K-nearest Neighbor Research Articles

Mechanical behavior of composite pipe structures under compressive force and its prediction using different machine learning algorithms

Abstract Thanks to machine learning algorithms, the performance of composites with high energy absorption capacity can be predicted with high accuracy rates with a small number of data. The aim of this study is to experimentally and numerically determine the crushing performances of glass/epoxy composite pipe structures under compressive force and to predict their compression behavior with the help of different machine learning algorithms. In the study, the crushing performances of composite pipes (peak force (PF), peak force displacement (PFD), mean crushing force (MCF), specific energy absorption (SEA), and total inner energy (TIE)) were determined for different specimen thicknesses, specimen lengths, mesh sizes, numbers of integration points, diameters (D), and compression directions (axial and radial). Additionally, the maximum strength values of composite pipes under force were estimated with the help of Linear Regression (LR), K-Nearest Neighbors (KNN), and Artificial Neural Networks (ANN) machine learning algorithms. The data taken from the ANN algorithm were found to be more reliable in estimating the PF and TIE values, with an accuracy rate of 92 %. When determining the MCF value, it was found that the data obtained from the LR algorithm was more reliable than other algorithms, with an accuracy rate of 80 %.

A neighborhood classifier based on adaptive radius selection and attribute reduction

Due to the capacity to process numerical data and generate highly interpretable results, neighborhood rough set theory has been widely used for data classification. However, there are two drawbacks that limit its performance. One is that the existing neighborhood classifiers suffer from insufficient information extraction due to the lack of a training process with feature selection. The other is that the neighborhood radius requires artificial parameters to adapt to different data distributions. To overcome the above shortcomings, a neighborhood classifier based on adaptive radius selection and attribute reduction (NRS-ASAR) is proposed. First, the K-nearest neighbor algorithm and the majority voting principle are introduced to eliminate noisy samples. Next, the training correlation radius is defined based on attribute reduction in which a relationship between the neighborhood radius and the features is established. Then, to overcome the subjectivity of the parameter in neighborhood classifier, an adaptive neighborhood radius called nearest neighborhood radius is defined. Finally, experimental results demonstrate that NRS-ASAR has better classification performance.

Classification model for blast furnace status based on multi-source information

The blast furnace (BF) is the key equipment for smelting, which has a significant impact on the sustainable development of global environment and energy. Therefore, it will be helpful for the operators if accurate evaluation to the status of the BF is realized. The classification model based on multi-source information is proposed in this paper, where the kernel fisher (KF) algorithm is enhanced to simplify the complexity both in algorithm and data dimensionality. The proposed model can realize the classification of both the distribution of gas flow (GF) and the geometric information of the burden surface (BS), and this paper also proposes a dynamic process adaptive kernel fisher (DPAKF) algorithm to enhance the adaptive ability of the classifier, where the online algorithm updating mechanism is designed. The experimental results demonstrate that the classification accuracy was increased from 90% to 95%, and the time expense of DPAKF was 236 s, which is prior compared with the existing support vector machine (SVM), k-nearest neighbor (KNN) and genetic algorithm (GA) models. The results of the student's t-distribution show that the statistics between DPAKF and KF, SVM, KNN and GA were 1.25, 2.81, 2.84 and 2.96, respectively, which demonstrate that there are significant statistical differences between DPAKF and SVM, KNN, and GA. This research provides a potential solution to the classification problem of the BF.

Advancing Agricultural Land Suitability in Urbanized Semi-Arid Environments: Insights from Geospatial and Machine Learning Approaches

Rising food demands are increasingly threatened by declining crop yields in urbanizing riverine regions of Southern Asia, exacerbated by erratic weather patterns. Optimizing agricultural land suitability (AgLS) offers a viable solution for sustainable agricultural productivity in such challenging environments. This study integrates remote sensing and field-based geospatial data with five machine learning (ML) algorithms—Naïve Bayes (NB), extra trees classifier (ETC), random forest (RF), K-nearest neighbors (KNN), and support vector machines (SVM)—alongside land-use/land-cover (LULC) considerations in the food-insecure Dharmapuri district, India. A grid searches optimized hyperparameters using factors such as slope, rainfall, temperature, texture, pH, electrical conductivity, organic carbon, available nitrogen, phosphorus, potassium, and calcium carbonate. The tuned ETC model showed the lowest root mean squared error (RMSE = 0.15), outperforming RF (RMSE = 0.18), NB (RMSE = 0.20), SVM (RMSE = 0.22), and KNN (RMSE = 0.23). The AgLS-ETC map identified 29.09% of the area as highly suitable (S1), 19.06% as moderately suitable (S2), 16.11% as marginally suitable (S3), 15.93% as currently unsuitable (N1), and 19.21% as permanently unsuitable (N2). By incorporating Landsat-8 derived LULC data to exclude forests, water bodies, and settlements, these suitability estimates were adjusted to 19.08% (S1), 14.45% (S2), 11.40% (S3), 10.48% (N1), and 9.58% (N2). Focusing on the ETC model, followed by land-use analysis, provides a robust framework for optimizing sustainable agricultural planning, ensuring the protection of ecological and social factors in developing countries.

