Support Vector Machine Classification Algorithm Research Articles

Research on Visual Inspection Method of Tree Whitening Quality based on Multi-level Feature Fusion

Background: At present, the traditional manual whitewashing method is used in tree whitewashing in China. The quality of tree whitening can only be judged by the naked eye. Objective: Up to now, the quality of tree whitewashing is still judged manually. In order to improve work efficiency, an automatic evaluation method of tree whitewashing quality based on multi-level feature fusion is proposed. Methods: The images extract texture features from white-washed trees (gray-level co-occurrence matrix) and shape features (gradient direction histogram) from pixel-level fusion to obtain a global characteristic matrix. Using a support vector machine (SVM), random forests, and clustering algorithm (KNN), three classifiers were selected to identify the integration characteristics of the training test. In order to reduce the correlation of feature information and improve the execution efficiency of the classifier, the global feature matrix was optimized by combining principal component analysis (PCA) with pixel-level fusion and feature-level fusion. Results: The experiment results showed that the classification accuracy of the support vector machine is 94.00%, which is higher than that of the random forest classifier (92.67) and KNN classifier (92.67%). Meanwhile, the support vector machine is superior to the other two classification algorithms in recall rate, accuracy rate, and algorithm execution efficiency. The results showed that the execution efficiency of each classification algorithm was improved after the optimization of the feature data. The support vector machine classification algorithm is more stable than the other two algorithms. Conclusion: The feature fusion method combined with PCA can improve the execution efficiency and recognition precision of classifiers to a certain extent. For the feature matrix obtained by different data processing methods, the SVM classifier performs more stably and reliably than the random forest classifier and KNN in the inspection of tree whitening quality.

477 Identifying the Neural Correlates of the Triple Code Model in Numerical Cognition Using Stereotactic Electroencephalography in Epilepsy Patients

INTRODUCTION: The capacity to process numerical quantities is an essential human trait. The “Triple Code Model” (TCM) in numerical cognition, theorizes that different neural substrates encode the processing of visual, auditory, and nonsymbolic numerical representations formats. Limited data exists examining the direct electrophysiological activity involved in numerical cognition. METHODS: Thirteen patients with medically refractory epilepsy that underwent sEEG for the localization of seizures performed a passive numerical recognition task. Subjects were presented with a number quantity from 1 to 9 in one of five different representation formats: two were auditory (spoken numbers, sequential beeps) and three were visual (Arabic numeral, written word, assortment of dots). A total of 2,482 electrode contacts were analyzed. Time-frequency spectrograms were reduced with principal component analysis (PCA) and passed into a linear support vector machine (SVM) classification algorithm to identify neural correlates of number processing. RESULTS: The highest classification accuracy in number processing, irrespective of representation format occurred in the bilateral parietal lobes, right lingual cortex, and bilateral putamen. A total of 407, 776, 854, 475, and 513 contacts had significant classification values during Arabic, beep, spoken, written, and dots, respectively. Bilateral superior temporal cortices demonstrated the best classification value for the processing of auditory formats, whereas visual formats (Arabic and written number) showed greatest involvement in the frontal lobe and inferior precentral gyrus. Classification of dots indicated preferential engagement of the left parietal cortex. Spectral analyses revealed that non-gamma frequency bands held greater than chance classification values to characterize format-specific number representations. CONCLUSIONS: Using a linear SVM classifier with PCA, we examined the Triple Code Model in numerical cognition and identified several neural structures with high classification values involved in numerical processing.

Blood platelet RNA profiles do not enable for nivolumab response prediction at baseline in patients with non-small cell lung cancer.

