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Summary Objective Tessitura—the habitual pitch range of a musical piece or role—is widely accepted as a significant factor in determining whether a singer should perform a given piece or role. However, attempts to quantify tessitura have historically relied on laborious hand calculations. The current study introduces a novel MATLAB program (“Tessa”) that automates tessitura analysis of digital sheet music in the MusicXML format. This program will allow voice teachers and singing voice specialists to assess the appropriateness of musical pieces or roles for their students and clients. Methods Tessa converts sheet music in the open-source MusicXML (MXL or XML) format to a MATLAB-compatible structure array (MAT). Once converted, the program extracts pitch, duration, and lyric information from the structure file, then saves relevant arrays to the user's computer. Finally, Tessa performs analyses on the piece's tessitura data, including histogram, box plot, and descriptive statistical analyses. Variables such as tempo, score part number, and the piece's title are automatically saved in order to facilitate repeated analyses. Results A sample analysis of Franz Schubert's song cycle Winterreise, D.911 is presented, with specific reference to the songs “3. Gefrorne Tranen” and “4. Erstarrung” as examples of pieces with markedly different tessituras—and, consequently, markedly different levels of vocal demand. Discussion Tessa is hosted on a GitHub repository ( https://github.com/Allerseelen/Tessa ) and is intended for open-source use and modification under the GNU General Public License v3.0. Voice teachers, singing voice specialists, and musicians may compare Tessa's analyses with voice range profiles or other assessment tools in order to evaluate repertoire and plan pacing strategies for extended performances.

The rapid increase in collateral omics and phenotypic data has enabled data-driven studies for the fast discovery of cancer targets and biomarkers. Thus, it is necessary to develop convenient tools for general oncologists and cancer scientists to carry out customized data mining without computational expertise. For this purpose, we developed innovative software that enables user-driven analyses assisted by knowledge-based smart systems. Publicly available data on mutations, gene expression, patient survival, immune score, drug screening and RNAi screening were integrated from the TCGA, GDSC, CCLE, NCI, and DepMap databases. The optimal selection of samples and other filtering options were guided by the smart function of the software for data mining and visualization on Kaplan-Meier plots, box plots and scatter plots of publication quality. We implemented unique algorithms for both data mining and visualization, thus simplifying and accelerating user-driven discovery activities on large multiomics datasets. The present Q-omics software program (v0.95) is available at http://qomics.sookmyung.ac.kr.

Sentiment analysis is a type of contextual text mining that assesses how users feel about emotive topics that are frequently discussed on social media. To analyze the sentiments of the textual data, a number of sentiment analysis methods such as lexicon-based, machine learning-based, and hybrid methods have been proposed. Among all methods, unsupervised methods, especially clustering methods are generally preferred, as they can directly be applied over the unlabelled datasets. Therefore, in this paper, a roulette wheel-based cuckoo search clustering method has been proposed for sentiment analysis. The proposed clustering method finds the optimal cluster centroids from the contents of sentimental datasets which are further used for determining the sentiment polarity of a document. The efficiency of the proposed roulette wheel cuckoo search clustering method has been evaluated on nine sentimental datasets including Twitter and Spam review datasets and compared with K-means, cuckoo search, grey wolf optimizer, grey wolf optimizer with simulated annealing, hybrid step size based cuckoo search, and spiral cuckoo search. The experimental analysis shows that the proposed methods attain the best mean accuracy, mean precision, and mean recall over 80% of the datasets. To statistically validate the efficacy of the proposed approach, box plots and paired t-test are also carried out. From the statistical analysis and experimental findings, the efficacy of the proposed method can be observed. The proposed clustering approach has theoretical implications for further studies to examine the sentimental data. Furthermore, the proposed method has significant practical implications for establishing a system that can generate conclusive comments on any societal issue.

The box and whiskers plot (boxplot) has shown to be an effective data visualization tool in proficiency testing. The boxplot provides a quick visual summary of the considered data set . All the descriptive aspects of data reported in a round of a proficiency test can be studied with this plot. Location (through median and quartiles), spread (by means of the interquartile range), shape (position of the median in the box), and possible outliers (values beyond of the fences) are simultaneously observed with the boxplot. In this paper, we propose a modification of the inner fences of the boxplot, in such a way that they are in accordance with the approach based on the z score (standard ISO 13528) and therefore useful to recognize graphically questionable and unacceptable values in proficiency testing.

Association rule mining is typically used to uncover the enthralling interdependencies between the set of variables and reveals the hidden pattern within the dataset. The associations are identifie...

Notwithstanding the superior performance of the Whale optimization algorithm (WOA) on a wide range of optimization issues, the exploitation in WOA gets more preference during the search process, thereby compromising the solution accuracy and diversity and also increases the chance of premature convergence. In this study, a novel modified WOA (m-SDWOA) is presented where the conventional WOA is combined with the modified mutualism phase of symbiotic organisms search (SOS), $$DE/rand/1/bin$$ mutation strategy of differential evolution (DE), and commensalism phase of SOS. A new selection parameter γ is introduced to select between exploration and exploitation phases of the algorithm. This overall arrangement balances the ability of the algorithm to explore or exploit. The algorithm’s efficiency is verified through 42 benchmark functions and IEEE CEC 19 test suite and comparing the results with various state−of-the−art algorithms comprising basic methods, WOA variants, and DE variants. Statistical analyses like Friedman’s test, box plot comparison, and Nemenyi multiple comparison tests are employed to check the proposed algorithm's consistency and statistical superiority. Finally, four real-life engineering design problems have been solved to confirm the problem-solving capability of the proposed m-SDWOA. All these analyses demonstrate the superiority of the proposed algorithm over the compared algorithms.

