Feature Analysis Techniques Research Articles

Robust prediction of workability properties for 3D printing with steel slag aggregate using bayesian regularization and evolution algorithm

This study addresses the growing interest in utilizing steel slag as a sustainable alternative to river sand in additive manufacturing of concrete, driven by the increasing scarcity of natural resources. The rheological properties of fresh material significantly impact the quality of 3D-printed filament, necessitating suitable workability for printability. The research focuses on evaluating the influence of steel slag aggregate and key admixtures, such as silica fume and superplasticizer, on the workability properties of fresh concrete. Through an extensive series of 90 slump tests, optimal combinations ensuring the desired workability for 3D printing applications were identified. To enhance the manufacturing of fresh concrete and develop user-friendly tools, a novel soft computing approach is introduced—Adaptive Elitist Differential Evolution coupled with Bayesian Regularization Artificial Neural Network (aeDE-BRANN). This advanced model considers five critical input parameters: silica fume to Portland cement ratio, steel slag to cement ratio, water to cement ratio, cement content, and superplasticizer dosage. The model outputs crucial workability metrics, including slump flow and slump. In a comprehensive comparative study, the aeDE-BRANN framework demonstrates superior performance in terms of both simplicity and efficiency when compared to other forecasting models. Feature analysis techniques, including Shapley values from game theory and partial dependence plots, provide valuable insights into the intricate relationships between input variables and workability properties. The findings underscore the water to cement ratio as the most influential factor on workability, followed by silica fume to Portland cement ratio and steel slag to cement ratio. This study contributes a reliable tool for predicting workability properties, aiding in the selection of suitable steel slag aggregate and admixtures. Ultimately, it facilitates the sustainable and innovative evolution of 3D concrete printing practices.

Investigating the contribution of author- and publication-specific features to scholars’ h-index prediction

Evaluation of researchers’ output is vital for hiring committees and funding bodies, and it is usually measured via their scientific productivity, citations, or a combined metric such as the h-index. Assessing young researchers is more critical because it takes a while to get citations and increment of h-index. Hence, predicting the h-index can help to discover the researchers’ scientific impact. In addition, identifying the influential factors to predict the scientific impact is helpful for researchers and their organizations seeking solutions to improve it. This study investigates the effect of the author, paper/venue-specific features on the future h-index. For this purpose, we used a machine learning approach to predict the h-index and feature analysis techniques to advance the understanding of feature impact. Utilizing the bibliometric data in Scopus, we defined and extracted two main groups of features. The first relates to prior scientific impact, and we name it ‘prior impact-based features’ and includes the number of publications, received citations, and h-index. The second group is ‘non-prior impact-based features’ and contains the features related to author, co-authorship, paper, and venue characteristics. We explored their importance in predicting researchers’ h-index in three career phases. Also, we examined the temporal dimension of predicting performance for different feature categories to find out which features are more reliable for long- and short-term prediction. We referred to the gender of the authors to examine the role of this author’s characteristics in the prediction task. Our findings showed that gender has a very slight effect in predicting the h-index. Although the results demonstrate better performance for the models containing prior impact-based features for all researchers’ groups in the near future, we found that non-prior impact-based features are more robust predictors for younger scholars in the long term. Also, prior impact-based features lose their power to predict more than other features in the long term.

Consolidated modeling and prediction of heat transfer coefficients for saturated flow boiling in mini/micro-channels using machine learning methods

