Power Of Support Vector Machines Research Articles

Enhanced Approaches for Safeguarding Communication Channels from Illicit Messages

This research project presents a comprehensive strategy aimed at mitigating the pervasive threat of human trafficking through the innovative application of machine learning methodologies. The primary objective revolves around the development and deployment of sophisticated algorithms to identify and intercept human trafficking- related communications. Leveraging the power of Support Vector Machine (SVM) classification, the system meticulously scrutinizes textual data streams, flagging messages indicative of trafficking activities for further investigation. Moreover, our approach extends beyond mere message analysis by incorporating cutting-edge Utilize Convolutional Neural Network (CNN) models for performing facial recognition, age estimation, and gender identification. By harnessing the rich visual information embedded in images and videos, the system enhances its capability to identify potential victims and perpetrators with unprecedented accuracy and efficiency. A pivotal component of our solution is the seamless integration of an alert mechanism facilitated by a Simple Mail Transfer Protocol (SMTP) server. This critical feature ensures that pertinent authorities are promptly notified upon the detection of suspicious activities, enabling swift and decisive intervention. Through this amalgamation of advanced technological frameworks, our research endeavors to empower law enforcement agencies and humanitarian organizations in their tireless efforts to combat the heinous crime of human trafficking. In essence, this research represents a significant stride towards the realization of a technologically fortified defense against the exploitation of vulnerable individuals. By amalgamating state-of-the- art machine learning techniques with real-time alert systems, we aspire to create a formidable deterrent against the perpetrators of this egregious crime, thereby Ensuring the protection of human dignity and advocating for social justice.

Optimizing ‘Explorer’ Rose Production Data with SVM in Smart Agriculture

In the context of modern flower cultivation, this study leverages the power of Support Vector Machines (SVM) to revolutionize the production process. By deploying SVM technology, the study aims to optimize various stages of flower production, with a keen focus on the ever-popular ‘Explorer’ rose variety. The SVM model showcases an exceptional precision rate, achieving a remarkable 99.21% accuracy in classifying production stages. This precision is further validated through k-fold cross-validation, resulting in an impressive average precision of 99.34%. The successful implementation of SVM underscores its potential to not only enhance the quality and profitability of rose production but also serve as a stepping stone for prospective investigations into the seamless integration of other machine learning algorithms within the realm of smart farming.

Advancing Intrusion Detection in Fog Computing: Unveiling the Power of Support Vector Machines for Robust Protection of Fog Nodes against XSS and SQL Injection Attacks

Fog computing, characterized as a cloud infrastructure in close proximity to end devices, faces substantial security challenges that necessitate robust intrusion detection mechanisms for fog nodes. The resource-constrained nature of fog nodes renders them particularly susceptible to attacks, making the development of efficient intrusion detection systems imperative. In this study, we propose a comprehensive approach to protect fog nodes, taking into account their limited resources. Leveraging the power of Support Vector Machines (SVMs), a widely adopted machine learning technique in IoT security, our method overcomes challenges associated with local optima, overfitting, and high-dimensional data. A thorough literature review underscores the prevalent use of SVMs in IoT security research. Specifically, we focus on addressing two prevalent web attacks: Cross-Site Scripting (XSS) and SQL injection attacks, based on global statistical data. To evaluate our approach, we employ the CSE-CIC-IDS2018 dataset and a pseudo-real dataset. Precision, recall, and accuracy are employed as evaluation metrics, along with the Mean Average Precision (MAP). Our evaluation results demonstrate an exceptional level of accuracy, achieving an impressive 98.28% accuracy in terms of average performance when compared to existing methods. Comparative analysis with state-of-the-art approaches further validates the superior efficacy and efficiency of our proposed method.

A new method for employee promotion detection based on the hybrid ANN-SVM model

A companys ability to achieve its ultimate long-term goal highly depends on the potential and motives of its employees. The Human Resource Management team must be able to accurately evaluate a workers ability in quality work and incentivize him/her using promotion. While it has been quite difficult to measure an employees aptness for promotion, previously, machine learning classification methods has been researched upon. The aim of this study is to analyze a newly proposed hybrid classification model in detecting potential employee for promotion. The hybrid model combines the strength in feature selection of Artificial Neural Network model and the binary classification power of Support Vector Machine. The model is tested and trained after its hyperparameters are tuned. Afterward, the model is evaluated on the accuracy, precision, recall and F1 score, and compared to the individual SVM and ANN models for performance. The predictions of this proposed model have proven to exceed those two models with a range of 0.15% to 7.26% increase in performances. Note, the precision of ANN model is somewhat higher than the hybrid model. In summary, the hybrid ANN-SVM model has proven to work effectively with both imbalanced and balanced datasets.

