Vector Regression Research Articles

Novel deep learning radiomics nomogram-based multiparametric MRI for predicting the lymph node metastasis in rectal cancer: A dual-center study

PurposeTo develop and evaluate a nomogram that integrates clinical parameters with deep learning radiomics (DLR) extracted from Magnetic Resonance Imaging (MRI) data to enhance the predictive accuracy for preoperative lymph node (LN) metastasis in rectal cancer.MethodsA retrospective analysis was conducted on 356 patients diagnosed with rectal cancer. Of these, 286 patients were allocated to the training set, and 70 patients comprised the external validation cohort. Preprocessed T2-weighted and diffusion-weighted imaging performed preoperatively facilitated the extraction of DLR features. Five machine learning algorithms—k-nearest neighbor, light gradient boosting machine, logistic regression, random forest, and support vector machine—were utilized to develop DLR models. The most effective algorithm was identified and used to establish a clinical DLR (CDLR) nomogram specifically designed to predict LN metastasis in rectal cancer. The performance of the nomogram was evaluated using receiver operating characteristic curve analysis.ResultsThe logistic regression classifier demonstrated significant predictive accuracy using the DLR signature, achieving an Area Under the Curve (AUC) of 0.919 in the training cohort and 0.778 in the external validation cohort. The integrated CDLR nomogram exhibited robust predictive performance across both datasets, with AUC values of 0.921 in the training cohort and 0.818 in the external validation cohort. Notably, it outperformed both the clinical model, which had AUC values of 0.770 and 0.723 in the training and external validation cohorts, respectively, and the stand-alone DLR model.ConclusionThe nomogram derived from multiparametric MRI data, referred to as the CDLR model, demonstrates strong predictive efficacy in forecasting LN metastasis in rectal cancer.

A Bag-of-Words Approach for Information Extraction from Electricity Invoices

In the context of digitization and automation, extracting relevant information from business documents remains a significant challenge. It is typical to rely on machine-learning techniques to automate the process, reduce manual labor, and minimize errors. This work introduces a new model for extracting key values from electricity invoices, including customer data, bill breakdown, electricity consumption, or marketer data. We evaluate several machine learning techniques, such as Naive Bayes, Logistic Regression, Random Forests, or Support Vector Machines. Our approach relies on a bag-of-words strategy and custom-designed features tailored for electricity data. We validate our method on the IDSEM dataset, which includes 75,000 electricity invoices with eighty-six fields. The model converts PDF invoices into text and processes each word separately using a context of eleven words. The results of our experiments indicate that Support Vector Machines and Random Forests perform exceptionally well in capturing numerous values with high precision. The study also explores the advantages of our custom features and evaluates the performance of unseen documents. The precision obtained with Support Vector Machines is 91.86% on average, peaking at 98.47% for one document template. These results demonstrate the effectiveness of our method in accurately extracting key values from invoices.

Evaluating the Performance of Five Classifiers for Twitter Sentiment Analysis Using Bag of Words

Abstract: Twitter is a social medium for millions of people to express their opinions on different subjects. Natural Language Processing is widely used for sentiment analysis. This Research paper compares five machine learning classifier performances using a bag of words technique. A Kaggle Dataset is used. The Naïve Bayes classifier gave better predictions having an accuracy achieved to 91.59 percent for the four type sentiments from the text, Negative, Positive, Neutral, and Irrelevant. The classifiers used in this research are Logistic Regression, XGBoost, Naïve Bayes, Random Forest, and Support Vector Machine. The bag of words with having n-grams to four words and retaining all the stop words gave better accuracy than having n-grams to one word and removing all stop words. For further improvement in results Transformer based models like Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformers (GPT) must be taken into account. Deep learning models are more likely to give better accuracy than traditional machine learning models, and they use word embeddings rather than a bag of words technique to understand semantics better.

Noninvasive assessment of single kidney glomerular filtration rate using multiple diffusion weighted imaging models.

