SVM Optimization Research Articles

Feature extraction of hyperspectral images based on SVM optimization of 2D-EMD and GWO

Feature extraction of hyperspectral images based on SVM optimization of 2D-EMD and GWO

Simultaneous feature selection and SVM optimization based on fuzzy signature and chaos GOA

Simultaneous feature selection and SVM optimization based on fuzzy signature and chaos GOA

Application of inertial navigation high precision positioning system based on SVM optimization

With the advancement of semiconductor technology, pedestrian navigation and positioning technology based on smartphones is becoming increasingly important in people's travel. However, precise positioning is challenging due to the use of inertial measurement units in low-cost smartphones and the complex motion states of pedestrians. To navigate and locate pedestrians in complex motion states, a method for converting between smartphone coordinate systems and navigation coordinate systems was studied and designed, and the errors of the built-in sensors of smartphones were analyzed and calibrated. In addition, support vector machines were used to optimize pedestrian trajectory prediction algorithms, and a pedestrian motion state recognition algorithm was designed based on this. To solve the classification problem of multiple human motion states, a multi classification model was constructed and adjacent gait correlation constraints were introduced to correct the classification results. Experiments indicated that the sum of squared errors for traditional algorithms estimating pedestrian trajectories was 0.92, whereas the optimized algorithms produced an improved sum of squared errors of 0.26. Consequently, the average sum of squared errors was reduced by 71.74 %, and the convergence speed increased by 55.56 %. The pedestrian trajectory prediction algorithm optimized by support vector machine could significantly lift the positioning and navigation efficiency, with a correct recognition rate of over 93 % and a position recognition accuracy of 78.8 % - 88.4 %. By optimizing recognition of the motion state of pedestrians, more accurate determination of their position and motion state can be achieved.

Fault diagnosis of analogue circuits based on artificial intelligence algorithms

Abstract The fault diagnosis problem of analog circuits has been paid more and more attention, and the realization of circuit fault feature extraction and pattern classification are two of the key problems. This paper first combines wavelet packet and energy entropy to design a fault feature extraction method based on wavelet packet entropy to solve the problem of analog circuit fault feature extraction. Secondly, the principle of SVM and particle swarm optimization algorithm are combined to design the fault diagnosis process of analog circuits based on an artificial intelligence algorithm. Finally, take the sallen-key circuit as an example to analyze the effectiveness of the wavelet packet entropy algorithm for fault feature extraction and analyze the effectiveness of this paper’s method based on the analog circuit of Wen’s bridge oscillation circuit. The results show that after wavelet packet entropy extraction of the feature vector values is less than 0.001, and the accuracy of the extraction is more than 0.98, the best parameters of the optimization is (182.4, 0.05), the false alarm rate of the fault diagnostic method is 0, the misdiagnosis rate is 0.08, the omission rate is 0, and the correctness rate of the diagnosis of the fault is 0.92. Based on this research is able to carry out the fault diagnosis of the analog circuit.

Aspect-Based Sentiment Analysis on iPhone Users on Twitter Using the SVM Method and Optimization of Hyperparameter Tuning

Aspect-Based Sentiment Analysis on iPhone Users on Twitter Using the SVM Method and Optimization of Hyperparameter Tuning

Enhancing Sustainable Rice Grain Quality Analysis with Efficient SVM Optimization Using Genetic Algorithm

The most extensively cultivated and indispensable crops in agriculture, ensuring sustainable rice cultivation practices is crucial, and accurately determining rice grain quality is a critical component of this effort. To achieve this goal, researchers have developed a novel technique that combines Support Vector Machine classification with Genetic Algorithm optimization. Using this technique, they were able to categorize rice grain quality with 92.81% accuracy, which improved to 93.31% after optimizing feature weights and classification configurations using the Genetic Algorithm. Compared to other classification algorithms, SVM showed the highest accuracy value, while k-NN had the lowest accuracy of 88.32%. The study’s results suggest that the combination of SVM classification with Genetic Algorithm optimization is an effective method for accurately analyzing rice grain quality. Furthermore, the SVM-based method outperformed other commonly used classification algorithms in terms of accuracy. This study’s findings could be valuable in promoting sustainable rice cultivation practices by improving the accuracy and efficiency of rice grain quality analysis and enhancing the overall productivity of the rice cultivation process. To further improve the classification accuracy of rice grain quality, the researchers employed the Genetic Algorithm optimization method to refine the feature weights and classification configurations.