Generalized Support Vector Quantile Regression

Support vector regression (SVR) is devised to solve the regression problem by utilizing the excellent predictive power of Support Vector Machine. In particular, the є-insensitive loss function, which is a loss function often used in SVR, is a function thatdoes not generate penalties if the difference between the actual value and the estimated regression curve is within є. In most studies, the є-insensitive loss function is used symmetrically, and it is of interest to determine the value of є. In SVQR (Support Vector Quantile Regression), the asymmetry of the width of є and the slope of the penalty was controlled using the parameter p. However, the slope of the penalty is fixed according to the p value that determines the asymmetry of є. In this study, a new e-insensitive loss function with p1 and p2 parameters was proposed. A new asymmetric SVR called GSVQR (Generalized Support Vector Quantile Regression) based on the new e-insensitive loss function can control the asymmetry of the width of є and the slope of the penalty using the parameters p1 and p2 , respectively. Moreover, the figures show that the asymmetry of the width of є and the slope of the penalty is controlled. Finally, through an experiment on a function, the accuracy of the existing symmetric Soft Margin, asymmetric SVQR, and asymmetric GSVQR was examined, and the characteristics of each were shown through figures.

Forecasting stock price movements with multiple data sources: Evidence from stock market in China

We employ multiple heterogeneous data sources, including historical transaction data, technical indicators, stock posts, news and Baidu index, to predict the directions of stock price movements. We focus on the distinctive predicting patterns of active and inactive stocks, and we examine the predictive power of support vector machine (SVM) in different levels of activity for a single stock. We construct a total of 14 data source combinations according to the above 5 heterogeneous data sources, and choose three forecasting horizons, namely 1 day, 2 days and 3 days, so that we can investigate the forecast effects of stock price movements in China A-share market under different data source combinations and forecasting horizons. It is concluded that the optimal data source combinations of active and inactive stocks are different. Active stocks achieve the highest accuracy when combining multiple non-traditional data sources, while inactive stocks obtain the highest accuracy when combining traditional data sources with non-traditional data sources. We further divide each stock into inactive periods, active periods and very active periods, and compare the forecast effects of the same stocks in different periods. We conclude that, for most combinations of data sources, the more active the stock is, the more accurate we achieve, which indicates that our approach is more powerful for predicting the price movements of stocks in active and very active periods.

SP-SVM: Large Margin Classifier for Data on Multiple Manifolds

As one of the most important state-of-the-art classification techniques, Support Vector Machine (SVM) has been widely adopted in many real-world applications, such as object detection, face recognition, text categorization, etc., due to its competitive practical performance and elegant theoretical interpretation. However, it treats all samples independently, and ignores the fact that, in many real situations especially when data are in high dimensional space, samples typically lie on low dimensional manifolds of the feature space and thus a sample can be related to its neighbors by being represented as a linear combination of other samples on the same manifold. This linear representation, which is usually sparse, reflects the structure of underlying manifolds. It has been extensively explored in the recent literature and proven to be critical for the performance of classification. To benefit from both the underlying low dimensional manifold structure and the large margin classifier, this paper proposes a novel method called Sparsity Preserving Support Vector Machine(SP-SVM), which explicitly considers the sparse representation of samples while maximizing the margin between different classes. Consequently, SP-SVM inherits both the discriminative power of support vector machine and the merits of sparsity. A set of experiments on real-world benchmark data sets show that SP-SVM achieves significantly higher precision on recognition task than various competitive baselines including the traditional SVM, the sparse representation based method and the classical nearest neighbor classifier.

A System for Offline Recognition of Handwritten Characters in Malayalam Script

In this paper, we propose a handwritten character recognition system for Malayalam language. The feature extraction phase consists of gradient and curvature calculation and dimensionality reduction using Principal Component Analysis. Directional information from the arc tangent of gradient is used as gradient feature. Strength of gradient in curvature direction is used as the curvature feature. The proposed system uses a combination of gradient and curvature feature in reduced dimension as the feature vector. For classification, discriminative power of Support Vector Machine (SVM) is evaluated. The results reveal that SVM with Radial Basis Function (RBF) kernel yield the best performance with 96.28% and 97.96% of accuracy in two different datasets. This is the highest accuracy ever reported on these datasets.

