Modified Support Vector Machine Research Articles

A Support Vector Machine-Assisted Metabolomics Approach for Non-Targeted Screening of Multi-Class Pesticides and Veterinary Drugs in Maize.

The contamination risks of plant-derived foods due to the co-existence of pesticides and veterinary drugs (P&VDs) have not been fully understood. With an increasing number of unexpected P&VDs illegally added to foods, it is essential to develop a non-targeted screening method for P&VDs for their comprehensive risk assessment. In this study, a modified support vector machine (SVM)-assisted metabolomics approach by screening eligible variables to represent marker compounds of 124 multi-class P&VDs in maize was developed based on the results of high-performance liquid chromatography-tandem mass spectrometry. Principal component analysis and orthogonal partial least squares discriminant analysis indicate the existence of variables with obvious inter-group differences, which were further investigated by S-plot plots, permutation tests, and variable importance in projection to obtain eligible variables. Meanwhile, SVM recursive feature elimination under the radial basis function was employed to obtain the weight-squared values of all the variables ranging from large to small for the screening of eligible variables as well. Pairwise t-tests and fold changes of concentration were further employed to confirm these eligible variables to represent marker compounds. The results indicate that 120 out of 124 P&VDs can be identified by the SVM-assisted metabolomics method, while only 109 P&VDs can be found by the metabolomics method alone, implying that SVM can promote the screening accuracy of the metabolomics method. In addition, the method's practicability was validated by the real contaminated maize samples, which provide a bright application prospect in non-targeted screening of contaminants. The limits of detection for 120 P&VDs in maize samples were calculated to be 0.3~1.5 µg/kg.

Methods for Identifying Effective Microseismic Signals in a Strong-Noise Environment Based on the Variational Mode Decomposition and Modified Support Vector Machine Models

The environment for acquiring microseismic signals is always filled with complex noise, leading to the presence of abundant invalid signals in the collected data and greatly disturbing effective microseismic signals. Regarding the identification of effective microseismic signals with a low signal-to-noise ratio, a method for identifying effective microseismic signals in a strong-noise environment by using the variational mode decomposition (VMD) and genetic algorithm (GA)-based optimized support vector machine (SVM) model is proposed. Microseismic signals with a low signal-to-noise ratio are adaptively decomposed into several intrinsic mode functions (IMFs) by using VMD. The characteristics of such IMFs are extracted and used as a basis for the determination of signal validity. The SVM model is optimized by utilizing GA to obtain the optimal penalty factor c and the kernel function parameter g. The availability of IMF components is judged by the optimized SVM model, based on which the effectiveness of microseismic signals is further identified. By applying the algorithm to the microseismic signals with artificially added noise, the effective microseismic signals and ineffective noise are discriminated, verifying the feasibility of the algorithm. After processing the microseismic records collected in the field, we effectively judge the effectiveness of microseismic signals, suppress the interfering noise in the data and greatly improve the signal-to-noise ratio of the seismic records. The results show that the method for identifying effective microseismic signals based on VMD and GA-SVM can well discriminate between effective and ineffective microseismic signals, which is very significant and provides technical support for microseismic monitoring in a strong-noise environment.

Hybrid GA-mSVM: Dimensionality reduction using hybrid genetic algorithm and modified support vector machine classifier

<p>The expansion of technology results in the generation of enormous amounts of data in all areas. The researchers face a difficult challenge when they attempt to categorize these high-dimensional data. A method called as feature selection is used to reduce the high-dimensionality of the data. It is possible to consider selecting features as an issue of global combinatorial optimization in the field of machine learning. This minimizes on the total number of features, gets rid of data that is insignificant, noisy, or duplicative, and ultimately achieves an acceptable level of classification accuracy. For the purpose of dimensionality reduction, a unique approach known as the Hybrid Genetic Algorithm – modified Support Vector Machine Classifier (Hybrid GA-mSVM) is presented in this study. The genetic algorithm component conducts a search using the principles underlying the evolutionary process in order to find the best feature set and after that the trimmed dataset is provided to the SVMs. The results of the experiments reveal that the proposed approach efficiently minimizes the features and achieves a better classification accuracy than other feature selection methods.</p>

Sentiment analysis and classification of COVID-19 tweets using machine learning classifier

