Support Vector Machine Classifier Research Articles

AG-MSTLN-EL: A Multi-source Transfer Learning Approach to Brain Tumor Detection.

The analysis of medical images (MI) is an important part of advanced medicine as it helps detect and diagnose various diseases early. Classifying brain tumors through magnetic resonance imaging (MRI) poses a challenge demanding accurate models for effective diagnosis and treatment planning. This paper introduces AG-MSTLN-EL, an attention-aided multi-source transfer learning ensemble learning model leveraging multi-source transfer learning (Visual Geometry Group ResNet and GoogLeNet), attention mechanisms, and ensemble learning to achieve robust and accurate brain tumor classification. Multi-source transfer learning allows knowledge extraction from diverse domains, enhancing generalization. The attention mechanism focuses on specific MRI regions, increasing interpretability and classification performance. Ensemble learning combines k-nearest neighbor, Softmax, and support vector machine classifiers, improving both accuracy and reliability. Evaluating the model's performance on a dataset with 3064 brain tumor MRI images, AG-MSTLN-EL outperforms state-of-the-art models in terms of all classification measures. The model's innovative combination of transfer learning, attention mechanism, and ensemble learning provides a reliable solution for brain tumor classification. Its superior performance and high interpretability make AG-MSTLN-EL a valuable tool for clinicians and researchers in medical image analysis.

Differently Distributed Iron Deposition in Mild and Moderate Stroke with Small Artery Occlusion: A Pilot Voxel-Based Susceptibility Mapping Study.

Small artery occlusion (SAO) is a common ischemic stroke subtype. However, its clinical outcome can be more severe than commonly understood. The severity of SAO can vary, ranging from mild to moderate. Iron deposition has been associated with the development and progression of stroke. However, its specific distribution and relationship with stroke severity in SAO remain unclear. The study's purpose is to investigate the differences in iron deposition between mild stroke with SAO (SAO-MiS) and moderate stroke with SAO (SAO-MoS) through quantitative susceptibility mapping (QSM) and its association with neurological deficits. Sixty-eight SAO participants within 24 hours of first onset were enrolled and separated into SAO-MiS and SAO-MoS according to the National Institutes of Health Stroke Scale (NIHSS) scores. QSM helped calculate the susceptibility maps, reflecting the iron content within the brain. The susceptibility maps were analyzed using voxel-wise statistical analysis to compare the iron deposition between SAO-MiS and SAO-MoS. Then, differentially distributed iron deposition helped differentiate between mild and moderate stroke using support vector machine (SVM) methods. Compared with SAO-MiS, SAO-MoS depicted elevated iron deposition in the left pallidum, parahippocampal gyrus, and superior frontal gyrus medial region, and is lower in the right superior/middle frontal gyrus and bilateral supplementary motor area. Based on iron deposition, the SVM classifier's analysis revealed a high power to discriminate SAO-MoS from SAO-MiS. In addition, fibrinogen, triglyceride (TG), and total cholesterol (TC) were linked with QSM values in specific brain regions. Our study first revealed the brain iron distribution after SAO and differently distributed iron deposition in SAO-MiS and SAO-MoS. The results indicate that iron deposition could play a role in the pathophysiology of SAO and its correlation with stroke severity.

Machine learning classification of functional neurological disorder using structural brain MRI features

BackgroundBrain imaging studies investigating grey matter in functional neurological disorder (FND) have used univariate approaches to report group-level differences compared with healthy controls (HCs). However, these findings have limited translatability...

Development of a diagnostic model for biliary atresia based on MMP7 and serological tests using machine learning.

To develop a machine learning diagnostic model based on MMP7 and other serological testing indicators for early and efficient diagnosis of biliary atresia (BA). A retrospective analysis was conducted on patient information from those hospitalized for pathological jaundice at Beijing Children's Hospital between January 1, 2019, and December 31, 2023. Patients with serum MMP7, liver stiffness measurements, and other routine serological tests were included in the study. Six machine learning models were constructed, including logistic regression (LR), random forest (RF), decision tree (DET), support vector machine classifier (SVC), neural network (MLP), and extreme gradient boosting (XGBoost), to diagnose BA. The area under the receiver operating characteristic curve was used to evaluate the diagnostic efficacy of the various models. A total of 98 patients were included in the study, comprising 64 BA patients and 34 patients with other cholestatic liver diseases. Among the six machine learning models, the XGBoost algorithm model and RF algorithm model achieved the best predictive performance, with an AUROC of nearly 100% in both the training and validation sets. In the training set, these two algorithm models achieved an accuracy, precision, recall, F1 score, and AUROC of 1. Through model interpretation analysis, serum MMP7 levels, serum GGT levels, and acholic stools were identified as the most important indicators for diagnosing BA. The nomogram constructed based on the XGBoost algorithm model also demonstrated convenient and efficient diagnostic efficacy. Machine learning models, especially the XGBoost algorithm and RF algorithm models, constructed based on preoperative serum MMP7 and serological tests can diagnose BA more efficiently and accurately. The most important influencing factors for diagnosis are serum MMP7, serum GGT, and acholic stools.

