Support Vector Machine Algorithm Research Articles

Prediction of building cooling load: an innovative design for GA-SVM and IG-SVM system for the estimation of hourly precision and computational fluctuation range

ABSTRACT Anticipating the loads that buildings will bear is a crucial strategy in the pursuit of energy efficiency and the reduction of emissions. In this paper, we introduce an interactive and comprehensive joint prediction model, specifically crafted to elevate the precision of forecasts related to building loads. The resultant Genetic Algorithm-Support Vector Machine (GA-SVM) prediction model is put into action to provide hourly predictions of cooling loads for buildings. In scenarios where the prediction of building cooling load (BCL) fluctuations becomes imperative, especially in the face of extreme weather conditions, the information granulation (IG) method is employed. The findings of this validation process unveil significant improvements. The coefficient of variation of root mean square error (CV-RMSE) and mean absolute percentage error (MAPE) for the GA-SVM model are reduced by 58.85% and 68.04%, respectively, when compared to the conventional SVM model. Furthermore, in a comparative analysis against three widely utilized prediction models, the SVM model demonstrates a reduction in CV-RMSE and MAPE ranging from 2.04% to 68.04%. The application of the joint prediction model showcases impressive results, with R 2 values ranging from 97.27% to 99.68%, MAPE ranging from 2.59% to 2.84%, and CV-RMSE ranging from only 0.0249 to 0.0319.

Enhancing Education and Teaching Management Through Data Mining and Support Vector Machine Algorithm

This study investigates the effectiveness of the SVM+TLBO approach for predicting student performance and evaluating learning abilities in educational settings. By integrating Support Vector Machines (SVM) with Teaching-Learning-Based Optimization (TLBO), the research aims to enhance predictive accuracy and efficiency compared to traditional methods, including Decision Trees, Ant Colony Algorithms, Clustering Algorithms, Convolutional Neural Networks (CNN), Neural Networks, Support Vector Machine (SVM) without Optimization. Results indicate that the SVM+TLBO model significantly outperforms these methods, providing robust predictions and valuable insights into student learning patterns. The findings underscore the importance of data-informed decision-making in education, enabling educators to identify at-risk students and tailor instructional strategies effectively. This research contributes to the growing field of educational data mining, highlighting the potential of advanced machine learning techniques to optimize educational outcomes and foster personalized learning experiences.

Assessment of dyna clue model for prediction of spatio-temporal dynamics of urbanisation and green space in Jaipur district, Rajasthan (India)

Urbanisation in Jaipur, Rajasthan’s capital, has significantly impacted land use and land cover (LULC) over the past four decades. This study addresses the pressing issue of how rapid urban expansion affects green spaces and environmental balance. Utilizing satellite imagery and a support vector machine (SVM) algorithm, LULC maps for 1993, 2003, 2013, and 2023 were generated to analyze changes in land cover, including agriculture, barren land, built-up areas, vegetation, wasteland, fallow land, and water bodies. The Dyna-CLUE model, incorporating variables such as elevation, slope, aspect, and proximity to streams and roads, was employed to forecast LULC changes for 2033 and 2043. The findings reveal a marked increase in built-up areas at the expense of vegetative cover, driven by urbanization, infrastructural development, agricultural expansion, climate change, and policy decisions. With Kappa coefficients indicating high agreement between classified land cover and ground truth data (84–90% accuracy), the study projects continued urban growth and further decline in vegetation. This underscores the urgent need for sustainable land use planning and environmental conservation in Jaipur. Proposed strategies to address these issues include establishing ecological zones, enforcing green building regulations, promoting mixed-use development, and implementing a 15-min green space policy. These measures aim to enhance green space distribution and support Jaipur’s transition towards a greener urban environment.