Anti-PD-(L)1 immunotherapy has emerged as a promising treatment approach for non-small cell lung cancer (NSCLC), though the response rates remain low. Pre-treatment response prediction may improve patient allocation for immunotherapy. Blood platelets act as active immune-like cells, thereby constraining T-cell activity, propagating cancer metastasis, and adjusting their spliced mRNA content. We investigated whether platelet RNA profiles before start of nivolumab anti-PD1 immunotherapy may predict treatment responses. We performed RNA-sequencing of platelet RNA samples isolated from stage III-IV NSCLC patients before treatment with nivolumab. Treatment response was scored by the RECIST-criteria. Data were analyzed using a predefined thromboSeq analysis including a particle-swarm-enhanced support vector machine (PSO/SVM) classification algorithm. We collected and processed a 286-samples cohort, separated into a training/evaluation and validation series and subjected those to training of the PSO/SVM-classification algorithm. We observed only low classification accuracy in the 107-samples validation series (area under the curve (AUC) training series: 0.73 (95% -CI: 0.63-0.84, n = 88 samples), AUC evaluation series: 0.64 (95% -CI: 0.51-0.76, n = 91 samples), AUC validation series: 0.58 (95% -CI: 0.45-0.70, n = 107 samples)), employing a five-RNAs biomarker panel. We concluded that platelet RNA may have minimally discriminative capacity for anti-PD1 nivolumab response prediction, with which the current methodology is insufficient for diagnostic application.

A novel automatic detection and classification algorithm for elderly cognitive impairment using CSVM

Alzheimer’s disease (AD) is a degenerative disease with insidious onset and chronic nervous system, which can be roughly classified into three stages: preclinical AD, mild cognitive impairment (MCI), and clinical AD. Given that timely treatment of MCI can delay the course of AD, identifying what stage of the disease is at the time of early diagnosis is critical in clinical practice. Based on the survey data of elderly residents in Changsha community, this paper constructs the cognitive impairment data set and explores the application of the emerging classification algorithm Convolutional Support Vector Machine(CSVM) in identifying elderly cognitive impairment, a small data classification problem. CSVM is an improved Support Vector Machine classification(SVC) algorithm, which uses the convolution filtering idea of the Convolutional Neural Networks(CNN) algorithm to preprocess the nonlinearly separable sample set to improve the classification ability of SVM. In addition, the optimal convolution filter (solution) is found by the Simplified Swarm Optimization(SSO) algorithm and orthogonal array experimental design. A variable update amplitude method is also proposed to optimize the SSO update strategy further. Aiming at the problem of an unbalanced sample set, a stratified sampling method is used to divide and cross-validate the sample data, and a variety of evaluation indicators are used to evaluate the performance of the model. Experimental results show that the data set processed by a specific convolution filter can significantly improve the classification performance of linear SVM. The accurate differentiation of normal cognition(NC), MCI, and AD helps intervene in adjuvant treatment for potential patients timely.

Smart Urban Cadastral Map Enrichment—A Machine Learning Method

Smart Urban Cadastral Map Enrichment—A Machine Learning Method

PENERAPAN METODE SUPPORT VECTOR MACHINE (SVM) PADA KLASIFIKASI JENIS CENGKEH BERDASARKAN FITUR TEKSTUR DAUN

Leaves are a very important plant component because they play an important role in differentiating plant species, including clove plants. Currently, the identification of clove species, namely Afo, Siputih, and Zanzibar, relies on manual observation of the characteristics of the fruit and flowers, which can take a long time, especially considering the long fruiting period of the clove plant. To answer this problem, the authors conducted a study to classify the three types of clove leaves based on the characteristics and texture of the Gray gray-level co-occurrence Matrix (GLCM), which includes four parameters: Contrast, Correlation, Energy, and Homogeneity. 
 The Support Vector Machine (SVM) classification algorithm processes extracted feature values and accurately class leaves. This study achieves the highest accuracy of 56.67% on an image size of 250x250 pixels and 48.33% on an image size of 150x150 pixels using 150 training data and 60 test data. These results indicate the potential of automatic leaf classification in efficiently identifying clove plant species.
 Keywords : Clove, Leaf, Processing, Texture, SVM

Rotation-Tolerant Wireless Power Transmission Scheme with Smart Positioning for Cognitive Research on Moving Animals.

