Development Of Learning Model Research Articles

Development and optimization of machine learning models for estimation of mechanical properties of linear low-density polyethylene

A hybrid methodology was developed and implemented for estimation of polymeric mechanical properties in rotational moulding process. The considered polymer in this study is linear low-density polyethylene, known as LLDPE, which has extensive application in plastic industry. The mechanical properties of the polymer were assessed and correlated to the oven residence time to build the predictive model of moulding process. A tiny dataset containing only 25 data rows via a number of machine learning models were assessed. Oven residence time is the only input, while the LLDPE's properties including tensile strength, impact strength, and flexure strength are the outputs considered in the machine learning models. We used tree-based ensemble methods for modeling in this work and they are tuned using FA (Firefly Algorithm) optimizer to find optimal hyper-parameters of them. Finally, the optimal models had shown a great performance to predict the output accurately. For tensile strength, the best model (FA-ET) has an R2 value of 0.9994, this score is 0.9995 for impact strength and 0.9968 for flexure strength. The tree-based models tuned in this study revealed to be robust in estimation of polymeric properties and can be used to obtain the products with the best quality.

Learning together: Towards foundation models for machine learning interatomic potentials with meta-learning

The development of machine learning models has led to an abundance of datasets containing quantum mechanical (QM) calculations for molecular and material systems. However, traditional training methods for machine learning models are unable to leverage the plethora of data available as they require that each dataset be generated using the same QM method. Taking machine learning interatomic potentials (MLIPs) as an example, we show that meta-learning techniques, a recent advancement from the machine learning community, can be used to fit multiple levels of QM theory in the same training process. Meta-learning changes the training procedure to learn a representation that can be easily re-trained to new tasks with small amounts of data. We then demonstrate that meta-learning enables simultaneously training to multiple large organic molecule datasets. As a proof of concept, we examine the performance of a MLIP refit to a small drug-like molecule and show that pre-training potentials to multiple levels of theory with meta-learning improves performance. This difference in performance can be seen both in the reduced error and in the improved smoothness of the potential energy surface produced. We therefore show that meta-learning can utilize existing datasets with inconsistent QM levels of theory to produce models that are better at specializing to new datasets. This opens new routes for creating pre-trained, foundation models for interatomic potentials.

Strategies of Automated Machine Learning for Energy Sustainability in Green Artificial Intelligence

Automated machine learning (AutoML) is recognized for its efficiency in facilitating model development due to its ability to perform tasks autonomously, without constant human intervention. AutoML automates the development and optimization of machine learning models, leading to high energy consumption due to the large amount of calculations involved. Hyperparameter optimization algorithms, central to AutoML, can significantly impact its carbon footprint. This work introduces and investigates energy efficiency metrics for advanced hyperparameter optimization algorithms within AutoML. These metrics enable the evaluation and optimization of an algorithm’s energy consumption, considering accuracy, sustainability, and reduced environmental impact. The experimentation demonstrates the application of Green AI principles to AutoML hyperparameter optimization algorithms. It assesses the current sustainability of AutoML practices and proposes strategies to make them more environmentally friendly. The findings indicate a reduction of 28.7% in CO2e emissions when implementing the Green AI strategy, compared to the Red AI strategy. This improvement in sustainability is achieved with a minimal decrease of 0.51% in validation accuracy. This study emphasizes the importance of continuing to investigate sustainability throughout the life cycle of AI, aligning with the three fundamental pillars of sustainable development.

Development of A Learning Model Based on RME for Improving Mathematical Literacy Skills and Students' Mathematical Disposition

This study aims to develop of a learning model based on Realistic Mathematics Education (RME) that are valid, practical, and effective to improve mathematical literacy skills and students' mathematical disposition. This study uses a research and development method with modified Plomp model. This research was carried out at SMP Negeri 9 Medan in the odd semester of the 2023/2024 academic year. The instruments in this study were the Mathematical Literacy Ability Test and the Mathematical Disposition Questionnaire. The data analysis technique in this study used descriptive statistical analysis. The results of this research showed that: (1) the RME-based learning model developed met the criteria for validity, practicality and effectiveness of the learning model; (2) increasing students' mathematical literacy skills using the realistic RME-based learning model developed in terms of the N-Gain in trial I of 0.29 with low criteria and in trial II of 0.59 with medium criteria. (3) increasing students' mathematical disposition abilities using the developed RME learning model in terms of the average in trial I and trial II, namely 5.04.