Machine Learning for Early Detection of Cognitive Decline in Parkinson’s Disease Using Multimodal Biomarker and Clinical Data

Background: Parkinson’s disease (PD) is the second most common neurodegenerative disease, primarily affecting the middle-aged to elderly population. Among its nonmotor symptoms, cognitive decline (CD) is a precursor to dementia and represents a critical target for early risk assessment and diagnosis. Accurate CD prediction is crucial for timely intervention and tailored management of at-risk patients. This study used machine learning (ML) techniques to predict the CD risk over five-year in early-stage PD. Methods: Data from the Early Parkinson’s Disease Longitudinal Singapore (2014 to 2018) was used to predict CD defined as a one-unit annual decrease or a one-unit decline in Montreal Cognitive Assessment over two consecutive years. Four ML methods—AutoScore, Random Forest, K-Nearest Neighbors and Neural Network—were applied using baseline demographics, clinical assessments and blood biomarkers. Results: Variable selection identified key predictors of CD, including education year, diastolic lying blood pressure, diastolic standing blood pressure, systolic lying blood pressure, Hoehn and Yahr scale, body mass index, phosphorylated tau at threonine 181, total tau, Neurofilament light chain and suppression of tumorigenicity 2. Random Forest was the most effective, achieving an AUC of 0.93 (95% CI: 0.89, 0.97), using 10-fold cross-validation. Conclusions: Here, we demonstrate that ML-based models can identify early-stage PD patients at high risk for CD, supporting targeted interventions and improved PD management.

Customer Churn Prediction Approach Based on LLM Embeddings and Logistic Regression

Nowadays, predicting customer churn is essential for the success of any company. Loyal customers generate continuous revenue streams, resulting in long-term success and growth. Moreover, companies are increasingly prioritizing the retention of existing customers due to the higher costs associated with attracting new ones. Consequently, there has been a growing demand for advanced methods aimed at enhancing customer loyalty and satisfaction, as well as predicting churners. In our work, we focused on building a robust churn prediction model for the telecommunications industry based on large embeddings from large language models and logistic regression to accurately identify churners. We conducted extensive experiments using a range of embedding techniques, including OpenAI Text-embedding, Google Gemini Text Embedding, bidirectional encoder representations from transformers (BERT), Sentence-Transformers, Sent2vec, and Doc2vec, to extract meaningful features. Additionally, we tested various classifiers, including logistic regression, support vector machine, random forest, K-nearest neighbors, multilayer perceptron, naive Bayes, decision tree, and zero-shot classification, to build a robust model capable of making accurate predictions. The best-performing model in our experiments is the logistic regression classifier, which we trained using the extracted feature from the OpenAI Text-embedding-ada-002 model, achieving an accuracy of 89%. The proposed model demonstrates a high discriminative ability between churning and loyal customers.

Employing machine learning models to predict pregnancy termination among adolescent and young women aged 15–24 years in East Africa

Pregnancy termination is still a sensitive and continuing public health issue due to several political, economic, religious, and social concerns. This study assesses the applications of machine learning models in the prediction of pregnancy termination using data from eleven national datasets in East Africa. Nine machine learning models, namely: Random Forests (RF), Decision Tree, Logistic Regression, Support Vector Machine, eXtreme Gradient Boosting (XGB), AdaBoost, CatBoost, K-nearest neighbor, and feedforward neural network models were used to predict pregnancy termination, with six evaluation criteria utilized to compare their performance. The pooled prevalence of pregnancy termination in East Africa was found to be 4.56%. All machine learning models had an accuracy of at least 71.8% on average. The RF model provided accuracy, specificity, precision, and AUC of 92.9%, 0.87, 0.91, and 0.93, respectively. The most important variables for predicting pregnancy termination were marital status, age, parity, country of residence, age at first sexual activity, exposure to mass media, and educational attainment. These findings underscore the need for a tailored approach that considers socioeconomic and regional disparities in designing policy initiatives aimed at reducing the rate of pregnancy terminations among younger women in the region.