This study aimed to assess single kidney glomerular filtration rate (GFR) using various diffusion weighted imaging (DWI) models. We reviewed adult patients with kidney diseases who underwent magnetic resonance imaging (MRI) examination from February 2021 to December 2023. DWI with 13 b-values was performed using 3.0-T scanners. Diffusion parameters were calculated with multi-slice ROIs positioned in renal parenchyma using four DWI models, including monoexponential model (MEM), diffusion kurtosis imaging (DKI), stretched exponential model (SEM), and intravoxel incoherent motion (IVIM). The split GFRs were measured by 99mTc-DTPA scintigraphy using Gates' method. Four different regression algorithms including the linear regression, regression tree, Gaussian regression and support vector machine (SVM) regression were employed to predict the GFR value based on different diffusion parameters. The leave-one-out cross validation was used to evaluate prediction ability of different models, and the performance of each model was quantified using the root mean square error (RMSE) and correlation coefficient. Fifteen (male/female, 10/5; age, 41.60±10.83 years) patients were included in this study. Among the four DWI models, the IVIM parameters with SVM regression model achieved the best performance with 0.184 RMSE and 0.789 correlation coefficient ( ). The parameters combining the four DWI models with SVM regression algorithm achieved the best performance in this study, with 0.171 RMSE and 0.815 correlation coefficient ( ). The DWI characteristics are able to serve as imaging biomarkers for assessing the function of single kidney. The integration of DWI into clinical practice could contribute to the advancement of non-invasive diagnostic methodologies.

Wind Speed Modeling under Quasi‐Linear Autoregressive Neural Network Model for Prediction of Production Power

Accurate wind speed modeling is beneficial for the design of wind energy conversion systems. Models of wind speed are used to assess the adequacy and dependability of a power supply. However, precise wind speed modeling is challenging due to the sporadic availability of wind speed. In this note, we propose a wind speed model with an autoregressive (AR) structure. A hybrid model is developed under linear and nonlinear parts based on a quasi‐linear autoregressive exogenous neural network (Q‐ARX‐NN). The model's structure is composed of a regression vector and its coefficients. The coefficients are divided into linear and nonlinear coefficients. A set of linear coefficients is identified under the algorithm of least square error (LSE), and a set of nonlinear coefficients is modeled by using a neural network to refine the residual error of the nonlinear part. Some artificial neural network (ANN) models can be set as nonlinear part sub‐models to sharpen the model's accuracy. The proposed model is tested for wind speed modeling to estimate wind energy production. Various nonlinear parts of the sub‐model are tested, such as neural networks, radial basis function networks, and ANN networks. Moreover, we evaluate the effects of the order of the model by varying hidden and output nodes, which can be summarized as the number of coefficients of the regression vector. Using specific wind turbine performance data, prediction models estimate production power. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Comparison between statistical and machine learning methods to detect the hematological indices with the greatest influence on elevated serum levels of low-density lipoprotein cholesterol

IntroductionElevated levels of low-density lipoprotein-cholesterol (LDL-C) is a significant risk factor for the development of cardiovascular diseases (CVD)s. Furthermore, studies have revealed an association between indices of the complete blood count (CBC) and dyslipidemia. We aimed to investigate the relationship between CBC parameters and serum levels of LDL. MethodIn a prospective study involving 9704 participants aged 35–65 years, comprehensive screening was conducted to estimate LDL-C levels and CBC indicators. The association between these biomarkers and high LDL-C (LDL-C≥130 mg/dL (3.25 mmol/L)) was investigated using various analytical methods, including Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) methodologies. ResultThe present study found that age, hemoglobin (HGB), hematocrit (HCT), platelet count (PLT), lymphocyte (LYM), PLT-LYM ratio (PLR), PLT-High-Density Lipoprotein (HDL) ratio (PHR), HGB-LYM ratio (HLR), red blood cell count (RBC), Neutrophil-HDL ratio (NHR), and PLT-RBC ratio (PRR) were all statistically significant between the two groups (p<0.05). Another important finding was that red cell distribution width (RDW) was a significant predictor for higher LDL levels in women. Furthermore, in men, RDW-PLT ratio (RPR) and PHR were the most important indicators for assessing the elevated LDL levels. ConclusionThe study found that sex increases LDL-C odds in females by 52.9 %, while age and HCT increase it by 4.1 % and 5.5 %, respectively. RPR and PHR were the most influential variables for both genders. Elevated RPR and PHR were negatively correlated with increased LDL levels in men, and RDW levels was a statistically significant factor for women. Moreover, RDW was a significant factor in women for high levels of HDL-C.The study revealed that females have higher LDL-C levels (16 % compared to 14 % of males), with significant differences across variables like age, HGB, HCT, PLT, RLR, PHR, RBC, LYM, NHR, RPR, and key factors like RDW and SII.