Development of a machine learning model to improve estimates of material stock and embodied emissions of roads

Material flow analysis is an important tool for estimating material flows and embedded emissions of transport infrastructure. Missing attributes tend to be a major barrier to accurate estimates. In this study a machine learning model is developed to estimate the missing data in a statistics dataset of roads, to enable a bottom-up material stock and flow analysis. The proposed approach was applied to the Swedish road network to predict missing data for road width in the statistical dataset. The predicted hybrid dataset was then used to estimate material stocks, flows, and embodied emissions from Year 2020 to Year 2045 using decarbonization scenarios with a supply chain perspective. The study demonstrates that machine learning models can be used to enable national-level material stock and flow analyses of roads. Multiple machine learning algorithms were tested, and the best performing model achieved an R2 value of 0.784. In the scenario-based analysis, the embodied emissions of Swedish roads could be reduced by up to 51% using available materials.

Development and validation of machine learning models for predicting HER2-zero and HER2-low breast cancers.

To develop and validate machine learning models for HER2-zero and -low using MRI features pre-neoadjuvant therapy (pre-NAT). 516 breast cancer patients post-NAT surgery were randomly divided into training (n = 362) and internal validation sets (n = 154) for model building and evaluation. MRI features (tumor diameter, enhancement type, background parenchymal enhancement, enhancement pattern, percentage of enhancement, signal enhancement ratio, breast edema, and ADC) were reviewed. Logistic regression (LR), support vector machine (SVM), k-nearest neighbor (KNN), and extreme gradient boosting (XGBoost) models utilized MRI characteristics for HER2 status assessment in training and validation datasets. The best-performing model generated a HER2 score, subsequently correlated with pathological complete response (pCR) and disease-free survival (DFS). The XGBoost model outperformed LR, SVM, and KNN, achieving an area under the ROC curve (AUC) of 0.783(95% CI: 0.733-0.833) and 0.787(95% CI: 0.709-0.865) in the validation dataset. Its HER2 score for predicting pCR had an AUC of 0.708 in the training datasets and 0.695 in the validation dataset. Additionally, the low HER2 score was significantly associated with shorter DFS in the validation dataset (HR: 2.748,95% CI: 1.016-7.432, P = 0.037). The XGBoost model could help distinguish HER2-zero and HER2-low breast cancers, and has the potential to predict pCR and prognosis in breast cancer patients undergoing NAT. Human epidermal growth factor receptor 2 (HER2)-low-expressing breast cancer can benefit from the HER2 targeted therapy. Prediction of HER2-low expression is crucial for appropriate management. MRI features offer a solution to this clinical issue.

Prediction of hydroxyl radical exposure during ozonation using different machine learning methods with ozone decay kinetic parameters

The abatement of micropollutants by ozonation can be accurately calculated by measuring the exposures of molecular ozone (O3) and hydroxyl radical (•OH) (i.e., ∫[O3]dt and ∫[•OH]dt). In the actual ozonation process, ∫[O3]dt values can be calculated by monitoring the O3 decay during the process. However, calculating ∫[•OH]dt is challenging in the field, which necessitates developing models to predict ∫[•OH]dt from measurable parameters. This study demonstrates the development of machine learning models to predict ∫[•OH]dt (the output variable) from five basic input variables (pH, dissolved organic carbon concentration, alkalinity, temperature, and O3 dose) and two optional ones (∫[O3]dt and instantaneous ozone demand, IOD). To develop the models, four different machine learning methods (random forest, support vector regression, artificial neural network, and Gaussian process regression) were employed using the input and output variables measured (or determined) in 130 different natural water samples. The results indicated that incorporating ∫[O3]dt as an input variable significantly improved the accuracy of prediction models, increasing overall R2 by 0.01−0.09, depending on the machine learning method. This suggests that ∫[O3]dt plays a crucial role as a key variable reflecting the •OH-yielding characteristics of dissolved organic matter. Conversely, IOD had a minimal impact on the accuracy of the prediction models. Generally, machine-learning-based prediction models outperformed those based on the response surface methodology developed as a control. Notably, models utilizing the Gaussian process regression algorithm demonstrated the highest coefficients of determination (overall R2 = 0.91−0.95) among the prediction models.

