Basic Feature Set Research Articles

Multiscale Feature Fusion and Graph Convolutional Network for Detecting Ethereum Phishing Scams

With the emergence of blockchain technology, the cryptocurrency market has experienced significant growth in recent years, simultaneously fostering environments conducive to cybercrimes such as phishing scams. Phishing scams on blockchain platforms like Ethereum have become a grave economic threat. Consequently, there is a pressing demand for effective detection mechanisms for these phishing activities to establish a secure financial transaction environment. However, existing methods typically utilize only the most recent transaction record when constructing features, resulting in the loss of vast amounts of transaction data and failing to adequately reflect the characteristics of nodes. Addressing this need, this study introduces a multiscale feature fusion approach integrated with a graph convolutional network model to detect phishing scams on Ethereum. A node basic feature set comprising 12 features is initially designed based on the Ethereum transaction dataset in the basic feature module. Subsequently, in the edge embedding representation module, all transaction times and amounts between two nodes are sorted, and a gate recurrent unit (GRU) neural network is employed to capture the temporal features within this transaction sequence, generating a fixed-length edge embedding representation from variable-length input. In the time trading feature module, attention weights are allocated to all embedding representations surrounding a node, aggregating the edge embedding representations and structural relationships into the node. Finally, combining basic and time trading features of the node, graph convolutional networks (GCNs), SAGEConv, and graph attention networks (GATs) are utilized to classify phishing nodes. The performance of these three graph convolution-based deep learning models is validated on a real Ethereum phishing scam dataset, demonstrating commendable efficiency. Among these, SAGEConv achieves an F1-score of 0.958, an AUC-ROC value of 0.956, and an AUC-PR value of 0.949, outperforming existing methods and baseline models.

Combining homomorphic filtering and recurrent neural network in gait signal analysis for neurodegenerative diseases detection

Automatic, cost-effective, and reliable detection of neurodegenerative diseases (NDs) is one of the important issues in clinical practice. The main idea of the proposed method in this study is to utilize the advantages of gait time series that can provide low-cost and non-invasive measures, homomorphic filtering that can effectively separate muscle activity from body dynamic and recurrent neural network or cascade forward neural network that can learn sequential time-varying data. Experimental results on gait time series of 16 healthy control subjects, 13 patients with amyotrophic lateral sclerosis, 15 patients with Parkinson’s disease and 20 patients with Huntington’s disease have demonstrated high detection performance with an accuracy rate of 100 % using K-fold cross validation for all three types of NDs outperforming other existing methods. The results have also indicated thatthe use of real cepstral coefficients, oscillation components, or basic statistics feature set has improved the detection performance.

Machine Learning Models for Weight-Bearing Activity Type Recognition Based on Accelerometry in Postmenopausal Women

Hip-worn triaxial accelerometers are widely used to assess physical activity in terms of energy expenditure. Methods for classification in terms of different types of activity of relevance to the skeleton in populations at risk of osteoporosis are not currently available. This publication aims to assess the accuracy of four machine learning models on binary (standing and walking) and tertiary (standing, walking, and jogging) classification tasks in postmenopausal women. Eighty women performed a shuttle test on an indoor track, of which thirty performed the same test on an indoor treadmill. The raw accelerometer data were pre-processed, converted into eighteen different features and then combined into nine unique feature sets. The four machine learning models were evaluated using three different validation methods. Using the leave-one-out validation method, the highest average accuracy for the binary classification model, 99.61%, was produced by a k-NN Manhattan classifier using a basic statistical feature set. For the tertiary classification model, the highest average accuracy, 94.04%, was produced by a k-NN Manhattan classifier using a feature set that included all 18 features. The methods and classifiers within this study can be applied to accelerometer data to more accurately characterize weight-bearing activity which are important to skeletal health.

Named Entity Recognition on Software Requirements Specification Documents

Software Requirements Specifications (SRS) documents set the requirements and expectations of software development projects. This textual information is considered as a guideline for identifying various requirements for the developers. The content of an SRS document is concentrated around software requirement specific entities which can be used to improve software-based information retrieval systems. In this paper, we consider Named Entity Recognition (NER) over SRS documents and classify the software requirement specific entities using ML-based methods. Existing NER methods are typically restricted to four basic sets of entities that are not pertinent to NER in the software requirement domain. We define ten software requirement-based entities which cover the essence of the SRS documents. In order to predict the appropriate entity tags, we first create a limited feature set and experiment with various NER models including ML- based probabilistic models and DL models. We conduct a detailed numerical study to evaluate the effectiveness of the NER models over the SRS datasets. NER models with a basic feature set demonstrate promising performance with an F1-macro score of 81%. Our analysis shows that the entities can be extracted with high accuracy from the SRS documents, which then can be used for various purposes in practice such as extractive summarization of large SRS documents and effectively grouping the requirements.