PurposeThis paper presents a new approach of productivity estimation of cutter suction dredger operation through data mining and learning from real-time big data.Design/methodology/approachThe paper used big data, data mining and machine learning techniques to extract features of cutter suction dredgers (CSD) for predicting its productivity. ElasticNet-SVR (Elastic Net-Support Vector Machine) method is used to filter the original monitoring data. Along with the actual working conditions of CSD, 15 features were selected. Then, a box plot was used to clean the corresponding data by filtering out outliers. Finally, four algorithms, namely SVR (Support Vector Regression), XGBoost (Extreme Gradient Boosting), LSTM (Long-Short Term Memory Network) and BP (Back Propagation) Neural Network, were used for modeling and testing.FindingsThe paper provided a comprehensive forecasting framework for productivity estimation including feature selection, data processing and model evaluation. The optimal coefficient of determination (R2) of four algorithms were all above 80.0%, indicating that the features selected were representative. Finally, the BP neural network model coupled with the SVR model was selected as the final model.Originality/valueMachine-learning algorithm incorporating domain expert judgments was used to select predictive features. The final optimal coefficient of determination (R2) of the coupled model of BP neural network and SVR is 87.6%, indicating that the method proposed in this paper is effective for CSD productivity estimation.

Synchrophasor technology improves power grid visibility by installing phasor measurement units (PMUs) over a wide area in the power system. Big data received from PMUs contains important information about grid behavior. This information is useful in monitoring the safety and security of the grid. An extensive state-of-the-art review of big data analytics and its prime applications in power systems are expressed in this paper. It presents, a general background in data analysis techniques such as exploratory data analysis, statistical data analysis, and unsupervised data mining techniques like clustering. Two 400 kV transmission line tripping events are analyzed from the data recorded by the PMUs installed in the western part of the Indian grid i.e., Maharashtra State Electricity Transmission Company Limited (MSETCL) grid. The box plots, Correlogram, and the formation of clusters carried out for the PMU data recorded under ambient and disturbance events. This provides insights on how effective big data helps to make the right decision at right time for effective management of the power grid under normal and contingency conditions.

While Doppler radar can be used to measure cardiopulmonary vital signs during sleep, meaningful diagnostic assessments are often subject to knowledge of a subject’s changing sleep posture. The torso Effective Radar Cross Section (ERCS) and displacement magnitude were studied for 20 human subjects in three imitated sleep posture categories using a dual-frequency Doppler radar system in an exploratory examination of the feasibility of using radar to recognize body orientation. Box plot statistical analyses were performed for comparative assessment of ratio variations in ERCS and respiration depth for three different imitated sleep postures. The observed statistical trends and correlations were applied to a physical model to develop posture decision algorithms with initial supine posture data used as a reference. A single-frequency algorithm tracked postures without error for 90% of the subjects using 2.4 GHz data, and 80% using 5.8 GHz data. As accuracy limitations were complementary, a dual-frequency algorithm was developed which recognized postures without error for 100% of the subjects.

Forests play a vital role in the regulation of climate, absorption of carbon dioxide, hydrological cycle, conservation of water, soil and biodiversity and help mitigate natural disasters. With the help of various remote sensors, high-resolution satellite images are being collected nowadays, which helps in tackling the global challenges of forest mapping in remote areas. Each landscape will grow different types of trees and in turn substantiate a part of the country’s economy. This paper uses visualization and machine learning (ML) processes to classify the forest land on the terrain dataset composed of the advanced spaceborne thermal emission and reflection radiometer (ASTER) imaging instrument to get the insight of the cumulated data by using Box Plot and Heat Map. The accuracy obtained by utilizing different machine learning techniques like Support Vector Machine (SVM) gives 95.4%, Logistic Regression (LR) gives 94.5%, K-Nearest Neighbor (K-NN) gives 93.7%, Decision Tree (DT) with 89.5%, Stochastic Gradient Descendent (SGD) with 92.4% and CN2 Rule Induction (RI) gives 85.3% are allied which gives appreciable results in forest mapping substantiated the same with confusion matrix and ROC. We also obtained the DT and rules for the considered dataset.

Optimization algorithms have made considerable advancements in solving complex problems with the ability to be applied to innumerable real-world problems. Nevertheless, they are passed through several challenges comprising of equilibrium between exploration and exploitation capabilities, and departure from local optimums. Portioning the population into several sub-populations is a robust technique to enhance the dispersion of the solution in the problem space. Consequently, the exploration would be increased, and the local optimums can be avoided. Furthermore, improving the exploration and exploitation capabilities is a way of increasing the authority of optimization algorithms that various researches have been considered, and numerous methods have been proposed. In this paper, a novel hybrid multi-population algorithm called HMPA is presented. First, a new portioning method is introduced to divide the population into several sub-populations. The sub-populations dynamically exchange solutions aiming at balancing the exploration and exploitation capabilities. Afterthought, artificial ecosystem-based optimization (AEO) and Harris Hawks optimization (HHO) algorithms are hybridized. Subsequently, levy-flight strategy, local search mechanism, quasi-oppositional learning, and chaos theory are utilized in a splendid way to maximize the efficiency of the HMPA. Next, HMPA is evaluated on fifty unimodal, multimodal, fix-dimension, shifted rotated, hybrid, and composite test functions. In addition, the results of HMPA is compared with similar state-of-the-art algorithms using five well-known statistical metrics, box plot, convergence rate, execution time, and Wilcoxon’s signed-rank test. Finally, the performance of the HMPA is investigated on seven constrained/unconstrained real-life engineering problems. The results demonstrate that the HMPA is outperformed the other competitor algorithms significantly.

