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With the rise of ready-to-assemble furniture, driven by international giants like IKEA, assembly services were increasingly offered by the same retailers. When planning orders with assembly services, the estimation of the service time leads to additional difficulties compared to standard delivery planning. Assembling large wardrobes or kitchens can take hours or even days while assembling a chair can be done in a few minutes. Combined with the usually vast amounts of offered products, a lot of knowledge is required to plan efficient and exact delivery routes. This paper shows how an artificial neural network (ANN) can be used to accurately predict the service time of a delivery based on factors such as the goods to be delivered or the personnel providing the service. The data used include not only deliveries with assembly of furniture, but also deliveries of goods without assembly and delivery of goods requiring electrical installation. The goal is to create a solution that can predict the time needed based on criteria such the type of furniture, the weight of the goods, and the experiences of the service technicians. The findings show that ANNs can be applied to this scenario and outperform more classical approaches, such as multiple linear regression or support vector machines. Still existing problems are largely due to the provided data, e.g., a large difference between the number of short and longer duration orders, which made it harder to accurately predict orders with longer duration. [ABSTRACT FROM AUTHOR]

Accurate stock price prediction is critical for investment decisions in the stock market. To improve the performance of stock price prediction, this paper proposes a novel two-stage prediction model that consists of a decomposition algorithm, a nonlinear ensemble strategy, and three individual machine learning models. Specifically, in the first stage, the stock price time series is decomposed into a finite number of sub-series by variational mode decomposition (VMD). Subsequently, three individual machine learning models, namely support vector machine regression (SVR), extreme learning machine (ELM), and deep neural network (DNN), are separately employed to predict decomposed sub-series, and then the obtained sub-series predictions of each individual prediction model are aggregated to generate the preliminary stock price predictions. In the second stage, an ELM-based nonlinear ensemble strategy is employed to combine preliminary stock price predictions. To verify the effectiveness of the proposed two-stage model, it is compared with a total of fourteen models in terms of accuracy evaluation, improvement percentage comparison, and statistical test. The empirical results demonstrate that the proposed two-stage model can obtain better performance than other competitor models. [ABSTRACT FROM AUTHOR]

This paper aims to design a multimodal teaching platform to facilitate users by learning oral Chinese as a Foreign language. The designed system intends to improve the oral learning experience, efficiency and fluency. Firstly, this paper describes the overall design of Chinese as a Foreign language system for speaking using a multimodal teaching mode. Secondly, it analyzes the architecture of the oral Chinese language as a Foreign language system by introducing specific functional modules and system processes. Thirdly, it uses the optimization algorithm of Support Vector Machine (SVM) based on fruit fly optimization to optimize the parameters by enhancing the degree of oral fluency of Chinese as a Foreign language, gradually accumulate the rules of oral learning, and complete the design of oral Chinese as a Foreign language system based on multimodal Teaching mode. Finally, it incorporates SVM and the improved SVM for Drosophila Algorithm (ISVM-DA) to optimize language learning by accurately classifying vocabulary and enhancing learning. The experimental results showed that the proposed system has the maximum Stability Ratio of 90%, substantial Sensitivity of 85%, and Recall values of 88% compared to existing systems by proving the good robustness of the system. The system designed in this paper can stimulate users' interest in learning oral Chinese as a Foreign language and improve the efficiency and level of users' learning oral Chinese as a Foreign language. [ABSTRACT FROM AUTHOR]

Correctly predicting up and down trends for stock prices is of immense important in the financial market. To further improve the prediction performance, in this paper we introduce five penalties: ridge, least absolute shrinkage and selection operator, elastic net, smoothly clipped absolute deviation and minimax concave penalty to logistic regressions with 19 technical indicators, and propose the five penalized logistic regressions to predict up and down trends for stock prices. Firstly, we translate the five penalized logistic log-likelihood functions into the five penalized weighted least squares functions and combine them with the tenfold cross-validation method to calculate the solution path to parameter estimators. Secondly, we combine the binomial deviation with cross-validation error as a risk measure to choose an appropriate tuning parameter for the penalty functions and apply the training set and the coordinate descent algorithm to obtain parameter estimators and probability estimators. Thirdly, we employ the testing set and the chosen optimal thresholds to construct two-class confusion matrices and receiver operating characteristic curves to assess the prediction performances to the five regressions. Finally, we compare the proposed five penalized logistic regressions with logistic regression, support vector machine and artificial neural network and found that the minimax concave penalty logistic regression performs the best in terms of the prediction performance to up and down trends for Google's stock prices. Therefore, in this paper we propose the five new prediction methods to improve the prediction accuracy of stock returns and bring economic benefits for investors. [ABSTRACT FROM AUTHOR]

The body of high-speed train is the most closely related part with passengers in the process of train operation. Traction motor is not only the power source of high-speed train, it is also the basic unit form that can reflect the reliable and stable operation of high-speed trains. The running safety of the train can be effectively protected by diagnosing the traction fault of the train. Based on research data on the development process of high-speed rail, this paper introduces the importance and pertinence of this research. Based on the study of separable linear SVM and nonlinear SVM, the composite fault diagnosis for traction motor using SVM technology is realized. After discussing the actual faults of current sensor and position sensor and their causes, a dynamic model based on traction motor is established and its parameters are simulated. Finally, a diagnostic observer is designed based on an example to determine the sensitivity of the composite fault to the high-speed rail traction motor, and experiments are designed to verify the detection results. Finally, this paper concludes by describing, and then points out some deficiencies and shortcomings of the research, which guides the direction and lays the data foundation for the subsequent research work. This paper studies the load fault of traction motor through a comprehensive analysis of SVM and sensor technology, which improves the effectiveness and practical efficiency of fault detection. [ABSTRACT FROM AUTHOR]

In binary classification, kernel-free quadratic support vector machines are proposed to avoid difficulties such as finding appropriate kernel functions or tuning their hyper-parameters. Furthermore, Universum data points, which do not belong to any class, can be exploited to embed prior knowledge into the corresponding models to improve the general performance. This paper designs novel kernel-free Universum quadratic surface support vector machine models. Further, this paper proposes the ℓ 1 norm regularized version that is beneficial for detecting potential sparsity patterns in the Hessian of the quadratic surface and reducing to the standard linear models if the data points are (almost) linearly separable. The proposed models are convex, so standard numerical solvers can be utilized to solve them. Moreover, a least squares version of the ℓ 1 norm regularized model is proposed. We also design an effective tailored algorithm that only requires solving one linear system. Several theoretical properties of these models are then reported and proved as well. The numerical results show that the least squares version of the proposed model achieves the highest mean accuracy scores with promising computational efficiency on some artificial and public benchmark data sets. Some statistical tests are conducted to show the competitiveness of the proposed models. [ABSTRACT FROM AUTHOR]

In recent years, technological advancements have been replicated in various industries, including sports medicine. Recent developments, such as big data analytics and data mining, which have revolutionized medical services in sports, are apparent in this transformation. This technological shift is motivated by the need to enhance athletic performance, prevent injuries, and offer individualized health advice. Modern lifestyles have simultaneously increased people's attention to their health, creating a demand for better medical services. However, China's ability to provide superior medical care needs to be improved due to a lack of medical resources and an ever-increasing patient population. To address these challenges, this research paper presents an integrated framework that leverages Spark-based big data analytics and the XGBoost algorithm. The framework aims to provide a robust sports medical service encompassing real-time health monitoring and data-driven insights. Powered by the formidable distributed computing platform Spark, it adeptly manages extensive sports data generated during training and events, facilitating instant health evaluations. Incorporating the XGBoost algorithm for data mining amplifies health prediction and recommendation capabilities. Renowned for its predictive prowess, XGBoost excels in discerning intricate sports data patterns and trends. Its proficiency in tackling intricates feature selection and modeling tasks ensures precision and actionable insights. Empirical findings underscore substantial enhancements in sports medical services. When applied to chronic disease datasets, the XGBoost algorithm garnered an impressive 93% trust rate. In contrast to conventional methods like K-Nearest Neighbors (KNN), Random Forest (RF), Decision Trees (DT), Support Vector Machines (SVM), Naïve Bayes (NB), and Logistic Regression (LR), the proposed framework consistently outperforms these established techniques. This remarkable performance underscores the transformative potential of the integrated framework in revolutionizing sports medical services. [ABSTRACT FROM AUTHOR]

