Optimal Learning Research Articles

Optimization and Multimachine Learning Algorithms to Predict Nanometal Surface Area Transfer Parameters for Gold and Silver Nanoparticles.

Interactions between gold metallic nanoparticles and molecular dyes have been well described by the nanometal surface energy transfer (NSET) mechanism. However, the expansion and testing of this model for nanoparticles of different metal composition is needed to develop a greater variety of nanosensors for medical and commercial applications. In this study, the NSET formula was slightly modified in the size-dependent dampening constant and skin depth terms to allow for modeling of different metals as well as testing the quenching effects created by variously sized gold, silver, copper, and platinum nanoparticles. Overall, the metal nanoparticles followed more closely the NSET prediction than for Förster resonance energy transfer, though scattering effects began to occur at 20 nm in the nanoparticle diameter. To further improve the NSET theoretical equation, an attempt was made to set a best-fit line of the NSET theoretical equation curve onto the Au and Ag data points. An exhaustive grid search optimizer was applied in the ranges for two variables, 0.1≤C≤2.0 and 0≤α≤4, representing the metal dampening constant and the orientation of donor to the metal surface, respectively. Three different grid searches, starting from coarse (entire range) to finer (narrower range), resulted in more than one million total calculations with values C=2.0 and α=0.0736. The results improved the calculation, but further analysis needed to be conducted in order to find any additional missing physics. With that motivation, two artificial intelligence/machine learning (AI/ML) algorithms, multilayer perception and least absolute shrinkage and selection operator regression, gave a correlation coefficient, R2, greater than 0.97, indicating that the small dataset was not overfitting and was method-independent. This analysis indicates that an investigation is warranted to focus on deeper physics informed machine learning for the NSET equations.

A Python-Based GUI Approach for Efficient Component Design of Compound Die for Composite Material: 3D and 2D CAD Modeling

This article presents a Python-based parametric Computer-Aided Design (CAD) system coupled with the AutoCAD API for the automation of compound die design. The system seeks to minimize the duration allocated to repeated operations, therefore promoting innovation. The graphical user interface facilitates the straightforward entry of design parameters and the automated production of precise manufacturing drawings. Advanced approaches, including Finite Element Analysis (FEA), topology optimization, and machine learning, are utilized to improve product design, reduce material consumption, and forecast component lifespan under operational stressors. The technology markedly decreases design time relative to conventional methods, rendering it optimal for sectors emphasizing bulk customization and batch production. Moreover, it enhances Time-To-Market (TTM) by providing an efficient process from design to manufacturing, while reducing errors and material waste. This tool has the potential for extensive utilization in mechanical design, especially within small and mediumsized enterprises.

TEKNOLOGI AUGMENTED REALITY SEBAGAI MEDIA BELAJAR BERBASIS ANDROID BAGI SISWA KELAS 4 SD NEGERI 11 SESETAN

Low learning interest among students, especially fourth-grade elementary school students, often becomes an obstacle in achieving optimal learning outcomes. To help make the learning process more effective and efficient, educational media are used as intermediaries between educators and learners that connect, provide information, and deliver messages. With the advancement of technology, the education world faces new challenges in applying technology to learning activities, particularly in the development of educational media. One technology that can support learning is Augmented Reality (AR). The main component of Augmented Reality is a 3D object that appears when a marker is scanned by software or an application. This will make students more interested in learning because of the animated 3D objects. In this research, the researcher developed "Augmented Reality Technology as an Android-Based Learning Media for Fourth-Grade Students at SD Negeri 11 Sesetan" to assist teachers and students in the learning process. With it students will become more interested in the learning process when using this AR-based educational media

Identifying climate-ready plant for urban environment: Integrating machine learning with traditional plant selection tools