Prospective teachers and cryptography students use programming, statistics and guile to enrich both their courses.

The method of automatic recognizing of various valvular syndromes from the heart sound is a difficult job in cardiology. The features extracted from this may be time, frequency, or time-frequency domain and it bears a significant role in the automated systems to detect cardiovascular diseases. The examination of Phonocardiogram (PCG) signal provides very useful evidence related to the working of the heart. The time-domain based feature extraction techniques are logically simple and computationally less complex. In this paper, the statistical significance of Wilson amplitude at threshold levels (5–50 mV) is inspected towards the identification and classification of murmur from the Phonocardiogram. It has been noticed that the Wilson Amplitude at the threshold 5 mV is statistically more significant and it differ with a “P” value of 1.153 × 10–5.

One of the most important activities in data science is defining a membership function in fuzzy system. Although there are few ways to describe membership function like artificial neural networks, genetic algorithms etc.; they are very complex and time consuming. On the other hand, the presence of outlier in a data set produces deceptive results in the modeling. So it is important to detect and eliminate them to prevent their negative effect on the modeling. This paper describes a new and simple way of constructing fuzzy membership function by using five-number summary of a data set. Five states membership function can be created in this new method. At the same time, if there is any outlier in the data set, it can be detected with the help of this method. Usually box plot is used to identify the outliers of a data set. So along with the new approach, the box plot has also been drawn so that it is understood that the results obtained in the new method are accurate. Several real life examples and their analysis have been discussed with graph to demonstrate the potential of the proposed method. The results obtained show that the proposed method has given good results. In the case of outlier, the proposed method and the box plot method have shown similar results. Primary advantage of this new procedure is that it is not as expensive as neural networks, and genetic algorithms.

Control Charts are one of the most powerful tools used to detect aberrant behavior in industrial processes. A valid performance measure for a control chart is the average run length (ARL); which is the expected number of runs to get an out of control signal. At the same time, robust estimators are of vital importance in order to estimate population parameters. Median absolute deviation (MAD) and quantiles are such estimators for population standard deviation. In this study, alternative control charts to the Tukey control chart based on the robust estimators are proposed. To monitor the control chart's performance, the ARL values are compare for many symmetric and skewed distributions. The simulation results show that the in-control ARL values of proposed control charts are higher than Tukey's control chart in all cases and more efficient to detect the process mean. However, the out- of- control ARL values for the all control charts are worse when the probability distribution is non-normal. As a result, it is recommended to use control chart based on the estimator Qn for the process monitoring performance when data are from normal or non-normal distribution. An application example using real-life data is provided to illustrate the proposed control charts, which also supported the results of the simulation study to some extent.

Mahalanobis-Taguchi system (MTS) is a kind of data mining and pattern recognition method which can identify the attribute characteristics of multidimensional data by constructing Mahalanobis distance (MD) measurement scale. In this paper, considering the influence of irregular distribution of the sample data and abnormal variation of the normal data on accuracy of MTS, a feature recognition and selection model of the equipment state based on the improved MTS is proposed, and two aspects of the model namely construction of the original Mahalanobis space (MS) and determination of the threshold are studied. Firstly, the original training sample space is statistically controlled by the X-bar-S control chart, and extreme data of the single characteristic attribute is filtered to reduce the impact of extreme condition on the accuracy of the model, so as to construct a more robust MS. Furthermore, the box plot method is used to determine the threshold of the model. And the stability of the model and the tolerance to the extreme condition are improved by leaving sufficient range of the variation for the extreme condition which is identified as in the normal range. Finally, the improved model is compared with the traditional one based on the unimproved MTS by using the data from the literature. The result shows that compared with the traditional model, the accuracy and sensitivity of the improved model for state identification can be greatly enhanced.

Classification of focal and non-focal Electroencephalogram (EEG) signals is an important problem especially for the identification of epileptogenic sites in the brain. However, the number of research works reported in the literature is limited and most of them suffer from validation on a limited scale and moderate accuracy. In this paper, focal and non-focal EEG signals are analyzed in variational mode decomposition (VMD) and discrete wavelet transform (DWT) domain and features such as refined composite multiscale dispersion entropy, refined composite multiscale fuzzy entropy, and autoregressive model (AR) coefficients are extracted in VMD, DWT and VMD-DWT domain. Statistical analysis of the features is carried out by one way ANOVA to demonstrate their discriminating ability by means of p-values and visual inspection of the box plots of the features. A feature reduction algorithm based on neighborhood component analysis is used to reduce the model complexity and select the features with the highest discriminating abilities. An ensemble stacking classification approach is adopted to improve the accuracy of classification. The performance of the proposed method is studied using a publicly available benchmark database that contains 3750 pairs of focal and 3750 pairs of non-focal EEG signals. It is shown that the stacking configuration improves the accuracy significantly compared to a standalone classifier. It gives high values of sensitivity (96.1%), Specificity (94.4%), Accuracy (95.2%) and area under curve (0.989). Comparison with various existing methods reveals that the proposed method outperforms the others in accuracy. It may help researchers in developing a computer-aided system to identify epileptogenic sites in the brain.