Automated detection of stale beef from electronic nose data

AbstractAccurate detection of stale beef on the market is important for protecting the legitimate rights and interests of consumers. To this end, we combined electronic nose measurements with machine learning technology to classify beef samples. We used an electronic nose to collect information about the odor characteristics of different beef samples and used linear discriminant analysis to reduce data dimensionality. We then classified samples using the following algorithms: extreme gradient boosting, logistic regression, K‐nearest neighbor, random forest, support vector machine, and neural networks for pattern recognition. We assessed model performance using a 10‐fold cross‐validation technique. All these methods reached an accuracy of 95% or above, with F1 scores and AUC values above 0.96. The support vector machine algorithm outperformed all other models, achieving perfect recognition with 100% accuracy and F1/AUC scores of 1.0. Our study demonstrates that electronic nose data combined with support vector machine can be used to successfully discriminate between stale and fresh beef, paving the way for novel research directions in the detection of stale beef.

Personality changes during adolescence predict young adult psychosis proneness and mediate gene-environment interplays of schizophrenia risk.

Psychotic symptoms in adolescence are associated with social adversity and genetic risk for schizophrenia. This gene-environment interplay may be mediated by personality, which also develops during adolescence. We hypothesized that (i) personality development predicts later Psychosis Proneness Signs (PPS), and (ii) personality traits mediate the association between genetic risk for schizophrenia, social adversities, and psychosis. A total of 784 individuals were selected within the IMAGEN cohort (Discovery Sample-DS: 526; Validation Sample-VS: 258); personality was assessed at baseline (13-15 years), follow-up-1 (FU1, 16-17 years), and FU2 (18-20 years). Latent growth curve models served to compute coefficients of individual change across 14 personality variables. A support vector machine algorithm employed these coefficients to predict PPS at FU3 (21-24 years). We computed mediation analyses, including personality-based predictions and self-reported bullying victimization as serial mediators along the pathway between polygenic risk score (PRS) for schizophrenia and FU3 PPS. We replicated the main findings also on 1132 adolescents recruited within the TRAILS cohort. Growth scores in neuroticism and openness predicted PPS with 65.6% balanced accuracy in the DS, and 69.5% in the VS Mediations revealed a significant positive direct effect of PRS on PPS (confidence interval [CI] 0.01-0.15), and an indirect effect, serially mediated by personality-based predictions and victimization (CI 0.006-0.01), replicated in the TRAILS cohort (CI 0.0004-0.004). Adolescent personality changes may predate future experiences associated with psychosis susceptibility. PPS personality-based predictions mediate the relationship between PRS and victimization toward adult PPS, suggesting that gene-environment correlations proposed for psychosis are partly mediated by personality.

Predicting Prognosis of Early-Stage Mycosis Fungoides with Utilization of Machine Learning

Mycosis fungoides (MF) is the most prevalent type of cutaneous T cell lymphomas. Studies on the prognosis of MF are limited, and no research exists on the potential of artificial intelligence to predict MF prognosis. This study aimed to compare the predictive capabilities of various machine learning (ML) algorithms in predicting progression, treatment response, and relapse and to assess their predictive power against that of the Cox proportional hazards (CPH) model in patients with early-stage MF. The data of patients aged 18 years and over who were diagnosed with early-stage MF at Ankara University Faculty of Medicine Hospital from 2006 to 2024 were retrospectively reviewed. ML algorithms were utilized to predict complete response, relapse, and disease progression using patient data. Of the 185 patients, 94 (50.8%) were female, and 91 (49.2%) were male. Complete response was observed in 114 patients (61.6%), while relapse and progression occurred in 69 (37.3%) and 54 (29.2%) patients, respectively. For predicting progression, the Support Vector Machine (SVM) algorithm demonstrated the highest success rate, with an accuracy of 75%, outperforming the CPH model (C-index: 0.652 for SVM vs. 0.501 for CPH). The most successful model for predicting complete response was the Ensemble model, with an accuracy of 68.89%, surpassing the CPH model (C-index: 0.662 for the Ensemble model vs. 0.543 for CPH). For predicting relapse, the decision tree classifier showed the highest performance, with an accuracy of 78.17%, outperforming the CPH model (C-index: 0.782 for the decision tree classifier vs. 0.505 for CPH). The results suggest that ML algorithms may be useful in predicting prognosis in early-stage MF patients.