In recent years, the escalating global concerns surrounding climate change have placed a growing emphasis on achieving dual objectives: reducing carbon emissions and achieving carbon neutrality. Businesses and organizations are under mounting pressure to align their operations with these crucial environmental goals. This paper introduces the concept of an enterprise carbon performance evaluation index system (ECPIS) to strike a balance between economic development and environmental protection and enhance overall enterprise management and development strategies. The ECPIS framework is constructed using machine learning and advanced data mining techniques, particularly support vector machines (SVM). Its core purpose is to provide enterprises with a systematic tool to gauge, analyze, and enhance their carbon performance, addressing dual carbon objectives. ECPIS development hinges on data mining techniques, extracting insights from diverse data sources to construct a comprehensive system that accommodates these dual carbon goals' intricacies. Its methodology includes data collection, preprocessing, feature selection, and data mining algorithms to unveil vital patterns and relationships within data. It conforms to international standards, establishing a tailored carbon performance index system aligned with China's national conditions. It validates carbon-related enterprise data and employs data mining's association rules to uncover pertinent carbon performance information. The results obtained from ECPIS are auspicious, boasting an experiential accuracy rate of 97.5%. This level of accuracy surpasses that achieved by other algorithms like K-Nearest Neighbors (KNN), Random Forest (RF), Decision Trees (DT), Naïve Bayes (NB), and Logistic Regression (LR). ECPIS stands out by considering various factors, including carbon emissions reduction, energy consumption, supply chain efficiency, and financial performance indicators. This multifaceted approach enables enterprises to gain a comprehensive understanding of their carbon performance and identify areas for improvement. [ABSTRACT FROM AUTHOR]

In recent years, the rapid development of the Internet has promoted the continuous expansion of the scale of China's tourism industry, and the amount of tourism data has surged. However, tourists need help bringing personal interest and high-value data from the plethora of tourism information. The rise of artificial intelligence has transformed traditional tourism into an intelligent, data-driven industry. This shift has generated vast tourism data, offering both opportunities and challenges. The paper discusses an AI and IoT-based Intelligent Tourism Recommendation System (ITRS) that offers travelers predefined itineraries, personalized suggestions, and tourism insights. This system simplifies attraction discovery, unveiling hidden value within tourism data at the intersection of AI and IoT. The present study thoroughly investigates AI-based recommendation algorithms before delving into the system's architecture. It categorizes user-based, project-based, and article-based collaborative filtering methodologies tailored to specific goals. First, thoroughly examine AI-based recommendation algorithms before delving into the system architecture. Second, categorize collaborative filtering methods as user-based, project-based, and article-based, each tailored to specific objectives. Third, delve into the Apriori algorithm's complexity within the context of weighted association rules and introduce an enhanced iteration for improved efficiency. The proposed scheme encompasses an elaborate ITRS plan featuring a user interest model and a client module, crucial for the computation and analysis of users' long-term and short-term interests. Rigorous performance testing confirms the ITRS's superiority across varying support levels, with experimental results demonstrating the Apriori algorithm's exceptional accuracy, achieving a 94.3% improvement over other methods. The Apriori algorithm is better than traditional recommendation algorithms such as Linear Regression, Logistic Regression, Decision Trees, Random Forest, Support Vector Machines, K-nearest neighbor, Naive Bayes, and XGBoost. [ABSTRACT FROM AUTHOR]

The development of the Internet and broadcasting technologies has made it possible to access a huge amount of multimedia content. Several cutting-edge technologies are working in parallel to make electronic gadgets automatic. The Internet of things (IoT) has become the main player in this race due to its significance in data collection and smart control. However, with rapid data generation from IoT devices, it has become difficult to manage such a huge volume of data and ensure its security and privacy. Among other fields, the music industry is also going to benefit from the power of these cutting-edge technologies, such as machine learning and IoT. Music can be created by utilizing several tools and instruments; the piano is one of them. The piano, as a representative of stringed instruments, is rapidly spreading to millions of households with its wide range, beautiful melodies, and strong aural complex. As existing keyword-based music recognition methods are increasingly limited in their application to large-scale song searches on the Internet, more and more researchers are turning to the study of content-based music recognition techniques. In this paper, we propose a musical identification system for piano performance evaluation by utilizing the power of IoT and support vector machine (SVM). The data from the piano was collected by utilizing different IoT devices and microphones. After the data was collected, we performed several preprocessing steps to prepare the collected data for SVM. To concentrate on the important information, key features were extracted from the input data, which include the Mel frequency cepstral coefficient and other piano-relevant features such as pedal usage patterns, pressure fluctuations, and keypress timings. These features were given as input to the SVM model for music recognition. The results of the experiment indicate that the average recognition rate of SVM for five songs is 61.2%, and the average recognition rate of HMM is 47.44%. The average recognition rate of SVM is 13.76% higher than that of HMM. Therefore, the SVM-based piano performance musical identification system is the basis of automatic music annotation and plays a very important role in the fields of computer music accompaniment, massive search of cover songs, and audio signal segmentation and matching. [ABSTRACT FROM AUTHOR]

It is found that, chronic kidney disease (CKD) is prevalence worldwide. Quality of life (QoL) in terms of health became an essential measure for patients with CKD. This paper uses the real-time dataset of CKD patients collected from reputed medical dialysis unit in Chennai, India. We measure the inter and intra class variations between the clinical and non-clinical attributes. Principal component analysis (PCA) is applied on twelve clinical (biomarkers) and eight non-clinical (comorbiditity) attributes to find salient among them. ANOVA is applied only on reduced attributes to calculate the correlation between the target variables such as mortality, age and gender. The characteristics of the attributes and its discriminating nature is evaluated using various well known classifiers such as Logistic Regression, K-nearest neighbor, support vector machine, Neive Bayes, decision tree, random forest and artificial neural network. The performance of the classifiers are evaluated using parameters such as confusion matrix, accuracy, F-measure, precision and recall. It is found that, the covariance of the attributes linearly separates the output space of target variables that are considered and the performance is encouraged. [ABSTRACT FROM AUTHOR]

Ovarian cancer is one of the most common types of cancer in women. Correct differentiation between benign and malignant ovarian tumors is of immense importance in medical fields. In this paper, we introduce group penalized logistic regressions to enhance diagnosis accuracy. Firstly, we divide 349 ovarian cancer patients into two sets: one for learning model parameters, and the other for assessing prediction performance, and select 46 variables from 49 traits as the predictor vector to construct GLASSO/GSCAD/GMCP penalized logistic regressions with 11 groups. Secondly, we develop group coordinate descent (GCD) algorithm and its specific pseudo code to simultaneously complete group selection and group estimation, introduce the tenfold cross validation (CV) procedure to select the relatively optimal tuning parameter, and apply the testing set and Youden index to obtain class probability estimator and class index information. Finally, we compute the accuracy, precision, specificity, sensitivity, F1-score and the area under ROC curve (AUC) to assess the prediction performance to the proposed group penalized methods, and found that GLASSO/GSCAD/GMCP penalized logistic regressions outperform three machine learning methods (ANN, artificial neural network; SVM, support vector machine; XGBoost, eXtreme gradient boosting) and three deep learning methods (CNN, convolutional neural network; DNN, deep neural network; RNN, recurrent neural network) in terms of accuracy, F1-score and AUC. [ABSTRACT FROM AUTHOR]