• A consolidated database for flow boiling heat transfer in mini/micro-channels is investigated using data science tools. • Optimal model gave a Mean Absolute Percentage Error of 11.3% when predicting the full test database and performed better than prior universal correlations. • Models could generally predict heat transfer coefficient well but struggled to predict extremely high outlier datapoints. Flow boiling has become a reliable mode of compensating with larger power densities and greater functions of devices because it is able to utilize both the latent and sensible heat contained within a specified coolant. There are currently very few available tools proven reliable when predicting heat transfer coefficients during flow boiling in mini/micro-channels. The most popular methods rely on semi-empirical correlations derived from experimental data. These correlations can only be applied to a very narrow subset of testing conditions. This study uses a number of data science methods and techniques to accurately predict the heat transfer coefficient during flow boiling in mini/micro-channels on a database consisting of 16,953 observations collected across 50 experiments using 12 working fluids. Exploratory data science is used to obtain confidence in the data and investigate relationships between feature variables before employing machine learning algorithms. Missing data is imputed using random forest nonparametric imputation. A variety of feature analysis techniques are employed to combine and select different optimal feature variables as input values such as principal component analysis to reduce the overall dimensionality of the dataset and the Boruta package, recursive feature elimination, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and stepwise selection to reduce the number of original variables used when modeling while preserving as much information as possible. A variety of models including linear modeling, generalized additive modeling, random forests, support vector machines, and neural networks are used to predict the heat transfer coefficient and compare the results with existing universal correlations. The support vector machine model performed best, with a Mean Absolute Percentage Error (MAPE) of 11.3%. The heat flux, vapor-only Froude number, and quality proved to be especially significant contributing variables across 90% of over 110 different models. Machine learning proved to be an extremely useful tool when predicting the heat transfer coefficient across a variety of different fluids but did struggle to predict extremely high outlier data where water was the working fluid.

Evaluation of the Image Feature Analysis Techniques by Image Strength Fluctuation for Alzheimer’s Disease

This article compares the effectiveness of both image strength fluctuation (ISF) and statistical parametric mapping (SPM), which is used for analyzing cerebral blood flow traced by single photon emission computed tomography (SPECT) in patients with Alzheimer’s disease (AD). This is a retrospective study that uses the built-in SPECT template of the SPM software to analyze the differences in cerebral blood flow (CBF) between two groups after using the ISF and SPM software to normalize and smooth the data. Using Z score analysis, the ISF method revealed unusual fluctuations in the range of image strength. The hot and cold areas were considered to be the difference between normal and abnormal images and the analysis of the gray intensities of cold and hot areas was used to show that cold and hot were in characteristic locations that correlate with brain functional areas. The results of the SPM analysis revealed significantly reduced blood flow in the bilateral inferior parietal lobule, the middle or superior gyrus of the left temporal lobe, the superior gyrus and the sub-lobar insula of the right temporal lobe. The results from SPM were similar to those obtained with the ISF and indicated the same regions (83.3%).

Queue behavioural patterns for passengers at airport terminals: A machine learning approach

Passengers go through different handling processes inside airport terminal buildings. The quality of these processes is usually measured by the time passengers require and by the level of comfort experienced by them. We present an analysis of behavioural patterns in queues at check-in desks and security controls, which are two of the most critical processes regarding passenger service. The passengers' flow is simulated to obtain queue lengths at one busy European airport between 2014 and 2016, supported by real flight data. Simulation is designed as a store-and forward cell-based system, whose parameters have been tuned and validated with real data from observations and empirical capacity and demand studies within the airport. Random Forest algorithms are then implemented to develop different models for each parameter prediction, after a data analysis stage based on statistical and visualization methods. Feature analysis techniques between dependent variables and the target outputs (queue lengths) determine which are the fundamental elements to explain queue behaviour and to predict target variables. We provide a method to forecast behavioural patterns at check-in desks and security controls, to help airport operators to implement adequate response policies. Queue behavioural patterns are captured by Machine Learning models, which can be used to offer improved passenger services (such as real-time predictions for expected waiting time at queues), or can be considered in a dynamic approach for terminal services design (as the entire progress of terminal handling depends on the stochastic behaviour of passengers). This could be a key tool for managing passengers demand and optimise the infrastructure's capacity through resource allocation.

