Implementing Machine Learning Research Articles

Application of Machine Learning Techniques in Calibration and Data Reduction of Multi-Hole Probes

Abstract This work presents procedures to implement machine learning methods in the existing algorithms for multi-hole probe calibrations and data reduction. It is shown here, that utilizing artificial neural networks (ANNs) can reduce the amount of calibration data that needs to be acquired in order to obtain a specific calibration uncertainty, by more than 50% while simultaneously reducing data reduction times significantly. ANNs were used instead of the surface fitting methods, where first, the directional calibration coefficients related to the flow angles are calculated based on the pressure measurements, and then the flow angles are used as a set of the input parameters for the following ANNs to define Mach number and static and total pressure iteratively. In a second approach, novel calibration coefficients were used to directly relate the pressure measurements from five-hole probe to the quantities of interest thus, eliminating the need for iterative algorithms used in the conventional surface fitting methods. The advantageous features of this method are an average increase of less than 1% in the calibration uncertainty for flow angles and significant reduction of the data reduction times (few seconds). In addition, we confirmed the methodology to avoid over-fitting and under-fitting.

Systematic Review of Machine Learning and Deep Learning Techniques for Spatiotemporal Air Quality Prediction

Background: Although computational models are advancing air quality prediction, achieving the desired performance or accuracy of prediction remains a gap, which impacts the implementation of machine learning (ML) air quality prediction models. Several models have been employed and some hybridized to enhance air quality and air quality index predictions. The objective of this paper is to systematically review machine and deep learning techniques for spatiotemporal air prediction challenges. Methods: In this review, a methodological framework based on PRISMA flow was utilized in which the initial search terms were defined to guide the literature search strategy in online data sources (Scopus and Google Scholar). The inclusion criteria are articles published in the English language, document type (articles and conference papers), and source type (journal and conference proceedings). The exclusion criteria are book series and books. The authors’ search strategy was complemented with ChatGPT-generated keywords to reduce the risk of bias. Report synthesis was achieved by keyword grouping using Microsoft Excel, leading to keyword sorting in ascending order for easy identification of similar and dissimilar keywords. Three independent researchers were used in this research to avoid bias in data collection and synthesis. Articles were retrieved on 27 July 2024. Results: Out of 374 articles, 80 were selected as they were in line with the scope of the study. The review identified the combination of a machine learning technique and deep learning techniques for data limitations and processing of the nonlinear characteristics of air pollutants. ML models, such as random forest, and decision tree classifier were among the commonly used models for air quality index and air quality predictions, with promising performance results. Deep learning models are promising due to the hyper-parameter components, which consist of activation functions suitable for nonlinear spatiotemporal data. The emergence of low-cost devices for data limitations is highlighted, in addition to the use of transfer learning and federated learning models. Again, it is highlighted that military activities and fires impact the O3 concentration, and the best-performing models highlighted in this review could be helpful in developing predictive models for air quality prediction in areas with heavy military activities. Limitation: This review acknowledges methodological challenges in terms of data collection sources, as there are equally relevant materials on other online data sources. Again, the choice and use of keywords for the initial search and the creation of subsequent filter keywords limit the collection of other relevant research articles.

A study of machine learning applications in healthcare

Abstract. Machine learning, a sub-field of artificial intelligence, has numerous algorithms with applications in image recognition and classification, natural language processing, data prediction, medical diagnosis, and more, achieving significant results. Nowadays, the application of many algorithms of machine learning in the medical field has become a research hotspot and attracted wide attention from society, which plays a key role in disease screening, disease prediction, and assisted diagnosis of diseases. In this paper, we have developed a new approach to the medical field that can be applied in the healthcare industry by using technologies such as k-nearest neighbor (KNN), recurrent neural network (RNN), plain Bayesian (NB), decision tree (DTT), random forest (RF), deep convolutional adversarial network (DCAN), generative adversarial network (GAN), deep convolutional generative adversarial network (DCGAN),deep convolutional neural network (CNN), and support vector machine (SVM). By referring to the existing related literature and application practice results, this paper provides a systematic introduction to the application and research of machine learning in the medical field. This paper also analyses and describes the challenges and difficulties in the implementation of machine learning in the medical field.

