Performance Of Support Vector Machine Classifier Research Articles

Machine learning classifiers for signal detection in ambient backscatter communication under time-selective fading

In the world of the Internet of Things (IoT), ambient backscatter communication, or AmBaC, is a cutting-edge technology that has attracted a lot of interest. It meets the requirement for low-power communication in IoT devices, which are often located in a multitude of settings and scenarios where power efficiency is essential. In the literature, most signal detection is carried out using an energy detector or Minimum Mean Square Error (MMSE) detector under static conditions. In this work, we perform signal detection using multiple supervised machine learning (ML) classifiers, namely random forest, K-nearest neighbors (KNN), and support vector machine (SVM), and we examine the system performance in terms of bit error rate (BER) for both stationary and moving radio-frequency (RF) source. The simulation findings demonstrate that the proposed supervised ML classifiers surpass the conventional signal detector. In particular, the performance of the SVM classifier is best among all the other ML classifiers. Additionally, we examined the performance of different kernels of the SVM classifier and discovered that the Gaussian kernel offers the highest performance when it comes to SVM classifier performance.

Application of terahertz spectroscopy in identification of transgenic rapeseed oils: A support vector machine model based on modified mayfly optimization algorithm

To achieve rapid and accurate identification of genetically modified (GM) and non-GM rapeseed oils, a support vector machine (SVM) model based on an improved mayfly optimization algorithm and coupled with the terahertz time-domain spectroscopy, is proposed. Two types of GM rapeseed oils and two types of non-GM rapeseed oils are selected as research subjects. Their spectral information is acquired by using the terahertz time-domain spectroscopy. The observations show that GM rapeseed oils exhibit stronger terahertz absorption characteristics than non-GM rapeseed oils. However, their absorption spectra are highly similar, making direct differentiation difficult through visual inspection alone. Therefore, SVM is used for spectral recognition. Considering that the classification performance of SVM is significantly affected by its parameters, the mayfly optimization algorithm is combined to optimize these parameters. Furthermore, adaptive inertia weight and Lévy flight strategies are introduced to enhance the global search capability and robustness of the mayfly optimization algorithm, thus addressing the issue of easily becoming trapped in local optima in the optimization process. Moreover, principal component analysis is used to reduce the dimensionality of the absorbance data in a 0.3–1.8 THz range, aiming to extract critical features, thereby enhancing modeling efficiency and reducing redundancy in spectral data. Experimental results demonstrate that the improved mayfly optimization algorithm effectively identifies the optimal parameter combination for SVM, thereby enhancing the overall performance of the identification model. The proposed SVM model, in which the improved mayfly optimization algorithm is used, can achieve a recognition accuracy of 100% for the four types of rapeseed oils, surpassing the 98.15% accuracy achieved by the SVM model with the original mayfly optimization algorithm. Thus, this study presents a rapid and effective new approach for identifying GM rapeseed oils and offers a valuable reference for identifying other genetically modified substances.

Text Mining - A Comparative Review of Twitter Sentiments Analysis

Background: Text mining derives information and patterns from textual data. Online social media platforms, which have recently acquired great interest, generate vast text data about human behaviors based on their interactions. This data is generally ambiguous and unstructured. The data includes typing errors and errors in grammar that cause lexical, syntactic, and semantic uncertainties. This results in incorrect pattern detection and analysis. Researchers are employing various text mining techniques that can aid in Topic Modeling, the detection of Trending Topics, the identification of Hate Speeches, and the growth of communities in online social media networks. Objective: This review paper compares the performance of ten machine learning classification techniques on a Twitter data set for analyzing users' sentiments on posts related to airline usage. Methods: Review and comparative analysis of Gaussian Naive Bayes, Random Forest, Multinomial Naive Bayes, Multinomial Naive Bayes with Bagging, Adaptive Boosting (AdaBoost), Optimized AdaBoost, Support Vector Machine (SVM), Optimized SVM, Logistic Regression, and Long-Short Term Memory (LSTM) for sentiment analysis. Results: The results of the experimental study showed that the Optimized SVM performed better than the other classifiers, with a training accuracy of 99.73% and testing accuracy of 89.74% compared to other models. Conclusion: Optimized SVM uses the RBF kernel function and nonlinear hyperplanes to split the dataset into classes, correctly classifying the dataset into distinct polarity. This, together with Feature Engineering utilizing Forward Trigrams and Weighted TF-IDF, has improved Optimized SVM classifier performance regarding train and test accuracy. Therefore, the train and test accuracy of Optimized SVM are 99.73% and 89.74% respectively. When compared to Random Forest, a marginal of 0.09% and 1.73% performance enhancement is observed in terms of train and test accuracy and 1.29% (train accuracy) and 3.63% (test accuracy) of improved performance when compared with LSTM. Likewise, Optimized SVM, gave more than 10% of enhanced performance in terms of train accuracy when compared with Gaussian Naïve Bayes, Multinomial Naïve Bayes, Multinomial Naïve Bayes with Bagging, Logistic Regression and a similar enhancement is observed with AdaBoost and Optimized AdaBoost which are ensemble models during the experimental process. Optimized SVM also has outperformed all the classification models in terms of AUC-ROC train and test scores.

