Predictive Machine Research Articles

Research on short-term photovoltaic power generation forecasting model based on multi-strategy improved squirrel search algorithm and support vector machine

Solar photovoltaic (PV) power generation is susceptible to environmental factors, and redundant features can disrupt prediction accuracy. To achieve rapid and accurate online prediction, we propose a method that combines Principal Component Analysis (PCA) with a multi-strategy improved Squirrel Search Algorithm (SSA) to optimize Support Vector Machine (MISSA-SVM) for prediction. Initially, to mitigate the impact of redundant features on prediction accuracy, KPCA is employed for feature dimensionality reduction. Subsequently, SVM is suggested as the foundational algorithm for constructing the prediction model. Furthermore, to address the influence of hyperparameter selection on model performance, SSA is introduced for optimizing SVM hyperparameters, with the aim of establishing the optimal prediction model. Moreover, to enhance solution efficiency and accuracy, a multi-strategy approach termed MISSA is proposed, which integrates Population Initialization based on the Tent map, Nonlinear Predator Presence Probability, Chaotic-based Dynamic Opposition-based Learning, and Selection Strategy, to refine SSA. Finally, through case studies, the performance of MISSA optimization is assessed using challenging CEC2021 test functions, demonstrating its high optimization performance, stability, and significance. Subsequently, the performance of the prediction model is validated using two datasets, showcasing that the proposed prediction method achieves high accuracy and robust prediction stability.

Approved AI-based fluid monitoring to identify morphological and functional treatment outcomes in neovascular age-related macular degeneration in real-world routine

AimTo predict antivascular endothelial growth factor (VEGF) treatment requirements, visual acuity and morphological outcomes in neovascular age-related macular degeneration (nAMD) using fluid quantification by artificial intelligence (AI) in a real-world...

Quinolone Antibiotics Inhibit the Rice Photosynthesis by Targeting Photosystem II Center Protein: Generational Differences and Mechanistic Insights.

Soil antibiotic pollution profoundly influences plant growth and photosynthetic performance, yet the main disturbed processes and the underlying mechanisms remain elusive. This study explored the photosynthetic toxicity of quinolone antibiotics across three generations on rice plants and clarified the mechanisms through experimental and computational studies. Marked variations across antibiotic generations were noted in their impact on rice photosynthesis with the level of inhibition intensifying from the second to the fourth generation. Omics analyses consistently targeted the light reaction phase of photosynthesis as the primary process impacted, emphasizing the particular vulnerability of photosystem II (PS II) to the antibiotic stress, as manifested by significant interruptions in the photon-mediated electron transport and O2 production. PS II center D2 protein (psbD) was identified as the primary target of the tested antibiotics, with the fourth-generation quinolones displaying the highest binding affinity to psbD. A predictive machine learning method was constructed to pinpoint antibiotic substructures that conferred enhanced affinity. As antibiotic generations evolve, the positive contribution of the carbonyl and carboxyl groups on the 4-quinolone core ring in the affinity interaction gradually intensified. This research illuminates the photosynthetic toxicities of antibiotics across generations, offering insights for the risk assessment of antibiotics and highlighting their potential threats to carbon fixation of agroecosystems.

Machine learning attack detection based-on stochastic classifier methods for enhancing of routing security in wireless sensor networks

Wireless Sensor Networks (WSNs) are vulnerable to attacks during data transmission, and many techniques have been proposed to detect and secure routing data. In this paper, we introduce a novel stochastic predictive machine learning approach designed to discern untrustworthy events and unreliable routing attributes, aiming to establish an artificial intelligence-based attack detection system for WSNs. Our methodology leverages real-time analysis of the features of simulated WSN routing data. By integrating Hidden Markov Models (HMM) with Gaussian Mixture Models (GMM), we develop a robust classification framework. This framework effectively identifies outliers, pinpoints malicious network behaviors from their origins, and categorizes them as either trusted or untrusted network activities. In addition, dimensionality reduction techniques are used to improve interpretability, reduce computation and processing time, extract uncorrelated features from network data, and optimize performances. The main advantage of our approach is to establish an efficient stochastic machine learning method capable of analyzing and filtering WSN traffic to prevent suspicious and unsafe data, reduce the large dissimilarity in the collected routing features, and rapidly detect attacks before they occur. In this work, we exploit a well-tuned data set that provides a lot of routing information without losing any data. The experimental results show that the proposed stochastic attack detection system can effectively identify and categorize anomalies in wireless sensor networks with high accuracy. The classification rates of the system were found to be around 83.65%, 84.94% and 94.55%, which is significantly better than the existing classification approaches. Furthermore, the proposed system showed a positive prediction value of 11.84% higher than the existing approaches.