This paper deals with the design and optimization of EDFA-Raman hybrid optical amplifier. Nature inspired optimization techniques such as Real Coded Genetic Algorithm (RGA) and Grey Wolf Optimization (GWO) are employed, and for the first time GWO is used in such a problem. Simultaneous optimization of four major parameters of hybrid amplifier, that is, lengths of EDFA and Raman sections, and their respective pump powers are carried out to achieve maximum gain. To compare the performance of both the optimization techniques, convergence curve and box plot are plotted. Simulation results reveal that the GWO has better convergence and lower computational complexity (run time) than that achieved by the RGA algorithm.

At present, most power quality online monitoring devices do not carry out accuracy test, and the measurement data may not be accurate due to the degraded performance of software and hardware and environmental impact after a certain number of years of operation. In this paper, a measurement accuracy evaluation method based on LSTM in proposed. Referring to the regulation that the precision inspection cycle of power secondary equipment is more than one year and the power quality online monitoring device must pass the accuracy test before putting into operation, it is considered that the measurement data of the device in the first year is accurate and reliable. Firstly, box plot is used to clean the data. Aiming at the coexistence section of reliable data and abnormal data interval in box plot, the slope law of abnormal data is used to complete the data cleaning. Then, the standard voltage and current value is given by the voltage and current prediction model based on LSTM. Finally, DTW cumulative distance is used to evaluate the accuracy, so as to avoid the problem that the timing of standard value and measured value does not correspond. By using the PQTS automatic test system, the feasibility of this method is verified.

The quick and accurate picking of the first arrival on microseismic signals is one of the critical processing steps of microseismic monitoring. This study proposed a first arrival picking method for application to microseismic data with a low signal-to-noise ratio (SNR). This approach consisted of two steps: feature selection and clustering. First of all, the optimal feature was searched automatically using the ReliefF algorithm according to the weight distribution of the signal features, and without manual design. On that basis, a k-means clustering method was adopted to classify the microseismic data with symmetry (0–1), and the first arrival times were accurately picked. The proposed method was validated using the synthetic data with different noise levels and real microseismic data. The comparative study results indicated that the proposed method had obviously outperformed the classical STA/LTA and the k-means without feature selection. Finally, the microseismic localization of the first arrivals picked using the various methods were compared. The positioning errors were analyzed using box plots with symmetric effect, and those of the proposed method were the smallest, and stable (all of which were less than 0.5 m), which further verified the superiority of this study’s proposed method and its potential in processing complicated microseismic datasets.

Environmental and ecosystem models can help to guide management of changing natural systems by projecting alternative future states under a common set of scenarios. Combining contrasting models into multi-model ensembles (MMEs) can improve the skill and reliability of projections, but associated uncertainty complicates communication of outputs, affecting both the effectiveness of management decisions and, sometimes, public trust in scientific evidence itself. Effective data visualisation can play a key role in accurately communicating such complex outcomes, but we lack an evidence base to enable us to design them to be visually appealing whilst also effectively communicating accurate information. To address this, we conducted a survey to identify the most effective methods for visually communicating the outputs of an ensemble of global climate models. We measured the accuracy, confidence, and ease with which the survey participants were able to interpret 10 visualisations depicting the same set of model outputs in different ways, as well as their preferences. Dot and box plots outperformed all other visualisations, heat maps and radar plots were comparatively ineffective, while our infographic scored highly for visual appeal but lacked information necessary for accurate interpretation. We provide a set of guidelines for visually communicating the outputs of MMEs across a wide range of research areas, aimed at maximising the impact of the visualisations, whilst minimizing the potential for misinterpretations, increasing the societal impact of the models and ensuring they are well-placed to support management in the future.

[EN] The optimization methods and, in particular, metaheuristics must be constantly improved to reduce execution times, improve the results, and thus be able to address broader instances. In particular, addressing combinatorial optimization problems is critical in the areas of operational research and engineering. In this work, a perturbation operator is proposed which uses the k-nearest neighbors technique, and this is studied with the aim of improving the diversification and intensification properties of metaheuristic algorithms in their binary version. Random operators are designed to study the contribution of the perturbation operator. To verify the proposal, large instances of the well-known set covering problem are studied. Box plots, convergence charts, and the Wilcoxon statistical test are used to determine the operator contribution. Furthermore, a comparison is made using metaheuristic techniques that use general binarization mechanisms such as transfer functions or db-scan as binarization methods. The results obtained indicate that the KNN perturbation operator improves significantly the results., The first author was supported by the Grant CONICYT/FONDECYT/INICIACION/11180056.

Energy produces by blood sugar in human body. Diabetes mellitus is a condition in which human body cannot manage energy. Blood glucose, Insulin etc., and their functions become unmanaged in the glucose energy system in blood. This unbalanced system generates many dangerous diseases as like blood pressure, diabetes etc., in body. Many different recourses of energy are available in the nature. In this paper we present dataset with their pattern by box whisker plot, histograms and extract nine best features by Chi-Square and plot correlation matrix for correlation of each feature with their heat_map. We have applied rule-based classification algorithms as Decision table, OneR and JRIP on prepared dataset. In proposed work we have managed these three selected algorithms by Bagging and Boosting ensemble methods. These ensemble methods calculate Classification Accuracy, Precision, Recall and F1-Score for diabetes UCI dataset. The summary with the results, finally we concluded that the highest accuracy (98%) of Bagging Ensemble Method and the accuracy, precision, recall and f1-score of the Decision Table, OneR, Jrip and Boosting algorithms were carrying less.

