Learning Based Support Vector Machine Research Articles

Optimizing Sensor Placement and Machine Learning Techniques for Accurate Hand Gesture Classification

Millions of individuals are living with upper extremity amputations, making them potential beneficiaries of hand and arm prostheses. While myoelectric prostheses have evolved to meet amputees’ needs, challenges remain related to their control. This research leverages surface electromyography sensors and machine learning techniques to classify five fundamental hand gestures. By utilizing features extracted from electromyography data, we employed a nonlinear, multiple-kernel learning-based support vector machine classifier for gesture recognition. Our dataset encompassed eight young nondisabled participants. Additionally, our study conducted a comparative analysis of five distinct sensor placement configurations. These configurations capture electromyography data associated with index finger and thumb movements, as well as index finger and ring finger movements. We also compared four different classifiers to determine the most capable one to classify hand gestures. The dual-sensor setup strategically placed to capture thumb and index finger movements was the most effective—this dual-sensor setup achieved 90% accuracy for classifying all five gestures using the support vector machine classifier. Furthermore, the application of multiple-kernel learning within the support vector machine classifier showcases its efficacy, achieving the highest classification accuracy amongst all classifiers. This study showcased the potential of surface electromyography sensors and machine learning in enhancing the control and functionality of myoelectric prostheses for individuals with upper extremity amputations.

Linking metabolomics to machine learning reveals the metabolic fates of the refractory industrial pollutant 1-Hexadecene

In order to solve the low removal efficiency of 1-Hexadecene in industrial wastewater treatment plants and fill the knowledge gap of its microbial metabolic mechanisms, this work studied the 1-Hexadecene degrading microorganisms enrichment and its metabolic pathway using continuous bioreactors and batch incubation tests. With the successful enrichment of hexadecene-degrading microorganisms, the biodegradation rates of 1-Hexadecene at 50–200 mg/L could range from 58.6 % to 72.5 %. The 16S rRNA fragment and microbial community analysis identified Flavihumibacter, Gemmatimonas, and Caulobacter as putative 1-Hexadecene degraders. Through mass spectrum qualification analysis, Principal component analysis and orthogonal partial least squares discriminant analysis, totally 81 featured metabolites associated with 1-Hexadecene biogradation were screened out. The machine learning based support vector machines and random forests further identified 18 biomarkers during those featured metabolites. Based on the coupling metabolomics analysis with machine learning, three potential metabolic pathways with Heptane, 2,5-dimethyl hexane, 3-ethyl-3-methyl-heptane as the respective end products were proposed through biomarkers. These results provide new insights for the analysis of metabolic pathway of 1-Hexadecene, and theoretical support for the bioaugmentation of its treatment processes.

Energy-efficient multisensor adaptive sampling and aggregation for patient monitoring in edge computing based IoHT networks

The need for remote healthcare monitoring systems that utilize limited resources’ biosensors is growing. These biosensors increase the amount of transmitted data across the Internet of Healthcare Things (IoHT) network. Therefore, it is necessary to decrease the transmitted data and make a decision at the edge gateway to save the energy of the biosensors and produce a quick response for the medical staff. This paper proposes an energy-efficient multisensor adaptive sampling and aggregation (EMASA) for patient monitoring in edge computing-based IoHT networks. In the edge-based IoHT network, EMASA operates on two levels: biosensors and the edge gateway. Each biosensor removes the redundant sensed data using the local emergency detection and sampling rate adaptation algorithms. In the edge gateway, it implements a machine learning-based Support Vector Machine (SVM) model to provide a suitable decision about the status of the monitored patient. We accomplished various examinations using real data from the patients’ biosensors. According to the simulation results, EMASA reduced the size of transmitted data from 93.5% to 99% and saved 78.35% of energy when compared to a previous study. It keeps the whole score with a good representation at the Edge gateway and provides accurate and fast decisions based on the patient’s condition.

