Hybrid Support Vector Machine Research Articles

Binary gravity search algorithm and support vector machine for forecasting and trading stock indices

A hybrid Support Vector Machine (SVM) model is proposed and applied to the task of forecasting the daily returns of five popular stock indices in the world, including the S&P500, NKY, CAC, FTSE100 and DAX. The originality of this work is that the Binary Gravity Search Algorithm (BGSA) is utilized, in order to optimize the parameters and inputs of SVM. The results show that the forecasts made by this model are significantly better than the Random Walk (RW), SVM, best predictors and Buy-and-Hold. The average accuracy of BGSA-SVM for five stock indices is 52.87%. In general, this study proves that a profitable trading strategy based on BGSA-SVM prediction can be realized in a real stock market.

Hybrid support vector machine and K‐nearest neighbor‐based software testing for educational assistant

AbstractIn terms of training students for work in diverse firms, traditional and out‐of‐date teaching techniques cannot compete with digital teaching methods. To overcome this problem, the teaching approach and content must be changed. An Educational Assistant for Software Testing (EAST) framework is developed in this work to train students to improve their skills in software testing via Computer Assisted Instruction (CAI) built using Natural Language Processing (NLP), Machine learning, and information retrieval techniques. In this paper, a Group Search Optimized two‐stage hybrid Support Vector Machine‐K‐Nearest Neighbor (SVM‐KNN) classifier is used to develop a novel approach for analyzing the parameters that introduce bugs in bug reports. To decrease the data sparsity problem, the group search optimization (GSO) algorithm is used to improve the parameter selection process of the two‐stage hybrid classifier by generating optimal values for parameters such as k, c, and gamma. Two bug report datasets were used to test the model. The database for our application is built by collecting bug reports from a wide open‐source community as well as several mobile application development companies. Based on the extensive experiments conducted via different performance metrics, we can conclude that the EAST framework can improve outdated teaching methodologies.

A Hybrid Support Vector Machine Algorithm for Big Data Heterogeneity Using Machine Learning

Big data technology has gained attention in all fields, particularly with regard to research and financial institutions. This technology has changed the world tremendously. Researchers and data scientists are currently working on its applicability in different domains such as health care, medicine, and the stock market, among others. The data being generated at an unexpected pace from multiple sources like social media, health care contexts, and Internet of things have given rise to big data. Management and processing of big data represent a challenge for researchers and data scientists, as there is heterogeneity and ambiguity. Heterogeneity is considered to be an important characteristic of big data. The analysis of heterogeneous data is a very complex task as it involves the compilation, storage, and processing of varied data based on diverse patterns and rules. The proposed research has focused on the heterogeneity problem in big data. This research introduces the hybrid support vector machine (H-SVM) classifier, which uses the support vector machine as a base. In the proposed algorithm, the heterogeneous Euclidean overlap metric (HEOM) and Euclidean distance are introduced to form clusters and classify the data on the basis of ordinal and nominal values. The performance of the proposed learning classifier is compared with linear SVM, random forest, and k-nearest neighbor. The proposed algorithm attained the highest accuracy as compared to other classifiers.

Hybrid GA-SVM Approach for Postoperative Life Expectancy Prediction in Lung Cancer Patients

Medical outcomes must be tracked in order to enhance quality initiatives, healthcare management, and mass education. Thoracic surgery data have been acquired for those who underwent major lung surgery for primary lung cancer, a field in which there has been little research and few reliable recommendations have been made for lung cancer patients. Early detection of lung cancer benefits therapy choices and increases the odds of a patient surviving a lung cancer infection. Using a Hybrid Genetic and Support Vector Machine (GA-SVM) methodology, this study proposes a method for identifying lung cancer patients. To estimate postoperative life expectancy, ensemble machine-learning techniques were applied. The article also presents a strategy for estimating a patient’s life expectancy following thoracic surgery after the detection of cancer. To perform the prediction, hybrid machine-learning methods were applied. In ensemble machine-learning algorithms, attribute ranking and selection are critical components of robust health outcome prediction. To enhance the efficacy of algorithms in health data analysis, we propose three attribute ranking and selection procedures. Compared to other machine-learning techniques, GA-SVM achieves an accuracy of 85% and a higher F1 score of 0.92. The proposed algorithm was compared with two recent state-of-the-art techniques and its performance level was ranked superior to those of its counterparts.