This article presents a novel wireless power transmission scheme designed for moving loads. Specifically focused on compensating for the tilt misalignment of the receiver. By utilizing phase-shifted excitation signals from an array of transmitters, the proposed scheme effectively mitigates the impact of misalignment, locally. The application of this scheme holds particular relevance for studying the behavior of moving animals in cognitive research. The system incorporates a cage with two transmitter arrays positioned on the top and bottom sides. To smart determining the receiver's position, the proposed structure utilizes current feedback from the driving circuits and employs SVM (Support Vector Machine) classification algorithms for positioning. Furthermore, when the receiver coil is tilted, a phase shift mechanism significantly enhances the power delivered to the receiver. Additionally, the use of an overlapped transmitter array enhances rotation tolerance and improves the uniformity of the magnetic fields for moving objects. The performance of the proposed scheme is validated through extensive simulations and measurements using a fabricated prototype. Notably, the designed system achieves a power delivery of 296 mW to the load at a 90° angular misalignment, compared to 1.67 µW delivered by conventional array system.

The Childfree Phenomenon in Indonesia: An Analysis of Sentiments on YouTube Video Comments

Childfree is a condition in which a person or couple decides not to have children in marriage. Childfree became popular in Indonesia when YouTuber and influencer Gita Savitri uploaded an Instagram story about it. This brought many pros and cons among the people towards the freedom to have children. Many TV broadcasts and YouTube videos cover this phenomenon. Several YouTube channels that broadcast this phenomenon are Menjadi Manusia and Analisa Channel. We collect YouTube comment data using web scraping techniques. From September 2021 to September 2022, 674 sample data points were obtained from two YouTube videos. Data is labelled (positive, negative, and neutral) using the Indonesian language lexicon approach as well as the Support Vector Machine (SVM) and Random Forest algorithms to determine the best model for classifying YouTube comments. The purpose of this research is to understand the public's perception of childfree and to compare the accuracy and AUC values of the two methods. Based on the results of the analysis, 128 comments are classified as positive, the remaining 39 comments are classified as neutral, and 503 comments are classified as negative. This shows that that the commentators on YouTube do not support or give a negative stigma to people who adhere to childfree. The solution to the balanced data problem for each sentiment class uses the random oversampling (ROS) approach. The RBF kernel SVM classification algorithm is a suitable method for classifying commentary data with an accuracy of 98.01% and an AUC of 98.58%, while the Random Forest algorithm only obtains an accuracy of 94.37% and an AUC of 95.87%.

Accuracy Assessment of Land Use/Land Cover Indices for Al-Rusafa in Baghdad Governorate by Remote Sensing Technology and GIS

The management and planning of natural and artificial resources depend on accurately monitoring land cover changes. Land cover change mapping and monitoring used to require expensive field surveys. Remote sensing is cheaper and more practical for mapping land use and cover changes. The Tigris River divides the Iraqi capital, Baghdad, into two parts: Karkh and Rusafa. Al-Rusafa was selected as a study area for current research, which has had rapid population and urban growth in recent decades. The current research applies the support vector machine technique to supervised LU/LC maps’ classification into barren regions, water bodies, vegetation cover and built-up regions. Spectral indicators were calculated: Enhanced Vegetation Index, Modified Normalized Difference Water Index, Normalized Built-Up Area Index, Dry Bareness Index in addition to calculating the accuracy assessment and Kappa coefficient. Using the Landsat 9 satellite image, ArcGIS 10.8 and Envi5.3 software were used to analyze and evaluate the results and field points observed by GPS devices. The results showed that the SVM classification algorithm accurately revealed the categories of LU/LC, where the classification accuracy reached 95%, and that the arid lands covered most of the study area 848.864 km2 and water bodies 76.747 km2, the vegetation and the built-up regions 466.459 km2 and 439.077 km2, respectively. The spectral indices showed slightly different areas of barren lands (DBSI 752.589 km2, 93% accuracy), vegetation (EVI 423.651 km2, 96% accuracy), and water bodies (MNDWI 73.187 km2, 98% accuracy) and built-up areas (NBAI 501,731 km2, 90%accuracy). The Support Vector Machine method outperforms other classification methods, and the spectral indicators employed in this work are useful and dependable for extracting each LU/LC category. In conclusion, Landsat 9 satellite data can reliably and swiftly detect ground cover.