Cross-subject emotion recognition using hierarchical feature optimization and support vector machine with multi-kernel collaboration

Objective. Due to individual differences, it is greatly challenging to realize the multiple types of emotion identification across subjects. Approach. In this research, a hierarchical feature optimization method is proposed in order to represent emotional states effectively based on peripheral physiological signals. Firstly, sparse learning combined with binary search is employed to achieve feature selection of single signals. Then an improved fast correlation-based filter is proposed to implement fusion optimization of multi-channel signal features. Aiming at overcoming the limitations of the support vector machine (SVM), which uses a single kernel function to make decisions, the multi-kernel function collaboration strategy is proposed to improve the classification performance of SVM. Main results. The effectiveness of the proposed method is verified on the DEAP dataset. Experimental results show that the proposed method presents a competitive performance for four cross-subject types of emotion identification with an accuracy of 84% (group 1) and 85.07% (group 2). Significance. The proposed model with hierarchical feature optimization and SVM with multi-kernel function collaboration demonstrates superior emotion recognition accuracy compared to state-of-the-art techniques. In addition, the analysis based on DEAP dataset composition characteristics presents a novel perspective to explore the emotion recognition issue more objectively and comprehensively.

A SENTIMENT ANALYSIS MODEL FOR ELECTROENCEPHALOGRAM SIGNALS OF STUDENTS IN UNIVERSITIES USING A CONVOLUTIONAL NEURAL NETWORK AND SUPPORT VECTOR MACHINE MODELS

Sentiment analysis in teaching evaluation has significant implications. By analyzing students’ sentiments toward instructors, educational institutions can gain valuable insights into teaching effectiveness. These data can guide curriculum development, instructional improvements, and faculty training initiatives. Positive sentiment indicates effective teaching methods, engagement, and student satisfaction; negative sentiment flags areas that need attention. Sentiment analysis can help identify patterns, trends, and outliers, aiding in targeted interventions and personalized support. It also enables comparisons across different courses, instructors, and departments. However, it is crucial to ensure the accuracy and fairness of sentiment analysis algorithms, considering potential biases and the contextual nature of the feedback. This study proposes a sentiment classification model CNN–SVM that combines a convolutional neural network (CNN) and a support vector machine (SVM). Taking students majoring in art in comprehensive colleges and universities as the research object, by collecting the electroencephalogram (EEG) signals of students during teaching evaluation. CNN–SVM is used as the emotional analysis model to obtain the emotional analysis of teaching evaluation results. EEG is a typical physiological signal, and data based on this signal can more truly reflect student emotions. The adaptive CNN feature extraction function and the super generalization classification performance of SVM can reduce the individual differences and data noise between data, thereby improving sentiment classification performance. The experimental results demonstrate that using technology to analyze sentiment can assist educational institutions in more properly comprehending the feedback and opinions of students on instruction. With regard to sentiment analysis, the CNN–SVM method that is derived to produce the fusion algorithm has solid performance.