<p>In March of 2020, the World Health Organization identified COVID-19 as a new pandemic and issued a statement to that effect. This fatal virus was able to disperse and propagate throughout several countries all over the world. During the progression of the pandemic, social networking sites like Twitter generated significant and substantial volumes of data that helped improve the quality of decisions pertaining to health care applications. In this paper, we proposed a sentiment classification using various feature extraction and machine leavening techniques for social media dataset. The system has divided into four phase data collection, preprocessing and normalization, feature extraction and feature selection and finally classification. In first phase we collect data from social media sources such as twitter using Twitter API. In second phase the tweets, data was ready for preprocessing and it was sorted into three categories: positive, neutral, and negative. During the third phase, various features were extracted from the tweets by employing a number of widely utilized approaches, including as bag of words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), Word2Vec, and FastText, to gather feature datasets. These methods were employed to extract distinct datasets for the features. The final phase different machine learning classification algorithms are applied for detection of sentiment using machine learning. In the extensive experimental analysis, the BoW performed better results with modified support vector machine (mSVM) than existing machine learning algorithms. The proposed mSVM performed superiorly to the other classifiers by 98.15% accuracy rate. Once the tweets are correctly classified as COVID-19 tweets, it is further categorized into three sentiments that is positive, negative and neural. Proposed mSVM achieves 93% of accuracy rate for positive sentiment which better as compared to other Machine Learning (ML) classifiers.</p>

An effective classification approach to categorize the breast cancer using modified support vector machine as a classifier

AbstractBreast cancer is a type of cancer which threatens the health of women and occupies first in female cancer mortality. In recent days, the machine learning techniques are widely utilized in various medical fields particularly in the application related to detection and classification. However, the accuracy obtained from the existing machine learning techniques was reliably low and results in poor classification. To overcome the issues in existing algorithms, this research introduced a Modified Support Vector Machine (MSVM) to classify the breast cancer as benign and malignant. The input data is obtained from Wisconsin Breast Cancer Dataset (WBCD) and the pre‐processing is performed to remove the inappropriate information from the raw data. Then, Principle Component Analysis (PCA) is used in the process of dimensionality reduction. The reduced dimensions are subjected into the stage of feature extraction using linear Discriminant analysis (LDA) and finally the classification is performed using the proposed MSVM. The experimental results show that the proposed approach achieved better classification accuracy of 99.34% which is comparatively higher than the existing Deer canid based Deep Convolutional Neural Network (DCNN), LDA‐SVM, Fuzzy rule based system and Intelligent Ensemble Classification method on the basis of Multi‐Layer Perceptron (IEC‐MLP) with classification accuracy of 98.12%, 97.65%, 97.22% and 98.74% respectively.

Electric Vehicle Lithium-ion Battery Ageing Analysis under Dynamic Condition: A Machine Learning Approach

Currently, the smart cities, smart vehicles, and smart gadgets will improve the way of living standard. Cloud connectivity of IoT sensed devices will capture real-time data in the cloud which helps to improve the system performance and quick response to queries. Electric Vehicle battery health diagnosis plays an important role in the proper functioning of the battery management system, guarantees safety, and warranty claim. Society 5.0 develops with the advancement in the road, infrastructure, better connectivity, transportation, and options available to purchase. Battery health cannot be measured directly. There are internal and external factors that affect battery health such as State of Charge, model parameters, charging/discharging method, temperature, Depth of Discharge, C-rate, battery chemistry, form factor, thermal management, and load change effect. Battery degrades due to both calendar ageing and cyclic ageing. Artificial Intelligence plays a significant role in Battery management system due to the nonlinear behavior of lithium-ion battery. Prediction of battery health accurately and in due time will reduce the risk of recklessness. Timely maintenance will reduce the risk of fatal accidents. This paper presents different batteries analysis under different discharge voltage and capacity conditions. Different machine learning algorithms such as Neural Network, Modified Support Vector Machine (M-SVM) and Linear Regression are used to predict state of health. The proposed M-SVM performs well with less error for all four-battery discharge data.

