Proposed Support Vector Machine Research Articles

SVM‐SFL based malicious UAV detection in wireless sensor networks

SummaryIn the modern era, unmanned aerial vehicle (UAV) based wireless sensor networks (WSN) are rising technologies in wireless communication. Through UAV, the sensed data can be forwarded to the base station. However, the increase in network users leads to several malicious attacks on UAVs. Hence, it affects the performance of a WSN platform while transmitting private information through UAVs. Therefore, the proposed study intends to develop an effective malicious UAV detection approach using a machine‐learning algorithm. Initially, the deployed sensor nodes in WSN are utilized to collect the environmental data. These sensor nodes transmit the collected data to the UAV. During data transmission, the sensor nodes generate a feed packet (authentication parameter) and forward it to the UAV along with the sensed information. The feedback packet is encrypted through a proxy re‐encryption scheme to secure the input data. These encrypted packets with the sensed input data are then transmitted to the base station. Finally, the feedback packet is decrypted and attains the actual input information. From the received data, the classification is performed using a proposed support vector machine with a shuffled frog leap (SVM‐SFL) approach. The proposed approach is implemented with the NS3 Python tool, and the results are analyzed by evaluating several performance matrices. Compared with other existing methods, the proposed study obtained improved results in terms of accuracy (98.61%), precision (98.5%), sensitivity (98.63%), and F‐measure (98.62%).

Localization of HIFS in primary distribution networks using voltage and current sequence components

In the power system protection when a conductor accidentally makes contact with a highly resistive medium, a HIF problem arises. Because of their lowest current amplitude and incredibly varied features, the HIF are challenging to detect. To detect the fault type this work proposes Support Vector Machine (SVM) classifier and Convolution Neural Network (CNN) classifier to localize the detected fault based on the trained statistical features attained from current and voltage sequence components at distribution feeders. The weight parameters of the SVM classifier and the CNN classifiers are tuned by a novel Hybrid AqSea Optimization Algorithm. The performance of the presented HIFD method is compared to assess proficiency. The adopted model at 70 % learning attains an accuracy of 93 %, which is 0.89 %, 7.69 %, 4.60 %, 1.21 % and 30.37 %, better than the previous models like SVM, SVM + GWO, SVM + DFA, SVM + AQO, SVM + SOA, and Aqsea + SVM, respectively.

Bayes' Theorem for Multi-Bearing Faults Diagnosis

During the process of fault diagnosis for automated machinery, support vector machines is one of the suitable choices to categorize multiple faults for machinery. Regardless of the volume of sampling data, support vector machines can handle a high number of input features. It was learned that support vector machines could only sense binary fault classification (such as faulty or healthy). However, the classification accuracy was found to be lower when using support vector machines to diagnose multi-bearing faults classifications. This is because the multiple classification problem will be reduced into several sub-problems of binary classification when support vector machines adapt to multi-bearing faults classifications. From there, many contradictory results will occur from every support vector machine model. In order to solve the situation, the combination of Support Vector Machines and Bayes’ Theorem is introduced to every single support vector machine model to overcome the conflicting results. This method will also increase classification accuracy. The proposed Support Vector Machines - Bayes’ Theorem method has resulted in an increase in the accuracy of the fault diagnosis model. The analysis result has shown an accuracy from 72% to 95%. It proved that Support Vector Machines - Bayes’ Theorem continuously eliminates and refines conflicting results from the original support vector machine model. Compared to the existing support vector machine, the proposed Support Vector Machines - Bayes’ Theorem has proven its effectiveness in diagnosing the multi-bearing faults problem classification.

Machine learning classifiers for detection of glaucoma

Glaucoma is a disease that affects the optic nerve. This disease, over a period of time, can lead to loss of vision. Which is known as ‘silent thief of sight’. There are several methods in which the disease can be treated, if detected at an early stage It is not possible for any technology, including artificial intelligence, to replace a doctor. However, it is possible to develop a model based on several classical image processing algorithms, combined with artificial intelligence that can detect onset of glaucoma based on certain parameters of the retinal fundus. This model would play an important role in early detection of the disease and assist the doctor. The traditional methods to detect glaucoma, as efficient as they may be, are usually expensive, a machine learning approach to diagnose from fundus images and accurately classify its severity can be considered to be efficient. Here we propose support vector machine (SVM) method to segregate, train the models using a high-end graphics processor unit (GPU) and augment the hull convex approach to boost the accuracy of the image processing mechanisms along with distinguishing the different stages of glaucoma. A web application for the screening process has also been adopted.