SGO: An innovative oversampling approach for imbalanced datasets using SVM and genetic algorithms

Imbalanced datasets present a challenging problem in machine learning and artificial intelligence. Since most models typically assume balanced data distributions, imbalanced positive and negative examples can lead to significant bias in prediction or classification tasks. Current over-sampling methods frequently encounter issues like overfitting and boundary bias. A novel imbalanced data augmentation technique called SVM-GA over-sampling (SGO) is proposed in this paper, which integrates Support Vector Machines (SVM) with Genetic Algorithms (GA). Our approach leverages SVM to identify the decision boundary and uses GA to generate new minority samples along this boundary, effectively addressing both over-fitting and boundary biases. It has been experimentally validated that SGO outperforms the traditional methods on most datasets, providing a novel and effective approach to address imbalanced data problems, with potential application prospects and generalization value.

Machine learning approach to flare-up detection and clustering in chronic obstructive pulmonary disease (COPD) patients.

Chronic obstructive pulmonary disease (COPD) is a prevalent and preventable condition that typically worsens over time. Acute exacerbations of COPD significantly impact disease progression, underscoring the importance of prevention efforts. This observational study aimed to achieve two main objectives: (1) identify patients at risk of exacerbations using an ensemble of clustering algorithms, and (2) classify patients into distinct clusters based on disease severity. Data from portable medical devices were analyzed post-hoc using hyperparameter optimization with Self-Organizing Maps (SOM), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Isolation Forest, and Support Vector Machine (SVM) algorithms, to detect flare-ups. Principal Component Analysis (PCA) followed by KMeans clustering was applied to categorize patients by severity. 25 patients were included within the study population, data from 17 patients had the required reliability. Five patients were identified in the highest deterioration group, with one clinically confirmed exacerbation accurately detected by our ensemble algorithm. Then, PCA and KMeans clustering grouped patients into three clusters based on severity: Cluster 0 started with the least severe characteristics but experienced decline, Cluster 1 consistently showed the most severe characteristics, and Cluster 2 showed slight improvement. Our approach effectively identified patients at risk of exacerbations and classified them by disease severity. Although promising, the approach would need to be verified on a larger sample with a larger number of recorded clinically verified exacerbations.

Optimal process parameter determination in selective laser melting via machine learning-guided sequential quadratic programing

Selective Laser Melting (SLM) is a widely used additive manufacturing method with critical processing parameters such as Laser Power, Scanning Speed, Hatch Distance, and Laser Beam Diameter, which significantly impact process quality and mechanical properties of fabricated parts like Relative Density, Hardness, and Surface Roughness. This study investigates an effective approach to modeling the SLM process for predicting and optimizing processing parameters using a combination of Machine Learning (ML) and Sequential Quadratic Programing (SQP). The proposed method aims to predict the mechanical properties of SLM-produced AlSi10Mg parts using Machine Learning techniques and then transform the process into a constrained multiple-objective optimization model employing the SQP algorithm to determine optimal process parameters. Artificial Neural Networks (ANN), Gaussian Process Regression (GPR), and Support Vector Machine (SVM) algorithms are applied to establish correlations between process parameters and mechanical properties, represented as mathematical functions. A cost function is formulated using these functions and minimized using the SQP method. Statistical analysis is implemented to validate the influence of processing parameters on mechanical properties accordingly. The outcomes of the method are verified through additional experiments and analyses using Micro-CT scanning techniques. The results demonstrate that processing parameters have a significant impact on part properties across different scales, with Micro-CT scanning proving instrumental in validating mechanical properties. Overall, this study establishes that multiple objectives can be efficiently optimized through the integration of ML and SQP methods, complemented by a judicious number of experiments.