Peptipedia v2.0: a peptide sequence database and user-friendly web platform. A major update.

In recent years, peptides have gained significant relevance due to their therapeutic properties. The surge in peptide production and synthesis has generated vast amounts of data, enabling the creation of comprehensive databases and information repositories. Advances in sequencing techniques and artificial intelligence have further accelerated the design of tailor-made peptides. However, leveraging these techniques requires versatile and continuously updated storage systems, along with tools that facilitate peptide research and the implementation of machine learning for predictive systems. This work introduces Peptipedia v2.0, one of the most comprehensive public repositories of peptides, supporting biotechnological research by simplifying peptide study and annotation. Peptipedia v2.0 has expanded its collection by over 45% with peptide sequences that have reported biological activities. The functional biological activity tree has been revised and enhanced, incorporating new categories such as cosmetic and dermatological activities, molecular binding, and antiageing properties. Utilizing protein language models and machine learning, more than 90 binary classification models have been trained, validated, and incorporated into Peptipedia v2.0. These models exhibit average sensitivities and specificities of 0.877±0.0530 and 0.873±0.054, respectively, facilitating the annotation of more than 3.6 million peptide sequences with unknown biological activities, also registered in Peptipedia v2.0. Additionally, Peptipedia v2.0 introduces description tools based on structural and ontological properties and user-friendly machine learning tools to facilitate the application of machine learning strategies to study peptide sequences. Database URL: https://peptipedia.cl/.

Harnessing Big Data for Machine Learning (Strategies, Approaches, and Challenges)

This paper explores the dynamic relationship between big data and machine learning, highlighting the key strategies, methodologies, and challenges associated with their integration. The convergence of these technologies presents transformative opportunities across industries, but it also introduces complexities in terms of data management, infrastructure, and real-time processing. This study examines the role of big data in fueling machine learning models, discusses critical success factors, and identifies best practices for implementing machine learning at scale.

Pesticide Residue Coverage Estimation on Citrus Leaf Using Image Analysis Assisted by Machine Learning

Globally, the agricultural industry has benefited from using pesticides to minimize crop losses. Nevertheless, the indiscriminate overuse of pesticides has led to significant risks associated with a detrimental impact on the environment and human health. Therefore, emerging concerns of pesticide residue found in crops, food, and livestock are a pressing issue. To address the above challenges, there have been many efforts made towards implementing machine learning to enable precision agricultural practices to reduce pesticide overuse. As of today, there are no guiding digital tools available for citrus growers to provide pesticide residue leaf coverage analysis after foliar applications. Herein, we are the first to report software assisted by lightweight machine learning (ML) to determine the Kocide 3000 and Oxytetracycline (OTC) residue coverage on citrus leaves based on image data analysis. This tool integrates a foundational Segment Anything Model (SAM) for image preprocessing to isolate the area of interest. In addition, Kocide 3000 and Oxytetracycline (OTC) residue coverage analysis was carried out using a specialized Mask Region-Based Convolutional Neural Network (CNN). This CNN was pre-trained on the MS COCO dataset and fine-tuned by training with acquired datasets in laboratory and field conditions. The developed software demonstrated excellent performance on both pesticides’ accuracy, precision, and recall, and F1 score metrics. In summary, this tool has the potential to assist growers with the decision-making process for controlling pesticide use rate and frequency, minimizing pesticide overuse.

