Kernel-based Support Vector Machine Research Articles

Shallow and deep learning for event relatedness classification

In the two recent decades various security authorities around the world acknowledged the importance of exploiting the ever-growing amount of information published on the web on various types of events for early detection of certain threats, situation monitoring and risk analysis. Since the information related to a particular real-world event might be scattered across various sources and mentioned on different dates, an important task is to link together all event mentions that are interrelated. This article studies the application of various statistical and machine learning techniques to solve a new application-oriented variation of the task of event pair relatedness classification, which merges different fine-grained event relation types reported elsewhere into one concept. The task focuses on linking event templates automatically extracted from online news by an existing event extraction system, which contain only short text snippets, and potentially erroneous and incomplete information. Results of exploring the performance of shallow learning methods such as decision tree-based random forest and gradient boosted tree ensembles (XGBoost) along with kernel-based support vector machines (SVM) are presented in comparison to both simpler shallow learners as well as a deep learning approach based on long short-term memory (LSTM) recurrent neural network. Our experiments focus on using linguistically lightweight features (some of which not reported elsewhere) which are easily portable across languages. We obtained F1 scores ranging from 92% (simplest shallow learner) to 96.4% (LSTM-based recurrent neural network) evaluated on a newly created event linking corpus.

An end-to-end graph convolutional kernel support vector machine

A novel kernel-based support vector machine (SVM) for graph classification is proposed. The SVM feature space mapping consists of a sequence of graph convolutional layers, which generates a vector space representation for each vertex, followed by a pooling layer which generates a reproducing kernel Hilbert space (RKHS) representation for the graph. The use of a RKHS offers the ability to implicitly operate in this space using a kernel function without the computational complexity of explicitly mapping into it. The proposed model is trained in a supervised end-to-end manner whereby the convolutional layers, the kernel function and SVM parameters are jointly optimized with respect to a regularized classification loss. This approach is distinct from existing kernel-based graph classification models which instead either use feature engineering or unsupervised learning to define the kernel function. Experimental results demonstrate that the proposed model outperforms existing deep learning baseline models on a number of datasets.

Machine learning augmented dead oil viscosity model for all oil types

Dead oil viscosity is one of the most unreliable properties to predict with classical black oil correlations. This results mostly from the large effect that oil type has on viscosity. Two dead oil samples with identical APIs (https://www.eia.gov/today), (https://en.wikipedia.org/a) and can have even an order of magnitude difference in viscosity at the same temperature (Dindoruk and Christman, 2004). In this work, we tried to limit this spread to a certain degree by incorporating a parameter such as MW to capture additional information for the character of the oil. Limitations of the classical black oil correlations became even more prominent when a wide spectrum of viscosity values coupled with a wide range of temperatures are considered. Given the constraints of limited input variables, the problem becomes particularly challenging for heavy-extra heavy oils with high asphaltene content (Sinha et al., 2019), where prediction errors could easily be as high as a couple of log-cycles. Even though there are several viscosity correlations available in the industry (Ali, 2003)- (Bergman and Sutton, 2009), (De Ghetto and Villa, 1994), (Dindoruk and Christman, 2004), (Mehrotra, 1992), (Motahhari et al., 2013), (Naseri et al., 2005), (Lindeloff et al., 2003), (Lohrenz et al., 1964), (Pedersen and Fredenslund, 1987) (Petrosky and Farshad, 1995), (Peng and Robinson, 1976), (Standing, 1977), (Teja and Rice, 1981), (Yarranton et al., 2013) most of those correlations are only applicable to the oil samples belonging to specific geographical regions and/or for structurally similar oils, because of the inherent bias in the training datasets used in the development of correlations. Therefore, they are predictively valid for a relatively narrower range of oils and/or viscosity. In this work, we considered a very wide range of oils (6° API to 50° API). Therefore, producing two easy to use viscosity correlations for API (https://www.eia.gov/today), (https://en.wikipedia.org/a) gravities above and below 20° API that can readily predict the viscosity at any desired temperature within an extended temperature range (15 °C to 160 °C). Also, the two sets of correlations were kept compatible in a region in the vicinity of switching points (20 API and 36 cp) so that they will have a proper transition from one branch to another. While the range of fluid properties is very wide, we were able to keep the input parameters to a minimum in terms of defining the character of the fluid (molecular weight and specific gravity). We demonstrated that the proposed correlation along with the given methodology performs much better than the leading correlations with the similar input proxies published in the literature for a wide range of viscosities (0.42 cp to 860, 000 cp). The use cases for the proposed correlation can be divided into three parts: 1) Prediction of the dead oil viscosity with limited input data, 2) use of limited in-hand viscosity or reference viscosity data to generate viscosities for the conditions that are hard to perform accurate experimentation or simply not having the physical sample in hand to do additional experiments(for example, for thermal recovery processes where viscosity is needed at elevated temperatures, or to construct the lift curves and/or tables for pipeline flow at lower temperatures)) and 3) it can be used to check the consistency and the quality of the existing data. In addition to classical correlation development efforts using known but limited physical control parameters, we have also attempted to model the viscosity with various machine learning methods K-Nearest Neighbor (KNN) (Cover and Hart, 1967) and Kernel-based Support Vector Machine (KSVM)) (https://docs.oracle.com/c, 2835), (Suykens and Vandewalle, 1999) and compared the outcome to each other and as well as against the proposed correlation. Based on calculated statistical parameters and cross-plots, the proposed correlation performed better than the other leading viscosity correlations (Bergman and Sutton, 2009), (Lindeloff et al., 2003), (Lohrenz et al., 1964), (Pedersen and Fredenslund, 1987), (Pedersen et al., 1984), (Petrosky and Farshad, 1995), (Standing, 1977), (Teja and Rice, 1981), (Yarranton et al., 2013) and as well as the selected supervised machine learning regression principles (https://en.wikipedia.org/b), (https://machinelearningma) such as KNN and KSVM. The subject correlation also helps in improving the accuracy as well as guiding the performance of these otherwise “Blackbox” machine learning principles as it can fill the gaps in the data especially in the context of extending the tuned viscosities based on a single point measurement (reference viscosity) in temperature domain. Furthermore, we also explain how it can be combined with Sinha et al. (Sinha et al., 2019) relative viscosity correlation to include the impact of asphaltene concentration to be able to estimate vertical or areal viscosity variations which can also ultimately help to improve the mobility cut-off predictions of the asphaltene/tar mat zones or heavier fluids.