A hybrid artificial bee colony algorithm and support vector machine for predicting blast-induced ground vibration

A hybrid artificial bee colony algorithm and support vector machine for predicting blast-induced ground vibration

Targeting Sustainable Transportation Development: The Support Vector Machine and the Bayesian Optimization Algorithm for Classifying Household Vehicle Ownership

Predicting household vehicle ownership (HVO) is a crucial component of travel demand forecasting. Furthermore, reliable HVO prediction is critical for achieving sustainable transportation development objectives in an era of rapid urbanization. This research predicted the HVO using a support vector machine (SVM) model optimized using the Bayesian Optimization (BO) algorithm. BO is used to determine the optimal SVM parameter values. This hybrid model was applied to two datasets derived from the US National Household Travel Survey dataset. Thus, two optimized SVM models were developed, namely SVMBO#1 and SVMBO#2. Using the confusion matrix, accuracy, receiver operating characteristic (ROC), and area under the ROC, the outcomes of these two hybrid models were examined. Additionally, the results of hybrid SVM models were compared with those of other machine learning models. The results demonstrated that the BO algorithm enhanced the performance of the standard SVM model for predicting the HVO. The BO method determined the Gaussian kernel to be the optimal kernel function for both datasets. The performance of the SVM#1 model was improved by 4.27% and 5.16% for the training and testing phases, respectively. For SVM#2 model, the performance of this model was improved by 1.20% and 2.14% for the training and testing phases, respectively. Moreover, the BO method enhanced the AUC of the SVM models used to predict the HVO. The hybrid SVM models also outperformed other machine learning models developed in this study. The findings of this study showed that SVM models hybridized with the BO algorithm can effectively predict the HVO and can be employed in the process of travel demand forecasting.

Hybrid feature selection method for SVM classification and its application for fault diagnosis of wear and peeling in journal bearing with a little muddy water using long-term real data

Rotating machines are widely used as components of various industries around the world, and its normal operation of rotating machines is important. Thus, condition monitoring and fault diagnosis of rotating machines have considerable attention in recent years. Industrial statistics illustrate that 40% of total large machine breakdowns happened due to broken bearings, while for small machines, the analogous number reaches up to 90%. This study aimed at researching fault diagnosis of journal bearings using the support vector machine (SVM) method. The experimental systems of vertical and horizontal rotating shafts were developed. There was no adding any initial artificial failure in the bearing, and a little muddy water was used, and long-term vibration data in both systems were obtained in the normal operation of the machines until bearing damages occurred in the journal bearing (3-hour tests were conducted repeatedly and total 128 datasets for vertical shaft and 24 datasets for horizontal shaft were obtained). A feature selection method is focused, and a hybrid feature selection method by combining Fisher score (FS) and a sequential forward selection (SFS) method was proposed. Its accuracy and efficiency was proved experimentally with 97.14% and 100% for vertical shaft and 100% for horizontal shaft. Furthermore, as a result of the SVM model and hybrid feature selection method, the most important feature for journal bearing of horizontal rotor system was clarified as mean value of RMS, and only this feature can give good diagnosis result. It is useful suggestion in selecting the features for the fault diagnosis of horizontal rotating machines.

SRCLoc: Synthetic Radio Map Construction Method for Fingerprinting Outdoor Localization in Hybrid Networks

A precise localization system is a key enabling technology for Internet of Things (IoT) applications and location-based services. Fingerprint-based localization methods are well-known and widely used solutions. These methods, however, are time-consuming and laborious for radio map construction during an offline site survey in large-scale applications. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network-based radio map construction method for real-time device localization. The proposed synthetic radio map construction method for fingerprinting outdoor localization (SRCLoc) combined the hybrid support vector machine and deep gated recurrent unit algorithms sequentially. The SRCLoc reduced the workload of site surveying required to build the fingerprint database by up to 85.7%. The results show that the average positioning error of SRCLoc is less than 39 cm, and more than 90% of the errors are less than 82 cm. That is, numerical results proved that, in comparison to traditional methods, the proposed SRCLoc method can significantly improve positioning performance and reduce radio map construction costs.

