K-nearest Neighbor Research Articles

Optimization of machine learning models for sentiment analysis in social media

The primary goal of this paper is to develop optimized classical machine learning models for sentiment analysis of social media posts. The methodology includes the application of k-fold cross-validation and feature selection using the χ2 independence test to prevent overfitting and improve classifier accuracy. Classical algorithms such as naïve Bayes, support vector machines, decision trees, and k-nearest neighbors are applied, along with preprocessing techniques, across five distinct datasets for model construction and evaluation. These optimized classical models are compared with recurrent neural network architectures, including LSTM and GRU, to evaluate the relative efficiency of both approaches. In total, 34 classification models were generated, with hyperparameter optimization for classical methods performed by grid search. The highest accuracy achieved was 82.45% for data without preprocessing and 78.83% for fully preprocessed data, both with the naïve Bayes algorithm. After hyperparameter optimization, some models achieved an accuracy greater than 90%. An analysis of variance indicated statistically significant differences among the models, confirming that feature selection and hyperparameter optimization are important factors in classifier performance. The proposed approach proved efficient for analyzing unstructured textual data, allowing the development of optimized models even with limited labeled data, and providing valuable insights into user opinions across different contexts.

Machine learning-driven design and performance analysis of microstrip antennas for sub-6 GHz/mm Wave 5G networks

In the realm of modern communication systems, antennas are crucial components, with the microstrip patch antenna being particularly notable for its low profile and seamless integration. Despite its widespread use, designing this antenna involves complex simulations to optimize parameters, requiring significant expertise and consuming considerable time and energy. To streamline this process, machine learning (ML) algorithms are being utilized. This paper introduces an innovative approach that employs ML techniques to design a rectangular microstrip patch antenna operating within the sub-6 GHz frequency range (1-6 GHz) and the millimeter frequency range (28-40 GHz). The antenna design maintains consistent patch dimensions positioned strategically at the center, with a thorough examination of patch length and width to enhance performance. Datasets are meticulously prepared, covering output parameters such as beam area, directivity, gain, and radiation efficiency across the specified frequency ranges. By employing various ML algorithms, this study conducts a comprehensive analysis to identify the most effective algorithm for accurately predicting antenna characteristics. The K-nearest neighbor (KNN) algorithm achieved high accuracy across all parameters: gain at 94.23% under sub-6 GHz and 95.93% under millimeter frequency range, directivity at 99.02% and 98.59%, radiation efficiency at 93.94% and 94.28%, and beam area at 99.07% and 98.59% respectively. These results optimize microstrip antenna designs and enhance understanding of the relationship between design parameters and performance outcomes with ML.

WEKA-based machine learning for traffic congestion prediction in Amman City

<p>Traffic congestion leads to wasted time, pollution, and increased fuel consumption. Traffic congestion prediction has become a developing research topic in recent years, particularly in the field of machine learning (ML). The evaluation of various traffic parameters is used to predict traffic congestion by relying on historical data. In this study, we will predict traffic congestion in Amman City, specifically at the 8th circle, using different ML classifiers. The 8th circle links four main streets: Westbound, Northbound, Eastbound, and Southbound. Datasets were collected from the greater Amman municipality hourly. The logistic regression (LR), k-nearest neighbor (KNN), decision tree (DT), random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP) classifiers have been chosen to predict traffic congestion at each street linked with the 8th circle. The waikato environment for knowledge analysis (WEKA) data mining tool is used to evaluate chosen classifiers by determining accuracy, F-measure, sensitivity, and precision evaluation metrics. The results obtained from all experiments have demonstrated that SVM is the best classifier to predict traffic congestion. The accuracy of SVM to predict traffic congestion at Westbound Street, Northbound Street, Eastbound Street, and Southbound Street was 99.4%, 99.7%, 99.6%, and 99.1%, respectively.</p>

A comparative analysis of PlanetScope 4-band and 8-band imageries for land use land cover classification