A new fuzzy support vector machine with pinball loss

The fuzzy support vector machine (FSVM) assigns each sample a fuzzy membership value based on its relevance, making it less sensitive to noise or outliers in the data. Although FSVM has had some success in avoiding the negative effects of noise, it uses hinge loss, which maximizes the shortest distance between two classes and is ineffective in dealing with feature noise near the decision boundary. Furthermore, whereas FSVM concentrates on misclassification errors, it neglects to consider the critical within-class scatter minimization. We present a Fuzzy support vector machine with pinball loss (FPin-SVM), which is a fuzzy extension of a reformulation of a recently proposed support vector machine with pinball loss (Pin-SVM) with several significant improvements, to improve the performance of FSVM. First, because we used the squared L2- norm of errors variables instead of the L1 norm, our FPin-SVM is a strongly convex minimization problem; second, to speed up the training procedure, solutions of the proposed FPin-SVM, as an unconstrained minimization problem, are obtained using the functional iterative and Newton methods. Third, it is proposed to solve the minimization problem directly in primal. Unlike FSVM and Pin-SVM, our FPin-SVM does not require a toolbox for optimization. We dig deeper into the features of FPin-SVM, such as noise insensitivity and within-class scatter minimization. We conducted experiments on synthetic and real-world datasets with various sounds to validate the usefulness of the suggested approach. Compared to the SVM, FSVM, and Pin-SVM, the presented approaches demonstrate equivalent or superior generalization performance in less training time.

UWB Sensor-Based Indoor LOS/NLOS Localization With Support Vector Machine Learning

Ultrawideband (UWB) sensor technology is known to achieve high-precision indoor localization accuracy in line-of-sight (LOS) environments, but its localization accuracy and stability suffer detrimentally in non-LOS (NLOS) conditions. Current NLOS/LOS identification based on channel impulse response’s (CIR) characteristic parameters (CCPs) improves location accuracy, but most CIR-based identification approaches did not sufficiently exploit the CIR information and are environment specific. This article derives three new CCPs and proposes a novel two-step identification/classification methodology with dynamic threshold comparison (DTC) and the fuzzy credibility-based support vector machine (FC-SVM). The proposed support vector machine (SVM)-based classification methodology leverages the derived CCPs obtained from the waveform and its channel analysis, which are more robust to environment and obstacles dynamic. This is achieved in two-step with a coarse-grained NLOS/LOS identification with the DTC strategy followed by FC-SVM to give the fine-grained result. Finally, based on the obtained identification results, a real-time ranging error mitigation strategy is then designed to improve the ranging and localization accuracy. Extensive experimental campaigns are conducted in different LOS/NLOS scenarios to evaluate the proposed methodology. The results show that the mean LOS/NLOS identification accuracy in various testing scenarios is 93.27%, and the LOS and NLOS recalls are 94.27% and 92.57%, respectively. The ranging errors in LOS (NLOS) conditions are reduced from 0.106 (1.442 m) to 0.065 (0.739 m), demonstrating an improvement of 38.85% (48.74%) with 0.041 (0.703 m) error reduction. In contrast, the average positioning accuracy is also reduced from 0.250 to 0.091 m with an improvement of 63.49% (0.159 m), which outperforms the state-of-the-art approaches of the least-squares SVM (LS-SVM) and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> -nearest neighbor (KNN) algorithms.

Securing healthcare data management using machine learning and blockchain technology: A comparative performance evaluation of support vector machine and conventional classifiers

As access to healthcare has become more central to people’s daily lives, the amount of medical big data has grown exponentially. Wearable Internet of Things (IoT)-reliant technology is gaining popularity in the medical field as a means to improve patient care and reduce wait times. In recent years, billions of sensors, devices, and machines have been hooked up to the web. One such technology, remote patient monitoring, is increasingly used in modern patient care and treatment. In addition, these developments pose substantial security issues about the recording of transaction data and the transmission of information itself, and pose considerable dangers to users’ privacy. Concerns about the privacy of a patient’s medical records have the ability to discontinue treatment, putting the patient’s life in threat. Thus, a system is proposed using machine learning in combination with blockchain technology to allow secure management as well as analysis of large amounts of healthcare data. With the help of machine learning, it is feasible to sort through all of the data and extract out only the most pertinent information. This is accomplished with the help of trained methodologies. After this data has been saved, the next issue will be the exchange of data and ensuring its trustworthiness. The concept of blockchain is introduced at this point. The Blockchain technology relies on consensus to ensure that all data is accurate and that all transactions are conducted in a safe manner. The management of healthcare is one area where blockchain technology offers the ability to have a huge impact by placing patients at the center of the system and improving the privacy and portability of health records. This study is primarily concerned with finding solutions to issues relating to the administration of healthcare data by utilizing Blockchain technology and incorporating some crucial characteristics developed with Machine Learning. The performance evaluation of proposed Support Vector Machine is compared with the other conventional machine learning classifiers. It is observed from the experimental finding that performance accuracy of Support vector machine is 98% which is better as compared to other traditional machine learning classifiers.

