SVM Approach Research Articles

Artificial Neural Network Based Approach for Food Recognition Using Various Filters

Food image recognition system has various applications now a day. In this paper, we have used a machine learning supervised approach and Support Vector Machine to classify different food images. SVM has been classified to detect and recognize food images with the least modification. By applying various filters like a texture filter, a segmentation method, clustering, and a SVM approach we have achieved more accuracy than other machine learning approaches with manually extracting features. Sustenance is an indivisible piece of people groups lives. we tend to apply a convolution neural network (CNN) to the undertakings of analyst work and perceiving sustenance pictures. Clarification for the wide decent variety of styles of nourishment, and picture acknowledgment of sustenance things are typically unpleasant difficulties. Nevertheless, profound learning has been demonstrated starting late to be a genuinely extreme picture acknowledgment framework, and CNN could be a dynamic approach to managing profound learning. CNN showed on a very basic level higher precision than did old-fashioned help vector-machine-based courses with carefully assembled decisions. For sustenance picture disclosure, CNN likewise demonstrated fundamentally count higher precision than a standard technique. Generally higher precision than standard techniques.

Biocomposite’s Multiple Uses for a New Approach in the Diagnosis of Parkinson’s Disease Using a Machine Learning Algorithm

Neurodegenerative diseases drastically affect human beings without distinction; it does not matter if they are male or female. Sometimes, it is not clear why a person in their life developed a well-known disease in the world such as Parkinson’s disease (PD). Nowadays, various novel machine learning-based algorithms for evaluating Parkinson’s disease have been designed. The most recent strategy, which was developed using deep learning and can forecast the severity of Parkinson’s disease, is the one described here. To identify this disease, a thorough medical history, previous treatment history, physical examinations, and some blood tests and brain films must be completed. Diagnoses are more critical since they are less expensive and less time-consuming. Voice data from 253 people used in the current study corroborates the doctor’s diagnosis of Parkinson’s disease. To acquire the best results from the data, preprocessing is done. To perform the balancing procedure, a systematic sampling strategy was used to select the data that would be analyzed. Several data groups were constructed using a feature selection technique based on the label’s effect strength. Classification algorithms and performance evaluation criteria employ DT, SVM, and kNN. The classification algorithm and data group with the highest performance value were chosen, and the model was created due to this selection. The SVM approach was employed when constructing the model, and 45% of the original data set data were used. The data was sorted from most relevant to least important. 86% performance accuracy was achieved, in addition to excellent results in all other areas of the project. As a result, it has been established that medical decision support will be provided to the doctor with the assistance of the data set obtained from the speech recordings of the individual who may have Parkinson’s disease and the model that has been developed.

Stochastic Variational Approach to Small Atoms and Molecules Coupled to Quantum Field Modes in Cavity QED.

In this work, we present a stochastic variational calculation (SVM) of energies and wave functions of few particle systems coupled to quantum fields in cavity QED. The spatial wave function and the photon spaces are optimized by a random selection process. Using correlated basis functions, the SVM approach solves the problem accurately and opens the way to the same precision that is reached the nonlight coupled quantum systems. Examples for a two-dimensional trion and confined electrons as well as for the He atom and the H_{2} molecule are presented showing that the light-matter coupling drastically changes the electronic states.

Sparse coded spatial pyramid matching and multi-kernel integrated SVM for non-linear scene classification

AbstractSupport vector machine (SVM) techniques and deep learning have been prevalent in object classification for many years. However, deep learning is computation-intensive and can require a long training time. SVM is significantly faster than Convolution Neural Network (CNN). However, the SVM has limited its applications in the mid-size dataset as it requires proper tuning. Recently the parameterization of multiple kernels has shown greater flexibility in the characterization of the dataset. Therefore, this paper proposes a sparse coded multi-scale approach to reduce training complexity and tuning of SVM using a non-linear fusion of kernels for large class natural scene classification. The optimum features are obtained by parameterizing the dictionary, Scale Invariant Feature Transform (SIFT) parameters, and fusion of multiple kernels. Experiments were conducted on a large dataset to examine the multi-kernel space capability to find distinct features for better classification. The proposed approach founds to be promising than the linear multi-kernel SVM approaches achieving 91.12 % maximum accuracy.