Mastering descriptive statistics is mandatory for a geologist. Chapter 5 shows how to describe a geological data set using Python programming, starting with basic metrics such as the location, dispersion, and degree of symmetry of a univariate data set. It then shows how to perform descriptive statistics in pandas and introduces box plot diagrams.

In this chapter, we adopt Six Sigma (SS) approach and reliability, availability, and maintainability (RAM) analysis in a plastic industry. We present a step-by-step application of the SS methodology for identifying and reducing the downtimes in the industry and examine the current operations management. Thus, failure and repair data are analyzed on the basis of statistical techniques. Histograms, box plots, and descriptive statistics on line-level failure data are conducted. The mathematical distribution parameters that best fit the fault data are calculated. Models for reliability, maintainability, failure rate, and repair rate have been established for the entire system. The models could be a useful tool for determining current conditions as well as forecasting the performance of improving the line's operations management policies. The main goals of the applicable approach are to understand the nature of the downtime dynamics and to measure accurately quantitatively the RAM characteristics of the industrial system. The analysis determines the system's critical points needing additional improvement through an appropriate maintenance plan. The benefit of the approach is that the production process is constantly tracked by relevant indices, the use of which contributes to a continuous improvement process.

VGI (Volunteered Geographic Information) refers to spatial data collected, created, and shared voluntarily by users. Georeferenced tracks are one of the most common components of VGI, and, as such, are not free from errors. The cleaning of GNSS (Global Navigation Satellite System) tracks is usually based on the detection and removal of outliers using their geometric characteristics. However, according to our experience, user profile differentiation is still a novelty, and studies delving into the relationship between contributor efficiency, activity, and quality of the VGI produced are lacking. The aim of this study is to design a procedure to filter GNSS traces according to their quality, the type of activity pursued, and the contributor efficiency with VGI. Source data are obtained Wikiloc. The methodology includes tracks classification according mobility types, box plot analysis to identify outliers, bivariate user segmentation according to level of activity and efficiency, and the study of its spatial behavior using kernel-density maps. The results reveal that out of 44,326 tracks, 8096 (18.26%) are considered erroneous, mainly (73.02%) due to contributors&rsquo, poor practices and the remaining being due to bad GNSS reception. The results also show a positive correlation between data quality and the author&rsquo, s efficiency collecting VGI.

In order to improve the efficiency of the contour size detection process of rotating parts, the methods of rotating parts, and the stepping movement of the laser sensor for non-contact detection was adopted. Due to the discontinuity of the detection area, the singularity of the detection data was inevitable, which results in the instability of the detection data. Aiming at this problem, the characteristics of data distribution are studied, the causes of singular values are summarized, and a method based on the combination of improved K-means clustering method and evaluation index was proposed. First, according to the characteristics of the data distribution, the negative data in the detection data was replaced by the adjacent value, and then the K value and the initial cluster center of the improved K-Means algorithm were calculated. Finally, the indexes of eligibility and singular value evaluation were established. The experimental results show that this method can accurately judge the qualification of the parts compared with the box plot method and 3$\sigma$ method, and effectively identify the singular value of the detection data and deal with it.

Classification of tropical cyclone (TC) using cloud pattern is an evolving area of research. The classification result may use for intensity detection of a TC to mitigate the damage. TC classification using image processing technique is a stimulating task due to complexity of the problem. This paper attempted to classify TC images using a modified local binary pattern (LBP). The LBP of an image describes its local structure in an easy and efficient manner. The proposed blocked LBP (B-LBP) is an improvised approach to generate central pixels form an input image. The input image is distributed into 3 × 3 identical blocks and the central pixels of each block are used to construct a new image pattern. This new image further analyzed by histogram and box plot to classify images. This paper shows a significant improvement in result toward classification. The B-LBP approach can improve the performance of the intensity prediction through classification with 91% accuracy.

Heart sound signal is an important sign about the mechanical performance of the cardiac valves. Enhancement of heart sound signal is a crucial issue to identify cardiac disease relevant to valve disorder. This study, presents a new approach based on the use of Dual Tree Complex Wavelet transform (DTCWT). The current approach has been employed in order to identify the normal heart sound from the pathological disorder. Twenty analyzed signals obtained from the PhysioNet database. After the preprocessing procedure the DTCWT has been implemented and the reconstructed signal were employed to extract five statistical features for both sounds. The result of the implementation and box plot showing the robust of DTCWT with apparent significance amongst the traditional discreet wavelet transform (DWT).

Automatic voice pathology detection can provide objective estimation and prevention in the early stages of voice diseases. A voice pathology detection and multi-classification method using an audio feature set extracted from glottal flow waveform is proposed in this work. In addition, a feature selection method of Fisher discrimination criterion is used to screen more valuable features and eliminate the features with low efficiency. The discrimination ability and effectiveness of the selected features were verified using three dimensional scatter plot and box plot in this study. One of the contributions of this paper is to investigate and evaluate the performance of different machine learning classifiers value for voice pathology detection and multi-classification. All experiments were carried out using the Massachusetts eye and ear infirmary database. Accuracy, sensitivity, specificity and receiver operating characteristic area are used as evaluation indexes to compare the performance of different machine learning classifiers. Each machine learning classifier had a good performance using the proposed feature set with nearly 100% accuracy in detection and higher than 90% accuracy in multi-classification. The experimental results indicate that the proposed method is valuable for voice pathology detection and multi-classification, especially in multi-classification.