A Hybrid SVR-Based Prediction Model for the Interfacial Bond Strength of Externally Bonded FRP Laminates on Grooves with Concrete Prisms

The current work presents a comparative study of hybrid models that use support vector machines (SVMs) and meta-heuristic optimization algorithms (MOAs) to predict the ultimate interfacial bond strength (IBS) capacity of fiber-reinforced polymer (FRP). More precisely, a dataset containing 136 experimental tests was first collected from the available literature for the development of hybrid SVM models. Five MOAs, namely the particle swarm optimization, the grey wolf optimizer, the equilibrium optimizer, the Harris hawks optimization and the slime mold algorithm, were used; five hybrid SVMs were constructed. The performance of the developed SVMs was then evaluated. The accuracy of the constructed hybrid models was found to be on the higher side, with R2 ranges between 0.8870 and 0.9774 in the training phase and between 0.8270 and 0.9294 in the testing phase. Based on the experimental results, the developed SVM–HHO (a hybrid model that uses an SVM and the Harris hawks optimization) was overall the most accurate model, with R2 values of 0.9241 and 0.9241 in the training and testing phases, respectively. Experimental results also demonstrate that the developed hybrid SVM can be used as an alternate tool for estimating the ultimate IBS capacity of FRP concrete in civil engineering projects.

Deep Sentiment Analysis of Twitter Data Using a Hybrid Ghost Convolution Neural Network Model.

Several problems remain, despite the evident advantages of sentiment analysis of public opinion represented on Twitter and Facebook. On complicated training data, hybrid approaches may reduce sentiment mistakes. This research assesses the dependability of numerous hybrid approaches on a variety of datasets. Across domains and datasets, we compare hybrid models to singles. Text tweets and reviews are included in our deep sentiment analysis learning systems. The support vector machine (SVM), Long Short-Term Memory (LSTM), and ghost model convolution neural network (CNN) are combined to get the hybrid model. The dependability and computation time of each approach were evaluated. On all datasets, hybrid models outperform single models when deep learning and SVM are combined. The traditional models were less trustworthy, and deep learning algorithms have recently shown their enormous promise in sentiment analysis. Linear transformations are used in feature maps to eliminate duplicate or related features. The ghost unit makes ghost features by taking away attributes that are both similar and duplicated from each intrinsic feature. LSTM produces higher results but takes longer to process, while CNN needs less hyperparameter adjusting and monitoring. The effectiveness of the integrated model varies depending on the work, and all performed better than the others. For hybrid deep sentiment analysis learning models, LSTM networks, CNNs, and SVMs are needed. Hybrid models are used to compare SVM, LSTM, and CNN, and we tested each method's accuracy and errors. Deep learning-SVM hybrid models improve sentiment analysis accuracy. Experimental results have shown the accuracy of the proposed model shown 91.3 percent and 91.5 percent for datasets type 1 and 8, respectively.

Application of Hybrid Support Vector Machine model for Streamflow Prediction in Barak valley, India

Forecasting streamflow (Qflow) is vital in flood and water management, determining potential of river water flow, agricultural practices, hydropower generation, and environmental flow study. This research aims to explore capability of hybrid support vector machines (SVM) with Whale Optimisation Algorithm (WOA) model for forecasting streamflow at Badarpur Ghat gauging station of Barak river basin and evaluate its enactment with the conventional SVM model. Root mean squared error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency (NSE) statistical measures are considered as evaluating standards. Assessment of outcomes indicates that the optimization algorithm could enhance the accurateness of standalone SVM model in monthly streamflow forecasting. Compared to conventional artificial intelligence methods without a data pre-processing system, the comparatively good performance of applied hybrid model gives an effective alternate to achieve better precision in streamflow forecasting. Results confirm that enhanced SVM model can better process a multifaceted hydrogeological data set, have higher prediction accuracy, and possess better generalisation capability.

Predicting daily soil temperature at multiple depths using hybrid machine learning models for a semi-arid region in Punjab, India.

Prediction of soil temperature (ST) at multiple depths is important for maintaining the physical, chemical, and biological activities in soil for various scientific aspects. The present study was conducted in a semi-arid region of Punjab to predict the daily ST at 5-cm (ST5), 15-cm (ST15), and 30-cm (ST30) soil depths by employing the three-hybrid machine learning (ML) paradigms, i.e. support vector machine (SVM), multilayer perceptron (MLP), adaptive neuro-fuzzy inference system (ANFIS) optimized with slime mould algorithm (SMA), particle swarm optimization (PSO), and spotted hyena optimizer (SHO) algorithms. Five scenarios with different input variables were constructed using daily meteorological parameters, and the optimal one was extracted by exploiting the GT (gamma test). The feasibility of the proposed hybrid SVM, MLP, and ANFIS models was inspected based on performance metrics and visual interpretation. According to the results, the SVM-SMA model yields better estimates than other models at 5-cm, 15-cm, and 30-cm soil depths, respectively, for scenario 5 in the validation phase. Furthermore, conferring to the results, the SMA algorithm-based SVM model had lower (higher) values of mean absolute error, root mean square error, and index of scattering (Nash-Sutcliffe efficiency, coefficient of correlation, and Willmott index of agreement) and proved the better feasibility of SVM models in predicting daily ST at multiple depths on the study site.