Multi-channel EEG-based sleep staging using brain functional connectivity and domain adaptation

Objective. Sleep stage recognition has essential clinical value for evaluating human physical/mental condition and diagnosing sleep-related diseases. To conduct a five-class (wake, N1, N2, N3 and rapid eye movement) sleep staging task, twenty subjects with recorded six-channel electroencephalography (EEG) signals from the ISRUC-SLEEP dataset is used. Approach. Unlike the exist methods ignoring the channel coupling relationship and non-stationarity characteristics, we developed a brain functional connectivity method to provide a new insight for multi-channel analysis. Furthermore, we investigated three frequency-domain features: two functional connectivity estimations, i.e. synchronization likelihood (SL) and wavelet-based correlation (WC) among four frequency bands, and energy ratio (ER) related to six frequency bands, respectively. Then, the Gaussian support vector machine (SVM) method was used to predict the five sleep stages. The performance of the applied features is evaluated in both subject dependence experiment by ten-fold cross validation and subject independence experiment by leave-one-subject-out cross-validation, respectively. Main results. In subject dependence experiment, the results showed that the fused feature (fusion of SL, WC and ER features) contributes significant gain the performance of SVM classifier, where the mean of classification accuracy can achieve 83.97% ± 1.04%. However, in subject-independence experiment, the individual differences EEG patterns across subjects leads to inferior accuracy. Five typical domain adaptation (DA) methods were applied to reduce the discrepancy of feature distributions by selecting the optimal subspace dimension. Results showed that four DA methods can significantly improve the mean accuracy by 1.89%–5.22% compared to the baseline accuracy 57.44% in leave-one-subject-out cross-validation. Significance. Compared with traditional time-frequency and nonlinear features, brain functional connectivity features can capture the correlation between different brain regions. For the individual EEG response differences, domain adaptation methods can transform features to improve the performance of sleep staging algorithms.

Wavelet Based Detection and Classification Power Quality Disturbance using SVM and PSO

This paper introduces a novel approach to detect and classify power quality disturbance in the power system using Support Vector Machine (SVM). The proposed method requires less number of features as compared to conventional approach for the identification. For the classification, 8 types of disturbances are taken in to account. The classification performance of SVM is compared with Radial basis Function neural network (RBNN).The classification accuracy of the SVM network is improved, just by rewriting the weights and updating the weights with the help of cognitive as well as the social behaviour of particles along with fitness value by using Particle Swarm Optimization (PSO). The simulation results possess significant improvement over existing methods in signal detection and classification with lesser number of features

Breast cancer classification application based on QGA-SVM

Early diagnosis of breast cancer plays an important role in improving survival rate. Physiological changes of breast tissue can be observed and measured through medical electrical impedance, and the results can be used as a preliminary diagnosis by doctors before treatment. In this paper, quantum genetic algorithm (QGA) and support vector machine (SVM) were combined to classify breast tissues to help clinicians in diagnosis. The algorithm uses QGA to optimize the parameters of SVM and improve the classification performance of SVM. In this experiment, the electrical impedance data measured from breast tissue provided by UCI [58] was used as the data set. Objectively speaking, the data volume of the data set is small and the representativeness is not strong enough. However, the experimental results show that QGA-SVM shows better classification performance, and it is better than SVM.

Improving SVM performance for type II diabetes prediction with an improved non-linear kernel: Insights from the PIMA dataset

Type 2 diabetes is a chronic metabolic disease that affects a significant portion of the worldwide people. Prediction of this disease using different machine learning (ML) based algorithms has gained substantial attention due to its potential for early detection and effective intervention. One of the most powerful ML algorithm support vector machines (SVM) has proven to be effective in a variety of classification tasks, including diabetes prediction. However, the kernel function chosen has a substantial effect on the performance of SVM classifiers. This paper proposes an improved non-linear kernel for the SVM model to enhance Type 2 diabetes classification. The new kernel uses radial basis function (RBF) and RBF city block kernels that enable SVM to learn complex decision boundaries and adapt to the intricacies of the PIMA dataset. The PIMA dataset contains various clinical and demographic features of individuals. To address missing values and outliers, we impute them using the median, ensuring the integrity of the dataset. We tackle the class imbalance issue by leveraging a robust synthetic-based over-sampling approach.A comparative analysis is performed against several existing kernel functions to show that the proposed approach is superior in terms of various prediction evaluation matrices. Our recommended integrated kernel model also showed improved performance (ACC = 85.5, Recall = 87.0, Precision = 83.4, F1 score = 85.2, and AUC = 85.5) when compared to other approaches in the literature. The results of this study indicate that the proposed non-linear kernel in SVM outperforms existing kernel functions for predicting Type 2 diabetes using the PIMA dataset. Furthermore, a simulation study is carried out to robustify the proposed kernel in SVM and perform well. The improved accuracy and robustness of the model suggest its potential utility in clinical settings, aiding in the early identification and management of individuals at risk for developing diabetes.