A Novel Combination of Whale Optimization Algorithm and Support Vector Machine with Different Kernel Functions for Prediction of Blasting-Induced Fly-Rock in Quarry Mines

This study proposed a novel data-driven model for estimating distance of fly-rock in bench blasting in open-pit mines using a robust combination of the whale optimization algorithm (WOA), support vector machine (SVM) and kernel functions. Four kernel functions were investigated for embedding in the SVM model, including linear (L), radius basis function (RBF), polynomial (P), and hyperbolic tangent (HT) functions. Then, the WOA was applied to optimize the kernel-based SVM models, namely WOA–SVM–L, WOA–SVM–P, WOA–SVM–RBF, and WOA–SVM–HT. A variety of conventional data-driven models were also developed for predicting fly-rock distance, including adaptive neuro-fuzzy inference system (ANFIS), gradient boosting machine (GBM), random forest (RF), classification and regression tree (CART), and artificial neural network (ANN). The blasting parameters and maximum fly-rock distance, as well as their relationship, were carefully investigated for this aim. The predictive results of the models were evaluated through two performance indices: root-mean-squared error (RMSE) and correlation coefficient (R2). These indices indicated that the linear function-based WOA–SVM model (i.e., WOA–SVM–L) seems to be not fit for predicting fly-rock with the largest error (i.e., RMSE = 9.080 and R2 = 0.937). In contrast, the WOA–SVM–RBF model yielded the highest accuracy in predicting the distance of fly-rock (i.e., RMSE = 5.241, R2 = 0.977). Meanwhile, the WOA–SVM–P and WOA–SVM–HT models provided lower performances than those of the WOA–SVM–RBF model, but they are acceptable. The conventional models (i.e., ANFIS, GBM, RF, CART, and ANN) are pretty well (i.e., RMSE in the range of 5.804 to 6.567; R2 in the range of 0.965 to 0.973); however, their performance is lower than those of the WOA–SVM–RBF model as well. Based on these results, the WOA–SVM model was proposed as a useful data-driven model for predicting fly-rock with high reliability in practical engineering.