Feature Analysis and Detection Techniques for Piracy Sites

In recent years, digital content has become easily accessible because of internet technology. Representative examples of such digital content include various types, such as music, TV, (program, sport, drama etc.) and films. However, there are cases where internet technology is used in illegal ways without the authorization of the copyright holder for digital content. Such actions have a direct impact on copyright owners' profits and further affect the development of the digital culture industry adversely. Therefore, in this study, we analyze features to detect piracy sites that cause copyright infringement. Further, we develop a piracy site detection crawler based on these features and present the analysis of its performance.

Nail Feature Analysis and Classification Techniques for Disease Detection

Nail Feature Analysis and Classification Techniques for Disease Detection

Joint feature extraction technique for text detection from natural scene image

Detection text detection and extraction from natural scenes (i.e. video or images) can deliver significant information for various applications. To address the issue of text detection, a novel approach for text detection from natural scene image is introduced by developing a joint feature extraction method by considering shape and scale invariant feature transform (SIFT) feature analysis techniques. Shape extraction is improved by applying curvature-based shape analysis model. To construct the feature descriptor, input image is passed through canny edge detection process in which gradients are computed of each image. Later, we perform SIFT analysis and SIFT-based feature matching to formulate the SIFT feature descriptor. Finally, these two descriptors are merged together, and a combined descriptor is presented for text detection. Experimental study is carried out by considering benchmark ICDAR 2003, 2013 and 2015 data sets. Experimental study shows that proposed approach outperforms when compared with state-of-art text detection model.

Time-Varying and Multiresolution Envelope Analysis and Discriminative Feature Analysis for Bearing Fault Diagnosis

This paper presents a reliable fault diagnosis methodology for various single and multiple combined defects of low-speed rolling element bearings. This method temporally partitions an acoustic emission (AE) signal and selects a portion of the signal, which contains intrinsic information about the bearing failures. This paper then performs frequency analysis for the selected time-domain AE signal by using multilevel finite-impulse response filter banks to obtain the most informative subband signals involving abnormal symptoms of the bearing defects. It does this by using a 2-D visualization tool that represents the percentage of the Gaussian-mixture-model-based residual component-to-defect component ratios via time-varying and multiresolution envelope analysis (TVMREA). Then, fault signatures in the time and frequency domains are extracted in the informative subband signals. Since all the extracted fault features may not be equally useful for diagnosis, the proposed genetic algorithm (GA)-based discriminative feature analysis (GADFA) selects the most discriminative subset of fault signatures. In experiments, single and multiple combined bearing defects under various conditions are used to validate the effectiveness of this fault diagnosis scheme using TVMREA and GADFA. Experimental results indicate that this reliable fault diagnosis methodology accurately identifies bearing failure type across a variety of conditions. In addition, GADFA outperforms other state-of-the-art feature analysis techniques, yielding 7.3%–46.6% performance improvements in average classification accuracy.

The role of quantitative PET in predicting cancer treatment outcomes

Recent years have witnessed increasing use of positron emission tomography (PET) in cancer staging and monitoring. In particular, 18F-fluoro-2-deoxy-d-glucose (FDG) has frequently been used in clinical practice for tumor detection, staging, and gross tumor volume definition in different cancer sites. More recently, there has been accumulating evidence that pre- and intra-treatment FDG uptake could be used as a prognostic factor for predicting cancer treatment outcomes. Typically, quantitative analysis of FDG uptake is based on observed changes in the standardized uptake value (SUV). However, SUV descriptors themselves might be influenced by the initial FDG uptake intensity, radiotracer distribution, and changes in tumor volume. These factors and others might make this approach prone to significant intra- and inter-observer variability. Alternatively, more recent efforts have been directed toward the use of advanced image feature analysis techniques as part of the emerging field of radiomics. In this work, we review recent advances and discuss current challenges regarding the use of PET imaging for predicting cancer outcomes. We discuss issues related to image feature extraction, statistical modeling, tracer type, and combination with other imaging modalities for the purpose of developing robust PET-based models of treatment outcomes. We provide examples based on our experience, and that of others, of PET imaging used alone or with other complementary modalities for predicting outcomes in radiotherapy and their application in personalizing treatment through dose painting. We also highlight the potential opportunities in this field for personalizing cancer treatment and improving clinical decision making.