Due to increasingly global cultural exchanges, people are more conscious about the importance of mastering a foreign language. Language translation and helping students in the colleges with their learning abilities to master one or more languages using computer-aided translation and training system has become an important research. Intelligibility is one of the most important indicators for translation evaluation of Chinese language spoken by English learners, but there is a big gap between the automatic translation intelligibility score and the human expert score. Based on this concept, this paper addresses the existing challenges faced by translation detection software to analyze their limitations and characteristics, especially related to learners' Chinese translation experience. To enhance translation recognition accuracy, we employ a data mining approach to identify imaging factors associated with incorrect translation by college students. First, we develop a data mining recognition and detection method, leading to improved accuracy in translation recognition by utilizing association specification and classification. The classifier is then utilized to distinguish various types of translation errors, while also incorporating a translation intelligibility evaluation to provide error correction feedback to learners. The proposed model outperforms standard translation methods and alternative approaches, achieving impressive identification accuracy (94.58%), efficiency (88.12%), and a low missed detection rate (5.32%). Moreover, it significantly reduces the translation mistakes, including grammatical (5.21%), semantic (3.45%), and contextual (2.18%) errors. The support vector machine (SVM) classifier outshines artificial neural network (ANN) classification in error detection, and the suggested scoring system demonstrates its superiority with the highest average correlation coefficient (0.822) compared to expert scoring. Experimental results confirm the improved accuracy and effectiveness of the proposed translation recognition and detection method. [ABSTRACT FROM AUTHOR]

To effectively analyze the working state of the air circulation system of the aircraft at high altitude, it is necessary to conduct simulation analysis on the ground. In this paper, a simulated annealing-grasshopper optimization algorithmtion algorithm support vector machine is proposed to establish the overall simulation model of the circulation system of the aircraft and to conduct fault injection analysis. By introducing the support vector machine to classify the results of the system and applying of grasshopper algorithm to optimize the support vector machine with methods such as simulated annealing and position migration, the optimal parameter values can be obtained. The results indicate that the simulation system can effectively simulate the temperature changes of the aircraft in various operating states; the optimized support vector machine can effectively distinguish the fault types of the aircraft component outlet; meanwhile, the system convergence is accelerated to avoid falling into the local optimal problems. [ABSTRACT FROM AUTHOR]

Automatic target detection plays a significant role during war operations. The concept behind automatic target detection in war is military object recognition from the captured images. For object recognition in the given image, convolutional neural network (CNN) architectures are used efficiently. However, CNN suffers from a problem of location invariant and their performance depends mainly on the size of the training set. Generally, the available training data will be in less proportion for military objects due to its operational and security issues. Due to these two issues of CNN, the performance of CNN may degrade abruptly. To address military object recognition, a relatively new neural network architecture based on capsule network (CapsNet) is introduced in this paper. A variant of CapsNet called the multi-level CapsNet framework is projecting in this paper for efficient military object recognition under the case of a small training dataset. The validation of the introduced framework is done on a dataset of military objects collected from the Internet. The dataset contains particularly five different military objects and similar civil ones. Experiments demonstrate that the proposed framework can accomplish a high recognition precision, superior to many other algorithms such as conventional support vector machines and transfer learning-based CNNs. [ABSTRACT FROM AUTHOR]

Nowadays, a healthcare field produces a huge amount of data; for processing those data, some efficient techniques are required. In this paper, a classification model is developed for heart disease prediction and the attribute selection is carried out through a modified bee algorithm. The prediction of heart disease through models will help the practitioners to make a precise decision about patient health. Heart disease dataset is obtained from the UCI repository. Dataset consists of 76 features and all those seventy-six features have not contributed equal information during the time of classification. In the entire attributes, some of the attributes will contribute a large amount of information at the time of classification task and some of the attributes will contribute only a small amount of information during the classification task. In this paper, a modified bee algorithm is used to identify the best subset of attributes from the entire features in the dataset; i.e., the training phase of classification only retains those features that are contributing more information during classification task and it will reduce the training time of classifiers. The experiment is analyzed with an obtained reduced subset of attributes by using the following classifiers such as support vector machine, Naïve Bayes and KNN. The experimental result shows that the support vector machine classifier will provide a good classification accuracy, true positive rate, true negative rate, false positive rate and false negative rate compared to Naïve Bayes and KNN. [ABSTRACT FROM AUTHOR]

This paper designs a maritime anomaly detection algorithm based on a support vector machine (SVM) that considers the spatiotemporal and motion features of trajectories. Since trajectories are two-dimensional, it is difficult to present their motion features. To accurately describe trajectory features, a novel trajectory feature extraction method based on statistical theory is proposed in this paper. This method maps trajectories onto a high-dimensional space, which can account for both the spatiotemporal features and motion features of the trajectories. With the proposed feature extraction method, the density-based spatial clustering of applications with noise algorithm is employed to recognize vessel traffic patterns by simultaneously considering the spatiotemporal and motion features. Then, an improved SVM is designed by employing a weighted hybrid kernel function and differential operator to detect anomalous behaviours from recognized vessel traffic patterns that include the spatiotemporal and motion characteristics. Compared with standard SVM, it can adaptively determine the optimal kernel function according to sample set. Finally, a numerical example based on automatic identification system data from the waters off Chengshan Jiao is fulfilled to verify the proposed algorithm effectiveness and accuracy. [ABSTRACT FROM AUTHOR]

Pattern recognition and data mining using support vector machine (SVM) have been the focus of widespread researches in recent decades. In SVM, a hyper-plane is designed to classify the training data. A challenge in SVM is that the parameters of hyper-planes are constants. As a result, there may be some critical points that will be classified into a wrong set. It should be mentioned that finding this hyper-plane is very similar to solving a regression problem using parametric or semi-parametric models in statistics. This is the main motivation of this paper. The contribution of this paper is combining SVM classifier and semi-parametric models (SP-SVM) to solve the aforementioned challenge. In fact, using semi-parametric linear model results in some serial linear decision boundaries with several slopes and intercepts. In other words, there are two types of kernels in the proposed SP-SVM: the kernels that perform nonlinear transformation of the input features and the kernels needed in the semi-parametric model. The validations have been done on Iris data set and also some other linearly non-separable classification problems. The accuracy of the proposed SP-SVM outperforms some related algorithms such as K-nearest neighbor (KNN)-based weighted multi-class twin support vector machines (KWMTSVM), support vector classification–regression machine for K-class classification (K-SVCR), twin multi-class classification support vector machines (twin-KSVC), intelligent particle swarm classifier (IPS-classifier) and random forest. The accuracy of SP-SVM is 97.33%. Thus, SP-SVM can play an important role in increasing the accuracy of industrial machines that perform classifications, for example, agricultural products. [ABSTRACT FROM AUTHOR]

This paper develops an integrated framework to forecast the volatility of crude oil prices by considering the impacts of extreme events (structural breaks). The impacts of extreme events are vital to improving prediction accuracy. Aiming to demonstrate the crude oil price fluctuation and the impacts of external events, this paper employs the complementary ensemble empirical mode decomposition (CEEMD). It decomposes the crude oil price into some constituents at various frequencies to extract a market fluctuation, a shock from extreme events and a long-term trend. The shock from extreme events is found to be the most crucial element in deciding the crude oil prices. Then we combine the iterative cumulative sum of squares (ICSS) test with the Chow test to get the structural breaks and analyze the extreme event impacts. Finally, this paper combines the structural breaks, the autoregressive integrated moving average (ARIMA) model, and the support vector machine (SVM) to make a forecast of the crude oil prices. The empirical process proves that the CEEMD-ARIMA-SVM model with structural breaks performs the best when compared with the other ARIMA-type models and SVM-type models. The framework offers an insightful view to help decision-makers and can be used in many areas. [ABSTRACT FROM AUTHOR]