Enhanced extraction of brominated flame retardants from e-waste plastics

Acrylonitrile butadiene styrene (ABS) is a high utility engineering thermoplastic, abundantly present in waste electronic and electrical goods. The demand for this plastic resin increases continuously with rapid industrial growth across the world. During the recycling of e-waste plastics, it is a common occurrence to find brominated flame retardants, such as Tetrabromobisphenol A (TBBPA) in the plastic. These flame retardants are intentionally added to the plastics as additives. Three processes were investigated to find the optimum solvent/antisolvent combinations, process parameters for the efficient recovery of TBBPA free plastics: microwave assisted extraction (MAE), ultrasound assisted extraction (UAE), and dissolution reprecipitation (DR). Rigorous solvent screening was carried out for both TBBPA and ABS using Hansen Solubility Parameters (HSP), and their interactions with the solvents were investigated with a molecular based Conductor like Screening Model for Realistic Solvents (COSMO-RS) software platform. A 4:1 (v/v) solvent mixture of 1-propanol and heptane was found to be ideal for the solid–liquid extraction process. The optimal extraction conditions (Temperature = 110 – 130 °C, time = 60 to 70 min solid/liquid ratio of 0.025) in MAE were predicted using machine learning based Support Vector Regression (SVR) technique. MAE was found to be superior to UAE and DR processes in terms of quantity and quality of recovered plastics. The maximum extraction efficiencies for TBBPA removal were achieved with MAE, UAE, and DR processes at 95.81%, 93.92%, and 98.15%, respectively. However, the DR process had a relatively lower plastic recovery.

English

Self-medication (SM) is a medicinal product used to treat symptoms or self-medicate disorders identified by oneself. Overusing the medication recommended by a medical doctor for oneself or other family members (particularly when considering a child or an elder) falls into the description of SM. Treating medicine for oneself without specialist guidance might cause many side effects like prolonged disease course, drug resistance and complications. Even though informed SM is one method to decrease healthcare costs, inappropriate self-treatment poses different risks involving the recurrence of symptoms, drug resistance, drug side effects, etc. The study aims to examine the pervasiveness of SM in undergraduate students worldwide through a meta-analysis and systematic review of the study published on SM. A self-administered survey will collect data on demographic variables and practice SM to perform this study. The data is collected among undergraduate students from Aldayer University college, Jazan University, and KSA under three disciplines: English, maths, and nursing. The study aims to assess undergraduate students' knowledge regarding SM, identify drugs that students have taken without a prescription in the last six months, identify the reason for SM, identify sources of information about SM, and detect the sources of drugs utilized by students for SM. One hundred eighty samples were selected by purposive sampling per inclusive criteria at L Aldayer University College, Jazan University, KSA. The total number of samples is 180, of which 60 are collected from each discipline. In addition, machine learning-based support vector machine models can be used to generate predictive models to identify which studies are taking high SM. The experimental results stated that nursing college studies involve more SM practices than other undergraduate students.

Traffic Classification of IoT Devices by Utilizing Spike Neural Network Learning Approach

Whenever, the Internet of Things (IoT) applications and devices increased, the capability of the its access frequently stressed. That can lead a significant bottleneck problem for network performance in different layers of an end point to end point (P2P) communication route. So, an appropriate characteristic (i.e., classification) of the time changing traffic prediction has been used to solve this issue. Nevertheless, stills remain at great an open defy. Due to of the most of the presenting solutions depend on machine learning (ML) methods, that though give high calculation cost, where they are not taking into account the fine-accurately flow classification of the IoT devices is needed. Therefore, this paper presents a new model based on the Spike Neural Network (SNN) called IoT-Traffic Classification (IoT-TCSNN) to classify IoT devices traffic. The model consists of four phases: data preprocessing, feature extraction, classier and evaluation. The proposed model performance is evaluated according to evaluation metrics: accuracy, precision, recall and F1-score and energy usage in comparison with two models: ML based Support Vector Machine IoT-TCSVM and ML based Deep Neural Network (IoT-TCDNN). The evaluations result has been shown that IoT-TCSNN consumes less energy in contrast to IoT-TCDNN and IoT-TCSVM. Also, it gives high accuracy in comparison with IoT-TCSVM.

Machine learning methods to predict cadmium (Cd) concentration in rice grain and support soil management at a regional scale

Rice is a major dietary source of the toxic metal cadmium (Cd). Concentration of Cd in rice grain varies widely at the regional scale, and it is challenging to predict grain Cd concentration using soil properties. The lack of reliable predictive models hampers management of contaminated soils. Here, we conducted a three-year survey of 601 pairs of soil and rice samples at a regional scale. Approximately 78.3% of the soil samples exceeded the soil screening values for Cd in China, and 53.9% of rice grain samples exceeded the Chinese maximum permissible limit for Cd. Predictive models were developed using multiple linear regression and machine learning methods. The correlations between rice grain Cd and soil total Cd concentrations were poor (R2 < 0.17). Both linear regression and machine learning methods identified four key factors that significantly affect grain Cd concentrations, including Fe-Mn oxide bound Cd, soil pH, field soil moisture content, and the concentration of soil reducible Mn. The machine learning-based support vector machine model showed the best performance (R2 = 0.87) in predicting grain Cd concentrations at a regional scale, followed by machine learning-based random forest model (R2 = 0.67), and back propagation neural network model (R2 = 0.64). Scenario simulations revealed that liming soil to a target pH of 6.5 could be one of the most cost-effective approaches to reduce the exceedance of Cd in rice grain. Taken together, these results show that machine learning methods can be used to predict Cd concentration in rice grain reliably at a regional scale and to support soil management and safe rice production.