Machine learning-driven evaluation and optimisation of compression yielded FRP-reinforced concrete beam with T section

Fibre reinforced polymer (FRP)-reinforced concrete beams usually encounter brittle failure due to the linear behaviour of FRP. To solve this issue, compression yielding (CY) concept was proposed recently to improve the ductility of FRP-reinforced concrete beams. However, because of the complexity of the compression yielding mechanism, the calculation of flexural capacity and ductility of FRP-reinforced concrete beam with CY block (CY beam) is challenging, especially for the CY beam with T section due to the lack of closed form solution. In this study, an integrated model is proposed based on machine learning method to evaluate the moment capacity and ductility of the CY beam with T section. To improve the prediction accuracy of the artificial neural network model for moment capacity, different activation functions are tested. The section effectiveness of CY beam with T section is evaluated using the proposed support vector machine model, which combines different kernel functions. Gaussian process regression is then employed to predict the ductility of CY beam with T section. It demonstrates that the developed integrated model can produce highly accurate predictions. In addition, a genetic algorithm (GA) is developed for identifying optimal CY beam section design solutions. Finally, the robustness of the model in the optimisation design for the CY beams with rectangular section and T section is demonstrated by using two numerical examples.

Fault Feature Enhanced Extraction and Fault Diagnosis Method of Vibrating Screen Bearings

For mechanical equipment, bearings have a high incidence area of faults. A problem for bearings is that their fault characteristics include a vibrating screen exciter which is weak and thus easily covered in strong background noise, hence making the noise difficult to remove. In this paper, a noise reduction method based on singular value decomposition, improved by singular value’s unilateral ascent method (SSVD), and a fault feature enhancement method, i.e., variational mode decomposition, improved by revised whale algorithm optimization (RWOA-VMD), are proposed. These two methods are used in vibration signal processing with early faults of bearings having a vibrating screen and they have achieved significant application results. This paper also aims to construct a multi-modal feature matrix composed of energy entropy, singular value entropy, and power spectrum entropy, and then the early fault diagnosis of bearings of a vibrating screen exciter bearing is realized by using the proposed support vector machine, improved by the aquila optimizer algorithm (AO-SVM).

Enhanced gray wolf optimization for estimation of time difference of arrival in WSNs

Purpose Intelligent prediction of node localization in wireless sensor networks (WSNs) is a major concern for researchers. The huge amount of data generated by modern sensor array systems required computationally efficient calibration techniques. This paper aims to improve localization accuracy by identifying obstacles in the optimization process and network scenarios. Design/methodology/approach The proposed method is used to incorporate distance estimation between nodes and packet transmission hop counts. This estimation is used in the proposed support vector machine (SVM) to find the network path using a time difference of arrival (TDoA)-based SVM. However, if the data set is noisy, SVM is prone to poor optimization, which leads to overlapping of target classes and the pathways through TDoA. The enhanced gray wolf optimization (EGWO) technique is introduced to eliminate overlapping target classes in the SVM. Findings The performance and efficacy of the model using existing TDoA methodologies are analyzed. The simulation results show that the proposed TDoA-EGWO achieves a higher rate of detection efficiency of 98% and control overhead of 97.8% and a better packet delivery ratio than other traditional methods. Originality/value The proposed method is successful in detecting the unknown position of the sensor node with a detection rate greater than that of other methods.

Random forest and support vector machine based hybrid liver disease detection

This study develops an automated liver disease detection system using a support vector machine and random forest detection techniques. These techniques are trained on data containing the information collected from the Mayo Clinic trial in primary biliary cirrhosis (PBC) of the liver conducted between 1974 and 1984. The proposed system can detect the presence of liver disease in the test set. The random forest model is used for recursive feature elimination at the pre-processing stage and the support vector machine is trained on the optimal feature set. The experimental result shows that the proposed support vector machine (SVM) model has achieved 78.3% accuracy.