Discovering rules for protein-ligand specificity using support vector inductive logic programming

Structural genomics initiatives are rapidly generating vast numbers of protein structures. Comparative modelling is also capable of producing accurate structural models for many protein sequences. However, for many of the known structures, functions are not yet determined, and in many modelling tasks, an accurate structural model does not necessarily tell us about function. Thus, there is a pressing need for high-throughput methods for determining function from structure. The spatial arrangement of key amino acids in a folded protein, on the surface or buried in clefts, is often the determinants of its biological function. A central aim of molecular biology is to understand the relationship between such substructures or surfaces and biological function, leading both to function prediction and to function design. We present a new general method for discovering the features of binding pockets that confer specificity for particular ligands. Using a recently developed machine-learning technique which couples the rule-discovery approach of inductive logic programming with the statistical learning power of support vector machines, we are able to discriminate, with high precision (90%) and recall (86%) between pockets that bind FAD and those that bind NAD on a large benchmark set given only the geometry and composition of the backbone of the binding pocket without the use of docking. In addition, we learn rules governing this specificity which can feed into protein functional design protocols. An analysis of the rules found suggests that key features of the binding pocket may be tied to conformational freedom in the ligand. The representation is sufficiently general to be applicable to any discriminatory binding problem. All programs and data sets are freely available to non-commercial users at http://www.sbg.bio.ic.ac.uk/svilp_ligand/.

Support Vector Machine Learning Model for the Prediction of Sentinel Node Status in Patients With Cutaneous Melanoma

Currently, approximately 80% of melanoma patients undergoing sentinel node biopsy (SNB) have negative sentinel lymph nodes (SLNs), and no prediction system is reliable enough to be implemented in the clinical setting to reduce the number of SNB procedures. In this study, the predictive power of support vector machine (SVM)-based statistical analysis was tested. The clinical records of 246 patients who underwent SNB at our institution were used for this analysis. The following clinicopathologic variables were considered: the patient's age and sex and the tumor's histological subtype, Breslow thickness, Clark level, ulceration, mitotic index, lymphocyte infiltration, regression, angiolymphatic invasion, microsatellitosis, and growth phase. The results of SVM-based prediction of SLN status were compared with those achieved with logistic regression. The SLN positivity rate was 22% (52 of 234). When the accuracy was > or = 80%, the negative predictive value, positive predictive value, specificity, and sensitivity were 98%, 54%, 94%, and 77% and 82%, 41%, 69%, and 93% by using SVM and logistic regression, respectively. Moreover, SVM and logistic regression were associated with a diagnostic error and an SNB percentage reduction of (1) 1% and 60% and (2) 15% and 73%, respectively. The results from this pilot study suggest that SVM-based prediction of SLN status might be evaluated as a prognostic method to avoid the SNB procedure in 60% of patients currently eligible, with a very low error rate. If validated in larger series, this strategy would lead to obvious advantages in terms of both patient quality of life and costs for the health care system.

Support-vector-based fuzzy neural network for pattern classification

Fuzzy neural networks (FNNs) for pattern classification usually use the backpropagation or C-cluster type learning algorithms to learn the parameters of the fuzzy rules and membership functions from the training data. However, such kinds of learning algorithms usually cannot minimize the empirical risk (training error) and expected risk (testing error) simultaneously, and thus cannot reach a good classification performance in the testing phase. To tackle this drawback, a support-vector-based fuzzy neural network (SVFNN) is proposed for pattern classification in this paper. The SVFNN combines the superior classification power of support vector machine (SVM) in high dimensional data spaces and the efficient human-like reasoning of FNN in handling uncertainty information. A learning algorithm consisting of three learning phases is developed to construct the SVFNN and train its parameters. In the first phase, the fuzzy rules and membership functions are automatically determined by the clustering principle. In the second phase, the parameters of FNN are calculated by the SVM with the proposed adaptive fuzzy kernel function. In the third phase, the relevant fuzzy rules are selected by the proposed reducing fuzzy rule method. To investigate the effectiveness of the proposed SVFNN classification, it is applied to the Iris, Vehicle, Dna, Satimage, Ijcnn1 datasets from the UCI Repository, Statlog collection and IJCNN challenge 2001, respectively. Experimental results show that the proposed SVFNN for pattern classification can achieve good classification performance with drastically reduced number of fuzzy kernel functions.