Radiomics analysis using MR imaging of subchondral bone for identification of knee osteoarthritis

BackgroundTo develop a magnetic resonance imaging (MRI)-based radiomics predictive model for the identification of knee osteoarthritis (OA), based on the tibial and femoral subchondral bone, and compare with the trabecular structural parameter-based model.MethodsEighty-eight consecutive knees were scanned with 3T MRI and scored using MRI osteoarthritis Knee Scores (MOAKS), in which 56 knees were diagnosed to have OA. The modality of sagittal three-dimensional balanced fast-field echo sequence (3D BFFE) was used to image the subchondral bone. Four trabecular structural parameters (bone volume fraction [BV/TV], trabecular thickness [Tb.Th], trabecular separation [Tb.Sp], and trabecular number) and 93 radiomics features were extracted from four regions of the lateral and medial aspects of the femur condyle and tibial plateau. Least absolute shrinkage and selection operator (LASSO) was used for feature selection. Machine learning-based support vector machine models were constructed to identify knee OA. The performance of the models was assessed by area under the curve (AUC) of the receiver operator characteristic (ROC). The correlation between radiomics features and trabecular structural parameters was analyzed using Pearson’s correlation coefficient.ResultsOur radiomics-based classification model achieved the AUC score of 0.961 (95% confidence interval [CI], 0.912–1.000) when distinguishing between normal and knee OA, which was higher than that of the trabecular parameter-based model (AUC, 0.873; 95% CI, 0.788–0.957). The first-order, texture, and Laplacian of Gaussian-based radiomics features correlated positively with Tb.Th and BV/TV, but negatively with Tb.Sp (P < 0.05).ConclusionsOur results suggested that our MRI-based radiomics models can be used as biomarkers for the classification of OA and are superior to the conventional structural parameter-based model.

Application of Support Vector Machine Model Based on Machine Learning in Art Teaching

The purpose of the evaluation is to reflect on whether education provides a good environment and conditions for the development of students and to reflect on the effect of teaching and the practicability of the talents cultivated by teaching to the society. When art education is evaluated, a number of positive outcomes have been achieved in terms of the development of art education, including the improvement of art education as a whole, the development of art talent, and a stronger role for the educational and social communities concerned about the quality of art education. Machine learning-based support vector machine (SVM) can better tackle issues like nonlinearity, high dimensionality, and local minima, which have been effectively implemented in the area of teaching quality evaluation (TQE) with the fast growth of information technology and the Internet. The main work of this paper is as follows: (1) it briefly expounds the research progress of TQE and multiclassification algorithm based on SVM at home and abroad and introduces the relevant basic theories of these two aspects. (2) One-to-one combination method is used in this research, reducing training time to a certain degree. Tests prove the procedure to be objective and equitable. (3) This research claims that an art TQE approach based on SVM is suited for limited professional assessment sample data and provides a method for this purpose.

Object Recognition in High-Resolution Indoor THz SAR Mapped Environment.

Synthetic aperture radar (SAR) at the terahertz (THz) spectrum has emerging short-range applications. In comparison to the microwave spectrum, the THz spectrum is limited in propagation range but benefits from high spatial resolution. The THz SAR is of significant interest for several applications which necessitate the mapping of indoor environments to support various endeavors such as rescue missions, map-assisted wireless communications, and household robotics. This paper addresses the augmentation of the high-resolution indoor mapped environment for object recognition, which includes detection, localization, and classification. Indoor object recognition is currently dominated by the usage of optical and infrared (IR) systems. However, it is not widely explored by radar technologies due to the limited spatial resolution at the most commonly used microwave frequencies. However, the THz spectrum provides a new paradigm of possible adaptation of object recognition in the radar domain by providing image quality in good compliance to optical/IR systems. In this paper, a multi-object indoor environment is foremost mapped at the THz spectrum ranging from 325 to 500 GHz in order to investigate the imaging in highly scattered environments and accordingly create a foundation for detection, localization, and classification. Furthermore, the extraction and clustering of features of the mapped environment are conducted for object detection and localization. Finally, the classification of detected objects is addressed with a supervised machine learning-based support vector machine (SVM) model.