Improved Methodology of SVM to Classify Acoustic Signal by Spectral Centroid

Acoustic signal classification issues are addressed in this work using spectral examination, channel extracting the features from the input and machine learning algorithm. This brief article examines the effect of various settings on feature extraction. This feature-level channel combination's accuracy increase is then observed. To categorise things, pattern recognition utilises a variety of classification schemes. "Pattern" refers to the measures that must be categorised with accurate feature extracted. Images and audio signals are among the most common kinds of measurements. The proposed Support Vector Machine (SVM) is used for the necessity of an effective categorization of acoustic signals driven by the continual improvements in multimedia technology. This study uses two machine learning algorithms to enhance audio classification and categorization. The proposed SVM achieves superior performance than the other ML algorithm by spectral features.

Data-driven intrinsic localized mode detection and classification in one-dimensional crystal lattice model

In this work we propose Support Vector Machine classification algorithms to classify one-dimensional crystal lattice waves from locally sampled data. Different learning datasets of particle displacements, momenta and energy density values are considered. Efficiency of the classification algorithms is further improved by two dimensionality reduction techniques: Principal Component Analysis and Locally Linear Embedding. Robustness of classifiers is investigated and demonstrated. Developed algorithms are successfully applied to detect localized intrinsic modes in three numerical simulations considering a case of two localized stationary breather solutions, a single stationary breather solution in noisy background and two mobile breather collision.

Assessment of damage in hydraulic concrete by gray wolf optimization‐support vector machine model and hierarchical clustering analysis of acoustic emission

Acoustic emission (AE) is a useful method for recording fracture processes in concrete. In this work, AE data are recorded during three-point bending tests to fracture of hydraulic concrete. First, AE data are used to analyze concrete's damage development using hits distribution, b-value, Ib-value, and average frequency versus RA value. Second, clustering analysis of AE signals is performed by hierarchical clustering. Third, a support vector machine model based on the gray wolf optimization algorithm is proposed to quantify the degree of damage. Via b-value analysis it is shown that the fracture process of hydraulic concrete can be divided into three stages: microcracks nucleation; microcracks coalescence into macrocracks (macrocrack nucleation); and macrocrack propagation. Further, it is shown that rise time, ringdown counts, energy, duration, amplitude, and central frequency can be used to characterize the failure modes. Specifically, it is found that microcrack nucleation stage is dominated by tensile failures, macrocrack nucleation stage is characterized by rapid increase of shear failures, which become dominant over tensile failures, and macrocrack propagation stage is dominated by shear failures. Via hierarchical cluster analysis, it is found that the fracture process can be divided into three clusters, which corresponds to the three stages obtained via b-value analysis. Finally, the proposed support vector machine model based on gray wolf optimization is found to predict the degree of damage in excellent agreement with experiment. This offers an effective practical method for damage assessment by combining AE with machine learning.

Forecasting Daily Electricity Price by Hybrid Model of Fractional Wavelet Transform, Feature Selection, Support Vector Machine and Optimization Algorithm

This paper proposes a novel hybrid forecasting model with three main parts to accurately forecast daily electricity prices. In the first part, where data are divided into high- and low-frequency data using the fractional wavelet transform, the best data with the highest relevancy are selected, using a feature selection algorithm. The second part is based on a nonlinear support vector network and auto-regressive integrated moving average (ARIMA) method for better training the previous values of electricity prices. The third part optimally adjusts the proposed support vector machine parameters with an error-base objective function, using the improved grey wolf and particle swarm optimization. The proposed method is applied to forecast electricity markets, and the results obtained are analyzed with the help of the criteria based on the forecast errors. The results demonstrate the high accuracy in the MAPE index of forecasting the electricity price, which is about 91% as compared to other forecasting methods.

Mahalanobis-ANOVA criterion for optimum feature subset selection in multi-class planetary gear fault diagnosis

The empirical analysis of a typical gear fault diagnosis of five different classes has been studied in this article. The analysis was used to develop novel feature selection criteria that provide an optimum feature subset over feature ranking genetic algorithms for improving the planetary gear fault classification accuracy. We have considered traditional approach in the fault diagnosis, where the raw vibration signal was divided into fixed-length epochs, and statistical time-domain features have been extracted from the segmented signal to represent the data in a compact discriminative form. Scale-invariant Mahalanobis distance–based feature selection using ANOVA statistic test was used as a feature selection criterion to find out the optimum feature subset. The Support Vector Machine Multi-Class machine learning algorithm was used as a classification technique to diagnose the gear faults. It has been observed that the highest gear fault classification accuracy of 99.89% (load case) was achieved by using the proposed Mahalanobis-ANOVA Criterion for optimum feature subset selection followed by Support Vector Machine Multi-Class algorithm. It is also noted that the developed feature selection criterion is a data-driven model which will contemplate all the nonlinearity in a signal. The fault diagnosis consistency of the proposed Support Vector Machine Multi-Class learning algorithm was ensured through 100 Monte Carlo runs, and the diagnostic ability of the classifier has been represented using confusion matrix and receiver operating characteristics.