Incorporation of Data-Mined Knowledge into Black-Box SVM for Interpretability

The lack of interpretability often makes black-box models challenging to be applied in many practical domains. For this reason, the current work, from the black-box model input port, proposes to incorporate data-mined knowledge into the black-box soft-margin SVM model to enhance accuracy and interpretability. The concept and incorporation mechanism of data-mined knowledge are successively developed, based on which a partially interpretable soft-margin SVM ( pTsm -SVM) optimization model is designed and then solved through reformulating the optimization problem as standard quadratic programming. An algorithm for mining linear positive (negative) class knowledge from general data sets is also proposed, which generates a linear two-dimensional discriminative rule with specificity (sensitivity) equal to 1 and the highest possible sensitivity (specificity) among all two-dimensional feature spaces. The knowledge-integrated pTsm -SVM works by achieving a good trade-off among the “large margin”, “high specificity”, and “high sensitivity”. Our experimental results on eight UCI datasets demonstrate the superiority of the proposed pTsm -SVM over the standard soft-margin SVM both in terms of accuracy and interpretability.

A variational inequality approach with SVM optimization algorithm for identifying mineral lithology

In this paper, we describe a method for SVM optimization that converts SVM into a variational inequality issue and applies it to mineral lithology classification. To begin, we use the null space approach to convert the SVM constraint conditions into a linear optimization of inequality constraints. The optimization problem is then transformed into the equivalent variation inequality. We then solve the SVM algorithm using the IPPA technique and show that it is convergent. Finally, we demonstrate the method's benefits by using the logging data set and core sets.

Improved marine predators algorithm for feature selection and SVM optimization

Improved marine predators algorithm for feature selection and SVM optimization

Return Strategy and Machine Learning Optimization of Tennis Sports Robot for Human Motion Recognition.

At present, there are many kinds of intelligent training equipment in tennis sports, but they all need human control. If a single tennis player uses the robot to return the ball, it will save some human resources. This study aims to improve the recognition rate of tennis sports robots in the return action and the return strategy. The human-oriented motion recognition of the tennis sports robot is taken as the starting point to recognize and analyze the return action of the tennis sports robot. The OpenPose traversal dataset is used to recognize and extract human motion features of tennis sports robots under different classifications. According to the return characteristics of the tennis sports robot, the method of tennis return strategy based on the support vector machine (SVM) is established, and the SVM algorithm in machine learning is optimized. Finally, the return strategy of tennis sports robots under eight return actions is analyzed and studied. The results reveal that the tennis sports robot based on the SVM-Optimization (SVM-O) algorithm has the highest return recognition rate, and the average return recognition rate is 88.61%. The error rates of the backswing, forward swing, and volatilization are high in the return strategy of tennis sports robots. The preparation action, backswing, and volatilization can achieve more objective results in the analysis of the return strategy, which is more than 90%. With the increase of iteration times, the effect of the model simulation experiment based on SVM-O is the best. It suggests that the algorithm proposed has a reliable accuracy of the return strategy of tennis sports robots, which meets the research requirements. Human motion recognition is integrated with the return motion of tennis sports robots. The application of the SVM-O algorithm to the return action recognition of tennis sports robots has good practicability in the return action recognition of tennis sports robot and solves the problem that the optimization algorithm cannot be applied to the real-time requirements. It has important research significance for the application of an optimized SVM algorithm in sports action recognition.

A parallel grid-search-based SVM optimization algorithm on Spark for passenger hotspot prediction

A parallel grid-search-based SVM optimization algorithm on Spark for passenger hotspot prediction

Retracted] HANA: A Performance‐Based Machine Learning and Neural Network Approach for Climate Resilient Agriculture

The importance of agricultural crops in India has been understated in terms of production during the last two decades, owing to global warming and other factors. Policymakers and farmers would benefit from imagining crop yields well before harvest to help them make appropriate marketing and storage decisions. Crop yields will benefit from such estimates as well. Several systems for predicting and modelling agricultural yields have been developed in the past, with varying degrees of effectiveness, due to the fact that they do not objectively account for meteorological aspects and seasonal climate variations. The importance of agricultural crops in India has been understated in terms of production during the last two decades, owing to global warming and other factors. Policymakers and farmers would benefit from imagining crop yields well before harvest to help them make appropriate marketing and storage decisions. Crop yields will benefit from such estimates as well. Several systems for predicting and modelling agricultural yields have been developed in the past, with varying degrees of effectiveness, due to the fact that they do not objectively account for meteorological aspects and seasonal climate variations. The method makes use of climate data from Thanjavur, India’s soils. To choose the optimal crop for a particular set of input and climate conditions, the system leverages real‐time input of location‐specific soil attributes. The model is been tested with various machine learning techniques such as NB, KNN, SVM, and decision tree. Among all these methods, the SVM gives the good results. The accuracy of the model was determined using LSTM, SVM, and Tabu search optimization. TSO with SVM has an 89% accuracy value.