A generic error-related potential classifier based on simulated subjects.

Error-related potentials (ErrPs) are brain signals known to be generated as a reaction to erroneous events. Several works have shown that not only self-made errors but also mistakes generated by external agents can elicit such event-related potentials. The possibility of reliably measuring ErrPs through non-invasive techniques has increased the interest in the brain-computer interface (BCI) community in using such signals to improve performance, for example, by performing error correction. Extensive calibration sessions are typically necessary to gather sufficient trials for training subject-specific ErrP classifiers. This procedure is not only time-consuming but also boresome for participants. In this paper, we explore the effectiveness of ErrPs in closed-loop systems, emphasizing their dependency on precise single-trial classification. To guarantee the presence of an ErrPs signal in the data we employ and to ensure that the parameters defining ErrPs are systematically varied, we utilize the open-source toolbox SEREEGA for data simulation. We generated training instances and evaluated the performance of the generic classifier on both simulated and real-world datasets, proposing a promising alternative to conventional calibration techniques. Results show that a generic support vector machine classifier reaches balanced accuracies of 72.9%, 62.7%, 71.0%, and 70.8% on each validation dataset. While performing similarly to a leave-one-subject-out approach for error class detection, the proposed classifier shows promising generalization across different datasets and subjects without further adaptation. Moreover, by utilizing SEREEGA, we can systematically adjust parameters to accommodate the variability in the ErrP, facilitating the systematic validation of closed-loop setups. Furthermore, our objective is to develop a universal ErrP classifier that captures the signal's variability, enabling it to determine the presence or absence of an ErrP in real EEG data.

Classification of Time–Frequency Maps of Guided Waves Using Foreground Extraction

Guided waves propagating in mechanical structures have proved to be an essential technique for applications, such as structural health monitoring. However, it is a well-known problem that when using non-stationary guided wave signals, dispersion, and high-order vibrational modes are excited, it becomes cumbersome to detect and identify relevant information. A typical method for the characterization of these non-stationary signals is based on time–frequency (TF) mapping techniques. This method produces 2D images, allowing the study of specific vibration modes and their evolution over time. However, this approach has low resolution, increases the size of the data, and introduces redundant information, making it difficult to extract relevant features for their accurate identification and classification. This paper presents a method for identifying discontinuities by analyzing the data in the TF maps of Lamb wave signals. Singular Value Decomposition (SVD) for low-rank optimization and then perform foreground feature extraction on the maps were proposed. These foreground features are then analyzed using Principal Component Analysis (PCA). Unlike traditional PCA, which operates on vectorized images, our approach focuses on the correlation between coordinates within the maps. This modification enhances feature detection and enables the classification of discontinuities within the maps. To evaluate unsupervised clustering of the dimensionally reduced data obtained from PCA, we experimentally tested our method using broadband Lamb waves with various vibrational modes interacting with different types of discontinuity patterns in a thin aluminum plate. A Support Vector Machine (SVM) classifier was then implemented for classification. The results of the experimental data yielded good classification effectiveness within reasonably low computational time despite the large matrixes of the TF maps used.

Intrusion Detection System with Ensemble Machine Learning Approaches using VotingClassifier

Internets have become a part of our everyday life due to the advancement in the electronics and signal processing technologies during past decades. The tremendous growth of internet leads towards the network threats. Many times firewalls and anti-viruses fails to manage the network because of this Intrusion Detection System (IDS) comes to assists us. In this paper we use IDS with Ensemble methodologies utilized in machine learning involve the fusion of multiple classifiers to improve predictive performance, while voting classifiers combine predictions from individual models to reach conclusive decisions. The paper employs a voting ensemble method combing decision tree, logistic regression and support vector machine classifier models. We test our proposedmodel to classify the NSL-KDD dataset. Our ensemble methodologies of proposed algorithmproduce a good result.