Climate change has intensified the urban heat island effect and increased extreme weather conditions, posing risks to public health and urban vegetation. To address these challenges, selecting climate-ready urban plant species is crucial. Traditional climate niche-based methods often fall short in urban contexts due to neglecting anthropogenic factors. Our study addresses this research gap by introducing an innovative urban plant selection method that integrates vulnerability metrics, expert consensus, and plant introduction records through machine learning. We identified eight climatic variables essential for the survival of urban plants in Beijing, China, and established safety margins for eight climate variables of 1070 urban plant species across two periods: the baseline (1981–2010) and the future (2041–2070). Based on existing assessment data, expert consensus and plant introduction records, 247 plant species were classified into three levels of adaptability: backbone (highly adaptable, prevalent in Beijing), general (moderately adaptable, requiring specific care), and maladapted (poorly adaptable). Subsequently, we investigated the dynamic relationship between safety margins for eight climate variables across two time periods and the adaptability levels of plants by constructing an optimal machine learning model to predict urban plant adaptability levels, enhancing its accuracy through model comparisons and hyperparameter tuning. Our findings indicate that nearly half (49.0 %) of the plant species in Beijing may face reduced adaptability to future climate conditions. However, a majority (75.9 %) perform well under baseline climate conditions and are expected to adapt to future climate conditions. The results reaffirm that the species can grow well out of the niche limit, suggesting that the traditional climate niche-based method may be limited in urban contexts. Our approach overcomes the limitations of binary classification of traditional niche-based methods and the neglect of anthropogenic factors by incorporating an urban plant adaptability classification schema and ground truth derived from expert consensus and records of plant introductions through machine learning methods. This study provides a method for selecting climate-ready plant species for urban environments and supports evidence-based urban forestry management amidst climate change.

Generating a Landslide Susceptibility Map Using Integrated Meta-Heuristic Optimization and Machine Learning Models

A landslide susceptibility assessment is one of the critical steps in planning for landslide disaster prevention. Advanced machine learning methods can be used as data-driven approaches for landslide susceptibility zonation with several landslide conditioning factors. Despite there being a number of studies on landslide susceptibility assessment, the literature is limited in several contexts, such as parameter optimization, an examination of the factors in detail, and study area. This study addresses these lacks in the literature and aims to develop a landslide susceptibility map of Kentucky, US. Four machine learning methods, namely artificial neural network (ANN), k-nearest neighbor (KNN), support vector machine (SVM), and stochastic gradient boosting (SGB), were used to train the dataset comprising sixteen landslide conditioning factors after pre-processing the data in terms of data encoding, data scaling, and dimension reduction. The hyperparameters of the machine learning methods were optimized using a state-of-the-art artificial bee colony (ABC) algorithm. The permutation importance and Shapley additive explanations (SHAP) methods were employed to reduce the dimension of the dataset and examine the contributions of each landslide conditioning factor to the output variable, respectively. The findings show that the ABC-SGB hybrid model achieved the highest prediction performance. The SHAP summary plot developed using the ABC-SGB model shows that intense precipitation, distance to faults, and slope were the most significant factors affecting landslide susceptibility. The SHAP analysis further underlines that increases in intense precipitation, distance to faults, and slope are associated with an increase in the probability of landslide incidents. The findings attained in this study can be used by decision makers to develop the most effective resource allocation plan for preventing landslides and minimizing related damages.

The Impact of Online Learning on the Learning Interest of Students at Anak Bangsa Christian High School in Surabaya

Learning interest is an internal drive that motivates individuals to learn something willingly and without coercion, leading to optimal learning outcomes. Online learning, on the other hand, refers to the teaching and learning process conducted without face-to-face interaction, supported by internet access and technological devices such as smartphones, laptops, or computers. The implementation of online learning during the pandemic has had varying effects on students' learning interest. This study aims to determine whether online learning influences the learning interest of students at Anak Bangsa Christian High School in Surabaya and to measure the extent of its impact. A quantitative method was employed in this research, with data collected through questionnaires completed by 60 respondents, representing the student population of Anak Bangsa Christian High School. The findings indicate a positive correlation between online learning and the learning interest of these students. The study reveals that students' learning interest increased with the enhancement of online learning methods. The contribution of online learning to shaping the learning interest variable among students at Anak Bangsa Christian High School in Surabaya was 57.5%. The study concludes that well-structured online learning can have a positive impact on students' learning interest. Online learning, when implemented interactively and creatively and supported by appropriate resources, can serve as an alternative learning system for the future, allowing students to more easily pursue study programs aligned with their talents and interests.