Intelligence analysis of membrane distillation via machine learning models for pharmaceutical separation

This study investigates simulation of pharmaceutical separation via membrane distillation process by computational simulation and machine learning modeling strategy. The efficacy of three regression models, i.e., Multi-layer Perceptron (MLP), Gamma Regression, and Support Vector Regression (SVR) in predicting the solute concentration, C(mol/m³), was evaluated. The hyper-parameters were optimized by fine-tuning the models using the Red Deer Algorithm (RDA). Computational analyses were carried out for removal of pharmaceuticals from solution by membrane distillation in continuous mode. Mass transfer and machine learning models were implemented focusing on concentration of solute in the feed section of membrane. Results indicate that the Multi-layer Perceptron model achieved great accuracy with an R2 of 0.9955, an MAE of 0.0084, and an RMSE of 0.0148, effectively capturing complex nonlinear relationships in the data. Gamma Regression also performed acceptably, with fitting R2 of 0.9214, showing its suitability for positively skewed data. The Support Vector Regression model, while capturing the general trend, showed the lowest performance with an R2 of 0.8710. These findings suggest that the Multi-layer Perceptron is the most accurate model for this dataset, followed by Gamma Regression and Support Vector Regression. This underscores the importance of careful model selection and optimization in regression analysis in combination with computational simulation of membrane processes.

Multitemporal UAV study of phenolic compounds in slash pine canopies

Phenolic compounds (PC) are important secondary metabolites in plants, playing a crucial role in plant defense mechanisms against pathogens and other plants. Monitoring PC levels is important for understanding tree stress and implementing effective breeding programs. However, traditional methods for monitoring PC are time-consuming, prone to altering the phenolic composition, and mostly applicable only on a small scale. In this study, we evaluated the performance of Unoccupied Aerial Vehicles (UAV) multispectral imaging in estimating the canopy phenolic content in slash pine over an 11-month period in 2021 and a seven-month period in 2022. Three machine learning models including Partial least squares regression (PLSR), Random forest (RF) and Support Vector Machine (SVM) were compared to determine the optimal predictive model for canopy PC. The RF model provided the best predictive results, with R2 values of 0.82 for the validation set and 0.94 for the calibration set. Additionally, the study assesses the heritable variation in canopy PC over time, with the monthly heritability (h2) of PC ranging from 0 to 0.26 in 2021 and from 0 to 0.35 in 2022; The highest h2 levels were observed in July and September 2021and July 2022. The findings demonstrate significant genetic control over the variation of PC. Furthermore, we observed higher breeding values and genetic gains in July and November, which further supports the strong correlation between PC and environmental factors such as temperature and light intensity. To the best of our knowledge, this is the first study to employ time-series UAV multispectral imaging to predict secondary metabolites in pine trees and estimate their genetic variation over time. As a proof of concept, these findings provide more reliable information for tree breeding programs, ultimately enhancing their overall performance.

A machine learning model based on results of a comprehensive radiological evaluation can predict the prognosis of basal ganglia cerebral hemorrhage treated with neuroendoscopy.