Methods of Intellectual Analysis in Medical Diagnostic Tasks Using Smart Feature Selection

The paper deals with a computer technique for high-performance processing, analysis and interpretation of medical and diagnostic images. We propose a new approach to the analysis of different classes of images based on evaluation of aggregate geometric and texture parameters of allocated regions of interest which are supposed to be a basic feature set. The developed efficient feature-space generation technique is based on Big Data mining of unstructured information by applying the discriminative analysis methods. The technique makes it possible to extract regions of interest on fundus images containing four classes of objects: exudates, intact areas, thick vessels, and thin vessels. The use of Big Data technology made it possible, due to involving large amounts of data, to improve the training sample and reduce classification errors that ensured an increase of diagnosis accuracy up to 95%. The proposed technique has been applied to the coagulate location problem, that is a crucial problem of diabetic retinopathy treatment. The experiment results on real eye fundus images proved a considerable increase of treatment effectiveness.

Fuzzy Double C-Means Clustering Based on Sparse Self-Representation

This paper introduces the popular sparse representation method into the classical fuzzy c-means clustering algorithm, and presents a novel fuzzy clustering algorithm, called fuzzy double c-means based on sparse self-representation (FDCM_SSR). The major characteristic of FDCM_SSR is that it can simultaneously address two datasets with different dimensions, and has two kinds of corresponding cluster centers. The first one is the basic feature set that represents the basic physical property of each sample itself. The second one is learned from the basic feature set by solving a spare self-representation model, referred to as discriminant feature set, which reflects the global structure of the sample set. The spare self-representation model employs dataset itself as dictionary of sparse representation. It has good category distinguishing ability, noise robustness, and data-adaptiveness, which enhance the clustering and generalization performance of FDCM_SSR. Experiments on different datasets and images show that FDCM_SSR is more competitive than other state-of-the-art fuzzy clustering algorithms.

Learning representations for the early detection of sepsis with deep neural networks

BackgroundSepsis is one of the leading causes of death in intensive care unit patients. Early detection of sepsis is vital because mortality increases as the sepsis stage worsens. ObjectiveThis study aimed to develop detection models for the early stage of sepsis using deep learning methodologies, and to compare the feasibility and performance of the new deep learning methodology with those of the regression method with conventional temporal feature extraction. MethodStudy group selection adhered to the InSight model. The results of the deep learning-based models and the InSight model were compared. ResultsWith deep feedforward networks, the area under the ROC curve (AUC) of the models were 0.887 and 0.915 for the InSight and the new feature sets, respectively. For the model with the combined feature set, the AUC was the same as that of the basic feature set (0.915). For the long short-term memory model, only the basic feature set was applied and the AUC improved to 0.929 compared with the existing 0.887 of the InSight model. ConclusionsThe contributions of this paper can be summarized in three ways: (i) improved performance without feature extraction using domain knowledge, (ii) verification of feature extraction capability of deep neural networks through comparison with reference features, and (iii) improved performance with feedforward neural networks using long short-term memory, a neural network architecture that can learn sequential patterns.

Study of Machine-Learning Classifier and Feature Set Selection for Intent Classification of Korean Tweets about Food Safety

In recent years, several studies have proposed making use of the Twitter micro-blogging service to track various trends in online media and discussion. In this study, we specifically examine the use of Twitter to track discussions of food safety in the Korean language. Given the irregularity of keyword use in most tweets, we focus on optimistic machine-learning and feature set selection to classify collected tweets. We build the classifier model using Naive Bayes & Naive Bayes Multinomial, Support Vector Machine, and Decision Tree Algorithms, all of which show good performance. To select an optimum feature set, we construct a basic feature set as a standard for performance comparison, so that further test feature sets can be evaluated. Experiments show that precision and F-measure performance are best when using a Naive Bayes Multinomial classifier model with a test feature set defined by extracting Substantive, Predicate, Modifier, and Interjection parts of speech.

Patient-level temporal aggregation for text-based asthma status ascertainment.

To specify the problem of patient-level temporal aggregation from clinical text and introduce several probabilistic methods for addressing that problem. The patient-level perspective differs from the prevailing natural language processing (NLP) practice of evaluating at the term, event, sentence, document, or visit level. We utilized an existing pediatric asthma cohort with manual annotations. After generating a basic feature set via standard clinical NLP methods, we introduce six methods of aggregating time-distributed features from the document level to the patient level. These aggregation methods are used to classify patients according to their asthma status in two hypothetical settings: retrospective epidemiology and clinical decision support. In both settings, solid patient classification performance was obtained with machine learning algorithms on a number of evidence aggregation methods, with Sum aggregation obtaining the highest F1 score of 85.71% on the retrospective epidemiological setting, and a probability density function-based method obtaining the highest F1 score of 74.63% on the clinical decision support setting. Multiple techniques also estimated the diagnosis date (index date) of asthma with promising accuracy. The clinical decision support setting is a more difficult problem. We rule out some aggregation methods rather than determining the best overall aggregation method, since our preliminary data set represented a practical setting in which manually annotated data were limited. Results contrasted the strengths of several aggregation algorithms in different settings. Multiple approaches exhibited good patient classification performance, and also predicted the timing of estimates with reasonable accuracy.

SMOQ: a tool for predicting the absolute residue-specific quality of a single protein model with support vector machines.