Interpretable machine learning models for displacement demand prediction in reinforced concrete buildings under pulse-like earthquakes

This work proposes a novel procedure to guide the development of machine learning models for estimating the seismic demand in existing reinforced concrete (RC) buildings. The proposed approach is organized across two scales. A large-scale (nonparametric) machine learning model is first obtained by means of Gaussian Process Regression (GPR) using all candidate building attributes and intensity measures. SHapley Additive exPlanations (SHAP) values are utilized to facilitate its interpretation and to assist the rational selection of a small subset of intensity measures, which is finally employed to develop a (symbolic) reduced-scale machine learning model by means of Genetic Programming (GP). Simplified models of archetype buildings are adopted to develop machine learning techniques at both scales, in such a way to alleviate the simulation time for preparing large datasets. Refined models representative of actual buildings are instead considered for the unbiased final assessment.The proposed approach is applied to develop predictive machine learning models for the maximum inter-storey drift in bare frames, pilotis frames and frames with infills under pulse-like seismic ground motions. Consequently, the critical examination of the SHAP values revealed the most significant intensity measures and unfolded interesting patterns depending on the occupancy rate of the infills. Moreover, the final assessment demonstrates that this approach allows the management of a non-homogeneous building stock consisting of very diverse structural systems (i.e., spanning from existing buildings designed against gravity loads only to buildings that comply with outdated seismic codes) while providing satisfactory predictions of the seismic demand with minimum computational effort.

Development of an Intelligent Road-Crossing Sensor System for Enhanced Pedestrian Safety

Pedestrian safety at road crossings in urban environments is a critical issue due to the increasing vehicular traffic and high pedestrian density. Traditional traffic control measures often fail to adequately address the dynamic interactions between vehicles and pedestrians, leading to significant risks of accidents. This study proposes the development of an intelligent road-crossing sensor system leveraging advanced technologies such as sensors, artificial intelligence (AI), and the Internet of Things (IoT). The system aims to enhance pedestrian safety by providing real-time alerts and adaptive traffic control, thereby reducing the risk of pedestrian-vehicle collisions. The methodology includes sensor selection, data collection, machine learning model development, and system implementation and testing. The potential benefits and challenges of deploying such systems are also discussed, offering insights into creating smarter and safer urban environments. Keywords: Pedestrian safety, intelligent sensor systems, road crossing, vehicle detection, traffic signals

CALiSol-23: Experimental electrolyte conductivity data for various Li-salts and solvent combinations

Ion transport in non-aqueous electrolytes is crucial for high performance lithium-ion battery (LIB) development. The design of superior electrolytes requires extensive experimentation across the compositional space. To support data driven accelerated electrolyte discovery efforts, we curated and analyzed a large dataset covering a wide range of experimentally recorded ionic conductivities for various combinations of lithium salts, solvents, concentrations, and temperatures. The dataset is named as ’Conductivity Atlas for Lithium salts and Solvents’ (CALiSol-23). Comprehensive datasets are lacking but are critical to building chemistry agnostic machine learning models for conductivity as well as data driven electrolyte optimization tasks. CALiSol-23 was derived from an exhaustive review of literature concerning experimental non-aqueous electrolyte conductivity measurement. The final dataset consists of 13,825 individual data points from 27 different experimental articles, in total covering 38 solvents, a broad temperature range, and 14 lithium salts. CALiSol-23 can help expedite machine learning model development that can help in understanding the complexities of ion transport and streamlining the optimization of non-aqueous electrolyte mixtures.