Spontaneous intracerebral hemorrhage is the second most common subtype of stroke. Therefore, this study aimed to investigate the risk factors affecting the prognosis of patients with basal ganglia cerebral hemorrhage after neuroendoscopy. Between January 2020 and January 2024, 130 patients with basal ganglia cerebral hemorrhage who underwent neuroendoscopy were recruited from two independent centers. We split this dataset into training (n = 79), internal validation (n = 22), and external validation (n = 29) sets. The least absolute shrinkage and selection operator-regression algorithm was used to select the top 10 important radiomic features of different regions (perioperative hemorrhage area [PRH], perioperative surround area [PRS], postoperative hemorrhage area [PSH], and postoperative edema area [PSE]). The black hole, island, blend, and swirl signs were evaluated. The top 10 radiomic features and 4 radiological features were combined to construct the k-nearest neighbor classification (KNN), logistic regression (LR), and support vector machine (SVM) models. Finally, the performance of the perioperative hemorrhage and postoperative edema machine learning models was validated using another independent dataset (n = 29). The primary outcome is mRS at 6 months after discharge. The mRS score greater than 3 defined as functional independence. A total of 12 models were built: PRH-KNN, PRH-LR, PRH-SVM, PRS-KNN, PRS-LR, PRS-SVM, PSH-KNN, PSH-LR, PSH-SVM, PSE-KNN, PSE-LR, and PSE-SVM, with corresponding areas under the curve (AUC) values in the internal validation set of 0.95, 0.91, 0.94, 0.52, 0.91, 0.54, 0.67, 0.9, 0.72, 0.92, 0.92, and 0.95, respectively. The AUC values of the PRH-KNN, PRH-LR, PRH-SVM, PSE-KNN, PSE-LR, and PSE-SVM in the external validation were 0.9, 0.92, 0.89, 0.91, 0.92, and 0.88, respectively. The model built based on computed tomography images of different regions accurately predicted the prognosis of patients with basal ganglia cerebral hemorrhage treated with neuroendoscopy. The models built based on the preoperative hematoma area and postoperative edema area showed excellent predictive efficacy in external verification, which has important clinical significance.

Modeling drilling fluid density at high-pressure high-temperature conditions using advanced machine-learning techniques

The management of oil and gas wells under high-pressure high-temperature (HPHT) conditions is a significant challenge in the oil industry, leading to increased operational and maintenance costs. One method for calculating the density of drilling fluids involves the use of sensors placed at the bottom of the well, which measure the fluid density in real time. Despite their accuracy, these sensors are costly and perform suboptimal under HPHT conditions. In the present study, seven robust machine-learning models, namely generalized regression neural networks (GRNN), wavelet neural networks (WNN), and cascade forward neural networks (CFNN) combined with two different training algorithms containing Levenberg-Marquardt and Bayesian regression (CFNN-LM and CFNN-BR), respectively, and support vector regression (SVR) models combined with three optimization algorithms containing (farmland fertility algorithm (FFA) grasshopper optimization algorithm (GOA), and particle swarm optimization (PSO) were utilized to predict mud density at HPHT conditions. Moreover, mathematical models were developed using a group data management algorithm (GMDH) to predict this parameter. WNN, SVR model combined with (PSO, GOA, FFA), CFNN, and GRNN algorithms have better adaptability than other artificial intelligence models. These models can improve performance by changing the data and different situations. Also, these models can better resistance against noisy data, which means that they can show good performance compared to uneven and discontinuous data. To this aim, using 986 experimental data of drilling fluid at HPHT, including 117 water-base fluid data, 440 colloidal gas aphron fluid data, 219 oil-base fluid data, and 210 synthetic fluid data the drilling fluid density was modeled. Input parameters including temperature, pressure, initial mud density, and volume fraction of solid content were used for modeling. The results showed that the volume fraction of the solid content as an input variable improves the accuracy of intelligent models in this work. Based on average absolute percent relative error (AAPRE) the best outcomes for predicting the mud density without the input parameter of solid fraction were observed by the PSO-SVR model with an AAPRE of 0.5569%. Following, the GOA-SVR, WNN, and FFA-SVR delivered the next best results with AAPRE of 0.5860%, 0.5786%, and 0.6148%, respectively. Moreover, with the input parameter of solid fraction, the WNN model demonstrated the highest accuracy among all models, achieving an AAPRE of 0.2475%. Also, the correlation developed using the GMDH method based on input parameters of initial mud density, temperature, and pressure yielded satisfactory outcomes and can provide precise and swift estimates. Eventually, Sensitivity analysis revealed that initial mud density had the highest impact on mud density. Lastly, the leverage analysis indicates that most data points are trustworthy based on experimentation, with only a few falling outside the PSO-SVR and WNN models' scope.