The Prediction of Running Velocity during the 30-15 Intermittent Fitness Test Using Accelerometry-Derived Metrics and Physiological Parameters: A Machine Learning Approach.

Measuring exercise variables is one of the most important points to consider to maximize physiological adaptations. High-intensity interval training (HIIT) is a useful method to improve both cardiovascular and neuromuscular performance. The 30–15IFT is a field test reflecting the effort elicited by HIIT, and the final velocity reached in the test is used to set the intensity of HIIT during the training session. In order to have a valid measure of the velocity during training, devices such as GPS can be used. However, in several situations (e.g., indoor setting), such devices do not provide reliable measures. The aim of the study was to predict exact running velocity during the 30–15IFT using accelerometry-derived metrics (i.e., Player Load and Average Net Force) and heart rate (HR) through a machine learning (ML) approach (i.e., Support Vector Machine) with a leave-one-subject-out cross-validation. The SVM approach showed the highest performance to predict running velocity (r = 0.91) when compared to univariate approaches using PL (r = 0.62), AvNetForce (r = 0.73) and HR only (r = 0.87). In conclusion, the presented multivariate ML approach is able to predict running velocity better than univariate ones, and the model is generalizable across subjects.

A temporal-based SVM approach for the detection and identification of pollutant gases in a gas mixture

Air toxicity and pollution phenomena are on the rise across the planet. Thus, the detection and control of gas pollution are nowadays major economic and environmental challenges. There exists a wide variety of sensors that can detect gas pollution events. However, they are either gas-specific or weak in the presence of gas mixtures. This paper handles this issue by presenting method based on a Temporal-based Support Vector Machine for for the detection and identification of several toxic gases in a gas mixture. The considered gases are carbon monoxide (CO), ozone (O3) and nitrogen dioxide (NO2). Furthermore, an incremental algorithm is proposed in this paper for the selection of the best performing kernel function in terms of accuracy and simplicity of implementation. Then, a decision-making algorithm based on the rate of appearance of a class on a moving window is proposed to improve decision making in presence of uncertainties. This algorithm allows the user to master the false-alarms and no-detection dilemma, and quantify the level of confidence attributed to the decision. Experimental results, obtained with different gas mixtures, show the effectiveness of the proposed approach with 100% of accuracy in the learning and testing stages.

Machine learning-assisted distinct element model calibration: ANFIS, SVM, GPR, and MARS approaches

Machine learning-assisted distinct element model calibration: ANFIS, SVM, GPR, and MARS approaches

Construction of Accurate Crack Identification on Concrete Structure using Hybrid Deep Learning Approach

In general, several conservative techniques are available for detecting cracks in concrete bridges but they have significant limitations, including low accuracy and efficiency. Due to the expansion of the neural network method, the performance of digital image processing based crack identification has recently diminished. Many single classifier approaches are used to detect the cracks with high accuracy. The classifiers are not concentrating on random fluctuation in the training dataset and also it reflects in the final output as an over-fitting phenomenon. Though this model contains many parameters to justify the training data, it fails in the residual variation. These residual variations are frequent in UAV recorded photos as well as many camera images. To reduce this challenge, a noise reduction technique is utilized along with an SVM classifier to reduce classification error. The proposed technique is more resourceful by performing classification via SVM approach, and further the feature extraction and network training has been implemented by using the CNN method. The captured digital images are processed by incorporating the bending test through reinforced concrete beams. Moreover, the proposed method is determining the widths of the crack by employing binary conversion in the captured images. The proposed model outperforms conservative techniques, single type classifiers, and image segmentation type process methods in terms of accuracy. The obtained results have proved that, the proposed hybrid method is more accurate and suitable for crack detection in concrete bridges especially in the unmanned environment.