Improving SVM Classification Performance on Unbalanced Student Graduation Time Data Using SMOTE

Student graduation accuracy is one of the indicators of the success of higher education institutions in carrying out the teaching and learning process and as a component of higher education accreditation. So it is not surprising that building a system that can predict or classify students graduating on time or not on time is necessary for universities to monitor the exact number of students graduating on time using educational technology. Unfortunately, educational technology or machine learning with data mining approaches is less accurate in classifying classes with unbalanced data. Therefore, this research purpose is to build a machine learning system that can improve classification performance on unbalanced class data between students who graduate on time and graduate late. This study applies the Synthetic Minority Oversampling Technique (SMOTE) method to improve the classifying performance of the Support Vector Machine (SVM) data mining method. The results of the study concluded that using the SMOTE method increased the accuracy, precision, and sensitivity of the SVM method in classifying class data of unbalanced student graduation times. The SVM performance score rises by 3% for classification accuracy, 8% for classification precision, and 25% for classification sensitivity.

Automatic Modulation Classification Using a Support Vector Machine-Based Pattern Recognition Algorithm

Modulation format recognition is an essential part of intelligent receivers of wireless communication systems, especially for adaptive radio systems (ARS). This paper presents a detailed investigation of automatic modulation classification (AMC) using pattern recognition classifiers (PRC) under fading and AWGN conditions. A variety of classifiers with different kernel functions and Support Vector Machine (SVM) classifiers have been developed for the classification of higher-order digital modulation signals. In addition, an extensive investigation of the extraction of various higher-order statistical features from each of the modulated classes and the choice of appropriate features for training classifiers are presented. In addition, the performance of the SVM classifier is evaluated under a variety of training rates and suboptimal channel conditions. Further, the performance of SVM classifiers is compared to that of existing techniques to demonstrate the effectiveness of the SVM classifiers for modulation categorization.

Radar signal recognition exploiting information geometry and support vector machine

Aiming at the recognition of low-probability-of-intercept (LPI) radar signals, a support vector machine (SVM)-based algorithm is proposed, where the information geometry theory is utilised to optimise the kernel function of the SVM. Since signals with different modulations have different characteristics in the time-frequency domain, the time-frequency transformation result of the LPI radar signal is considered as an image, referred to as the time-frequency image, and computer vision techniques are utilized to perform recognition. Specifically, the time-frequency images of different LPI radar signals are obtained via the Choi-Williams distribution (CWD) transform, and the AlexNet network, one improved convolutional neural network (CNN), is used to extract time-frequency features. Then, an SVM is adopted to recognise LPI radar signals due to its superiority in addressing the dimension disaster and non-linear inseparability issue. The extracted time-frequency features are fed into the SVM for classification and recognition. Note that the classification performance of SVM depends on the kernel function. Therefore, in the proposed algorithm, information geometry theory is exploited to improve the Gaussian kernel function, and the maximum margin between different categories of samples is further enlarged. As a consequence, the recognition accuracy for LPI radar signals with similar time-frequency images is effectively improved. In addition, the proposed algorithm has better robustness to small samples than other deep learning-based algorithms, since the SVM method minimises the structural risk instead of the empirical risk. Simulation results verify the effectiveness of the proposed algorithm.

Fault Detection and Classification in Interconnected System with Wind Generation Using ANN and SVM

Protective relays are installed in generation, transmission, and distribution system for detection, classification, and estimation of faults. To match the future load demand and to get uninterrupted power supply, use of renewable energy sources are increasing day by day. Faults can occur in transmission lines, transformers, generators, and busbars but the nature of these faults may change many times when renewable energy sources are considered. This research paper introduce techniques to detect and classify different faults on transmission line in the presence of wind energy sources using efficient tools of artificial intelligence. The main challenges of the system fault detection, in presence of wind turbine lie in their non-linearity, uncertainty and unknown disturbances. PSCAD/EMTDC software tool is used to simulate the power system model with RES which is implemented in MATLAB and Python software. Artificial Neural Network (ANN) and Support Vector Machine (SVM) algorithms have been used to classify and detect faults on transmission lines connected with wind energy source. The proposed technique has been validated for internal faults on transmission line and external faults on power system. In total of 4320 internal and external fault cases with wide variation in system parameters have been used for validation of the proposed model. The proposed model gives an overall fault zone identification accuracy of more than 99% in presence of wind energy source. The results obtained from validation show that the performance of SVM classifier is better than ANN in term of efficacy and classification time.