Textile Wastewater Treatment in a Spinning Disc Reactor: Improved Performances—Experimental, Modeling and SVM Optimization

The paper presents an experimental study regarding the treatment of a real textile wastewater using the spinning disc (SD) technology, either individually or associated with an advanced Fenton oxidation step. The SD efficiency was investigated by studying the color, suspended solids, or turbidity removals, at distinctive feeding flowrates (10–30 L/h) and disc rotating speeds (100–1500 rpm). The data revealed increasing removal trends and allowed to establish the highest removal values. Based on obtained experimental results, the wastewater treatment efficiency by SD technology was reasonably good and thus, the WW indicators can be improved within relatively short periods of time. Additionally, based on supervised learning algorithms, the study includes treatment modeling for turbidity and color removal, followed by turbidity removal optimization relying on the best learned models. Satisfactory results obtained with the modeling and optimization procedures provide useful predictions for the approached treatment processes. Furthermore, within this study, a Fenton oxidation process was applied to SD technology to minimize the color and solids content. The influence of pH, hydrogen peroxide and ferrous ions concentrations was also investigated in order to establish the highest removal efficiencies. Overall, the SD technology applied in textile effluents treatment proved to be an appropriate and efficient alternative to classical mechanical step applied within the primary treatment step and, when associated with an advanced oxidative process in the secondary step, rendered good improvement, namely of 62.84% and 69.46% for color and respectively, suspended solids removal.

Intelligent system for IoT botnet detection using SVM and PSO optimization

Intelligent system for IoT botnet detection using SVM and PSO optimization

Application of improved sparrow search algorithm in SVM optimization

Sparrow search algorithm has good global search performance, but there is still a probability of falling into local optimum. In order to optimize the algorithm, K-means is proposed to make the population evenly distributed and improve the efficiency at the beginning. Then, the sine-cosine search and adaptive local search strategies are introduced to reduce the probability of falling into the local optimum in the middle and late stages, so that the optimal solution can be obtained easily. Finally, the two strategies are discussed and applied to SVM parameter optimization. The UCI dataset classification results show that the algorithm with the two strategies is suitable for SVM parameter optimization, and the algorithm with sine-cosine search has better optimization ability and stability.

Intelligent system for IoT botnet detection using SVM and PSO optimization

Botnet attacks involving Internet-of-Things (IoT) devices have skyrocketed in recent years due to the proliferation of internet IoT devices that can be readily infiltrated. The botnet is a common threat, exploiting the absence of basic IoT security technologies and can perform several DDoS attacks. Existing IoT botnet detection methods still have issues, such as relying on labeled data, not being validated with newer botnets, and using very complex machine learning algorithms, making the development of new methods to detect compromised IoT devices urgent to reduce the negative implications of these IoT botnets. Due to the vast amount of normal data accessible, anomaly detection algorithms seem to promise for identifying botnet attacks on the Internet of Things (IoT). For anomaly detection, the One-Class Support vector machine is a strong method (ONE-SVM). Many aspects influence the classification outcomes of the ONE-SVM technique, like that of the subset of features utilized for training the ONE-SVM model, hyperparameters of the kernel. An evolutionary IoT botnet detection algorithm is described in this paper. Particle Swarm Optimization technique (PSO) is used to tune the hyperparameters of the ONE-SVM to detect IoT botnet assaults launched from hacked IoT devices. A new version of a real benchmark dataset is used to evaluate the proposed method's performance using traditional anomaly detection evaluation measures. This technique exceeds all existing algorithms in terms of false positive, true positive and rates, and G-mean for all IoT device categories, according to testing results. It also achieves the shortest detection time despite lowering the number of picked features by a significant amount.