The art of systematic financial trading evolved with an array of approaches, ranging from simple strategies to complex algorithms, all relying primarily on aspects of time-series analysis (e.g., Murphy, 1999; De Prado, 2018; Tsay, 2005). After visiting the trading floor of a leading financial institution, we noticed that traders always execute their trade orders while observing images of financial time-series on their screens. In this work, we build upon image recognition's success (e.g., Krizhevsky et al., 2012; Szegedy et al., 2015; Zeiler and Fergus, 2014; Wang et al., 2017; Koch et al., 2015; LeCun et al., 2015) and examine the value of transforming the traditional time-series analysis to that of image classification. We create a large sample of financial time-series images encoded as candlestick (Box and Whisker) charts and label the samples following three algebraically-defined binary trade strategies (Murphy, 1999). Using the images, we train over a dozen machine-learning classification models and find that the algorithms efficiently recover the complicated, multiscale label-generating rules when the data is visually represented. We suggest that the transformation of continuous numeric time-series classification problem to a vision problem is useful for recovering signals typical of technical analysis.

This research paper examines the full lifecycle of the case of turning the gas and oil industry to a data-driven operation model. The study presents the approach of re-engineering production data, building a predictive model for temperature forecast, statistical analysis, and visualization of Distributed Temperature Sensing (DTS) data provided by the oil-gas industry. For better analysis, the raw data have been pre-processed and organized according to the proper model. Furthermore, after the data organization, we proceed with observing relationships among three features (Date, Depth and Temperature), analyze vague upheavals and similarities utilizing plot histograms, scatterplots, box plots, heatmaps, violin plots for better visualization. Since the drastic temperature change indicates the anomaly, several alternative Outlier Detection Techniques are offered to predict early equipment failure and prevent production outage. Our results indicated a high correlation between depth and temperature, presence of trend in temperature distribution, and temperature drops in specific ranges. Proper analysis of the data allows the specialist to understand reservoir performance and prolong the production file of the wells.

This paper addresses the problem of the unsupervised approach of credit card fraud detection in unbalanced datasets using the ARIMA model. The ARIMA model is fitted to the regular spending behaviour of the customer and is used to detect fraud if some deviations or discrepancies appear. Our model is applied to credit card datasets and is compared to four anomaly detection approaches, namely, the K-means, box plot, local outlier factor and isolation forest approaches. The results show that the ARIMA model presents better detecting power than that of the benchmark models.

With the increasing application of disturbance waveform data, a general disturbance detection technology faced with various transient waveforms has practical significance for simplifying the detection process of specific disturbances and improving the accuracy of the specific disturbance detection. To this end, a detection algorithm for transient disturbance waveforms based on the probability density of remaining data is proposed in this paper. Firstly, the method of obtaining the remaining waveform data and its probability density is presented. Then, the probability distribution law of noise in power waveform data is studied based on the field measured data. Finally, according to the probability density difference of the remaining waveform data in adjacent periods, a transient waveform detection algorithm based on Wasserstein distance between probability densities is established. The threshold is determined by the box plot. A large amount of field measured data verifies the effectiveness of the proposed disturbance detection algorithm.

The boxplot is a powerful visualization tool of sampled continuous data sets because of its rich information delivered, compact size, and effective visual expression. The advantage of boxplots is not widely appreciated. Many top journals suggest that boxplots should be used in place of bar charts, but have been wrongly replaced by bar charts. One technical barrier to the usage of boxplots in reporting quantitative data is that bench scientists are not competent in generating boxplots, and are afraid of R, a programming tool. This tutorial provides an effective training material in that even a novice without prior R experience can become competent, within one day, in generating professional boxplots. The available R scripts for boxplots are very limited in scope and are aimed at specialists, and the bench scientists have difficulty in following these scripts. This tutorial provides extensive step-by-step R scripts and instructions, as well as 29 illustrations for customizing every detail of the boxplot structures. Basic R commands and concepts are introduced for users without prior R experiences, which can be skipped by audiences with R knowledge. Violin plots are the enhanced version of boxplots, and therefore, this tutorial also provides a brief introduction and usage of the R package vioplot with one additional illustration. While the protocol is prepared for the newbies and trainees it will be a handy tool for infrequent users, and may benefit the experienced users as well since it provides scripts for customizing every detail of boxplots.

The main focus of this paper is to predict clusters of patient records such as Fibrosis, Malign Lymph, Metastases, Normal Lymphogram obtained via different classification techniques and compare the presentation of classification algorithms based on some parameters such as precision, recall, F1 value (F-measure), classification accuracy (CA) and area under the curve (AUC). The Lymphographic dataset consist of 18 predictor attributes. Experimental results display that the Neural network provides highest classification accuracy and precision as well as F1 value among the other four classification algorithm which are KNN, SVM (Support vector machine) learner, Random forest learner, Ada boost. It permits the classifier to precisely perform multi-class medical data classification. Confusion Matrix has also been described for the above mentioned classifier using the lymphographic dataset. This paper also visualizes the ranking of Lymphogram dataset using the scoring method and analysis of scatter plot, box plot and distribution plot.