Enhanced Occupational Safety in Agricultural Machinery Factories: Artificial Intelligence-Driven Helmet Detection Using Transfer Learning and Majority Voting

The objective of this study was to develop an artificial intelligence (AI)-driven model for the detection of helmet usage among workers in tractor and agricultural machinery factories with the aim of enhancing occupational safety. A transfer learning approach was employed, utilizing nine pre-trained neural networks for the extraction of deep features. The following neural networks were employed: MobileNetV2, ResNet50, DarkNet53, AlexNet, ShuffleNet, DenseNet201, InceptionV3, Inception-ResNetV2, and GoogLeNet. Subsequently, the extracted features were subjected to iterative neighborhood component analysis (INCA) for feature selection, after which they were classified using the k-nearest neighbor (kNN) algorithm. The classification outputs of all networks were combined through iterative majority voting (IMV) to achieve optimal results. To evaluate the model, an image dataset comprising 662 images of individuals wearing helmets and 722 images of individuals without helmets sourced from the internet was constructed. The proposed model achieved an accuracy of 90.39%, with DenseNet201 producing the most accurate results. This AI-driven helmet detection model demonstrates significant potential in improving occupational safety by assisting safety officers, especially in confined environments, reducing human error, and enhancing efficiency.

Smartphone-Based Pupillometry Using Machine Learning for the Diagnosis of Sports-Related Concussion

Background: Quantitative pupillometry has been proposed as an objective means to diagnose acute sports-related concussion (SRC). Objective: To assess the diagnostic accuracy of a smartphone-based quantitative pupillometer in the acute diagnosis of SRC. Methods: Division I college football players had baseline pupillometry including pupillary light reflex (PLR) parameters of maximum resting diameter, minimum diameter after light stimulus, percent change in pupil diameter, latency of pupil constriction onset, mean constriction velocity, maximum constriction velocity, and mean dilation velocity using a smartphone-based app. When an SRC occurred, athletes had the smartphone pupillometry repeated as part of their concussion testing. All combinations of the seven PLR parameters were tested in machine learning binary classification models to determine the optimal combination for differentiating between non-concussed and concussed athletes. Results: 93 football athletes underwent baseline pupillometry testing. Among these athletes, 11 suffered future SRC and had pupillometry recordings repeated at the time of diagnosis. In the machine learning pupillometry analysis that used the synthetic minority oversampling technique to account for the significant class imbalance in our dataset, the best-performing model was a random forest algorithm with the combination of latency, maximum diameter, minimum diameter, mean constriction velocity, and maximum constriction velocity PLR parameters as feature inputs. This model produced 91% overall accuracy, 98% sensitivity, 84.2% specificity, area under the curve (AUC) of 0.91, and an F1 score of 91.6% in differentiating between baseline and SRC recordings. In the machine learning analysis prior to oversampling of our imbalanced dataset, the best-performing model was k-nearest neighbors using latency, maximum diameter, maximum constriction velocity, and mean dilation velocity to produce 82% accuracy, 40% sensitivity, 87% specificity, AUC of 0.64, and F1 score of 24%. Conclusions: Smartphone pupillometry in combination with machine learning may provide fast and objective SRC diagnosis in football athletes.

An adaptive thresholding approach for open set fault diagnosis

Abstract Cross-domain fault diagnosis using deep learning plays a critical role in ensuring the reliability and safety of mechanical systems. However, real-world industrial scenarios often involve unknown fault classes, which introduce significant challenges beyond environmental differences between training and testing phases. These unknown fault classes, which do not appear in the training data, create a cross-domain open set fault diagnosis problem where the target domain includes both known and unknown fault types with distinct distribution characteristics. Traditional domain adaptation methods that align source and target domains often overlook the spatial distribution of each class in the feature space, leading to potential negative transfer and misclassification of unknown faults. To address these challenges, this paper proposes a k-nearest neighbors based adaptive thresholding (KNNAT) method, which dynamically adjusts classification thresholds based on the spatial distribution of each class in the feature space. This approach effectively isolates unknown faults, reducing their impact on domain adaptation and improving the reliability of the diagnostic process. Extensive experiments on the publicly available CWRU bearing and PHM09 datasets demonstrate that the proposed KNNAT method outperforms other state-of-the-art methods, achieving higher accuracy and robustness in identifying known faults while successfully isolating unknown faults. These results highlight the potential of using the KNNAT method to enhance the reliability of mechanical systems in cross-domain fault diagnosis applications.