Lung cancer is a leading deadly form of the illness that is the cause of one million deaths around the world every year. Identification of lung nodules on chest computed tomography (CT) scans has become an extremely necessary procedure in modern medicine as a result of the present state of things in the field of medicine. This occurs as a result of the fact that lung cancer does not display symptoms until it has progressed. Early detection and precise diagnosis of suspected malignant lung nodules will improve therapy and lower lung cancer mortality. This paper proposes a transfer learning-based approach to classify lung CT images and detect the cancer. The input images are first sent to the pre-trained hybrid model consisting of AlexNet, VGG and GoogleNet. The model extracts features from the images, and these features are sent to the multi-class support vector machine (SVM) for classification. As in any deep learning model, the data are first trained and then tested on real-time images. The training of the model is done with the help of IQ-OTH/NCCD dataset which contains 1190 images belonging to three categories, normal, benign and malignant. The data are divided into different ratios while training and validation stages to test the efficiency of the proposed model. The proposed model obtained a superior accuracy of 97% which is much higher than the existing models. [ABSTRACT FROM AUTHOR]

Deception is a well-known term that involves acting in a way that it leads another person to believe something that is not true. Deception is a very common occurrence in daily life, and many times it becomes a big problem for national security. To cope with this problem, deception detection has gained a lot of attention recently. In this paper, we have tried to come up with a deceit identification system where we have used electroencephalograph (EEG) data collected by performing a concealed information test. To improve the performance of the system, first we have tried to select the optimal subset of the EEG channels using binary particle swarm optimization and secondly performed support vector machine hyper-parameter optimization using continuous version of PSO. The proposed model is validated using EEG dataset. The performance of the proposed system has resulted in increase of accuracy from 76.98 to 96.45% which is a significant improvement. Also, the proposed approach outperformed in terms of sensitivity, specificity, F1-score and G-measure when compared with state-of-the-art models. [ABSTRACT FROM AUTHOR]

Achieving accurate turning point (TP) forecasting techniques can provide investors with tools to make profitable trading decisions, by offering the opportunity to buy = low and sell = high. Forecasting stock time series TP is known to be one of the most important but also extremely challenging issues. The stock TP forecasting problem is a classification problem where the decision to buy or sell a stock has to be made at each point in time. The first step to achieve this goal is to identify and label the TPs present in the history of the stock time series through a detection technique. Once the labels are achieved, the corresponding classifier is trained to assign labels to new, invisible observations. According to the literature, improving the accuracy of the classifier leads to an increase in the forecasted TPs' accuracy. Various intelligent classification techniques with different properties have been developed, Over the past decades, to achieve more accurate classification results. However, the parameter estimation of classifiers is done by optimizing a continuous distance-based cost function; and since the goal of the classification problem is discrete, applying a continuous cost function is unreasonable, or at least quite inefficient. To address this, we introduce a novel discrete learning-based methodology for estimating the unknown parameters of the classifier and thereby forecasting stock TPs. In contrast to the existing classification techniques, the proposed methodology is tailored to the discrete goal function of the classification. The present study chooses the multilayer perceptron (MLP), which is one of the most widely-used intelligent classification techniques, to implement the proposed methodology. Although it can be generally shown that the forecasting performance of the proposed discrete learning-based multilayer perceptron model (DIMLP) will not be worse than that of its continuous learning-based counterparts, this paper compares it to other classification techniques to determine the superiority of the proposed DIMLP model. Experimental results using randomly selected datasets from the Shanghai Stock Exchange show that the proposed DIMLP model is superior to its counterparts including classical MLP, Support Vector Machine, Random Forest, Decision Tree, K-Nearest Neighbor, Logistic Regression, Radial Basis Function Network, Generalized Logistic Regression, Probabilistic Neural Network, and AdaBoost. Consequently, further support is provided for the hypothesis that using a discrete learning-based function as a cost function for classification purposes is appropriate and effective. [ABSTRACT FROM AUTHOR]

Multi-view learning improves the performance of existing learning tasks by using complementary information between multiple feature sets. In the latest research, multi-view learning model using privileged information is proposed; specific models are PSVM-2V and MCPK. In these models, views complement each other by acting as privileged information policies; however, a single view contains privileged information that can guide the classifier, and the existing framework does not consider it. In order to use this information to correct multi-view support vector machine classifier, we propose a framework for generating a series of small-scale views based on information hidden in a single view, which extends the original multi-view parallel structure to a hierarchical structure with sub-view mechanism. In this paper, two sub-view learning structures SL-PSVM-2V and SL-MCPK are constructed. The two new models fully exploit the data features in the view. Similarly, they follow the principles of consistency and complementarity. We use the standard quadratic programming solver to solve the new model. In 55 groups of classification experiments, the proposed model improves the classification accuracy by about 1.91% on the original basis. SL-MCPK ranks 1.3846 on average in the accuracy experiment, indicating that they have good classification ability. In addition, the computational time statistics and noise data set experiments are carried out to prove the effectiveness of the proposed method from multiple perspectives. [ABSTRACT FROM AUTHOR]

In the prediction of turning points (TPs) of time series, the improved model of integrating piecewise linear representation and weighted support vector machine (IPLR-WSVM) has achieved good performance. However, due to the single data source and the limitation of algorithm, IPLR-WSVM has encountered challenges in profitability. In this paper, a model fusion method based on multi-source heterogeneous data and different learning algorithms is proposed for the prediction of TPs (MF-MSHD). Multi-source heterogeneous data include weighted unstructured and structured information with different granularities. RF, WSVM, BPNN, GBDT, and LSTM are selected to be the learning algorithms. The differences among meta-models are constructed by different inputs and algorithms as much as possible, and a model fusion rule is designed to determine the final TPs. Moreover, the TPs are generated based on the characteristics of individual stock. For sentiment analysis, a more accurate sentiment dictionary of stock market comments is established. Specifically, the fine-grained data is introduced to jointly determine the accurate trading moment. The prediction level of the proposal improves the accuracy and profitability, and also outperforms the composite indexes. Experimental results show that the profit rate of randomly selected stocks in MF-MSHD reaches 0.5172, while the highest value is 0.2841 in single meta-model and 0.0992 in buy and hold strategy, respectively. The other indicators including the accuracy are also modified. Compared with the increases of 0.1648, 0.4051, and 0.3397 in Shanghai Composite Index, Shenzhen Composite Index, and CSI 300 Index, MF-MSHD shows higher profitability in stock trading signal prediction. [ABSTRACT FROM AUTHOR]

Data Envelopment Analysis (DEA) is a widely used performance analysis tool, but for every newly added unit in a data set, it requires complete re-processing of the DEA model. In real applications, data sets are usually imprecise and enormously increasing, which demands a large computer processing time. A predictive capability of DEA, typically in uncertain environment, can solve this problem. This paper aims to establish an integrated DEA and support vector machine for regression (SVR) approach with imprecise data for efficiency estimation and prediction in optimistic and pessimistic environments. To illustrate the potential of the proposed approach, it is applied on Indian banks for evaluation and prediction of different efficiency measures (technical, pure technical, and scale) during the period 2011–2020. To study the sensitivity analysis and productivity of Indian banks' efficiency with time, DEA window analysis (window width five years) and Malmquist productivity index (MPI) in optimistic and pessimistic environments have been improvised with interval data. The new approach results in very reliable and accurate prediction (mean square error: 2 × 10 - 3 to 5 × 10 - 3 ) of different efficiency measures that benefits bank experts for planning and decision making on bank addition/merger. Other findings conclude that (i) technical efficiency is more influenced by managerial (pure technical) inefficiencies rather than bank size (scale efficiency) in all windows, and (ii) on the contrary to all other banks, Yes Bank Ltd. examined decreased productivity in both the environments, (iii) technical efficiency change has higher impact on MPI in both environments as compared to technological change. [ABSTRACT FROM AUTHOR]