Earth-observing satellites have become essential in comprehending human impacts on the landscape. Satellite-based imagery is indispensable for mapping Earth's features, managing resources, and studying environmental changes. Readily available remote sensing data with improved radiometric, spectral, spatial, and temporal resolution presents opportunities for advanced data analysis. Precise and accurate land use land cover (LULC) information is essential for the surveillance of environmental conditions and the effective management of natural resources. This research assesses the performance of PlanetScope product SuperDove sensor (PSB.SD), having two different band combinations, including 4-band (Red, Blue, Green and Near-Infrared (NIR)) and 8-band (Blue, Green II, Red, NIR, Coastal Blue, Green, Yellow, Red-Edge) imagery in ArcGIS Pro for the month of July 2021. Four different supervised classifiers, including support vector machine (SVM), k-nearest neighbours (KNN), random forest (RF), and maximum likelihood (ML) classifiers. This study was carried out for the three major areas, i.e., City of Summerside, City of Charlottetown, and Town of Three Rivers in Prince Edward Island (PEI), Canada and LULC classification scheme consist of six major classes which includes Agriculture, Forest, Vegetation, Bare Land, Urban and Water bodies. For accuracy assessment, overall accuracy as well as kappa coefficient were estimated to identify the most accurate combination of LULC classifier and different band combination imagery from PlanetScope. Results show that the highest overall accuracy of 0.94 for Town of Three Rivers and 0.93 for City of Summerside and City of Charlottetown were observed using 8-band imagery with SVM classifier. The lowest overall accuracy of 0.78 for Town of Three Rivers, 0.83 for City of Charlottetown, and 0.82 for City of Summerside was observed using 4-band imagery using ML classifier. Further, the SVM classifier performs well in accuracy with 8-band imagery of PlanetScope, showcasing its potential in LULC classification compared to previous PlanetScope 4-band imagery.

A Comprehensive Survey of Arrhythmia Classification Techniques

Arrhythmia classification is a critical area of research in cardiology, given the significant health risks associated with irregular heart rhythms. This research study presents a comprehensive survey of current methodologies employed for the detection and classification of arrhythmias from electrocardiogram (ECG) signals. The study examines a variety of methods, including sophisticated deep learning techniques like Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM) in addition to more conventional machine learning algorithms like Support Vector Machines (SVM), Random Forests (RF), and K-Nearest Neighbors (KNN). Additionally, the research study provides critical information that is relevant to medical diagnostics, such as the weak generalizability hampered by imbalanced datasets, whose management is well illustrated by the research study as a successful way to increase model performance.

Classification of IPB variety of cayenne pepper genotypes using physical characteristics during the growing period until harvest using machine learning

In agrarian sectors, the prevalence of adulteration from seed selection to final agricultural outputs is a significant concern. This deceptive practice not only spans across distinct plant species but also extends to varieties within the same species, as observed in chili cultivation. Addressing this issue, the current study aims to develop a machine learning-based classification model that discriminates between cayenne pepper genotypes of IPB varieties and others. The model harnesses a range of plant physiological parameters monitored from the growth phase through to harvest. These parameters include plant and dichotomous heights, age at flowering, age at harvest, fruit stalk length, chili dimensions, individual chili weight, total number of chilies per plant, and overall yield productivity. Employing a dataset of 45 plant samples—30 from IPB varieties and 15 from non-IPB varieties—this research evaluates four machine learning algorithms: Linear Discriminant Analysis (LDA), k-Nearest Neighbors (kNN), Decision Tree (DT), and Random Forest (RF). To ascertain the robustness of the proposed model, the study also investigates the impact of varying data split ratios, including 90/10, 75/25, and 50/50, on model performance. Preliminary results indicate that the DT classifier, with an accuracy exceeding 80%, successfully differentiates the IPB cayenne pepper genotypes from other varieties. This promising conceptual model warrants further validation and enhancement through future research on a larger dataset, paving the way for its practical application in ensuring the authenticity of agricultural produce.