Machine Learning to Predict Interim Response in Pediatric Classical Hodgkin Lymphoma Using Affordable Blood Tests.

Response assessment of classical Hodgkin lymphoma (cHL) with positron emission tomography-computerized tomography (PET-CT) is standard of care in well-resourced settings but unavailable in most African countries. We aimed to investigate correlations between changes in PET-CT findings at interim analysis with changes in blood test results in pediatric patients with cHL in 17 South African centers. Changes in ferritin, lactate dehydrogenase (LDH), erythrocyte sedimentation rate (ESR), albumin, total white cell count (TWC), absolute lymphocyte count (ALC), and absolute eosinophil count were compared with PET-CT Deauville scores (DS) after two cycles of doxorubicin, bleomycin, vinblastine, and dacarbazine in 84 pediatric patients with cHL. DS 1-3 denoted rapid early response (RER) while DS 4-5 denoted slow early response (SER). Missing values were imputed using the k-nearest neighbor algorithm. Baseline and follow-up blood test values were combined into a single difference variable. Data were split into training and testing sets for analysis using Python scikit-learn 1.2.2 with logistic regression, random forests, naïve Bayes, and support vector machine classifiers. Random forest analysis achieved the best validated test accuracy of 73% when predicting RER or SER from blood samples. When applied to the full data set, the optimal model had a predictive accuracy of 80% and a receiver operating characteristic AUC of 89%. The most predictive variable was the differences in ALC, contributing 21% to the model. Differences in ferritin, LDH, and TWC contributed 15%-16%. Differences in ESR, hemoglobin, and albumin contributed 11%-12%. Changes in low-cost, widely available blood tests may predict chemosensitivity for pediatric cHL without access to PET-CT, identifying patients who may not require radiotherapy. Changes in these nonspecific blood tests should be assessed in combination with clinical findings and available imaging to avoid undertreatment.

An Ensemble Learning Algorithm with Hyperparameter Optimization (ELAHO) Model for Early Detection of Maternal Health Risk (MHR) Level Using Machine Learning

Despite the advancement in the healthcare system, maternal health risk remains high, which is the most challenging aspect nowadays. There is a need to develop an effective model for early detection, monitoring, and prediction of maternal health risk levels during pregnancy. The machine learning intelligence model has proven its effectiveness and robustness in providing accurate and reliable prediction, analysis, and interpretation of medical data, reducing several risk factors for early diagnosis in healthcare. In this research work, we proposed an ensemble learning algorithm with a hyperparameter optimization (ELAHO) model using machine learning algorithms to improve its robustness, effectiveness, and model performance. The proposed method uses a hybrid model of logistic regression and support vector machines (LG-SVM) to predict maternal health risk levels during pregnancy. The method utilized Python software for training, testing, and validation. We evaluated the performance of our proposed model using accuracy, precision, sensitivity, f1-score, and ROC-AUC score. The proposed models outperformed the conventional models and achieved 100% predictive accuracy. The proposed approach has the potential to be adapted as an intelligence-monitoring system for early medical diagnosis during pregnancy. The proposed techniques will help medical professionals make quick decisions accurately and enhance monitoring to improve the level of care offered to pregnant mothers and their unborn children.