Kernel SVM Classifiers based on Fractal Analysis for Estimation of Hearing Loss

Hearing screening consists of analyzing the hearing capacity of an individual, regardless of age. It identifies serious hearing problems, degree, type and cause of the hearing loss and the needs of the person to propose a solution. Auditory evoked potentials (AEPs) which are detected on the EEG auditory cortex area are very small signals in response to a sound stimulus (or electric) from the inner ear to the primary auditory areas of the brain. AEPs are noninvasive methods used to detect hearing disorders and to estimate hearing thresholds level. In this paper, due to the nonlinear characteristics of EEG, Detrented Fluctuation Analysis (DFA) is used to characterize the irregularity or complexity of EEG signals by calculating the Fractal Dimension (FD) from the recorded AEP signals of the impaired hearing and the normal subjects. This is to estimate their hearing threshold. In order to classify both groups, hearing impaired and normal persons, support vector machine (SVM) is used. For comparably evaluating the performance of SVM classifier, three kernel functions: linear, radial basis function (RBF) and polynomial are employed to distinguish normal and the abnormal hearing subjects. Grid search technique is selected to estimate the optimal kernel parameters. Our results indicate that the RBF kernel SVM classifier is promising; it is able to obtain a high training as well as testing classification accuracy.

Impact analysis of feature selection techniques on cyberstalking detection

Internet-based applications are making the habitual society and exploring new ways to perform online-based crimes. Numerous cybercriminals are engaged in the different platforms of the internet-based virtual world, carrying out cybercrimes according to predetermined and preplanned agendas. As technology advances, cyberstalking, cyberbullying, and other forms of cyber harassment are growing on social media, email, and other online platforms. Cyberstalking uses internet-based technology to harass, intimidate, and undermine individuals online with different approaches. In order to examine the impact of feature selection strategies for improving model performance, this paper proposes a machine learning-based cyberstalking detection model. The proposed model used the Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction method to extract features, and three distinct approaches, TF-IDF + Chi-Square Test, and TF-IDF + Information Gain, were used to select the different numbers of relevant features. In the cyberstalking detection model, a Support Vector Machine (SVM) was employed for classification purposes. Based on the SVM classifier's performance, each feature selection approach's impact on the various feature sets was assessed. According to experimental findings, the TF-IDF + Chi-Square Test outperformed other applied approaches and improved detection mode performance. Additionally, experimental findings demonstrate that the TFIDF + Chi-Square Test approach also performs better in a small collection of relevant features than other approaches that have been utilized.

Hybrid Approach of EEG Stress Level Classification Using K-Means Clustering and Support Vector Machine

Support vector machine (SVM) algorithms are prevalent in classifying electroencephalogram (EEG) signals for the detection of mental stress at various levels. This study aimed to reduce the subjective bias in form of human stress reactivity, by employing clustering methods to pre-label stress levels according to the inherent homogeneity and, perform SVM to classify the stress level. Brainwave signals at the prefrontal cortex (Fp1 and Fp2) from 50 participants were captured related to the stress induced by the virtual reality (VR) horror video and intelligence quotient (IQ) test. The power spectral density (PSD) values of Theta, Alpha, and Beta frequency bands were extracted, and Wilcoxon signed-rank test were reported to show a significant difference in the absolute power between resting baseline and post-stimuli. The extracted features were further clustered into three groups of stress level. The labelled data based on k-means clustering method were fed into SVM to classify the stress levels. The performance of SVM classifier was validated by 10-fold cross validation method and the result affirmed the highest performance of 98% accuracy by using only the feature of Beta-band absolute power at right (Fp2) prefrontal region on account of the significant changes of Beta activity during pre- and post-stimuli. In essence, stress pattern has been found in the brain activity of Beta frequency band within right prefrontal cortex which has been shown to be significantly more active under stimuli. The hybrid approach of classification using k-means clustering and SVM has been proven to be an effective method in lieu of pre-labelling the stress level to reduce individual differences in stress response, and in turn to improve the reliability and detection rate of mental stress.