Integration of new moving object segmentation and classification techniques using optimal salp swarm-based feature fusion with linear multi k-SVM classifier

The feature extraction technique is applied on least enclosing rectangle (LER) of the segmented object to increase the processing speed. The main intuition of this salp swarm algorithm relays on reducing the computational load of the proposed classifier by removing the repetitive and unrelated features from the feature vector. Also, increased training samples of similarly shaped classes when applied on the classifier can generate the misclassification results. Thus, a new layered kernel-based support vector machine (k-SVM) classifier is developed by means of integrating the k-neural network classifier and layered SVM classifier. Because of the high dimensional features, a difficulty occurs in the application of a single classifier. In order to ease the computational load, this multi classifier is integrated with a shadow elimination technique to classify the object categories of intelligent transportations system such as motorcycles, bicycles, cars, and pedestrians.

Diabetic Prediction Using Kernel Based Support Vector Machine

Diabetic Prediction Using Kernel Based Support Vector Machine

Preoperative assessment of lymph node metastasis in Colon Cancer patients using machine learning: a pilot study

BackgroundPreoperative detection of lymph node (LN) metastasis is critical for planning treatments in colon cancer (CC). The clinical diagnostic criteria based on the size of the LNs are not sensitive to determine metastasis using CT images. In this retrospective study, we investigated the potential value of CT texture features to diagnose LN metastasis using preoperative CT data and patient characteristics by developing quantitative prediction models.MethodsA total of 390 CC patients, undergone surgical resection, were enrolled in this monocentric study. 390 histologically validated LNs were collected from patients and randomly separated into training (312 patients, 155 metastatic and 157 normal LNs) and test cohorts (78 patients, 39 metastatic and 39 normal LNs). Six patient characteristics and 146 quantitative CT imaging features were analyzed and key variables were determined using either exhaustive search or least absolute shrinkage algorithm. Two kernel-based support vector machine classifiers (patient-characteristic model and radiomic-derived model), generated with 10-fold cross-validation, were compared with the clinical model that utilizes long-axis diameter for diagnosis of metastatic LN. The performance of the models was evaluated on the test cohort by computing accuracy, sensitivity, specificity, and area under the receiver operating curve (AUC).ResultsThe clinical model had an overall diagnostic accuracy of 64.87%; specifically, accuracy of 65.38% and 62.82%, sensitivity of 83.87% and 84.62%, and specificity of 47.13% and 41.03% for training and test cohorts, respectively. The patient-demographic model obtained accuracy of 67.31% and 73.08%, the sensitivity of 62.58% and 69.23%, and specificity of 71.97% and 76.23% for training and test cohorts, respectively. Besides, the radiomic-derived model resulted in an accuracy of 81.09% and 79.49%, sensitivity of 83.87% and 74.36%, and specificity of 78.34% and 84.62% for training and test cohorts, respectively. Furthermore, the diagnostic performance of the radiomic-derived model was significantly higher than clinical and patient-demographic models (p < 0.02) according to the DeLong method.ConclusionsThe texture of the LNs provided characteristic information about the histological status of the LNs. The radiomic-derived model leveraging LN texture provides better preoperative diagnostic accuracy for the detection of metastatic LNs compared to the clinically accepted diagnostic criteria and patient-demographic model.

Support Vector Machines-based Heart Disease Diagnosis using Feature Subset, Wrapping Selection and Extraction Methods

Heart disease is one of the leading causes of human death and in the absence of an accurate diagnosis, there are limitations to beat it. In this research, an automatic diagnostic methodology for clinical heart disease is presented. The proposed approach computes the most relevant feature subset by taking advantage of feature selection and extraction techniques. To accomplish the feature selection, two algorithms (Mean Fisher based feature selection algorithm(MFFSA) and accuracy based feature selection algorithm(AFSA)) are presented. The selected feature subset is then further refined through the feature extraction technique i.e., principle component analysis. The proposed technique is validated over Cleveland, Hungarian, Switzerland and combination of all of them. In order to classify a human as a heart disease patient(HDP) or a normal control subject(NCS), Radial basis function kernel-based support vector machines are used. The proposed methodology is evaluated through accuracy, specificity and sensitivity metrics.