Assessing cellulose micro/nanofibre morphology using a high throughput fibre analysis device to predict nanopaper performance

Characterising cellulose nanofibre (CNF) morphology has been identified as a grand challenge for the nanocellulose research field. Direct techniques for CNF morphology characterisation exhibit various difficulties related to the material network structure and equipment cost, while indirect techniques that investigate fibre-light interaction, fibre-solvent interaction, fibre-fibre interaction, or specific fibre surface area involve relatively facile methods but may be more unreliable. Nanopaper mechanical testing is a prevalent metric for assessing fibre-fibre interaction, but is an off-line, time-consuming, and destructive methodology. In this study, an optical fibre morphology analyser (MorFi, Techpap) was employed as an on-line, high throughput, fast turnaround tool to assess micro/nanofibre pulp morphology and predict the properties of nanopaper material. Correlation analysis identified fibre content and fibre kink properties as most correlated with nanopaper strength and toughness, while fibre width and coarseness were most inversely correlated with nanopaper performance. Principal component analysis (PCA) was employed to visualise interdependent morphological and mechanical data. Subsequently, two data driven statistical models—multiple linear regression (MLR) and machine learning based support vector regression (SVR)—were established to predict nanopaper properties from fibre morphology data, with SVR generating a more accurate prediction across all nanopaper properties (NRMSE = 0.13–0.33) compared to the MLR model (NRMSE = 0.33–0.51). This study highlights that statistical methods are useful to disentangle and visualise interdependent morphological data from an on-line fibre analysis device, while regression models are also capable of predicting paper mechanical properties from CNF samples even though these devices do not operate at nanoscale resolution.Graphical abstract

QSPCA: A Two-Stage Efficient Power Control Approach in D2D Communication for 5G Networks

The existing literature on device-to-device (D2D) architecture suffers from a dearth of analysis under imperfect channel conditions. There is a need for rigorous analyses on the policy improvement and evaluation of network performance. Accordingly, a two-stage transmit power control approach (named QSPCA) is proposed: First, a reinforcement Q-learning based power control technique and; second, a supervised learning based support vector machine (SVM) model. This model replaces the unified communication model of the conventional D2D setup with a distributed one, thereby requiring lower resources, such as D2D throughput, transmit power, and signal-to-interference-plus-noise ratio as compared to existing algorithms. Results confirm that the QSPCA technique is better than existing models by at least 15.31% and 19.5% in terms of throughput as compared to SVM and Q-learning techniques, respectively. The customizability of the QSPCA technique opens up multiple avenues and industrial communication technologies in 5G networks, such as factory automation.

Potential of Machine Learning Based Support Vector Regression for Solar Radiation Prediction

AbstractMeasurements of the solar radiation quantities profoundly affect on the energy output ratios. A decline in solar radiation measurements in many countries, which is due to reasons high cost, difficulty of measurement that necessitated developing different methods to estimate the proportion of solar radiation. Many empirical models have been developed using special variables and coefficients, such as Angstrom and Prescott models. The development of machine-learning algorithms makes these algorithms as a possible application instead of the empirical models to decrease the error rate and obtaining better results. In this paper, radial basis function is applied as the kernel function of support vector regression (SVR) method to calculate the amount of monthly average daily of the global solar radiation in four sites in Egypt. Five variables used as input (sunshine duration, air temperature, relative humidity, solar declination angle and extraterrestrial solar radiation). The experimental results have a good estimation in all locations according to root mean square error, however, this study proved that SVR models can be as an efficient machine-learning technique with a higher accuracy.