Coalition: Mapping the Development of Inclusive Practices in Initial Teacher Training for Fostering Student Participation

In this article, we present the progress of the Erasmus+ Coalition European project. The aim of this project is to promote the development of inclusive practices in higher education so that optimal learning scenarios can be experienced which allow for the rethinking of participation processes in an educational context. In the first phase of the project, a study was carried out using a mixed methodological approach of teaching competences for the development of inclusive practices. University teaching staff and students from several European institutions participated in this study with the aim of gathering information on the following key areas: academic perceptions of possible institutional obstacles and resources; beliefs of the teaching body with regard to inclusive pedagogical approaches; and strategies for adapting to different learning styles and encouraging student participation. Here, we present the results obtained in relation to the implementation of procedures for inclusive teaching, placing our emphasis on the strategies used to favour the monitoring of student learning. Based on the achievements of the initial stage, our intention is to generate a community of practice that supports the training scaffolding of those concerned, and to develop online processes of inquiry to improve initial teacher training in fomenting student participation.

Guided listening as meaningful learning: A study of Israeli secular Jewish High School music programs

Guided listening is vital in music education, fostering self-expression, skill development, and academic achievement. Yet, its limited use in Israeli high schools creates a gap between its recognized value and practice. This mixed-method study examines guided listening integration in secular Jewish-Israeli high school music programs based on four philosophical and pedagogical approaches—esthetic, Praxial, active, and peer instruction—to understand teachers’ objectives, methodologies, implementation, and optimal learning environments. Data was collected from an online questionnaire, structured interviews, direct observations of Western Art Music History lessons, and the researcher’s reflective journal. Key findings: (1) teachers tend to use a repeated five-step teaching pattern (reviewing, lecturing, analyzing, listening while following a score, summarizing); (2) teachers usually do not write lesson plans with detailed, step-by-step guidelines; and (3) teachers use recommended guided listening approaches in a limited capacity, likely due to pressure from preparing students for exams, and the perception that such methods are not fully suitable for Israeli high school music students. The paper explores teachers’ challenges in adopting guided listening pedagogical methods while balancing curriculum demands and promoting inclusivity. It offers recommendations for policymakers and educators on professional development and curriculum revisions, emphasizing detailed lesson planning for effective learning outcomes.

Integration of investor behavioral perspective and climate change in reinforcement learning for portfolio optimization

Addressing environmental impact is increasingly imperative for individual investors and large financial institutions, making it a key objective of socially responsible investing. However, there is a noticeable gap in research on integrating sustainability and low-carbon considerations into machine learning-based portfolio optimization. To meet this challenge, this study introduces a Portfolio Emissions Sentiment Attention Aware Reinforcement Learning (PESAARL) model based on the Proximal Policy Optimization (PPO) algorithm to optimize a portfolio of Dow Jones Industrial Average (DJIA) stocks. PESAARL uniquely integrates environmental impact considerations, specifically carbon footprint using the firm level scope 1 and scope 2 emissions data, alongside firm-level investor sentiment and attention, into the investment decision-making process. Through multiple experiments, PESAARL demonstrates significant advantages, in terms of financial and environmental performance, over the benchmarks.

A survey on person and vehicle re‐identification

AbstractPerson/vehicle re‐identification aims to use technologies such as cross‐camera retrieval to associate the same person (same vehicle) in the surveillance videos at different locations, different times, and images captured by different cameras so as to achieve cross‐surveillance image matching, person retrieval and trajectory tracking. It plays an extremely important role in the fields of intelligent security, criminal investigation etc. In recent years, the rapid development of deep learning technology has significantly propelled the advancement of re‐identification (Re‐ID) technology. An increasing number of technical methods have emerged, aiming to enhance Re‐ID performance. This paper summarises four popular research areas in the current field of re‐identification, focusing on the current research hotspots. These areas include the multi‐task learning domain, the generalisation learning domain, the cross‐modality domain, and the optimisation learning domain. Specifically, the paper analyses various challenges faced within these domains and elaborates on different deep learning frameworks and networks that address these challenges. A comparative analysis of re‐identification tasks from various classification perspectives is provided, introducing mainstream research directions and current achievements. Finally, insights into future development trends are presented.

Integration of transcriptomics and machine learning for insights into breast cancer: exploring lipid metabolism and immune interactions.