Automated phase classification in cyclic alternating patterns in sleep stages using Wigner–Ville Distribution based features

Sleep is one of the most important body mechanisms responsible for the proper functioning of human body. Cyclic alternating patterns (CAP) play an indispensable role in the analysis of sleep quality and related disorders like nocturnal front lobe epilepsy, insomnia, narcolepsy etc. The traditional manual segregation methods of CAP phases by the medical experts are prone to human fatigue and errors which may lead to inaccurate diagnosis of sleep stages. In this paper, we present an automated approach for the classification of CAP phases (A and B) using Wigner–Ville Distribution (WVD) and Rényi entropy (RE) features. The WVD provides a high-resolution time–frequency analysis of the signals whereas RE provides least time–frequency uncertainty with WVD. The classification is performed using medium Gaussian kernel-based support vector machine with 10-fold cross-validation technique. We have presented the results for randomly sampled balanced data sets. The proposed approach does not require any pre-processing or post-processing stages, making it simple as compared to the existing techniques. The proposed method is able to achieve an average classification accuracy of 72.35% and 87.45% for balanced and unbalanced data sets respectively. The proposed method can aid the medical experts to analyze the cerebral stability as well as the sleep quality of a person.

Fuzzy Particle Swarm Optimization Feature Selection and Aggrandized Classifier for Uncovering Frauds in Credit Card Deals

In today's economy, credit card plays a very important role. The rise of credit card customers improved, credit card scam cases were also on the rise. Numerous procedures are anticipated to challenge the evolution of the frauds in credit cards. In this research work, proposed an innovative fraud detection method which utilizes the similar cardholder’s behavioral patterns to construct a current cardholder’s interactive profile in order to stay away from the credit card scams. However, the selection of optimal features from the samples and the decision cost for accuracy becomes main important problem. To illuminate these issues this proposed research work presents an innovative fraud detection technique that makes out of four phases: 1. To augment a cardholder’s behavioral styles, first we divide all cardholders into distinctive groups making use of the cardholder’s historical transaction data such that the members of each group have the similar transaction behavior by K-means. 2. Introduces a new Fuzzy Particle Swarm Optimization (FPSO) feature selection for the amplification of fraud detection in credit cards. 3. By means of a prolonged wrapper method, an ensemble classification are performed by Aggrandized Kernel based Support Vector Machine (AKSVM).4.Refreshing the cardholder’s social profile with an input system. This Proposed work adopts the external quality metrics as Accuracy, Recall, Concept drift rate and Fraud feature rate. The UCI dataset is used and is done in MATLAB framework. The analytical measures were used to estimate the routine of the mentioned fraud detection technique. The simulation results show that this proposed innovative fraud detection method provides better accuracy results than other fraud detection techniques. The low concept drift rate results the gain of the innovative method to classify the transactions accurately.

Construction of sheep forage intake estimation models based on sound analysis

Forage intake is one of the most important indicators of health and productivity of ruminant livestock, such as sheep. Forage intake estimation can also make an important contribution to the design and implementation of rotational grazing systems. In this paper, sheep forage intake estimation models were developed based on acoustic analysis from data gathered by an audio recorder mounted on a sheep. A data set with 114 pieces of audio was constructed by collecting ingestion sound of eight female sheep (2 years old with 50 ± 3 kg body mass). A Gaussian kernel-based support vector machine (SVM) classifier was trained to identify chewing sound segments from sheep ingestion audio. Seven explanatory variables were extracted from each chewing sound segment. These variables were used to establish single variable and multiple variable-based forage estimation models. Least squares regression and elastic network approach were employed to determine the coefficients of the single variable and multiple variable-based forage intake estimation models, respectively. Validation results showed that the best single variable and multiple variable-based model could explain 71.02% and 80.94% of forage intake changes, respectively. The average accuracy of the two best models was 86.13% and 89.32%, respectively. The results suggested that an automatic system to estimate the forage intake of sheep based on a wearable audio-recorder could be developed in the future. This could contribute to sheep health disorder identification, decisions on the number of sheep for a given grassland unit, and so on. The latter is the foundation of sheep rotational grazing.