Euclidean and Poincaré space ensemble Xgboost

The Hyperbolic space has garnered attention for its unique properties and efficient representation of hierarchical structures. Recent studies have explored hyperbolic alternatives to hyperplane-based classifiers, such as logistic regression and support vector machines. Hyperbolic methods have even been fused into random forests by constructing data splits with horosphere, which proved effective for hyperbolic datasets. However, the existing incorporation of the horosphere leads to substantial computation time, diverting attention from its application on most datasets. Against this backdrop, we introduce an extension of Xgboost, a renowned machine learning (ML) algorithm to hyperbolic space, denoted as PXgboost. This extension involves a redefinition of the node split concept using the Riemannian gradient and Riemannian Hessian. Our findings unveil the promising performance of PXgboost compared to the algorithms in the literature through comprehensive experiments conducted on 64 datasets from the UCI ML repository and 8 datasets from WordNet by fusing both their Euclidean and hyperbolic-transformed (hyperbolic UCI) representations. Furthermore, our findings suggest that the Euclidean metric-based classifier performs well even on hyperbolic data. Building upon the above finding, we propose a space fusion classifier called, EPboost. It harmonizes data processing across various spaces and integrates probability outcomes for predictive analysis. In our comparative analysis involving 19 algorithms on the UCI dataset, our EPboost outperforms others in most cases, underscoring its efficacy and potential significance in diverse ML applications. This research marks a step forward in harnessing hyperbolic geometry for ML tasks and showcases its potential to enhance algorithmic efficacy.

Optimized polycystic ovarian disease prognosis and classification using AI based computational approaches on multi-modality data

Polycystic Ovarian Disease or Polycystic Ovary Syndrome (PCOS) is becoming increasingly communal among women, owing to poor lifestyle choices. According to the research conducted by National Institutes of Health, it has been observe that PCOS, an endocrine condition common in women of childbearing age, has become a significant contributing factor to infertility. Ovarian abnormalities brought on by PCOS carry a high risk of miscarriage, infertility, cardiac problems, diabetes, uterine cancer, etc. Ovarian cysts, obesity, menstrual irregularities, elevated amounts of male hormones, acne vulgaris, hair loss, and hirsutism are some of the symptoms of PCOS. It is not easy to determine PCOS because of its different combinations of symptoms in different women and various criteria needed for diagnosis. Taking biochemical tests and ovary scanning is a time-consuming process and the financial expenses have become a hardship to the patients. Thus, early prognosis of PCOS is crucial to avoid infertility. The goal of the proposed work is to analyse PCOS symptoms based on clinical data for early diagnosis and to classify into PCOS affected or not. To achieve this objective, clinical features dataset and ultrasound imaging dataset from Kaggle is utilized. Initially 541 instances of 45 clinical features such as testosterone, hirsutism, family history, BMI, fast food, menstrual disorder, risk etc. are considered and correlation-based feature extraction method is applied to this dataset which results in 17 features. The extracted features are applied to various machine learning algorithms such as Logistic Regression, Naïve Bayes and Support Vector Machine. The performance of each method is evaluated based on accuracy, precision, recall, F1-score and the result shows that among three models, Support Vector Machine model achieved high accuracy of 94.44%. In addition to this, 3856 ultrasound images are analysed by CNN based deep learning algorithm and VGG16 transfer learning algorithm. The performance of these models is evaluated using training accuracy, loss and validation accuracy, loss and the result depicts that VGG16 outperforms than CNN model with validation accuracy of 98.29%.