A hybrid classification approach for automatically recognizing COVID-19 using deep transfer learning using chest radiographs

Coronavirus 2019 causes COVID-19, a worldwide epidemic. It endangers millions globally. Early illness detection improves recovery and control. X-ray image processing is used to categorise and identify COVID-19 in the present study. Preprocessing, feature extraction using local binary pattern (LBP) and edge orient histogram (EOH), and classification utilising K-nearest neighbour (KNN), Navie Bayes, support vector machine (SVM), and transfer learning convolution neural networks (CNNs) are some of the stages that are implemented in the process. Other phases in the process include preprocessing, feature extraction, and preprocessing. LBP+KNN, EOH+KNN, LBP+SVM, EOH +SVM, CNN+LBP, and CNN+EOH are the outputs derived from the combinations of feature extraction operators and classifiers. Other possible outcomes are CNN+EOH and CNN+LBP. A total of 4,000 pictures were used as the basis for conducting an analysis of the performance of six different models. In order to train the models, 10-fold cross-validation was used, and their accuracy was measured accordingly. The evaluation results indicate a high level of accuracy in diagnosis, ranging from 90.2% to 97.56%. The CNN+LBP and CNN+EOH models have demonstrated superior performance compared to other models, achieving average accuracies ranging from 96.66% and 98.54%.

Quasi-Newton Optimized Kolmogorov-Arnold Networks for Wind Farm Power Prediction

Having accurate and effective wind energy forecasting that can be easily incorporated into smart networks is important. Appropriate planning and energy generation predictions are necessary for these infrastructures. The production of wind energy is linked to instability and unpredictability. Wind energy forecasting has traditionally been performed using statistical models, but with the advent of artificial intelligence (AI), research indicates that AI is more accurate than the statical technique. In this study, the nominal power of six wind farms in China was predicted using Kolmogorov-Arnold Networks (KAN) and Multilayer Perceptron (MLP) models. KAN as an alternative to the conventional MLP, has the ability to handle problems with scalability, vanishing gradients, and interpretability associated with MLP. The KAN uses learnable B-Spline as activation functions prompting it to address the issues of the MLP. We employed the Radial Basis Function (RBF) with Gaussian kernels to approximate the 3-order B-spline basis. In most deep learning models stochastic gradient-based optimisation algorithms such as Adaptive Moment Estimation (ADAM) and Stochastic Gradient Descent (SGD) optimizer are mostly employed, a quasi-Newton optimization technique Limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm LBFGS was employed in this work to approximate the Hessian matrix and estimate the parameter space's curvature. Also, in the preprocessing of the data, the Interquartile Range (IQR) technique is used to handle outliers and a clustering-based K-Nearest Neighbor (KNN) imputer to handle missing values. Based on different sites, the KAN-LBFGS shows superior performance based on the performance evaluation metrics with site 5 achieving MSE of 0.0039, RMSE of 0.0622, MAE of 0.0352, and DC of 0.9468. The study highlights the importance of the model’s architecture, preprocessing and optimization techniques.

Ensemble stacking classifier model for prediction of diabetes

Diabetes, being a chronic condition, possesses the capacity to instigate a global healthcare catastrophe. This condition can be managed and potentially cured with prompt diagnosis and treatment. Integrating machine learning technology with medical science enables precise prognosis of an individual’s susceptibility to diabetes. The proposed work presents the ensemble stacking classifier model. This efficient and effective diabetes prediction model predicts a patient’s diabetes risk by combining the output of multiple machine-learning techniques into a single model. The performance parameters of four distinct machine learning classification algorithms K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), and decision tree (DT) are compared in this study with those of the proposed stacked classifier model. The suggested model is developed using ensemble methods, where the previously discussed algorithms are integrated to create the base classifier layer of the stack classifier. The meta-classifier is implemented in the form of the logistic regression (LR) algorithm. Upon evaluating the performance of both the developed model and its algorithms, it is proved that the proposed model attains a testing accuracy of 88.5%, surpassing the accuracy of all baseline classification algorithms. As a result, this work determines that the ensemble stacking classifier model exhibits higher prediction accuracy than the base classifier algorithms. This finding underscores the model’s potential as a viable instrument for predicting diabetes in individuals.