Big Data Sentimental Analysis Using Document to Vector and Optimized Support Vector Machine

With the rapid evolution of the internet, using social media networks such as Twitter, Facebook, and Tumblr, is becoming so common that they have made a great impact on every aspect of human life. Twitter is one of the most popular micro-blogging social media that allow people to share their emotions in short text about variety of topics such as company’s products, people, politics, and services. Analyzing sentiment could be possible as emotions and reviews on different topics are shared every second, which makes social media to become a useful source of information in different fields such as business, politics, applications, and services. Twitter Application Programming Interface (Twitter-API), which is an interface between developers and Twitter, allows them to search for tweets based on the desired keyword using some secret keys and tokens. In this work, Twitter-API used to download the most recent tweets about four keywords, namely, (Trump, Bitcoin, IoT, and Toyota) with a different number of tweets. “Vader” that is a lexicon rule-based method used to categorize downloaded tweets into “Positive” and “Negative” based on their polarity, then the tweets were protected in Mongo database for the next processes. After pre-processing, the hold-out technique was used to split each dataset to 80% as “training-set” and rest 20% “testing-set.” After that, a deep learning-based Document to Vector model was used for feature extraction. To perform the classification task, Radial Bias Function kernel-based support vector machine (SVM) has been used. The accuracy of (RBF-SVM) mainly depends on the value of hyperplane “Soft Margin” penalty “C” and γ “gamma” parameters. The main goal of this work is to select best values for those parameters in order to improve the accuracy of RBF-SVM classifier. The objective of this study is to show the impacts of using four meta-heuristic optimizer algorithms, namely, particle swarm optimizer (PSO), modified PSO (MPSO), grey wolf optimizer (GWO), and hybrid of PSO-GWO in improving SVM classification accuracy by selecting the best values for those parameters. To the best of our knowledge, hybrid PSO-GWO has never been used in SVM optimization. The results show that these optimizers have a significant impact on increasing SVM accuracy. The best accuracy of the model with traditional SVM was 87.885%. After optimization, the highest accuracy obtained with GWO is 91.053% while PSO, hybrid PSO-GWO, and MPSO best accuracies are 90.736%, 90.657%, and 90.557%, respectively.

MRI radiomics for early prediction of response to vaccine therapy in a transgenic mouse model of pancreatic ductal adenocarcinoma

BackgroundThere is a lack of well-established clinical tools for predicting dendritic cell (DC) vaccination response of pancreatic ductal adenocarcinoma (PDAC). DC vaccine treatment efficiency was demonstrated using histological analysis in pre-clinical studies; however, its usage was limited due to invasiveness. In this study, we aimed to investigate the potential of MRI texture features for detection of early immunotherapeutic response as well as overall survival (OS) of PDAC subjects following dendritic cell (DC) vaccine treatment in LSL-KrasG12D;LSL-Trp53R172H;Pdx-1-Cre (KPC) transgenic mouse model of pancreatic ductal adenocarcinoma (PDAC).Materials and methodsKPC mice were treated with DC vaccines, and tumor growth was dynamically monitored. A total of a hundred and fifty-two image features of T2-weighted MRI images were analyzed using a kernel-based support vector machine model to detect treatment effects following the first and third weeks of the treatment. Moreover, univariate analysis was performed to describe the association between MRI texture and survival of KPC mice as well as histological tumor biomarkers.ResultsOS for mice in the treatment group was 54.8 ± 22.54 days while the control group had 35.39 ± 17.17 days. A subset of three MRI features distinguished treatment effects starting from the first week with increasing accuracy throughout the treatment (75% to 94%). Besides, we observed that short-run emphasis of approximate wavelet coefficients had a positive correlation with the survival of the KPC mice (r = 0.78, p < 0.001). Additionally, tissue-specific MRI texture features showed positive association with fibrosis percentage (r = 0.84, p < 0.002), CK19 positive percentage (r = − 0.97, p < 0.001), and Ki67 positive cells (r = 0.81, p < 0.02) as histological disease biomarkers.ConclusionOur results demonstrate that MRI texture features can be used as imaging biomarkers for early detection of therapeutic response following DC vaccination in the KPC mouse model of PDAC. Besides, MRI texture can be utilized to characterize tumor microenvironment reflected with histology analysis.