Deep Spiking Delayed Feedback Reservoirs and Its Application in Spectrum Sensing of MIMO-OFDM Dynamic Spectrum Sharing

In this paper, we introduce a deep spiking delayed feedback reservoir (DFR) model to combine DFR with spiking neuros: DFRs are a new type of recurrent neural networks (RNNs) that are able to capture the temporal correlations in time series while spiking neurons are energy-efficient and biologically plausible neurons models. The introduced deep spiking DFR model is energy-efficient and has the capability of analyzing time series signals. The corresponding field programmable gate arrays (FPGA)-based hardware implementation of such deep spiking DFR model is introduced and the underlying energy-efficiency and recourse utilization are evaluated. Various spike encoding schemes are explored and the optimal spike encoding scheme to analyze the time series has been identified. To be specific, we evaluate the performance of the introduced model using the spectrum occupancy time series data in MIMO-OFDM based cognitive radio (CR) in dynamic spectrum sharing (DSS) networks. In a MIMO-OFDM DSS system, available spectrum is very scarce and efficient utilization of spectrum is very essential. To improve the spectrum efficiency, the first step is to identify the frequency bands that are not utilized by the existing users so that a secondary user (SU) can use them for transmission. Due to the channel correlation as well as users' activities, there is a significant temporal correlation in the spectrum occupancy behavior of the frequency bands in different time slots. The introduced deep spiking DFR model is used to capture the temporal correlation of the spectrum occupancy time series and predict the idle/busy subcarriers in future time slots for potential spectrum access. Evaluation results suggest that our introduced model achieves higher area under curve (AUC) in the receiver operating characteristic (ROC) curve compared with the traditional energy detection-based strategies and the learning-based support vector machines (SVMs).

Wavelet‐based real‐time stator fault detection of inverter‐fed induction motor

This work proposes a novel real-time detection scheme for incipient stator inter-turn short circuit fault in voltage sources inverter-fed induction machines. Both non-sinusoidal input voltage and the short circuit fault causes harmonics in the motor stator current and these combined harmonic components complicate the spectral analysis-based diagnosis in inverter-fed motors. Aim of the analysis is to identify the effect of inverter fundamental/switching frequency on early detection and classification of the inter-turn fault. Discrete wavelet transform based analysis is performed on stator current using daubechies1 wavelet and statistical parameter L2 norm has been computed for the detailed and approximate coefficients at different decomposition levels to obtain the most precise feature of fault. Support vector machine-based learning algorithm is used for the accurate classification of the incipient fault. The proposed method is independent of switching and fundamental frequency, the modulation index and mechanical load. Real-time detection is possible even with infinitesimal fault current of 350 mA by the proposed method. The competency of the proposed algorithm is validated using simulation and verified by hardware with VSI-fed induction motor drive.

Retinal blood vessel segmentation from diabetic retinopathy images using tandem PCNN model and deep learning based SVM

Diabetic Retinopathy (DR) occurs due to Type-II diabetes. It causes damages to the retinal blood vessels and reason for visual impairment. The predicted center is around the probability of variation in the estimation of retinal veins, and the crisp enrolls vessel development inside the retina. To witness the changes segmentation of retinal blood vessels has to be made. A framework to upgrade the quality of the segmentation results over morbid retinal images is proposed. This framework utilizes Contrast Limited Adaptive Histogram Equalization (CLAHE) for eliminating the background from the source image and enhances the foreground blood vessel pixels, Tandem Pulse Coupled Neural Network (TPCNN) model is endorsed for automatic feature vectors generation, and Deep Learning Based Support Vector Machine (DLBSVM) is proposed for classification and extraction of blood vessels. The DLBSVM parameters are fine-tuned via Firefly algorithm. The STARE, DRIVE, HRF, REVIEW, and DRIONS fundus image datasets are deliberated to assess the recommended techniques. The results render that the proposed technologies improve the segmentation with 80.61% Sensitivity, 99.54% Specificity, and 99.49% Accuracy.

Fallen People Detection Capabilities Using Assistive Robot

One of the main problems in the elderly population and for people with functional disabilities is falling when they are not supervised. Therefore, there is a need for monitoring systems with fall detection functionality. Mobile robots are a good solution for keeping the person in sight when compared to static-view sensors. Mobile-patrol robots can be used for a group of people and systems are less intrusive than ones based on mobile robots. In this paper, we propose a novel vision-based solution for fall detection based on a mobile-patrol robot that can correct its position in case of doubt. The overall approach can be formulated as an end-to-end solution based on two stages: person detection and fall classification. Deep learning-based computer vision is used for person detection and fall classification is done by using a learning-based Support Vector Machine (SVM) classifier. This approach mainly fulfills the following design requirements—simple to apply, adaptable, high performance, independent of person size, clothes, or the environment, low cost and real-time computing. Important to highlight is the ability to distinguish between a simple resting position and a real fall scene. One of the main contributions of this paper is the input feature vector to the SVM-based classifier. We evaluated the robustness of the approach using a realistic public dataset proposed in this paper called the Fallen Person Dataset (FPDS), with 2062 images and 1072 falls. The results obtained from different experiments indicate that the system has a high success rate in fall classification (precision of 100% and recall of 99.74%). Training the algorithm using our Fallen Person Dataset (FPDS) and testing it with other datasets showed that the algorithm is independent of the camera setup.