Recognition of Automated Hand-written Digits on Document Images Making Use of Machine Learning Techniques

The purpose of this study is to create an automated framework that can recognize similar handwritten digit strings. For starting the experiment, the digits were separated into different numbers. The process of defining handwritten digit strings is then concluded by recognizing each digit recognition module's segmented digit. This research utilizes various machine learning techniques to produce a strong performance on the digit string recognition challenge, including SVM, ANN, and CNN architectures. These approaches use SVM, ANN, and CNN models of HOG feature vectors to train images of digit strings. Deep learning methods organize the pictures by moving a fixed-size monitor over them while categorizing each sub-image as a digit pass or fail. Following complete segmentation, complete recognition of handwritten digits is accomplished. To assess the methods' results, data must be used for machine learning training. Following that, the digit data is evaluated using the desired machine learning methodology. The Experiment findings indicate that SVM and ANN also have disadvantages in precision and efficiency in text picture recognition. Thus, the other process, CNN, performs better and is more accurate. This paper focuses on developing an effective system for automatically recognizing handwritten digits. This research would examine the adaptation of emerging machine learning and deep learning approaches to various datasets, like SVM, ANN, and CNN. The test results undeniably demonstrate that the CNN approach is significantly more effective than the ANN and SVM approaches, ranking 71% higher. The suggested architecture is composed of three major components: image pre-processing, attribute extraction, and classification. The purpose of this study is to enhance the precision of handwritten digit recognition significantly. As will be demonstrated, pre-processing and function extraction are significant elements of this study to obtain maximum consistency.

Recognition of Automated Hand-written Digits on Document Images Making Use of Machine Learning Techniques

The purpose of this study is to create an automated framework that can recognize similar handwritten digit strings. For starting the experiment, the digits were separated into different numbers. The process of defining handwritten digit strings is then concluded by recognizing each digit recognition module's segmented digit. This research utilizes various machine learning techniques to produce a strong performance on the digit string recognition challenge, including SVM, ANN, and CNN architectures. These approaches use SVM, ANN, and CNN models of HOG feature vectors to train images of digit strings. Deep learning methods organize the pictures by moving a fixed-size monitor over them while categorizing each sub-image as a digit pass or fail. Following complete segmentation, complete recognition of handwritten digits is accomplished. To assess the methods' results, data must be used for machine learning training. Following that, the digit data is evaluated using the desired machine learning methodology. The Experiment findings indicate that SVM and ANN also have disadvantages in precision and efficiency in text picture recognition. Thus, the other process, CNN, performs better and is more accurate. This paper focuses on developing an effective system for automatically recognizing handwritten digits. This research would examine the adaptation of emerging machine learning and deep learning approaches to various datasets, like SVM, ANN, and CNN. The test results undeniably demonstrate that the CNN approach is significantly more effective than the ANN and SVM approaches, ranking 71% higher. The suggested architecture is composed of three major components: image pre-processing, attribute extraction, and classification. The purpose of this study is to enhance the precision of handwritten digit recognition significantly. As will be demonstrated, pre-processing and function extraction are significant elements of this study to obtain maximum consistency.

An Interpretable Hand-Crafted Feature-Based Model for Atrial Fibrillation Detection.

Atrial Fibrillation (AF) is the most common type of cardiac arrhythmia. Early diagnosis of AF helps to improve therapy and prognosis. Machine Learning (ML) has been successfully applied to improve the effectiveness of Computer-Aided Diagnosis (CADx) systems for AF detection. Presenting an explanation for the decision made by an ML model is considerable from the cardiologists' point of view, which decreases the complexity of the ML model and can provide tangible information in their diagnosis. In this paper, a range of explanation techniques is applied to hand-crafted features based ML models for heart rhythm classification. We validate the impact of the techniques by applying feature selection and classification to the 2017 CinC/PhysioNet challenge dataset. The results show the effectiveness and efficiency of SHapley Additive exPlanations (SHAP) technique along with Random Forest (RF) for the classification of the Electrocardiogram (ECG) signals for AF detection with a mean F-score of 0.746 compared to 0.706 for a technique based on the same features based on a cascaded SVM approach. The study also highlights how this interpretable hand-crafted feature-based model can provide cardiologists with a more compact set of features and tangible information in their diagnosis.