Minimum-energy virtual machine placement using embedded sensors and machine learning

Cloud data centers (DCs) consume large amounts of energy and contribute significantly to environmental concerns. Furthermore, with the advent of 5G and B5G networks, increasingly software-oriented and becoming highly dependent on cloud computing, it becomes imperative to optimize their energy consumption. Thus, in this study, we present a virtual machine placement algorithm that minimizes the energy consumption of a cluster of server machines. Our solution is embodied through the use of sensors embedded inside physical server machines, enabling the introduction of new features for sensitive thermal awareness and proactive hot spot avoidance. Leveraging this significantly enhanced feature space, we implement data-driven predictive machine learning models along with a heuristic placement algorithm (CPP), enabling proactive VM placements that are both energy-aware and thermal-aware. Indeed, experiments carried out on a cluster of physical server machines demonstrate high performance by both the ML models and the placement algorithm (CPP). Compared with the best baseline algorithm, our solution reduced power consumption and temperature by 7% and 2%, respectively, while avoiding hot spots and maintaining efficient load distribution, thereby reducing the overhead of physical machines by 28%.

Developing a predictive machine learning model and a kinetic model for the bioremediation of terrestrial diesel spills

Terrestrial diesel spills significantly threaten the natural environment and human health, necessitating effective bioremediation strategies for diesel-contaminated soil. This study aims to evaluate the impact of diesel spills on soil water retention capacity and the effectiveness of different bioremediation methods. Four tanks (A-D) were used to compare natural attenuation, bioaugmentation, biostimulation, and a combination of bioaugmentation and biostimulation in enhancing diesel degradation. The findings demonstrated that soil water retention decreases with higher diesel concentrations and increases with more compost. After 21 days, the Diesel Range Organics (DRO) removal efficiencies for Tanks A, B, C, and D were 15.70 %, 23.31 %, 29.65 %, and 49.78 %, respectively. The degradation kinetics primarily followed first-order reaction models, with combined bioaugmentation and biostimulation showing the fastest reaction rate. The projected timelines for complete bioremediation were 44 days for the combined method, 88 days for biostimulation, 116 days for bioaugmentation, and 178 days for natural attenuation. Machine Learning models further supported these findings, with the bilateral Artificial Neural Network outperforming the Linear Regression model (R2 of 0.9990 vs. 0.9713, respectively). This study contributes to understanding the efficiency of different bioremediation techniques and offers practical insights for managing diesel-contaminated soils.

Identifying heterogeneity for increasing the prediction accuracy of machine learning models

In recent years, the significance of machine learning in agriculture has surged, particularly in post-harvest monitoring for sustainable aquaculture. Challenges like heterogeneity, irrelevant variables and multicollinearity hinder the implementation of smart monitoring systems. However, this study focuses on investigating heterogeneity among drying parameters that determine the moisture content removal during seaweed drying due to its limited attention, particularly within the field of agriculture. Additionally, a heterogeneity model within machine learning algorithms is proposed to enhance accuracy in predicting seaweed moisture content removal, both before and after the removal of heterogeneity parameters and also after the inclusion of single-eliminated heterogeneity parameters. The dataset consists of 1914 observations with 29 independent variables, but this study narrows down to five: Temperature (T1, T4, T7), Humidity (H5), and Solar Radiation (PY). These variables are interacted up to second-order interactions, resulting in 55 variables. Variance inflation factor and boxplots are employed to identify heterogeneity parameters. Two predictive machine learning models, namely random forest and elastic net are then utilized to identify the 15 and 20 highest important parameters for seaweed moisture content removal. Evaluation metrics (MSE, SSE, MAPE, and R-squared) are used to assess model performance. Results demonstrate that the random forest model outperforms the elastic net model in terms of higher accuracy and lower error, both before and after removing heterogeneity parameters, and even after reintroducing single-eliminated heterogeneity parameters. Notably, the random forest model exhibits higher accuracy before excluding heterogeneity parameters.