Ensemble Learning with Extremely Randomized k-Nearest Neighbors for Accurate and Efficient Classification

Ensemble Learning with Extremely Randomized k-Nearest Neighbors for Accurate and Efficient Classification

T1WI Radiomics Analysis of Anterior Scalene Muscle: A Preliminary Application in Neurogenic Thoracic Outlet Syndrome.

This study aimed to analyze the differences in radiomic features of the anterior scalene muscle and evaluate the diagnostic performance of MRI-based radiomics model for neurogenic thoracic outlet syndrome (NTOS). Imaging data of patients with NTOS who underwent preoperative brachial plexus magnetic resonance neurography were collected and were randomly divided into training and test groups. The anterior scalene muscle area was sliced in the T1WI sequence as the region of interest for the extraction of radiomics features. The most significant features were identified using feature selection and dimensionality-reduction methods. Various machine learning algorithms were applied to construct regression models. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC). Totally, 267 radiomics features were extracted, of which 57 showed significant differences (P ≤ 0.05) between the abnormal and normal anterior scalene muscle groups. The least absolute shrinkage and selection operator regression model identified 13 optimal radiomic features with nonzero coefficients for constructing the model. In the training set, the AUROCs of diagnostic models built by different machine learning algorithms, ranked from highest to lowest, were as follows: support vector machine (SVM), 0.953; multilayer perception (MLP), 0.936; logistic regression (LR), 0.926; light gradient boosting machine (LightGBM), 0.906; and K-nearest neighbors (KNN), 0.813. In the testing set, the rankings were as follows: LR, 0.933; SVM, 0.886; KNN, 0.843; LightGBM, 0.824; and MLP, 0.706. NTOS is attributed to anterior scalene muscle abnormalities and exhibits distinct radiomic features. Integrating these features with machine learning can improve traditional manual image interpretation, offering further clarity in NTOS diagnosis.

Architecture-Aware Augmentation: A Hybrid Deep Learning and Machine Learning Approach for Enhanced Parkinson’s Disease Detection

Parkinson’s Disease (PD) is a progressive neurodegenerative disorder affecting millions worldwide. Early detection is crucial for improving patient outcomes. Spiral drawing analysis has emerged as a non-invasive tool to detect early motor impairments associated with PD. This study examines the performance of hybrid deep learning and machine learning models in detecting PD using spiral drawings, with a focus on the impact of data augmentation techniques. We compare the accuracy of Vision Transformer (ViT) with K-Nearest Neighbors (KNN), Convolutional Neural Networks (CNN) with Support Vector Machines (SVM), and Residual Neural Networks (ResNet-50) with Logistic Regression, evaluating their performance on both augmented and non-augmented data. Our findings reveal that ViT with KNN, initially achieving 96.77% accuracy on unaugmented data, experienced a notable decline across all augmentation techniques, suggesting it relies heavily on global patterns in spiral drawings. In contrast, ResNet-50 with Logistic Regression showed consistent improvement with data augmentation, reaching 93.55% accuracy when rotation and flipping techniques were applied. These results highlight that hybrid models respond differently to augmentation, and careful selection of augmentation strategies is necessary for optimizing model performance. Our study provides important insights into the development of reliable diagnostic tools for early PD detection, emphasizing the need for appropriate augmentation techniques in medical image analysis.

Desertification Monitoring Using Machine Learning Techniques with Multiple Indicators Derived from Sentinel-2 in Turkmenistan

This research offers a fresh understanding of desertification in Turkmenistan by utilizing satellite remote sensing data and machine learning techniques. With 80% of its area covered by desert, Turkmenistan has particular difficulties as a result of the harsh effects of desertification, which are made worse by climate change and irresponsible land use. Despite the fact that desertification has been the subject of numerous studies conducted worldwide, this study is among the first to use a multi-index approach to specifically focus on Turkmenistan. It does this by integrating six important desertification indicators within machine learning models like random forest (RF), eXtreme Gradient Boosting (XGBoost), naïve Bayes (NB), and K-nearest neighbors (KNN). These indicators include the Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Moisture Index (NDMI), Bare Soil Index (BSI), Enhanced Vegetation Index (EVI), and land surface temperature (LST). Based on Sentinel-2 satellite data processed by the Google Earth Engine (GEE) platform, the findings show that the country’s northern, central, and eastern regions are undergoing severe desertification. Moreover, RF and XGBoost performed better than the straightforward models like NB and KNN in terms of accuracy (96% and 96.33%), sensitivity (both 100%), and kappa (0.901 and 0.9095). By concentrating on Turkmenistan, this study fills a significant gap and provides a framework for tracking desertification in similar regions around the world.