Predicting financial performance with intellectual capital using machine learning

Purpose This study aims to apply machine learning techniques to efficiently predict leisure firms’ financial performance. Accurate financial forecasting is crucial in leisure and tourism, greatly affecting firms’ strategic decisions and competitive positioning. This study emphasizes the roles of intellectual capital to offer a nuanced understanding of how these types of capital influence firm success. Design/methodology/approach Using comprehensive firm-level data, this study examines several machine learning algorithms’ predictive capacity across a spectrum of industry sectors (general, manufacturing, service) to identify the most effective model and training dataset. These tools are used to evaluate financial metrics such as return on sales, return on assets and sales growth. A range of variables are incorporated into this process to enhance model accuracy and relevance. Findings Results demonstrate the support vector machine algorithm’s exceptional performance based on a training data set from the service sector in predicting leisure firms’ return on sales and sales growth. This algorithm is thus an efficacious strategic forecasting instrument. The variables significantly affecting firm performance include demand variation; organizational, product and technological innovation; synergistic innovation between multiple domains; salary levels; market strategy; and the number of employees. Originality/value By integrating advanced machine learning techniques with the strategic management of intellectual capital, this study presents a sophisticated approach to predicting leisure firms’ financial performance. Findings enrich the discourse on firm performance forecasting and offer actionable insights into strategic planning and resource allocation for practitioners in the leisure and tourism sectors.

Grade Classification of Camellia Seed Oil Based on Hyperspectral Imaging Technology.

To achieve the rapid grade classification of camellia seed oil, hyperspectral imaging technology was used to acquire hyperspectral images of three distinct grades of camellia seed oil. The spectral and image information collected by the hyperspectral imaging technology was preprocessed by different methods. The characteristic wavelength selection in this study included the continuous projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS), and the gray-level co-occurrence matrix (GLCM) algorithm was used to extract the texture features of camellia seed oil at the characteristic wavelength. Combined with genetic algorithm (GA) and support vector machine algorithm (SVM), different grade classification models for camellia seed oil were developed using full wavelengths (GA-SVM), characteristic wavelengths (CARS-GA-SVM), and fusing spectral and image features (CARS-GLCM-GA-SVM). The results show that the CARS-GLCM-GA-SVM model, which combined spectral and image information, had the best classification effect, and the accuracy of the calibration set and prediction set of the CARS-GLCM-GA-SVM model were 98.30% and 96.61%, respectively. Compared with the CARS-GA-SVM model, the accuracy of the calibration set and prediction set were improved by 10.75% and 12.04%, respectively. Compared with the GA-SVM model, the accuracy of the calibration set and prediction set were improved by 18.28% and 18.15%, respectively. The research showed that hyperspectral imaging technology can rapidly classify camellia seed oil grades.

Automatic detection of fake reviews at marketplaces using expert-based features and consumers’ reactions

The article presents the results of a practical study of the features of fake reviews that are described by marketers and other experts. Due to the abundance of fake reviews on marketplaces, consumer trust falls not only in the seller or platform, but in the genre itself. The paper presents the results of automatic classification of reviews from Russian marketplaces into potentially fake and honest ones using modelling of features that experts call labels of a fake review (presence of template words, exclamation marks, emoji, positive sentiment), and machine learning algorithms. To solve the problem, a corpus of 6 288 texts from the Russian marketplaces Wildberries and Megamarket has been collected. The target variable (predicted class) is the ratio of likes and dislikes given to the review by other buyers. The best result is demonstrated by the support vector machine algorithm in binary classification into reviews with a low and high ratings (without neutral ones). The classification model confirms that the formal features identified by experts as indicating fake reviews indeed have predictive potential. The quality of the model is reduced by the imbalance in classes and insufficient number of reviews with buyer reactions in our corpus, which leaves room for further work.