Handheld confocal Raman spectroscopy (CRS) for objective assessment of skin barrier function and stratification of severity in atopic dermatitis (AD) patients

BackgroundWe developed the first-of-its-kind handheld confocal Raman spectroscopy (CRS) system to quantify the concentration of natural moisturizing factors in the skin. ObjectiveTo evaluate the feasibility of our handheld CRS system and propose a novel quantitative index to measure skin barrier function. MethodsThis prospective study included 30 atopic dermatitis (AD) patients and 14 healthy volunteers. All AD participants were assessed using the Scoring Atopic Dermatitis (SCORAD) severity instrument, a vapometer for trans-epidermal water loss and a moisture meter for skin surface moisture. A handheld CRS operating at 785 nm laser was used to measure the biochemical constituents of the skin up to a depth of ∼100 μm. We trained a linear kernel-based support vector machine (SVM) model for eczema classification based on the water, ceramide and urocanic acid content. A novel Eczema Biochemical Index (EBI) was then formulated using the skin constituents measured from the AD participants to stage disease severity. ResultsThe SVM model used to classify healthy participants and AD patients obtained high cross-validated area under the curve of 0.857 and accuracy of 0.841, with high sensitivity and specificity values of 0.857 and 0.833 respectively. EBI can be used to stratify AD patients of varying severity, based on the biochemical constituents in the skin. ConclusionAs compared to the standard CRS system, the handheld CRS offers higher portability and provides Raman measurements at various body regions with similar sensitivity. This suggests that a handheld CRS device could be a valuable point-of-care resource in both research and clinical use.

Displacement Prediction of a Complex Landslide in the Three Gorges Reservoir Area (China) Using a Hybrid Computational Intelligence Approach

Displacement prediction of reservoir landslide remains inherently uncertain since a complete understanding of the complex nonlinear, dynamic landslide system is still lacking. An appropriate quantification of predictive uncertainties is a key underpinning of displacement prediction and mitigation of reservoir landslide. A density prediction, offering a full estimation of the probability density for future outputs, is promising for quantification of the uncertainty of landslide displacement. In the present study, a hybrid computational intelligence approach is proposed to build a density prediction model of landslide displacement and quantify the associated predictive uncertainties. The hybrid computational intelligence approach consists of two steps: first, the input variables are selected through copula analysis; second, kernel-based support vector machine quantile regression (KSVMQR) is employed to perform density prediction. The copula-KSVMQR approach is demonstrated through a complex landslide in the Three Gorges Reservoir Area (TGRA), China. The experimental study suggests that the copula-KSVMQR approach is capable of construction density prediction by providing full probability density distributions of the prediction with perfect performance. In addition, different types of predictions, including interval prediction and point prediction, can be derived from the obtained density predictions with excellent performance. The results show that the mean prediction interval widths of the proposed approach at ZG287 and ZG289 are 27.30 and 33.04, respectively, which are approximately 60 percent lower than that obtained using the traditional bootstrap-extreme learning machine-artificial neural network (Bootstrap-ELM-ANN). Moreover, the obtained point predictions show great consistency with the observations, with correlation coefficients of 0.9998. Given the satisfactory performance, the presented copula-KSVMQR approach shows a great ability to predict landslide displacement.

Quantitative Assessment of Speech in Cerebellar Ataxia Using Magnitude and Phase Based Cepstrum.

The clinical assessment of speech abnormalities in Cerebellar Ataxia (CA) is time-consuming and inconsistent. We have developed an automated objective system to quantify CA severity and thereby facilitate remote monitoring and optimisation of therapeutic interventions. A quantitative acoustic assessment could prove to be a viable biomarker for this purpose. Our study explores the use of phase-based cepstral features extracted from the modified group delay function as a complement to the features obtained from the magnitude cepstrum. We selected a combination of 15 acoustic measurements using RELIEF feature selection algorithm during the feature optimisation process. These features were used to segregate ataxic speakers from normal speakers (controls) and objectively assess them based on their severity. The effectiveness of our study has been experimentally evaluated through a clinical study involving 42 patients diagnosed with CA and 23 age-matched controls. A radial basis function kernel based support vector machine (SVM) classifier achieved a classification accuracy of 84.6% in CA-Control discrimination [area under the ROC curve (AUC) of 0.97] and 74% in the modified 3-level CA severity estimation (AUC of 0.90) deduced from the clinical ratings. The strong classification ability of selected features and the SVM model supports this scheme's suitability for monitoring CA related speech motor abnormalities.

