analysis of influencing factors and trend prediction of infectious diseases at the public health level in the future, as well as providing a reference for incidence rate prediction of infectious diseases.

Optimizing Machine Learning-Based Intrusion Detection Systems for Enhanced Security in Cloud Computing Environments.

In today’s cloud computing environments, robust network security is crucial due to the inherently distributed and dynamic nature of cloud systems. Traditional Network Intrusion Detection Systems (NIDS) often face challenges in handling large-scale, rapidly evolving data and sophisticated attack vectors unique to cloud settings. This paper presents an optimized Machine Learning (ML)-based NIDS framework designed specifically for cloud infrastructures, leveraging feature selection, dimensionality reduction, and algorithmic tuning techniques to enhance detection accuracy and reduce false positives. Integrating supervised and unsupervised learning methods, this NIDS system can detect both known and novel attack types efficiently. By utilizing cloud-specific datasets and real-time traffic monitoring, it adapts well to the scalability and elasticity requirements of cloud environments. Additionally, the system incorporates automated optimization techniques, such as hyperparameter tuning and ensemble learning, to further enhance performance. Experimental results show improvements in detection rates, reduced computational overhead, and better scalability, making it a promising solution for modern cloud-based infrastructures.

Multi-criteria study and machine learning optimization of a novel heat integration for combined electricity, heat, and hydrogen production: Application of biogas-fueled S-Graz plant and biogas steam reforming

The current research introduces an environmentally friendly heat design method by employing biogas fuel, aiming to yield electricity, hydrogen, and heating load simultaneously. The proposed arrangement consists of a biogas-powered S-Graz plant and a biogas steam reforming cycle. Although methane-fueled S-Graz plants for multigeneration purposes have been studied in previous studies, research on employing biogas fuel to launch a S-Graz plant and integrating a biogas steam reforming cycle with such a plant has yet to be examined. The model is simulated using the engineering equation solver software, and the study includes thermodynamic, exergoeconomic, and sustainability assessments to show the potential of the suggested configuration. By conducting a sensitivity study, a machine learning optimization method within MATLAB is implemented to exhibit the final optimal solution for the proposed arrangement. This optimization uses artificial neural networks and a non-dominated sorting genetic algorithm-II algorithm in a triple-objective framework based on energy efficiency, sustainability index, and products’ specific cost. The optimization demonstrates that the mentioned objectives reach optimal values of 58.26 %, 4.56, and 15.56 $/GJ, respectively. Also, the optimal net output power and hydrogen production rate equal 5746 kW and 1.45 m3/s, respectively. Besides, the process determines the optimal exergy efficiency, total net present value, and payback period as 52.70 %, 50.3 M$, and 8.96 years, respectively. The total investment cost rate for this system also is found to be 219.8 $/h.

Optimized Machine Learning Model for Predicting Compressive Strength of Alkali-Activated Concrete Through Multi-Faceted Comparative Analysis

Alkali-activated concrete (AAC), produced from industrial by-products like fly ash and slag, offers a promising alternative to traditional Portland cement concrete by significantly reducing carbon emissions. Yet, the inherent variability in AAC formulations presents a challenge for accurately predicting its compressive strength using conventional approaches. To address this, we leverage machine learning (ML) techniques, which enable more precise strength predictions based on a combination of material properties and cement mix design parameters. In this study, we curated an extensive dataset comprising 1756 unique AAC mixtures to support robust ML-based modeling. Four distinct input variable schemes were devised to identify the optimal predictor set, and a comparative analysis was performed to evaluate their effectiveness. After this, we investigated the performance of several popular ML algorithms, including random forest (RF), adaptive boosting (AdaBoost), gradient boosting regression trees (GBRTs), and extreme gradient boosting (XGBoost). Among these, the XGBoost model consistently outperformed its counterparts. To further enhance the predictive accuracy of the XGBoost model, we applied four state-of-the-art optimization techniques: the Gray Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), beetle antennae search (BAS), and Bayesian optimization (BO). The optimized XGBoost model delivered superior performance, achieving a remarkable coefficient of determination (R2) of 0.99 on the training set and 0.94 across the entire dataset. Finally, we employed SHapely Additive exPlanations (SHAP) to imbue the optimized model with interpretability, enabling deeper insights into the complex relationships governing AAC formulations. Through the lens of ML, we highlight the benefits of the multi-faceted synergistic approach for AAC strength prediction, which combines careful input parameter selection, optimal hyperparameter tuning, and enhanced model interpretability. This integrated strategy improves both the robustness and scalability of the model, offering a clear and reliable prediction of AAC performance.