In the lung computer-aided detection (Lung CAD) system, the region of interest (ROI) of lung nodules has more false positives, making the imbalance between positive and negative (true positive and false positive) samples more likely to lead to misclassification of true positive nodules, a cost-sensitive multikernel learning support vector machine (CS-MKL-SVM) algorithm is proposed. Different penalty coefficients are assigned to positive and negative samples, so that the model can better learn the features of true positive nodules and improve the classification effect. To further improve the detection rate of pulmonary nodules and overall recognition accuracy, a score function named F-new based on the harmonic mean of accuracy (ACC) and sensitivity (SEN) is proposed as a fitness function for subsequent particle swarm optimization (PSO) parameter optimization, and a feasibility analysis of this function is performed. Compared with the fitness function that considers only accuracy or sensitivity, both the detection rate and the recognition accuracy of pulmonary nodules can be improved by this new algorithm. Compared with the grid search algorithm, using PSO for parameter search can reduce the model training time by nearly 20 times and achieve rapid parameter optimization. The maximum F-new obtained on the test set is 0.9357 for the proposed algorithm. When the maximum value of F-new is achieved, the corresponding recognition ACC is 91%, and SEN is 96.3%. Compared with the radial basis function in the single kernel, the F-new of the algorithm in this paper is 2.16% higher, ACC is 1.00% higher and SEN is equal. Compared with the polynomial kernel function in the single kernel, the F-new of the algorithm is 3.64% higher, ACC is 1.00% higher and SEN is 7.41% higher. The experimental results show that the F-new, ACC and SEN of the proposed algorithm is the best among them, and the results obtained by using multikernel function combined with F-new index are better than the single kernel function. Compared with the MKL-SVM algorithm of grid search, the ACC of the algorithm in this paper is reduced by 1%, and the results are equal to those of the MKL-SVM algorithm based on PSO only. Compared with the above two algorithms, SEN is increased by 3.71% and 7.41%, respectively. Therefore, it can be seen that the cost sensitive method can effectively reduce the missed detection of nodules, and the availability of the new algorithm can be further verified. [ABSTRACT FROM AUTHOR]

Students' performance prediction systems play a vital role in enhancing the educational performance inside universities, schools, and training centers. Big data can come from different resources such as examination centers, virtual courses, registration departments, e-learning systems. Extracting meaningful knowledge from educational data is a complex task, so reducing the data dimensionality is needed. In this paper, we proposed an enhanced binary genetic algorithm (EBGA) as a wrapper feature selection algorithm. Novel hybrid selection mechanism based on a k-means algorithm and electromagnetic-like mechanism (EM) method is proposed. K-means will cluster the population into a set of clusters, while EM will determine a value called a total force (TF) for each solution. Each cluster has an accumulated total force (ATF) (i.e., adding all TFs together). Selection process will select two solutions with the highest TF from the cluster, which has the highest ATF. We employed a hybrid machine learning approach between the proposed EBGA and five different classifiers (i.e., k-Nearest Neighbors (k-NN), Decision Trees (DT), Naive Bayes (NB), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA)). Two real case studies obtained from UCI Machine Learning Repository are used in this paper. Obtained results showed the ability of the proposed approach to enhance the performance of the binary genetic algorithm. Moreover, the performances of all classifiers are improved between 1 % and 11 % . [ABSTRACT FROM AUTHOR]

At present, the COVID-19 epidemic is still spreading at home and abroad, and the foreign exchange market is highly volatile. From financial institutions to individual investors, foreign exchange asset allocation has become important contents worthy of attention. However, most intelligent optimization algorithms (hereinafter IOAS) adopt the existing data and ignore the forecasted one in the foreign exchange portfolio allocation, which will result in a huge difference between portfolio allocation and actual demand; at the same time, many IOAS are less adaptable and have lower optimization ability in portfolio problems. To solve the aforementioned problems, this paper first proposed a DETS based on hybrid tabu search and differential evolution algorithms (DEAs), which has excellent optimization ability. Subsequently, the DETS algorithm was applied to support vector machine (SVM) model. Experiments show that, compared with other algorithms, the MAE and RMSE obtained by using DETS optimization parameters are reduced by at least 3.79 and 1.47%, while the CTR is improved by at least 2.19%. Then combined with the DETS algorithm and Pareto sorting theory, an algorithm suitable for multi-objective optimization was further proposed, named NSDE-TS. Finally, by applying NSDE-TS algorithm, the optimal foreign exchange portfolio is acquired. The empirical analysis shows that the Pareto front obtained by this algorithm is better than that of NSGA-II. Since the lower the uniformity index and convergence index, the stronger the optimization performance of the corresponding algorithm, compared with NSGA-II, its uniformity and convergence index decreased by 15.7 and 39.6%. [ABSTRACT FROM AUTHOR]

Feature selection and instance selection are two data preprocessing methods widely used in data mining and pattern recognition. The main goal is to reduce the computational cost of many learning tasks. Recently, joint feature and instance selection has been approached by solving some global optimization problems using meta-heuristics. This approach is not only computationally expensive, but also does not exploit the fact that the data usually have a structured manifold implicitly hidden in the data and its labels. In this paper, we address joint feature and instance selection using scores derived from discriminant analysis theory. We present three approaches for joint feature and instance selection. The first scheme is a wrapper technique, while the other two schemes are filtering techniques. In the filtering approaches, the search process uses a genetic algorithm where the evaluation criterion is mainly given by the discriminant analysis score. This score depends simultaneously on the feature subset candidate and the best corresponding subset of instances. Thus, the best feature subset and the best instances are determined by finding the best score. The performance of the proposed approaches is quantified and studied using image classification with Nearest Neighbor and Support Vector Machine Classifiers. Experiments are conducted on five public image datasets. We compare the performance of our proposed methods with several state-of-the-art methods. The experiments performed show the superiority of the proposed methods over several baseline methods. [ABSTRACT FROM AUTHOR]

Software fault prediction (SFP) plays a vital role into fostering high quality throughout the software development process. It allows to identify the fault-prone modules in early development phases and facilitates the focused and effective testing over the fault-prone modules. Machine learning (ML)-based classifiers are prominently being used for fault prediction in the software industry. The accuracy of the ML models depends upon the training data and its quality. The curse of high dimensionality adversely impacts the classification power of a ML model. The presence of inter-correlated, insignificant and/or redundant features (or attributes) in the training data hinders the performance of ML classifiers. Feature preprocessing (or feature selection (FS)) is the solution to this issue. Meta-heuristics is the key method to find out the most significant feature subset. In this paper, a novel feature selection method is devised using mathematical diversification for genetic evolution. It avoids the local optimums by utilizing arithmetic diversification among the candidate solutions (or populations). The survival of fittest is the working principle of evolving populations with crossover and mutation operations. The selected feature subset is fed to five classification algorithms, namely artificial neural network, support vector machine, decision tree, k-nearest neighbor and naïve Bayes. The proposed model is trained and tested over five datasets from NASA corpus, namely CM1, JM1, KC1, KC2 and PC1. In total, 100 SFP models are implemented (4 feature selection methods × 5 datasets × 5 classification algorithms). From the experiments, it is observed that the SFP models with proposed feature selection technique of evolving populations with mathematical diversification (FS-EPwMD) are better than other models. It can be concluded that the proposed SFP model built using proposed FS-EPwMD with artificial neural networks performs statistically best among all the competing 100 SFP models irrespective of the datasets used. [ABSTRACT FROM AUTHOR]