NCMNet: Neighbor Consistency Mining Network for Two-View Correspondence Pruning.

Correspondence pruning plays a crucial role in a variety of feature matching based tasks, which aims at identifying correct correspondences (inliers) from initial ones. Seeking consistent k-nearest neighbors in both coordinate and feature spaces is a prevalent strategy employed in previous approaches. However, the vicinity of an inlier contains numerous irregular false correspondences (outliers), which leads them to mistakenly become neighbors according to the similarity constraint of nearest neighbors. To tackle this issue, we propose a global-graph space to seek consistent neighbors with similar graph structures. This is achieved by using a global connected graph to explicitly render the affinity relationship between correspondences based on the spatial and feature consistency. Furthermore, to enhance the robustness of method for various matching scenes, we develop a neighbor consistency block to adequately leverage the potential of three types of neighbors. The consistency can be progressively mined by sequentially extracting intra-neighbor context and exploring inter-neighbor interactions. Ultimately, we present a Neighbor Consistency Mining Network (NCMNet) to estimate the parametric models and remove outliers. Extensive experimental results demonstrate that the proposed method outperforms other state-of-the-art methods on various benchmarks for two-view geometry estimation. Meanwhile, four extended tasks, including remote sensing image registration, point cloud registration, 3D reconstruction, and visual localization, are conducted to test the generalization ability.

Adaptive Graph [formula omitted]-Means

Clustering large-scale datasets has received increasing attention recently. However, existing algorithms are still not efficient in scenarios with extremely large number of clusters. To this end, Adaptive Graph K-Means (AGKM) is proposed in this work. Its idea originates from k-means, but it operates on an adaptive k-Nearest Neighbor (k-NN) graph instead of data features. First, AGKM is highly efficient for processing datasets where both the numbers of samples and clusters are very large. Specifically, the time and space complexity are both linear w.r.t the number of samples and, more importantly, independent to the cluster number. Second, AGKM is designed for balanced clusters. This constraint is realized by adding a regularization term in loss function, and a simple modification of the graph in optimization algorithm, which does not increase the computational burden. Last, the indicator and dissimilarity matrices are learned simultaneously, so that the proposed AGKM obtains the final partition directly with higher efficacy and efficiency. Experiments on several datasets validate the advantages of AGKM. In particular, over 29X and 46X speed-ups with respect to k-means are observed on the two large-scale datasets WebFace and CelebA, respectively.

Breast cancer identification using machine learning and hyperparameter optimization

Breast cancer identification can be analyzed through genomic analysis using gene expression data, one type of which is mRNA. This involves analyzing gene expression patterns of breast tissue samples to distinguish breast cancer from healthy tissue or to differentiate subtypes of different breast cancers. This research developed the right computational model for breast cancer classification using machine learning and hyperparameter optimization algorithms. The primary objective of this research is to utilize various machine learning algorithms to classify breast cancer based on gene expression and enhance the models developed in previous studies. This paper provides an extensive literature review of prior breast cancer classification research and offers new theoretical perspectives. This research used a problem-solving approach with conventional machine learning techniques, most notably the decision tree. It also evaluates other machine learning algorithms for comparison, including k-nearest neighbor, naïve bayes, random forest, extra tree classifier, and support vector machine. The evaluation process used classification reports that provide insight into the precision, recall, F1-score, and accuracy of each machine learning model. The evaluation results show that the performance of the decision tree algorithm model is superior and impressive, achieving 99.73% accuracy and a score of 1 for precision, recall, and F1-score.