Global mapping of lunar refractory elements: multivariate regression vs. machine learning

Context.The quantitative estimation of elemental concentrations at the spatial resolution of hyperspectral near-infrared (NIR) images of the lunar surface is an important tool for understanding the processes relevant for the origin and evolution of the Moon.Aims.We aim to map the abundances of the elements Fe, Ca, and Mg at a typical accuracy of about 1 wt.% at the spatial resolution of the Moon Mineralogy Mapper (M3) instrument on-board Chandrayaan-1 lunar mission.Methods.The NIR reflectance of the lunar regolith is an integrated response to the presence of refractory elements and soil alteration processes. Our approach was to define a combination of spectral parameters that are robust with respect to the effects of soil maturity. We calibrated the spectral parameters with respect to elemental abundances measured by the Lunar Prospector Gamma Ray Spectrometer (LP GRS) and the Kaguya GRS (KGRS). For this purpose, we compared a classical multivariate linear regression (MLR) approach and the machine learning based support vector regression (SVR) technique applied to M3global observations.Results.The M3-based global elemental maps are consistent in distribution and range with the LP GRS and KGRS elemental maps and do not show artifacts in immature areas such as small fresh craters. The results derived using MLR and SVR are compared to sample-based ground truth data of the Apollo and Luna sample-return sites, where the root-mean-square deviations obtained by the two regression models are similar.Conclusions.The main advantage of the proposed new algorithm is its ability to minimize artifacts due to space-weathering effects. The elemental maps of Mg and Ca provide additional information and reveal structures not always visible in the Fe map. The global elemental abundance maps derived for the fully calibrated M3observations might thus serve as important tools to investigate the lunar geology and evolution.

Identification of repurposed protein kinase B binders from FDA-approved drug library: a hybrid–structure activity relationship and systems modeling based approach

Food and Drug Administration (FDA)-approved drugs may be repurposed against those diseases, for which their therapeutic action has not been described. The present study deals with repurposing FDA-approved drugs for selective targeting of protein kinase B (PKB/Akt) for anti-cancer activity, through a two-tier (Cell and Target) model hybridization protocol implemented with support vector machine-based learning method. The hybridization was done as per rules of reaction kinetics. The hybridization process was facilitated as a standalone application for free access at https://github.com/undwivedi/Akt-Selective.git. The selectivity of the ligands for PKB/Akt binding was also evaluated on the basis of mitophagy system model for anti-apoptotic activity. Screening of the FDA-approved drug library, using the developed H- SAR model, led to identification of four compounds (Cas nos. 94749-08-3, 57808-66-9, 62-13-5, 76-43-7), bearing the selectivity for PKB/Akt. Since, the identified compounds have already crossed the barriers of absorption, distribution, metabolism, excretion, toxicity in clinical trials, therefore are safe to be considered for repurposing individually or in combination with other drugs.Communicated by Ramaswamy H. Sarma

Recognition and Classification of Power Quality Disturbances by DWT-MRA and SVM Classifier

Electrical power system is a large and complex network, where power quality disturbances (PQDs) must be monitored, analyzed and mitigated continuously in order to preserve and to re-establish the normal power supply without even slight interruption. Practically huge disturbance data is difficult to manage and requires the higher level of accuracy and time for the analysis and monitoring. Thus automatic and intelligent algorithm based methodologies are in practice for the detection, recognition and classification of power quality events. This approach may help to take preventive measures against abnormal operations and moreover, sudden fluctuations in supply can be handled accordingly. Disturbance types, causes, proper and appropriate extraction of features in single and multiple disturbances, classification model type and classifier performance, are still the main concerns and challenges. In this paper, an attempt has been made to present a different approach for recognition of PQDs with the synthetic model based generated disturbances, which are frequent in power system operations, and the proposed unique feature vector. Disturbances are generated in Matlab workspace environment whereas distinctive features of events are extracted through discrete wavelet transform (DWT) technique. Machine learning based Support vector machine classifier tool is implemented for the classification and recognition of disturbances. In relation to the results, the proposed methodology recognizes the PQDs with high accuracy, sensitivity and specificity. This study illustrates that the proposed approach is valid, efficient and applicable.