Breast cancer (BRCA) represents a substantial global health challenge marked by inadequate early detection rates. The complex interplay between the tumor immune microenvironment and fatty acid metabolism in BRCA requires further investigation to elucidate the specific role of lipid metabolism in this disease. We systematically integrated nine machine learning algorithms into 184 unique combinations to develop a consensus model for lipid metabolism-related prognostic genes (LMPGS). Additionally, transcriptomics analysis provided a comprehensive understanding of this prognostic signature. Using the ESTIMATE method, we evaluated immune infiltration among different risk subgroups and assessed their responsiveness to immunotherapy. Tailored treatments were screened for specific risk subgroups. Finally, we verified the expression of key genes through in vitro experiments. We identified 259 differentially expressed genes (DEGs) related to lipid metabolism through analysis of the cancer genome atlas program (TCGA) database. Subsequently, via univariate Cox regression analysis and C-index analysis, we developed an optimal machine learning algorithm to construct a 21-gene LMPGS model. We used optimal cutoff values to divide the lipid metabolism prognostic gene scores into two groups according to high and low scores. Our study revealed distinct biological functions and mutation landscapes between high-scoring and low-scoring patients. The low-scoring group presented a greater immune score, whereas the high-scoring group presented enhanced responses to both immunotherapy and chemotherapy drugs. Single-cell analysis highlighted significant upregulation of CPNE3 in epithelial cells. Moreover, by employing molecular docking, we identified niclosamide as a potential targeted therapeutic drug. Finally, our experiments demonstrated high expression of MTMR9 and CPNE3 in BRCA and their significant correlation with prognosis. By employing bioinformatics and diverse machine learning algorithms, we successfully identified genes associated with lipid metabolism in BRCA and uncovered potential therapeutic agents, thereby offering novel insights into the mechanisms and treatment strategies for BRCA.

Integrating Spirituality into Neurological Healthcare

Brain health, as defined by Owolabi et al.⁸, encompassesthe integration of cognitive, emotional, and motor functions that areessential for optimal learning, adaptation, and overall lifeperformance. This broad understanding is further enriched byvarious perspectives, which highlight critical elements such aslearning, memory, and the adaptability of cognitive and emotionalcapacities. In particular, cognitive health plays a vital role infostering social connectivity, especially in older adults. The ability tomaintain meaningful relationships as one ages is closely tied to thebrain's capacity for adaptation and resilience. Viewed as a dynamicand multifaceted state influenced by numerous factors, brain healthinvolves not only the preservation of brain integrity but also thecultivation of overall well-being. Ultimately, these aspects convergeto enable individuals to reach their full potential throughout life. Byemphasizing the interconnectedness of cognitive, emotional, andsocial functions, this holistic approach to brain health underscoresits fundamental role in achieving a fulfilling and balanced life atevery stage of development.

Bio-inspired disease prediction: harnessing the power of electric eel foraging optimization algorithm with machine learning for heart disease prediction

Heart disease is the most significant health problem around the world. Thus, it emphasizes the need for accurate and efficient predictive models for early diagnosis. This study proposes an innovative approach integrating the Electric Eel Foraging Optimization Algorithm (EEFOA) with the Random Forest (RF) algorithm for classifying heart disease prediction. EEFOA draws inspiration from the foraging behaviour of electric eels, a bio-inspired optimization framework capable of effectively exploring complex solutions. The objective is to improve the predictive performance of heart disease diagnosis by integrating optimization and Machine learning methodologies. The experiment uses a heart disease dataset comprising clinical and demographic features of at-risk individuals. Subsequently, EEFOA was applied to optimize the features of the dataset and classification using the RF algorithm, thereby enhancing its predictive performance. The results demonstrate that the Electric Eel Foraging Optimization Algorithm Random Forest (EEFOARF) model outperforms traditional RF and other state-of-the-art classifiers in terms of predictive accuracy, sensitivity, specificity, precision, and Log_Loss, achieving remarkable scores of 96.59%, 95.15%, 98.04%, 98%, and 0.1179, respectively. The proposed methodology has the potential to make a significant contribution, thereby reducing morbidity and mortality rates.

Hybrid FCMG-OP-FIS model approach to convert regression into classification data for machine learning-based AQI prediction