Building Scalable MLOps: Optimizing Machine Learning Deployment and Operations

As machine learning (ML) models become increasingly integrated into mission-critical applications and production systems, the need for robust and scalable MLOps (Machine Learning Operations) practices has grown significantly. This paper explores key strategies and best practices for building scalable MLOps pipelines to optimize the deployment and operation of machine learning models at an enterprise scale. It delves into the importance of automating the end-to-end lifecycle of ML models, from data ingestion and model training to testing, deployment, and monitoring. Approaches for implementing continuous integration and continuous deployment (CI/CD) pipelines tailored for ML workflows are discussed, enabling efficient and repeatable model updates and deployments. The paper emphasizes the criticality of implementing comprehensive monitoring and observability mechanisms to track model performance, detect drift, and ensure the reliability and trustworthiness of deployed models. The paper also addresses the challenges of managing model versioning and governance at scale, including techniques for maintaining a centralized model registry, enforcing access controls, and ensuring compliance with regulatory requirements. The paper aims to provide a comprehensive guide for organizations seeking to establish scalable and robust MLOps practices, enabling them to unlock the full potential of machine learning while mitigating risks and ensuring responsible AI deployment. Keywords—Machine Learning Operations (MLOps), Scalable AI Deployment, Continuous Integration and Continuous Deployment (CI/CD) for ML, ML Monitoring and Observability, Model Reproducibility, Model Versioning and Governance, Centralized Model Registry, Responsible AI Deployment, Ethical AI Practices, Enterprise MLOps

Enhancing gait recognition in lower limb exoskeletons: adaptive feature selection and random forest with Bayesian optimization

Abstract To improve human-machine cooperation and enhance the accuracy of gait recognition in wearable lower limb exoskeletons, an enhancement method of gait recognition based on adaptive feature selection and an optimized machine learning algorithm was proposed. In this study, surface electromyography (sEMG) signals of rectus femoris, medialis femoris, lateralis femoris, semitendinosus and biceps femoris were recorded during level-ground walking. Then, time domain, frequency domain, time-frequency domain and nonlinear features were extracted. The integrated value of electromyography, variance, root mean square and wavelength were selected as the time domain features, and the mean power frequency and median frequency were selected as the frequency domain features. Wavelet packet energy was selected as the time-frequency domain feature. Nonlinear features including approximate entropy, sample entropy and fuzzy entropy of sEMG were extracted. To reduce feature dimension and improve the calculation efficiency, adaptive feature selection was performed by particle swarm optimization combined with sigmoid function. Then, the feature matrix was determined as the input for a random forest classifier to recognize different gait phases. An adaptive optimization mechanism based on Bayesian optimization was applied to random forest. Compared with random forest, the overall performance of the optimized model was improved. Its accuracy was increased by 3.57%. The feature selection and adaptive optimization mechanisms in gait recognition not only enhance the accuracy of random forest algorithms applied to sEMG for gait prediction, but also facilitate the flexibility and consistency required for lower limb exoskeleton gait control.

Repurposing MALDI-TOF MS for effective antibiotic resistance screening in Staphylococcus epidermidis using machine learning

The emergence of Staphylococcus epidermidis as a significant nosocomial pathogen necessitates advancements in more efficient antimicrobial resistance profiling. However, existing culture-based and PCR-based antimicrobial susceptibility testing methods are far too slow or costly. This study combines machine learning with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to develop predictive models for various antibiotics using a comprehensive dataset containing thousands of S. epidermidis isolates. Optimized machine learning models utilized feature selection and achieved high AUROC scores ranging from 0.80 to 0.95 while maintaining AUPRC scores up to 0.97. Shapley Additive exPlanations were employed to analyze relevant features and assess the significance of corresponding protein biomarkers while also verifying that predictive power was derived from the detection of proteins rather than noise. Antimicrobial resistance models were validated externally to evaluate model performance outside the original data collection site. The approaches and findings in this study demonstrate a significant advancement in rapid, cost-effective antimicrobial resistance profiling, offering a promising solution for improving treatments for nosocomial infections and being potentially applicable to other microbial pathogens in the future.

Optimized XGBoost Model with Whale Optimization Algorithm for Detecting Anomalies in Manufacturing

Anomalies and defects in the manufacturing process hinder operating efficiency and product quality. The Whale Optimization Algorithm (WOA) optimizes the XGBoost model for better anomaly identification by iteratively refining hyperparameters. Experiments using real-world manufacturing datasets prove proposed model works. Comparing the proposed model to traditional anomaly detection methods shows its superior performance in industry patent concept. The optimized XGBoost model's interpretability and anomaly detection features are also discussed. In this paper, WOA is applied in this work to optimize hyperparameters of XGBoost, a robust gradient boosting technique for accurate anomaly detection in manufacturing systems. Optimized XGBoost gained 1.00 precision value, 0.9 recall value and 0.96 f1-score for class 0.0 and gained a 0.95 precision value, 1.00 recall value, and a 0.97 f1-score for class 1.0. The proposed model gained 0.993 Train Score and 0.964 Test Score. Our findings suggest that integrating XGBoost with the WOA may uncover manufacturing process irregularities. Optimization improves detection accuracy and provides a flexible and interpretable framework, helping modern industrial processes maintain quality and efficiency. This research encourages machine learning optimization for industrial patent applications, advancing anomaly detection methods.