SEMG-Based hand gestures classification using a semi-supervised multi-layer neural networks with Autoencoder

This work presents a semi-supervised multilayer neural network (MLNN) with an Autoencoder to develop a classification model for recognizing hand gestures from electromyographic (EMG) signals. Using a Myo armband equipped with eight non-invasive surface-mounted biosensors, raw surface EMG (sEMG) sensor data were captured corresponding to five hand gestures: Fist, Open hand, Wave in, Wave out, and Double tap. The sensor collected data underwent preprocessing, feature extraction, label assignment, and dataset organization for classification tasks. The model implementation, validation, and testing demonstrated its efficacy after incorporating synthetic sEMG data generated by an Autoencoder. In comparison to the state-of-the-art techniques from the literature, the proposed model exhibited strong performance, achieving accuracy of 99.68%, 100%, and 99.26% during training, validation, and testing, respectively. Comparatively, the proposed MLNN with Autoencoder model outperformed a K-Nearest Neighbors model established for comparative evaluation.

Improving the risk profile of Indonesian enterprise taxpayers using multilabel classification

Optimizing tax revenues is difficult in Indonesia due to obstacles such as tax evasion and tax avoidance. It is closely related to an organization's compliance with tax regulations, known as the taxpayers risk profile. However, this mechanism does not accurately detect tax avoidance and tax evasion risks. To overcome this limitation, we use a multilabel classification machine learning method in this study, which classifies a single observation into one or more labels at once. The approach involves problem transformation (binary relevance and label powerset), algorithm adaptation (multilabel k-nearest neighbor (ML-kNN) and multilabel-adaptive resonance associative map (ML ARAM)), and ensemble (label space partitioning and random k-label sets with disjoint (RAkELd)). Based on the model performance comparisons, we discovered that the ML-ARAM method based on deep learning is the best, with an average F1-score of 95.5% and a hamming loss of 7.4%. We also examine the feature importance of the best model to reduce the dimensions of features so that we can identify the dominant factors that encourage a taxpayer entity to engage in tax avoidance or tax evasion. The findings of this study improve the accuracy of tax avoidance risk detection and tax evasion risk profiles using machine learning methods, ensuring maximum tax revenues in Indonesia.

Comparative Analysis of Machine Learning Algorithms: KNN, SVM, Decision Tree and Logistic Regression for Efficiency and Performance

This paper presents a comparative study of machine learning models namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, and Logistic Regression on two datasets: 20Newsgroups and Wine datasets. Based on basic accuracy, and precision, and recall of the models, F1 measures were assessed. When compared with other classifiers such as KNN and Decisions Trees, SVM and Logistic regression gave better results especially in the case of the 20Newsgroups dataset dominated by textual high dimensional data. KNN had poor recall results and Decision Tree was moderate. In the Wine dataset, since the structure of data is comparatively less complex in our context then all the models yielded almost similar results with accuracy and precision factors very close to 1.0 which of course manifested that the choice of the model does not affect much on simple data. These results stress the importance of the choice of the appropriate model for tasks with a certain level of data complexity; detailed models show the greatest efficiency at the accomplishment of complicated tasks, though at the same time, they are not required for simple, structured data.

A Hybrid Deep Learning Model with Data Augmentation to Improve Tumor Classification Using MRI Images

Background: Cancer ranks second among the causes of mortality worldwide, following cardiovascular diseases. Brain cancer, in particular, has the lowest survival rate of any form of cancer. Brain tumors vary in their morphology, texture, and location, which determine their classification. The accurate diagnosis of tumors enables physicians to select the optimal treatment strategies and potentially prolong patients’ lives. Researchers who have implemented deep learning models for the diagnosis of diseases in recent years have largely focused on deep neural network optimization to enhance their performance. This involves implementing models with the best performance and incorporating various network architectures by configuring their hyperparameters. Methods: This paper presents a novel hybrid approach for improved brain tumor classification by combining CNNs and EfficientNetV2B3 for feature extraction, followed by K-nearest neighbors (KNN) for classification, which has been described as one of the simplest machine learning algorithms based on supervised learning techniques. The KNN algorithm assumes similarities between new cases and available cases and assigns new cases to the category that most closely resembles the available categories. Results: To evaluate the recommended method’s efficacy, two widely known benchmark MRI datasets were utilized in the experiments. The initial dataset consisted of 3064 MRI images depicting meningiomas, gliomas, and pituitary tumors. Images from two classes, consisting of healthy brains and brain tumors, were included in the second dataset, which was obtained from Kaggle. Conclusions: In order to enhance the performance even further, this study concatenated the CNN and EfficientNetV2B3’s flattened outputs before feeding them into the KNN classifier. The proposed framework was run on these two different datasets and demonstrated outstanding performance, with accuracy of 99.51% and 99.8%, respectively.