Determining the Remaining Functional Life of Power Transformers Using Multiple Methods of Diagnosing the Operating Condition Based on SVM Classification Algorithms

Starting from the current need for the safety of energy systems, in which power transformers play a key role, the study of the health of power transformers in service is a difficult and complex task, since the assessment consists of identifying indicators that can provide accurate data on the extent of degradation of transformer components and subcomponents, in order to establish a model for predicting the remaining life of transformers. Therefore, this paper proposes a model for assessing the remaining service life by diagnosing the condition of the transformer based on the health index (HI) obtained from a multi-parameter analysis. To determine the condition of power transformers, a number of methods are presented based on the combination of the combined Duval pentagon (PDC) method and ethylene concentration (C2H4) to determine the fault condition, the combination of the degree of polymerisation (DP) and moisture to determine the condition of the cellulose insulation and the use of the oil quality index (OQIN) to determine the condition of the oil. For each of the classification methods presented, applications based on machine learning (ML), in particular support vector machine (SVM), have been implemented for automatic classification using the Matlab development environment. The global algorithmic approach presented in this paper subscribes to the idea of event-based maintenance. Two case studies are also presented to validate SVM-based classification methods and algorithms.

Development of a patient health monitoring system based on the internet of things with a module for predicting vital signs

<span>Recent issues related to human health in the world have shown the importance of telemedicine considering necessities to perform the remote monitoring of patients. In this study, using a patient smart monitoring system (PSMS), we collected 5,000 samples of heart rate and blood saturation vital signs from 4 volunteers and tried to find better correlation algorithms to develop a module to predict what these vital signs will be in the next 60 seconds. The following regression algorithms recurrent neural network (long short-term memory) (RNN(LSTM)), autorregresive integrated moving average (ARIMA), value-added reseller vector autoregression (VAR) were used to forecast the patient's state of health in the next 60 seconds. Further, the support vector machine (SVM) and Naive Bayes classification algorithms use the data forecasted by the regression algorithms as input to predict the health status of the patients. When comparing algorithms, we focused on the F measure, a metric used to evaluate the performance of machine learning algorithms. As a result, RNN(LSTM) and SVM showed the performance score value of machine learning algorithms F 0.84, RNN(LSTM) and Naive Bayes 0.81, VAR and SVM 0.82, and VAR and Naive Bayes 0.75. Compared to them, the correlation of ARIMA regression algorithms and SVM classification showed a better F score of 0.86 for machine learning algorithms than the others.</span>

Consumer behavior prediction and marketing strategy optimization based on big data analysis

Abstract A large amount of user data on network platforms contains rich treasures, and the mining and development of user consumption behavior in combination with big data provide new possibilities for enterprise precision marketing. In this paper, a consumption behavior prediction model is constructed based on context-aware data and support vector machine classification algorithm, and interactive information collection is used to collect contextual data of user consumption behavior and consumption behavior cycle data. Combined with the support vector machine classification algorithm, the collected behavioral consumption data is divided into hyperplanes, and for nonlinear data, the method of placing the corresponding behavioral data in hyperplanes in the feature space is proposed. The selection of consumption behavior features is obtained using the min-max normalization process, and behavior prediction is carried out on this basis. The results show that the context-aware behavioral prediction model in this paper has the highest R-value, F1 value and NDCG among all models in Top-10, which are 0.796, 0.645 and 0.878, respectively. The two-stage prediction method in the behavioral prediction model can achieve a 98.15% data capture rate, which can accurately obtain the number of these users so as to formulate an accurate marketing strategy.

Optimal gas sensor combination selection method for low cost machine olfaction applicated in food discrimination

In this paper an electronic nose system is proposed, together with odor-related gas information data processing and recognizing methods and an optimal gas sensor combination selecting procedure. The constructed electronic nose system consists of three parts, including a gas sensor chamber, data collecting circuits loaded with gas sensors and odor-related information data processing AI-driving programs. Utilizing the constructed electronic nose system, we accomplish the classifying missions of most 7 fruits and 8 vegetables to classifying accuracy of 96.7% using SVM classification algorithm and 10 sensor units. All probable gas sensor combinations are checked by the standard of their classifying performance to find the gas sensor combination match best when working together. The outcome turned to find that a specified gas sensor combination composed of 4 sensor units work best in the fruits and vegetables classification. This outcome suggests that these 4 sensor units are most suitable utilizing in related fruits and vegetables classification mission.