MSVM: An Approach for Customer Churn Prediction using modified Support Vector Machine and various Machine Learning Techniques

Consumer attrition is a major issue across the world companies and that is one of their primary worries. Organizations, specifically in the telecommunication sector, are working to build technologies to forecast future customer churn due to the obvious direct impact on profits. It's essential to define the variables that lead to customer churn before making the required efforts to minimize churn. An important impact was the creation of an attrition estimation method that aids telecommunication corporations in predicting which consumers are willing to churn. The methodology proposed in this study employs mathematical methodologies on a big data framework to provide a unique strategy to feature design and evaluation. This research work has proposed a customer churn prediction using a modified Support Vector Machine Learning (mSVM) classifier. The significant contribution of this research is that we have changed the distance function of SVM in both training and testing. Similar machine learning algorithms are also validated on similar datasets such as Naïve Bayes (NB), K-nearest Neighbor (KNN) and Decision Tree (DT) with J48 classifiers. The BigML dataset has been used for detecting churn in the telecom industry in a real-time scenario. In an extensive experimental analysis, we demonstrate mSVM which produces higher accuracy over the traditional machine learning algorithm for different cross-validation methods.

3D MIMO Beamforming Using Spatial Distance SVM Algorithm and Interference Mitigation for 5G Wireless Communication Network

In recent decades, Multiple Input Multiple Output beamforming is deliberated as the vital technology enablers for 5G mobile radio services. Since, it provides noticeable improvement regarding throughput and coverage measures in 5G networks. Primarily, executed 3D MIMO beamforming using the modified Support Vector Machine algorithm which forms beam effectually to the users. The interference is mitigated in two stages that are small cell interference and macro cell interference by measuring the interference power from the cells. To provide better security to the data transmitted over Device-to-Device communication, Advanced Encryption Standard algorithm is used. The results attained from the simulations are auspicious in terms of metrics including throughput, Signal to Interference plus Noise Ratio (SINR) and Signal to Noise Ratio (SNR). From the simulation results, we prove that our ML-3DIM method increases throughput, SINR, SNR by up to 20%, 30% and 35% respectively compared to the existing methods including PABM, ULABM, and NOMA.

Varying combination of feature extraction and modified support vector machines based prediction of myocardial infarction

Varying combination of feature extraction and modified support vector machines based prediction of myocardial infarction

Rapid classification of steel via a modified support vector machine algorithm based on portable fiber-optic laser-induced breakdown spectroscopy

The littering of steel waste increases annually, and it causes environmental pollution and resource wastage. Rapid classification of steel is a critical step in the process of recycling to conserve resources. The method of principal components analysis (PCA) combined with support vector machine (SVM) was developed to establish a model for rapidly classifying 14 kinds of special steel samples, whose spectra were acquired via a portable fiber-optic laser-induced breakdown spectroscopy (FO-LIBS) system. Fifty-one preselected characteristic lines of trace elements were chosen as input variables because the samples of special steel differed in terms of the concentration of trace elements. Results showed that the recognition accuracy of the PCA-SVM model was gradually improved by the increase in principal components (PCs) and reached 100% when 13 PCs were extracted as input; this accuracy value was significantly higher than the 95% accuracy of the SVM model. This finding suggests that FO-LIBS combined with the PCA-SVM algorithm can achieve rapid classification of steel materials and provide a new approach for online detection in the industrial field.

Enhanced packet reordering procedure to improve TCP communication

PurposeThe research work is attained by Spurious Transmission–based Enhanced Packet Reordering Method (ST-EPRM). The packet reordering necessity is evaded by presenting random linear network coding process on wireless network physical layer which function on basis of sequence numbers. The spurious retransmission happening over wireless network is obtained by presenting monitoring concept for reducing number of spurious retransmissions because it might need more than three DUPACKs for triggering fast retransmit. This monitoring node performs as centralized node as well variation amid buffer length and number of packets being sent can be predicted. This information helps in differentiating spurious retransmission from the packet loss.Design/methodology/approachBased on transmission detection, action is accomplished whether to retransmit or evade transmission. Monitoring node selection is achieved by presenting improved cuckoo search algorithm. The modified support vector machine algorithm is greatly used for variation-based spurious transmission.FindingsThe research work which is attained by ST-EPRM. The packet reordering necessity is evaded by presenting random linear network coding process on wireless network physical layer which function on basis of sequence numbers. The spurious retransmission happening over wireless network is obtained by presenting monitoring concept for reducing number of spurious retransmissions because it might need more than three DUPACKs for triggering fast retransmit. This monitoring node performs as centralized node as well variation amid buffer length and number of packets being sent can be predicted. This information helps in differentiating spurious retransmission from the packet loss.Originality/valueBased on transmission detection, action is accomplished whether to retransmit or evade transmission. Monitoring node selection is achieved by presenting improved cuckoo search algorithm. The modified support vector machine algorithm is greatly used for variation-based spurious transmission.