Improving Forest Above-Ground Biomass Estimation Accuracy Using Multi-Source Remote Sensing and Optimized Least Absolute Shrinkage and Selection Operator Variable Selection Method

Estimation of forest above-ground biomass (AGB) using multi-source remote sensing data is an important method to improve the accuracy of the estimate. However, selecting remote sensing factors that can effectively improve the accuracy of forest AGB estimation from a large amount of data is a challenge when the sample size is small. In this regard, the Least Absolute Shrinkage and Selection Operator (Lasso) has advantages for extensive redundant variables but still has some drawbacks. To address this, the study introduces two Least Absolute Shrinkage and Selection Operator Lasso-based variable selection methods: Least Absolute Shrinkage and Selection Operator Genetic Algorithm (Lasso-GA) and Variance Inflation Factor Least Absolute Shrinkage and Selection Operator (VIF-Lasso). Sentinel 2, Sentinel 1, Landsat 8 OLI, ALOS-2 PALSAR-2, Light Detection and Ranging, and Digital Elevation Model (DEM) data were used in this study. In order to explore the variable selection capabilities of Lasso-GA and VIF-Lasso for remote sensing estimation of forest AGB. It compares Lasso-GA and VIF-Lasso with Boruta, Random Forest Importance Selection, Pearson Correlation, and Lasso for selecting remote sensing factors. Additionally, it employs eight machine learning models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Bayesian Regression Neural Network (BRNN), Elastic Net (EN), K-Nearest Neighbors (KNN), Extremely Randomized Trees (ETR), and Stochastic Gradient Boosting (SGBoost)—to estimate forest AGB in Wuyi Village, Zhenyuan County. The results showed that the optimized Lasso variable selection could improve the accuracy of forest biomass estimation. The VIF-Lasso method results in a BRNN model with an R2 of 0.75 and an RMSE of 16.48 Mg/ha. The Lasso-GA method results in an ETR model with an R2 of 0.73 and an RMSE of 16.70 Mg/ha. Compared to the optimal SGBoost model with the Lasso variable selection method (R2 of 0.69, RMSE of 18.63 Mg/ha), the VIF-Lasso method improves R2 by 0.06 and reduces RMSE by 2.15 Mg/ha, while the Lasso-GA method improves R2 by 0.04 and reduces RMSE by 1.93 Mg/ha. From another perspective, they also demonstrated that the RX sample count and sensitivity provided by LiDAR, as well as the Horizontal Transmit, Vertical Receive provided by Microwave Radar, along with the feature variables (Mean, Contrast, and Correlation) calculated from the Green, Red, and NIR bands of optical remote sensing in 7 × 7 and 5 × 5 windows, play an important role in forest AGB estimation. Therefore, the optimized Lasso variable selection method shows strong potential for forest AGB estimation using multi-source remote sensing data.