BackgroundIt is important to predict the quality of a protein structural model before its native structure is known. The method that can predict the absolute local quality of individual residues in a single protein model is rare, yet particularly needed for using, ranking and refining protein models.ResultsWe developed a machine learning tool (SMOQ) that can predict the distance deviation of each residue in a single protein model. SMOQ uses support vector machines (SVM) with protein sequence and structural features (i.e. basic feature set), including amino acid sequence, secondary structures, solvent accessibilities, and residue-residue contacts to make predictions. We also trained a SVM model with two new additional features (profiles and SOV scores) on 20 CASP8 targets and found that including them can only improve the performance when real deviations between native and model are higher than 5Å. The SMOQ tool finally released uses the basic feature set trained on 85 CASP8 targets. Moreover, SMOQ implemented a way to convert predicted local quality scores into a global quality score. SMOQ was tested on the 84 CASP9 single-domain targets. The average difference between the residue-specific distance deviation predicted by our method and the actual distance deviation on the test data is 2.637Å. The global quality prediction accuracy of the tool is comparable to other good tools on the same benchmark.ConclusionSMOQ is a useful tool for protein single model quality assessment. Its source code and executable are available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/.

Sampling and classifying interference patterns in a wireless sensor network

The low-powered transmissions in a wireless sensor network (WSN) are highly susceptible to interference from external sources. Our work is a step towards enabling WSN devices to better understand the interference in their environment so that they can adapt to it and communicate more efficiently. We extend our previous work in which we collected received signal strength traces using mote-class synchronized receivers at sample rates that are, to the best of our knowledge, higher than previously described in the literature. These traces contain distinct interference patterns, each with a different potential for being exploited by cognitive radio strategies. In order to exploit a pattern, however, a node must first recognize it. Given the energy and space constraints of a node, we explore succinct decision tree classifiers for the two most disruptive patterns. We expand on a basic feature set to incorporate attributes based on the dip statistic and the Lomb periodogram, both of which address specific, empirically observed behaviour, and we show their positive impact on both the decision tree structure and the overall classification performance. Moreover, we present an approximation of the periodogram that makes its construction feasible for mote-class devices, and we describe the simplification's impact on classification performance.

Viewpoints: A High-Performance High-Dimensional Exploratory Data Analysis Tool

ABSTRACTScientific data sets continue to increase in both size and complexity. In the past, dedicated graphics systems at supercomputing centers were required to visualize large data sets, but as the price of commodity graphics hardware has dropped and its capability has increased, it is now possible, in principle, to view large complex data sets on a single workstation. To do this in practice, an investigator will need software that is written to take advantage of the relevant graphics hardware. The Viewpoints visualization package described herein is an example of such software. Viewpoints is an interactive tool for exploratory visual analysis of large high-dimensional (multivariate) data. It leverages the capabilities of modern graphics boards (GPUs) to run on a single workstation or laptop. Viewpoints is minimalist: it attempts to do a small set of useful things very well (or at least very quickly) in comparison with similar packages today. Its basic feature set includes linked scatter plots with brushing, dynamic histograms, normalization, and outlier detection/removal. Viewpoints was originally designed for astrophysicists, but it has since been used in a variety of fields that range from astronomy, quantum chemistry, fluid dynamics, machine learning, bioinformatics, and finance to information technology server log mining. In this article, we describe the Viewpoints package and show examples of its usage.

Introducing djatoka

The ISO-standardized JPEG 2000 image format has started to attract significant attention. Support for the format is emerging in major consumer applications, and the cultural heritage community seriously considers it a viable format for digital preservation. So far, only commercial image servers with JPEG 2000 support have been available. They come with significant license fees and typically provide the customers with limited extensibility capabilities. Here, we introduce djatoka, an open source JPEG 2000 image server with an attractive basic feature set, and extensibility under control of the community of implementers. We describe djatoka, and point at demonstrations that feature digitized images of marvelous historical manuscripts from the collections of the British Library and the University of Ghent. We also caIl upon the community to engage in further development of djatoka.

Multivariate models for the concentration of oxygen-18 in precipitation based on meteorological and geographical features

Concentrations of the stable isotope oxygen-18 in precipitation samples have been modeled by geographical and meteorological features of the sampling stations. Precipitation samples are from 30 locations in Austria; oxygen-18 concentrations have been determined by isotope ratio mass spectrometry; each location has been characterized by three geographical features (longitude, latitude and elevation), and five meteorological features (relative humidity, fresh snow, wind speed, precipitation, and air temperature). All data are monthly means for the summer period April to September computed as long term averages for a time period of typically 20 years. The basic feature set has been augmented by 49 nonlinear transformations of the original features giving a data set with 57 features. The number of objects was 180 corresponding to the 30 sampling stations times six months. Different methods of feature selection (correlation coefficient, backward elimination, all subsets regression, and genetic algorithm) have been applied, and for the 20 best feature subsets the prediction errors of PLS models have been estimated from test sets. The final best model for a prediction of oxygen-18 concentrations contains ten features selected by a genetic algorithm. This model has been applied to the computation of a geographical oxygen-18 distribution map from Austria based on computed oxygen-18 values for 200 locations, and experimental values of 30 locations.