Skin diseases are the most important worrying problems in societies because it affects the patients both physically and psychologically. Skin disease is one of the highly prone to risk with an association of climatic factors around the world. Predicting the skin disease cases associated with influencing factors is the most crucial task. It is very difficult task to identify the appropriate and optimal features for skin disease from the large volume of health sector data available in the world. Previous researchers applied different types of ensemble features selection techniques for the appropriate selection of features which gives highest accuracy with minimum computation time. Classification rate of any algorithm depends on feature extraction techniques and classifier used for classification purpose. Data availability is one of the most significant drawbacks in the health sector if data are available that might be in raw format. Filling missing value and type conversion almost takes 70% of the time. The missing value can be addressed by statistical parameters such as mean, average and median with stand mechanism in machine learning. The objective of this paper is the selection of significant attributes and removes irrelevant features that affect model performance. The performance of skin disease data can be experimented through K-nearest neighbor, support vector machine and random forest classifier. The entire research is carried out on the real-time dataset. The efficiency of the proposed approach is measured through confusion matrix, accuracy, F-measure, precision and recall. [ABSTRACT FROM AUTHOR]

Support vector machine (SVM) is a popular and important machine learning technique based on the central idea of maximizing the minimummargin , i.e., the smallest distance from the training points to the classification boundary. However, recent theoretical studies revealed that maximizing the minimum is not crucial to the generalization performance, while the margindistribution is essential. Furthermore, kernel-free quadratic support vector machine becomes a new trend in the SVM area since it can avoid some difficulties such as finding appropriate kernel functions and tuning their hyper-parameters. This paper proposes two novel non-kernel quadratic surface optimal margin distribution machine (QSODM L and QSODM) models. Their basic idea is to optimize the margin distribution of training points by maximizing the margin mean, minimizing the margin variance and classifying data points by directly constructing a quadratic surface in the original space. These proposed models are convex so that they can be solved by some well-known solvers. Moreover, in order to improve the efficiencies of proposed models on large-scale data sets, a stochastic gradient descent with variance reduction (SVRG) algorithm is proposed to solve the proposed models. The numerical results on some artificial and benchmark data sets verify that QSODM L and QSODM are superior to some other well-known SVM models in classification accuracy and computational efficiency. [ABSTRACT FROM AUTHOR]

Deep learning grabs a center attraction in industries, deep learning techniques are having great potential and recently these potentials are applied to healthcare problems, including computer-aided detection/diagnosis, disease prediction. Deep learning techniques are playing an important role in the classification and prediction of the diseases. The popularity of deep learning approaches is because of their ability to handle a large amount of data related to the patients with accuracy, reliability in a short span of time. However, the practitioners may take time in analyzing and generating the reports. In this paper, we have proposed a Deep Neural Network-based classification model for the classification of Parkinson's disease. Our proposed method is one such good example giving faster and more accurate results for the classification of Parkinson's disease patients with excellent accuracy of 94.87%. We have also compared the results with other existing approaches like linear discriminant analysis, support vector machine, K-nearest neighbor, decision tree, classification and regression trees, random forest, linear regression, logistic regression, multi-layer perceptron, and Naive Bayes. [ABSTRACT FROM AUTHOR]

The K-nearest neighbor-weighted multi-class twin support vector machine (KWMTSVM) is an effective multi-classification algorithm which utilizes the local information of all training samples. However, it is easily affected by the noises and outliers owing to the use of the hinge loss function. That is because the outlier will obtain a huge loss and become the support vector, which will shift the separating hyperplane inappropriately. To reduce the negative influence of outliers, we use the ramp loss function to replace the hinge loss function in KWMTSVM and propose a novel sparse and robust multi-classification algorithm named ramp loss K-nearest neighbor-weighted multi-class twin support vector machine (RKWMTSVM) in this paper. Firstly, the proposed RKWMTSVM restricts the loss of outlier to a fixed value, thus the negative influence on the construction of hyperplane is suppressed and the classification performance is further improved. Secondly, since outliers will not become support vectors, the RKWMTSVM is a sparser algorithm, especially compared with KWMTSVM. Thirdly, because RKWMTSVM is a non-differentiable non-convex optimization problem, we use the concave–convex procedure (CCCP) to solve it. In each iteration of CCCP, the proposed RKWMTSVM solves a series of KWMTSVM-like problems. That also means RKWMTSVM inherits the merits of KWMTSVM, namely, it can exploit the local information of intra-class to improve the generalization ability and use inter-class information to remove the redundant constraints and accelerate the solution process. In the end, the clipping dual coordinate descent (clipDCD) algorithm is employed into our RKWMTSVM to further speed up the computational speed. We do numerical experiments on twenty-four benchmark datasets. The experimental results verify the validity and effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

Statistical process control (SPC) has proven to be an effective tool for measuring, controlling, and improving a process through the application of statistical procedures. The most valuable SPC tool is control charts, however, if data assumptions are not met, alterations of the statistical performance of traditional control charts may be expected. In practice, the independence assumption is commonly violated in many discrete and continuous industrial processes. Recently, machine learning has been applied in the field of control chart pattern recognition (CCPR) in order to detect and identify abnormal patterns associated with assignable causes. In several instances, the performance of SVMs has been proven superior to other machine learning algorithms. In this paper, a SVM-GA-based monitoring system is presented for fault identification in AR(1) auto-correlated processes. A two stages classification scheme is presented. The initial classification stage is centered on abnormal pattern identification. The integration of a genetic algorithm in this stage boosts the performance of the SVM by simultaneously optimizing the input feature vector, the classifiers' hyper-parameters, and the window size. A second stage provides additional information on the detected abnormal pattern in order to facilitate the fault diagnosis. The results showed a good accuracy performance on both classification stages. The proposed monitoring system yielded better classification accuracies for positive autocorrelation levels than it did for their negative counterparts. The worst classification accuracies were obtained when autocorrelation was not present. [ABSTRACT FROM AUTHOR]

AdaBoost methods find an accurate classifier by combining moderate learners that can be computed using traditional techniques based, for instance, on separating hyperplanes. Recently, we proposed a strategy to compute each moderate learner using a linear ensemble of weak classifiers that are built through the kernel support vector machine (KSVM) hypersurface geometry. In this way, we apply AdaBoost procedure in a nested loop: Each iteration of the inner loop boosts weak classifiers to a moderate one while the outer loop combines the moderate classifiers to build the global decision rule. In this paper, we explore this methodology in two ways: (a) For classification in principal component analysis (PCA) spaces; (b) For multi-class nonlinear discriminant PCA, named MNDPCA. Up to the best of our knowledge, the former is a new AdaBoost-based classification technique. Besides, in this paper we study the influence of kernel types for MNDPCA in order to set a near optimum configuration for feature selection and ranking in PCA subspaces. We compare the proposed methodologies with counterpart ones using facial expressions of the Radboud Faces database and Karolinska Directed Emotional Faces (KDEF) image database. Our experimental results have shown that MNDPCA outperforms counterpart techniques for selecting PCA features in the Radboud database while it performs close to the best technique for KDEF images. Moreover, the proposed classifier achieves outstanding recognition rates if compared with the literature techniques. [ABSTRACT FROM AUTHOR]

Recognition of Devanagari ancient handwritten character is an important task for resourceful contents' exploitation of the priceless information contained in them. There are numerous Devanagari ancient handwritten documents from fifteenth to the nineteenth century. This paper presents an optical character recognition system for the recognition of Devanagari ancient manuscripts. In this paper, improved recognition results for Devanagari ancient characters have been presented using the scale-invariant feature transform (SIFT) and Gabor filter feature extraction techniques. Support vector machine (SVM) classifier is used for the classification task in this work. For experimental results, a database consisting of 5484 samples of Devanagari characters was collected from various ancient manuscripts placed in libraries and museums. SIFT- and Gabor filter-based features are used to extract the properties of the handwritten Devanagari ancient characters for recognition. Principle component analysis is used to reduce the length of the feature vector for reducing training time of the model and to improve recognition accuracy. Recognition accuracy of 91.39% has been achieved using the proposed system based on tenfold cross-validation technique and poly-SVM classifier. [ABSTRACT FROM AUTHOR]