Sell Quality determination for Chemical Fertilizer Data using Expert System

There is an growing appetite for food due to the ever increasing global population, so new technologies need to be created to increase crop yield. This paper proposes an intelligent way of forecasting crop yield and recommends the best variables for optimising crop yield. With technical developments, the emphasis has now moved to the use of computers and control systems for process management and efficiency enhancement. we estimate the crop yield per acre, in this research work to proposed hybrid approach for Chemical Fertilizer Data classification using SVM and neural network approach with expert system improvement. Yield and data obtained from Madhya Pradesh of Agriculture are used in the proposed process. Humidity, yield, temperature and rainfall are the different parameters used in the dataset.

A Multi-View SVM Approach for Seizure Detection from Single Channel EEG Signals

Seizures are the part of the epilepsy that occurs in central nervous system which leads to abnormal brain activity. Electroencephalogram (EEG) signal recordings are mostly used in epileptic seizure detection process. Detection of seizures is a crucial part for further treatment of patients. This paper proposes a multi-view SVM model for seizure detection using the single channel EEG signals. In this experiment, two views of the EEG data have been extracted, (1) the time domain features using Independent Component Analysis (ICA) and (2) power spectral densities are obtained in the frequency domain. Extracted features have been fed to multi-view SVM classification model. In this study, a single channel EEG dataset is used for seizure detection. Performance estimation parameters namely Accuracy, Sensitivity, Specificity, F1-score, and AUC value have been estimated for evaluating the proposed model. The model classified seizure and non-seizure over the sets A vs E and B vs E with an accuracy greater than 99% using k-fold cross validation. The classification accuracy obtained by multi-view SVM is better by 1–4% than single view SVM using the same features. Furthermore, the proposed model is also compared with existing single view SVM models. It is observed that the multi view SVM model performed significantly better, compare to a single view SVM model over the same features.

An efficient approach for optimum shape design of steel shear panel dampers under cyclic loading

The low-cycle fatigue performance of shear panel damper (SPD) highly depends on the geometry of its shape and the criterion considered for its design. The main contribution of the current study is to find the optimum shape of the SPD subjected to cyclic loading by considering two different objective functions. The maximum equivalent plastic strain and the ratio of energy dissipation through plastic deformation to the maximum equivalent plastic strain are selected as the first and second objective functions, respectively. Since the optimization procedure requires high computational efforts, a hybrid computational approach is used to perform two paramount phases of estimating the inelastic responses of the SPD and solving the optimization problem. In the first phase, as an alternative for the time-consuming finite element analysis of the SPD, a weighted-support vector machine model is developed to predict the inelastic responses of the SPDs during the optimization process. In the second phase, the optimum shape of the SPD is found by using the whale optimization algorithm (WOA). The results indicate that both design criteria lead to the optimum-shaped SPDs with a significant improvement in their low cycle fatigue performance in comparing with the initial rectangular shape while a slight reduction in their energy dissipation capacity. Moreover, the second design criterion is slightly better in the performance improvement of the optimum-shaped SPDs compared with the first one. In addition, the weighted-based SVM approach can accurately predict the inelastic responses of the SPDs under cyclic loading, and its combination with WOA results in finding the optimum solutions quickly.

SVM Approach for Forecasting International Tourism Arrival In East Java

The maneuver of international tourists visiting Indonesia, especially the East Java area, in positive correlation with time. In accordance with the government’s work program to increase GDP from the tourism sector. However, the condition necessary to be supported by the existence of feasibility and adequate tourism services. Facility improvements certainly require a projection of international tourist arrivals visiting East Java. SVM is a machine learning method that has shown excellent performance in predicting time series. In addition, analysis results have shown that SVM had able to predict foreign tourist visits with a high degree of accuracy. Therefore, this research can be used as a support for tourism facility procurement policies, especially in East Java.