ENHANCING AIR POLLUTION CONTROL WITH MACHINE LEARNING IN THE AUTOMATION FIELD

The integration of machine learning with real-time data collection offers a transformative approach to optimizing pollution control strategies. This study explores the application of these advanced technologies in various environments, including urban, industrial, coastal, and rural areas. Using predictive machine learning models, significant reductions in pollutants such as PM2.5, SO2, NOx, VOCs, PM10, and NH3 were achieved through targeted and timely interventions. In urban areas, air quality improved notably due to proactive measures informed by high-accuracy predictions. Industrial areas saw a 20% reduction in sulfur dioxide emissions, while coastal areas effectively managed volatile organic compounds. In rural areas, optimizing agricultural practices led to substantial decreases in particulate matter and ammonia emissions. These findings validate the efficacy of machine learning in enhancing pollution control efforts, highlighting its potential to revolutionize air quality management. This study underscores the importance of continued investment in advanced, data-driven approaches to address the growing challenge of air pollution, advocating for more sophisticated, adaptive, and effective strategies to protect public health and the environment.

Multi-objective optimization of molten salt microreactor shielding perturbations employing machine learning

Nuclear reactor shielding is a requirement for all reactors to prevent radiation from escaping the core. Shielding design can be an arduous process that requires significant computational resources to model even a single concept, for either deterministic and stochastic computations. As such, many developed shield concepts are under-optimized and unnecessarily large and/or expensive. Machine learning models have been previously trained and paired with advanced optimizers to assist in this process, providing a low-cost estimate of neutronic codes like MCNP. However, traditional models often require a large quantity of data, usually hundreds or thousands of iterations, to be accurately trained and deployed. Additionally, many neutronic modelers do not have the optimization background to build or deploy an effective shielding optimizer and would benefit from a simple Python framework.A molten salt reactor shielding problem is presented for optimization, utilizing a predictive machine learning model requiring only 21 iterations for training. This model is paired with a closed-loop optimizer using the Gekko Optimization Suite for Python. A baseline shield around the core is presented, and mass and cost constraints are applied at 172 metric tons and $1.4 million, respectively. An optimal shield is generated using the closed-loop optimizer (0.0377mREM/hr), allowing a similarly small dose to that of the baseline, but at a 10.8% reduced mass, a 11.9% reduced material cost, and 61 times computational cost reduction (98.4% improvement). The simple shield optimization framework resulting from this work offers acceleration and simplification of work for shield designers, allowing them to create improved designs in a fraction of the time.

Retraction Note: Hybrid approaches of convolutional network and support vector machine for American sign language prediction

Retraction Note: Hybrid approaches of convolutional network and support vector machine for American sign language prediction

Prediction Models for Intravenous Immunoglobulin Non-Responders of Kawasaki Disease Using Machine Learning.