With the rise of network on handheld devices, security of the network has become critical issue. Intrusion detection system is used to predict intrusive packets on network; two-step procedure has been used to predict the intrusion, i.e., feature selection and then classification. Firstly, unwanted and expandable features in data lead to network classification problem which affect the decision capability of the classifiers, so we need optimize feature selection technique. Feature selection technique used in this paper is based on the correlation information known as correlation-based feature selection (CFS). In this paper, CFS's search algorithm is implemented using Chaotic Flower Pollination Algorithm (CFPA) that logically selects the most favorable features for classification referred as CFPA-CFS. Further, hybridization of CFPA and support vector machine classifier is implemented and named as CFPSVM. Finally, novel IDS framework uses CFPA-CFS and CFPSVM in sequence to predict the intrusion. Further, performance of proposed framework is evaluated using two intrusion detection evaluation datasets, namely KDDCup99 and NSL-KDD. The results demonstrate that proposed CFPA-CFS contributes more critical features for CFPSVM to achieve better accuracy compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Support vector machine (SVM) is a popular supervised machine learning technique extensively applied to various real-life applications. A weakness of SVM, though, is that its efficiency rapidly decreases as the size of training dataset increases. This issue is more challenging for the devices and applications of IoT where the computing resource is quite constrained. As a result, numerous schemes have been proposed to decrease the training time of SVM, especially for the large-scale dataset. In this paper, a survey on the state-of-the-art methods for reducing the number of training data points and increasing the speed of SVM is presented. The existing methods are categorized into five types based on the approach employed for data reduction process, namely clustering-based, geometrical, distance-based, random sampling, and evolutionary methods. The key characteristics of the schemes are reviewed and compared against six practical benchmarks. This survey will be useful to select an appropriate method for the given target problem and develop a new approach for improving the performance of SVM dealing with the large-scale dataset. [ABSTRACT FROM AUTHOR]

Label-specific features learning is a multi-label learning framework that utilizes label feature extraction to solve a single example where multiple class labels exist simultaneously. As an essential multi-label learning method, label correlation learning has been widely used in multi-label classification learning. However, in the existing label-specific features learning, the label correlation measurement often assumes that the label correlations are a global structure or that the label correlations only have a local smoothness. In actual application scenarios, the two situations may occur together. This paper proposes a multi-label classification method by joint Label-Specific features and Global and Local label correlation learning, named LSGL. Firstly, we obtain the weight of the label-specific features of each class label utilizing the l 1 -norm and then learn high-order global label correlation and label local smoothness. By adding manifold regularization terms, we fully utilize the structural relationship between features and labels and mine global and local label association information. These processes are carried out in a unified optimization model, and each part learns and promotes each other. Finally, in the low-dimensional label-specific features representation learning is to carry out multi-label classification learning through the support vector machine and the extreme learning machine, respectively. A comparative study with state-of-the-art approaches and statistical hypothesis testing manifests the validity of the LSGL method and the features learned from label-specific features learning. [ABSTRACT FROM AUTHOR]

Road sign detection and recognition is an integral part of intelligent transportation systems. It increases protection by reminding the driver of the current condition of the route, such as notices, bans, limitations, and other valuable driving information. This paper describes a novel system for automatic detection and recognition of road signs, which is achieved in two main steps. First, the initial image is pre-processed using DBSCAN clustering algorithm. The clustering is performed based on color information, and the generated clusters are segmented using artificial neural networks (ANN) classifier. The resulting ROIs are then carried out based on their aspect ratio and size to retain only significant ones. Then, a shape-based classification is performed using ANN as classifier and HDSO as feature to detect the circular, rectangular and triangular shapes. Second, a hybrid feature is defined to recognize the ROIs detected from the first step. It involves a combination of the so-called GLBP-Color which is an extension of the classical gradient local binary patterns feature to the RGB color space and the local self-similarity feature. ANN, AdaBoost, and support vector machine have been tested with the introduced hybrid feature and the first one is selected as it outperforms the other two. The proposed method has been tested in outdoor scenes, using a collection of common databases, well known in the traffic sign community (GTSRB, GTSDB, and STS). The results demonstrate the effectiveness of our method when compared to recent state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Symbolic data are usually composed of some categorical variables used to represent discrete entities in many real-world applications. Mining of symbolic data is more difficult than numerical data due to the lack of inherent geometric properties of this type of data. In this paper, we use two kinds of kernel learning methods to create a kernel estimation model and a nonlinear classification algorithm for symbolic data. By using the kernel smoothing method, we construct a squared-error consistent probability estimator for symbolic data, followed by a new data transformation model proposed to embed symbolic data into Euclidean space. Based on the model, the inner product and distance measure between symbolic data objects are reformulated, allowing a new Support Vector Machine (SVM), called SVM-S, to be defined for nonlinear classification on symbolic data using the Mercer kernel learning method. The experiment results show that SVM can be much more effective for symbolic data classification based on our proposed model and measures. [ABSTRACT FROM AUTHOR]

Fuzzy parameterized fuzzy soft matrices (fpfs-matrices) which can model problems involving fuzzy objects and parameters are one of the mathematical tools used to deal with decision-making problems. To utilize soft decision-making methods via fpfs-matrices in machine learning is likely to draw much scholarly attention. In this paper, we propose Comparison Matrix-Based Fuzzy Parameterized Fuzzy Soft Classifier (FPFS-CMC) in order to transfer modeling success of fpfs-matrices to machine learning. We then compare FPFS-CMC with Fuzzy Soft Set Classifier (FSSC), FussCyier, Fuzzy Soft Set Classification Using Hamming Distance (HDFSSC), and Fuzzy k-Nearest Neighbor (Fuzzy kNN) in consideration of accuracy, precision, recall, macro-F-score, and micro-F-score performance metrics, and 15 datasets in UCI Machine Learning Repository. Besides, we compare the proposed classifier with the state-of-the-art Support Vector Machine (SVM), Decision Tree (DT), and Adaptive Boosting (AdaBoost) in terms of five performance metrics herein. Afterward, the results from the experiments are analyzed by employing the Friedman and Nemenyi tests to assess the statistical significance of the differences in performances. Both experimental and statistical results show that FPFS-CMC outperforms the others. Finally, we provide the conclusive remarks and some suggestions for further research. [ABSTRACT FROM AUTHOR]

The quality of information extracted from the vibration signals, and the accuracy of the bearing status detection depend on the methods used to process the signal and select the informative features. In this paper, a new hybrid approach is introduced in which the relatively new swarm decomposition (SWD) method and the optimized compensation distance evaluation technique (OCDET) are used to enhance the signal processing stage and to improve the optimal features selection process, respectively. Firstly, the vibration signals are decomposed into their Oscillatory Components (OCs) using the SWD. The feature matrix is constructed by computing the time-domain features for the OCs. The CDET method is consequently utilized to select the most sensitive features corresponding to the bearing status. On the other hand, The CDET approach contains a parameter called threshold which affects the number of the selected features. In this way, the hybrid optimization algorithm, which is a combination of the Particle Swarm Optimization (PSO) algorithm with the Sine–Cosine Algorithm (SCA) and the Levy flight distribution, has been used to select the optimal CDET threshold and improve the support vector machine (SVM) classifier. The proposed technique ability is evaluated by vibration signals corresponding to different bearing defects and various speeds. The results indicate the capability of the proposed fault diagnosis method in identifying the very small-size defects under various bearing conditions. Finally, the presented method shows better performance in comparison with other well-known methods in the most of the case studies. [ABSTRACT FROM AUTHOR]