EXPLORING THE ROLE OF ARTIFICIAL INTELLIGENCE IN MANAGING EMERGING RISKS: AN IN-DEPTH STUDY OF AI APPLICATIONS IN FINANCIAL INSTITUTIONS' RISK FRAMEWORKS

This research focuses on using approaches such as ML and ANNs in FRM while will look at and try to analyze their effectiveness compared to logistic regression, random forest, and support vector machine. Training and testing of the models were done using accuracy, precision, recall and F1-score with a sample database comprising of 15000 financial records. Imputation of missing values; selection of informative variables; and data scaling, were performed to enhance the reliability of the models used. Analysis of the results revealed that ANNs and more so DNNs surpassed conventional approaches in the prediction of financial risks. Still, the integration of traditional and AI-based approaches resulted in improved performance outcomes as well as proved to be more resilient to multiple risk factors. Thus, the work concludes that the enhancement of the integration of AI in the management of financial risk can enhance the accuracy of risk assessment. The future work should include improvements regarding the interpretability of the model, testing on a more substantial number of data and experimenting with reinforcement learning to apply it to decision making in the financial risk cases.

Mathematical modeling of ions adsorption from water/wastewater sources via porous materials: A machine learning-based approach

This paper developed the predictive modeling of substance concentration (C) utilizing the input parameters x and y, for analysis of adsorption process. Employing three distinct machine learning models—Multilayer Perceptron (MLP), polynomial regression (PR), and Support Vector Machine (SVM)—the study investigates the efficacy of models in capturing the relationships between the inputs and output. The models are trained from data obtained from mass transfer calculations for removal of solute from solution via porous adsorbent. Furthermore, the hyper-parameters for each model are optimized through the utilization of the Political Optimizer (PO). The Multilayer Perceptron model emerges as a standout performer, showcasing an exceptional R-squared score of 0.9981, indicative of a robust fit to the data. Complemented by impressively low MAE and MSE values (7.94043E-01 and 2.0420E+00, respectively), the MLP model attests to its ability to provide accurate predictions and discern underlying patterns within the dataset. The polynomial regression model, while slightly trailing behind the MLP in terms of R-squared score (0.95929), revealed commendable predictive performance. Support Vector Machine also proves to be a formidable contender, boasting a robust R-squared score of 0.96055.

MACHINE LEARNING APPROACHES FOR DEMAND FORECASTING: THE IMPACT OF CUSTOMER SATISFACTION ON PREDICTION ACCURACY

This study investigates the effectiveness of various machine learning models in predicting product demand based on customer satisfaction data. Four models—Linear Regression, Random Forest, Gradient Boosting, and Support Vector Machine (SVM)—were evaluated using performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R² score. The results indicate that Gradient Boosting achieved the highest accuracy, with an MAE of 2.56, MSE of 12.75, RMSE of 3.57, and R² score of 0.82, effectively capturing the complex, non-linear relationships inherent in customer satisfaction factors. Random Forest also demonstrated strong performance, while Linear Regression and SVM showed limitations in handling intricate datasets. These findings underscore the importance of utilizing advanced machine learning techniques for accurate demand forecasting, highlighting the critical role of customer satisfaction data in enhancing predictive capabilities. The insights gained from this research can guide organizations in optimizing inventory management and improving customer satisfaction in a rapidly evolving market.

Visible, near-infrared, and shortwave-infrared spectra as an input variable for digital mapping of soil organic carbon