A SUPPLY CHAIN MANAGEMENT BASED PATIENT FORECASTING MODEL FOR DENTAL HOSPITAL

Management is in fact on the verge of a major breakthrough in finding out exactly how service sector success is actually determined by the interactions between the flows of information, materials, money, manpower, and capital equipment. The way these five flow principles interlock to be able to amplify one another as well as to bring about switch and fluctuation will build the grounds for anticipating the look of decisions, policies, organizational forms, and investment choices . A concept of distribution management which recognized the integrated dynamics of organizational associations since companies are so intertwined, expert argued that technique attributes can influence the general functionality of abilities as investigation, engineering, sales, and promotion. Illustrated phenomena is actually utilizing a laptop computer or perhaps computer simulation of order information flow along with the impact of its effect on development in addition to distribution general functionality for each supply chain member, in addition to the entire supply chain application. A Machine Learning based proposed model for patient forecasting is implemented to show the research analysis work for patient forecasting for the dental hospital located in Bhopal, Madhya Pradesh, India. This research paper is indicating about the use, application and implementation of Kernel based Support Vector Machine for regression analysis purpose and also select the best Kernel for the implementation of the predicted model.

Applying support-vector machine learning algorithms toward predicting host-guest interactions with cucurbit[7]uril.

Machine learning is a valuable tool in the development of chemical technologies but its applications into supramolecular chemistry have been limited. Here, the utility of kernel-based support vector machine learning using density functional theory calculations as training data is evaluated when used to predict equilibrium binding coefficients of small molecules with cucurbit[7]uril (CB[7]). We find that utilising SVMs may confer some predictive ability. This algorithm was then used to predict the binding of drugs TAK-580 and selumetinib. The algorithm did predict strong binding for TAK-580 and poor binding for selumetinib, and these results were experimentally validated. It was discovered that the larger homologue cucurbit[8]uril (CB[8]) is partial to selumetinib, suggesting an opportunity for tunable release by introducing different concentrations of CB[7] or CB[8] into a hydrogel depot. We qualitatively demonstrated that these drugs may have utility in combination against gliomas. Finally, mass transfer simulations show CB[7] can independently tune the release of TAK-580 without affecting selumetinib. This work gives specific evidence that a machine learning approach to recognition of small molecules by macrocycles has merit and reinforces the view that machine learning may prove valuable in the development of drug delivery systems and supramolecular chemistry more broadly.

A Speech Emotion Recognition Model Based on Multi-Level Local Binary and Local Ternary Patterns

Interpreting a speech signal is quite challenging because it consists of different frequencies and features that vary according to emotions. Although different algorithms are being developed in the speech emotion recognition (SER) domain, the success rates vary according to the spoken languages, emotions, and databases. In this study, a new lightweight effective SER method has been developed that has low computational complexity. This method, called 1BTPDN, is applied on RAVDESS, EMO-DB, SAVEE, and EMOVO databases. First, low-pass filter coefficients are obtained by applying a one-dimensional discrete wavelet transform on the raw audio data. The features are extracted by applying textural analysis methods, a one-dimensional local binary pattern, and a one-dimensional local ternary pattern to each filter. Using neighborhood component analysis, the most dominant 1024 features are selected from 7680 features while the other features are discarded. These 1024 features are selected as the input of the classifier which is a third-degree polynomial kernel-based support vector machine. The success rates of the 1BTPDN reached 95.16%, 89.16%, 76.67%, and 74.31% in the RAVDESS, EMO-DB, SAVEE, and EMOVO databases, respectively. The recognition rates are higher compared to many textural, acoustic, and deep learning state-of-the-art SER methods.

User Recognition Based on Human Body Impulse Response: A Feasibility Study

Human recognition technologies for security systems require high reliability and easy accessibility in the advent of the internet of things (IoT). While several biometric approaches have been studied for user recognition, there are demands for more convenient techniques suitable for the IoT devices. Recently, electrical frequency responses of the human body have been unveiled as one of promising biometric signals, but the pilot studies are inconclusive about the characteristics of human body as a transmission medium for electric signals. This paper provides a multi-domain analysis of human body impulse responses (HBIR) measured at the receiver when customized impulse signals are passed through the human body. We analyzed the impulse responses in the time, frequency, and wavelet domains and extracted representative feature vectors using a proposed accumulated difference metric in each domain. The classification performance was tested using the $k$ -nearest neighbors (KNN) algorithm and the support vector machine (SVM) algorithm on 10-day data acquired from five subjects. The average classification accuracies of the simple classifier KNN for the time, frequency, and wavelet features reached 92.99%, 77.01%, and 94.55%, respectively. In addition, the kernel-based SVM slightly improved the accuracies of three features by 0.58%, 2.34%, and 0.42%, respectively. The result shows potential of the proposed approach for user recognition based on HBIR.