Proposing WPOD-NET combining SVM system for detecting car number plate

<span lang="EN-US">Nowadays, there are many smart parking lots using plate detection system to control in/out vehicles. However, the disadvantages of systems are a fixed environment and necessity of manual labor and requirement of checkpoints in entrances. To solve the problems, a novel algorithm for wide-angle detecting car number plate using warped planar object detection (WPOD-NET) and a modified support vector machine (SVM) system is proposed. Comparing to other models, the proposal improves not only the range of detection angle but also the accuracy of detecting in shady conditions. The results show that the accuracy of proposal model is up to 95.1% with 1000 testing images in various scenarios.</span>

Lipid profiling using Raman and a modified support vector machine algorithm.

Lipid droplets are dynamic organelles that play important cellular roles. They are composed of a phospholipid membrane and a core of triglycerides and sterol esters. Fatty acids have important roles in phospholipid membrane formation, signaling, and synthesis of triglycerides as energy storage. Better non-invasive tools for profiling and measuring cellular lipids are needed. Here we demonstrate the potential of Raman spectroscopy to determine with high accuracy the composition changes of the fatty acids and cholesterol found in the lipid droplets of prostate cancer cells treated with various fatty acids. The methodology uses a modified least squares fitting (LSF) routine that uses highly discriminatory wavenumbers between the fatty acids present in the sample using a support vector machine algorithm. Using this new LSF routine, Raman micro-spectroscopy can become a better non-invasive tool for profiling and measuring fatty acids and cholesterol for cancer biology.

Enhancing the precision and accuracy of renal failure diagnosis using the modified support vector machine algorithm and dragonfly algorithm

Enhancing the precision and accuracy of renal failure diagnosis using the modified support vector machine algorithm and dragonfly algorithm

A firefly algorithm modified support vector machine for the credit risk assessment of supply chain finance

PurposeNowadays, Supply Chain Finance (SCF) has been developing rapidly since the emergence of credit risk. Therefore, this paper used SVM optimized by the firefly algorithm, which is called firefly algorithm support vector machine (FA-SVM), and applied it to SCF evaluation with a different indicator selection. Design/methodology/approachIn this paper, we used FA-SVM to assess the credit risk of supply chain finance with extracted index through correlation and appraisal analysis, and finally determined 3 first-level indicators and 15 third-level indicators. Through the application analysis, 39 SMEs (117 sample data) were selected from the Computer and Electronic Communications Manufacturing Industry as the characteristics for the input variables, to verify the improvement effect of the method relative to the LIBSVM and the classification pretest effect in the credit risk assessment of the SCF. FindingsThe results showed that FA-SVM could improve the accuracy of classification prediction compared with LIBSVM, and decrease the error rate of falseness recognize credible enterprise to untrusted enterprise. Originality/valueThis paper appliedthe firefly support vector machine in the supply chain financial evaluation for the first time. The output variable was described in a more detailed manner during the index define, and the random selection set in the process of FA-SVM data training.

A novel quality prediction method based on feature selection considering high dimensional product quality data

<p style='text-indent:20px;'>Product quality is the lifeline of enterprise survival and development. With the rapid development of information technology, the semiconductor manufacturing process produces multitude of quality features. Due to the increasing quality features, the requirement on the training time and classification accuracy of quality prediction methods becomes increasingly higher. Aiming at realizing the quality prediction for semiconductor manufacturing process, this paper proposes a modified support vector machine (SVM) model based on feature selection, considering the high dimensional and nonlinear characteristics of data. The model first improves the Radial Basis Function (RBF) in SVM, and then combines the Duelist algorithm (DA) and variable neighborhood search algorithm (VNS) for feature selection and parameters optimization. Compared with some other SVM models that are based on DA, genetic algorithm (GA), and Information Gain algorithm (IG), the experiment results show that our DA-VNS-SVM can obtain higher classification accuracy rate with a smaller feature subset. In addition, we compare the DA-VNS-SVM with some common machine learning algorithms such as logistic regression, naive Bayes, decision tree, random forest, and artificial neural network. The results indicate that our model outperform these machine learning algorithms for the quality prediction of semiconductor.</p>