Unsupervised Modelling of E-Customers’ Profiles: Multiple Correspondence Analysis with Hierarchical Clustering of Principal Components and Machine Learning Classifiers

The rapid growth of e-commerce has transformed customer behaviors, demanding deeper insights into how demographic factors shape online user preferences. This study performed a threefold analysis to understand the impact of these changes. Firstly, this study investigated how demographic factors (e.g., age, gender, education) influence e-customer preferences in Serbia. From a sample of n = 906 respondents, conditional dependencies between demographics and user preferences were tested. From a hypothetical framework of 24 tested hypotheses, this study successfully rejected 8/24 (with p < 0.05), suggesting a high association between demographics with purchase frequency and reasons for quitting the purchase. However, although the reported test statistics suggested an association, understanding how interactions between categories shape e-customer profiles was still required. Therefore, the second part of this study considers an MCA-HCPC (Multiple Correspondence Analysis with Hierarchical Clustering on Principal Components) to identify user profiles. The analysis revealed three main clusters: (1) young, female, unemployed e-customers driven mainly by customer reviews; (2) retirees and older adults with infrequent purchases, hesitant to buy without experiencing the product in person; and (3) employed, highly educated, male, middle-aged adults who prioritize fast and accurate delivery over price. In the third stage, the clusters are used as labels for Machine Learning (ML) classification tasks. Particularly, Gradient Boosting Machine (GBM), Decision Tree (DT), k-Nearest Neighbors (kNN), Gaussian Naïve Bayes (GNB), Random Forest (RF), and Support Vector Machine (SVM) were used. The results suggested that GBM, RF, and SVM had high classification performance in identifying user profiles. Lastly, after performing Permutation Feature Importance (PFI), the findings suggested that age, work status, education, and income are the main determinants of shaping e-customer profiles and developing marketing strategies.

Availability Evaluation of Cross Project Software Based on GAKT and Supervised Learning in Personalized Innovation Education for College Students

The availability evaluation of cross project software is crucial for personalized innovation education and learning of college students. Ensuring the stability and efficiency of educational resources and tools is the key to improving learning outcomes. This study proposes an evaluation model that integrates MeanShift clustering method and K-nearest neighbor algorithm. The approach enhanced the feature selection of the model and improved the model by combining attention mechanism. The experimental results showed that the proposed model performed well in multiple aspects. In addition to exhibiting superior convergence performance, the proposed model also demonstrated a significant advantage over naive Bayesian and gradient enhanced decision tree models in terms of delay performance. The optimal delay was 2.47s, and the data volume was as low as 14.12MB. Moreover, its accuracy reached 94.11%, which was 6.61% and 19.11% higher than naive Bayesian and gradient enhanced decision trees, respectively. This model was of practical significance for optimizing the software tools for personalized and innovative education and learning of college students. The MeanShift K-nearest neighbor model can provide educational institutions with a more reliable and efficient evaluation tool. The proposed model can better adapt to and meet the personalized learning needs of students, while also reducing potential learning interruptions caused by software unavailability. In addition, the efficient data processing and accuracy performance of the model means that high-quality availability assessments can also be achieved in resource constrained environments.

Enhancing Cardiovascular Disease Prediction Accuracy through an Ensemble Machine Learning Approach

This study explores the efficacy of an ensemble machine learning approach, specifically a Voting Classifier combining Decision Tree, k-Nearest Neighbors, and Gaussian Naive Bayes classifiers, in predicting cardiovascular diseases (CVDs). Utilizing a dataset consisting of 70,000 clinical records, the model was rigorously tested through 5-fold cross-validation, achieving remarkable results with average accuracies, precision, recall, and F1-scores all exceeding 99%. The findings validate the hypothesis that ensemble models, due to their capacity to leverage multiple learning algorithms, provide superior prediction accuracy and reliability compared to single predictor models. This research not only confirms the effectiveness of ensemble methods in medical diagnostics but also highlights their potential to enhance decision-making in clinical settings. Given the model's success in identifying various stages of cardiovascular conditions with high accuracy, it offers significant implications for early intervention and personalized patient management. Future research should aim to validate these results across more diverse populations and explore the integration of additional predictive factors that could refine the model's applicability. This study contributes to the computational health field by demonstrating how advanced machine learning techniques can be effectively applied in predicting health outcomes.