Machine learning and FPGA implementation for predicting luminance decay and temperature distribution in micro-LED displays

AbstractA machine learning-based method predicts the luminance decay of micro light emitting diode (micro-LED) displays, utilizing temperature distribution and degradation experiments alongside implementation on field-programmable gate array (FPGA). Micro-LEDs, used in outdoor and indoor billboards, experience degradation due to harsh environmental conditions. To model temperature distribution in indoor advertising displays using minimal data, a temperature model is constructed based on sensor from the panel and thermal images captured by a camera, in relation to the display pattern. The input data is processed using an FPGA, which transmits the sensed temperatures and display patterns to the micro-LED panel. Multilayer Perceptron (MLP), a type of neural network, predicts the temperature distribution over the panel surface, achieving an error of less than 1.1 °C. Separate degradation models forecast luminance decay, factoring in enclosure temperature, input current, and usage time, with distinct models for red, green, and blue LEDs. Exponential curve-fitting and interpolation, following TM-21 standards, ensure long-term accuracy. The luminance decay predictions have an average error below 1.05% (approximately 9 nits). The FPGA implementation minimizes resource consumption while maintaining prediction accuracy, making it suitable for real-time applications. The degradation model accurately predicts performance over tens or even hundreds of thousands of hours, aligning with the exponential decay trends defined by TM-21.

FCG-MFD: Benchmark function call graph-based dataset for malware family detection

Cyber crimes related to malware families are on the rise. This growth persists despite the prevalence of various antivirus software and approaches for malware detection and classification. Security experts have implemented Machine Learning (ML) techniques to identify these cyber-crimes. However, these approaches demand updated malware datasets for continuous improvements amid the evolving sophistication of malware strains. Thus, we present the FCG-MFD, a benchmark dataset with extensive Function Call Graphs (FCG) for malware family detection. This dataset guarantees resistance against emerging malware families by enabling security systems. Our dataset has two sub-datasets (FCG & Metadata) (1,00,000 samples) from VirusSamples, Virusshare, VirusSign, theZoo, Vx-underground, and MalwareBazaar curated using FCGs and metadata to optimize the efficacy of ML algorithms. We suggest a new malware analysis technique using FCGs and graph embedding networks, offering a solution to the complexity of feature engineering in ML-based malware analysis. Our approach to extracting semantic features via the Natural Language Processing (NLP) method is inspired by tasks involving sentences and words, respectively, for functions and instructions. We leverage a node2vec mechanism-based graph embedding network to generate malware embedding vectors. These vectors enable automated and efficient malware analysis by combining structural and semantic features. We use two datasets (FCG & Metadata) to assess FCG-MFD performance. F1-Scores of 99.14% and 99.28% are competitive with State-of-the-art (SOTA) methods.

Building performance optimization through sensitivity Analysis, and economic insights using AI

Optimizing building designs for energy efficiency and occupant comfort presents significant challenges due to the complex and often conflicting requirements of various stakeholders. Consequently, this study conducts a multifaceted sensitivity and economic impact analysis that aims to improve building performance in terms of energy efficiency and occupant comfort by implementing machine learning techniques. Using a broad dataset comprising of 12,000 energy simulation runs for Tvedestrand Upper Secondary School in Norway, several machine learning models were employed with Multi-Layer Perceptron outperforming the others. In addition, several sensitivity analysis methods were used to explore the influence of individual parameters on building performance. The analysis reveals that ventilation rate, room depth, U-value of the facade, and heat gains significantly affect energy consumption. Economic impact analysis was also carried out to compare the cost-effectiveness of traditional HVAC systems with Building Management System (BMS) HVAC solutions. The BMS HVAC system shows significantly lower operational costs over time, with investment costs averaging around 1200 Norwegian kroner (NOK)/m2 and operational costs of approximately 150 NOK/m2 per year. Sensitivity analysis under different economic scenarios highlights the economic viability of the BMS HVAC system. This study identifies optimal building parameters that balance energy efficiency and thermal comfort, achieving total energy consumption between 11.05 and 22.51 kWh/m2 and zero discomfort hours (h > 26 °C). In sum, the findings offer valuable insights for stakeholders, enabling informed decisions about sustainable building design and energy efficiency improvements, ensuring both technical soundness and financial viability under a wide range of conditions, while using the tested tools.