Air pollution from vehicle emissions, industrial activities, and medical facilities poses significant health risks in urban areas, underscoring the necessity for robust air quality index (AQI) monitoring. This paper presents a novel method for AQI prediction by integrating a fuzzy centre merge graph with an optimal value-based fuzzy inference system (FCMG-OP-FIS) and machine learning (ML). Traditional ML techniques encounter difficulties when converting regression datasets into classification formats, particularly when unable to label the dataset using the traditional method. The proposed FCMG-OP-FIS model efficiently converts regression data into a classification framework. Unlike traditional AQI prediction methods that rely solely on pollutant data, this approach incorporates both pollutant and meteorological data to improve prediction accuracy. The innovative fuzzy centre merge graph (FCMG) balances the dataset for optimal solutions and facilitates input grouping for Simulink, simplifying rule management. The FCMG-OP-FIS model generates a regression output for AQI, which is subsequently classified into levels (healthy, moderate, or unhealthy) using IF-THEN rules. To enhance accuracy further, a random forest classifier (RFC) is trained on the FCMG-OP-FIS classified output data. The regression output of the FCMG-OP-FIS model is validated using metrics such as RMSE (0.48), MSE (0.23), MAE (0.23), and MAPE (1.77%). Additionally, the classification output from the RFC model employs advanced validation techniques including stratified shuffle validation, grid search cross-validation, and confusion matrix analysis, achieving an accuracy rate of 99%, with the F1 score, precision, and recall over all at 99%. These results demonstrate the effectiveness of the proposed model in accurately labelling data for classification and predicting AQI through ML, highlighting its potential for practical application in environmental monitoring and management.

Improving students’ responsibility in tennis courses: “The Silent Movement” model for physical education programme

Background: Tennis courses are a mandatory component prepared for prospective physical education teachers in universities. However, many students do not achieve optimal learning outcomes due to the irresponsible attitude of students, which can hinder the learning process. Research Objective: The purpose of this study is to develop and produce a tennis learning model (Silent Movement Tennis) for increasing the sense of personal and social responsibility in students. Silent Movement Tennis is the latest model in tennis learning, and there has been no previous research. Method: This research used the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) method in producing a learning model called “Silent Movement Tennis,” which consists of 4 movement skill activity posts that must be carried out by every student with a sense of responsibility. The respondents of this study are all 4th semester students (academic year 2023-2024), totalling 30 people, consisting of 9 females and 21 males at a university in the city of Palembang. The Silent Movement Tennis model was also validated by 3 experts consisting of 1 learning planning expert, 1 tennis practitioner, and 1 developmental psychologist and analysed using the Content Validity Index (CVI) and Content Validity Ratio (CVR). Finding/Result: The results of validation by experts who have been analysed show the value of the Content Validity Index (CVI) shows an average score of 3.63, and the Content Validity Ratio (CVR) analysis shows an average score of 0.27, which means the model developed is appropriate, relevant, or good, and also high content validity to increase the sense of social and personal responsibility for students. Conclusion: Silent Movement Tennis model as an alternative model to improve basic tennis skills and student responsibility. Future research efforts must focus on the design and implementation of sports values programs, namely by developing learning games and other sports based on the TPSR model to foster and develop a sense of personal and social responsibility that is important for students to become an adaptive and resilient generation, which ultimately contributes to their future success.

Finite Element Analysis and Machine Learning in the Loop Additive Manufacturing Process Parameter Optimization to Acquire the Desired Dynamics of Manufactured Parts

Finite Element Analysis and Machine Learning in the Loop Additive Manufacturing Process Parameter Optimization to Acquire the Desired Dynamics of Manufactured Parts

Skin Cancer Image Classification Using Artificial Intelligence Strategies: A Systematic Review.

The increasing incidence of and resulting deaths associated with malignant skin tumors are a public health problem that can be minimized if detection strategies are improved. Currently, diagnosis is heavily based on physicians' judgment and experience, which can occasionally lead to the worsening of the lesion or needless biopsies. Several non-invasive imaging modalities, e.g., confocal scanning laser microscopy or multiphoton laser scanning microscopy, have been explored for skin cancer assessment, which have been aligned with different artificial intelligence (AI) strategies to assist in the diagnostic task, based on several image features, thus making the process more reliable and faster. This systematic review concerns the implementation of AI methods for skin tumor classification with different imaging modalities, following the PRISMA guidelines. In total, 206 records were retrieved and qualitatively analyzed. Diagnostic potential was found for several techniques, particularly for dermoscopy images, with strategies yielding classification results close to perfection. Learning approaches based on support vector machines and artificial neural networks seem to be preferred, with a recent focus on convolutional neural networks. Still, detailed descriptions of training/testing conditions are lacking in some reports, hampering reproduction. The use of AI methods in skin cancer diagnosis is an expanding field, with future work aiming to construct optimal learning approaches and strategies. Ultimately, early detection could be optimized, improving patient outcomes, even in areas where healthcare is scarce.