Development and validation of machine learning models to predict MDRO colonization or infection on ICU admission by using electronic health record data

BackgroundMultidrug-resistant organisms (MDRO) pose a significant threat to public health. Intensive Care Units (ICU), characterized by the extensive use of antimicrobial agents and a high prevalence of bacterial resistance, are hotspots for MDRO proliferation. Timely identification of patients at high risk for MDRO can aid in curbing transmission, enhancing patient outcomes, and maintaining the cleanliness of the ICU environment. This study focused on developing a machine learning (ML) model to identify patients at risk of MDRO during the initial phase of their ICU stay.MethodsUtilizing patient data from the First Medical Center of the People’s Liberation Army General Hospital (PLAGH-ICU) and the Medical Information Mart for Intensive Care (MIMIC-IV), the study analyzed variables within 24 h of ICU admission. Machine learning algorithms were applied to these datasets, emphasizing the early detection of MDRO colonization or infection. Model efficacy was evaluated by the area under the receiver operating characteristics curve (AUROC), alongside internal and external validation sets.ResultsThe study evaluated 3,536 patients in PLAGH-ICU and 34,923 in MIMIC-IV, revealing MDRO prevalence of 11.96% and 8.81%, respectively. Significant differences in ICU and hospital stays, along with mortality rates, were observed between MDRO positive and negative patients. In the temporal validation, the PLAGH-ICU model achieved an AUROC of 0.786 [0.748, 0.825], while the MIMIC-IV model reached 0.744 [0.723, 0.766]. External validation demonstrated reduced model performance across different datasets. Key predictors included biochemical markers and the duration of pre-ICU hospital stay.ConclusionsThe ML models developed in this study demonstrated their capability in early identification of MDRO risks in ICU patients. Continuous refinement and validation in varied clinical contexts remain essential for future applications.

The Prophet's Style Learning Method: Prophetic Learning in English Language Teaching (ELT) to Students

This research describes the prophet's style learning method and prophetic learning in English Language Teaching (ELT). This research is a library research. The researcher uses sources from books and national journals. The analysis shows that in Indonesia, teaching English is a lifelong endeavor, starting from elementary school and continuing through university. The Prophet Muhammad SAW is a role model for Muslims, as he pioneered education and developed learning models applicable in various contexts, including English study. Teachers/lecturers should prioritize sincerity, patience, and honesty in the classroom to create strong relationships with students and maintain a strong connection with Allah SWT. Prophet Muhammad is a Muslim trendsetter, demonstrating cognitive and affective learning methods. To incorporate prophetic learning into English teaching, they can use prophetic learning strategies such as 1) teacher as a model, 2) stimulus-response/questioning, 3) storytelling, 4) analogy/logical order, 5) direct practice, 6) applied materials, 7) offering advice, 8) closing statement. Prophets possess virtuous qualities such as honesty, trust, communication, and intelligence. Besides, teachers/lecturers should give examples, play the role of guides, and support students in learning progress. Teachers/lecturers should also be able to create curiosity and enthusiasm about the next meeting and be prepared to answer questions and provide answers. In conclusion, prophetic learning is a crucial aspect of a student's life, and educators should emulate the learning methods to ensure their students are equipped with the necessary skills to succeed in their studies.

Unraveling Brain Synchronisation Dynamics by Explainable Neural Networks using EEG Signals: Application to Dyslexia Diagnosis.

The electrical activity of the neural processes involved in cognitive functions is captured in EEG signals, allowing the exploration of the integration and coordination of neuronal oscillations across multiple spatiotemporal scales. We have proposed a novel approach that combines the transformation of EEG signal into image sequences, considering cross-frequency phase synchronisation (CFS) dynamics involved in low-level auditory processing, with the development of a two-stage deep learning model for the detection of developmental dyslexia (DD). This deep learning model exploits spatial and temporal information preserved in the image sequences to find discriminative patterns of phase synchronisation over time achieving a balanced accuracy of up to 83%. This result supports the existence of differential brain synchronisation dynamics between typical and dyslexic seven-year-old readers. Furthermore, we have obtained interpretable representations using a novel feature mask to link the most relevant regions during classification with the cognitive processes attributed to normal reading and those corresponding to compensatory mechanisms found in dyslexia.