Early Diagnosis of Brain Tumour MRI Images Using Hybrid Techniques between Deep and Machine Learning.

Cancer is considered one of the most aggressive and destructive diseases that shortens the average lives of patients. Misdiagnosed brain tumours lead to false medical intervention, which reduces patients' chance of survival. Accurate early medical diagnoses of brain tumour are an essential point for starting treatment plans that improve the survival of patients with brain tumours. Computer-aided diagnostic systems have provided consecutive successes for helping medical doctors make accurate diagnoses and have conducted positive strides in the field of deep and machine learning. Deep convolutional layers extract strong distinguishing features from the regions of interest compared with those extracted using traditional methods. In this study, different experiments are performed for brain tumour diagnosis by combining deep learning and traditional machine learning techniques. AlexNet and ResNet-18 are used with the support vector machine (SVM) algorithm for brain tumour classification and diagnosis. Brain tumour magnetic resonance imaging (MRI) images are enhanced using the average filter technique. Then, deep learning techniques are applied to extract robust and important deep features via deep convolutional layers. The process of combining deep and machine learning techniques starts, where features are extracted using deep learning techniques, namely, AlexNet and ResNet-18. These features are then classified using SoftMax and SVM. The MRI dataset contains 3,060 images divided into four classes, which are three tumours and one normal. All systems have achieved superior results. Specifically, the AlexNet+SVM hybrid technique exhibits the best performance, with 95.10% accuracy, 95.25% sensitivity, and 98.50% specificity.

Hybrid Support Vector Machine and Distance Classifier in Breast Tumor Detection

It is time to look back to balance life style as cancer is affecting all stage of life. Several research studies are going on to make the detection process of cancer painless. Artificial Intelligence is used to detect MRI images and help in decision making. There are two hybrid models proposed. The first model is the combination of Support Vector Machine and Modified Back Propagation Neural Network. The second model is a combination of Distance Classifier and Modified Back Propagation. We have collected more than five thousand MRI dataset related to breast cancer. Dataset was preprocessed and analyzed through hybrid models proposed. In first section we provide introduction to Support Vector Machine. In second and third sections, hybrid models of Support Vector Machine and Distance Classifier are discussed. In result and discussion, we have presented the sample of statistical data and output. The model shows 97% accuracy in breast tumor detection.

Disease detection on plant leaf using K-means segmentation with fuzzy logic SVM algorithm

Detection of plant diseases requisite at its early stage to manage the large crop field. In plants existence of diseases result reduced yields of crops and therefore it is imperative to identify at its early stage. Leaf is the main part where the diseases symptoms are shown in the initial stage itself. Image processing techniques are used at the computing part whereas in this research a hybrid KMFLs (K-Means Fuzzy logics) and SVMs (Support Vector Machines) are implemented to identify and categorize diseased plants based on leaf disease grades. This work’s proposed method is implemented by examining images of leaves for diseases including Alternaria alternates, Anthracnoses, Bacterial blights and Cercospora leaf spots. The input leaf image features are extracted which are subsequently used for categorizations into classes.

Consumer-oriented sensory optimization of yogurt: An artificial intelligence approach

With the emergence of the ‘chain reversal’ phenomenon, the design and development of food products has gradually tended to the consumer-oriented pattern. Catering to consumer preference for sensory attributes is a key factor for food enterprises striving for favor in the market. In this paper, a sensory optimization process is proposed for searching for optimal attributes and to provide further sensory improvement suggestions. An intelligent hybrid genetic algorithm (GA)-particle swarm optimization (PSO)-tabu search (TS) algorithm is used for hybrid deep convolutional auto-encoder and support vector machine (DCAE-SVM) grey-box model optimization in high-dimensional variable space. A comparison of GA and GA-PSO on the given grey-box models and dataset demonstrated that the performance of the proposed GA-PSO-TS was effective. The entire framework is also applicable to explain the influence of brands and storage conditions on sensory attributes and consumer preference.