Sentiment Analysis of the MK Decision Trial of the Result of the 2024 President and Vice President General Election on Social Media X Using the Support Vector Machine Method

Support Vector Machine (SVM) is a method of machine learning often used in classification and regression issues, especially in the classification of commentary reviews on social media such as Twitter. The Constitutional Court (MK) has the authority to resolve disputes resulting from the general election, including the 2024 presidential election. As an institution that maintains fairness and transparency in the democratic process, the Constitutional Court's decisions are often at the center of public attention and debate, especially on social media. In the 2024 general election, various allegations of fraud led to protests from several parties who felt aggrieved. The final and binding Constitutional Court's decision is expected to resolve the conflict that arises, but it often does not satisfy all parties, causing political and social tensions. This conflict can be reflected through public opinion expressed on social media, such as Twitter, where various responses and sentiments to the decision are essential analysis materials. This Research uses the Support Vector Machine (SVM) algorithm with a dataset of 1383 review comments divided by an 80:20 ratio for training and testing. The system was implemented using the Python programming language, with evaluations showing the highest accuracy at 61.00%, precision at 61.00%, and recall at 62.00%. This study aims to analyze public sentiment regarding the Constitutional Court's decision using the SVM method and identify the tendency of public opinion as positive, negative, or neutral. Through this study, it is expected that a deeper understanding of the public's perception of the Constitutional Court's decision is obtained. In addition, this Research is likely to contribute to developing sentiment analysis methods in the future and provide a basis for recommendations for the Constitutional Court in handling election result disputes better.

Assessing critical flood-prone districts and optimal shelter zones in the Brahmaputra Valley: Strategies for effective flood risk management

Frequent flooding has become a persistent issue in floodplain regions, causing significant disasters during each rainy season due to insufficient disaster management planning. This study proposes a methodology to prioritize flood susceptibility areas at the district level and identify suitable sites for flood shelters using a combination of machine learning algorithms and multi-criteria analysis, supported by geospatial technology. Flood shelter suitability mapping was conducted using the Analytical Hierarchy Process (AHP), while flood susceptibility zones were assessed using four different machine learning models: Support Vector Machine (SVM), Random Forest, Decision Tree, and Naive Bayes. The integration of machine learning models with the AHP technique is vital in situations where conventional numerical models face challenges due to limited data, such as river discharge and water levels. The methodology includes a multicollinearity assessment to ensure the independence of selected flood-causing factors, information gain ratio to identify the most influential factors, Spearman's rho test to verify correlations between the machine learning models, and ROC-AUC along with statistical regression for validating the accuracy of the flood susceptibility maps. The findings indicate that the SVM algorithm, given its strong performance and effective training datasets, is recommended for areas with similar physical characteristics. The district-wise priority map generated from the weighted results of flood susceptibility assessments will be useful for flood management and mitigation strategies. Additionally, the study found that applying the AHP technique to determine flood shelter suitability, after assessing flood-prone areas, enhanced the efficiency of the flood management process. This research offers valuable insights for authorities to better address flooding and improve flood prevention and management efforts in floodplain regions, contributing to broader climate change adaptation strategies.

Autism Spectrum Disorder and Atypical Brain Connectivity: Novel Insights from Brain Connectivity-Associated Genes by Combining Random Forest and Support Vector Machine Algorithm.

It is estimated that approximately one in every 100 children is diagnosed with autism spectrum disorder (ASD) around the globe. Currently, there are no curative pharmacological treatments for ASD. Discoveries on key molecular mechanisms of ASD are essential for precision medicine strategies. Considering that atypical brain connectivity patterns have been observed in individuals with ASD, this study examined the brain connectivity-associated genes and their putatively distinct expression patterns in brain samples from individuals diagnosed with ASD and using an iterative strategy based on random forest and support vector machine algorithms. We discovered a potential gene signature capable of differentiating ASD from control samples with a 92% accuracy. This gene signature comprised 14 brain connectivity-associated genes exhibiting enrichment in synapse-related terms. Of these genes, 11 were previously associated with ASD in varying degrees of evidence. Notably, NFKBIA, WNT10B, and IFT22 genes were identified as ASD-related for the first time in this study. Subsequent clustering analysis revealed the existence of two distinct ASD subtypes based on our gene signature. The expression levels of signature genes have the potential to influence brain connectivity patterns, potentially contributing to the manifestation of ASD. Further studies on the omics of ASD are called for so as to elucidate the molecular basis of ASD and for diagnostic and therapeutic innovations. Finally, we underscore that advances in ASD research can benefit from integrative bioinformatics and data science approaches.