Quantification of urinary albumin in clinical samples using smartphone enabled LFA reader incorporating automated segmentation

Abstract Smartphone-assisted urine analyzers estimate the urinary albumin by quantifying color changes at sensor pad of test strips. These strips yield color variations due to the total protein present in the sample, making it difficult to relate to color changes due to specific analyte. We have addressed it using a Lateral Flow Assay (LFA) device for automatic detection and quantification of urinary albumin. LFAs are specific to individual analytes, allowing color changes to be linked to the specific analyte, minimizing the interference. The proposed reader performs automatic segmentation of the region of interest (ROI) using YOLOv5, a deep learning-based model. Concentrations of urinary albumin in clinical samples were classified using customized machine learning algorithms. An accuracy of 96% was achieved on the test data using the k-Nearest Neighbour (k-NN) algorithm. Performance of the model was also evaluated under different illumination conditions and with different smartphone cameras, and validated using standard nephelometer.

IoT Traffic Parameter Classification based on Optimized BPSO for Enabling Green Wireless Networks

The rapid expansion of artificial intelligence (AI) integrated with the Internet of Things (IoT) has fueled the development of various smart devices, particularly for smart city applications. However, the heterogeneity of these devices necessitates a robust communication network capable of maintaining a consistent traffic flow. This paper employs Machine Learning (ML) models to classify continuously received network parameters from diverse IoT devices, identifying necessary adjustments to enhance network performance. Key network traffic parameters, such as packet data, are transmitted through gateways via specialized tools. Six different ML techniques with default parameters were used: Decision Tree (DT), Random Forest (RF), Support Vector Machines (SVMs), K-Nearest Neighbors (KNN), Naive Bayes (NB), and Stochastic Gradient Descent Classifiers (SGDC), to classify the traffic of the environment (IoT / non IoT). The models' performance was evaluated in a real-time smart laboratory environment comprising 38 IoT devices from various vendors with the following metrics: Accuracy, F1-score, Recall and Precision. The RF model achieved the highest Accuracy of 95.6%. Also the Binary Particle Swarm Optimizer (BPSO) was used across the RF. The results demonstrated that the BPSO-RF with hyperparameter optimization enhanced the Accuracy from 95.6% to 99.4%.

Universal bovine identification via depth data and deep metric learning

This paper proposes and evaluates, for the first time, a top-down (dorsal view), depth-only deep learning system for accurately identifying individual cattle and provides associated code, datasets, and training weights for immediate reproducibility. An increase in herd size skews the cow-to-human ratio at the farm and makes the manual monitoring of individuals more challenging. Therefore, real-time cattle identification is essential for the farms and a crucial step towards precision livestock farming. Underpinned by our previous work, this paper introduces a deep-metric learning method for cattle identification using depth data as a novel biometric measure acquired with an off-the-shelf 3D camera. In contrast to our previous work, which was limited to breeds with distinct coat patterns, this study introduces a breed-agnostic pipeline for universal cattle identification. The results show that depth, as a biometric, can potentially broaden the real-world applicability of our method to the rest 68% of UK cattle breeds that lack distinctive coat patterns. The method relies on Convolutional Neural Network (CNN) and Multi-Layered Perceptron (MLP) backbones that learn well-generalised embedding spaces from the body shape to differentiate individuals — requiring neither species-specific coat patterns nor close-up muzzle prints for operation. The network embeddings are clustered using a simple algorithm such as k-Nearest Neighbours (k-NN) for highly accurate identification, thus eliminating the need to retrain the network for enrolling new individuals. We evaluate two backbone architectures, Residual Neural Network (ResNet), as previously used to identify Holstein Friesians using RGB images, and PointNet, which is specialised to operate on 3D point clouds. We also present CowDepth2023, a new dataset containing 21,490 synchronised colour-depth image pairs of 99 cows, to evaluate the backbones. Both ResNet and PointNet architectures, which consume depth maps and point clouds, respectively, led to high accuracy that is on par with the coat pattern-based backbone. This new universal methodology also addresses the case of all-black and all-white breeds, where the previous coat pattern-based approach fell short. The ResNet colour backbone resulted in 99.97% k-NN identification accuracy, while the PointNet accuracy was 99.36%. Furthermore, we also show that the PointNet architecture is robust to noise and missing data by significantly reducing the number of 3D points and observing the drop in accuracy. Our research indicates that these techniques can identify animals using dorsal-view depth maps alone. Regardless of the substantial inter-class variety in the body shape, we show that the models spatially rely on similar body surfaces using Gradient-weighted Class Activation Mapping (Grad-CAM) and Point Cloud Saliency Mapping (PC-SM).