This study proposes a novel methodology to employ discrete point spectra as input variable for digital mapping of soil organic carbon (SOC). Accordingly, two SOC modeling approaches were used in three agricultural sites in Czech Republic: i) machine learning (ML) including partial least square regression (PLSR), cubist, random forest (RF), and support vector regression (SVR), and ii) regression kriging (RK) by the combination of ordinary kriging (OK) and PLSR (PLSR-K), cubist (cubist-K), RF (RF-K), and SVR (SVR-K). Models were developed on environmental predictor covariates (EPCs) and thirty genetic algorithms (GA)-selected visible, near-infrared, and shortwave-infrared (VNIR–SWIR) wavelengths spectra, individually and combined. Thirty rasters were then created using interpolation of the selected spectra and served as the input variables – with and without EPCs – to test and compare the developed models and SOC predictive maps with each other and with those retrieved from the third approach: iii) kriging using OK of the measured and ML-predicted SOC. The impact of employing selected wavelengths’ spectra and EPCs on models' performance was investigated using independent test samples and the uncertainty associated with the produced maps. Using interpolated spectra as the only input variable yielded a relatively acceptable accuracy (Nová Ves: RMSE = 0.19%, Údrnice: RMSE = 0.12%, Klučov: RMSE = 0.13%). In comparison, the interpolated spectra coupled with EPCs enhanced the results. Regarding the uncertainty, however, the ML-based SOC maps were more reliable, than RK-based ones. Furthermore, maps produced using both spectra and EPCs showed less uncertainty than those constructed on the individual datasets.

Joint estimation of SOH and RUL for lithium batteries based on variable frequency and model integration

The safe and stable operation of lithium-ion batteries in electric vehicles is crucial. Aiming at the problems of a large workload of online estimation and prediction and inability to weigh the whole life cycle of the battery in the joint estimation of SOH (State of Health) and RUL (Remaining Useful Life) for traditional lithium-ion batteries, this paper proposes an optimization scheme for the joint analysis of SOH-RUL. First, the article extracts suitable health features. It utilizes an integrated Extreme Learning Machine (ELM) to estimate the SOH of the battery and inputs the estimates into a Regression Vector Machine (RVM) model for RUL prediction. To better cope with the phenomenon of "sudden capacity change" in batteries, this paper divides the entire life cycle into the early and late stages. A lower estimation frequency and model integration are employed in the early stage.In contrast, the estimation frequency and model integration are increased later to balance speed and safety in total life cycle estimation. Finally, validation was performed on the NASA and CALCE datasets, comparing the effect of constant estimated frequency and model integration with that of varying estimated frequency and model integration at different aging stages. The RMSE of SOH estimation error for both datasets is within 2% during the early and late stages, and the SOH estimation error for the entire lifecycle is within 2%. The absolute RUL prediction error for the NASA dataset is below five times; for the CALCE dataset, it is below 20 times in most cases.

New Heuristics Method for Malicious URLs Detection Using Machine Learning

Malicious URLs are a very prominent, dangerous form of cyber threats in view of the fact that they can enable many evils like phishing attacks, malware distribution, and several other kinds of cyber fraud. The techniques of detection conventionally applied are based on blacklisting and heuristic analyses, which are gradually becoming inefficient against sophisticated, rapidly evolving threats. In this paper, the authors present various machine learning techniques applied in malicious URL detection. In the present paper, we will look at three machine learning models: Logistic Regression, Random Forest, and Support Vector Machines. We used a methodology that involved collecting data and feature extraction, training a model, then evaluating its performance with different metrics such as accuracy, precision, recall, and F1-score. We implemented and optimized three models—Logistic Regression, Random Forest, and Support Vector Machines (SVM)—based on the literature available that indicates the effectiveness of these models. Logistic Regression shows promising results to detect the malicious URLs, according to Vanitha and Vinodhini. Random Forest models are found to be very robust and accurate according to Cui et al. and Vanhoenshoven et al., SVM models are evidenced to have very high accuracy according to Manjeri et al., Further works on deep learning models emphasized their potentials. In our study, the optimized Random Forest model in our case showed the best performance, and its training accuracy was 99%, while validation accuracy was 90.5%, also logistic Regression and SVM achieved training accuracy was 89.31%, while validation accuracy was 90.5%. All the optimization processes, model performances, and integration into the real-time cybersecurity infrastructures, along with the strengths and limitations, are discussed in this paper. The paper will, therefore, discuss the benefits and challenges for each model in this aspect—emphasizing continuous updating of the models and integrating them into real-time cybersecurity infrastructures.