Statistical Process Control (SPC) methodologies are a set of statistical methods and techniques that were initially designed for industrial processes but could be adopted for non-industrial applications. The current prospective study aimed to provide a unique quantitative investigation of an epidemiological disease using the SPC program platform. The selected case herein was a long-term monitoring record of the yearly cancer mortality rates in women worldwide. Multidimensional segregation of the dataset into subgroups was conducted to visualize the clustering pattern based on nations (42 countries as boxplot), time and the Gaussian Mixture Model (two-interfering bell-shaped distributions approach). The trend of death rates versus the elapsed years would demonstrate a moderately negative correlation with the time following the theory of splines. Construction of control chart based on the fitted Weibull distribution showed a gradual steady improvement in survivability rates from malignancy. The greatest variations in the mortality ratios existed within the European countries.

A deep knowledge of radio propagation is necessary before deploying wireless networks. In indoor environments, when people are moving, experimental analysis is needed, as many elements are difficult to model with a deterministic simulation tool. Thus, before deploying 5G systems at the 3.5 GHz band, insights on the effect of people moving in the indoor channel are required. This contribution describes the results of a measurement campaign considering different conditions. For analyzing the results, box plots of measured attenuations due to the people moving are compiled. Both the speed of the people and their concentration seem to influence the narrowband parameters of the radio channel. However, the position of the people seems to provide fewer effects on the radio channel.

It is a great challenge of identification as well as formation of groups of infectious disease data set. Data mining, a process of uncovering silent characteristics of big data is one of such techniques which have nowadays become more popular for treating massive volume of infectious disease data set. In the current study, we apply cluster analysis, one of the data mining techniques to classify real groups of infectious disease “novel corona virus disease (COVID-19)” data set of different states and union territories (UTs) in India according to their high similarity to each other. The results obtained permit us to have a sense of clusters of affected Indian states and UTs. The main objective of clustering in this study is to optimize monitoring techniques in affected states and UTs in India which will be very valuable to the government, doctors, the police and others involved in understanding seriousness of the spread of novel coronavirus (COVID-19) to improve government policies, decisions, medical facilities (ventilators, testing kits, masks etc.), treatment etc. to reduce number of infected and deceased persons.

Multi-site testing saves test time and tests cost by screening multiple chips at once. However, it comes with its issues. As test engineers increase the number of sites on each tester to further save test time and cost, variations are now being observed in measurements from site to site which do not correspond to actual problems in the devices under test. Thus, a cost-effective way to investigate site to site variations and identify sites with issues needs to be developed to ensure high test quality and to rule out possible problems arising from the test hardware. In this paper, regression fitting on a quantile-quantile curve is used to compare the distribution of each site to a theoretical and expected distribution. This is shown to pronounce site to site variations inherent in test data, hence identifying issue-ridden sites with ease. The quantile-quantile plot compares the integrals of two probability density functions in a single plot, thus capturing the location, scale, and skewness of the test data set. This method provides more information to the test engineer than classical statistical methods that rely on single test statistics for distribution comparison and is at no extra cost.

A long-term multichannel electroencephalogram recording plays a crucial role in recognizing the epileptic seizure activities from the brain lobes. This research study investigates the automated detection of epileptic seizures from multichannel electroencephalogram recordings using Teager energy feature. A supervised back-propagation neural network model was implemented to classify the inter-ictal seizures. The study was conducted on multichannel electroencephalogram data that was obtained from Institute of Neuroscience, Ramaiah Memorial Hospital, Bengaluru, India, after ethical clearance from the from the Institutional Ethics Board. Initially, notch filter was applied to remove the 50 Hz power line noise from raw electroencephalogram followed by independent component analysis to remove eye blinks and muscular activities. A time domain feature called Teager energy was estimated which detects the rapid changes in the given electroencephalogram time series. A 1 s windowing was introduced to ensure stationarity for estimation of Teager energy. The descriptive and box plot analysis ensures the suitability of the Teager energy for the seizure detection. The performance of the multilayer perceptron neural network classifier was evaluated using sensitivity, specificity, and false detection rate. Simulation results showed the highest sensitivity, specificity and false detection rate of 96.66%, 99.15%, and 0.30 per hour respectively. It can be concluded that procedure can be applied for real-time seizure detection.

In univariate and in multivariate analyses, it is difficult to identify outliers in the case of skewed or heavy-tailed distributions. In this article, we propose simple univariate and multivariate outlier identification procedures that perform well with these types of distributions while keeping the computational complexity low. We describe the commands gboxplot (univariate case) and sdasym (multivariate case), which implement these procedures in Stata.

Data collection related to the flow pattern has always been associated with outliers due to various reasons. Outlier detection in flow pattern experiments is of high importance and results in a better and more accurate understanding of the flow pattern. In this study, six data mining methods have been used to identify the outliers in flow pattern experiments. The discussed methods include box plot, histograms, linear regression, k-nearest neighbors, local outlier factor, k-medoids clustering, multilayer perceptron, and self-organizing map. The main aim of this study is to detect the outliers in data collection in order to conduct flow pattern experiments using the data mining methods. These methods have been analyzed and compared with each other in a case study and their performance evaluated. The experimental outliers under investigation were emanated from flow pattern experiments around a spur dike located in a 90° bend using Vectrino velocimeter (ADV). The range of velocity measurement of this device is between ± 0.01 and ± 4 m/s, and measurement accuracy is 1 mm/s. Also, the frequency is set at 50 Hz. The comparisons of different outlier detection methods results demonstrated that the box plot and the local outlier factor methods have the best performance.