Pattern classification of bearing faults in PMSM based on time domain feature ensembles

This research aims to identify an effective feature-based pattern classification technique that uses vibration and current data to identify bearing conditions. The authors attempted non-conventional time-domain features to detect the bearing conditions in permanent magnet synchronous motors (PMSM). This work employs two case studies utilizing eight datasets from Paderborn University to identify the bearing conditions of three and twelve classes. In this work, support vector machine, k-nearest neighbor, random forest, decision tree, and naive Bayes classifiers with 10% holdout validation are applied to study 31 feature combinations. This study also examines the Henry gas solubility optimization technique for feature selection to identify the most discriminating features. The results indicate that four feature ensembles consisting of 2 to 5 features performed better with the support vector machine, k-nearest neighbor, and random forest classifiers. In contrast to previous relevant studies, the proposed features are useful in identifying PMSM-bearing conditions with the highest accuracy of 99.8% and 99% using current signals for 3 and 12 classes respectively for combined current operating conditions.

Asymmetric lesion detection with geometric patterns and CNN-SVM classification

In dermoscopic images, which allow visualization of surface skin structures not visible to the naked eye, lesion shape offers vital insights into skin diseases. In clinically practiced methods, asymmetric lesion shape is one of the criteria for diagnosing Melanoma. Initially, we labeled data for a non-annotated dataset with symmetrical information based on clinical assessments. Subsequently, we propose a supporting technique—a supervised learning image processing algorithm—to analyze the geometrical pattern of lesion shape, aiding non-experts in understanding the criteria of an asymmetric lesion. We then utilize a pre-trained convolutional neural network (CNN) to extract shape, color, and texture features from dermoscopic images for training a multiclass support vector machine (SVM) classifier, outperforming state-of-the-art methods from the literature. In the geometry-based experiment, we achieved a 99.00 % detection rate for dermatological asymmetric lesions. In the CNN-based experiment, the best performance is found 94 % Kappa Score, 95 % Macro F1-score, and 97 % weighted F1-score for classifying lesion shapes (Asymmetric, Half-Symmetric, and Symmetric).

Voice Recognition Based on Machine Learning Classification Algorithms: A Review

One essential component of biometric identity is voice recognition technology, which uses speech pattern analysis to authenticate people. With an emphasis on machine learning classification techniques, this review article thoroughly examines the field of speech recognition. We examine the effectiveness of random forest (RF), multilayer perceptrons (MLP), k-nearest neighbours (KNN), and support vector machine (SVM) classifiers via painstaking analysis and empirical evaluation. Utilizing a collection of Sepedi speech audio files, our results demonstrate the remarkable accuracy of 99.86% that RF is capable of producing. Aside from visual aids for better understanding, assessment indicators like as accuracy, precision, recall, F-measure, and root mean square error (RMSE) clarify the effectiveness of the model. The research highlights how machine learning algorithms, especially reinforcement learning (RF), have the capacity to revolutionize speech recognition technology in a variety of contexts.

A novel deep CNN model with entropy coded sine cosine for corn disease classification

Corn diseases significantly impact crop yields, posing a major challenge to agricultural productivity. Early and accurate detection of these diseases is crucial for effective management and mitigation. Existing methods, mostly relying on analyzing corn leaves, often lack the precision to identify and classify a wide range of diseases under varying conditions. This study introduces a novel approach to detecting corn diseases using image processing and deep learning techniques, aiming to enhance detection accuracy through pre-processing, improved feature extraction and selection, and classification algorithms. A new deep Convolutional Neural Network (CNN) model named TreeNet, with 35 layers and 38 connections, is proposed. TreeNet is pre-trained using the Plant Village imaging dataset. For image pre-processing, the YCbCr color space is utilized to improve color representation and contrast. Feature extraction is performed using TreeNet and two pre-trained models, Darknet-53, and DenseNet-201, with features fused using a serial-based fusion method. The Entropy-coded Sine Cosine Algorithm is applied for feature selection, optimizing the feature set for classification. The selected features are used to train Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifiers, with extensive experiments conducted using both 5-fold and 10-fold cross-validation, and feature sizes ranging from 200 to 1150. The proposed method achieves classification accuracy, precision, recall, and F1-score of 99.8%, 99%, 100%, and 99%, respectively, surpassing existing benchmarks. The integration of TreeNet with Darknet-53 and DenseNet-201, along with robust pre-processing and feature selection, significantly improves corn disease detection, highlighting the potential of advanced CNN architectures in agriculture.