Implementing machine learning to characterize solar cells and optimize their efficiency

Implementing machine learning to characterize solar cells and optimize their efficiency

The Role of Machine Learning in Enhancing Corporate Financial Planning

Aim: To examine the role of machine learning in enhancing corporate financial planning. Problem Statement: In the past, corporate financial planning relied on statistical models and expert judgment which are often constrained by predefined assumptions and historical data. By so doing, they usually struggled to handle the non-linear and dynamic nature of financial markets causing limitations in their responsiveness and predictive power. Significance of Study: There is need to incorporate machine learning in corporate financial planning to provide a transformative technique via the introduction of advanced algorithms which are capable of analyzing diverse and vast datasets to uncover sophisticated relationships and patterns. Methodology: Recent relevant published articles in the area of machine learning in enhancing corporate financial planning were consulted. Relevant articles were sourced from the internet using the Google search engine. The abstract of the consulted published articles were thoroughly examined to study their relevance to the subject matter. Discussion: This review article examines the role of machine learning in enhancing corporate financial planning. It was found that machine learning techniques have crucial roles to play in the context of corporate financial planning via decision-making improvement, risk management, operational efficiency enhancement, fraud detection and enabling personalized experiences. Also, Machine Learning technologies have a wide variety of uses in corporate financial planning which enable banks and insurance companies in delivering tailored experiences that give their customers some preferences. Common identified machine learning approaches in corporate financial planning include predictive analytics, natural language processing, computer vision, reinforcement learning and anomaly detection approaches. Major areas of machine learning application include dynamic pricing and offers; regulatory compliance; personalized customer service; predictive analytics for customer retention; personalized product recommendations and so on. Furthermore, the implementation of Machine Learning (ML) in corporate financial planning and financial services industry provides many chances for enhancing customer experiences, improving efficiency and driving innovation. However, ML poses numerous challenges which should be addressed to attain the complete potential of these technologies. Conclusion: As technology continues to advance, financial institutions will need to route the challenges of bias mitigation, transparency and regulatory compliance while the ML full potential is equally leveraged. Also, machine learning has the potential to transform corporate financial planning via the provision of more adaptive, accurate and real-time insights.

Unraveling Plasmonic Tilted Fiber Bragg Gratings (TFBG): A Journey From “Anomalous Resonances” to Refined Refractometry

AbstractPlasmonic tilted fiber Bragg gratings (TFBGs) have emerged as versatile tools for refractometric analyses and biochemical sensing. Their applications have significantly blossomed these last years, from proteins and cellular bioassays to operando monitoring in batteries, to cite just a few. They are widely recognized for their cutting‐edge performance and low limits of detection, arising from their dense multimodal spectral nature featuring tens of narrowband cladding mode resonances. Their comb‐like spectrum is so rich that numerous demodulation techniques have been reported, without benchmark of their relative performance while they possess important distinctions. This review highlights developments in detangling techniques from the pioneering works based on single‐peak analysis up to the most recent approaches involving Fourier analysis, the implementation of machine learning, and cascaded spectral decomposition processes. To fairly compare the different techniques of the literature, we implemented each analysis on original experimental refractometric calibrations, revealing the assets of the most updated methods. This paper therefore reviews these demodulation techniques based on the same datasets, obtained under the same conditions. We show and discuss the results obtained from bioassays and pinpoint the importance of advanced analytical methodologies to maximize the reproducibility, reliability and performance of plasmonic‐based TFBGs biosensors.