Identification of transient overvoltage using discrete wavelet transform with minimised border distortion effect and support vector machine

The existing border distortion effect at signal edges can produce inaccurate detection of transient signals when deploying signal processing method. Therefore, there is a need to develop a technique to minimise this border distortion effect through the use of Discrete Wavelet Transform (DWT). In this study, the extension mode has been proposed to minimise border distortion effect. DWT based on one-cycle window technique is used to extract the features of transient disturbances signal. The disturbances contain imprecision of data and provide insufficient information, thereby leading to the failure of the conventional method to identify any power quality (PQ) problems. Thus, the detection and classification method using Support Vector Machine (SVM) is deployed to acquire reliable and accurate classification technique. The novel approached of one-cycle sliding window with the association of extension mode are validated through the SVM classification. From the results obtained, the performance of absolute reconstructed signal after threshold technique shows that smooth padding is the most effective extension mode to reduce the border distortion effect using one-cycle sliding window. Overall, the SVM classification performance based on one-versus-one (OVO) coding design can detect transient and non-transient events subsequent to undergoing all subsequent processes.

Research on diagnosis algorithm of mechanical equipment brake friction fault based on MCNN-SVM

Brakes in mechanical equipment are crucial for operational safety, and their effects are directly affected by friction performance. The fault signal induced by friction interface presents the phenomenon of multi-source, and the fault samples are difficult to obtain in practical engineering. Both aspects yield unsatisfactory recognition performance of diagnosis models. To address the issues, in this article, we proposed an algorithm based on a modified convolutional neural network (CNN) and support vector machine (SVM). First, dynamic features were extracted from the friction factor and friction surface temperature as samples, which could effectively present the state of brake friction. Next, CNN was used to learn feature knowledge from dynamic feature set, the Mish activation function, batch normalisation and dropout were employed to complete the training of modified CNN (MCNN). Then, the dynamic feature set was input into the trained MCNN again to learn the feature representations of friction state. Finally, the feature representations were migrated to SVM to establish the mapping between feature space and label space, and the final fault recognition was completed. The proposed algorithm fully combined the powerful feature learning ability of MCNN and the excellent classification performance of SVM on small samples. Experiment results showed that MCNN-SVM had faster convergence speed, and the accuracy of the proposed algorithm reached 100%. Its diagnosis effect was better than counterpart algorithms.

Application of Genetic Algorithm-Based Support Vector Machine in Identification of Gene Expression Signatures for Psoriasis Classification: A Hybrid Model.

Background Psoriasis is a chronic autoimmune disease impairing significantly the quality of life of the patient. The diagnosis of the disease is done via a visual inspection of the lesional skin by dermatologists. Classification of psoriasis using gene expression is an important issue for the early and effective treatment of the disease. Therefore, gene expression data and selection of suitable gene signatures are effective sources of information. Methods We aimed to develop a hybrid classifier for the diagnosis of psoriasis based on two machine learning models of the genetic algorithm and support vector machine (SVM). The method also conducts gene signature selection. A publically available gene expression dataset was used to test the model. Results A number of 181 probe sets were selected among the original 54,675 probes using the hybrid model with a prediction accuracy of 100% over the test set. A number of 10 hub genes were identified using the protein-protein interaction network. Nine out of 10 identified genes were found in significant modules. Conclusions The results showed that the genetic algorithm improved the SVM classifier performance significantly implying the ability of the proposed model in terms of detecting relevant gene expression signatures as the best features.

Computer Vision-Based Patched and Unpatched Pothole Classification Using Machine Learning Approach Optimized by Forensic-Based Investigation Metaheuristic

During the phase of periodic asphalt pavement survey, patched and unpatched potholes need to be accurately detected. This study proposes and verifies a computer vision-based approach for automatically distinguishing patched and unpatched potholes. Using two-dimensional images, patched and unpatched potholes may have similar shapes. Therefore, this study relies on image texture descriptors to delineate these two objects of interest. The texture descriptors of statistical measurement of color channels, the gray-level cooccurrence matrix, and the local ternary pattern are used to extract texture information from image samples of asphalt pavement roads. To construct a classification model based on the extracted texture-based dataset, this study proposes and validates an integration of the Support Vector Machine Classification (SVC) and the Forensic-Based Investigation (FBI) metaheuristic. The SVC is used to generalize a classification boundary that separates the input data into two class labels of patched and unpatched potholes. To optimize the SVC performance, the FBI algorithm is utilized to fine-tune the SVC hyperparameters. To establish the hybrid FBI-SVC framework, an image dataset consisting of 600 samples has been collected. The experiment supported by the Wilcoxon signed-rank test demonstrates that the proposed computer vision is highly suitable for the task of interest with a classification accuracy rate = 94.833%.