BACKGROUNDAND OBJECTIVE: Intravenous immunoglobulin (IVIG) is a prominent therapeutic agent for Kawasaki disease(KD) that significantly reduces the incidence of coronary artery anomalies. Various methodologies, including machine learning, have been employed to develop IVIG non-responder prediction models; however, their validation and reproducibility remain unverified.This study aimed to develop a predictive scoring system for identifying IVIG nonresponders andrigorously test the accuracy and reliability of this system. METHODS: The study included an exposure group of 228 IVIG non-responders and a control group of 997 IVIG responders. Subsequently, a predictive machine learning model was constructed. The Shizuoka score, including variables such as the "initial treatment date" (cutoff: < 4 days), sodium level (cutoff: < 133 mEq/L), total bilirubin level (cutoff: ≥ 0.5 mg/dL), and neutrophil-to-lymphocyte ratio (cutoff: ≥ 2.6), was established. Patients meeting two or more of these criteria were grouped as high-risk IVIG non-responders. Using the Shizuoka score to stratify IVIG responders, propensity score matching was used to analyze 85 patients each for IVIG and IVIG-added prednisolone treatment in the high-risk group. In the IVIG plus prednisolone group, the IVIG non-responder count significantly decreased (p < 0.001), with an odds ratio of 0.192 (95% confidence interval 0.078-0.441). CONCLUSIONS: Intravenous immunoglobulin non-responders were predicted using machine learning models and validated using propensity score matching. The initiation of initial IVIG-added prednisolone treatment in the high-risk group identified by the Shizuoka score, crafted using machine learning models, appears useful for predicting IVIG non-responders.

Implementation of exact thermal analysis in extended Oxley’s predictive machining theory

Finding the exact solution to the generated temperatures that are more closely aligned with the actual nature of the process is important while conducting analytical modeling of the chip formation process in metal cutting. The extended Oxley’s predictive machining theory is implemented with the exact thermal analysis approach. This study presents temperature distributions near the shear plane on the workpiece side along with an exact temperature prediction for orthogonal cutting using the Komanduri-Hou model for the shear plane heat source. An explicit solution is also derived to obtain the tool-chip interface temperatures. The intricate formulae utilized in the original research study of Komanduri-Hou are solved using MATLAB code. The approach used here makes it easier to use the exact solution in future research for various categories of materials. The temperature distribution plots help to clarify the metal-cutting procedure and understand the different heat zones. The outcomes of shear angle, forces, stresses, strains, strain rates, and average temperatures are consistent with those reported in earlier studies.

Leveraging Machine Learning Approaches to Predict Organic Carbon Abundance in Mars‐Analog Hypersaline Lake Sediments

AbstractModern advancements in laboratory and instrumental techniques in astrobiology have improved our life detection capabilities on both Earth and beyond. These advancements have also increased the complexity of data often resulting in data sets that are characterized by complex and non‐linear relationships. Machine learning methods are underutilized in astrobiology; however, these methods are extremely effective at revealing structure and patterns in complex data sets when paired with the right algorithms. Here, we employ a series of classification and regression algorithms to predict the abundance of organic carbon (OC) from X‐ray fluorescence (XRF) heavy element (&gt;Mg) data in dynamic Mars‐analog hypersaline lake sediments. More specifically, we constructed models using the random forest, k‐nearest neighbors (KNN), support vector machine, and logistic regression algorithms. Overall, our trained models showed good performance with predicting the abundance of OC, with accuracies from 80% to 94%. Machine learning approaches such as classification and regression algorithms offer insight into complex data while providing agnostic insights, ultimately creating a more efficient search for OC. We applied our trained model on XRF data from Martian soil using rover‐based (PIXL) and orbital (Odyssey) data sets to produce probability predictions of OC abundance. Our predictions show a high probability that OC abundance is low which is comparable to OC data from recently landed missions. These results highlight the potential for predictive machine learning models to be trained on data from analog environments on Earth and then applied to extraterrestrial targets, ultimately, improving life detection efforts.

DESIGN A CIRCUIT FOR HUMIDITY PREDICTION USING CMOS TECHNOLOGY AND IMPLEMENTING ANN

This research paper presents the knowledge about Humidity prediction model based on Arduino Uno, DHT11 Sensor and 16*2 LCD display. The model combines real-time data acquisition with predictive machine learning algorithm to estimate future humidity levels. The microcontroller of Arduino processes from the DHT sensor and shows the predicted and actual humidity data in the LCD display. This system gives a low-cost and user-friendly solution for humidity monitoring in various environments. Keyword: Arduino UNO, DHT-11, 16*2 LCD

Predictive machine learning approaches for perovskites properties using their chemical formula: towards the discovery of stable solar cells materials

Predictive machine learning approaches for perovskites properties using their chemical formula: towards the discovery of stable solar cells materials