Multi-expert ensemble ECG diagnostic algorithm using mutually exclusive–symbiotic correlation between 254 hierarchical multiple labels

Electrocardiograms (ECGs) are a cheap and convenient means of assessing heart health and provide an important basis for diagnosis and treatment by cardiologists. However, existing intelligent ECG diagnostic approaches can only detect up to several tens of ECG terms, which barely cover the most common arrhythmias. Thus, further diagnosis is required by cardiologists in clinical settings. This paper describes the development of a multi-expert ensemble learning model that can recognize 254 ECG terms. Based on data from 191,804 wearable 12-lead ECGs, mutually exclusive–symbiotic correlations between hierarchical multiple labels are applied at the loss level to improve the diagnostic performance of the model and make its predictions more reasonable while alleviating the difficulty of class imbalance. The model achieves an average area under the receiver operating characteristics curve of 0.973 and 0.956 on offline and online test sets, respectively. We select 130 terms from the 254 available for clinical settings by considering the classification performance and clinical significance, providing real-time and comprehensive ancillary support for the public.

Machine Learning Model Development to Predict Power Outage Duration (POD): A Case Study for Electric Utilities.

In the face of increasing climate variability and the complexities of modern power grids, managing power outages in electric utilities has emerged as a critical challenge. This paper introduces a novel predictive model employing machine learning algorithms, including decision tree (DT), random forest (RF), k-nearest neighbors (KNN), and extreme gradient boosting (XGBoost). Leveraging historical sensors-based and non-sensors-based outage data from a Turkish electric utility company, the model demonstrates adaptability to diverse grid structures, considers meteorological and non-meteorological outage causes, and provides real-time feedback to customers to effectively address the problem of power outage duration. Using the XGBoost algorithm with the minimum redundancy maximum relevance (MRMR) feature selection attained 98.433% accuracy in predicting outage durations, better than the state-of-the-art methods showing 85.511% accuracy on average over various datasets, a 12.922% improvement. This paper contributes a practical solution to enhance outage management and customer communication, showcasing the potential of machine learning to transform electric utility responses and improve grid resilience and reliability.

Pengembangan Flipbook “Dukung” tentang Peduli Lingkungan sebagai Media Dakwah pada Pembelajaran PAI Kelas 6 SD

This research aims to develop a flipbook entitled "SUPPORT" (Da'wah Cares for the Environment) as a medium for da'wah in 6th-grade elementary school Islamic Religious Education (PAI) learning that is effective for conveying environmental care da'wah messages, as well as testing the effectiveness of the flipbook in increasing understanding and a caring attitude towards the importance of caring for the environment in the context of Islamic religious teachings that are appropriate according to the assessment of material experts, media experts and students. The research method used in this development research is Design and Development (D&D), which has a focused objective on the process of designing and developing educational products, analyzing and evaluating these products from a design and development perspective. This research procedure uses the Analyze, Design, Develop, Implement, Evaluate (ADDIE) model, which is a learning development model that is effective, dynamic, and supports ongoing learning. The instruments for collecting this data are observation, material expert validation questionnaire, media expert validation questionnaire, student response questionnaire, and documentation. The results of the research show that the "SUPPORT" flipbook developed is very good and suitable for use in conveying environmental care da'wah messages. The conclusion of this research is that the development of flipbooks as a medium for preaching environmental care in PAI learning for grade 6 elementary schools can make a new contribution to delivering preaching material and environmental education in elementary schools.

Predicting subacute ruminal acidosis from milk mid-infrared estimated fatty acids and machine learning on Canadian commercial dairy herds