Development of an Intelligent Decision Support System for Attaining Sustainable Growth within a Life Insurance Company

Consumer behaviour is one of the most important and complex areas of research. It acknowledges the buying behaviour of consumer clusters towards any product, such as life insurance policies. Among various factors, the three most well-known determinants on which human conjecture depends for preferring a product are demographic, economic and psychographic factors, which can help in developing an accurate market design and strategy for the sustainable growth of a company. In this paper, the study of customer satisfaction with regard to a life insurance company is presented, which focused on comparing artificial intelligence-based, data-driven approaches to classical market segmentation approaches. In this work, an artificial intelligence-based decision support system was developed which utilises the aforementioned factors for the accurate classification of potential buyers. The novelty of this paper lies in developing supervised machine learning models that have a tendency to accurately identify the cluster of potential buyers with the help of demographic, economic and psychographic factors. By considering a combination of the factors that are related to the demographic, economic and psychographic elements, the proposed support vector machine model and logistic regression model-based decision support systems were able to identify the cluster of potential buyers with collective accuracies of 98.82% and 89.20%, respectively. The substantial accuracy of a support vector machine model would be helpful for a life insurance company which needs a decision support system for targeting potential customers and sustaining its share within the market.

Comparison study on speech emotion prediction using machine learning

Speech Emotion recognition is the process of analyzing human emotions. People vary widely in their precision at recognizing the emotion of others. Support vector machine (SVM) is one of the best algorithms for prediction and handling the voice data. In this speech emotion identification is a difficult task that includes various preprocessing steps. The proposed one, the method is based on the Cepstral Mel frequency Coefficients (MFCC) and voice signal energy as Input and use of the RAVASS emotional database function discourse. In this research work support vector machine algorithm, linear kernel processing is used to predict the human emotions.In Feature Extraction using Librosa module to handling audio and music type of data. Librosa can extract the values of Mfcc-Mel-frequency Cepstral coefficients it’s used for identifying monosyllabic words in voice recognition. Chroma mainly used for similarity and audio matching. In voice data each data are applied to feature extraction technique. Then generated matrix format of values for calculation. In proposed Support vector machine algorithm for act as a classifier to find emotions. Mfcc, chroma, Mel features are directly send out the Support vector machine (SVM) algorithm. Its take a class of eight emotions like happy, sad, calm, angry, neutral, fearful, disgust. Based on our features (mfcc, chroma, Mel) to classify emotions. The comparison study on SVM and KNN algorithms, SVM has predicted 4% better accuracy than KNN.

WITHDRAWN: Embedded Hardware System and Sensor Robot Used In Goal Tracking Technology Of Sports Football Goalkeeper

ABSTRACT The purpose of the software-only system built into the hardware of the design is called embedded systems. The system is maybe a stand-alone system or a larger system. Software is usually built into the memory module. It does not require any auxiliary storage device in the computer. Telecommunications, smart cards, missiles, computer networks, digital consumer electronics, and satellites are part of the sensor's robot used in embedded systems. In embedded systems, hardware platforms need to interact with various real-time input and output or variable. In the previous method Principal Component Analysis (PCA) Algorithm and Independent Component Analysis (ICA), several countries encourage football practice for rehabilitation and social inclusion purposes. For visually impaired people, football is purely sound-based, where the ball and the players are constantly emitting sounds for localization purposes in the field. The proposed Support Vector Machine (SVM) algorithm Embedded Hardware System and Sensor Robot Used in Goal Tracking Technology of Sports Football Goalkeeper Functions The electronics' goal, the position and the ball is by returning the feedback based on the sound that are affecting the location. It is to design the four functions for training. The system has confirmed the volunteers from the Chilean national football team.

Ethnicity Classification Based On Facial Features Using Viola-Jones Algorithm

Biometric recognition system can use race classification to identify the human globally with a particular identity. This paper proposes Support Vector Machine and will compare the result with K-Nearest Neighbor for classification of people into two major races namely Indonesian western and eastern races. Firstly, the proposed classification method extracts the distinct primary facial feature and skin color model of the given face with Viola-Jones Algorithm to effectively classify the races. To increase the accuracy, the sample must not contain any background of other people skin, no movement and the pictures were taken from the mobile camera with no beauty filter.