Predictive machine learning model for mechanical dilatation in transvenous lead extraction procedures

Abstract Background Transvenous lead extraction (TLE) remains a procedure that requires a high level of expertise, with a doubled risk of death and clinical failure when performed in low-volume centers compared to high-volume ones (1). Purpose The aim of this study was to create a machine learning (ML)-based risk stratification system for predicting the need for mechanical dilatation in patients undergoing TLE due to infection. Methods We designed a ML-based risk stratification system trained with data from our registry to predict the need for mechanical dilatation in patients undergoing TLE for infection. An extensive evaluation of 5 different ML models (k-nearest neighbors, support vector machine, decision tree, and decision tree ensembles, such as random forest and gradient boosting machine) was conducted to identify a classifier with the highest potential to correctly predict previously unseen patients. Results Data to train the model was extracted from our 25-year registry of patients undergoing TLE (June 1998 – March 2023), for a total of 491 patients (77.8% male; age 69.7 ± 12.8 years) and 938 leads (ICD 21.2%; pacing 78.8%; indwelling time 61 ± 60 months) removed with success in 100% of cases. Each patient was represented by a set of 21 attributes (14 clinical, 7 device-related). Manual traction (MT) was used in 27.5% of cases, and mechanical dilatation (MD) was employed in the remaining 72.5% of cases. 5-fold nested cross validation was used to estimate performances: in turn, 393 patients were used for training and model selection, and 98 patients were used for independent testing. According to the evaluation, Gradient Boosting Machine performed best, achieving test accuracy of 89% (+/- 2% std. dev.), test sensitivity of 95% (+/- 3% std. dev.), test specificity of 73% (+/- 8% std. dev.), test AUROC of 92% (+/- 1% std. dev.). A further interpretability analysis on the best performing decision tree was conducted, showing remarkable adherence between the internal decisions taken by the model to make predictions and the current clinical practice for TLE. Conclusion ML models have enhanced the prediction of MD requirements in TLE procedures. This data may support the referral of patients to specialized and high-volume centers, thereby improving preoperative risk assessment.Feature importanceROC Curves

Machine-Learning-Based Predictive Models for Compressive Strength, Flexural Strength, and Slump of Concrete

The process of concrete production involves mixing cement, water, and other materials. The quantity of each of these materials results in a performance that is particularly estimated in terms of compressive or flexural strength. It has been observed that the final performance of concrete has a high variance and that traditional formulation methods do not guarantee consistent results. Consequently, designs tend to be over-designed, generating higher costs than required, to ensure the performance committed to the client. This study proposes the construction of predictive machine learning models to estimate compressive or flexural strength and concrete slump. The study was carried out following the Team Data Science Process (TDSP) methodology, using a dataset generated by the Colombian Ready Mix (RMX) company Cementos Argos S.A. over five years, containing the quantity of materials used for different concrete mixes, as well as performance metrics measured in the laboratory. Predictive models such as XGBoost and neural networks were trained, and hyperparameter tuning was performed using advanced techniques such as genetic algorithms to obtain three models with high performance for estimating compressive strength, flexural strength, and slump. This study concludes that it is possible to use machine learning techniques to design reliable concrete mixes that, when combined with traditional analytical methods, could reduce costs and minimize over-designed concrete mixes.

Predicting the recurrence of spontaneous intracerebral hemorrhage using a machine learning model.

Recurrence can worsen conditions and increase mortality in ICH patients. Predicting the recurrence risk and preventing or treating these patients is a rational strategy to improve outcomes potentially. A machine learning model with improved performance is necessary to predict recurrence. We collected data from ICH patients in two hospitals for our retrospective training cohort and prospective testing cohort. The outcome was the recurrence within one year. We constructed logistic regression, support vector machine (SVM), decision trees, Voting Classifier, random forest, and XGBoost models for prediction. The model included age, NIHSS score at discharge, hematoma volume at admission and discharge, PLT, AST, and CRP levels at admission, use of hypotensive drugs and history of stroke. In internal validation, logistic regression demonstrated an AUC of 0.89 and precision of 0.81, SVM showed an AUC of 0.93 and precision of 0.90, the random forest achieved an AUC of 0.95 and precision of 0.93, and XGBoost scored an AUC of 0.95 and precision of 0.92. In external validation, logistic regression achieved an AUC of 0.81 and precision of 0.79, SVM obtained an AUC of 0.87 and precision of 0.76, the random forest reached an AUC of 0.92 and precision of 0.86, and XGBoost recorded an AUC of 0.93 and precision of 0.91. The machine learning models performed better in predicting ICH recurrence than traditional statistical models. The XGBoost model demonstrated the best comprehensive performance for predicting ICH recurrence in the external testing cohort.