Pattern Recognition for Classification and Matching of Car Tires

Abstract Commercial databases containing images of tire tread patterns are currently used by product designers, forensic specialists and product application personnel to identify whether a given tread pattern matches an existing tire. Currently, this pattern matching process is almost entirely manual, requiring visual searches of extensive libraries of tire tread patterns. Our work explores a first step toward automating this pattern matching process by building on feature analysis techniques from computer vision and image processing to develop a new method for extracting and classifying features from tire tread patterns and automatically locating candidate matches from a database of existing tread pattern images. Our method begins with a selection of tire tread images obtained from multiple sources (including manufacturers' literature, Web site images, and Tire Guides, Inc.), which are preprocessed and normalized using Two-Dimensional Fast Fourier Transforms (2D-FFT). The results of this preprocessing are feature-rich images that are further analyzed using feature extraction algorithms drawn from research in computer vision. A new, feature extraction algorithm is developed based on the geometry of the 2D-FFT images of the tire. The resulting FFT-based analysis allows independent classification of the tire images along two dimensions, specifically by separating “rib” and “lug” features of the tread pattern. Dimensionality of (0,0) indicates a smooth treaded tire with no pattern; dimensionality of (1,0) and (0,1) are purely rib and lug tires; and dimensionality of (1,1) is an all-season pattern. This analysis technique allows a candidate tire to be classified according to the features of its tread pattern, and other tires with similar features and tread pattern classifications can be automatically retrieved from the database.

Diagnosis of Visuo-Spatial Neglect Using Dynamic Sequence Features from a Cancellation Task

Visuo-spatial neglect is recognised as a major barrier to recovery following a stroke or head injury. A standard clinical assessment technique to assess the condition is a pencil-and-paper based cancellation task. Traditional static analysis of this task involves counting the number of targets correctly cancelled on the test sheet. Using a computer-based test capture system, this paper presents the novel application of using a series of standard pattern recognition techniques to examine the diagnostic capability of a number of dynamic features relating to the sequence in which the targets were cancelled. While none of the individual dynamic features is as sensitive to neglect as the conventional static analysis, a series of standard multi-dimensional feature analysis techniques are shown to improve the classification accuracy of the dynamic properties of task execution, and hence the sensitivity to the detection of neglect and the validity of this novel application. Combining the outcome of the dynamic sequence-based features with the conventional static analysis further improves the overall sensitivity of the two cancellation tasks included in this study. The algorithmic nature of the methodology for feature extraction objectively and consistently assesses patients, thereby improving the repeatability of the task.

Computerized analysis of interstitial disease in chest radiographs: improvement of geometric-pattern feature analysis.

We have been developing automated computerized schemes to assist radiologists in interpreting chest radiographs for interstitial disease based on texture analysis and geometric-pattern feature analysis. In this study, we attempted to improve the performance of the geometric-pattern feature analysis, because the current classification performance with geometric-pattern feature analysis is considerably lower than that of texture analysis. In order to improve the performance in distinguishing between normal lungs and abnormal lungs with interstitial disease, we attempted to remove rib edges in regions of interest (ROIs) by using an edge detection technique, and also to reduce false positives by using feature analysis techniques. In addition, the effects of many parameters on classification performance were investigated to identify proper threshold levels, and subsequently the specificity of the geometric-pattern feature analysis was improved from 69.5% to 86.1% at a sensitivity of 95.0%. Using a combined rule-based method with texture analysis and geometric-pattern feature analysis plus the artificial neural network (ANN) method for classification, a high specificity of 96.1% was obtained at a sensitivity of 95.0%.