Machine learning’s effectiveness in evaluating movement in one-legged standing test for predicting high autistic trait

IntroductionResearch supporting the presence of diverse motor impairments, including impaired balance coordination, in children with autism spectrum disorder (ASD) is increasing. The one-legged standing test (OLST) is a popular test of balance. Since machine learning is a powerful technique for learning predictive models from movement data, it can objectively evaluate the processes involved in OLST. This study assesses machine learning’s effectiveness in evaluating movement in OLST for predicting high autistic trait.MethodsIn this study, 64 boys and 62 girls participated. The participants were instructed to stand on one leg on a pressure sensor while facing the experimenter. The data collected in the experiment were time-series data pertaining to pressure distribution on the sole of the foot and full-body images. A model to identify the participants belonging to High autistic trait group and Low autistic trait group was developed using a support vector machine (SVM) algorithm with 16 explanatory variables. Further, classification models were built for the conventional, proposed, and combined explanatory variable categories. The probabilities of High autistic trait group were calculated using the SVM model.ResultsFor proposed and combined variables, the accuracy, sensitivity, and specificity scores were 1.000. The variables shoulder, hip, and trunk are important since they explain the balance status of children with high autistic trait. Further, the total Social Responsiveness Scale score positively correlated with the probability of High autistic trait group in each category of explanatory variables.DiscussionResults indicate the effectiveness of evaluating movement in OLST by using movies and machine learning for predicting high autistic trait. In addition, they emphasize the significance of specifically focusing on shoulder and waist movements, which facilitate the efficient predicting high autistic trait. Finally, studies incorporating a broader range of balance cues are necessary to comprehensively determine the effectiveness of utilizing balance ability in predicting high autistic trait.

MRI-based radiomics model for predicting endometrial cancer with high tumor mutation burden.

To evaluate the performance of MRI-based radiomics in predicting endometrial cancer (EC) with a high tumor mutation burden (TMB-H). A total of 122 patients with pathologically confirmed EC (40 TMB-H, 82 non-TMB-H) were included in this retrospective study. Patients were randomly divided into training and testing cohorts in a ratio of 7:3. Radiomics features were extracted from sagittal T2-weighted images and contrast-enhanced T1-weighted images. Then, the logistic regression (LR), random forest (RF), and support vector machine (SVM) algorithms were used to construct radiomics models. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the diagnostic performance of each model, and decision curve analysis was used to determine their clinical application value. Four radiomics features were selected to build the radiomics models. The three models had similar performance, achieving 0.771 (LR), 0.892 (RF), and 0.738 (SVM) in the training cohort, and 0.787 (LR), 0.798 (RF), and 0.777 (SVM) in the testing cohort. The decision curve demonstrated the good clinical application value of the LR model. The MRI-based radiomics models demonstrated moderate predictive ability for TMB-H EC and thus may be a tool for preoperative, noninvasive prediction of TMB-H EC.

Sentiment Analysis of Google Maps User Reviews on the Play Store Using Support Vector Machine and Latent Dirichlet Allocation Topic Modeling