Diagnosis of Epilepsy Using Signal Time Domain Specifications and SVM Neural Network

Epilepsy is a central nervous system (neurological) disorder that is caused by abnormal pathologic oscillating activity of a group of nerve cells in the brain. The electroencephalographic signals gained from brain electrical activities are mostly used for the diagnosis of neurological diseases. These signals indicate electrical activities in the brain and they contain some data about the brain; however, gaining long-term EEG data with seizure activities specifically in regions lacking medical centers and educated neurologists would be very costly and unpleasant. In this article based on electroencephalogram (EEG) signals, a new method is proposed for the automatic detection of Epilepsy. The aim of this article is to provide a model for the detection of Epilepsy by SVM optimization using genetic algorithm for the classification of EEG data. SVMs are one the powerful technics of machine learning, and they are widely applicable in many fields. The training and testing data were obtained from investigating EEG signals of 367 healthy and ill individuals. The data used in this paper have been derived from Barekat Imam Khomeini (RAH) Hospital in Miyaneh city. In this study the noise removal was done over the data by FIR Filter and genetic algorithm was used for the calculation of filter coefficients and optimal sample number. This method classifies the signals of both healthy individuals and the ones with Epilepsy with an accuracy of 100%.

Grid SVM: Aplikasi Machine Learning dalam Pengolahan Data Akuakultur

Water condition is the main factor that affects the success rate of aquaculture, especially in shrimp cultivation. However, the farmer often experiences difficulties in determining the condition which is stated based on the measurement of various water parameter. Therefore, a proper classification model is needed to help the farmer in classifying the water condition in a pond. By knowing the condition, then proper and correct treatment can be given. In this research, a machine learning algorithm called SVM is used to make a model from an aquaculture dataset. Another processing technique like data normalization and the usage of optimization algorithm named grid search is also performed to improve the modelling result. Furthermore, a test scheme with using k-fold cross-validation is performed to know the performance of the model which is measured by the value of accuracy, precision, recall, f-measure, and AUROC. Then, the SVM model is compared with several models which are made by using another machine learning algorithm such as KNN, CNB, RF, MLP, and LR in order to know the best model to be implemented on cultivation process. From the experiment results, the model which is made with SVM and grid search optimization has the best performance in the validation process with the performance score of 3.54383.

Extreme vector machine for fast training on large data

Quite often, different types of loss functions are adopted in SVM or its variants to meet practical requirements. How to scale up the corresponding SVMs for large datasets are becoming more and more important in practice. In this paper, extreme vector machine (EVM) is proposed to realize fast training of SVMs with different yet typical loss functions on large datasets. EVM begins with a fast approximation of the convex hull, expressed by extreme vectors, of the training data in the feature space, and then completes the corresponding SVM optimization over the extreme vector set. When hinge loss function is adopted, EVM is the same as the approximate extreme points support vector machine (AESVM) for classification. When square hinge loss function, least squares loss function and Huber loss function are adopted, EVM corresponds to three versions, namely, L2-EVM, LS-EVM and Hub-EVM, respectively, for classification or regression. In contrast to the most related machine AESVM, with the retainment of its theoretical advantage, EVM is distinctive in its applicability to a wide variety of loss functions to meet practical requirements. Compared with the other state-of-the-art fast training algorithms CVM and FastKDE of SVMs, EVM indeed relaxes the limitation of least squares loss functions, and experimentally exhibits its superiority in training time, robustness capability and number of support vectors.

Exploiting multiplex data relationships in Support Vector Machines

In this paper, a novel method for introducing multiplex data relationships to the SVM optimization process is presented. Different properties about the training data are encoded in graph structures, in the form of pairwise data relationships. Then, they are incorporated to the SVM optimization problem, as modified graph-regularized basekernels, each highlighting a different property about the training data. The contribution of each graph-regularized kernel to the SVM classification problem, is estimated automatically. Thereby, the solution of the proposed modified SVM optimization problem lies in a regularized space, where data similarity is expressed by a linear combination of multiple single-graph regularized kernels. The proposed method exploits and extends the findings of Multiple Kernel Learning and graph-based SVM method families. It is shown that the available kernel options for the former can be broadened, and the exhaustive parameter tuning for the latter can be eliminated. Moreover, both method families can be considered as special cases of the proposed formulation, hereafter. Our experimental evaluation in visual data classification problems denote the superiority of the proposed method. The obtained classification performance gains can be explained by the exploitation of multiplex data relationships, during the classifier optimization process.