Future of Pharmaceutical Industry: Role of Artificial Intelligence, Automation and Robotics

Background The future of smart factories and pharmaceutical industries has evolved significantly since the 19th century. Computers have been used in the pharmaceutical field since the 1980s with the emergence of Artificial Intelligence (AI). In addition, automation and robotics are used in the pharmaceutical industry to improve the efficiency of pharmaceutical development and production. Purpose The present review article covers the future roles of AI, automation and robotics in pharmaceutical industries. Methodology The current review article employed a comprehensive search strategy across relevant databases, including PubMed, Scopus and Web of Science, utilizing keywords such as AI, Automation, Robotics and Pharmaceutical Industries. The articles considered a focus on recent advancements and emerging trends in the intersection of AI, automation and robotics within pharmaceutical sectors. Results AI, incorporating predictive machine learning and reasoning techniques, aids in the preclinical identification of molecules and forecasting potential lead compounds before conducting clinical trials. automation offers significant benefits in monitoring and predictive maintenance of production lines, power distribution and control machines. Robotic process automation improves efficiency by connecting computer terminals to handle various manufacturing process elements. Conclusion Artificial intelligence, automation and Robotics have sparked innovations in the healthcare business, benefiting the global ecosystem and healthcare delivery. Incorporating these advanced tools in pharmaceutical industries from raw material selection to final product development could improve the quality and safety of the pharmaceutical products and reduce the time and cost.

TrG2P: A transfer-learning-based tool integrating multi-trait data for accurate prediction of crop yield

Yield prediction is the primary goal of genomic selection (GS)-assisted crop breeding. Because yield is a complex quantitative trait, making predictions from genotypic data is challenging. Transfer learning can produce an effective model for a target task by leveraging knowledge from a different, but related, source domain and is considered a great potential method for improving yield prediction by integrating multi-trait data. However, it has not previously been applied to genotype-to-phenotype prediction owing to the lack of an efficient implementation framework. We therefore developed TrG2P, a transfer-learning-based framework. TrG2P first employs convolutional neural networks (CNN) to train models using non-yield-trait phenotypic and genotypic data, thus obtaining pre-trained models. Subsequently, the convolutional layer parameters from these pre-trained models are transferred to the yield prediction task, and the fully connected layers are retrained, thus obtaining fine-tuned models. Finally, the convolutional layer and the first fully connected layer of the fine-tuned models are fused, and the last fully connected layer is trained to enhance prediction performance. We applied TrG2P to five sets of genotypic and phenotypic data from maize (Zea mays), rice (Oryza sativa), and wheat (Triticum aestivum) and compared its model precision to that of seven other popular GS tools: ridge regression best linear unbiased prediction (rrBLUP), random forest, support vector regression, light gradient boosting machine (LightGBM), CNN, DeepGS, and deep neural network for genomic prediction (DNNGP). TrG2P improved the accuracy of yield prediction by 39.9%, 6.8%, and 1.8% in rice, maize, and wheat, respectively, compared with predictions generated by the best-performing comparison model. Our work therefore demonstrates that transfer learning is an effective strategy for improving yield prediction by integrating information from non-yield-trait data. We attribute its enhanced prediction accuracy to the valuable information available from traits associated with yield and to training dataset augmentation. The Python implementation of TrG2P is available at https://github.com/lijinlong1991/TrG2P. The web-based tool is available at http://trg2p.ebreed.cn:81.