These days, traveling is made easier by utilizing easily accessible online directions such as Google Maps. Google Maps provides real-time routes by displaying and presenting the closest routes that users can take. However, lately, the routes provided by Google Maps services often get users lost by presenting routes such as forests, narrow roads, and even dead ends. Therefore, this study aims to determine the level of user satisfaction and sentiment into two categories, namely positive and negative, based on reviews on the Google Play Store platform using the Support Vector Machine (SVM) algorithm and topic modeling using Latent Dirichlet Allocation (LDA) to find out the collection of topics that are the main topics of conversation by users regarding Google Maps services. The results of this study show that the SVM algorithm is feasible to use in sentiment analysis classification with an accuracy value of 86%, precision of 93%, recall of 53%, and f1-score of 52%. In addition, topic modeling is applied to generate coherence values for each topic, which shows that the higher the coherence value, the more specific the topic is. The highest coherence value generated in this study was two topic models with a coherence value of 35.15%, but this study took five with a coherence value of 33.39%. The five topic models to be applied in this study are selected because they have a good enough coherence value to identify the main topics and hidden topics in Google Maps user reviews with the Latent Dirichlet Allocation model. The topic model shows five aspects users often discuss: Google Maps route accuracy, system and service errors, navigation application directions, lost time history, and convoluted route provision.

Estimation Stand Volume, Basal Area and Quadratic Mean Diameter Using Landsat 8 OLI and Sentinel‐2 Satellite Image With Different Machine Learning Techniques

ABSTRACTThe data required for sustainable forest planning is provided by traditional forest inventories, which are labor, time, and cost‐intensive. Providing this data quickly, reliably, and accurately is crucial for planners and researchers. The objective of this study was to predict stand basal area (BA), stand volume (V), and quadratic mean diameter (dq) by leveraging vegetation indices (VIs) and reflectance (R) derived from Landsat 8 OLI and Sentinel 2 satellite images, along with topographic (T) data obtained from ALOS‐PALSAR satellite imagery. Forest inventory data for a total of 250 sample plots were used for modeling in the study. Stand parameters were estimated using support vector machines (SVM), multiple linear regression (MLR), decision tree (DT), and random forest (RF) algorithms. In modeling V, BA, and dq, both individual and combinations of R, VIs, and T values obtained from satellite imagery were used as independent variables. Using the generated datasets, each of the stand parameters was modeled separately with MLR, SVM, RF, and DT algorithms, and the success of the models was compared to determine the modeling technique and dataset with the highest success for the relevant parameter. The results showed that for each stand parameter, the highest model success was achieved in the combined dataset, which was created by combining all datasets. However, in terms of modeling techniques, the highest success for each stand parameter was achieved with different modeling techniques. The highest success for V is obtained in the model using the SVM method (R2 = 0.78; RMSE = 0.28 m3/ha), the RF method yielded the highest model performance for BA (R2 = 0.70; RMSE = 2.53 m2/ha), and finally, the highest success for dq was obtained in the DT method (R2 = 0.74; RMSE = 0.02 cm). In general, the datasets obtained from Sentinel 2 images showed higher model success than the datasets obtained from Landsat 8 OLI images.

Predictive Maintenance of Induction Motor in the Cutting Section of Paper Industry

Induction Motors are vital to the paper industry because they power a variety of equipment needed for pulp processing, drying and cutting among other tasks. Ensuring the consistent manufacture of these motors and satisfying market demands depends heavily on their dependability. However, the extreme humidity, dust and fluctuating loads that occur in the paper sector present serious obstacles to the efficiency and durability of induction motors. To maintain continuous production and satisfy consumer demand, the paper industry significantly depends on the effective operation of a variety of machinery including induction motors in the cutting area. Unexpected malfunctions in these vital parts however might result in expensive downtime and lost output. By using data-driven strategies that anticipate and avoid breakdowns before they happen, predictive maintenance techniques provide a proactive way to reduce such risks. This report provides an extensive analysis of the application of predictive maintenance techniques designed especially for induction motors in the paper industry’s cutting division. The predictive maintenance techniques includes Random Forest Tree, Linear Regression and Support Vector Machines algorithm with these algorithm the Induction Motor’s variations in temperature, vibration, sound, and speed parameters were collected and trained for predicting the failure. Among these algorithms Support Vector Machines shows greater advantage in predicting the failure with the accuracy of 84% whereas Random Forest Tree with 75% and Linear regression with 72%. As well as the real time data’s were collected and stored in the database. The performance of the Induction Motor were efficiently improved and monitored under normal and fault condition by Machine Learning techniques.