Hybrid local phase quantization and grey wolf optimization based SVM for finger vein recognition

As a novelist and the most secure biometric method, finger vein recognition has gained substantial significance and various pertinent researches have been reported in literature. However, it is difficult to extract a more reliable and accurate finger vein pattern due to the random noise, poor lighting, illumination variation, image deformation and blur. Furthermore, improper parameter settings of SVMs lead to poor classification accuracy and apparently, not much relevant research has been conducted on its optimal parameter setting. To alleviate these problems, this paper proposes an efficient finger vein recognition framework consisting of the hybrid Local Phase Quantization (LPQ) for robust feature extraction and Grey Wolf Optimization based SVM (GWO-SVM) to compute the best parameter combination of SVM for optimal results of binary classification. Finger vein features are first extracted by integrating LPQ, which is invariant to motion blur and deformation, with Local Directional Pattern (LDP), which is robust to random noise and illumination variation, to augment the recognition performance and reduce the computational time. Then, GWO-SVM is used for classification in order to maximize the classification accuracy by determining the optimal SVM parameters. The extensive experimental results indicate remarkable performance and significant enhancements in terms of recognition accuracy by the proposed framework compared to the existing techniques and prove the effectiveness of the proposed framework on four tested finger vein datasets. It has outperformed the typical SVM approach and kNCN-SRC two-stage methodology via achieving the recognition accuracy of 98% and equal error rate as low as 0.1020%.

Cotton Leaf Diseases Recognition Using Deep Learning and Genetic Algorithm

Globally, Pakistan ranks 4 in cotton production, 6 as an importer of raw cotton, and 3 in cotton consumption. Nearly 10% of GDP and 55% of the country's foreign exchange earnings depend on cotton products. Approximately 1.5 million people in Pakistan are engaged in the cotton value chain. However, several diseases such as Mildew, Leaf Spot, and Soreshine affect cotton production. Manual diagnosis is not a good solution due to several factors such as high cost and unavailability of an expert. Therefore, it is essential to develop an automated technique that can accurately detect and recognize these diseases at their early stages. In this study, a new technique is proposed using deep learning architecture with serially fused features and the best feature selection. The proposed architecture consists of the following steps: (a) a self-collected dataset of cotton diseases is prepared and labeled by an expert; (b) data augmentation is performed on the collected dataset to increase the number of images for better training at the earlier step; (c) a pre-trained deep learning model named ResNet101 is employed and trained through a transfer learning approach; (d) features are computed from the third and fourth last layers and serially combined into one matrix; (e) a genetic algorithm is applied to the combined matrix to select the best points for further recognition. For final recognition, a Cubic SVM approach was utilized and validated on a prepared dataset. On the newly prepared dataset, the highest achieved accuracy was 98.8% using Cubic SVM, which shows the perfection of the proposed framework..

Ambient acoustic event assistive framework for identification, detection, and recognition of unknown acoustic events of a residence

In recent times, Ambient Assisted Living has emerged as Smart Living. Smart living is a subset of ambient intelligence, which uses the latest technologies, intellectual processes, and ambient intelligent methodologies to enable house residents to live independently with a virtual companion 24 × 7. Typically, these residents are highly engrossed in the daily routine activities that they tend to ignore certain acoustic events attributing them to the white noise caused due to tap water leakage, flush water leakage, the acoustics of door opening/closing, cupboard opening/closing, curtain opening/closing, television, shower, radio, chair and many more. These unattended events lead to a waste of critical energy resources such as electricity, water, and gas and may cause accidents in some cases. For the conducted experiments, a customized dataset termed as “unknown-2000” and ESC-50 has been used, which has more than 2000 audio sound classification samples. The customized dataset is used for the conducted experiments, consisting of various length acoustic events ranging from 2 s to 10 s. In the proposed review, we have identified, analyzed, and evaluated resident acoustic events using Librosa machine learning libraries, texture analysis using LBP methodology, LSTM-CNN, SVM, KNN, LSTM, Bi-LSTM, and Decision Tree-based classification approaches. Furthermore, in the proposed approach, based on the conducted rigorous and detailed analysis, we are also envisioning the prospective ways to enhance smart living concepts by proposing a novel Acoustic Event Detection and Classification System. The investigation results validate the success of the proposed approach. The obtained results indicate that the customized version of the LSTM-CNN based classification approach used in the conducted experiment has outperformed all the other customized classification approaches, such as SVM, KNN-based classification, C4.5 decision tree-based classification, LSTM, and Bi-LSTM based classification. The LSTM-CNN based classification model has achieved an average value of approximately 0.77 and a standard deviation of 0.2295. Furthermore, the obtained experiential results show that the proposed approach has produced a good performance in various noisy conditions such as SNR0, SNR3, SNR6, SNR9, SNR12, and SNR15. The system classification accuracy has been enhanced to 77% for various acoustic events of a residence. In the end, a detailed comparison of LBP and without LBP approaches has been carried out, which proves that the combination of LBP and LSTM-CNN classification approach provides better results than without the LBP classification approach. The proposed Ambient Acoustic Event Assistive Framework is a cost-effective alternative due to the use of low-cost microphone sensors in the conducted experiments.