Children have limited exposure to statistical concepts and processes, yet researchers have highlighted multiple benefits of experiences in which they design and/or engage informally with statistical modelling. A study was conducted with a classroom in which students developed and utilised data-based models to respond to the inquiry question, Which origami animal jumps the furthest? The students used hat plots and box plots in Tinkerplots to make sense of variability in comparing distributions of their data and to support them to write justified conclusions of their findings. The study relied on classroom video and student artefacts to analyse aspects of the students’ modelling experiences which exposed them to powerful statistical ideas, such as key repeatable structures and dispositions in statistics. Three principles—purpose, process and prediction—are highlighted as ways in which the problem context, statistical structures and inquiry dispositions and cycle extended students’ opportunities to reason in sophisticated ways appropriate for their age. The research question under investigation was, How can an emphasis on purpose, process and prediction be implemented to support children’s statistical modelling? The principles illustrated in the study may provide a simple framework for teachers and researchers to develop statistical modelling practices and norms at the school level.

Fire, one of the most serious disasters threatening human life, is a chemical event that can destroy forests, buildings, and machinery within minutes. For this reason, there have been numerous methods developed to extinguish the fire. Within the scope of this study, a sound wave flame extinction system was developed in order to extinguish the flames at an early stage of the fire. The data used in the study were obtained as a result of experiments conducted with the developed system. The created dataset consists of data obtained from 17,442 experiments. It is aimed to classify the fuel type, flame size, decibel, frequency, airflow and distance features, and the extinction-non-extinction status of the flame through rule-based machine learning methods. In the study, rule-based machine learning methods, ANFIS (Adaptive-Network Based Fuzzy Inference Systems), CN2 Rule and DT (Decision Tree) were used. The methods of Box Plot, Scatter Plot and Correlation Analysis were utilized for statistical analysis of the data. As a result of the classifications, respectively, 94.5%, 99.91%, and 97.28% success were achieved with the ANFIS, CN2 Rule, and DT methods. As a result of the evaluations made by using Box Plot, Scatter Plot and Correlation Analysis.

Inventory management in the healthcare industry is of prime importance to ensure the appropriate delivery of the medical and health products to the final customer in a reasonable time without shortage or overstocking of the goods. The achievement of this balance, in turn, is dependent on the production of the product in a timely manner through dynamic control of the inventory mobility of the input materials in the warehouse. In the present study, eight raw materials, which are used as excipients in healthcare products, were selected as a focus research group for analysis using statistical process control (SPC: process-behavior (trending/control) charts, Pareto plots, Box plot diagrams and Pie illustrations), namely Macrogolglycerides, MCC, Sodium Benzoic Sulfimide, Carmellose Sodium, Isofol 20, Citric Acid Hydrate, Ace K and Sodium Hydrate. Inventory autonomous management network was integrated with SPC software (Minitab®v17.1.0) using the generated database. A unique and simple index was created to prioritize and evaluate each material quantitatively based on material mass, rejection factor, deliveries intervals and the lag time before disposition. Isofol 20 and MCC contributed by about 70% as a material risk weight (MRW). This index is useful for objective comparative analysis, measurement of the degree of changes quantitatively and decision-making.

This chapter examines seven old tools, seven new tools, and other tools to solve problems, analyze, and visualize the data used in Six Sigma teams. The seven old tools are check sheets, histograms, fishbone diagrams – cause-and-effect diagrams, Pareto analysis and diagrams, scatter diagrams, stratification analysis, and control charts. The seven new tools are affinity diagrams, systematic diagrams, arrow diagrams, relationships diagrams, matrix diagrams, matrix data analysis, and process decision program charts. The other tools are brainstorming, 5 Whys analysis, Gantt charts, dot plots, run charts, box-and-whisker plots, probability plots, bar charts, line graphs, and stem-and-leaf plots.

This chapter discusses and illustrates descriptive statistics. The purpose of the procedures and fundamental concepts reviewed in this chapter is quite straightforward: to facilitate the description and summarisation of data. By ‘describe’ we generally mean either the use of some pictorial or graphical representation of the data (e.g. a histogram, box plot, radar plot, stem-and-leaf display, icon plot or line graph) or the computation of an index or number designed to summarise a specific characteristic of a variable or measurement (e.g., frequency counts, measures of central tendency, variability, standard scores). Along the way, we explore the fundamental concepts of probability and the normal distribution. We seldom interpret individual data points or observations primarily because it is too difficult for the human brain to extract or identify the essential nature, patterns, or trends evident in the data, particularly if the sample is large. Rather we utilise procedures and measures which provide a general depiction of how the data are behaving. These statistical procedures are designed to identify or display specific patterns or trends in the data. What remains after their application is simply for us to interpret and tell the story.

A novel hybrid data balancing method based on both undersampling and oversampling with ensemble technique has been presented in this paper for efficiently detecting the auto insurance frauds. Initially, the skewness from the original imbalance dataset is removed by excluding outliers from the majority class samples using Box and Whisker plot and synthetic samples are generated from the minority class samples by using synthetic minority oversampling (SMOTE) technique. We employed three supervised classifiers, namely, support vector machine, multilayer perceptron, and K-nearest neighbors for classification purpose. The final classification results are obtained by aggregating the results obtained from these classifiers using the majority voting ensemble technique. Our model has been experimentally evaluated with a real-world automobile insurance dataset.