Predicting survival of patients with heart failure by optimizing the hyperparameters

Heart failure is a prominent global cause of mortality. Heart failure is a medical condition characterized by the heart's inability to adequately circulate blood throughout the body or meet its needs. The rising expenses associated with conventional medical treatments for heart failure diagnosis have underscored the significance of developing diagnostic systems that utilize machine learning approaches. We employed various machine learning techniques on a heart failure dataset from the literature, specifically focusing on the survival status of heart failure patients. After employing the hold-out validation technique to prevent overfitting, the survival status of patients was evaluated by utilizing GridSearchCV hyperparameter optimization on classifiers such as Naive Bayes, Support Vector Machines, Decision Trees, Random Forests, K-Nearest Neighbor, Discriminant Analysis, and Extreme Gradient Boosting. Our performance measurement results show that the Decision Trees, Support Vector Machines, and Naive Bayes algorithms are prominent algorithms for the relevant data. While Decision Tree has the highest accuracy value, Support Vector Machines gives the lowest false negative rate, a crucial metric in medical decisions. Additionally, Naive Bayes has very similar results to the Support Vector Machines algorithm. While the straightforward and effective Decision Tree model, which has minimal pre-processing and does not require input scaling, is an easy-to-use method, Support Vector Machines and Naive Bayes which has low false negative rate values may help decision-making by reducing the risk of misdiagnosis.

Adsorption of Ibuprofen from Water Using Banana Peel Biochar: Experimental Investigation and Machine Learning Algorithms

Ibuprofen is a significant nonsteroidal anti-inflammatory drug that poses environmental and health risks when present in wastewater because of its persistence and probable toxicity. This study investigates the use of banana peel biochar (BPB) made at 600 °C to 900 °C to eliminate ibuprofen from aqueous solutions. The uniqueness of this work lies in the high-temperature pyrolysis process, which has not been previously explored for the ibuprofen removal efficiency using BPB. The batch experiment was conducted considering initial concentrations, pH, and contact time. The data were compared with different algorithms, with Linear Regression (LR), Support Vector Machines (SVM), Decision Trees (DT), Random Forest (RF), and k-Nearest Neighbor (k-NN) to forecast the performance. The results revealed that banana peel biochar at 900 °C exhibited the highest ibuprofen removal efficiency (69.28 ± 0.83%) at 125 mg/L concentration with the sequence of BPB900 > BPB800 > BPB700 > BPB600. A maximum removal efficiency of 72.67 ± 0.75% was observed at pH 9. Adsorption behavior was analyzed using isotherm and kinetic models, with the Freundlich isotherm model (R2 value 0.9620) indicating heterogeneous adsorption and the pseudo-second-order (PSO) kinetic model (R2 value 0.9969) suggesting that physicochemical interactions govern the process. FTIR analysis ensured the existence of functional groups (hydroxyl, carboxylic, carbonyl, and aromatic rings) responsible for adsorption. Machine learning algorithms, especially RF, demonstrated outstanding performance with 90.07% accuracy in predicting the experimental data. In comparison to other adsorbents, BPB demonstrated superior removal efficiency, underscoring its effectiveness. The study suggests that BPB, particularly at 900 °C, is effective in removing ibuprofen, and due to its sustainable production, it offers a potential solution for wastewater treatment.

Graphene Metasurface Based Biosensor for COVID-19 Detection in the Terahertz Regime with Machine Learning Optimization using K-Nearest Neighbours Regression

Graphene Metasurface Based Biosensor for COVID-19 Detection in the Terahertz Regime with Machine Learning Optimization using K-Nearest Neighbours Regression