A Fault Identification Method for Metal Oxide Arresters Combining Suppression of Environmental Temperature and Humidity Interference with a Stacked Autoencoder

Most existing methods aiming to solve the fault identification problem of metal oxide arresters (MOAs) are limited by strong subjectivity in judgment, the significant impact of environmental temperature and humidity on the online monitoring of the resistance current, and poor generalization ability. Therefore, in this article, we propose an MOA fault identification method that combines suppressing environmental temperature and humidity interference with a stacked autoencoder (SAE). Firstly, a functional relationship model between resistance current and environmental temperature and humidity is established. Then, a temperature and humidity interference suppression method based on weighted nonlinear surface modeling is proposed to normalize the resistance current to the same reference temperature and humidity conditions. Finally, an MOA fault identification method combining the suppression of environmental temperature and humidity interference with an SAE is proposed. Furthermore, a comprehensive comparison is conducted on the recall, accuracy, F1-score, and average accuracy of support vector machine, random forest, logistic regression, and SAE classification algorithms in three different scenarios to demonstrate the effectiveness of the proposed method. The results indicate that environmental temperature and humidity interference suppression for resistive current prior to MOA fault classification significantly reduce the number of false alarms. Compared with other methods, the MOA fault identification method, which combines environmental temperature and humidity interference suppression with an SAE, has the highest average accuracy of 99.7%.

Empowering Pregnancy Risk Assessment: A Web-Based Classification Framework with K-Means Clustering Enhanced Models

This study aims to determine whether there is an increase in accuracy results for predicting pregnancy risk with a classification algorithm that goes through and without going through the clustering stage. After that, compare which classification algorithm gets the best improvement. This study uses the K-Means clustering approach, as well as the SVM, Naive Bayes, and K-Nearest Neighbor (KNN) classification algorithms. The pregnancy risk dataset used comes from the UCI Machine Learning Repository. Evaluation metrics used include accuracy, precision, recall, and F1-score. The results of the study revealed that the K-Means model with KNN provided the highest performance compared to the other two, with an accuracy of 79.53% and an average F1-score of 0.8. The implementation of K-Means resulted in an increase in accuracy of 0.4%, 1.57%, and 2.76% on KNN, SVM, and Naive Bayes respectively, which confirms the impact of clustering in improving classification performance. The resulting model can be used in real-time via a website built using the Flask API, and offers tools that can help health practitioners to plan treatments effectively and minimize the risk of pregnancy.

Detection Method of Fungal Spores Based on Fingerprint Characteristics of Diffraction-Polarization Images.

The most significant aspect of promoting greenhouse productivity is the timely monitoring of disease spores and applying proactive control measures. This paper introduces a method to classify spores of airborne disease in greenhouse crops by using fingerprint characteristics of diffraction-polarized images and machine learning. Initially, a diffraction-polarization imaging system was established, and the diffraction fingerprint images of disease spores were taken in polarization directions of 0°, 45°, 90° and 135°. Subsequently, the diffraction-polarization images were processed, wherein the fingerprint features of the spore diffraction-polarization images were extracted. Finally, a support vector machine (SVM) classification algorithm was used to classify the disease spores. The study's results indicate that the diffraction-polarization imaging system can capture images of disease spores. Different spores all have their own unique diffraction-polarization fingerprint characteristics. The identification rates of tomato gray mold spores, cucumber downy mold spores and cucumber powdery mildew spores were 96.02%, 94.94% and 96.57%, respectively. The average identification rate of spores was 95.85%. This study can provide a research basis for the identification and classification of disease spores.

Potato late blight severity monitoring based on the relief-mRmR algorithm with dual-drone cooperation

Late Blight in Potato, a prevalent potato disease, significantly impacts potato yield and stands as one of the principal afflictions affecting the potato crop. Timely and effective monitoring of late blight severity in potato and its spatial distribution has become an immediate priority. In this study, we employed a dual-drone collaborative approach, utilizing the multispectral drone from DJI as well as an ultra-high-resolution RGB drone. By integrating the vegetation index derived from the multispectral UAV, the texture index from the RGB drone, and the estimated crown coverage feature, we combined the relief-mRmR technique with machine learning modeling algorithms to monitor late blight in potato. We compared and constructed severity monitoring models for late blight in potato using Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN) classification algorithms. The results indicate that the RF model exhibited the highest accuracy. For the training dataset, the overall accuracy (OA) and kappa coefficient were 92.50% and 0.90, respectively, while for the independent validation dataset, the OA and kappa coefficient reached 97.50% and 0.96, respectively. The findings also demonstrate that augmenting the vegetation index and texture index with the estimated high-resolution plant crown coverage information significantly enhances the accuracy of the late blight in potato monitoring model.