An improved shift-invariant artificial neural network for computerized detection of clustered microcalcifications in digital mammograms.

A shift-invariant artificial neutral network (SIANN) has been applied to eliminate the false-positive detections reported by a rule-based computer aided-diagnosis (CAD) scheme developed in our laboratory. Regions of interest (ROIs) were selected around the centers of the rule-based CAD detections and analyzed by the SIANN. In our previous study, background-trend correction and pixel-value normalization were used as the preprocessing of the ROIs prior to the SIANN. A ROI is classified as a positive ROI, if the total number of microcalcifications detected in the ROI is greater than a certain number. In this study, modifications were made to improve the performance of the SIANN. First, the preprocessing is removed because the result of the background-trend correction is affected by the size of ROIs. Second, image-feature analysis is employed to the output of the SIANN in an effort to eliminate some of the false detections by the SIANN. In order to train the SIANN to detect microcalcifications and also to extract image features of microcalcifications, the zero-mean-weight constraint and training-free-zone techniques have been developed. A cross-validation training method was also applied to avoid the overtraining problem. The performance of the SIANN was evaluated by means of ROC analysis using a database of 39 mammograms for training and 50 different mammograms for testing. The analysis yielded an average area under the ROC curve (A(z)) of 0.90 for the testing set. Approximately 62% of false-positive clusters detected by the rule-based scheme were eliminated without any loss of the true-positive clusters by using the improved SIANN with image feature analysis techniques.

Computerized detection of masses in digital mammograms: Investigation of feature-analysis techniques

Mammographic screening of asymptomatic women has shown effectiveness in the reduction of breast cancer mortality. We are developing a computerized scheme for the detection of mammographic masses as an aid to radiologists in mammographic screening programs. Possible masses on digitized screen/film mammograms are initially identified using a nonlinear bilateral-subtraction technique, which is based on asymmetric density patterns occurring in corresponding portions of right and left mammograms. In this study, we analyze the characteristics of actual masses and nonmass detections to develop feature-analysis techniques with which to reduce the number of nonmass (ie, false-positive) detections. These feature-analysis techniques involve (1) the extraction of various features (such as area, contrast, circularity and border-distance based on the density and geometric information of masses in both processed, and original breast images), and (2) tests of the extracted features to reduce nonmass detections. Cumulative histograms of both actual-mass detections and nonmass detections are used to characterize extracted features and to determine the cutoff values used in the feature tests. The effectiveness of the feature-analysis techniques is evaluated in combination with the computerized detection scheme that uses the nonlinear bilateral-subtraction technique using free-response receiver operating characteristic analysis and 77 patient cases (308 mammograms). Results show that the feature-analysis techniques effectively improve the performance of the computerized detection scheme: about 35% false-positive detections were eliminated without loss in sensitivity when the feature-analysis techniques were used.

Voice input output for the K.B.C.S. project of India

Financed by the Government of India in cooperation with the United Nations Development Program (UNDP), the Nodal Centre for Speech Input Output is concerned with the realization of a voice‐oriented interactive computing environment (VOICE) under a knowledge‐based computer system project. The VOICE system aims at speech input backed by provisions for (a) feedback by (i) oral repetition by the machine, (ii) visual display; and (b) correction by (i) repetition of the word, (ii) spelling by voice, (iii) typing in. Speech output of the machine, based on an earlier work [P. V. S. Rao and R. B. Thosar, IEEE Trans. ASSP ASSP‐22, 217–225 (1974)] will be either (a) a well‐formed sentence based on a restricted syntax, or (b) the speaker's utterance, recognized, resynthesized, and repeated for confirmation. For (a), predetermined intonational patterns are used. For (b), the speaker's intonational pattern is superimposed on the synthetic utterance. For recognition, conventional subword unit approaches, hidden Markov models, and rule‐based direct phonetic feature analysis techniques are all being evaluated. It is hoped that the final system will in some sense integrate the best features of these.