Manajemen Keuangan Madrasah dalam Pengadaan Sarana dan Prasarana Pendidikan

This study underscores the importance of effective financial management in the procurement of educational facilities and infrastructure at MA Al Washoya Ngoro Jombang, aiming to support optimal learning processes. Effective financial management is essential to ensure that the madrasah can provide adequate facilities in line with established educational standards. The objective of this research is to analyze how financial management is implemented at MA Al Washoya, focusing on planning, inventory, and evaluation related to the procurement of educational facilities and infrastructure.The research employs a qualitative descriptive approach, using data collection techniques such as observation, interviews, and documentation. The study examines the financial planning process involving the Waka Sarpras, fund allocation based on annual work meetings, and how inventory and financial evaluation are conducted to ensure proper and efficient use of funds.The findings reveal that financial management at MA Al Washoya is functioning reasonably well; however, there are areas requiring improvement, particularly in optimizing procurement and maintenance of existing facilities and infrastructure. The evaluation indicates that, although facilities and infrastructure have been planned and procured, better maintenance is needed to ensure their sustainability. The study recommends that the madrasah be more proactive in financial planning and asset maintenance strategies to ensure the achievement of high-quality and sustainable educational outcomes.

Combine multi-order representation learning and frame optimization learning for skeleton-based action recognition

Skeleton-based action recognition has broad application prospects in many fields such as virtual reality. Currently, the most popular way is to employ Graph Convolutional Networks (GCNs) or Hypergraph Convolutional Networks (HGCNs) for this task. However, GCN-based methods may heavily rely on the physical connectivity relationship between joints while lack the capture of higher-order information about interactions among distant joints, and HGCN-based methods usually introduce unnecessary noise when capturing low-order information of skeleton structures with simple topology. Besides, the current methods do not deal well with redundant frames and confusing frames. These limitations hinder the improvement of recognition accuracy. In this paper, we propose a novel network, called Hyper-Net, which combines multi-order representation learning and frame optimization learning for skeleton-based action recognition. Specifically, the proposed Hyper-Net contains Temporal-Channel Aggregation Graph Convolution (TCA-GC), Spatial-Temporal Aggregation Hypergraph Convolution (STA-HC) and Frame Optimization Learning (F-OL) modules. The TCA-GC aggregates low-order and local information from simple joint and bone topologies across different temporal and channel dimensions. The STA-HC captures high-order and global information from complex motion streams as well as solving the problem of spatial-temporal weight imbalance. The F-OL can adaptively extract key frames and distinguish confusing frames, thus improving the ability of the network to recognize confusing actions. A large number of experiments are conducted on the NTU RGB+D, NTU RGB+D 120 and NW-UCLA datasets for action recognition task. Experimental results demonstrate the superiority and effectiveness of the proposed network.

Machining performance analysis of wire-EDM for machining of titanium alloy using multi-objective optimization and machine learning approach

This study investigates the multi-objective optimization of process parameters in wire electrical discharge machining (WEDM) using titanium alloy grade 5 and zinc-coated brass microwire. The novelty lies in the comprehensive investigation of the effects of varying pulse on time (Ton), wire tension (WT), and wire feed rate (WF) on output responses, including surface roughness (SR), surface crack density (SCD), corner diameter (CD), and kerf width (KW). The Box-Behnken design, a sophisticated response surface methodology (RSM) technique, is employed to efficiently explore the complex relationships and interdependencies between these input and output parameters. The study's novel approach incorporates machine learning algorithms, including decision tree, light gradient boosting machine (LightGBM) and random forest, to predict the characteristics of the laser-cut components based on the input parameters. The contour plots generated provide valuable insights into the underlying patterns and associations between input and output features, aiding in the understanding of the WEDM process. The results reveal that the optimized values for SR, SCD, CD, and KW are obtained at specific machining conditions: Ton of 10 μs, WF of 3.79 m/min, and WT of 8 gf. Among the machine learning algorithms employed, random forest outperforms the others in predicting KW, SR, SCD and CD, exhibiting significantly lower mean squared error (MSE) and mean absolute error (MAE) values. This study contributes to the field of WEDM by providing a comprehensive analysis, incorporating advanced experimental design techniques and machine learning algorithms, which can aid in process optimization and quality control in manufacturing applications.