A Feature Extraction based Improved Sentiment Analysis on Apache Spark for Real-time Twitter Data

This paper aims to improve the accuracy of sentiment analysis on Apache Spark for a real-time general twitter data. A lot of works exist on sentiment analysis on offline or stored twitter data that uses several classification algorithms on relevant features extracted using well-known feature extraction methodologies on pre-processed text data. However, not much works exist for sentiment analysis of real-time twitter data and especially for the generic data on big data processing platforms such as Apache Spark. This paper proposes a real-time sentiment analysis for generic twitter data through Apache Spark using six classification algorithms on N-gram and Term Frequency – Inverse Document Frequency (TF-IDF) feature extraction methodologies on the pre-processed data. An exhaustive comparison is done using Logistic Regression (LR), Multinomial Naive Bayes (MNB), Random Forest Classfier (RFC), Support Vector Machine (SVM), K-Nearest Neighbour (K-NN), and Decision Tree (DT) classification algorithms. It is observed that the trigram feature extraction method performs the best on LR and SVM and the RFC results are also comparable on the considered general tweets data.

Comparing WorldView-2 and PlanetScope Imagery to Mapping Housing Types Using GEOBIA

The mapping accuracy of housing types plays a vital role in urban planning and development. Choosing the right imagery for urban geospatial analysis matters in terms of spatial or textural resolution. Here we compare the effectiveness of different satellite imagery, namely WorldView-2 (2m resolution) and PlanetScope (3m resolution) to map housing types. The segmentation algorithm employed is SNIC (Simple Non-Iterative Clustering) while SVM (Support Vector Machine) algorithm is for classification. This study assessed the performance of these satellite platforms in capturing to extract spatial and spectral elements of each housing class and differentiating between urban villages (Kampung Kota), government-based housing, and private-based gated housing classes in the Tangerang area. WorldView-2, with its high spatial resolution, provides detailed information, allowing for precise delineation of housing boundaries and distinctive features, whereas Planetscope imagery offers better textural information for the segmentation stage. Despite the coarser details, the SVM classification algorithm achieved an overall accuracy of 65.00% using PlanetScope imagery. Comparative analysis revealed that WorldView-2 imagery outperformed PlanetScope imagery in terms of overall accuracy, with an overall accuracy of 65.52%. The higher spatial resolution of WorldView-2 enables better discrimination of housing types, resulting in more accurate classification. However, PlanetScope imagery provides valuable information, particularly for large-scale urban planning applications. The findings of this study contribute to the field of remote sensing and assist urban planners in making informed decisions regarding housing development and infrastructure planning based on available satellite imagery resources, both of which have their own advantages and disadvantages.

Backward Elimination for Feature Selection on Breast Cancer Classification Using Logistic Regression and Support Vector Machine Algorithms

Breast cancer is a prevalent form of cancer that afflicts women across all nations globally. One of the ways that can be done as a prevention to reduce elevated fatality due to breast cancer is with a detection system that can determine whether a cancer is benign or malignant. Logistic Regression and Support Vector Machine (SVM) classification algorithms are often used to detect this disease, but the use of these two algorithms often doesn’t give optimal results when applied to datasets that have many features, so additional algorithm is needed to improve classification performance by using Backward Elimination feature selection. The comparison of Logistic Regression and SVM algorithms was carried out by applying feature selection to breast cancer data to see the best model. The breast cancer dataset has 30 features and two classes, Benign and Malignant. Backward Elimination has reduced features from 30 features to 13 features, thereby increasing the performance of both classification models. The best classification was obtained by using the Backward Elimination feature selection and linear kernel SVM with an increase in accuracy value from 96.14% to 97.02%, precision from 98.06% to 99.49%, recall from 90.48% to 92.38%, and the AUC from 0.95 to 0.96.