Actual evapotranspiration estimation over the Tuojiang River Basin based on a hybrid CNN-RF model

Actual evapotranspiration (AET) is a crucial variable linking water cycle, energy balance, and carbon cycle. Accurate regional AET estimation is a prerequisite for investigating its spatio-temporal characteristics and is critical to climate change assessment and hydrological cycle. Eddy covariance (EC) measurements are usually regarded as the ground truth to verify the reliability of other AET estimation models. However, EC flux towers are usually distributed sparsely and cannot fully reflect the actual spatial features of AET. This study proposes a hybrid machine learning model, called the hybrid Convolutional Neural Network and Random Forests (CNN-RF) model, and applies it to the regional AET estimation over the Tuojiang River Basin. The results demonstrate the excellent performance of the CNN-RF model at the site level and the superiority of the CNN-RF model over hybrid Convolutional Neural Network and Support Vector Machine (CNN-SVM), CNN, SVM, and RF models. The AET modeled by the CNN-RF model shows the highest correlation (correlation coefficient: 0.97) with the target value. Moreover, the mean absolute error (MAE) and the root mean square error (RMSE) for the CNN-RF-based modeling are the lowest, only 4.02 mm and 6.29 mm, respectively. Additionally, the CNN-RF-estimated AET shows good agreement with the AET simulated by the SWAT over the Tuojiang River Basin, further illustrating that the CNN-RF model based on remotely sensed variables has a strong generalization ability in space. The AET shows a high correlation with the air temperature and the precipitation; the correlation coefficients are 0.925 and 0.79, respectively. The evolution characteristics of these two meteorological variables govern the overall rise in annual AET during 1981–2020 (1.59 mm/year) over Tuojiang River Basin. This result may yield some insights into the interaction between various meteorological factors in the context of global warming and provide evidence for the impact of climate change on water and energy cycles.

Research on lithology identification based on multi-sensor hybrid domain information fusion and support vector machine

Research on lithology identification based on multi-sensor hybrid domain information fusion and support vector machine

Optimized Hardware Vision System for Vehicle Detection based on FPGA and Combining Machine Learning and PSO

Vision-based hardware driver assistance systems are the most important systems in the world because of their low cost and ability to provide information on driving environments. Improving safety and reducing accidents are the two main objectives of these systems. For this, in this paper a new Vision-based Hardware Advanced Driver Assistance System (VH-ADAS) based machine learning incorporating the hybridization of Support Vector Machine (SVM)-Histogram of Oriented Gradient (HOG) classifier and Particle Swarm Optimization (PSO) technique is proposed for traffic scenes from both video and captured images. First, the proposed system uses a feature extraction method based on the HOG. Then, the Particle Swarm Optimization technique is used for selection and so to optimize the features. The SVM method is applied to obtain fast detection and high accuracy. Finally, a hardware synthesizable architecture of the complete system was developed and then co-simulation validity was succeeded using the Matlab-Vivado System Generator (VSG) and a Field Programmable Gate Array (FPGA). The results show that the proposed new system supports real-time detection in both images and video. Also, they show that the proposed vehicle detection method is competitive in terms of parallel run time with only 1.483 ms and in terms of accuracy rate with only 97.84%.

Using Artificial Intelligent to Model Predict the Biological Resilience With an Emphasis on Population of cyanobacteria in Jajrood River in The Eastern Tehran, Iran.

Prediction of bio-resilience in water resources such as rivers is important for better management of land-use systems and water resources. This study has proposed the use of artificial intelligent (AI) models for assessing the relationship among the biological conditions in Jajrood River. For this purpose, the qualitative monthly data of the river related to 2008-2018 were applied. Different resilience indicators for preparation of scenarios were determined using the canonical correlation analysis (CCA) method. Appropriate time-series scenarios (5scenarios) were modelled via Gene Expression Programming (GEP) plus Support Vector Machine (SVM), the bio-indicators were predicted. In order to reduce the error, the wavelet hybrids (W-GEP and W-SVM) were also used for modelling. Validation of the models was performed using Nash-Sutcliffe efficiency (E), root mean square error (RMSE), and mean‏ absolute error (MAE). In all the models investigated, Scenario 3 and Scenario 4 had the highest and lowest accuracies as 0.98 and 0.33 in validation, respectively. The third scenario combined with NO3 -, BODt-1, BOD, PO3-, and Q provided the best results. Then, the values of 0.98, 0.94, 0.82, and 0.78 were obtained for its validation by WSVM, WGEP, SVM, and GEP models, respectively. These findings suggested the superiority of hybrid models and SVM over classical models and GEP in water quality assessment respectively. Examination of the scenarios revealed that NO3 - and DO had the highest and the lowest impact on Shannon index of Cyanophyceae algae over time, as a bio-indicator of water quality in the river, respectively.