In this paper, a new hybrid intelligent technique is presented based on the improvement in the feature selection method for multi-fault classification. The bearing conditions used in this study include healthy condition, defective inner ring, defective outer ring, and the faulty rolling element at different rotating motor speeds. To form the feature matrix, at first, the vibration signals are decomposed using empirical mode decomposition and wavelet packet decomposition. Then, the time and frequency domain features are extracted from the raw signals and the components are obtained from the signal decomposition. The high-dimensional feature matrix leads to increasing the computational complexity and reducing the efficiency in the classification accuracy of faults. Therefore, in the first stage of the feature selection process, the redundant and unnecessary features are eliminated by the FDAF-score feature selection method and the preselected feature set is formed. The FDAF-score technique is a combination of both F-score and Fisher discriminate analysis (FDA) algorithms. Since there may exist the features that are not susceptible to the presence of faults, the binary particle swarm optimization (BPSO) algorithm and the support vector machine (SVM) are used to select the optimal features from the preselected features. The BPSO algorithm is used to determine the optimal feature set and SVM classifier parameters so that the predictive error of the bearing conditions and the number of selected features are minimized. The results obtained in this paper demonstrate that the selected features are able to differentiate the different bearing conditions at various speeds. Comparing the results of this article with other fault detection methods indicates the ability of the proposed method. [ABSTRACT FROM AUTHOR]

Face recognition or verification remains a real challenge in the area of pattern recognition and image processing. The image acquisition process is a crucial step in which noise will inevitably be introduced, and in most cases this noise drastically decreases the accuracy of the classification rate of recognition systems, making them ineffective. This paper presents a novel approach to face recognition or verification, which increases the recognition rate in noisy environmental conditions. The latter is achieved by using the intrinsic face mode functions that result from applying a bi-dimensional empirical mode decomposition with Green's functions in tension to noisy images. Each image is individually decomposed, and noisy modes are discarded or filtered during reconstruction. Then, the extracted modes are used for classification purposes with canonical classifiers such as vector support machines or k-nearest neighbor classifiers. Experimental results show that this method achieves very stable results, almost independently of the amount of noise added to the image, due to the ability of decomposition to capture the noise in the first mode. Classification results using noisy images are at the same level as other algorithms proposed for the same databases but working on clean images and therefore are better than those obtained using classic image filters in noisy images. Moreover, unlike most of the available algorithms, the algorithm proposed in this paper is based on the input data (without the need to adjust parameters), making it transparent to the user. Finally, the proposed new approach achieves good results independently of the type of noise, the level of noise and the type of the database, which is not possible with other classical methods requiring parameter adjustment. [ABSTRACT FROM AUTHOR]

Tamil handwritten character recognition system enormously depends on its character features. This paper deals with the feature extraction and the three ways of feature predictions that are experimented in order to grasp features from various Tamil characters possessing variations in style and shape. Shape, shape ordering and location-based instances are the features predicted from the characters. The key features of this paper are the strip tree-based hierarchical formation which deals with the shape features of the characters, the implementation of the Z-ordering algorithm for addressing the structure ordering and finally the representation of PM-Quad tree that deals with extracting locations of the character features. A hierarchical classification algorithm based on support vector machine is used for predicting the character from its character features using divide-and-conquer procedure. Proof of this work shows that this work can address more characters and its varied shapes. [ABSTRACT FROM AUTHOR]

This paper deals with an adaptive technique-based design of load frequency controller for hybrid distributed generation systems. The hybrid system model is consisting of multiple power generating units and energy storage units. The proposed adaptive technique is the modified cuckoo search algorithm and support vector machine (SVM), which is utilized for obtaining the optimal solutions. In this paper, the proposed adaptive technique is utilized for optimized load frequency control (LFC) of a hybrid distributed generation systems model. The studied hybrid distributed generation systems consist of renewable/nonrenewable energy-based generating units such as wind turbine generator, solar photovoltaic, solar thermal power generator, diesel engine generator, fuel cell with aqua-electrolyzer, while energy storage units consist of battery energy storage system, flywheel energy storage system and ultra-capacitor. Here, fractional-order proportional–integral derivative (FOPID) controller is utilized to mitigate any frequency deviation owing to sudden generation/load change. The gain of the FOPID controller is optimized using the proposed adaptive technique. The proposed adaptive method-based LFC is implemented in the MATLAB/Simulink platform and analyzed their results. In order to analyze the effectiveness of the proposed adaptive technique, this is compared with that of other well-established techniques such as FOPID controller, GA with ANN-FOPID and SVM-FOPID controller. Moreover, the proposed tuned frequency controllers improve the overall dynamic response in terms of settling time, overshoot and undershoot in the profile of frequency deviation and power deviation of the interconnected hybrid power system. [ABSTRACT FROM AUTHOR]

Weighted twin support vector machine with local information (WLTSVM) is a novel algorithm for binary classification problems. It can exploit as much underlying correlation information as possible. Unfortunately, it remains challenging to apply WLTSVM into large-scale problems directly. Motivated by the sparse solution of WLTSVM, in this paper, a safe screening rule is proposed for WLTSVM, termed as SSR-WLTSVM. The SSR-WLTSVM can delete a majority of training samples before actually solving it to reduce the scale of WLTSVM. Therefore, computation time can be reduced greatly. More importantly, our screening rule is safe in the sense that the reduced problem can derive an identical optimal solution as the original one. Besides, a different neighbor k having a different effect on the performance of SSR-WLTSVM is further elaborated, that is, a bigger k will achieve a greater speedup. Sequential versions of SSR-WLTSVM are further introduced to substantially accelerate the parameter tuning process. And a fast algorithm clipDCD is introduced in this paper to handle large-scale datasets. In addition, Friedman test and paired-sample t test are used to verify the effectiveness of SSR-WLTSVM. Experimental results on 30 benchmark datasets confirm the efficiency of our proposed algorithm. [ABSTRACT FROM AUTHOR]

Fuzzy support vector machine and their variants are prominent classification techniques that reduce the adverse effects of noises and outliers as compare to classical support vector machine. However, in these methods, only the distance between the training pattern and the class center is considered, and hence, the edge support vectors cannot be distinguished from noises and outliers properly. These limitations are overcome by intuitionistic fuzzy-based support vector machine which allocate two parameters (membership and non-membership degrees) to each pattern of a dataset and hence define score number based on the importance of a pattern. In this paper, an intuitionistic fuzzy proximal support vector machine for multi-category classification problems is proposed. The method significantly reduces the impacts of noises and outliers present in the dataset by assigning the intuitionistic fuzzy score function to each training point based on its location and surroundings. Moreover, the method is computationally efficient as the robust classifiers are obtained by solving the system of linear equations instead of large size quadratic programming problems. In the proposed method, using polynomial and Gaussian kernels, the hyperplanes are also developed in the feature space. The geometrical advantages of the suggested method over the existing techniques are ascertained using the simulated two-dimensional artificial dataset having three target classes. Further, extensive experimental studies on ten UCI benchmark datasets have been performed which demonstrate that the proposed algorithm predicts more precisely about future data as comparison to some well-established algorithms. Figures are also illustrated by varying different parameters involved in the model which confirms the performance of the method over the existing algorithms. Moreover, the analysis of the predictive behavior of the proposed approach is done using the Friedman test, a nonparametric alternative to the analysis of variance test at 5 % significance level. Further, the proposed method has also been applied to image classification and gesture-phase segmentation problems which confirms the efficiency and handling capabilities of the proposed algorithm in practical applications. [ABSTRACT FROM AUTHOR]