Our objective was to validate the possibility of detecting SARA from milk Fourier transform mid-infrared spectroscopy estimated fatty acids (FA) and machine learning. Subacute ruminal acidosis is a common condition in modern commercial dairy herds for which the diagnostic remains challenging due to its symptoms often being subtle, nonexclusive, and not immediately apparent. This observational study aimed at evaluating the possibility of predicting SARA by developing machine learning models to be applied to farm data and to provide an estimated portrait of SARA prevalence in commercial dairy herds. A first data set composed of 488 milk samples of 67 cows (initial DIM = 8.5 ± 6.18; mean ± SD) from 7 commercial dairy farms and their corresponding SARA classification (SARA+ if rumen pH <6.0 for 300 min, else SARA-) was used for the development of machine learning models. Three sets of predictive variables: i) milk major components (MMC), ii) milk FA (MFA), and iii) MMC combined with MFA (MMCFA) were submitted to 3 different algorithms, namely Elastic net (EN), Extreme gradient boosting (XGB), and Partial least squares (PLS), and evaluated using 3 different scenarios of cross-validation. Accuracy, sensitivity, and specificity of the resulting 27 models were analyzed using a linear mixed model. Model performance was not significantly affected by the choice of algorithm. Model performance was improved by including fatty acids estimations (MFA and MMCFA as opposed to MMC alone). Based on these results, one model was selected (algorithm: EN; predictive variables: MMCFA; 60.4, 65.4, and 55.3% of accuracy, sensitivity, and specificity, respectively) and applied to a large data set comprising the first test-day record (milk major components and FA within the first 70 DIM of 211,972 Holstein cows (219,503 samples) collected from 3001 commercial dairy herds. Based on this analysis, the within-herd SARA prevalence of commercial farms was estimated at 6.6 ± 5.29% ranging from 0 to 38.3%. A subsequent linear mixed model was built to investigate the herd-level factors associated to higher within-herd SARA prevalence. Milking system, proportion of primiparous cows, herd size and seasons were all herd-level factors affecting SARA prevalence. Furthermore, milk production was positively, and milk fat yield negatively associated with SARA prevalence. Due to their moderate levels of accuracy, the SARA prediction models developed in our study, using data from continuous pH measurements on commercial farms, are not suitable for diagnostic purpose. However, these models can provide valuable information at the herd level.

Integration of Augmented Reality (AR) in Biology Education: A Systematic Literature Review

21st-century learning encourages the integration of Augmented Reality (AR) technology as asupport for learning media to improve learning effectiveness. This systematic literature review explores researchtrends and the integration of Augmented Reality (AR) in biology learning, especially at the formal educationlevel. This study analyzed 23 articles from Scopus, Sage, and ERIC databases published between 2015 and 2023to answer the six research questions posed. The analysis showed several key findings: 1) Most of the researchrelated to AR implementation in biology materials was conducted in Indonesia and the United States; 2) Collegestudents are the dominant research subjects in AR; 3) AR-related research has increased significantly since2015, reaching a peak in 2020, but experiencing fluctuations until 2023; 4) The majority of research focuses onusing RnD research methods, with efforts to develop AR technology in various biological topics; 5) The mostapplied biological material topic using AR technology is the anatomical structure of human organs; 6) Thechallenges of AR implementation in biology learning include accessibility, hardware availability, cost, training,and adaptation to pandemic conditions. Based on these findings, future research recommendations includediversification of research subjects, development of innovative AR learning models, and more in-depth researchon factors that influence the effectiveness of AR implementation, particularly in developing countries. It is alsoimportant to consider developing strategies to overcome the barriers identified to increase the success of ARimplementation in the context of biology learning. Evaluation of the impact of AR use on student learningoutcomes needs to be the focus of future research to complement the current findings.

Development of machine learning models predicting mortality using routinely collected observational health data from 0-59 months old children admitted to an intensive care unit in Bangladesh: critical role of biochemistry and haematology data

IntroductionTreatment in the intensive care unit (ICU) generates complex data where machine learning (ML) modelling could be beneficial. Using routine hospital data, we evaluated the ability of multiple ML models...

Semi-supervised learning with flexible threshold for non-intrusive load monitoring

Non-intrusive load monitoring (NILM) can obtain fine-grained power consumption information for individual appliances within the user without installing additional hardware sensors. With the rapid development of the deep learning model, many methods have been utilized to address NILM problems and have achieved enhanced appliance identification performance. However, supervised learning models require a substantial volume of annotated data to function effectively, which is time-consuming, laborious, and difficult to implement in real scenarios. In this paper, we propose a novel semi-supervised learning method that combines consistency regularization and pseudo-labels to help identification of appliances with limited labeled data and an abundance of unlabeled data. In addition, given the different learning difficulties of various appliance categories, for example, feature learning is more difficult for multi-state appliances than two-state appliances, the thresholds employed for different appliances are adjusted in a flexible way at each time step so that the informative unlabeled data and their pseudo-labels can be delivered. Experiments have been conducted on publicly available datasets, and the results indicate that the proposed method attains superior appliance identification performance compared to cutting-edge methods.