A Comparison of Machine Learning and Deep Learning Methods with Rule Based Features for Mixed Emotion Analysis

Multi-class classification of sentiments from text data still remains a challenging task to detect the sentiments hidden behind the sentences because of the probable existence of multiple meanings for some of the texts in the dataset. To overcome this, the proposed rule based modified Convolutional neural network-Global Vectors (RCNN-GloVe) and rule-based modified Support Vector Machine - Global Vectors (RSVM-GloVe) were developed for classifying the twitter complex sentences at twelve different levels focusing on mixed emotions by targeting abstract nouns and adjective emotion words. To execute this, three proposed algorithms were developed such as the optimized abstract noun algorithm (OABNA) to identify the abstract noun emotion words, optimized complex sentences algorithm (OCSA) to extract all the complex sentences in a tweet precisely and adjective searching algorithm (ADJSA) to retrieve all the sentences with adjectives. The results of this study indicates that our proposed RCNNGloVe method used in the sentiment analysis was able to classify the mixed emotions accurately from the twitter dataset with the highest accuracy level of 92.02% in abstract nouns and 88.93% in adjectives. It is distinctly evident from the research that the proposed deep learning model (RCNN-GloVe) had an edge over the machine learning model (RSVM-GloVe).

KDT-SPSO: A multimodal particle swarm optimisation algorithm based on k-d trees for palm tree detection

Palm tree detection, as any other problem of object localisation in an image, can be formulated mathematically as a multimodal optimisation problem that seeks for the best set of positions and scales that maximise the classification score. In recent decades, many bio-inspired multimodal optimisation algorithms using different niching techniques have been proven effective and robust in solving optimisation problems. In many niching-based algorithms, e.g., species-based particle swarm optimisation (SPSO), repetitive distance evaluations to search for nearest neighbours are unavoidable to find multiple optima in the search space, which are computationally expensive. In this paper, we propose an improved SPSO named KDT-SPSO to speed up the nearest neighbour search using a special tree-based structure, called k-d tree. KDT-SPSO reduces the computation complexity by only visiting the subtrees that most likely contain the neighbours. We statistically tested KDT-SPSO on a number of benchmark functions and the results showed that it was up to 48% faster than the original SPSO. After proving its effectiveness, we optimised the parameters of KDT-SPSO for palm tree detection and introduced a restart mechanism to speed up its convergence and to improve the recall rate. The local binary pattern (LBP) feature extractor and modified support vector machine (SVM) classifier with radial basis function (RBF) kernel were used to compute the fitness value of palm trees. The optimised KDT-SPSO was able to achieve on average 91.80% palm tree detection accuracy and 5 times faster than the traditional sliding window approach.

Identification of Gait Events in Healthy and Parkinson’s Disease Subjects Using Inertial Sensors: A Supervised Learning Approach

Automatic detection of gait phases through supervised learning is a feasible approach that takes advantage of the consistency of the gait cycle among healthy subjects. However, gait patterns among subjects with impairments are not as consistent and most of the existing algorithms have limited performance detecting phases during impaired gait. In this paper, we proposed one algorithm that used linear classifiers to detect in real-time the transition between consecutive gait phases. Our approach is a generalization of the rule- and threshold-based algorithms for event detection. Linear classifiers are parametric models that require appropriate values in order to perform correct classification of the gait phases. We introduced a modified Support Vector Machine (SVM) to compute such sub-optimal combinations of those values, and a further optimization with a hybrid meta-heuristic approach that integrates a Genetic and a Simulated Annealing Algorithm. We tested our approach on data collected by a single-IMU foot-mounted wearable device during overground and treadmill walking for two groups: one with healthy and one with Parkinson’s Disease subjects. The ${F}_1$ -scores were 0.987 and 0.953 for the two groups, which were comparable with our previously developed threshold-based method, which obtained 0.988 and 0.974, respectively. Our proposed approach achieved similar performance as the threshold-based scheme, with the advantage of not relying on any prior knowledge of specific features for any particular inertial signal.

Soil Classification Using Modified Support Vector Machine

Soil Classification Using Modified Support Vector Machine