Machine learning prediction models for individualized technical success of chronic total occlusion revascularization using anatomical input

Abstract Introduction Chronic total occlusion revascularization (CTO PCI) remains a challenging procedure in the realm of percutaneous coronary interventions. Efforts such as implementing the hybrid algorithm for CTO crossing have been made. Interventional techniques are evolving, and complexity scores such as the J-CTO score might get outdated. Machine learning models can be used to overcome this limitation by constantly providing new data and improving the models by continuous data feeding. In this study, we introduced machine learning models to predict individualized technical success of CTO PCI using anatomical coronary information and baseline information. Methods Consecutive patients undergoing percutaneous coronary intervention of chronic total occlusions were enrolled in this study. Angiographic data and lesion characteristics from coronary angiography were extracted. Machine learning models were applied to predict technical success. A total of 271 patients were used to validate and train the models and 50 separate patients for testing. Data was normalized and fed to several classifying machine learning models including decision trees (DT), random forest (RF), extreme gradient boosting (XGB), CatBoost (CB) classifiers, and light gradient boosting (LGB). Finally, accuracy, precision, recall and F1-score of the selected hyperparameters and cross-validation was performed. Results CB was the best performing model with an accuracy of 0.81, precision of 0.83 and recall of 0.98. The F1 score was 0.90. RF was the second-best performing model with accuracy of 0.77, precision of 0.82, recall of 0.92 and F1 score 0.87. The tuned models performed modestly with RF having a low F1 score of 0.44, due to small sample size. In the tuned models. LGB had a higher F1 score and a relatively high accuracy (0.782) and precision (0.508). The feature importance matrix found age, CTO length, reference diameter to have the highest value among the lesion complexity parameters. Discussion: Overall, the normal-run CB was the best model for predicting the technical success after a CTO PCI with a high accuracy. The fine-tuned and cross-validated models performed modestly because of the relatively small, unbalanced dataset and overfitting. However, data showed promising first results in implementing machine learning into predicting interventional outcomes. Further studies like data augmentation and providing a weighted dataset would increase the model performance and provide a very precise statement whether a patient would have increased chances to have a successful PCI based on baseline anatomic information. Conclusion This is one of the first studies to predict technical success of CTO PCI using angiographic baseline parameters using machine learning. Future studies with larger patient cohorts are needed to involve deploying the selected model in larger patient cohorts including external validation and assessing its performance with new, unseen data.Figure 1.Machine learning modelsFigure 2.Feature importance matrix

Discovery of Therapeutic Antibodies Targeting Complex Multi-Spanning Membrane Proteins.

Complex integral membrane proteins, which are embedded in the cell surface lipid bilayer by multiple transmembrane spanning polypeptides, encompass families of proteins that are important target classes for drug discovery. These protein families include G protein-coupled receptors, ion channels, transporters, enzymes, and adhesion molecules. The high specificity of monoclonal antibodies and the ability to engineer their properties offers a significant opportunity to selectively bind these target proteins, allowing direct modulation of pharmacology or enabling other mechanisms of action such as cell killing. Isolation of antibodies that bind these types of membrane proteins and exhibit the desired pharmacological function has, however, remained challenging due to technical issues in preparing membrane protein antigens suitable for enabling and driving antibody drug discovery strategies. In this article, we review progress and emerging themes in defining discovery strategies for a generation of antibodies that target these complex membrane protein antigens. We also comment on how this field may develop with the emerging implementation of computational techniques, artificial intelligence, and machine learning.

Machine Learning-Based Stroke Prediction: A Critical Analysis

Stroke is a critical public health issue that frequently has long-term impairment and negative effects. Devising innovative methods that enable timely and accurate identification and intervention is crucial. In this regard, machine learning (ML) and deep learning (DL) approaches of artificial intelligence (AI) play a crucial role in reducing the incidence of strokes. This study systematically analyzed articles from 2012 to 2022 using the PRISMA Method. PRISMA is a tool that facilitates researchers' access to an online platform for self-directed learning. The cumulative quantity of articles gathered for ten years reached 1405 from five databases. However, only 79 relevant articles were used for identification. The main objective was to provide a thorough taxonomy that classifies using and implementing machine learning approaches for stroke prediction. The results of this experiment confirm that machine-learning techniques have a great deal of potential for accurate stroke prediction. Nevertheless, challenges such as biased data and algorithms and the need for models that can be adjusted to accommodate various demographics and healthcare systems continue to exist. It is essential to recognize the need for additional research projects that thoroughly explore potential data biases, algorithmic biases, and the generalizability of models across various demographics and healthcare systems. More research is necessary to further the literature on the complete assessment of machine learning models in precisely forecasting stroke occurrences.