Prediction of melanoma metastasis using dermatoscopy deep features: an international multicentre cohort study.

Whether dermatoscopy deep features could serve as biomarker for the prediction of melanoma metastasis remains an underexplored area in medical research. In this cohort of 712 patients from 10 centres in 3 continents, a support vector machine classifier that analysed deep features on dermatoscopic images demonstrated similar prognostic performance for metastasis in terms of AUC and true positive rate to current benchmarks of melanoma staging, namely Breslow thickness and ulceration. Deep features derived from dermatoscopy could predict early-stage melanomas with high metastatic potential, tailoring further treatment strategies.

A hybrid model for the detection of retinal disorders using artificial intelligence techniques

The prevalence of vision impairment is increasing at an alarming rate. The goal of the study was to create an automated method that uses optical coherence tomography (OCT) to classify retinal disorders into four categories: choroidal neovascularization, diabetic macular edema, drusen, and normal cases. This study proposed a new framework that combines machine learning and deep learning-based techniques. The utilized classifiers were support vector machine (SVM), K-nearest neighbor (K-NN), decision tree (DT), and ensemble model (EM). A feature extractor, the InceptionV3 convolutional neural network, was also employed. The performance of the models was evaluated against nine criteria using a dataset of 18000 OCT images. For the SVM, K-NN, DT, and EM classifiers, the analysis exhibited state-of-the-art performance, with classification accuracies of 99.43%, 99.54%, 97.98%, and 99.31%, respectively. A promising methodology has been introduced for the automatic identification and classification of retinal disorders, leading to reduced human error and saved time.

Varroa Mite Counting Based on Hyperspectral Imaging.

Varroa mite infestation poses a severe threat to honeybee colonies globally. This study investigates the feasibility of utilizing the HS-Cam and machine learning techniques for Varroa mite counting. The methodology involves image acquisition, dimensionality reduction through Principal Component Analysis (PCA), and machine learning-based segmentation and classification algorithms. Specifically, a k-Nearest Neighbors (kNNs) model distinguishes Varroa mites from other objects in the images, while a Support Vector Machine (SVM) classifier enhances shape detection. The final phase integrates a dedicated counting algorithm, leveraging outputs from the SVM classifier to quantify Varroa mite populations in hyperspectral images. The preliminary results demonstrate segmentation accuracy exceeding 99% and an average precision of 0.9983 and recall of 0.9947 across all the classes. The results obtained from our machine learning-based approach for Varroa mite counting were compared against ground-truth labels obtained through manual counting, demonstrating a high degree of agreement between the automated counting and manual ground truth. Despite working with a limited dataset, the HS-Cam showcases its potential for Varroa counting, delivering superior performance compared to traditional RGB images. Future research directions include validating the proposed hyperspectral imaging methodology with a more extensive and diverse dataset. Additionally, the effectiveness of using a near-infrared (NIR) excitation source for Varroa detection will be explored, along with assessing smartphone integration feasibility.

Epileptic seizure forecasting with wearable-based nocturnal sleep features.

Non-invasive biomarkers have recently shown promise for seizure forecasting in people with epilepsy. In this work, we developed a seizure-day forecasting algorithm based on nocturnal sleep features acquired using a smart shirt. Seventy-eight individuals with epilepsy admitted to the Centre hospitalier de l'Université de Montréal epilepsy monitoring unit wore the Hexoskin biometric smart shirt during their stay. The shirt continuously measures electrocardiography, respiratory, and accelerometry activity. Ten sleep features, including sleep efficiency, sleep latency, sleep duration, time spent in non-rapid eye movement sleep (NREM) and rapid eye movement sleep (REM), wakefulness after sleep onset, average heart and breathing rates, high-frequency heart rate variability, and the number of position changes, were automatically computed using the Hexoskin sleep algorithm. Each night's features were then normalized using a reference night for each patient. A support vector machine classifier was trained for pseudo-prospective seizure-day forecasting, with forecasting horizons of 16- and 24-h to include both diurnal and nocturnal seizures (24-h) or diurnal seizures only (16-h). The algorithm's performance was assessed using a nested leave-one-patient-out cross-validation approach. Improvement over chance (IoC) performances were achieved for 48.7% and 40% of patients with the 16- and 24-h forecasting horizons, respectively. For patients with IoC performances, the proposed algorithm reached mean IoC, sensitivity and time in warning of 34.3%, 86.0%, and 51.7%, respectively for the 16-h horizon, and 34.2%, 64.4% and 30.2%, respectively, for the 24-h horizon. Smart shirt-based nocturnal sleep analysis holds promise as a non-invasive approach for seizure-day forecasting in a subset of people with epilepsy. Further investigations, particularly in a residential setting with long-term recordings, could pave the way for the development of innovative and practical seizure forecasting devices. Seizure forecasting with wearable devices may improve the quality of life of people living with epilepsy who experience unpredictable, recurrent seizures. In this study, we have developed a seizure forecasting algorithm using sleep characteristics obtained from a smart shirt worn at night by a large number of hospitalized patients with epilepsy (78). A daily seizure forecast was generated following each night using machine learning methods. Our results show that around half of people with epilepsy may benefit from such an approach.