Daily Suspended Sediment Prediction Using Seasonal Time Series and Artificial Intelligence Techniques

Estimating the amount of suspended sediment in rivers correctly is important due to the adverse impacts encountered during the design and maintenance of hydraulic structures such as dams, regulators, water channels and bridges. The sediment concentration and discharge currents have usually complex relationship, especially on long term scales, which can lead to high uncertainties in load estimates for certain components. In this paper, with several data-driven methods, including two types of perceptron support vector machines with radial basis function kernel (SVM-RBF), and poly kernel learning algorithms (SVM-PK), Library SVM (LibSVM), adaptive neuro-fuzzy (NF) and statistical approaches such as sediment rating curves (SRC), multi linear regression (MLR) are used for forecasting daily suspended sediment concentration from daily temperature of water and streamflow in the river. Daily data are measured at Augusta station by the US Geological Survey. 15 different input combinations (1 to 15) were used for SVM-PK, SVM-RBF, LibSVM, NF and MLR model studies. All approaches are compared to each other according to three statistical criteria; mean absolute errors (MAE), root mean square errors (RMSE) and correlation coefficient (R). Of the applied linear and nonlinear methods, LibSVM and NF have good results, but LibSVM generates a slightly better fit under whole daily sediment values.

FESD: An Approach for Biometric Human Footprint Matching Using Fuzzy Ensemble Learning

Biometric traits such as fingerprint, retina scan, and palm-prints are used to identify a person at attendance monitoring, banking, passport, travel, and many other applications. Biometric-based person identification is the only method that never changes according to time, and no one can copy it without knowledge. Footprint-based biometric is one way to recognize a person based on different features associated with human footprints. For example, some places, such as airports, nanotechnology laboratories, silicon industries, temples, and public areas, require high security. It is necessary to add a footprint-based biometric trait for such high alert areas. The number of subjects taken by existing footprint-based methods is limited to very few subjects. The above research gaps motivate to add more subjects for this study. The proposed algorithm utilizes the fuzzy logic-based method for personal identification. Considerably 220 subjects with temporal aspects are taken into account to fill the existing methods gap. Three approaches, Fine Gaussian SVM (FSVM), Fine KNN (FKNN), and Fuzzy Ensemble Subspace Discriminant (FESD), have been utilized to create the enhanced human footprint matcher. The Fine Gaussian SVM approach exhibits an accuracy of 84.7%, the FKNN approach results in an accuracy of 92.3%, and the FESD approach gives an accuracy of 98.89%. FESD approach rectifies the recognition rate(to reach the required accuracy of 98.88%) False Match Rate (FMR, the rate of falsely as genuine classified imposters) at 0.01, False Non-Match Rate at 0.093 which is the rate of falsely as imposter classified genuine users) to a set of different matchers for the identification task. It improves the speed of recognition with 220 subjects by implementing the prototype schemes for footprint biometric to evaluate system properties, including accuracy and performance.