Performance Comparison of Random Forest (RF) and Classification and Regression Trees (CART) for Hotel Star Rating Prediction

Purpose: This study proposes to evaluate the effectiveness of Random Forest (RF) compared to Classification and Regression Trees (CART) in prediction of hotel star ratings. The objective is to identify the algorithm that provides the most reliable and accurate classification outcomes based on diverse hotel attributes in accordance with the standard categorization of star hotel categories. This is necessary due to the important role of accurate star ratings in guiding consumer choices and enhancing competitive positioning in the hospitality industry. Method: This study conducted a comprehensive dataset about Hotel in Banyumas Regency, including location, facilities, the size of rooms, type of rooms, price of rooms, and customer reviews, subjected to training through both RF and CART algorithms. Both algorithms are evaluated using accuracy, precision, recall, and F1 score. Additionally, both algorithms due to in the same preprocessing while performing hyperparameter tuning improve the efficacy of each model. Result: The results showed that RF achieved the best overall accuracy and robustness than CART across all tests conducted. Furthermore, RF also outperformed CART in classification effectiveness among classes, including enhanced precision and recall scores across multiple stars rating categories, signifying increased generalization and consistency in classification tasks. RF classifier consistently surpassed the CART classifier in terms of both accuracy and F1-score throughout all random states and test sizes, with a highest score of 0.9932 at a random state of 100 and a test size of 0.4. The most reliable results were obtained using RF with 42 random states and a test size of 0.2, resulting in an accuracy of 0.9909, precision of 1.0, recall of 1.0, and F1 score of 1.0. Simultaneously, CART shows values of 0.9818, 1.0, 1.0, and 1.0, respectively, while maintaining the same variation. This consistent performance, regardless of fluctuations, illustrates the robustness and suitability of RF for classification tasks compared to CART. Novelty: This study offered new insights about the implementation of machine learning about hotel star rating predictions using RF and CART algorithms. Also, the novelty of the collected hotel dataset used in this study. A detailed comparative analysis was also provided, contributing to the existing literature by showing the effectiveness of RF over CART for this specific application. Future studies could explore the integration of additional machine learning methods to further enhance prediction accuracy and operational efficiency in the hospitality industry.

Real-time deep learning-assisted mechano-acoustic system for respiratory diagnosis and multifunctional classification

Epidermally mounted sensors using triaxial accelerometers have been previously used to monitor physiological processes with the implementation of machine learning (ML) algorithm interfaces. The findings from these previous studies have established a strong foundation for the analysis of high-resolution, intricate signals, typically through frequency domain conversion. In this study we integrate a wireless mechano-acoustic sensor with a multi-modal deep learning system for the real-time analysis of signals emitted by the laryngeal prominence area of the thyroid cartilage at frequency ranges up to 1 kHz. This interface provides real-time data visualization and communication with the ML server, creating a system that assesses severity of chronic obstructive pulmonary disease and analyzes the user’s speech patterns.

Unmasking phishers: ML for malicious certificate detection

Phishing attacks increasingly use digital certificates to appear safe to users, and the frequency of such attacks has surged in recent years. As an example, around 80% of the 2021 phishing attacks used digital certificates to appear legitimate. The most common methods today for detecting phishing websites rely on users reporting the websites to phishing repositories, where they are then confirmed. This process can be slow, allowing the attacker to have time to have their phishing attack out on the Internet. Newer methods that implement machine learning models for the detection of phishing websites based on their digital certificate have been shown to be effective. This paper presents a system that uses certificate and domain name related features along with machine learning methods for the detection of phishing websites. To develop the system, data was collected from PhishTank and Tranco for domain names, and Censys was used for certificate retrieval. The domain related features are partly engineered using a time-series based deep learning model to get a vector representation of the domain name. Using the features engineered from the certificate and domain name, classical machine learning classifiers are trained and compared. Enriching the feature set with the vector representation of the domain names results in higher performance in distinguishing suspicious certificates from benign ones, going from an F1-score of 0.77 for a feature set solely based on certificate-related features to a performance of 0.89 with the enriched feature set. A time-based evaluation reflects the same performance with an F1-score of 0.88, which is an improvement compared to existing approaches to feature engineering.