Different patterns of structural network impairments in two amyotrophic lateral sclerosis subtypes driven by 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance hybrid imaging.

The structural network damages in amyotrophic lateral sclerosis patients are evident but contradictory due to the high heterogeneity of the disease. We hypothesized that patterns of structural network impairments would be different in amyotrophic lateral sclerosis subtypes by a data-driven method using 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance hybrid imaging. The data of positron emission tomography, structural MRI and diffusion tensor imaging in fifty patients with amyotrophic lateral sclerosis and 23 healthy controls were collected by a 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance hybrid. Two amyotrophic lateral sclerosis subtypes were identified as the optimal cluster based on grey matter volume and standardized uptake value ratio. Network metrics at the global, local and connection levels were compared to explore the impaired patterns of structural networks in the identified subtypes. Compared with healthy controls, the two amyotrophic lateral sclerosis subtypes displayed a pattern of a locally impaired structural network centralized in the sensorimotor network and a pattern of an extensively impaired structural network in the whole brain. When comparing the two amyotrophic lateral sclerosis subgroups by a support vector machine classifier based on the decreases in nodal efficiency of structural network, the individualized network scores were obtained in every amyotrophic lateral sclerosis patient and demonstrated a positive correlation with disease severity. We clustered two amyotrophic lateral sclerosis subtypes by a data-driven method, which encompassed different patterns of structural network impairments. Our results imply that amyotrophic lateral sclerosis may possess the intrinsic damaged pattern of white matter network and thus provide a latent direction for stratification in clinical research.

Classification and Analysis of HIV Neurocognitive MRI Images using Support Vector Machine

Medical imaging has expanded thanks to advances in processing power and advanced image analysis techniques, especially with magnetic resonance imaging (MRI), which offers comprehensive body scans for diagnosis. This work proposes a simple yet efficient method to use a support vector machine (SVM) to classify HIV neurocognitive MRI pictures into normal and pathological categories. The model consists of four steps: data pre-processing, feature extraction, SVM classification, and model evaluation. To separate desired and undesired elements, such as the scalp and skull, pre-processed images were converted from grayscale to colour using support vector machines. The discrete wavelet transform (DWT) was used in the feature extraction stage to extract image properties. Colour moments (CMs) were then used to optimize the feature collection. Afterwards, the SVM classifier was used to determine the ideal feature set to classify images. For example, a dataset is used for training and testing, with a split ratio of 75% to 25% respectively. The experimental results show that the proposed model has a high classification accuracy of 94.4%

Machine learning-based CT radiomics enhances bladder cancer staging predictions: A comparative study of clinical, radiomics, and combined models.

Predicting the accurate preoperative staging of bladder cancer (BLCA), which markedly affects treatment decisions and patient outcomes, using traditional clinical parameters is challenging. Nevertheless, emerging studies in radiomics, especially machine learning-based computed tomography (CT) image-based radiomics, hold promise in improving stage prediction accuracy in various tumors. However, the comparative performance and clinical utility of models for BLCA are under investigation. We aimed to investigate the application value of machine learning-based CT radiomics in preoperative staging prediction by comparing the performance of clinical, radiomics, and clinical-radiomics combined models. A retrospective cohort of 105 patients with initial BLCA was randomized into training (70%) and testing (30%) cohorts. Radiomics features were extracted from CT images using the optimal feature filter, followed by the application of the least absolute shrinkage and selection operator algorithm for optimum feature selection. Furthermore, machine learning algorithms were used to establish a radiomics model within the training cohort. Independent risk factors for muscle-invasive BLCA (MIBC) obtained by multivariate logistic regression (LR) analysis were separately used to construct a clinical model. For a clinical-radiomics fusion model, radiomics features were combined with clinical parameters. Performance was evaluated based on receiver operating characteristic curves, calibration curves, decision curve analysis (DCA), and standard performance metrics. Patients exhibited a significantly higher age (p=0.029), larger tumor size (p=0.01), and an increased neutrophil-to-lymphocyte ratio (NLR; p=0.045) in the MIBC group than in the NMIBC group. LR analysis revealed age (p=0.026), tumor size (p=0.007), and NLR (p=0.019) as significant predictors for constructing the clinical model. In the testing cohort, the radiomics model, which used an Support Vector Machine classifier, achieved the highest area under the curve (AUC) value of 0.857. The clinical-radiomics model outperformed the remaining two models, with AUC values of 0.958 and 0.893 in the training and testing cohorts, respectively. DeLong's test indicated significant differences between the three models. Calibration curves showed good agreement, and DCA confirmed the superior clinical utility of the clinical-radiomics model. Machine learning-based CT radiomics combined with clinical parameters was a promising approach in staging BLCA accurately, which outperformed the individual models. Integrating radiomics features with clinical information holds the potential to improve personalized treatment planning and patient outcomes in BLCA.

In-vivo high-frequency quantitative ultrasound-derived parameters of the anterior sclera correlated with level of myopia and presence of staphyloma.

A high-frequency point-of-care (POC) ultrasound instrument was used to evaluate the microstructural and biomechanical properties of the anterior sclera in vivo using parameters computed from quantitative ultrasound (QUS) methods. In this cross-sectional study, both eyes of 85 enrolled patients were scanned with the POC instrument and ultrasound data were processed to obtain QUS parameters. Pearson correlation and multi-linear regression were used to identify relationships between QUS parameters and refractive error (RE) or axial length. After categorising eyes based on RE, binary support vector machine (SVM) classifiers were trained using the QUS or ophthalmic parameters (anterior chamber depth, central corneal thickness, corneal power, and intraocular pressure) to classify each eye. Classifier performance was evaluated by computing the area under the receiver-operating characteristic curve (AUC). Individual QUS parameters correlated with RE and axial length (p < 0.05). Multi-linear regression revealed significant correlation between the set of QUS parameters and both RE (R = 0.49, p < 0.001) and axial length (R = 0.46, p = 0.001). Classifiers trained with QUS parameters achieved higher AUC (𝑝 = 0.06) for identifying myopic eyes (AUC = 0.71) compared to classifiers trained with ophthalmic parameters (AUC = 0.63). QUS-based classifiers attained the highest AUC when identifying highly myopic eyes (AUC = 0.77). QUS parameters correlate with progressing myopia and may be indicative of myopia-induced microstructural and biomechanical changes in the anterior sclera. These methods may provide critical clinical information complementary to standard ophthalmic measurements for predicting myopia progression and risk assessment for posterior staphyloma formation.

Nanofluidic Detection Platform for Simultaneous Light Absorption and Scattering Measurement of Individual Nanoparticles in Flow.

Characterization and quantification of plasmonic nanoparticles at the single particle level have become increasingly important with the advancements in nanotechnology and their application to various biological analyses including diagnostics, photothermal therapy, and immunoassays. While various nanoparticle detection methodologies have been developed and widely used, simultaneous measurement of light absorption and scattering from individual plasmonic nanoparticles in flow is still challenging. Herein, we describe a novel nanofluidic detection platform that enables simultaneous measurement of absorption and scattering signals from individual nanoparticles within a nanochannel. Our detection platform utilized optical diffraction phenomena by a single nanochannel as both a readout signal for photothermal detection and a reference light for interferometric scattering detection. Through the elucidation of the frequency effect on the detection performance and optimization of experimental conditions, we achieved the classification of gold and silver nanoparticles with a diameter of 20-60 nm at an average accuracy score of 82.6 ± 2.1% by measured data sets of absorption and scattering signals. Furthermore, we demonstrated the concentration determination of plasmonic nanoparticle mixtures using a trained Support vector machine (SVM) classifier. Our simple yet sensitive nanofluidic detection platform will be a valuable tool for the analysis of nanoparticles and their applications to chemical and biological assays.