Identification of Severe Acute Exacerbations of Chronic Obstructive Pulmonary Disease Subgroups by Machine Learning Implementation in Electronic Health Records.

Acute exacerbations of COPD (AECOPD) are heterogeneous. Machine learning (ML) has previously been used to dissect some of the heterogeneity in COPD. The widespread adoption of electronic health records (EHRs) has led to the rapid accumulation of large amounts of patient data as part of routine clinical care. However, it is unclear whether the implementation of ML in EHR-derived data has the potential to identify subgroups of AECOPD. Determine whether ML implementation using EHR data from severe AECOPD requiring hospitalization identifies relevant subgroups. This study used two retrospective cohorts of patients with AECOPD (non-COVID-19 and COVID-19) treated at Yale-New Haven Hospital (YNHHS). K-means clustering was used to identify patient subgroups. We identified three subgroups in the non-COVID cohort (n=1,736). Each subgroup had distinct clinical characteristics. The reference subgroup was the largest (n=904), followed by cardio-renal (n = 548) and eosinophilic (n=284). The eosinophilic subgroup had milder severity of AECOPD, including a shorter hospital stay (p<0.01). The cardio-renal subgroup had the highest mortality during (5%) and in the year after hospitalization (30%). Validation of the severe AECOPD classifier in the COVID-19 cohort recapitulated the characteristics seen in the non-COVID cohort. AECOPD subgroups in the COVID-19 cohort had different IL-1 beta, IL-2R, and IL-8 levels (FDR ≤ 0.05. These specific leukocyte and cytokine profiles resulted in inflammatory differences between the AECOPD subgroups based on C-reactive protein levels. Incorporating ML with EHR-data allows the identification of specific clinical and biological subgroups for severe AECOPD.

Sustainable separation of molybdenum from mixed mineral acids generated as semiconductor industry waste streams using tributyl phosphate (TBP) by effects of hybrid machine learning models

This study explores the separation and optimization of molybdenum (Mo) from mixed mineral acids derived from semiconductor industry waste streams with tributyl phosphate (TBP) by implementing machine learning (ML) models. Considerable experimental tests were performed to evaluate the impact of various operational variables on the effectiveness of Mo extraction and stripping. The support vector regression (SVR) paired with harmony search algorithm (HSA), genetic algorithm (GA), and shuffled frog leaping algorithm (SFLA) were employed for enhancement in the separation process and structural optimization. The SVR-SFLA model yielded the most meticulous predictions, identifying optimal extraction conditions with a TBP concentration, mixing time, temperature, and O/A ratio of 50%, 30 min, 25 °C, and 1, respectively, achieving 77.8% efficiency. The derived results from the SVR-SFLA model, in tandem with the McCabe-Theil diagram, indicated a four-stage counter-current extraction process required to achieve a yield exceeding 99%. For the stripping process, the hybrid model indicated optimal conditions with 3 M NH4OH and an A/O ratio of 0.5 at 50 °C for 20 min, requiring two counter-current stages for nearly complete stripping. Feature importance analysis using a random forest algorithm (RFA) highlighted the NH4OH concentration and phase ratio as the most significant factors, contributing 40.3% and 29.1%, respectively, to the stripping from the loaded TBP phase. The final product, obtained after crystallization and thermal decomposition of the strip solutions, was characterized by X-ray diffraction (XRD), thermogravimetric analysis (TGA), scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDS), revealing 99.74% purity for molybdenum trioxide.